Annual Review of Psychology, Vol 56 2005

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Annu. Rev. Psychol. 2005. 56:1–23 doi: 10.1146/annurev.psych.56.091103.070239 c 2005 by Annual Reviews. All rights reserved Copyright First published online as a Review in Advance on June 10, 2004

IN SEARCH OF MEMORY TRACES Richard F. Thompson


Neuroscience Program, University of Southern California, Los Angeles, California 90089-2520; email: [email protected]

Key Words learning, hippocampus, cerebellum, inactivation, localization ■ Abstract The key issue in analyzing brain substrates of memory is the nature of memory traces, how memories are formed, stored, and retrieved in the brain. In order to analyze mechanisms of memory formation it is first necessary to find the loci of memory storage, the classic problem of localization. Various approaches to this issue are reviewed. A particular strategy is proposed that involves a number of different techniques (electrophysiological recording, lesions, electrical stimulation, pathway tracing) to identify the essential memory trace circuit for a given form of learning and memory. The methods of reversible inactivation can be used to localize the memory traces within this circuit. Using classical conditioning of eye blink and other discrete responses as a model system, the essential memory trace circuit is identified, the basic memory trace is localized (to the cerebellum), and putative higher-order memory traces are characterized in the hippocampus.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Simplified Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Strategies to Study Memory Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 OUR MODEL SYSTEM: EYEBLINK CONDITIONING . . . . . . . . . . . . . . . . . . . . . 6 Motor Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 The Hippocampus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 The Essential Circuitry: The Cerebellum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

INTRODUCTION A major achievement of recent research on brain substrates of learning and memory, to which our work has contributed, is the recognition that there are several different forms or aspects of memory involving different brain systems (Figure 1). Squire (1992) has argued eloquently for the distinction between declarative and nondeclarative forms of memory, as has Schacter (1987). The distinction between episodic and semantic memory—“What did you have for breakfast?” versus “Where is the Eiffel Tower?”—has been stressed by Tulving (1985). 0066-4308/05/0203-0001$14.00

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Figure 1 A current view of the various forms of learning and memory and their putative brain substrates.


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LOCALIZATION OF MEMORY TRACES

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Implicit or nondeclarative memory is very much a grab bag. In general, explicit memory involves awareness of the memory whereas implicit memory does not necessarily involve being aware of the memory (aware meaning verbal report). The schema of Figure 1 is of course oversimplified. When an organism learns something, a number of brain systems can become engaged. However, in most cases there is one critical brain system, which when damaged causes permanent impairment in the particular form of learning and memory. Many readers will recall Lashley’s (1950) pessimistic conclusion that his series of experiments “has yielded a good bit of information about what and where the memory trace is not” in his famous article, “In Search of the Engram.” In all fairness to Lashley, the existence of several different forms of memory with differing neuronal substrates was not recognized at that time, nor were modern analytic techniques available then. We adopt the following definitions from an earlier review (Lavond et al. 1993). The essential memory trace, together with its associated circuitry, is necessary and sufficient for the basic aspects of a given form of learning (e.g., acquisition and retention). Other brain structures may also form memory traces, defined loosely as learning-induced changes in neuronal activity, but these may not be essential for the basic learning. Thus, in eyeblink conditioning, long-lasting learning-induced increases in neuronal activity develop in the hippocampus (Berger et al. 1976), but the hippocampus is not necessary for basic delay associative learning and memory (Schmaltz & Theios 1972). Aversive classical conditioning can modify the receptive-field properties of neurons in sensory systems. Particularly striking are the studies of Weinberger and associates (Edeline & Weinberger 1991, Weinberger et al. 1984) showing that neurons in secondary areas of auditory thalamus and cortex shift their best frequencies toward the frequency of the conditioning stimulus in aversive Pavlovian conditioning (see also Bao et al. 2003). The motor area of the cerebral cortex provides yet another example. Classical eyeblink conditioning results in marked and persisting increases in excitability of pyramidal neurons in motor cortex (Woody et al. 1984). However, the motor cortex is not necessary for either learning or memory of the conditioned eyeblink response (Ivkovich & Thompson 1997). These “higherorder” memory traces appear to play important roles in the adaptive behavior of the organism. The obvious point here is that the organism can learn, remember, and perform the basic learned response following destruction of nonessential memory trace systems, but destruction of the essential memory trace system abolishes this ability. We draw a distinction between the essential memory trace and the essential memory trace circuit. The latter includes the necessary input and output circuits as well as the essential memory trace itself. These are all identified by lesions. But lesions, per se, cannot distinguish between the essential trace and the essential circuitry; lesions of either completely prevent and abolish the learned response.

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Simplified Systems How does one go about finding memory traces? One very productive approach is the use of simplified “model” systems, emphasized early by Thompson & Spencer (1966) (see also Kandel & Spencer 1968). Thus, vertebrate spinal reflexes and the gill-withdrawal reflex circuit in Aplysia have both served as very productive models to analyze processes of habituation and sensitization. In the case of habituation, at least, the memory trace is embedded within the reflex pathway under study so lesions abolish the reflex as well as the trace. For monosynaptic pathways in both Aplysia and spinal cord the mechanism of short-term habituation appears to be simply a decrease in the probability of transmitter release as a result of repeated activation, a presynaptic process of synaptic depression (Farel & Thompson 1976, Kandel 1975). But even for a simple behavioral phenomenon like habituation, the neural substrate can prove to be complex, particularly for long-lasting habituation, e.g., between sessions as opposed to within session response decrements. In the Aplysia cellular monosynaptic system, Ezzeddine & Glanzman (2003) have shown that prolonged habituation depends on protein synthesis, protein phosphatase activity, and postsynaptic glutamate receptors. Indeed, their data indicate that stimuli inducing habituation of the gill-withdrawal reflex appear to activate sensitizing processes as well, as proposed in the “dual-process” theory of habituation by Groves & Thompson (1970). Thus, much is still not known about the detailed mechanisms of memory traces in this simplest form of learning. By mechanisms we mean the physical-chemical substrate of memory store. Although there are many candidate mechanisms, as of this writing we do not yet know the detailed physical bases of memory storage for any form of learning and memory in the mammalian brain. But understanding the mechanisms of memory storage will not inform us of what the memories are. The actual memories are coded by the neuronal circuits that code, store, and retrieve the memories, what I term here the “essential circuits.”

Strategies to Study Memory Formation There appear to be two general strategies for the study of memory storage. One approach is to pick a mechanism of neural plasticity such as long-term potentiation (LTP) in a structure known to be important for memory and attempt to show that it is a substrate for memories. Richard Morris and associates published a heroic effort to demonstrate that activity-dependent synaptic plasticity, particularly LTP (and LTD, long-term depression), “is both necessary and sufficient for the information storage underlying the type of memory mediated by the brain area in which that plasticity is observed” (Martin et al. 2000). They established several “formal” criteria by which to judge this hypothesis. We examine these criteria below but here we simply note their conclusion that “synaptic plasticity is necessary for learning and memory, but that little data currently supports the notion of sufficiency” (p. 649).

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Actually, there are many forms of plasticity in the nervous system, synaptic plasticity being only one. There are also changes in neuron excitability due to factors intrinsic to the neurons, as in decreases in after-hyperpolarization, as well as structural changes in dendrites, formation of new neurons, and actions of glial cells. LTP has been the most intensely studied putative synaptic mechanism of memory storage, particularly in the hippocampus (e.g., Martin et al. 2000), but the evidence is still far from clear. There is a vast literature analyzing mechanisms of LTP but very little work attempting to show that it is a basis for the storage of memories (see Shors & Matzel 1997, Wilson & Tonegawa 1997). The alternative strategy is to begin with a well-characterized form of learning and memory and determine the memory traces involved. In order to do this it is first necessary to find where in the brain the memories are stored, the classical problem of localization. Because learning involves changes in behavior as a result of exposure to stimuli that do not change, there must be alterations at some loci in the brain. A given form of learning may involve one or multiple loci of memory storage, but they must exist. We have used this strategy in our search for memory traces. How does one go about finding a memory trace in the behaving mammal? Olds, Disterhoft, and associates wrestled with this issue in pioneering studies (Olds et al. 1972). Their basic approach was to record extracellular unit cluster discharges in many brain areas in freely moving rats, looking for increases in unit response on the conditioning day that were significantly greater than responses to the stimuli on the preceding pseudoconditioning day, i.e., a learning-induced increase in unit activity. The behavioral situation was simply an auditory conditioned stimulus (CS+) followed after 2 sec by a food pellet unconditioned stimulus (US) versus a CS− with no food pellet. The studies were very well controlled. The initial criterion they set for identification of the memory trace was shortest latency learned responses: The “earliest” conditioned brain responses (i.e., those appearing with the shortest latencies after application of the CS) might thus be considered to be “at the site” of conditioning, and other later conditioned brain responses might be considered to be secondary to them. Similarly (and hopefully validating this supposition) “early” new brain responses might be expected to emerge at the site of old responses indicating this site to be a junction point between old and new (Olds et al. 1972, p. 202). In a most interesting thought piece, Michael Gabriel (1976) argued against the use of the shortest latency response as the criterion for identification of the memory trace. He proposed an alternative “bias” hypothesis. In essence, he suggested that a tonic flow of impulses originating from a distant neuronal locus biased the activity of the neurons being recorded as the shortest latency learned response. So the increase of the shortest latency unit response could be due to tonic actions on these neurons from a distant site where learning has resulted in a tonic increase in activity. Gabriel draws the following rather strong conclusion: “clearly the bias

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hypothesis argues against the idea of mapping the brain for short-latency neuronal responses in order to localize engrams” (p. 279). In a companion piece to the Olds et al. (1972) article, Disterhoft & Olds (1972) do consider the possibility of tonic influences, as Gabriel notes. They suggest an additional criterion, namely the earliest increase in neuronal activity that develops over the trials of training. I return to this issue below. In the late 1960s and early 1970s we tried several different types of model systems to analyze neural substrates of mammalian associative learning and memory. Because of our success in using spinal reflexes for analysis of habituation we tackled classical conditioning of the flexion reflex in the acute spinal animal, with some success (Patterson et al. 1973). However, I concluded it was not a good model of associative learning in the behaving mammal.

OUR MODEL SYSTEM: EYEBLINK CONDITIONING Thanks in part to efforts by Michael Patterson, then a postdoc in my lab who had obtained his Ph.D. with Isadore Gormezano, and because of the elegant and comprehensive behavioral studies by Gormezano, we adopted his preparation: classical conditioning of the nictitating membrane (NM) eyeblink response in the restrained rabbit. We have detailed the advantages of this preparation elsewhere (Thompson et al. 1972, Thompson et al. 1976). Perhaps the most important advantages are: (a) The unconditioned response (UR) provides an independent measure of performance against which to compare effects of variables acting on the conditioned response (CR), and (b) the behavioral conditioned response is robust, reliable, and discrete, and the exact amplitude-time course of the response is measurable. There were a number of other advantages as well: no sensitization, pseudoconditioning, or alpha responding; many trials required to learn; and extensive parometric data on the properties of the CR (see Gormezano et al. 1983, and the important theoretical analyses by Wagner and associates, e.g., Wagner & Donegan 1989).

Motor Control We began our analysis with a focus on the motor nerves and nuclei that generated the reflex and learned eyeblink responses (Cegavske et al. 1976, Cegavske et al. 1979, Young et al. 1976). To oversimplify from our work and the work of others, the sixth nerve was critically important for the nictitating membrane (NM) response, but the third and fourth nerves were also involved and the seventh nerve was critical for closure of the external eyelid (obicularis oculi muscles). In terms of motor nuclei, the seventh nucleus controlled external eyelid closure and the accessory portion of the sixth nucleus innervated the retractor bulbous muscle, which retracted the eyeball and forced the nictitating membrane out across the front surface of the eye. The fourth and sixth nuclei acted synergistically with the accessory sixth and seventh. We showed with simultaneous recordings that NM extension and external eyelid closure [actually electromyographic (EMG)

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activity of the obicularis oculi muscles] were essentially perfectly correlated over the course of learning (McCormick et al. 1982b). Indeed, obicularis oculi EMG appears to be the most robust and sensitive measure of learning (Lavond et al. 1990). Recordings from the relevant motor nuclei, particularly the sixth, accessory sixth, and seventh, showed essentially identical patterns of learning-induced increases in neuronal activity. It is important to stress the fact that the pathways mediating the reflex eyeblink are in the brain stem and do not involve “higher” brain systems such as the cerebellum and hippocampus. Specifically, there are direct projections from the trigeminal nucleus (activated by stimulation of the cornea and surrounding tissues) to the relevant motor nuclei and indirect projections relaying via the brain stem reticular system to the motor nuclei (Hiraoka & Shimamura 1977). The basic logic of our approach seemed reasonable—identify the critical motor neurons involved in control of the learned response and trace the essential circuitry backward to the source, the “engram.” The fact that several motor nuclei showed the same pattern of learning-induced activation argued against any plasticity unique to one motor nucleus (e.g., the accessory sixth) or to processes of plasticity at the level of the motor nuclei. Instead, it seemed most likely that the learninginduced response in the motor nuclei was driven from a common central source. The pattern of learning-induced increase in the activity of neurons in the motor nuclei in the CS period in paired trials and on CS-alone trials (i.e., the neuronal CR) was strikingly similar in form to the amplitude-time course of NM extension, external eyelid closure, and of course the EMG recorded from the obicularis oculi muscles. Indeed, an envelope of increased frequency of discharge of neurons in the motor nuclei preceded in time and closely predicted the form of the behavioral eyeblink-NM response (Figure 2). This close predictive parallel was most evident with unit cluster recordings but was also true for single unit recordings in the motor nuclei. Our initial logic—to work backward from the motor nuclei—was not as simple as it might have seemed because of the very large number of central brain systems that project to the motor and premotor nuclei. So we adopted a different strategy. It was apparent that the amplitudetime course form of the conditioned eyeblink-NM response closely paralleled and followed the pattern of increased unit activity in the motor nuclei. So we focused on this behavioral model of the learning-induced increase in neural activity in the motor nuclei and used it as a template or model (Figure 2). The higher brain systems that acted to generate and drive the learning-induced neuronal CR, the “model” in the motor nuclei, must show the same pattern of learning-induced activity as do the motor nuclei, and hence the amplitude-time course of the learned eyeblink-NM response. Thus, we searched through higher brain systems looking for learning-induced neuronal activity that correlated closely with and preceded in time the form of the learned eyeblink-NM response. In essence, we mapped virtually the entire brain of the rabbit in 1 mm steps, searching for neuronal models

USE OF THE MOTOR NEURON NEURONAL MODEL

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Figure 2 Histograms of unit cluster recordings from the ABD and simultaneous recording of the eyeblink response (NM, the “third eyelid”) in classical conditioning in the rabbit summed and averaged over eight trials before (A) and after (B) learning. First cursor onset of tone CS, second cursor onset of corneal airpuff US, trace duration 750 msec, upward movement of the NM trace indicates extension (eyelid closure). Note that the pattern of neural discharges precedes and models the amplitude-time course of the behavioral response. ABD, abducens motor nucleaus; CS, conditioned stimulus; NM, nictitating membrane; US, unconditioned response. (From Cegavske et al. 1979.)

of the learned behavioral response (see, e.g., McCormick et al. 1983, Thompson et al. 1976). We did not search blindly but rather system-by-system. The basic assumption is that the neuronal sites of memory trace formation will show a pattern of increased frequency of unit cluster and single-unit discharges that precedes and closely predicts the pattern of response in the motor nuclei and hence in the amplitude-time course of the behavioral CR. We felt this criterion was preferable to the earlier ideas of shortest latency response, earliest response over trials, or first site to show increased tonic responses. This assumption proved to be the key that permitted us to localize a memory trace.

The Hippocampus Because of its involvement in memory, we began with the hippocampus (Berger et al. 1976). To our delight, both unit cluster recordings and single-unit responses recorded from CA1 and CA3 showed the requisite learning-induced responses, i.e., pattern of increased discharge frequency that preceded in time (within trials) and correlated virtually perfectly with the amplitude-time course of the behavioral CR (Figure 3). We showed that this neuronal model of a memory is generated by identified pyramidal neurons (Berger & Thompson 1978b). Furthermore, the hippocampal unit response began to develop in the US period within just a few trials of training (a criterion by Olds et al. 1972) and moved into the CS period in close association

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Figure 3 Behavioral eyeblink (nictitating membrane) and hippocampal unit response averaged over 100 trials in a well-trained rabbit. The unit response is of a single, identified pyramidal neuron in CA1. Upper trace, raw single 100-msec sweep to show the neuron response, middle trace averaged NM response, lower trace cumulated histogram of unit activity; cursors and durations in middle and lower traces as in Figure 2. Note that the pattern of response of this neuron both precedes and models the amplitude-time course of the behavioral response. (From Berger & Thompson 1978b.)

with the development of the behavioral CR (Berger & Thompson 1978a). It seemed like a perfect candidate for the engram. In addition, electroencephalogram (EEG) activity recorded from the hippocampus at the beginning of training predicted the rate of learning (Berry & Thompson 1978). We characterized this learning-induced response in the hippocampus in some detail (see Berger et al. 1986). Unfortunately, as we noted above, animals could learn the basic delay-conditioned NM-eyeblink response following hippocampal lesions (Schmaltz & Theios 1972). This apparent enigma illustrates a fundamental limitation of using neuronal recordings to localize memory traces. The fact that a neuronal model of the learned response occurs at a given locus does not necessarily mean it originates there. Indeed, the learning-induced neuronal model in the hippocampus, at least in the CS period, is abolished by appropriate cerebellum lesions (Clark et al. 1984). Actually, a number of loci in the brain exhibit the learning-induced neuronal model of the learned behavioral response. I return to this issue below. The breakthrough insofar as the hippocampus is concerned came in a study in our laboratory by Paul Solomon and Donald Weisz using the trace-conditioning

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paradigm, where a 500-msec period of no stimuli separated CS offset from US onset (Solomon et al. 1986). In brief, large bilateral hippocampal lesions made before training markedly impaired subsequent learning of the trace (but not delay) eyeblink CR, a result replicated exactly by Moyer et al. (1990). If the hippocampal lesions are made immediately after learning, the trace CR is abolished (with no effect on the delay CR), but if the lesions are made a month after training the trace CR remains intact (Kim et al. 1995). In sum, hippocampal lesions induced anterograde amnesia (impairment of postlesion learning) and marked but time-limited retrograde amnesia for the trace eyeblink CR. These are precisely the effects that such lesions have on declarative memory in humans (Squire 1987). These results suggest that trace eyeblink conditioning provides a simple model of hippocampal-dependent declarative memory, a possibility strongly supported by studies of humans with hippocampal-medial temporal lobe amnesia. Such patients are markedly impaired on acquisition of trace eyeblink conditioning if the trace interval is sufficiently long but not impaired at all on delay conditioning (see McGlinchey-Berroth et al. 1997). Clark & Squire (1998, 1999) made the striking observation that awareness of the training contingencies in normal human subjects correlated highly with the degree of learning of trace eyeblink conditioning. Recall that awareness is a key defining property of declarative memory. They also showed that awareness played no role in delay conditioning, and that expectancy of US occurrence influenced trace but not delay conditioning (Clark & Squire 2000, Clark et al. 2001). They conclude that delay and trace conditioning are fundamentally different phenomena, with delay conditioning inducing nondeclarative or procedural memory and trace conditioning inducing declarative memory. Hence, trace eyeblink conditioning in rabbits would seem to provide a very elementary instantiation of declarative memory in animals. These strikingly parallel results of hippocampal lesions in rabbits and humans for trace eyeblink conditioning suggest the possibility that a memory trace(s) may develop in the hippocampus in trace conditioning, but do not prove it. Recall that delay eyeblink conditioning results in the development of a pronounced neuronal model of the learned response in the hippocampus (as does trace training). Several lines of evidence support the view that localized changes do develop in the hippocampus in eyeblink conditioning. Thus, the properties of increased neuronal activity are strikingly similar to the properties of LTP (Berger et al. 1986, Thompson 1997, Weisz et al. 1984), and induction of LTP in the hippocampus can enhance eyeblink learning (Berger 1984). There is also a dramatic learninginduced decrease in the after-hyperpolarization in pyramidal neurons in eyeblink conditioning, most evident in trace conditioning, which leads to increased excitability. This alteration is intrinsic to the neurons due to a decrease in one or more calcium-dependent outward potassium currents (Disterhoft et al. 1986, Disterhoft & McEchron 2000). There is also a dramatic increase in dendritic membraneassociated protein kinase C after eyeblink conditioning (Olds et al. 1989). After trace eyeblink conditioning there is a substantial increase in the number of multiple

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synapse boutons in hippocampal neurons (Geinisman et al. 2001). Finally, there is a dramatic increase in the number of new neurons in the hippocampus in trace, but not delay, eyeblink conditioning (Gould et al. 1999, Shors et al. 2001). These sets of structural and chemical changes are certainly consistent with the formation of higher-order memory traces in the hippocampus. But recall that cerebellar lesions can abolish both the behavioral learned response and the increased neuronal response in the CS period in the hippocampus in both delay and trace learning (see below and Clark et al. 1984, Woodruff-Pak et al. 1985). So doubts remain. Interestingly, the early (over trials)-developing increase in neuronal activity in the hippocampus in the US period is not completely abolished by cerebellar lesions (Clark et al. 1984). In my view, it is necessary to identify the entire essential circuitry from stimulus input to motor output in order to establish definitively that the essential memory trace, e.g., for trace conditioning, develops and is stored, albeit temporarily, in the hippocampus (see below). To date, not all the essential circuitry has been identified (see Thompson & Kim 1996). Yet another remaining puzzle is where the trace memories go after the hippocampus is no longer necessary.

The Essential Circuitry: The Cerebellum So the hippocampus is not the locus of the essential memory trace for standarddelay classical eyeblink conditioning. In a detailed mapping study of the brain stem we identified several loci where neurons exhibited the requisite neuronal model of the CR—increase in frequency of discharge that preceded the onset of the USUR and modeled the amplitude time source of the behavioral CR, just as did the neuronal response in the hippocampus (McCormick et al. 1983). The following brain areas showed this neuronal response predictive of the learned behavioral CR: the relevant motor nuclei (as noted above), a region in the vicinity of the fifth nucleus, various reticular regions, the cerebellar cortex (ansiform and anterior lobes), the cerebellar interpositus nucleus, the pontine nuclei, and the red nucleus. The response in the interpositus nucleus was particularly robust (Figure 4). The fact that a neuronal model of the behavioral CR developed in the cerebellum was suggestive of a memory trace but we knew from our earlier work on the hippocampus that neuronal recordings, per se, could not identify the essential memory trace. We completed a series of lesion studies, initially involving large cerebellar lesions and later lesions limited to the interpositus nucleus (e.g., McCormick & Thompson 1984a,b; Clark et al. 1984), all of which completely prevented learning and completely and permanently abolished the CR with no effect on the UR (thus ruling out performance variables). In a long series of studies completed while we were at Stanford, we identified virtually the entire essential memory trace circuit using electrophysiological recordings, electrical stimulation, lesions (aspiration, electrolytic, and chemical), and anatomical pathway tracing methods (see Chapman et al. 1988; Christian & Thompson 2003; McCormick et al. 1982a, 1985; Lavond et al. 1985; Mauk et al. 1986; Steinmetz et al. 1986, 1987; J.K. Thompson et al. 1985; Woodruff-Pak et al.

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Figure 4 Neuronal model of learned eyeblink response recorded from the cerebellar interpositus nucleus. Each graph shows nictitating membrane movement (top trace) and a histogram of multiple unit activity (bottom trace) averaged and summed over 100 trials. Animals that received explicitly unpaired presentations of the conditioning stimuli do not develop altered patterns of neuronal activity (left column). Trained animals develop a neuronal model of the learned behavior. Total trace duration 750 msec. (From McCormick & Thompson 1984b.)

1985). In brief, the efferent CR pathway projects from the interpositus nucleus ipsilateral to the trained eye, via the superior cerebellar peduncle, to the contralateral magnocellular red nucleus and to the relevant motor nuclei ipsilateral to the trained eye. The cerebellar interpositus lesion abolition of the eyeblink CR is strictly ipsilateral and is due to damage to neuron somas, not fibers of passage. The inferior

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olive-climbing fiber system appears to be the critical US reinforcing or teaching pathway and the pontine nuclei and mossy fiber system appears to convey the necessary CS information to the cerebellum. A particularly satisfying aspect of our discovery of the essential role of the cerebellum in classical conditioning of discrete responses is the relevance of our work to the human condition. Irene Daum and associates, working in Germany, replicated in humans the fact that appropriate cerebellar damage completely prevents learning of the eyeblink CR (Daum et al. 1993), since replicated in many other studies. Christine Logan (a former graduate student of mine) and Scott Grafton, a neurologist then at the University of Southern California, completed an extensive positron emission tomography (PET) study of eyeblink conditioning in humans. They found significant activation in the cerebellar interpositus nucleus and several loci in cerebellar cortex, in close agreement with our recording studies in the rabbit cerebellum (Logan & Grafton 1995), again replicated in many other studies. A point that is obvious from our studies but seems not to be widely understood is that our results on the role of the cerebellum in classical conditioning apply to the learning of any discrete movement: eyeblink, head turn, forelimb flexion, hindlimb flexion, etc. (see Shinkman et al. 1996). Eyeblink conditioning is simply a convenient response to measure. Our findings to date seem to have identified perhaps the critical function of the cerebellum, namely the learning of discrete skilled movements, a basic notion proposed in classic theories of the cerebellum as a learning machine (see Albus 1971, Eccles 1977, Ito 1984, Marr 1969). Indeed, our work constitutes a compelling verification of these theories. Our data to this point strongly supported the hypothesis that the essential memory trace was formed and stored in the cerebellum, but did not prove it. Hence we adapted use of methods of reversible inactivation to localize the memory trace. The logic is straightforward. Having identified the essential memory trace circuit, reversibly inactivate each key locus in the circuit during training. If this completely prevents learning at a given locus, then this locus either conveys essential afferent information to the memory trace or is the site of the memory trace. However, if reversible inactivation of a given locus does not prevent learning at all, then this locus is efferent from the memory trace. But remember that inactivation of all these essential loci will completely prevent expression of the CR. We infused muscimol for inactivation of neuron cell bodies—it acts on gamma amino butyric acid (GABAA) receptors as an agonist and completely shuts down (hyperpolarizes) the neurons for several hours, after which they fully recover. To inactivate axons we infused tetrodotoxin (TTX) (see, e.g., Krupa et al. 1993; Krupa & Thompson 1995, 1997; Krupa et al. 1996; Thompson & Krupa 1994). David Lavond and his students completed parallel studies using reversible cooling (e.g., Clark et al. 1992, Clark & Lavond 1993). Results are completely consistent and have since been replicated in other laboratories. In brief (see Figure 5) inactivation limited to the anterior interpositus nucleus completely prevented learning. After removal of inactivation animals showed no signs of learning; with subsequent training they learned normally as though they

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Figure 5 Highly simplified schematic of the essential memory trace circuit for delay classical conditioning of the eyeblink response to illustrate the use of reversible inactivation to localize memory traces. Shading in a, b, and c indicate reversible inactivation of the key region of each structure during training using muscimol; d indicates inactivation of the axonal pathway exiting from the cerebellum, the superior cerebellar peduncle, using tetrodotoxin. Inactivation of the interpositus (c) completely prevents learning, but inactivation of the superior peduncle (d), the red nucleus (b), and the motor nuclei (a) does not prevent learning at all. (Modified from Thompson & Krupa 1994.)

had no prior training; there was no savings. In complete contrast, inactivation of the superior peduncle (the immediate efferent projection from the interpositus/dentate nuclei), the red nucleus, and the relevant motor nuclei, although completely preventing expression of the CR, did not prevent learning at all. After removal of the inactivation the animals had fully learned to asymptote. Thus, inactivation localized to the anterior interpositus nucleus completely prevented learning but inactivation of its immediate output pathway did not prevent learning at all. The fact that after interpositus inactivation there was no savings argues strongly that no part of the memory trace developed in structures afferent to the interpositus. Similarly, the fact that inactivation of structures efferent from the interpositus did not prevent learning at all argues that no part of the memory is formed in these structures. I noted above that several loci in the brain exhibited the neuronal model of the learned behavioral CR, e.g., trigeminal nuclear region, pontine nuclei, etc. In a series of studies Lavond and associates (and we) showed that with inactivation of

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the interpositus nucleus, all these models disappeared, i.e., they are all driven from the interpositus (Bao et al. 2000, Lavond & Cartford 2000). Because muscimol acts only on neuron cell bodies and not on axons, the inactivation of the interpositus nucleus does not prevent normal activation of cerebellar cortex by the two major afferent systems, climbing fibers from the inferior olive and mossy fibers from many sources. The fact that no learning at all occurred with inactivation limited to the interpositus nucleus would seem to argue against formation of memory traces in the cerebellar cortex. However, this inactivation blocks the direct projections from the interpositus nucleus to the cerebellar cortex. When we first discovered the essential role of the cerebellum in eyeblink conditioning I had hoped the memory traces would be formed and stored in the cerebellar cortex. The cellular machinery in the cortex is vast (each Purkinje neuron receives up to 200,000 synapses from granule neurons). However, we were never able to completely prevent or abolish the CR by lesions limited to the cerebellar cortex. Other workers, particularly Yeo and associates (Yeo et al. 1984), claimed to have done so. The problem is with the lesion method—it is impossible to remove all of the cerebellar cortex without damaging the critical region of the interpositus nucleus (see detailed discussion in Christian & Thompson 2003). However, we consistently found that with large cerebellar cortical lesions or reversible inactivation, learning was much slower and to a lesser degree and adaptive timing of the CR was lost. Normally the eyeblink closure CR peaks at the onset of the US; it is a maximally adaptive response. After large cortical lesions, the CR peaks earlier in time and is no longer adaptive (Garcia et al. 1999, McCormick & Thompson 1984b). So the cortex is critically important for normal adaptive learning and it seemed very likely that higher-order memory traces were established there. But again the lesion method is inconclusive. Fortunately nature provided us with an ideal preparation, the Purkinje cell degeneration (pcd) mutant mouse. The brain of this mutant develops normally until about two weeks after birth. Then over the next several weeks, all Purkinje neurons in the cerebellar cortex die; as a result the animals have no functional cerebellar cortex. But the interpositus nucleus remains functional. Results were clear: The pcd mice were able to learn (L. Chen et al. 1996). They learned much more slowly and to a lesser degree than normal mice of the same strain (littermates), but they still showed significant and substantial learning. The CRs were shorter latency in the pcd mice. They also showed rapid extinction with CS-alone training. The possibility that the memory trace was somehow formed in loci other than the cerebellum was ruled out by lesioning the interpositus nucleus bilaterally in pcd mice before training. The lesioned pcd mice were unable to learn the conditioned eyeblink response (L. Chen et al. 1999). These results were very similar to the effects of large cerebellar cortical lesions we had found earlier in rabbits. So the cortex is critically important for normal learning, and higher-order memory traces are likely formed there. There is considerable evidence supporting the view that a process of long-term potentiation

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(LTD) at the parallel fiber-Purkinje neuron dendritic synapses, discovered by Ito (see 1984), plays a key role in initial plasticity (C. Chen & Thompson 1995, C. Chen et al. 1995, Shibuki et al. 1996, Kim & Thompson 1997). Indeed, Purkinje neurons show substantial changes in CS-evoked discharge frequency over the course of training, with many showing learning-induced decrease in discharge frequency, as could be expected if LTD developed (Christian et al. 2002, Christian & Thompson 2003). Other lines of evidence also support the view that memory traces are formed in cerebellar cortex (Cooke et al. 2004). The evidence is now very strong that the basic essential memory trace is formed and stored in the anterior interpositus nucleus for classical conditioning of the eyeblink response. Indeed, the reversible inactivation studies provide the key evidence (Figure 5). It is clear that long-lasting changes in the critical neurons in the anterior interpositus nucleus do develop. Thus, infusion of protein synthesis inhibitors in this locus completely prevents learning (Bracha et al. 1998, G. Chen & Steinmetz 2000, Gomi et al. 1999). Further, training results in selective expression of a protein kinase in this locus, KKIAMRE, an enzyme for the cell division cycle (Gomi et al. 1999). Pharmacological isolation of the interpositus from the cerebellar cortex reveals clear learning-induced increases in excitability of interpositus neurons (Bao et al. 2002, Garcia & Mauk 1998). Perhaps most convincing, eyeblink conditioning results in a dramatic and highly significant increase in the number of excitatory synapses (but not inhibitory synapses) in the interpositus nucleus (Kleim et al. 2002).

CONCLUSION The essential circuitry for classical conditioning of the eyeblink response is shown in Figure 6, along with the site of memory trace formation in the interpositus nucleus and a putative site of plasticity in the cerebellar cortical neurons. The nature of the memory is defined by this circuit; the circuit is the memory. The CS activates the sensory afferent pathways to the site(s) of trace storage in the cerebellum, which activates the efferent pathways to the motor nuclei and the learned behavior. The content of the memory, the conditioned eyeblink response, is completely defined and completely predictable from the essential circuit. The formal criteria developed by Richard Morris and associates to demonstrate that a given set of phenomena establish what they term the “synaptic plasticity and memory” (SPM) hypothesis are as follows (Martin et al. 2000): 1. DETECTABILITY: If an animal displays memory of some previous experience, a change in synaptic efficacy should be detectable somewhere in its nervous system. 2. MIMICRY: Conversely, if it were possible to induce the same spatial pattern of synaptic weight changes artificially, the animal should display “apparent” memory for some past experience which did not in practice occur.

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Figure 6 Simplified schematic (most interneurons omitted) of the putative essential circuitry for delay classical conditioning of eyeblink (and other discrete responses) learned with an aversive US. (The sensory and motor nuclei activated depend of course on the nature of the CS and US; the more central portions of the circuit appear to be general.) The reflex US-UR pathway involves direct and indirect projection from the trigeminal nucleus to the motor nuclei (for the eyeblink UR and CR, primarily accessory 6 and 7). The tone CS pathway projects from auditory nuclei to the pontine nuclei and to the cerebellum as mossy fibers. The US pathway includes projections from the trigeminal to the inferior olive and to the cerebellum as climbing fibers. The CR pathway projects from the interpositus to the red nucleus and on to premotor and motor nuclei. There is also a direct GABAergic inhibitory projection from the interpositus to the inferior olive. Solid cell bodies and bar terminals indicate inhibitory neurons; open cell bodies and fork terminals indicate excitatory neurons. Stars indicate sites of plasticity based on current evidence. See text for details. (From Christian & Thompson 2003.) CR, conditioned response; CS, conditioned stimulus; UR, unconditioned response; US, unconditioned stimulus.

3. ANTEROGRADE ALTERATION: Interventions that prevent the induction of synaptic weight changes during a learning experience should impair the animal’s memory of that experience. 4. RETROGRADE ALTERATION: Interventions that alter the spatial distribution of synaptic weights induced by a prior learning experience (see detectability) should alter the animal’s memory of that experience (p. 651).

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I would expand the SPM notion to include nonsynaptic mechanisms of plasticity and memory as well, referring to all as the memory trace. I would also note that these criteria cannot be met unless the putative memory trace has been localized. Evidence to date on localization of the memory trace(s) for classical conditioning of eyeblink and other discrete responses would seem to satisfy Morris’ criteria. 1. DETECTABILITY: There is a dramatic increase in neuronal/synaptic efficacy in the cerebellum, both in the cortex of lobule HVI and in the anterior interpositus nucleus as a result of training (see, e.g., Shinkman et al. 1996, Bao et al. 2002). Indeed, long-term potentiation (LTP) has been reported to occur in the interpositus nucleus (Racine et al. 1986). 2. MIMICRY: Electrical microstimulation of the locus of the memory trace in the anterior interpositus nucleus can evoke the response to be learned before training (McCormick & Thompson 1984a, Chapman et al. 1988). 3. ANTEROGRADE ALTERATION: Infusion of muscimol into the site of the memory trace in the anterior interpositus nucleus completely prevents learning, i.e., induction of the memory trace. 4. RETROGRADE ALTERATION: Infusion of muscimol into the site of the memory trace in the anterior interpositus nucleus in a well-trained animal alters the synaptic weights (shuts them down) and abolishes the animal’s memory for that experience. These of course are only opinions of what constitutes demonstration of a memory trace. In my view the key is that the essential circuit defines the memory, as I noted above. But we still do not know the detailed nature of the memory trace in the interpositus and how it is formed. It will be necessary to identify all the steps in the causal chain from initial activation of the neurons at the beginning of training to the final form of the memory trace, from the biochemical/genetic processes to the structural changes in the synapses and neurons that code the permanent memory trace. ACKNOWLEDGMENTS Work described in this paper was supported in part by National Science Foundation Grant IBN 92,15069, National Institutes of Aging Grant AG14751, a grant from the Sankyo Company, and funds from the University of Southern California. The Annual Review of Psychology is online at http://psych.annualreviews.org

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LOCALIZATION OF MEMORY TRACES in the lateral pontine nucleus. Behav. Neurosci. 114:254–61 Bao S, Cheng EF, Davis JD, Gobeske KT, Merzenich MM. 2003. Progressive degradation and subsequent refinement of acoustic representations in the adult auditory cortex. J. Neurosci. 23:10765–75 Berger TW. 1984. Long-term potentiation of hippocampal synaptic transmission affects rate of behavioral learning. Science 224:627– 30 Berger TW, Alger BE, Thompson RF. 1976. Neuronal substrate of classical conditioning in the hippocampus. Science 192:483– 85 Berger TW, Berry SD, Thompson RF. 1986. Role of the hippocampus in classical conditioning of aversive and appetitive behaviors. In The Hippocampus, Vols. III and IV, ed. RL Isaacson, KH Pribram, pp. 203–39. New York: Plenum Berger TW, Thompson RF. 1978a. Neuronal plasticity in the limbic system during classical conditioning of the rabbit nictitating membrane response. I. The hippocampus. Brain Res. 145:323–46 Berger TW, Thompson RF. 1978b. Identification of pyramidal cells as the critical elements in hippocampal neuronal plasticity during learning. Proc. Natl. Acad. Sci. USA 75:1572–76 Berry SD, Thompson RF. 1978. Prediction of learning rate from the hippocampal EEG. Science 200:1298–300 Bracha V, Irwin KB, Webster ML, Wunderlich DA, Stachowiak MK, Bloedel JR. 1998. Microinjections of anisomycin into the intermediate cerebellum during learning affect the acquisition of classically conditioned responses in the rabbit. Brain Res. 788:169–78 Cegavske CF, Patterson MM, Thompson RF. 1979. Neuronal unit activity in the abducens nucleus during classical conditioning of the nictitating membrane response in the rabbit Oryctolagus cuniculus. J. Comp. Physiol. Psychol. 93:595–609 Cegavske CF, Thompson RF, Patterson MM, Gormezano I. 1976. Mechanisms of effer-

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ent neuronal control of the reflex nictitating membrane response in the rabbit (Oryctolagus cuniculus). J. Comp. Physiol. Psychol. 90:411–23 Chapman PF, Steinmetz JE, Thompson RF. 1988. Classical conditioning does not occur when direct stimulation of the red nucleus or cerebellar nuclei is the unconditioned stimulus. Brain Res. 442:97–104 Chen C, Kano M, Abeliovich A, Chen L, Bao S, et al. 1995. Impaired motor coordination correlates with persistent multiple climbing fiber innervation in PKCγ mutant mice. Cell 83:1233–42 Chen C, Thompson RF. 1995. Temporal specificity of long-term depression in parallel fiber-Purkinje synapses in rat cerebellar slice. Learn. Mem. 2:185–98 Chen G, Steinmetz JE. 2000. Microinfusion of protein kinase inhibitor H7 in the cerebellum impairs the acquisition but not retention of classical eyeblink conditioning in rabbits. Brain Res. 856:193–201 Chen L, Bao S, Lockard JM, Kim JJ, Thompson RF. 1996. Impaired classical eyeblink conditioning in cerebellar lesioned and Purkinje cell degeneration (pcd) mutant mice. J. Neurosci. 16:2829–38 Chen L, Bao S, Thompson RF. 1999. Bilateral lesions of the interpositus nucleus completely prevent eyeblink conditioning in Purkinje cell degeneration mutant mice. Behav. Neurosci. 113:204–10 Christian KM, Poulos AM, Thompson RF. 2002. Purkinje cell activity during classical conditioning of the eyeblink reflex in rabbits. Soc. Neurosci. Abstr. 79.9 Christian KM, Thompson RF. 2003. Neural substrates of eyeblink conditioning: acquisition and retention. Learn. Mem. 11:427–55 Clark GA, McCormick DA, Lavond DG, Thompson RF. 1984. Effects of lesions of cerebellar nuclei on conditioned behavioral and hippocampal neuronal responses. Brain Res. 291:125–36 Clark RE, Lavond DG. 1993. Reversible lesions of the red nucleus during acquisition and retention of a classically conditioned

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LOCALIZATION OF MEMORY TRACES mechanisms of the corneal blinking reflex in cats. Brain Res. 125:265–75 Ito M. 1984. The Cerebellum and Neural Control. New York: Raven Ivkovich D, Thompson RF. 1997. Motor cortex lesions do not affect learning or performance of the eyeblink response in rabbits. Behav. Neurosci. 111:727–38 Kandel ER. 1975. The Cellular Basis of Behavior: An Introduction to Behavioral Neurobiology. San Francisco: Freeman Kandel ER, Spencer WA. 1968. Cellular neurophysiological approaches in the study of learning. Physiol. Rev. 48:65–134 Kim JJ, Clark RE, Thompson RF. 1995. Hippocampectomy impairs the memory of recently, but not remotely, acquired trace eyeblink conditioned responses. Behav. Neurosci. 109:195–203 Kim JJ, Thompson RF. 1997. Cerebellar circuits and synaptic mechanisms involved in classical eyeblink conditioning. Trends Neurosci. 20:177–81 Kleim JA, Freeman JH Jr, Bruneau R, Nolan BC, Cooper NR, Zook A, et al. 2002. Synapse formation is associated with memory storage in the cerebellum. Proc. Natl. Acad. Sci. USA 99:13228–31 Krupa DJ, Thompson JK, Thompson RF. 1993. Localization of a memory trace in the mammalian brain. Science 260:989–91 Krupa DJ, Thompson RF. 1995. Inactivation of the superior cerebellar peduncle blocks expression but not acquisition of the rabbit’s classically conditioned eyeblink response. Proc. Natl. Acad. Sci. USA 92:5097–101 Krupa DJ, Thompson RF. 1997. Reversible inactivation of the cerebellar interpositus nucleus completely prevents acquisition of the classically conditioned eyeblink response. Learn. Mem. 3:545–56 Krupa DJ, Weng J, Thompson RF. 1996. Inactivation of brainstem motor nuclei blocks expression but not acquisition of the rabbit’s classically conditioned eyeblink response. Behav. Neurosci. 110:219–27 Lashley KS. 1950. In search of the engram. Soc. Exp. Biol. Symp. 4:454–82

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Lavond DG, Cartford MC. 2000. Eyeblink conditioning circuitry: tracing, lesion, and reversible lesion experiments. In Eyeblink Classical Conditioning: Animal Models, ed. DS Woodruff-Pak, JE Steinmetz, 2:51–80. Boston, MA: Kluwer Acad. Lavond DG, Hembree TL, Thompson RF. 1985. Effect of kainic acid lesions of the cerebellar interpositus nucleus on eyelid conditioning in the rabbit. Brain Res. 326:179–83 Lavond DG, Kim JJ, Thompson RF. 1993. Mammalian brain substrates of aversive classical conditioning. Annu. Rev. Psychol. 44:317–42 Lavond DG, Logan CG, Sohn JH, Garner WD, Kanzawa SA. 1990. Lesions of the cerebellar interpositus nucleus abolish both nictitating membrane and eyelid EMG conditioned responses. Brain Res. 514:238–48 Logan CG, Grafton ST. 1995. Functional anatomy of human eyeblink conditioning determined with regional cerebral glucose metabolism and positron-emission tomography. Proc. Natl. Acad. Sci. USA 92:7500–4 Marr D. 1969. A theory of cerebellar cortex. J. Physiol. 202:437–70 Martin SJ, Grimwood PD, Morris RGM. 2000. Synaptic plasticity and memory: an evaluation of the hypothesis. Annu. Rev. Neurosci. 23:649–711 Mauk MD, Steinmetz JE, Thompson RF. 1986. Classical conditioning using stimulation of the inferior olive as the unconditioned stimulus. Proc. Natl. Acad. Sci. USA 83:5349–53 McCormick DA, Clark GA, Lavond DG, Thompson RF. 1982a. Initial localization of the memory trace for a basic form of learning. Proc. Natl. Acad. Sci. USA 79:2731–42 McCormick DA, Lavond DG, Thompson RF. 1982b. Concomitant classical conditioning of the rabbit nictitating membrane and eyelid responses: correlations and implications. Physiol. Behav. 28:769–75 McCormick DA, Lavond DG, Thompson RF. 1983. Neuronal responses of the rabbit brainstem during performance of the classically conditioned nictitating membrane (NM/eyelid response). Brain Res. 271:73–88

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McCormick DA, Steinmetz JE, Thompson RF. 1985. Lesions of the inferior olivary complex cause extinction of the classically conditioned eyeblink response. Brain Res. 359: 120–30 McCormick DA, Thompson RF. 1984a. Cerebellum: essential involvement in the classically conditioned eyelid response. Science 223:296–99 McCormick DA, Thompson RF. 1984b. Neuronal responses of the rabbit cerebellum during acquisition and performance of a classically conditioned nictitating membraneeyelid response. J. Neurosci. 4:2811–22 McGlinchey-Berroth R, Carrillo MC, Gabrieli JD, Brawn CM, Disterhoft JF. 1997. Impaired trace eyeblink conditioning in bilateral, medial-temporal lobe amnesia. Behav. Neurosci. 111:873–82 Moyer JR Jr, Deyo RA, Disterhoft JF. 1990. Hippocampectomy disrupts trace eye-blink conditioning in rabbits. Behav. Neurosci. 104:243–52 Olds J, Anderson ML, McPhie DL, Staten LD, Alkon DL. 1989. Imaging of memoryspecific changes in the distribution of protein kinase C in the hippocampus. Science 245:866–69 Olds J, Disterhoft J, Segal M, Kornblith DL, Hirsh R. 1972. Learning centers of rat brain mapped by measuring latencies of conditioned unit responses. J. Neurophysiol. 35: 202–19 Patterson MM, Cegavske CF, Thompson RF. 1973. Effects of classical conditioning paradigm on hindlimb flexor nerve response in immobilized spinal cat. J. Comp. Physiol. Psychol. 84:88–97 Racine RJ, Wilson DA, Gingell R, Sunderland D. 1986. Long-term potentiation in the interpositus and vestibular nuclei in the rat. Exp. Brain Res. 63:158–62 Schacter DL. 1987. Implicit memory: history and current status. Exp. Psychol. Learn. Mem. Cogn. 13:501–18 Schmaltz LW, Theios J. 1972. Acquisition and extinction of a classically conditioned response in hippocampectomized rabbits

(Oryctolagus cuniculus). J. Comp. Physiol. Psychol. 79:328–33 Shibuki K, Gomi H, Chen L, Bao S, Kim JJ, et al. 1996. Deficient cerebellar long-term depression, impaired eyeblink conditioning and normal motor coordination in GFAP mutant mice. Neuron 16:587–99 Shinkman PG, Swain RA, Thompson RF. 1996. Classical conditioning with electrical stimulation of cerebellum as both conditioned and unconditioned stimulus. Behav. Neurosci. 110:914–21 Shors TJ, Matzel LD. 1997. Long-term potentiation: What’s learning got to do with it? Behav. Brain Sci. 20:597–614; discussion 614–55 Shors TJ, Miesegaes G, Beylin A, Zhao M, Rydel T, Gould E. 2001. Neurogenesis in the adult is involved in the formation of trace memories. Nature 410:372–76 Solomon PR, Vander Schaaf ER, Thompson RF, Weisz DJ. 1986. Hippocampus and trace conditioning of the rabbit’s classically conditioned nictitating membrane response. Behav Neurosci. 100:729–44 Squire LR. 1987. Memory and Brain. New York: Oxford Univ. Press Squire LR. 1992. Declarative and nondeclarative memory: multiple brain systems supporting learning and memory. J. Cogn. Neurosci. 4:232–43 Steinmetz JE, Logan CG, Rosen DJ, Thompson JK, Lavond DG, Thompson RF. 1987. Initial localization of the acoustic conditioned stimulus projection system to the cerebellum essential for classical eyelid conditioning. Proc. Natl. Acad. Sci. USA 84:3531–35 Steinmetz JE, Rosen DJ, Chapman PF, Lavond DG, Thompson RF. 1986. Classical conditioning of the rabbit eyelid response with a mossy fiber stimulation CS. I. Pontine nuclei and middle cerebellar peduncle stimulation. Behav. Neurosci. 100:871–80 Thompson JK, Lavond DG, Thompson RF. 1985. Cerebellar interpositus/dentate nuclei afferent seen with retrograde fluorescent tracers in the rabbit. Neurosci. Abstr. 11:1112 Thompson RF. 1997. Classical conditioning

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LOCALIZATION OF MEMORY TRACES has much to do with LTP. Behav. Brain Sci. 20:632–33 Thompson RF, Berger TW, Cegavske CF, Patterson MM, Roemer RA, et al. 1976. The search for the engram. Am. Psychol. 31:209– 27 Thompson RF, Kim JJ. 1996. Memory systems in the brain and localization of a memory. Proc. Natl. Acad. Sci. USA 93:13438–44 Thompson RF, Krupa DJ. 1994. Organization of memory traces in the mammalian brain. Annu. Rev. Neurosci. 17:519–49 Thompson RF, Patterson MM, Teyler TJ. 1972. Neurophysiology of learning. Annu. Rev. Psychol. 23:73–104 Thompson RF, Spencer WA. 1966. Habituation: a model phenomenon for the study of neuronal substrates of behavior. Psychol. Rev. 73:16–43 Tulving E. 1985. How many memory systems are there? Am. Psychol. 40:385–98 Wagner AR, Donegan NH. 1989. Some relationships between a computational model (SOP) and a neural circuit for Pavlovian (rabbit eyeblink) conditioning. In The Psychology of Learning and Motivation, ed. RD Hawkins, GH Bower, 22:157–203. San Diego: Academic Weinberger NM, Hopkins W, Diamond DM. 1984. Physiological plasticity of single neurons in auditory cortex of the cat during acquisition of the papillary conditioned re-

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sponse: I. Primary Field (AI). Behav. Neurosci. 98:171–88 Weisz DJ, Clark GA, Thompson RF. 1984. Increased activity of dentate granule cells during nictitating membrane response conditioning in rabbits. Behav. Brain Res. 12:145– 54 Wilson MA, Tonegawa S. 1997. Synaptic plasticity, place cells and spatial memory: study with second generation knockouts. Trends Neurosci. 20:102–6 Woodruff-Pak DS, Lavond DG, Thompson RF. 1985. Trace conditioning: abolished by cerebellar nuclear lesions but not lateral cerebellar cortex aspirations. Brain Res. 348:249– 60 Woody CD, Alkon DL, Hay B. 1984. Depolarization-induced effects of Ca2+-calmodulindependent protein kinase injection, in vivo, in single neurons of cat motor cortex. Brain Res. 321:192–97 Yeo CH, Hardiman MJ, Glickstein M. 1984. Discrete lesions of the cerebellar cortex abolish the classically conditioned nictitating membrane response of the rabbit. Behav. Brain Res. 13:261–66 Young RA, Cegavske CF, Thompson RF. 1976. Tone-induced charges in excitability of abducens motoneurons and the reflex path of the rabbit nictitating membrane response. J. Comp. Physiol. Psychol. 90:424– 34

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CONTENTS Frontispiece—Richard F. Thompson

xviii


PREFATORY In Search of Memory Traces, Richard F. Thompson

1

DECISION MAKING Indeterminacy in Brain and Behavior, Paul W. Glimcher

25

BRAIN IMAGING/COGNITIVE NEUROSCIENCE Models of Brain Function in Neuroimaging, Karl J. Friston

57

MUSIC PERCEPTION Brain Organization for Music Processing, Isabelle Peretz and Robert J. Zatorre

89

SOMESTHETIC AND VESTIBULAR SENSES Vestibular, Proprioceptive, and Haptic Contributions to Spatial Orientation, James R. Lackner and Paul DiZio

115

CONCEPTS AND CATEGORIES Human Category Learning, F. Gregory Ashby and W. Todd Maddox

149

ANIMAL LEARNING AND BEHAVIOR: CLASSICAL Pavlovian Conditioning: A Functional Perspective, Michael Domjan

179

NEUROSCIENCE OF LEARNING The Neuroscience of Mammalian Associative Learning, Michael S. Fanselow and Andrew M. Poulos

207

HUMAN DEVELOPMENT: EMOTIONAL, SOCIAL, AND PERSONALITY Behavioral Inhibition: Linking Biology and Behavior Within a Developmental Framework, Nathan A. Fox, Heather A. Henderson, Peter J. Marshall, Kate E. Nichols, and Melissa A. Ghera

235

BIOLOGICAL AND GENETIC PROCESSES IN DEVELOPMENT Human Development: Biological and Genetic Processes, Irving I. Gottesman and Daniel R. Hanson

263 vii

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SPECIAL TOPICS IN PSYCHOPATHOLOGY The Psychology and Neurobiology of Suicidal Behavior, Thomas E. Joiner Jr., Jessica S. Brown, and LaRicka R. Wingate

287

DISORDERS OF CHILDHOOD Autism in Infancy and Early Childhood, Fred Volkmar, Kasia Chawarska, and Ami Klin

315


CHILD/FAMILY THERAPY Youth Psychotherapy Outcome Research: A Review and Critique of the Evidence Base, John R. Weisz, Amanda Jensen Doss, and Kristin M. Hawley

337

ALTRUISM AND AGGRESSION Prosocial Behavior: Multilevel Perspectives, Louis A. Penner, John F. Dovidio, Jane A. Piliavin, and David A. Schroeder

365

INTERGROUP RELATIONS, STIGMA, STEREOTYPING, PREJUDICE, DISCRIMINATION The Social Psychology of Stigma, Brenda Major and Laurie T. O’Brien

393

PERSONALITY PROCESSES Personality Architecture: Within-Person Structures and Processes, Daniel Cervone

423

PERSONALITY DEVELOPMENT: STABILITY AND CHANGE Personality Development: Stability and Change, Avshalom Caspi, Brent W. Roberts, and Rebecca L. Shiner

453

WORK MOTIVATION Work Motivation Theory and Research at the Dawn of the Twenty-First Century, Gary P. Latham and Craig C. Pinder

485

GROUPS AND TEAMS Teams in Organizations: From Input-Process-Output Models to IMOI Models, Daniel R. Ilgen, John R. Hollenbeck, Michael Johnson, and Dustin Jundt

517

LEADERSHIP Presidential Leadership, George R. Goethals

545

PERSONNEL EVALUATION AND COMPENSATION Personnel Psychology: Performance Evaluation and Pay for Performance, Sara L. Rynes, Barry Gerhart, and Laura Parks

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PSYCHOPHYSIOLOGICAL DISORDERS AND PSYCHOLOGICAL EFFECTS ON MEDICAL DISORDERS Psychological Approaches to Understanding and Treating Disease-Related Pain, Francis J. Keefe, Amy P. Abernethy, and Lisa C. Campbell

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TIMELY TOPIC Psychological Evidence at the Dawn of the Law’s Scientific Age, David L. Faigman and John Monahan

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INDEXES Subject Index Cumulative Index of Contributing Authors, Volumes 46–56 Cumulative Index of Chapter Titles, Volumes 46–56

ERRATA An online log of corrections to Annual Review of Psychology chapters may be found at http://psych.annualreviews.org/errata.shtml

661 695 700

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Annu. Rev. Psychol. 2005. 56:25–56 doi: 10.1146/annurev.psych.55.090902.141429 c 2005 by Annual Reviews. All rights reserved Copyright First published online as a Review in Advance on September 10, 2004

INDETERMINACY IN BRAIN AND BEHAVIOR Paul W. Glimcher


Center for Neural Science, New York University, New York, New York 10003; email: [email protected]

Key Words randomness, decision-making, choice, game theory ■ Abstract The central goal of modern science that evolved during the Enlightenment was the empirical reduction of uncertainty by experimental inquiry. Although there have been challenges to this view in the physical sciences, where profoundly indeterminate events have been identified at the quantum level, the presumption that physical phenomena are fundamentally determinate seems to have defined modern behavioral science. Programs like those of the classical behaviorists, for example, were explicitly anchored to a fully deterministic worldview, and this anchoring clearly influenced the experiments that those scientists chose to perform. Recent advances in the psychological, social, and neural sciences, however, have caused a number of scholars to begin to question the assumption that all of behavior can be regarded as fundamentally deterministic in character. Although it is not yet clear whether the generative mechanisms for human and animal behavior will require a philosophically indeterminate approach, it is clear that behavioral scientists of all kinds are beginning to engage the issues of indeterminacy that plagued physics at the beginning of the twentieth century. CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Determinism and the Philosophy of Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Do Indeterminacies in the Physical World Matter for Behavioral Scientists? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THE RISING TIDE OF APPARENT INDETERMINACY . . . . . . . . . . . . . . . . . . . . . Indeterminacy in the Social Sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Empirical Measurements of Behavioral Indeterminacy . . . . . . . . . . . . . . . . . . . . . . Reducing Uncertainty: Looking for Determinacy with Neurophysiology . . . . . . . . Indeterminacy at the Cellular and Subcellular Levels . . . . . . . . . . . . . . . . . . . . . . . . THE CHALLENGE OF INDETERMINACY FOR BEHAVIORAL SCIENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25 26 27 29 29 35 40 46 50

INTRODUCTION Our modern view that the central function of scientific inquiry is to reduce uncertainty emerged early in the scientific revolution that constituted the Enlightenment; by the time of Galileo’s death (cf. Bacon 1620, Descartes 1637, Galilei 1630, 0066-4308/05/0203-0025$14.00

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Kepler 1618–1621) it was clear that improving the accuracy with which one could predict future events as determinate processes would be a central feature of the scientific method at both theoretical and empirical levels in the physical sciences. Over the course of the eighteenth and nineteenth centuries, the early social sciences emulated this trend, seeking to develop causal relationships in a testable and determinate fashion (cf. Keynes 1936, Smith 1776). By the twentieth century, the notion that scientific inquiry would reduce animal behavior to deterministic certainty had become a mainstream notion in psychological circles as well. Nowhere is this clearer than in the work of Pavlov. As Pavlov put it in Conditioned Reflexes: The physiologist must thus take his own path, where a trail has already been blazed for him. Three hundred years ago Descartes evolved the idea of the reflex. Starting from the assumption that animals behaved simply as machines, he regarded every activity of the organism as a necessary reaction to some external stimulus. . .. A bold attempt to apply the idea of the reflex to the activities of the [cerebral] hemispheres was made by the Russian physiologist I.M. Sechenov, on the basis of the knowledge available in his day of the physiology of the central nervous system. In a pamphlet entitled “Reflexes of the Brain,” published in Russia in 1863, he attempted to represent the activities of the cerebral hemispheres as reflex—that is to say, as determined. (Pavlov 1927) In the period that followed, Skinner and his students (cf. Skinner 1938) strengthened this notion, and psychologists as a group largely embraced the idea that a complete psychological theory would be a determinate one. By studying the causal relationships between environment, organism, and response, these scientists began the process of developing a predictive and testable theory of psychology. The twentieth century witnessed a similar trend in the effective application of the scientific method toward understanding the biological sources of behavior, and as a result, saw the development of a powerful deterministic program for understanding biological systems. Charles Sherrington (1906), for example, applied this programmatic approach to the physiological study of reflexes with great effect.

Determinism and the Philosophy of Science In philosophical circles, the central role of a determinate worldview in the classical scientific method also became a formalized principle in this period. In the early part of the twentieth century, the philosopher Karl Popper (1934) explicitly defined the goal of modern science as the falsification of extant theories through experimental inquiry. For Popper, theories could never be proven in practice, only subjected to the test of falsification. If experimental evidence falsifies a theory then it can be discarded; if experimental evidence corroborates a theory then it can be tentatively retained. What is critical about this logic is what it implies about indeterminacy. Consider the theoretical claim that if I flip a certain coin there is a 50% chance it will land

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heads-up. As Popper points out, this is not only an unverifiable theoretical claim, but also an untestable one; my assertion predicts all possible empirical outcomes and is thus unfalsifiable. Even more importantly, my theoretical claim predicts as an experimental result all possible finite series of coin flips that could ever be observed. If the coin is equally likely to land heads-up and tails-up, then any specific series of heads and tails is equally likely, whether that be six heads in a row or six flips that alternate between heads and tails. No formal prediction of any particular outcome is ever possible and for this reason Popper argued that probabilistic claims about indeterminate processes were irremediably problematic. Indeed, in his early writings Popper even used this to argue that the notion of a fundamentally indeterminate universe is at base a nonscientific proposition. In the 1920s and 1930s, however, the emerging discipline of quantum physics raised an important challenge to this notion that had evolved during the Enlightenment and motivated much of Popper’s work. Based initially on the work of Heisenberg and his colleagues (Heisenberg 1930, 1952), strong evidence arose suggesting that several phenomena that occur at the atomic and subatomic scales are, in fact, fundamentally indeterminate and thus could be described only probabilistically. This was a critical challenge to the existing philosophy of science as expressed by Popper because that philosophy argued that a theory of physics built upon probability theory was unfalsifiable, perhaps even unscientific. Nevertheless, the empirical evidence gathered during that period seemed to indicate unambiguously that at a small enough scale of analysis, events occur that are fundamentally indeterminate. This indicated that the philosophy of science, rather than the reality of our physical universe, might have to change.

Do Indeterminacies in the Physical World Matter for Behavioral Scientists? What, if any, are the implications of these issues for the study of behavior? Even if there are fundamental indeterminacies in the physical world, should this matter to behavioral scientists? Many scholars believe that the quantum physicist Edwin Schrodinger provided an answer to that question in his book, What Is Life (1944), in which he argued that for any organism to survive it must operate, in principle, in a fully determinate environment. Indeterminacy, he believed, would be lethal to living systems. Schrodinger’s own work (cf. 1951) had demonstrated that at the atomic and subatomic scales, matter can be described only in probabilistic terms, but it had also shown that large aggregates of these elementary particles behaved in an effectively determinate manner. His argument was that living cells were large enough objects that they would never interact with single atomic or subatomic particles, but only with these larger determinate aggregates. In essence, he argued that cells were large enough that quantum fluctuations in the properties of individual atoms would have no effect on them. Indeed, he went on to argue that biological cells are the size that they are specifically because quantum indeterminacy prevents them from surviving if they become any smaller. Biologists,

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psychologists, and social scientists, he assured us, need not be concerned with fundamental indeterminacy in the universe:


If it were not so, if we were organisms so sensitive that a single atom, or even a few atoms, could make a perceptible impression on our senses—Heavens, what would life be like! To stress one point: an organism of that kind would most certainly not be capable of developing the kind of orderly thought which, after passing through a long sequence of earlier stages, ultimately results in forming, among many other ideas, the idea of an atom. (Schrodinger 1951) Recently, however, evidence has begun to arise in the social, psychological, and neurobiological domains that suggests that, at larger scales of analysis than the one Schrodinger examined in What Is Life, living systems exhibit behavior that is apparently indeterminate (cf. Hastie 2001, Schall 2004, Shafir & LeBoeuf 2002, Staddon & Cerutti 2003). At the largest scale of analysis, social scientists working in areas such as the theory of games have begun to argue that for behavior to be efficient under some circumstances, it must be irreducibly uncertain from the point of view of other organisms and therefore must be studied with the tools of probability theory. In principle, this raises critical problems for game theory. For all of the reasons Popper identified, when game theory makes probabilistic predictions it does so in a manner that is nonfalsifiable. Of course, if Schrodinger was correct, the apparent indeterminacy of game theory presents only a temporary impediment to scientific inquiry. A reductionist approach to human behavior during strategic games would ultimately reveal the mechanisms that give rise to this apparent indeterminacy and thus should ultimately yield a falsifiable determinate theory of human behavior. Although contemporary game theory thus faces indeterminacy, empirical science can hope to resolve this apparent indeterminacy by reduction. Interestingly, however, psychologists working at a lower level of reduction than social scientists have also begun to find evidence of apparent indeterminacy in the systems they study (cf. Staddon & Cerruti 2003). Recently, psychologists have begun the analysis of apparently stochastic patterns of individual responses and have been able to demonstrate classes of individual behavior that appear to be as fully random as can be measured. Indeterminacy, in the hands of these psychologists, seems to be an apparent feature of the behavior of single humans and animals. At a yet deeper level of reduction, neurobiologists have also begun to gather evidence for the existence of apparently indeterminate processes within the architecture of the mammalian brain (cf. Schall 2004). The patterns of action potentials generated by individual neurons, for example, appear highly stochastic for reasons that are not yet well understood. Growing evidence that apparently indeterminate processes operate at social, psychological, and even neurobiological levels are bringing behavioral scientists face-to-face with the same philosophical and scientific issues faced by Popper, Heisenberg, Schrodinger, and others in the last century. Can such theories be scientific, or is calling a neural signal or a behavior a random process only an excuse for ignorance? It may be that behavioral scientists will choose to assert as an

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axiom that all of the physical phenomena we study are fundamentally determinate in order to avoid these issues, but on the other hand such an assertion may force us to neglect a growing body of compelling evidence.

THE RISING TIDE OF APPARENT INDETERMINACY


Indeterminacy in the Social Sciences Like scholars in the physical sciences, social scientists in the eighteenth and nineteenth centuries strongly emphasized a determinate scientific approach in their study of human behavior. The classic economic theory of that period, for example, rested on the foundation of a theory of determinate utility developed by Blaise Pascal (1670, Arnauld & Nicole 1662) and Daniel Bernoulli (1738). This utility theory argued that humans act predictably to maximize benefits and to minimize costs, and that the costs and benefits of any action can be reliably computed. Pascal had developed this basic logic in the seventeenth century, arguing that the “expected value” of an action was equal to the product of any possible gain or loss incurred by that action and the likelihood of the gain or loss. Bernoulli had extended this notion with the observation that humans appear at an empirical level to be more averse to risk than Pascal’s formulation predicts. Bernoulli’s conclusion was that humans made decisions based on the product of a subjective estimate of cost or benefit and the likelihood of that gain or loss, rather than based on an objective measure of gains or losses. Because of the precise form of his hypothesis, Bernoulli was able to show that this notion could successfully account for the empirically observed aversion of humans to risk. Thus, the critical idea that utility theory advanced was that one could compute the relative desirabilities of all possible actions to a chooser and, except in the presumably rare case where two actions have identical subjective desirabilities, one could then predict the actions of a chooser with determinate precision. Building on this foundation, Adam Smith (1776) argued that all economic actors could be seen as effectively trading off costs and benefits to maximize gain in a complex marketplace. The prices of goods, for example, were presumed to be set by the determinate interactions of supply and demand curves that represented the aggregate subjective desirabilities and costs of goods to consumers and producers. It was thus a central thesis of eighteenthand nineteenth-century economic theory that the rational process by which desirability was assessed could be accurately modeled and that these models made deterministic predictions about human behavior. Importantly, the incorporation of likelihoods into expected utility theory allowed the approach to make determinate predictions even when the environment in which human decision makers operated was unpredictable. Choosers were assumed to consider risk when they determined the desirability of an action, and the theory explicitly and convincingly predicted that no feature of this environmental uncertainty would be presumed to propagate into the behavior of the choosers. The only time that utility theory predicted indeterminacy in behavior was when

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two or more mutually exclusive actions had precisely equal subjective desirabilities, and the importance of that particular situation seemed limited to the classical economists. In the first half of the twentieth century, the theory of games developed by John VonNeumann, Oskar Morgenstern, and John Nash directly challenged this determinate approach (Nash 1950a,b, 1951; VonNeumann & Morgenstern 1944). Game theory represented a deviation from the classical tradition specifically because it proposed that when a rational chooser faces an intelligent and self-interested opponent, rather than a passive economic environment, situations could easily arise in which the subjective desirabilities of two or more actions are driven toward precise equality. The theory went on to make surprising and fundamentally indeterminate predictions about how rational humans would behave under many conditions that could be well described by game theory. To understand this theoretical insight, consider two opponents repeatedly playing the childhood game of rock-paper-scissors in which the loser pays the winner $2 on a round won by playing paper and $1 on a round won by playing scissors or rock. If the behavior of one’s opponent is unpredictable, any response can win, in principle. Paper will beat a play of rock for $2, scissors will beat a play of paper for $1, and rock will beat a play of scissors, again for $1. Classical utility theory assumes that humans choose between actions by multiplicatively combining the subjective value and likelihood of each outcome and then selecting the action with the outcome that yields the highest expected utility. Assuming naively that one’s opponent is equally likely to play rock, or paper, or scissors, the greater value of winning with paper should lead all players to select paper deterministically on each round. What VonNeumann recognized was that this assumption about the behavior of one’s opponent simply could not be correct. A competitor who simply selected scissors could reliably defeat any player who actually behaved in accord with this strategy. Game theory, as developed by VonNeumann & Morgenstern (1944), addresses this limitation of classical utility theory by making the assumption that both players are aware that they face an intelligent opponent who can anticipate their actions and that both players will shape their behavior accordingly to minimize losses and maximize gains. To accomplish this, players must take into account the potential payoffs associated with each choice, as specified by classical utility theory, but they must also consider how the actions of their opponent will influence those payoffs. Consider again the situation in rock-paper-scissors. Winning with paper yields twice as much money as winning with rock or scissors, but deterministically playing paper leads to certain defeat. What VonNeumann & Morgenstern showed was that under these conditions we can predict that a rational player will titrate risk against gain and play paper two-thirds of the time, scissors one-sixth of the time, and rock one-sixth of the time. Critically, however, he must avoid making his twothirds, one-sixth, one-sixth selections in a determinate fashion; for example, in a repeated version of the game by playing paper, then scissors, then paper, then rock, then paper, and then paper. Were his opponent to divine the determinate nature

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of such a strategy (through observation, for example), then winning would again become trivial for that opponent. He would only have to play scissors, then rock, then scissors, then paper, then scissors, and then scissors to assure a consistent win. The only way to avoid this trap is for a player to incorporate apparent indeterminacy directly into his behavior. He must in essence flip a weighted coin on each round to select between rock and paper and scissors. VonNeumann & Morgenstern were well aware of the implications of this observation. It suggested that under some conditions the study of economic choice would have to become a probabilistic process. As they put it: Consider now a participant in a social exchange economy. His problem has, of course, many elements in common with a maximum problem. [A problem in which a single economic actor seeks to maximize his gain by classically deterministic processes.] But it also contains some, very essential, elements of an entirely different nature. He too tries to obtain an optimum result. But in order to achieve this, he must enter into relations of exchange with others. If two or more persons exchange goods with each other, then the results for each one will depend in general not merely upon his own actions but on those of the others as well. Thus each participant attempts to maximize a function (his above-mentioned “result”) of which he does not control all of the variables. This is certainly no maximization problem, but a peculiar and disconcerting mixture of several conflicting maximum problems. Every participant is guided by another principle and neither determines all of the variables which affect his interest. This kind of problem is nowhere dealt with in classical mathematics. . .. We hope that the reader will be convinced by the above that they face here and now a really conceptual—and not merely technical—difficulty. And it is this problem which the theory of “games of strategy” is mainly devised to meet. (VonNeumann & Morgenstern 1944) VonNeumann & Morgenstern’s critical insight was that under conditions of this type choosers might not be able to identify a single course of action that is deterministically optimal. Instead, they may be forced to select a course of action in as random a fashion as possible. It is this strategy of random selection, known now as a mixed strategy, that distinguishes VonNeumann & Morgenstern’s approach from more classical deterministic approaches to the study of behavior. In sum, VonNeumann & Morgenstern argued that human behavior, under some conditions, must appear indeterminate in order to be efficient. They made this point elegantly when they described, in game theoretic form, a conflict between Sherlock Holmes and his archenemy, Professor Moriarity: Sherlock Holmes desires to proceed from London to Dover and hence to the continent in order to escape from Professor Moriarity who pursues him. Having boarded the train he observes, as the train pulls out, the appearance of Professor Moriarity on the platform. Sherlock Holmes takes it for granted—and

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in this he is assumed to be fully justified—that his adversary, who has seen him, might secure a special train and overtake him. Sherlock Holmes is faced with the alternative of going to Dover or of leaving the train at Canterbury, the only intermediate station. His adversary—whose intelligence is assumed to be fully adequate to visualize these possibilities—has the same choice. Both opponents must choose the place of their detrainment in ignorance of the other’s corresponding decision. If, as a result of these measures, they should find themselves, in fine, on the same platform, Sherlock Holmes may with certainty expect to be killed by Moriarity. If Holmes reaches Dover unharmed he can make good his escape. What are the good strategies, particularly for Sherlock Holmes? [Set the value] to Professor Moriarity [of] catching Sherlock Holmes [at], say 100. [Alternatively, consider what happens if] Sherlock Holmes successfully escaped to Dover, while Moriarity stopped at Canterbury. This is Moriarity’s defeat as far as the present action is concerned, and should be described by a big negative value of the matrix element [for Moriarity]—in the order of magnitude but smaller than the positive value mentioned above—say, −50. [Finally, consider what happens if] Sherlock Holmes escapes Moriarity at the intermediate station, but fails to reach the Continent. This is best viewed as a tie, and assigned the matrix element 0. [From a mathematical analysis of these values one can conclude that] the good strategies (e for Moriarity, n for Sherlock Holmes) [are]: e = {3/5, 2/5}, n = {2/5, 3/5} Thus Moriarity should go to Dover with a probability of 60% while Sherlock Holmes should stop at the intermediate station with a probability of 60%, the remaining 40% being left in each case for the other alternative.1 (VonNeumann & Morgenstern 1944, pp. 177–178) Of course, this theoretical formulation raises critical questions about the scientific nature of game theory. If game theory predicts that Holmes will get off the train at Canterbury with a 60% probability, any action Holmes takes is compatible with the theory. VonNeumann & Morgenstern recognized this but were adamant that this was still the only rational strategy for Holmes to adopt. Holmes must, they argued, be as indeterminate as possible in selecting a course of action. In essence, he must appear to have flipped a weighted coin (weighted 60% for Canterbury and 40% for Dover) in order to maximize his chance of survival. This was true, VonNeumann & Morgenstern suggested, irrespective of whether the theory of games preserved Popperian falsifiability. By the early 1950s, John Nash (1950a,b; 1951) had seen an interesting additional level of structure in game theoretic problems that required a mixed strategy, 1

Our result for e, n yields that Sherlock Holmes is as good as 48% dead when his train pulls out from Victoria Station.

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or apparently indeterminate, solution. Building on the work of VonNeumann & Morgenstern, he concluded that stable mixed strategies must in principle reflect an equilibrium point at which the subjective desirabilities of the two or more actions being mixed were precisely equivalent. He argued that it was only this equivalence that could produce the indeterminate behavior that VonNeumann & Morgenstern had predicted. Consider again the situation when a single player must, on repeated rounds, select rock or paper or scissors. If any one of these is truly preferable as a choice, then we can assume that the chooser will always select that option. Mixed strategies should thus emerge, Nash reasoned, only when the two or more actions that are being mixed have identical average desirabilities. Working from VonNeumann & Morgenstern’s insights, Nash argued that these two equivalent desirabilities emerge when the competitive interactions of the two players drive them toward an equilibrium at which the two or more actions being mixed are of equal desirability. What Nash argued was that mixed-strategy equilibriums emerge from dynamic interactions between the players, which yield equal average desirabilities, and thus totally indeterminate patterns of behavioral choice. Classical utility theory had presumed that situations in which two or more actions have precisely equal subjective desirabilities would be encountered only rarely. Nash’s insight was that not only are they encountered, but the dynamic interactions that occur during strategic games actively create these situations of equal subjective desirability. From the point of view of indeterminacy, the critical insight that VonNeumann, Morgenstern, and Nash offered was that indeterminacy is a requisite feature of efficient behavior in a competitive world. That insight means either that humans and animals appear indeterminate to each other under some conditions or they behave inefficiently. In 1982, the evolutionary biologist John Maynard Smith also engaged indeterminacy during strategic games, but from an evolutionary perspective. He argued that any species involved in an internal competition for resources could be described using game theory and that this mathematical formalism predicted that organisms capable of producing apparently indeterminate behavior would be favored by natural selection. Imagine, Maynard Smith proposed, a species of animals in which individuals compete for access to territories that increase the number of young an individual can produce. Individuals without territories produce a small number of young, while individuals with territories produce a large number of young. Obviously, under these conditions it is in the interest of individuals to obtain territories. Now consider a situation in which there are more individuals than territories. In this hypothetical species, territories change hands when an animal without a territory “displays” to an animal with a territory, essentially threatening that individual for control of the territory. In the hawk-dove game, as this competition has come to be known, after such a display each animal must make a decision: whether to escalate the conflict (to fight for the territory) or whether to retreat (give up the territory without a fight). If one of the animals elects to escalate, behaving as a hawk, and

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TABLE 1


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Payoffs for challenger in the hawk-dove game Challenger chooses hawk

Challenger chooses dove

Defender chooses hawk

50% chance of gaining territory 50% chance of injury

Nothing gained

Defender chooses dove

Value of territory gained

50% chance of gaining territory

one decides to retreat, behaving as a dove, then the hawk takes the territory. If both animals elect to be doves, then one of them at random takes the territory. Finally, if both animals elect to be hawks, then they fight, one sustains injuries that reduce the number of young that individual can produce, and the other gains the territory. Table 1 illustrates this simple game as a two-by-two matrix that specifies the costs and benefits of all possible actions to each player. What Maynard Smith realized at a mechanistic level was that each of these values could be expressed in terms of evolutionary fitness, the gain in reproductive success, that an individual achieves with each outcome. Gaining a territory confers an increase in fitness, whereas sustaining an injury confers a decrease. Thus, if the value of a territory is high and the magnitude of injury in a hawk versus hawk fight is low, then animals that behave as hawks are more fit than those that behave as doves. Under these conditions, Maynard Smith reasoned, the population will evolve toward a single pure strategy equilibrium: All animals in the population will always be hawks. Similarly, if the value of a territory is low and the magnitude of injury is high, then all animals that behave as doves will produce more offspring, be more fit, than animals that act as hawks. Under these conditions, the population should converge on a pure strategy equilibrium of dove. If, however, the value of a territory is high and the cost of an injury also is relatively high, then an interesting thing happens. The only reproductively stable strategy for the animal and its offspring is to behave sometimes as a hawk and sometimes as a dove. To be more specific, a single dominant and unbeatable strategy emerges in a population playing the hawk-dove game. The probability that on any given encounter an individual will choose to behave as a hawk must be equal to the value of a territory divided by the magnitude of the injury sustained in a hawk versus hawk conflict. Critically, on each encounter individuals have to behave in an unpredictable fashion, never allowing their opponent to know whether they will be a hawk or dove2. But across many such encounters the only stable and unbeatable solution for the population is for the probability of being a hawk to be equal to the value of a territory divided by the cost of injury. This theoretical analysis suggests that, at least from the point of view of other individuals in this same species, evolution would drive behavior toward 2

Maynard Smith showed mathematically that a population of unpredictable individuals would dominate a population in which separate individuals were committed at birth to playing hawk or dove. For details of that proof, see (Smith 1982).

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unpredictability. As in the game theoretic work of VonNeumann, Morgenstern, and Nash, the ability to generate apparently unpredictable behavior seems advantageous. One interesting feature of Maynard Smith’s argument, however, is the mechanism by which this uncertain behavior would be presumed to arise. We have strong reasons to believe that completely novel behaviors arise, at least in part, from genetic mutations. Random changes occur in the genomes of these animals and then selection operates to preserve useful variations in behavioral traits. Atomic-level fluctuations in DNA molecules, induced by quantum-level forces like cosmic radiation, produce unpredictable changes in the genetic make-up of a species. These random changes then influence behavior. We have every reason to believe that the mechanism by which apparently indeterminate behaviors would arise would itself be truly indeterminate. Game theory, whether directed toward the actions of an individual or the evolution of a species, predicts that under some conditions behavior must appear indeterminate in order for it to be efficient. What implications, if any, does this have for the determinate scientific method? Does this mean that social scientists have to abandon Popperian falsifiability? Probably not, for at least two important reasons. First, the theoretical observation that behavior should appear indeterminate does not mean that behavior does appear indeterminate. Physical constraints may make it impossible, or unlikely, for mutations to generate behavior that even appears indeterminate. If this is the case, then at an empirical level we may simply find that apparently indeterminate behavior does not occur. Second, even if apparently indeterminate behavior were to be observed, this would not require that the physical generative process for the behavior itself be indeterminate. The psychological system responsible for decision making during strategic games might operate on totally deterministic grounds. Like a modern digital computer, it may simply generate an appearance of indeterminacy sufficient to defy prediction by the opponent. In summary, there may be reasons why fundamental indeterminacies like those that arise at the quantum level cannot influence the systems that generate behavior. Either of these two observations would rescue Popperian falsifiability in its strongest form.

Empirical Measurements of Behavioral Indeterminacy The theory of games makes it clear that an organism with the ability to produce apparently indeterminate patterns of behavior would have a selective advantage over an animal that lacked this ability. Were apparently indeterminate behavior to have arisen in the evolutionary history of vertebrates, there seems every reason to believe that this behavioral phenotype would be preserved. Do humans have this ability? A common answer to this question, based on studies of humans, is no. Over the course of the last 40 years, a number of psychological studies have suggested that, perhaps because of some fundamental constraint in the human nervous system, humans cannot generate behavior that appears indeterminate (for a review of this literature see Wagenaar 1972). For example, in one of the first of

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these studies, Bakan (1960) asked humans to simulate the action of a random coin flip: subjects were asked to make up a sequence of heads and tails that was fully random in order. When Bakan analyzed the sequences generated by these subjects they were found to be highly nonrandom despite the instructions that the subjects received. Bakan found that the subjects tended to overproduce alternations between heads and tails and to underproduce the occasional long runs of heads or tails that would be predicted from a truly random process. In sum, the humans behaved in a fairly determinate fashion, despite their instructions to do otherwise. Since 1960, dozens of studies have replicated this basic result. When human subjects receive a verbal instruction to produce a random sequence, they reliably fail. On the basis of this evidence, many psychologists have concluded that humans lack the ability, in principle, to generate patterns of behavior that appear indeterminate. According to game theory, however, environmental conditions should arise in which apparently indeterminate behavior would be truly beneficial. Organisms in their natural environment would be reinforced for producing apparently indeterminate behavioral patterns under some conditions. Regardless of these human data, then, can nonhuman animals produce apparently indeterminate behaviors if they are reinforced for doing so? Blough (1966) was one of the first to ask this question directly by specifically reinforcing pigeons for producing behavior that approximated a random process. In that experiment, pigeons were trained to peck a key in a Skinner box, and the amount of grain that they received after each peck was contingent upon the length of time that had intervened since the last peck. The more closely the set of interresponse intervals produced by the pigeon approximated the output of a random Poisson-like process, the more grain the bird earned. Blough found that under these conditions the birds quickly adopted a response strategy that was virtually indistinguishable from the output of a truly random operator. Figure 1 shows the frequency distribution of interresponse intervals Blough obtained from a single pigeon and, plotted as a solid line, the pattern of intervals that would be expected from a fully random process. While Blough’s analysis did not show that the behavior of the pigeons was random by all possible measures, it did demonstrate that when an apparently indeterminate behavior was reinforced, pigeons could produce a behavior of this general type. This study was critical because it provided the first evidence that the ability to produce apparently indeterminate behavior had arisen in the vertebrate line. Since that original study, a voluminous literature has examined the ability of several species of animals to generate apparently indeterminate behavioral sequences when they are specifically reinforced for doing so, and tasks more closely approximating the conditions described by game theorists have also been examined (see Neuringer 2002 for a review of this literature). Shimp (1967) introduced one paradigm that has been particularly widely studied. In that paradigm, pigeons were trained to choose sequentially between left and right response keys for four responses during each of thousands of trials. The behavior of the pigeons on each trial thus produced one of 16 possible patterns, for example, left-right-left-left. The animals then were reinforced for producing the 16 possible patterns with an

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Figure 1 Mean interresponse times (IRTs) from two replications of three experimental conditions for a single pigeon (Blough 1966). The graph plots the frequency of each IRT in half-second bins. A fully indeterminate process would produce points that fall along straight lines in this space. (Reproduced with permission from Journal of the Experimental Analysis of Behavior, copyright 1966, Society for the Experimental Analysis of Behavior.)

apparently random frequency distribution. In one important and well-controlled study, Page & Neuringer (1985) employed a strategy of this type to examine indeterminacy in behavior and to see whether the ability of pigeons to produce random sequences depended specifically upon whether or not they were reinforced for apparent randomness. In that experiment, pigeons produced long sets of left and right responses, but under two reinforcement contingencies. Under the first contingency, the animals were specifically reinforced for producing patterns of left and right responses that had a random-like frequency distribution. Under the second contingency, the randomness of the emitted frequency distribution was irrelevant to the reward received. Page & Neuringer found that when reinforcement was contingent on variability, the variability of the pigeons’ responses increased, but when the level of variability was not reinforced directly, the pigeons adopted much more determinate response patterns. More specifically, they found that an information theoretic analysis of the response patterns showed nearly perfect indeterminacy when, but only when, indeterminacy was reinforced. These results suggest two interesting conclusions. First, they suggest that the degree of apparent indeterminacy included in behavior is variable. Animals can be more or less indeterminate based on the requirements of their environment. Second, they suggest that when

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indeterminacy is irrelevant, this species of animal prefers to adopt a fairly determinate response strategy. Machado (1989) employed a similar approach in another important study. In that experiment, pigeons once again emitted four left or right responses in each of thousands of sequential trials, and the variability of the response pattern they produced was assessed statistically to determine the amount of reward that the pigeon would receive. This was accomplished simply by counting the number of trials since that sequence had last been produced and assigning this number, a variability score, to that trial. To determine whether a reinforcement was delivered, the variability scores for the last 20 trials were cumulated and the variability score for the current trial was compared to the variability scores for those last 20 trials. If the percentile rank of the variability score for the current trial exceeded some fixed threshold, for example 50%, then a reinforcement was delivered. If pigeons responded truly randomly, then the probability of emitting all 16 possible sequences within 25 trials would be less than 1%. Accordingly, Machado adjusted the threshold requirement so that it never reinforced patterns of sequences shorter than 25 trials in length. Machado found that under these conditions the frequency distribution of variability scores actually produced by the birds was nearly identical to the frequency distribution that would be expected from a truly random process. Figure 2 plots this relationship for one of Machado’s animals. In this figure, the points plot the frequency with which each possible variability score was observed

Figure 2 Dots are used to plot the distribution of variability scores obtained from a single animal in the Machado (1989) paper. The solid line indicates the frequency distribution that would be expected from a perfectly indeterminate process. (Reproduced with permission from Journal of the Experimental Analysis of Behavior, copyright 1989, Society for the Experimental Analysis of Behavior.)

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during a reinforcement contingency that maximized indeterminate behavior. Low variabilities were observed often and high variability scores occurred more rarely. The solid line plots, for comparison, the pattern of variability scores that would be expected from a fully random process measured in this way. Machado’s critical observation is that this particular reinforcement protocol yields a fourth order pattern of responses indistinguishable from the pattern that would be expected from a fully indeterminate process. In sum, many studies (Neuringer 2002) that have yielded data similar to Blough’s, Page & Neuringer’s, and Machado’s suggest that when nonhuman animals are reinforced for producing apparently indeterminate patterns of behavior, they can produce behavior of this type. This set of observations thus led Neuringer (1986) to test the hypothesis that previous studies with human subjects had failed to yield apparently indeterminate behavior because human subjects had not been specifically reinforced for producing apparently indeterminate behaviors. In this study, Neuringer instructed human subjects to produce a random sequence of ones and twos on a computer keyboard. He then analyzed the resulting sequences for nonuniform distributions of ones and twos, and first and second order patterns in the ones and twos. He also analyzed the sequences with a set of statistics related to autocorrelation functions. What he found under these conditions was that the human subjects produced highly nonrandom sequences exactly as had been observed in previous studies. Neuringer provided feedback to these subjects by showing them, after a run of 100 trials, how the distribution they had produced deviated from the distribution that would be expected from a random sequence according to one of the statistical measures of randomness that he employed. In sequence, Neuringer then presented the subjects with each of the additional statistical metrics until they were receiving feedback according to all five metrics at the end of each 100-trial run. Finally, the feedback was terminated and the subjects were told that if they could produce a sequence that could not be discriminated from the product of a computer pseudorandom number generator, they would receive a cash bonus. Under these conditions, Neuringer found that the human subjects essentially all produced sequences that could not be discriminated from random sequences by any of the metrics he employed. From these data, he concluded that like pigeons and other vertebrates, human subjects could produce apparently indeterminate sequences under some conditions. In a similar experiment, Rapoport & Budescu (1992) examined the behavior of humans playing two-person games of the type VonNeumann, Morgenstern, and Nash studied. In Rapoport & Budescu’s experiments, random-like behaviors were reinforced monetarily, and they found that humans could produce behavioral sequences that appeared indeterminate. Under conditions in which game theory predicts that indeterminacy will be reinforced, apparently indeterminate behavior can be produced. Of course, these data do not demonstrate that humans can produce fully indeterminate behavior. All of these data suggest that humans and animals can produce behavior that appears indeterminate, but it seems probable that, like a randomnumber generator in a computer, the generative process for this behavior is likely

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determinate at a lower level of reduction. To test that hypothesis, however, one would have to turn to a neurophysiological level of analysis.


Reducing Uncertainty: Looking for Determinacy with Neurophysiology Perhaps the most influential study of choice behavior at the level of interacting neurons has been the work of William Newsome and his colleagues at Stanford University (for a review of Newsome’s work, see Batista & Newsome 2000; for a review of neurobiological choice literature, see Glimcher 2003). Newsome and his colleagues trained rhesus monkeys to monitor a visual display that presents a circular patch of chaotically moving spots of light (Figure 3). Upon viewing a display of this type, human observers report a chaotic blizzard of randomly moving white spots. However, when 15% of the spots move coherently in a single direction, humans subjects report a strong sense that the spots are moving, overall, in that direction. If the fraction of spots moving coherently is reduced, the strength of this perceived motion is reduced. By systematically varying the fraction of spots moving coherently, Newsome and his colleagues could therefore systematically manipulate how difficult it was for observers to determine the average direction in which the spots were moving. In their original experiment (Newsome et al. 1989), monkeys were presented with a display of this type for 2 seconds, after which they had to decide in which of two possible directions the spots, on average, were moving. The animals indicated their decision with an eye movement that shifted the animal’s point-of-gaze in the direction of perceived average motion. If the animals had judged the direction of spot motion correctly, they received a fruit juice reward. While animals made these decisions, the activity of single motion-sensitive neurons in the middle temporal visual cortex (area MT) was monitored. Under these conditions, Newsome and his colleagues found that if 15% of the dots in the display moved to the right, the monkeys always reported that they saw rightward motion, and cells in area MT activated by rightward motion rapidly generated action potentials. As the percentage of rightward dots was systematically decreased, both the probability that the monkey would report that he had seen rightward motion and the probability that the neurons would show an increase in firing rate decreased at roughly the same rate. What can we learn from data of this type about the mechanisms that give rise to apparently indeterminate behaviors? In one interesting study, Britten and colleagues (1996) examined the activity of MT neurons while monkeys viewed a display in which either all of the spots moved in random directions (there was no coherent direction of spot motion) or only a small fraction of the dots moved in a coherent direction. These conditions were selected to examine the relationship between neural activity in area MT and the decisions that an animal made when the visual stimulus was ambiguous. Interestingly, on a subset of the trials in which there was no coherent motion of the spots, the exact same pattern of randomly

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Figure 3 The Moving Spot Task. Monkeys fixate a central point while chaotically moving spots of light are presented within a circular aperture. On any given trial, a small fraction of the spots moves in a coherent manner in one of two possible directions. Across trials, the fraction of dots moving in this coherent fashion can be varied systematically to increase or decrease the strength of the perceived motion signal in the two possible directions. After viewing the display for 2 seconds, monkeys indicate the direction of perceived motion with a saccadic eye movement. Correct responses are reinforced with water or fruit juice. (From Shadlen & Newsome 2001. Reproduced with permission from Journal of Neuroscience.)

moving spots was presented. Under these conditions, the animals viewed the exact same stimulus while the activity of MT neurons was monitored. Perhaps surprisingly, even under these conditions the activity of the neurons varied from trial to trial. The precise number of action potentials generated and the precise pattern of action potential generation differed in an apparently random manner from trial to trial, even when the visual stimulus that the animal was evaluating was identical.

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Britten and colleagues also found that the perceptual judgments of the animals were unpredictable on these trials. Like the neurons, the behavior of the animals was variable. Finally, these authors found that the judgments of the animals were always correlated, although only weakly, with the activity of the neurons. In sum, the neurons appeared to be indeterminate with regard to the stimulus, and the decisions that the animals made were correlated with these apparently indeterminate neural events. These data led to the generation of a model (Figure 4) designed to simulate the brain circuits for making this perceptual decision about the direction of spot motion (Shadlen et al. 1996). The model proposed that a group of, for example, rightward motion–sensitive neurons in area MT pooled data according to a fully defined algorithm to yield an instantaneous estimate of the current strength of rightward motion in the moving spot display. In a similar way, a group of leftward motion–sensitive neurons was hypothesized to extract an estimate of the

Figure 4 Shadlen et al.’s (1996) model of a perceptual decision circuit. Pools of neurons in area MT extract the instantaneous strength of visual motion occurring in the display, for motion in all possible directions. The instantaneous pooled estimates of motion strength in each of the two possible directions are passed to elements that compute the time integral of that signal to derive an estimate of the average motion signal over a 2-second display interval. The process of pooling is presumed to involve the addition of a fundamental indeterminacy called the “pooling noise” in the original model and labeled here as the pooling noise generator. These integrative elements project, in turn, to eye movement–producing neurons. The integrative elements are postulated to be mutually inhibitory, assuring that only one eye movement is triggered at a time.

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instantaneous strength of leftward motion. Because the monkeys were allowed to view the motion stimulus for 2 seconds, this had allowed them, in principle, to sum 2 seconds of instantaneous motion information before making a choice. Accordingly, Shadlen and his colleagues proposed that the output of each of the neuronal pools of direction-sensitive neurons was summed, or integrated, over the 2-second period, to develop an estimate of the average direction of motion throughout the stimulus interval. They next proposed that the neurons that integrated rightward activity should be able to directly activate circuits that produced rightward eye movements and that leftward integrating neurons should be able to activate circuits for producing leftward eye movements. To make the system capable of decision making, in the sense of making choices, the model employed two inhibitory linkages that allowed the output of each integrator to inhibit the other integrator’s access to the eye movement control circuits. A quantitative analysis of the behavior of the model, however, revealed a surprising result. The behavior of the actual monkeys appeared much more random than would be predicted from the neurobiologically derived model. Moreover, interestingly, this apparent randomness could be accounted for only by assuming neural circuitry that specifically incorporated a degree of intrinsic randomness that they referred to as a neuronal pooling noise. Shadlen and colleagues were forced to incorporate into the model a fully random element in order to account for their results. The Shadlen model was intended to link the activity of neurons in area MT to behavior in as determinate a fashion as possible, but Shadlen and his colleagues concluded that this linkage could only be accomplished if it was presumed that the nervous system incorporated an indeterminate element. Of course, there was no specific claim about the mechanistic nature of this apparently indeterminate neural element. The pooling noise generator could be a determinate device that yields an apparently indeterminate signal, but it is interesting that even at this level of analysis an indeterminate process seemed to operate. Motivated in part by those findings, Dorris & Glimcher (2004) elected to examine the behavior and brains of monkeys employed in a game theoretic conflict that actually required an apparently indeterminate type of behavior, a mixed strategy game of the type VonNeumann and Nash had modeled. In the human version of their inspection game, two opponents face each other, an employer and an employee (Figure 5). In each round of the game, the employee must decide whether to go to work, in which case he earns a fixed wage, or to shirk, in hopes of earning his wage plus a bonus. The goal of the employee is simply to maximize his gains in terms of salary and bonus. The employer, on the other hand, must decide between trusting his employee to arrive for work or spending money to hire an inspector who can check and see whether the employee arrived for work that day. The goal of the employer is to spend as little money as possible on inspections while maximizing the employee’s incentive to work. In this game, both human and monkey contestants played the role of the employee against a standardized, and strategically sophisticated, computer employer. Each round began with the illumination of two lights, one for working and one for

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Figure 5 General form of the payoff matrix for the inspection game for both the experimental subjects (employees) and their opponents (employers). The variables in the bottom left of each cell determine the employee’s payoffs and the variables in the top right of each cell determine the employer’s payoffs for each combination of player’s responses. V = value of hypothetical product to the employer, fixed at 4; W = wage paid by employer to employee, fixed at 2; C = cost of working to employee, fixed at 1; I = cost of inspection to the employer, varied from 0.1 to 0.9 in steps of 0.2. Middle and right panels show payoff matrices for 70% and 30% employee-shirk rates. The predicted equilibrium strategy for the employer remains constant at a 50% inspect for all blocks of trials. One unit of payoff is 0.25 mL of water for monkey or 5 cents for human.


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shirking. At the end of each round, players selected one light and the computer employer simultaneously decided whether to pay for an inspection on that round. These responses were then compared by a computer arbiter that paid off players according to a fixed payoff matrix (paying off in juice for monkeys, real currency for humans, and virtual currency for the computer employer). Dorris & Glimcher (2004) found that the overall probability that a human playing the inspection game for money would chose to shirk was reasonably well predicted by the Nash equilibrium computations, but more importantly, they found that human subjects behaved almost perfectly randomly from trial to trial. An analysis of the human data revealed when the Nash solution in the game was for a player to shirk 50% of the time, not only did the players shirk about 50% of the time, but they also showed essentially no patterns in their behavior out to a third-order statistical analysis. As in the experiments of Neuringer (1986) and Rapoport & Budescu (1992), subjects appeared capable of producing largely random patterns of behavior when they were reinforced for doing so. When Dorris & Glimcher analyzed the behavior of their monkeys, they found that the behavior of the monkeys was surprisingly similar, even essentially identical, to the behavior of their human employees. Just like humans, the monkeys seemed to precisely track the Nash equilibrium solutions and to produce those average solutions using largely random sequences of working and shirking. When Dorris & Glimcher (2004) examined the activity of neurons in the posterior parietal cortex while monkeys played the inspection game, they found that the posterior parietal cortex carried a signal essentially identical to one predicted by game theory. The neural activity was correlated with the theoretical quantity economists refer to as expected utility. Importantly, however, this neural encoding of an economic choice variable was not accomplished in a totally deterministic fashion. The cortical neurons responded with an average rate that was correlated with expected utility, but on a moment-by-moment basis, the neurons behaved unpredictably. At a formal level, the neurons behaved roughly like Poisson devices, producing action potentials with random interspike intervals much like the interpeck intervals Blough’s (1966) pigeons produced. So what do we know of the mechanism that generates choice behavior under these conditions? Shadlen’s computational model of the choice process (Shadlen et al. 1996) seems to suggest that at the level of the neural computation we still can see evidence of apparent indeterminacy, and other models loosely related to the original Shadlen model seem to make a similar point (Barraclough et al. 2004, Corrado et al. 2003, Glimcher & Dorris 2005, Lau & Glimcher 2003). The absolute variability of primate behavior seems to be adjustable, and neural models of the machinery that generates this behavior at a neuronal level seem to include apparently random elements. What, then, is the implementation of this random element? One hopeful possibility is that the cellular-level mechanisms that implement this randomness may be, in fact, fully determinate processes. It is at least possible that if we better understood the mechanisms by which cells (for example, cortical neurons) generate action potentials, it would still be possible

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to reduce this apparent indeterminacy to a determinate process at a subcellular level.


Indeterminacy at the Cellular and Subcellular Levels Among the first scientists to examine the pattern of cortical neuronal firing rates with regard to indeterminacy were Tolhurst et al. (1981) and Dean (1981), who were extending studies of neuronal variability pioneered by Barlow & Levick (1969; see also Heggelund & Albus 1978). In two landmark papers, Tolhurst et al. (1981) and Dean (1981) examined the firing patterns of neurons in the visual cortices of anesthetized cats viewing visual displays that presented moving bars of light. When a given visual stimulus was presented to the animals, cortical neurons always responded with a fixed average rate of firing. A vertically oriented bar, for example, always produced a fixed rate of average action potential generation. As the bar was rotated toward a horizontal orientation, for example, the cell responded with a different, but also consistent, average rate of action potential production. They found, however, that the exact pattern of firing that gave rise to this average rate seemed to be almost completely unpredictable. Indeed, as the average firing rate increased, the moment-by-moment variability of the spike rate also increased, almost exactly in proportion to this mean rate. Put more formally, Tolhurst et al. and Dean found that the average firing rate was proportional to the square of the variance across a broad range of rates (Figure 6). This was a statistical distribution that would occur if the process of generating an action potential could be described in the following way: Immediately after an action potential is generated there is a 0% chance of generating an action potential for some largely fixed interval. After that interval has elapsed, the probability of generating an action potential in any given instant becomes fixed at a low level until an action potential occurs, after which the probability of action potential generation is again zero and the process repeats. Of course, during the interval when the probability of action potential generation was fixed at this low level, the spike generation could be characterized as, in principle, fully random. The time at which a spike occurred could be described as a fully random process that had all of the hallmarks of a truly stochastic Poisson operator. What Tolhurst et al. (1981) and Dean (1981) found, therefore, was that at the level of action potential generation, cortical neurons could be described as essentially stochastic. This was a surprising result at the time, and it has been widely confirmed (Rieke et al. 1997, Shadlen & Newsome 1998). What then is the source of this apparent stochasticity, and would a more detailed biophysical analysis of the spike generation mechanism reveal an underlying deterministic process that would yield this apparent indeterminacy? To examine one possible answer to that question, Mainen & Sejnowski (1995) sought to determine whether the biophysical process that actually generates action potentials in response to changes in membrane voltage was determinate. They performed intracellular manipulations of single cortical neurons in cortical networks by employing a brain slice preparation, inserting a microelectrode inside a single

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Figure 6 Tolhurst et al.’s (1981) plot of variance as a function of the mean firing rates for a cat’s visual-cortical neurons. The different symbols represent different averages of stimulus conditions; the straight lines plot regressions. The graph indicates that the square of variance and mean rate are related by a relatively fixed constant of proportionality. (Reproduced with permission from Vision Research. Copyright Pergamon Press.)

neuron and recording the pattern of membrane voltage produced in the cell by the network in which it was embedded. While membrane voltage was monitored, they also recorded the precise times at which the cell generated action potentials. This allowed them to determine the relationship between membrane voltage and action potential generation under reasonably normal conditions. Next, they disconnected this cell from the rest of the network in which it was embedded and used the microelectrode to reinject exactly the same pattern of membrane voltages that had occurred originally. They found that under these conditions the cell fired action potentials at exactly the same time, with regard to the membrane voltage signal, as it did previously. They found that the spike-generating mechanism was fully

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deterministic. A given pattern of membrane voltage gave rise to exactly the same pattern of action potentials no matter how many times it was injected into the cell. On the one hand, this was a reassuring result. At base, the pattern of action potential generation was found to be governed by a determinate device. However, on the other hand, it was puzzling. Spike rates are not determinate in this sense. Tolhurst et al. and Dean’s work indicates that spike rates are distributed in a Poisson-like fashion, and there clearly is nothing about the spike generator within each cell that produces this pattern. The Mainen & Sejnowski (1995) data indicate that the apparent randomness in spike patterns must be a function of apparent randomness in the underlying membrane voltages. What then are the sources of these Poisson-like fluctuations in membrane voltage? We know that membrane voltages are governed, ultimately, by the pattern of synaptic activations that a cell receives from the neurons that impinge upon it. Each cortical neuron receives about 10,000 synapses from the tissue that surrounds it. The fact that about half of these synapses are excitatory and half are inhibitory is also important. It means that net excitation and inhibition are largely balanced in an active neuron and small shifts in this balance cause the membrane voltage to rise and fall, and thus cause action potentials to be generated. Together, these observations make a clear suggestion. The source of the apparent stochasticity in the membrane voltage either is a determinate pattern of synaptic activations that carefully sculpts the membrane voltage to yield an apparently indeterminate pattern of action potentials for reasons we do not yet understand or the process of synaptic activation is itself apparently indeterminate. A number of groups have investigated this latter possibility by studying the activity of single synapses (see Auger & Marty 2000, Stevens 2003 for reviews of this literature). The basic approach taken by these groups has been to activate a neuron and then monitor the rate at which individual synaptic vesicles are released into the synaptic cleft. Before these experiments were undertaken one could have speculated that synapses were simple determinate mechanisms: When an action potential invades the presynaptic region, it might be presumed that synaptic vesicles of neurotransmitter were deterministically released into the synaptic cleft. Modern studies of this process seem to contradict this view, however. Current evidence indicates that when an action potential invades the presynaptic terminal, the chance that a single synaptic vesicle will be released can be as low as 20%. Examinations of the precise patterns of vesicular release suggest that the likelihood that a vesicle of neurotransmitter will be released in response to a single action potential can be described as a random Poisson-like process. Vesicular release seems to be an apparently indeterminate process. Careful study of other elements in the synapse seems to yield a set of similar, and highly stochastic, results. Postsynaptic membranes, for example, seem to possess only a tiny number of neurotransmitter receptors (cf. Takumi et al. 1999), and during synaptic transmission as few as one or two of a given type of receptor molecules may be activated (Nimchinski et al. 2004). Under these conditions, a single open ion channel may allow a countable number of calcium or sodium ions

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to enter the neuron, and there is evidence that the actions of a single receptor and the few ions that it channels into the cell may influence the postsynaptic membrane. Together, all of these data suggest that membrane voltage is the product of interactions at the atomic level, many of which are governed by quantum physics and thus are truly indeterminate events. Because of the tiny scale at which these processes operate, interactions between action potentials and transmitter release as well as interactions between transmitter molecules and postsynaptic receptors may be, and indeed seem likely to be, fundamentally indeterminate. In 1944, Schrodinger argued that the fundamental indeterminacy of the physical universe would have no effect on living systems. He argued that were biological systems to become so small that the actions of single atoms or molecules could influence cells, the resulting organisms would surely perish from the evolutionary landscape. Studies of the mammalian synapse, however, seem to indicate that Schrodinger (1944) was simply wrong in this regard. Single synapses appear to be indeterminate devices; not apparently indeterminate, but fundamentally indeterminate. At base, physical indeterminacy seems to be a fundamental property of the brain. But how sure can we be that this fundamental indeterminacy at the level of the synapse has anything to do with indeterminacy at the level of a single cortical neuron, at the level of a cortical network, at the level of behavior, or at the level of a social theory of behavior? The evidence that we have today suggests that membrane voltage can be influenced by quantum level events, like the random movement of individual calcium ions. So there is every reason to believe that membrane voltage can be viewed, at least under some circumstances, as a formally indeterminate process of the type that precludes Popperian falsifiability. How does this membrane voltage influence action potential generation? Recall that cells receive a mixture of excitation and inhibition from thousands of synapses and that the ratio of this mixture is variable. Imagine that the correlations between the activity of the individual synapses impinging on a given cell were variable. Under conditions in which the activity of many synapses is correlated and the membrane voltage is driven either way above or way below its threshold for action potential generation, the network of neurons itself would maintain a largely determinate characteristic even though the synapses themselves might appear stochastic. Alternatively, when the synaptic activity is uncorrelated and the forces of excitation and inhibition are balanced, small uncorrelated fluctuations in synaptic probabilities drive cells above or below threshold. Under these conditions, indeterminacy in the synapses propagates to the membrane voltage and thence to the pattern of action potential generation. Indeterminacy in the pattern of action potential generation, although variable, would reflect a fundamental indeterminacy in the nervous system. At the level of behavior, apparent indeterminacy is reinforced by the environment and has been observed. Animals can produce behavior that appears to scientists to be indeterminate. How does this apparent indeterminacy arise? Given what we know about the behavior of synapses and action potentials, two possibilities present themselves. The fundamental indeterminacy observed at the cellular

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level could be prevented from influencing higher-level phenomena in the nervous system, rendering these higher-level phenomena determinate. These determinate processes could then instantiate pseudorandom computations that emulate the underlying cellular indeterminacy and yield apparently indeterminate behavior. Alternatively, we can propose the hypothesis that indeterminacy observed at the cellular level could propagate to behavior under some circumstances, yielding truly indeterminate behavior under some conditions and more determinate behaviors under others.

THE CHALLENGE OF INDETERMINACY FOR BEHAVIORAL SCIENCE The traditional scientific method, or at least one interpretation of that method, suggests that the goal of an investigator should be to reduce uncertainty. We make predictions from our data about future states of the world, i, which have some error, ε. One goal of science is to reduce ε to the smallest possible value and then to use the i measured under these conditions to falsify incorrect theories. Formally, situations in which behavior appears highly indeterminate are those in which ε is large with regard to i. The argument that aspects of the world are, however, truly indeterminate necessitates a change in the way measurements of i are approached. In a fundamentally indeterminate world, ε would have a fixed minimum value beyond which the reduction of uncertainty would be impossible. If this is the case, and ε does have a fixed minimal value, then two critical problems arise for the scientific method. First, a measurement problem arises. If variability is observed during a scientific measurement, does that represent an error on the part of the scientist or variability in the world? Without a reliable technique for specifying the minimum value of ε under a given set of circumstances, there is no way to know if a measurement is accurate. This promotes anarchy in the method by permitting a confusion between error and observation. Second, a falsification problem arises. The existence of a lower limit on ε precludes hard falsification of the type Popper advocated. If a given set of scientific predictions must be couched in probabilistic terms, then—for all the reasons Popper outlined—rigorous falsification is impossible. Good examples of these measurement and falsification problems arise in the contemporary debate about what information is carried in the Poisson-like patterns of action potentials produced by cortical neurons. Cortical neurons produce variable patterns of interspike intervals. All efforts to reduce that variability to a determinate pattern have essentially failed. Some scientists conclude from this failure that spike trains are, at root, indeterminate and that the only information carried by these patterns of action potentials is encoded by the mean rate at which they occur (Shadlen & Newsome 1998). Others propose theories that would yield Poisson-like patterns of interspike intervals, but from underlying determinate processes. Is the first of these hypotheses testable, falsifiable, and scientific? The

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answer to that question seems far from clear. What is clear is that two kinds of indeterminacy in principle could occur: a fundamental indeterminacy for which ε cannot be reduced and an apparent indeterminacy for which ε can be reduced. Fundamental indeterminacy challenges the scientific method. Apparent indeterminacy only serves to challenge scientists. In which category does the Poisson-like variability of cortical neurons belong? Popper (1934) argued that science proceeds by falsification. A hypothesis is never proven; it is just discarded when it becomes clearly false. For this reason, Popper was deeply troubled by scientific theories that were fundamentally probabilistic in nature. If a theory proposes that a given neuron will fire an action potential with a probability of 0.2 in the next millisecond, any observation made during the next millisecond is commensurate with the theory. Of course, the longer the neuron is observed, the more robustly the frequency of action potential generation can be described in the past, but the ability of the theory to predict the future remains untestable and perhaps even unscientific, Popper argued. In the end, the theory is untestable because it predicts that given an infinitely long period of observation all possible patterns of action potentials will occur and thus no given pattern can be used to formally falsify the hypothesis. In the behavioral sciences, however, even determinate theories rarely proceed through a process of unambiguous falsification. Nearly all of the measurements made by behavioral scientists are clouded by variability. Variability results from measurement error, from uncontrolled factors that influence the outcome of the experiment, and perhaps even from variability intrinsic to the system under study. As a result, behavioral hypotheses typically are falsified not with unambiguous observations but with statistical generalizations. Further, falsification tends to be iterative. Instead of demonstrating that a single observation is incompatible with a given hypothesis, behavioral scientists gather a distribution of observations and use this distribution to assess the accuracy of the theory. An existing theory is replaced when a new theory can account for a portion of the residual variance unexplained by the old theory. Behavioral scientists accept that measurements are clouded by variance, ε; they work to minimize the magnitude of ε and they make statistical arguments that accommodate ε. However, at a fundamental level the goal of the scientific method remains a reduction in ε. Bacon argued that science must reduce uncertainty, and for working scientists, this usually means reducing ε. For this reason it is difficult to use the formal logical approach embodied by the Popperian scientific method to argue that variance itself, intrinsic indeterminacy, is a fundamental property of a behavioral system. Accepting the level of variance associated with our best theories as the lowest possible variance necessarily forces an abandonment of further inquiry. If some arbitrarily observed variation in a set of measurements is presumed, a priori, to reflect an irreducible feature of the system under study, then there is no reason to engage in further scientific examination. The search for new theories is, in essence, a technique for reducing ε. Over the last century, scholars seeking to understand behavior have struggled with this problem because they have again and again identified systems in which

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variability, ε, seems irreducible. Neuringer (1986), for example, demonstrated that under some conditions human behavior is indistinguishable from a fundamentally indeterminate process. Tolhurst et al. (1981) and Dean (1981) made similar observations in their studies of cortical neurons. How can we ever hope to rigorously test hypotheses that include irreducible variation if the scientific method always seeks to reduce variance to zero? One answer would be to employ a strategy first used by the quantum physicists who encountered evidence of fundamental uncertainty in the physical world. Consider as they did a fundamental process, like their quantum events, that impose a known amount of uncertainty, ω, on a set of scientific measurements. Under such conditions scientists would still make measurements and those measurements would still include an uncertain component, ε total, but under those conditions ε total would be the sum of ω and the variances due to factors like measurement error, ε error. As theories were replaced iteratively by increasingly accurate theories, ε total would begin to approach ω. Under these conditions, knowledge of the value of ω would solve the measurement problem posed by the existence of fundamental indeterminacy. Knowledge of the minimum possible level of indeterminacy would allow one to discriminate between an error on the part of the scientist and variability in the world. One of the two problems posed by uncertainty would become tractable. The existence of a known nonzero ω, however, would do nothing to resolve the falsification problem. Under conditions in which ω has a nonzero value, scientific predictions must always be couched in probabilistic terms and thus rigorous falsification would remain impossible. The two critical issues that would arise were the behavioral world to be indeterminate would therefore be whether ω could be determined, and how one could proceed without rigorous falsification as a scientific goal. Unfortunately, behavioral scientists do not yet have a theory that would allow them to specify the magnitude of ω, and it seems unlikely that such a theory is imminent. This is probably a very important problem, and one with which behavioral scientists are beginning to grapple. The most promising strategy for defining ω today may be to develop converging evidence, from several levels of analysis, for a specific value of ω under a specific set of behavioral conditions. For example, if game theoretic, behavioral, and neurobiological studies all suggested a specific value for ω under some set of conditions, then one could be much more confident that the traditional scientific method could be pursued. The data presented here suggest that the rudiments of just such an approach may be evolving, although it is far too early to suggest that estimates of the behavioral uncertainty intrinsic to any given situation can be made accurately. Research like that of VonNeumann, Neuringer, and Shadlen points to the existence of indeterminate elements that participate in the generation of behavior, and each provides quantitative estimates of that indeterminacy. One goal of these approaches, in the long run, will have to be quantitative convergence around specific predictions for ω. The loss of rigorous falsification may be a more difficult philosophical problem, but may pose fewer difficulties to us as working scientists, especially since quantum

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physicists have already begun to engage that problem. In practice, scientists rarely proceed through a process of unambiguous falsification. Instead, we often test theories against each other. We ask which theory provides more explanatory power, which yields a smaller ε, and we then discard the less-efficient theory. Theories are used to falsify each other in an iterative process, and there is nothing about this sequence of events that requires determinacy in the real world. This process of iterative falsification does work, although it is less elegant than the strong falsification Popper advocated. It will probably have to form the philosophical basis on which the study of indeterminate behavior is based, and it likely will form an entirely adequate base. Indeterminacy becomes particularly problematic, however, when we try to ask whether the best currently available theory is a good theory. We traditionally consider a theory good when the predictions it makes are accurate. However, under conditions in which ω is large, good theories—even perfect theories—may not be accurate. An interesting example of this may be contemporary models developed to explain the choice behavior of humans during economic games (Camerer 2003, Erev & Roth 1998). These models seek to explain the play-by-play actions of individuals engaged in repeated rounds of games like rock-paper-scissors. The models seek to explain how human players learn from their experiences, and then use what they have learned to generate actions. Under these game theoretic conditions, however, there may be good reasons to believe that behavior is at least partially indeterminate. If behavior is truly uncertain on a choice-by-choice level, then how much of the behavior should a good theory explain? We can only assess the overall quality of theories like these if we can discover the fraction of the behavior that they seek to model, ω, which is truly indeterminate. The critical point that all of these observations make is that if human behavior is at root indeterminate, we do not need to abandon the scientific method as it is practiced today. The existence of indeterminacy does raise measurement and falsification problems. The measurement problem can be addressed by efforts to bound ω. The falsification problem has to be addressed in a different way. If the behavioral world is indeterminate, we will have to abandon rigorous falsification. That would be a shame, but it is important to remember that as behavioral scientists, we typically rely on an iterative process of theory-by-theory falsification, and there is no compelling reason to believe that this iterative method is challenged by the existence of fundamental indeterminacy in behavior. These considerations suggest that behavioral indeterminacy may be a good deal less threatening to scientists and the scientific method than Popper may have feared originally. At the same time, the empirical observations presented in this review hint that behavioral indeterminacy may be much more likely to occur than Schrodinger imagined. He argued that fundamental indeterminacy would never arise in the living world because If it were not so, if we were organisms so sensitive that a single atom, or even a few atoms, could make a perceptible impression on our senses—Heavens, what would life be like! To stress one point: an organism of that kind would

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most certainly not be capable of developing the kind of orderly thought which, after passing through a long sequence of earlier stages, ultimately results in forming, among many other ideas, the idea of an atom. (Schrodinger 1944) Our existing data, although ambiguous, clearly challenge Schrodinger’s conclusion. The vertebrate nervous system is sensitive to the actions of single quantum particles. At the lowest levels of perceptual threshold, the quantum dynamics of photons, more than anything else, governs whether or not a human observer sees a light (Rieke & Baylor 1998). Synapses and neurotransmission also seem to violate this assumption of Schrodinger’s, and these are the building blocks from which neurocomputation is achieved. In the end, Schrodinger may be right, behavior may be fundamentally determinate, but it would be premature to draw that conclusion now. Behavioral scientists will have to continue to explore apparent indeterminacy in behavior and will have to develop the methodological tools for determining whether this apparent indeterminacy is fundamental. ACKNOWLEDGMENTS The author expresses his profound gratitude to B. Lau and H. Bayer for hours spent in discussion. This work was supported by the James S. McDonnell Foundation and the National Eye Institute. The Annual Review of Psychology is online at http://psych.annualreviews.org

LITERATURE CITED Arnauld A, Nicole P. 1662/1996. Logic or the Art of Thinking. Transl. JV Buroker, ed. London: Cambridge Univ. Press Auger C, Marty A. 2000. Quantal currents at single-site central synapses. J. Physiol. (London) 526:3–11 Bacon F. 1620/1994. Novum Organum. Transl. P Urbach, J Gibson. Chicago: Open Court Bakan P. 1960. Response-tendencies in attempts to generate binary series. Am. J. Psychiatry 73:127–31 Barlow HB, Levick WR. 1969. Three factors limiting the reliable detection of light by retinal ganglion cells of the cat. J. Physiol. (London) 200:1–24 Barraclough DJ, Conroy ML, Lee D. 2004. Prefrontal cortex and decision making in a mixed-strategy game. Nat. Neurosci. 7:404– 10 Batista AP, Newsome WT. 2000. Visuo-motor

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tion of the Nervous System. New York: Scribner’s Shimp CP. 1967. Reinforcement of leastfrequent sequences of choices. J. Exp. Anal. Behav. 10:57–65 Skinner BF. 1938. The Behavior of Organisms. East Norwalk, CT: Appleton-Century-Crofts Smith A. 1776/1991. Wealth of Nations. New York: Prometheus Staddon JER, Cerutti DT. 2003. Operant conditioning. Annu. Rev. Psychol. 54:115–44 Stevens CF. 2003. Neurotransmitter release at central synapses. Neuron 40:381–88 Takumi Y, Ramirez-Leon V, Laake P, Rinvik E, Ottersen OP. 1999. Differential modes of expression for AMPA and NMDA receptors in hippocampal synapses. Nat. Neurosci. 2:618–24 Tolhurst DJ, Movshon JA, Dean AF. 1981. The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision. Res. 23:775–85 VonNeumann JV, Morgenstern O. 1944. Theory of Games and Economic Behavior. Princeton, NJ: Princeton Univ. Press Wagenaar WA. 1972. Generation of random sequences by human subjects: a critical survey of literature. Psychol. Bull. 77:65–72

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Annu. Rev. Psychol. 2005. 56:57–87 doi: 10.1146/annurev.psych.56.091103.070311 c 2005 by Annual Reviews. All rights reserved Copyright First published online as a Review in Advance on August 24, 2004

MODELS OF BRAIN FUNCTION IN NEUROIMAGING Karl J. Friston


Wellcome Department of Cognitive Neurology, University College London, London, WC1N 3BG, United Kingdom; email: [email protected]

Key Words dynamic, inference, causal, Bayesian, fMRI, hemodynamics, connectivity ■ Abstract Inferences about brain function, using neuroimaging data, rest on models of how the data were caused. These models can be quite diverse, ranging from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamics. However, they all have to be internally consistent because they model the same thing. This consistency encompasses many levels of description and places constraints on the statistical models, adopted for data analysis, and the experimental designs they embody. The aim of this review is to introduce the key models used in imaging neuroscience and how they relate to each other. We start with anatomical models of functional brain architectures, which motivate some of the fundaments of neuroimaging. We then turn to basic statistical models (e.g., the general linear model) used for making classical and Bayesian inferences about where neuronal responses are expressed. By incorporating biophysical constraints, these basic models can be finessed and, in a dynamic setting, rendered causal. This allows us to infer how interactions among brain regions are mediated.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ANATOMIC MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Functional Specialization and Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Functional Specialization and Segregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . STATISTICAL MODELS OF REGIONAL RESPONSES . . . . . . . . . . . . . . . . . . . . . Statistical Parametric Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The General Linear Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classical and Bayesian Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dynamic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biophysical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MODELS OF FUNCTIONAL INTEGRATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Functional and Effective Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dynamic Causal Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0057$14.00

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INTRODUCTION Understanding the brain depends on conceptual, anatomical, statistical, and causal models that link ideas about how it works to observations and experimental data. The aim of this review is to highlight the relationships among the sorts of models that are employed in imaging neuroscience. These relationships reflect an increasing mechanistic finesse as one moves from simple statistical models used to identify where evoked brain responses are expressed (cf. neo-phrenology) to models of how neuronal responses are caused (e.g., causal modeling). In the near future, models of representational inference and learning may be used as observation models to confirm some fundamental hypotheses about what the brain is doing (e.g., predictive coding). We review a series of exemplar models that cover conceptual models, motivating experimental design, to detailed biophysical models of coupled neuronal ensembles that enable questions to be asked at a physiological and computational level. Anatomical models of functional brain architectures motivate the fundaments of neuroimaging. We start by reviewing the distinction between functional specialization and integration and how these principles serve as the basis for most analyses of neuroimaging data. We then turn to simple statistical models (e.g., the general linear model) used for making classical and Bayesian inferences about functional specialization in terms of where neuronal responses are expressed. Characterizing a region-specific effect rests on estimation and inference. Inferences in neuroimaging may be about differences expressed when comparing one group of subjects to another or, within subjects, inferences may be about changes over a sequence of observations. They may pertain to structural differences (e.g., in voxel-based morphometry; Ashburner & Friston 2000) or neurophysiological indices of brain functions (e.g., functional magnetic resonance imaging, or fMRI). The principles of data analysis are very similar for all these applications. We focus on the analysis of fMRI time-series because this covers most of the issues encountered in other modalities. By incorporating biophysical constraints, simple observation models can be rendered biologically more realistic and, in a dynamic framework, causal. This allows us to infer how interactions among brain regions are mediated. This sort of characterization speaks to functional integration and relies on the notions of functional and effective connectivity.

ANATOMIC MODELS Functional Specialization and Integration The brain appears to adhere to two fundamental principles of functional organization, functional specialization and functional integration, where the integration within and among specialized areas is mediated by effective connectivity. The distinction relates to that between “localizationism” and “[dis]connectionism” that dominated thinking about cortical function in the nineteenth century. Since the

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early anatomic theories of Gall, the identification of a particular brain region with a specific function has become a central theme in neuroscience. However, functional localization per se was not easy to demonstrate: For example, a meeting that took place on August 4, 1881, addressed the difficulties of attributing function to a cortical area, given the dependence of cerebral activity on underlying connections (Phillips et al. 1984). This meeting was entitled “Localization of Function in the Cortex Cerebri.” Goltz (1881), although accepting the results of electrical stimulation in dog and monkey cortex, considered that the excitation method was inconclusive, in that movements elicited might have originated in related pathways, or current could have spread to distant centers. In short, the excitation method could not be used to infer functional localization because localizationism discounted interactions, or functional integration among different brain areas. It was proposed that lesion studies could supplement excitation experiments. Ironically, it was observations on patients with brain lesions some years later (see Absher & Benson 1993) that led to the concept of “disconnection syndromes” and the refutation of localizationism as a complete or sufficient explanation of cortical organization. Functional localization implies that a function can be localized in a cortical area, whereas specialization suggests that a cortical area is specialized for some aspects of perceptual or motor processing, and that this specialization is anatomically segregated within the cortex. The cortical infrastructure supporting a single function may then involve many specialized areas whose union is mediated by the functional integration among them. In this view, functional specialization is only meaningful in the context of functional integration and vice versa.

Functional Specialization and Segregation The functional role of any component (e.g., cortical area, subarea, or neuronal population) of the brain is defined largely by its connections (Passingham et al. 2002). Certain patterns of cortical projections are so common that they could amount to rules of cortical connectivity. “These rules revolve around one, apparently, overriding strategy that the cerebral cortex uses—that of functional segregation” (Zeki 1990). Functional segregation demands that cells with common functional properties be grouped together. This architectural constraint necessitates both convergence and divergence of cortical connections. Extrinsic connections among cortical regions are not continuous but occur in patches or clusters. This patchiness has, in some instances, a clear relationship to functional segregation. For example, V2 has a distinctive cytochrome oxidase architecture consisting of thick stripes, thin stripes, and interstripes. When recordings are made in V2, directionally selective (but not wavelength or color-selective) cells are found exclusively in the thick stripes. Retrograde (i.e., backward) labeling of cells in V5 is limited to these thick stripes. All the available physiological evidence suggests that V5 is a functionally homogeneous area that is specialized for visual motion. Evidence of this nature supports the notion that patchy connectivity is the anatomical infrastructure that mediates functional segregation and specialization. If it is the case that neurons

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in a given cortical area share a common responsiveness, by virtue of their extrinsic connectivity, to some sensorimotor or cognitive attribute, then this functional segregation is also an anatomical one. In summary, functional specialization suggests that challenging a subject with the appropriate sensorimotor attribute or cognitive process should lead to activity changes in, and only in, the specialized areas. This is the anatomical and physiological model upon which the search for regionally specific effects is based. We deal first with models of regionally specific responses and later return to models of functional integration.

STATISTICAL MODELS OF REGIONAL RESPONSES Statistical Parametric Mapping Functional mapping studies are usually analyzed with some form of statistical parametric mapping. Statistical parametric mapping entails the construction of spatially extended statistical processes to test hypotheses about regionally specific effects (Friston et al. 1991). Statistical parametric maps (SPMs) are image processes with voxel values that, under the null hypothesis, are distributed according to a known probability density function, usually the student’s T or F distributions. These are known colloquially as T- or F-maps. The success of statistical parametric mapping is due largely to the simplicity of the idea. Namely, one analyzes every voxel using any standard (univariate) statistical test, usually one testing for activation or regression on some explanatory variable. The resulting statistical parameters are assembled into an image—the SPM. SPMs are interpreted as spatially extended statistical processes by referring to the probabilistic behavior of random fields (Adler 1981; Friston 1991; Worsley et al. 1992, 1996). Random fields model both the univariate probabilistic characteristics of an SPM and any nonstationary spatial covariance structure under the null hypothesis. “Unlikely” excursions of the SPM are interpreted as regionally specific effects, attributable to the sensorimotor or cognitive process that has been manipulated experimentally. Over the years, statistical parametric mapping (Friston et al. 1995b) has come to refer to the conjoint use of the general linear model (GLM) and Gaussian random field (GRF) theory to analyze and make classical inferences about spatially extended data through statistical parametric maps. The GLM is used to estimate some parameters that could explain the spatially continuous data in exactly the same way as in conventional analysis of discrete data. GRF theory is used to resolve the multiple-comparisons problem that ensues when making inferences over a volume of the brain. GRF theory provides a method for adjusting p values for the search volume of an SPM to control false positive rates. It plays the same role for continuous data (i.e., images or time-series) as the Bonferonni correction for a family of discontinuous or discrete statistical tests. Below we consider the Bayesian alternative to classical inference with SPMs. This rests on conditional inferences about an effect, given the data, as opposed to

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classical inferences about the data, given the effect is zero. Bayesian inferences about spatially extended effects use posterior probability maps (PPMs). Although less established than SPMs, PPMs are potentially very useful, not least because they do not have to contend with the multiple-comparisons problem induced by classical inference (see Berry & Hochberg 1999). In contradistinction to SPM, this means that inferences about a given regional response do not depend on inferences about responses elsewhere. Before looking at the models underlying Bayesian inference, we first consider estimation and classical inference in the context of the GLM.

The General Linear Model Statistical analysis of imaging data corresponds to (a) modeling the data to partition observed neurophysiological responses into components of interest, confounds, and error, and (b) making inferences about interesting effects using the variances of the partitions. A brief review of the literature may give the impression that there are numerous ways to analyze positron emission tomography (PET) and fMRI time-series, with a diversity of statistical and conceptual approaches. This is not the case. With very few exceptions (see Nichols & Holmes 2002 for an overview), every analysis is a variant of the general linear model. These include (a) simple T-tests on scans assigned to one condition or another, (b) correlation coefficients between observed responses and boxcar stimulus functions in fMRI (Bandettini et al. 1993), (c) inferences made using multiple linear regression, (d) evoked responses estimated using linear time invariant models, and (e) selective averaging to estimate event-related responses. Mathematically, all are identical and can be implemented with the same equations and algorithms. The only thing that distinguishes among them is the design matrix encoding the experimental design. The general linear model is an equation, y = Xβ + ε,

(1)

expressing the observed response y in terms of a linear combination of explanatory variables in the matrix X plus a well-behaved error term (i.e., an independently and identically distributed Gaussian random variable). The general linear model is variously known as “analysis of [co]variance” or “multiple regression” and subsumes simpler variants, like the T-test for a difference in means, to more elaborate linear convolution models such as finite impulse response (FIR) models. The matrix X that contains the explanatory variables (e.g., designed effects or confounds) is called the design matrix. Each column of the design matrix corresponds to some effect one has built into the experiment or that may confound the results. These are referred to as explanatory variables, covariates, or regressors. The example in Figure 1 relates to an fMRI study of visual stimulation under four conditions. The effects on the response variable are modeled in terms of functions of the presence of these conditions (i.e., box or stick functions smoothed with components of a hemodynamic response function; in Figure 1, two components were used for each


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of the four conditions, giving eight regressors in total). The relative contribution of each of these columns to the response is controlled by the parameters β. These are estimated using standard least squares, and inferences about the parameter estimates are made using T or F statistics, depending upon whether one is looking at a particular linear combination (e.g., a subtraction), or all of them together. The design matrix can contain covariates or indicator variables that take values of 0 or 1, to indicate the presence of a particular level of an experimental factor. Each column of X has an associated but unknown parameter. Some of these parameters will be of interest (e.g., the effect of a sensorimotor or cognitive condition or the regression coefficient of hemodynamic responses on reaction time). The remaining parameters will be of no interest and pertain to nuisance or confounding effects (e.g., the effect of being a particular subject or the regression slope of regional activity on global activity). Inferences about the parameter estimates are made using their estimated variance. This allows one to test the null hypothesis that some particular linear combination (e.g., a subtraction) of the estimates is zero using an SPM{T}. The T statistic obtains by dividing a contrast or compound (specified by contrast weights) of the parameter estimates by the standard error of that compound. Sometimes, several contrasts of parameter estimates are jointly interesting; for example, when using polynomial (Büchel et al. 1996) or basis function expansions of some experimental factor. In these instances, the SPM{F} is used and is specified with a matrix of contrast weights that can be thought of as a collection of “T contrasts” that one wants to test together. In most analyses, the design matrix contains indicator variables or parametric variables encoding the experimental manipulations. These are formally identical to classical analysis of [co]variance (i.e., AnCova) models. An important instance of the GLM, from the perspective of fMRI, is the linear time invariant model. Mathematically this is no different from any other GLM. However, it explicitly ←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− Figure 1 The general linear model. This model is an equation expressing the response variable Y in terms of a linear combination of explanatory variables in a design matrix X and an error term with assumed or known autocorrelation . In fMRI, the data can be filtered with a convolution or residual-forming matrix (or a combination) S, leading to a generalized linear model that includes (intrinsic) serial correlations and applied (extrinsic) filtering. Different choices of S correspond to different estimation schemes as indicated on the upper left. The parameter estimates obtain in a least squares sense using the pseudoinverse (denoted by +) of the filtered design matrix. Generally, an effect of interest is specified by a vector of contrast weights c that give a weighted sum or compound of parameter estimates referred to as a contrast. The T statistic is simply this contrast divided by the estimated standard error (i.e., the square root of its estimated variance). The ensuing T statistic is distributed with v degrees of freedom. The equations for estimating the variance of the contrast and the degrees of freedom are provided in the right-hand panel and accommodate the nonsphericity implied by .

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treats the data sequence as an ordered time-series and enables a signal processing perspective that can be useful. The equations summarized in Figure 1 can be used to implement a vast range of statistical analyses. The issue is therefore not so much the mathematics but the formulation of a design matrix X appropriate to the study design and inferences that are sought. Before considering general linear models as biophysical or causal models of brain responses, we focus on the design matrix as a device to specify experimental design. Here the explanatory variables encode treatment effects that we assume are expressed in a linear and instantaneous fashion in the data, without reference to any particular mechanism.

Experimental Design This section considers the different sorts of designs employed in neuroimaging studies. Experimental designs can be classified as single factor or multifactorial designs; within this classification, the levels of each factor can be categorical or parametric. The tenet of cognitive subtraction is that the difference between two tasks can be formulated as a separable cognitive or sensorimotor component and that regionally specific differences in hemodynamic responses, evoked by the two tasks, identify the corresponding functionally specialized area. Early applications of subtraction range from the functional anatomy of word processing (Petersen et al. 1989) to functional specialization in extrastriate cortex (Lueck et al. 1989). The latter studies involved presenting visual stimuli with and without some sensory attribute (e.g., color and motion). The areas highlighted by subtraction were identified with homologous areas in monkeys that showed selective electrophysiological responses to equivalent visual stimuli. Cognitive conjunctions (Price & Friston 1997) can be thought of as an extension of the subtraction technique, in the sense that they combine a series of subtractions. In subtraction, one tests a single hypothesis pertaining to the activation in one task relative to another. In conjunction analyses, several hypotheses are tested to determine whether all the activations, in a series of task pairs, are expressed conjointly. Consider the problem of identifying regionally specific activations due to a particular cognitive component (e.g., object recognition). If one can identify a series of task pairs whose differences have only that component in common, then the region that activates, in all the corresponding subtractions, can be associated with the common component. In short, conjunction analyses allow one to disclose context-invariant regional responses.

CATEGORICAL DESIGNS, COGNITIVE SUBTRACTION, AND CONJUNCTIONS

The premise behind parametric designs is that regional physiology will vary systematically with the degree of cognitive or sensorimotor processing or deficits thereof. Examples of this approach include the PET

PARAMETRIC DESIGNS

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experiments of Grafton et al. (1992) that demonstrated significant correlations between hemodynamic responses and the performance of a visually guided motor tracking task. On the sensory side, Price et al. (1992) demonstrated a remarkable linear relationship between perfusion in periauditory regions and frequency of aural word presentation. This correlation was not observed in Wernicke’s area, where perfusion appeared to correlate, not with the discriminative attributes of the stimulus, but with the presence or absence of semantic content. These relationships or neurometric functions may be linear or nonlinear. Using polynomial regression, in the context of the GLM, one can identify nonlinear relationships between stimulus parameters (e.g., stimulus duration or presentation rate) and evoked responses. To do this one usually uses a SPM{F} (see Büchel et al. 1996). The example provided in Figure 2 illustrates both categorical and parametric aspects of design and analysis. These data were obtained from an fMRI study of visual motion processing using radially moving dots (Chawla et al. 1999). The stimuli were presented over a range of speeds using isoluminant and isochromatic stimuli. To identify areas involved in visual motion, a stationary dots condition was subtracted from a moving dots conditions (see the contrast weights, upper right). To ensure significant motion-sensitive responses, using color and luminance stimuli, a conjunction of the equivalent subtractions was assessed under both viewing contexts. Areas V5 and V3a are seen in the ensuing SPM{T}. The T values in this SPM are simply the minimum of the T values for each subtraction. Thresholding this SPM ensures that all voxels survive a threshold in each subtraction separately. This conjunction SPM has an equivalent interpretation; it represents the intersection of the excursion sets, defined by the threshold of each component SPM. This intersection is the essence of a conjunction. The responses in left V5 are shown in the lower panel of Figure 2 and speak to a compelling inverted “U” relationship between speed and evoked response that peaks at around 8◦ per second. It is this sort of relationship that parametric designs try to characterize. Interestingly, the form of these speed-dependent responses was similar using both stimulus types, although luminance cues elicit a greater response. From the point of view of a factorial design, there is a main effect of stimulus (isoluminant versus isochromatic), a main (nonlinear) effect of speed, but no speed by stimulus interaction. Factorial designs are more prevalent than single-factor designs because they enable inferences about interactions. At its simplest, an interaction represents a change in a change. Interactions are associated with factorial designs where two or more factors are combined in the same experiment. The interaction term assesses the effect of one factor on the effect of the other. Factorial designs have a wide range of applications. An early application, in neuroimaging, examined physiological adaptation and plasticity during motor performance by assessing time by condition interactions (Friston et al. 1992). Factorial designs have an important role in the context of cognitive subtraction and additive factors logic by virtue of being able to test for interactions, or context-sensitive activations

MULTIFACTORIAL DESIGNS

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Figure 2 (Top right): An image representation of the design matrix. The vectors of contrast weights define the linear compounds of parameters tested. The contrast weights are displayed over the column of the design matrix that corresponds to the effects in question. The design matrix here includes condition-specific effects (boxcars convolved with a hemodynamic response function). Odd columns correspond to stimuli shown under isochromatic conditions and even columns model responses to isoluminant stimuli. The first two columns are for stationary stimuli and the remaining columns are for stimuli of increasing speed. The final column is a constant term. (Top left) A maximum intensity projection of the SPM{T} conforming to the standard anatomical space of Talairach & Tournoux (1988). The T values here are the minimum T values from both contrasts, thresholded at p = 0.001 uncorrected. The most significant conjunction is seen in left V5. (Lower panel) Plot of the condition-specific parameter estimates for this voxel. The T value was 9.25 (p < 0.001, corrected according to GRF theory). This example is based on data from Chawla et al. 1999.

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(i.e., to demonstrate the fallacy of “pure insertion”; see Friston et al. 1996c). These interaction effects can sometimes be interpreted as (a) the integration of the two or more (cognitive) processes or (b) the modulation of one (perceptual) process by another. In summary, the design matrix encodes the causes of observed data and, in particular, treatment effects caused by changes in the level of various experimental factors. These factors can have categorical or parametric levels and most experiments nowadays use multiple factors to test for both main effects and interactions. Before turning to mechanistically more informed formulations of the general linear model we consider briefly the two sorts of inferences that can be made about the parameter estimates.

Classical and Bayesian Inference To date, inference in neuroimaging has been restricted largely to classical inferences based upon statistical parametric maps. The statistics that comprise these SPMs are essentially functions of the data. The probability distribution of the chosen statistic, under the null hypothesis (i.e., the null distribution), is used to compute a p value. This p value is the probability of obtaining the statistic, or the data, given that the null hypothesis is true. If sufficiently small, the null hypothesis can be rejected and an inference is made. The alternative approach is to use Bayesian or conditional inference based upon the posterior distribution of the activation given the data (Holmes & Ford 1993). This necessitates the specification of priors (i.e., the probability distribution of the activation). Bayesian inference requires the posterior distribution and therefore rests upon a posterior density analysis. A useful way to summarize this posterior density is to compute the probability that the activation exceeds some threshold. This computation represents a Bayesian inference about the effect, in relation to the specified threshold. By computing a posterior probability for each voxel, we can construct posterior probability maps or PPMs that are a useful complement to classical SPMs (Friston et al. 2002, Friston & Penny 2003). The motivation for using conditional or Bayesian inference is that it has high face validity. This is because the inference is about an effect, or activation, being greater than some specified size that has some meaning in relation to underlying neurophysiology. This contrasts with classical inference, in which the inference is about the effect being significantly different from zero. The problem for classical inference is that trivial departures from the null hypothesis can be declared significant, with sufficient data or sensitivity. From the point of view of neuroimaging, posterior inference is especially useful because it eschews the multiple-comparisons problem. In classical inference, one tries to ensure that the probability of rejecting the null hypothesis incorrectly is maintained at a small rate, despite making inferences over large volumes of the brain. This induces a multiple-comparisons problem that, for continuous spatially extended data, requires an adjustment or correction to the p value using GRF theory as mentioned above. This correction means that classical inference becomes less sensitive or powerful with large search

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volumes. In contradistinction, posterior inference does not have to contend with the multiple-comparisons problem because there are no false-positives. The probability that activation has occurred, given the data, at any particular voxel is the same, irrespective of whether one has analyzed that voxel or the entire brain. For this reason, posterior inference using PPMs represents a relatively more powerful approach than classical inference in neuroimaging. PPMs require the posterior distribution or conditional distribution of the activation (a contrast of conditional parameter estimates) given the data. This posterior density can be computed, under Gaussian assumptions, using Bayes’ rule. Bayes’ rule requires the specification of a likelihood function and the prior density of the model’s parameters. The models used to form PPMs and the likelihood functions are the same as in classical SPM analyses, namely the GLM. The only extra bit of information that is required is the prior probability distribution of the parameters. Although it would be possible to specify them using independent data or some plausible physiological constraints, there is an alternative to this fully Bayesian approach. The alternative is empirical Bayes, in which the variances of the prior distributions are estimated directly from the data. Empirical Bayes requires a hierarchical observation model where the parameters and hyperparameters at any particular level can be treated as priors on the level below. There are numerous examples of hierarchical observation models in neuroimaging. For example, the distinction between fixed- and random/mixedeffects analyses of multisubject studies relies upon a two-level hierarchical model (Friston et al. 2002). However, in neuroimaging there is a natural hierarchical observation model that is common to all brain mapping experiments. This is the hierarchy induced by looking for the same effects at every voxel within the brain (or gray matter). The first level of the hierarchy corresponds to the experimental effects at any particular voxel and the second level of the hierarchy comprises the effects over voxels. Put simply, the variation in a particular contrast, over voxels, can be used as the prior variance of that contrast at any particular voxel. Generally, hierarchical linear models have the following form:


HIERARCHICAL MODELS AND EMPIRICAL BAYES

y = X (1) β (1) + ε (1) β (1) = X (2) β (2) + ε (2) . β (2) = . . .

(2)

This is the same as Equation 1, but now the parameters of the first level are generated by a supraordinate linear model and so on to any hierarchical depth required. These hierarchical observation models are an important extension of the GLM and are usually estimated using Expectation Maximization (Dempster et al. 1977). In the present context, the response variables comprise the responses at all voxels and β (1) are the treatment effects about which we want to make an inference. Because we have invoked a second level, the first-level parameters embody random effects and are generated by a second-level linear model. At the second level, β (2) is the

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average effect over voxels and ε (2) is voxel-to-voxel variation. By estimating the variance of ε(2), one is implicitly estimating an empirical prior on the first-level parameters at each voxel. This prior can then be used to estimate the posterior probability of β (1) being greater than some threshold at each voxel. An example of the ensuing PPM is provided in Figure 3 along with the classical SPM. In this section we have seen how the GLM can be used to test hypotheses about brain responses and how, in a hierarchical form, it enables empirical Bayesian or conditional inference. In the next section, we deal with dynamic systems and how they can be formulated as GLMs. These dynamic models take us closer to how experimental manipulations actually cause brain responses, and represent the next step in working toward causal models of brain responses.

Dynamic Models In Friston et al. (1994), the form of the hemodynamic impulse response function (HRF) was estimated using a least squares deconvolution and a time-invariant model, where evoked neuronal responses are convolved or smoothed with an HRF to give the measured hemodynamic response (see also Boynton et al. 1996). This simple linear convolution model is the cornerstone for making statistical inferences about activations in fMRI with the GLM. An impulse response function is the response to a single impulse, measured at a series of times after the input. It characterizes the inputoutput behavior of the system (i.e., voxel) and places important constraints on the sorts of inputs that will excite a response. Knowing the forms that the HRF can take is important for several reasons, not least because it allows for better statistical models of the data. The HRF may vary from voxel to voxel and this has to be accommodated in the GLM. To allow for different HRFs in different brain regions, the notion of temporal basis functions, to model evoked responses in fMRI, was introduced (Friston et al. 1995a) and applied to event-related responses in Josephs et al. (1997) (see also Lange & Zeger 1997). The basic idea behind temporal basis functions is that the hemodynamic response, induced by any given trial type, can be expressed as the linear combination of several (basis) functions of peristimulus time. The convolution model for fMRI responses takes a stimulus function encoding the neuronal responses and convolves it with an HRF to give a regressor that enters the design matrix. When using basis functions, the stimulus function is convolved with all the basis functions to give a series of regressors (in Figure 1 we used four stimulus functions and two basis functions to give eight regressors). Mathematically we can express this model as

CONVOLUTION MODELS AND TEMPORAL BASIS FUNCTIONS

y(t) = Xβ + ε X i = Ti (t) ⊗ u(t)

⇔

y(t) = u(t) ⊗ h(t) + ε , h(t) = β1 T1 (t) + β2 T2 (t) + . . .

(3)

where ⊗ means convolution. This equivalence illustrates how temporal basis functions allow one to take any convolution model (right) and convert it into a GLM (left). The parameter estimates β i are the coefficients or weights that determine the


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mixture of basis functions Ti(t) that best models h(t), the HRF for the trial type and voxel in question. We find the most useful basis set to be a canonical HRF and its derivatives with respect to the key parameters that determine its form (see below). Temporal basis functions are important because they enable a graceful transition between conventional multilinear regression models with one stimulus function per condition and FIR models with a parameter for each time point following the onset of a condition or trial type. Figure 4 illustrates this graphically. In short, temporal basis functions offer useful constraints on the form of the estimated response that retain the flexibility of FIR models and the efficiency of single regressor models.

Biophysical Models By adopting a convolution model for brain responses in fMRI, we are implicitly positing some underlying dynamic system that converts neuronal responses into observed hemodynamic responses. Our understanding of the biophysical and physiological mechanisms that underpin the HRF has grown considerably in the past few years (e.g., Buxton & Frank 1997, Hoge et al. 1999, Mandeville et al. 1999). Figure 5 shows some simulations based on the hemodynamic model described in Friston et al. (2000). Here, neuronal activity induces some autoregulated vasoactive signal that causes transient increases in regional cerebral blood flow (rCBF). The resulting flow increases lead to the dilation of a venous balloon, increasing its volume (v) and diluting venous blood to decrease deoxyhemoglobin content (q). The blood oxygenation level–dependent (BOLD) signal is roughly proportional to the concentration of deoxyhemoglobin (q/v) and follows the rCBF response with about a one-second delay. The model is framed in terms of differential equations, examples of which are provided in Figure 5. Notice that we have introduced variables, like volume and deoxyhemoglobin concentrations, that are not actually observed. These are referred to as the hidden states of input-state-output models. The state and output equations of any analytic dynamical system are

INPUT-STATE-OUTPUT SYSTEMS

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− Figure 3 Statistical parametric map (SPM) and posterior probability map (PPM) for a functional magnetic resonance imaging study of attention to visual motion (Büchel & Friston 1997). The display format in the lower panel uses an axial slice through extrastriate regions but the thresholds are the same as employed in maximum-intensity projections (upper panels). (Upper right) The activation threshold for the PPM was 0.7 a.u., meaning that all voxels shown had a 90% chance of an activation of 0.7% or more. (Upper left) The corresponding SPM using a corrected threshold at p = 0.05. Note the bilateral foci of motion-related responses in the PPM that are not seen in the SPM (gray arrows). As can be imputed from the design matrix (upper middle panel), the statistical model of evoked responses comprised boxcar regressors convolved with a canonical hemodynamic response function. The middle column corresponds to the presentation of moving dots and was the stimulus property tested by the contrast.


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x˙ (t) = f (x, u, θ)


y(t) = g(x, u, θ) + ε.

(4)

The first line is an ordinary differential equation and expresses the rate of change of the states as a parameterized function of the states and input. Typically, the inputs u(t) correspond to designed experimental effects (e.g., the stimulus function in fMRI). There is a fundamental and causal relationship (Fliess et al. 1983) between the outputs and the history of the inputs in Equation 4. This relationship conforms to a Volterra series, which expresses the output y(t) as a generalized convolution of the input u(t), critically without reference to the hidden states x(t). This series is simply a functional Taylor expansion of the outputs with respect to the inputs (Bendat 1990). The reason it is a functional expansion is that the inputs are a function of time1, y(t) =

i

0

t

t

...

κi (σ1 , . . . , σi )u(t − σ1 ), . . . , u(t − σi )dσ1 , . . . , dσi 0

κi (σ1 , . . . , σi ) =

∂ i y(t) , ∂u(t − σ1 ), . . . , ∂u(t − σi )

(5)

where κ i(σ 1, . . ., σ i) is the ith-order kernel. In Equation 5, the integrals are restricted to the past. This renders Equation 5 causal. The key thing here is that Equation 5 is simply a convolution and can be expressed as a GLM as in Equation 3. This means that we can take a neurophysiologically realistic model of hemodynamic responses and use it as an observation model to estimate parameters using observed data. Here the model is parameterized in terms of kernels that have a direct analytic 1

For simplicity, here and in Equation 7, we deal with only one experimental input.

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− Figure 4 Temporal basis functions offer useful constraints on the form of the estimated response that retain (a) the flexibility of finite impulse response (FIR) models and (b) the efficiency of single regressor models. The specification of these constrained FIR models involves setting up stimulus functions u(t) that model expected neuronal changes [e.g., boxcars of epoch-related responses or spikes (delta functions) at the onset of specific events or trials]. These stimulus functions are then convolved with a set of basis functions Ti(t) of peristimulus time that, in some linear combination, model the hemodynamic impulse response function (HRF). The ensuing regressors are assembled into the design matrix. The basis functions can be as simple as a single canonical HRF (middle), through to a series of delayed delta functions (bottom). The latter case corresponds to an FIR model and the coefficients constitute estimates of the impulse response function at a finite number of discrete sampling times. Selective averaging in event-related functional magnetic resonance imaging (Dale & Buckner 1997) is mathematically equivalent to this limiting case.


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relation to the original parameters θ of the biophysical system (through Equation 5). The first-order kernel is simply the conventional HRF. High-order kernels correspond to high-order HRFs and can be estimated using basis functions as described above. In fact, by choosing basis functions according to


T (σ )i =

∂κ(σ )1 , ∂θi

(6)

one can estimate the biophysical parameters because, to a first-order approximation, β i = θ i. The critical step we have taken here is to start with a causal dynamic model of how responses are generated and construct a general linear observation model that allows us to estimate and infer things about the parameters of that model. This is in contrast to the conventional use of the GLM with design matrices that are not informed by a forward model of how data are caused. This approach to modeling brain responses has a much more direct connection with underlying physiology and rests upon an understanding of the underlying system. Once a suitable causal model has been established (e.g., Figure 5), we can estimate second-order kernels. These kernels represent a nonlinear characterization of the HRF that can model interactions among stimuli in causing responses. One important manifestation of the nonlinear effects, captured by the second-order kernels, is a modulation of stimulus-specific responses by preceding stimuli that are proximate in time. This means that responses at high stimulus presentation rates saturate and, in some instances, show an inverted U behavior. This behavior appears to be specific to BOLD effects (as distinct from evoked changes in cerebral blood flow) and may represent a hemodynamic refractoriness. This effect has important implications for event-related fMRI, where one may want to present trials in quick succession. The results of a typical nonlinear analysis are given in Figure 6. The results in the right panel represent the average response, integrated over a 32-second train of stimuli as a function of stimulus onset asynchrony (SOA). These responses are based on the kernel estimates (left panels) using data from a voxel in the left posterior temporal region of a subject obtained during the presentation of single words at different rates. The solid line represents the estimated response and shows

NONLINEAR SYSTEM IDENTIFICATION

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− Figure 5 (Right) Hemodynamics elicited by an impulse of neuronal activity as predicted by a dynamical biophysical model (left). A burst of neuronal activity causes an increase in flow-inducing signal that decays with first-order kinetics and is downregulated by local flow. This signal increases regional cerebral blood flow (rCBF), which dilates the venous capillaries, increasing volume (v). Concurrently, venous blood is expelled from the venous pool, decreasing deoxyhemoglobin content (q). The resulting fall in deoxyhemoglobin concentration leads to a transient increase in the blood oxygenation level–dependent (BOLD) signal and a subsequent undershoot. (Left) Hemodynamic model on which these simulations were based (see Friston et al. 2000 for details).


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a clear maximum at just less than one second. The dots are responses based on empirical data from the same experiment. The broken line shows the expected response in the absence of nonlinear effects (i.e., that predicted by setting the second-order kernel to zero). It is clear that nonlinearities become important at around two seconds, leading to an actual diminution of the integrated response at subsecond SOAs. The implication of this sort of result is that the assumptions of the linear convolution models discussed above are violated with subsecond SOAs (see also Buckner et al. 1996, Burock et al. 1998). In summary, we started with models of regionally specific responses, framed in terms of the general linear model, in which responses were modeled as linear mixtures of designed changes in explanatory variables. Hierarchical extensions to linear observation models enable random-effects analyses and, in particular, an empirical Bayesian approach. The mechanistic utility of these models is realized through forward models that embody causal dynamics. Simple variants of these are the linear convolution models used to construct explanatory variables in conventional analyses of fMRI data. These are a special case of generalized convolution models that are mathematically equivalent to input-state-output systems comprising hidden states. Estimation and inference with these dynamic models tells us something about how the response was caused, but only at the level of single voxels. In the next section, we adopt the same perspective on models, but in the context of distributed responses and functional integration.

MODELS OF FUNCTIONAL INTEGRATION Functional and Effective Connectivity Imaging neuroscience has firmly established functional specialization as a principle of brain organization in man. The integration of specialized areas has proven ←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− Figure 6 (Left panels) Volterra kernels from a voxel in the left superior temporal gyrus at −56, −28, and 12 mm. These kernel estimates were based on a single-subject study of aural word presentation at different rates (from 0 to 90 words per minute) using a second-order approximation to a Volterra series expansion modeling the observed hemodynamic response to stimulus input (a delta function for each word). These kernels can be thought of as a characterization of the second-order hemodynamic response function. The first-order kernel κ 1 (upper panel) represents the (first-order) component usually presented in linear analyses. The second-order kernel (lower panel) is presented in image format. The color scale is arbitrary; white is positive and black is negative. The insert on the right represents κ1 κ1T , the second-order kernel predicted by a simple model that involves a linear convolution with κ1 followed by some static nonlinearity. (Right panel) Integrated responses over a 32-second stimulus train as a function of stimulus onset asynchrony. Solid line: estimates based on the nonlinear convolution model parameterized by the kernels on the left. Broken line: the responses expected in the absence of second-order effects (i.e., in a truly linear system). Dots: empirical averages based on the presentation of actual stimulus trains.

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more difficult to assess. Functional integration is usually inferred on the basis of correlations among measurements of neuronal activity. Functional connectivity has been defined as statistical dependencies or correlations among remote neurophysiological events. However, correlations can arise in a variety of ways; for example, in multiunit electrode recordings they can result from stimulus-locked transients evoked by a common input or reflect stimulus-induced oscillations mediated by synaptic connections (Gerstein & Perkel 1969). Integration within a distributed system is usually better understood in terms of effective connectivity: Effective connectivity refers explicitly to the influence that one neural system exerts over another, either at a synaptic (i.e., synaptic efficacy) or population level. It has been proposed that “the [electrophysiological] notion of effective connectivity should be understood as the experiment- and time-dependent, simplest possible circuit diagram that would replicate the observed timing relationships between the recorded neurons” (Aertsen & Preißl 1991). This speaks to two important points: (a) Effective connectivity is dynamic, i.e., activity- and time-dependent and (b) it depends upon a model of the interactions. The estimation procedures employed in functional neuroimaging can be divided into those based on (a) linear regression models (e.g., McIntosh & Gonzalez-Lima 1994) or (b) nonlinear dynamic causal models. There is a necessary link between functional integration and multivariate analyses because the latter are necessary to model interactions among brain regions. Multivariate approaches can be divided into those that are inferential in nature and those that are data-led or exploratory. We first consider multivariate approaches that generally are based on functional connectivity or covariance patterns (and generally are exploratory) and then turn to models of effective connectivity (that allow for some form of inference). In Friston et al. (1993), we introduced voxel-based principal component analysis (PCA) of neuroimaging time-series to characterize distributed brain systems implicated in sensorimotor, perceptual, or cognitive processes. These distributed systems are identified with principal components or eigenimages that correspond to spatial modes of coherent brain activity. This approach represents one of the simplest multivariate characterizations of functional neuroimaging time-series and falls into the class of exploratory analyses. Principal component or eigenimage analysis generally uses singular value decomposition to identify a set of orthogonal spatial modes that capture the greatest amount of variance expressed over time. As such, the ensuing modes embody the most prominent aspects of the variance-covariance structure of a given time-series. Noting that covariance among brain regions is equivalent to functional connectivity renders eigenimage analysis particularly interesting because it was among the first ways of addressing functional integration (i.e., connectivity) with neuroimaging data. Subsequently, eigenimage analysis has been elaborated in a number of ways. Notable among these is canonical variate analysis and multidimensional scaling (Friston et al. 1996a,b). Canonical variate

EIGENIMAGE ANALYSIS AND RELATED APPROACHES

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analysis was introduced in the context of ManCova (multiple analysis of covariance) and uses the generalized eigenvector solution to maximize the variance that can be explained by some explanatory variables relative to error. Canonical variate analysis can be thought of as an extension of eigenimage analysis that refers explicitly to some explanatory variables and allows for statistical inference. In fMRI, eigenimage analysis (e.g., Sychra et al. 1994) generally is used as an exploratory device to characterize coherent brain activity. These variance components may or may not be related to experimental design and endogenous coherent dynamics that have been observed in the motor system (Biswal et al. 1995). Despite its exploratory power, eigenimage analysis is fundamentally limited for two reasons. First, it offers only a linear decomposition of any set of neurophysiological measurements, and second, the particular set of eigenimages or spatial modes obtained is uniquely determined by constraints that are biologically implausible. These aspects of PCA confer inherent limitations on the interpretability and usefulness of eigenimage analysis of biological time-series and have motivated the exploration of nonlinear PCA and neural network approaches (e.g., Mørch et al. 1995). Two other important approaches deserve mention here. The first is independent component analysis (ICA). ICA uses entropy maximization to find, using iterative schemes, spatial modes or their dynamics that are approximately independent. This is a stronger requirement than orthogonality in PCA and involves removing highorder correlations among the modes (or dynamics). It was initially introduced as spatial ICA (McKeown et al. 1998) in which the independence constraint was applied to the modes (with no constraints on their temporal expression). Approaches that are more recent use, by analogy with magneto- and electrophysiological timeseries analysis, temporal ICA where the dynamics are enforced to be independent (e.g., Calhoun et al. 2001). This requires an initial dimension reduction (usually using conventional eigenimage analysis). Finally, there has been an interest in cluster analysis (Baumgartner et al. 1997). Conceptually, this can be related to eigenimage analysis through multidimensional scaling and principal coordinate analysis. All these approaches are interesting, but hardly anyone uses them. This is largely because the approaches tell you nothing about how the brain works and don’t allow one to ask specific questions. Simply demonstrating statistical dependencies among regional brain responses (i.e., demonstrating functional connectivity) does not address how these responses were caused. To address this, one needs explicit models of integration, or more precisely, effective connectivity.

Dynamic Causal Modeling This final section examines the modeling of interactions among neuronal populations, at a cortical level, using neuroimaging time-series. The aim of these models is to estimate, and make inferences about, the coupling among brain areas and how that coupling is influenced by changes in experimental context (e.g., time or

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cognitive set). The basic idea is to construct a reasonably realistic neuronal model of interacting cortical regions or nodes. This model is then supplemented with a forward model of how neuronal or synaptic activity translates into a measured response (see previous section). This enables the parameters of the neuronal model (i.e., effective connectivity) to be estimated from observed data. Intuitively, this approach regards an experiment as a designed perturbation of neuronal dynamics that are promulgated and distributed throughout a system of coupled anatomical nodes to change region-specific neuronal activity. These changes engender, through a measurement-specific forward model, responses that are used to identify the architecture and time constants of the system at a neuronal level. This represents a departure from conventional approaches (e.g., structural equation modeling and autoregression models; Büchel & Friston 1997, Harrison et al. 2003, McIntosh & Gonzalez-Lima 1994), in which one assumes the observed responses are driven by endogenous or intrinsic noise (i.e., innovations). In contradistinction, dynamic causal models assume the responses are driven by designed changes in inputs. An important conceptual aspect of dynamic causal models pertains to how the experimental inputs enter the model and cause neuronal responses. Experimental variables can illicit responses in one of two ways. First, they can elicit responses through direct influences on specific anatomical nodes. This would be appropriate, for example, in modeling sensory evoked responses in early visual cortices. The second class of input exerts its effect vicariously, through a modulation of the coupling among nodes. These sorts of experimental variables would normally be more enduring; for example, attention to a particular attribute or the maintenance of some cognitive set. These distinctions are seen most clearly in relation to particular forms of causal models used for estimation; for example, the bilinear approximation x˙ (t) = f (x, u) = Ax + u Bx + Cu y = g(x) + ε A=

∂f ∂x

B=

∂2 f ∂ x∂u

C=

∂f . ∂u

(7)

This is an approximation to any model of how changes in neuronal activity in one region, x(t)i, are caused by activity in the other regions. Here the output function g(x) embodies a hemodynamic model, linking neuronal activity to BOLD, for each region (e.g., that in Figure 5). The matrix A represents the connectivity among the regions in the absence of input u(t). Effective connectivity is the influence that one neuronal system exerts over another in terms of inducing a response ∂ x˙ /∂ x. This latent connectivity can be thought of as the intrinsic coupling in the absence of experimental perturbations. The matrix B is effectively the change in intrinsic coupling induced by the input. It encodes the input-sensitive changes in A or, equivalently, the modulation of effective connectivity by experimental manipulations. Because B is a second-order derivative, it is referred to as bilinear.

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Finally, the matrix C embodies the extrinsic influences of inputs on neuronal activity. The parameters θ = {A, B, C} are the connectivity or coupling matrices that we wish to identify and that define the functional architecture and interactions among brain regions at a neuronal level. Because Equation 7 has exactly the same form as Equation 4, we can express it as a GLM and estimate the parameters. Generally, estimation in the context of highly parameterized models like DCMs requires constraints in the form of priors. These priors enable conditional inference about the connectivity estimates. The sorts of questions that can be addressed with DCMs are now illustrated by looking at how attentional modulation might be mediated in sensory processing hierarchies in the brain. It has been established that the superior posterior parietal cortex (SPC) exerts a modulatory role on V5 responses using Volterra-based regression models (Friston & Büchel 2000), and that the inferior frontal gyrus (IFG) exerts a similar influence on SPC using structural equation modeling (Büchel & Friston 1997). The example here shows that DCM leads to the same conclusions but starting from a completely different construct (see Friston et al. 2003 for details). The experimental paradigm and data acquisition parameters are described in the Figure 7 legend. Figure 7 also shows the location of the regions that entered into the DCM. These regions were based on maxima from conventional SPMs testing for the effects of photic stimulation, motion, and attention. Regional time courses were taken as the first eigenvariate of 8 mm spherical volumes of interest centered on the maxima shown in the figure. The inputs, in this example, comprise one sensory perturbation and two contextual inputs. The sensory input was simply the presence of photic stimulation, and the first contextual input was presence of motion in the visual field. The second contextual input, encoding attentional set, was unity during attention to speed changes and zero otherwise. The model parameters were fitted such that the regional outputs from the model corresponded as closely as possible to the four regional eigenvariates (Figure 7, left panel). The intrinsic connections were constrained to conform to a hierarchical pattern in which each area was reciprocally connected to its supraordinate area. Photic stimulation entered at, and only at, V1. The effect of motion in the visual field was modeled as a bilinear modulation of the V1 to V5 connectivity and attention was allowed to modulate the backward connections from IFG and SPC. The results of the DCM are shown in Figure 7 (right panel). Of primary interest is the modulatory effect of attention that is expressed in terms of the bilinear coupling parameters for this input. Analysis of the posterior densities of the bilinear parameters shows that we can be highly confident that attention modulates the backward connections from IFG to SPC and from SPC to V5. Indeed, the influences of IFG on SPC are negligible in the absence of attention (dotted connection). It is important to note that the only way attentional manipulation could effect brain responses was through this bilinear effect. Attention-related responses are seen throughout the system (attention epochs are marked with arrows in the plot of IFG

DCM AND ATTENTIONAL MODULATION


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responses in Figure 7). This attentional modulation is accounted for, sufficiently, by changing just two connections. This change is, presumably, instantiated by an instructional set at the beginning of each epoch. The second idea this analysis illustrates is how DCM models functional segregation. Here one can regard V1 as “segregating” motion from other visual information and distributing it to the motion-sensitive area V5. This segregation is modeled as a bilinear “enabling” of V1 to V5 connections when, and only when, motion is present. Note that in the absence of motion the intrinsic V1 to V5 connection was trivially small (in fact the estimate was −0.04). The key advantage of entering motion through a bilinear effect, as opposed to a direct effect on V5, is that we can finesse the inference that V5 shows motion-selective responses with the assertion that these responses are mediated by afferents from V1. The two bilinear effects above represent two important aspects of functional integration that DCM is able characterize. ←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− Figure 7 Results of a dynamic causal modeling (DCM) analysis of attention to visual motion with functional magnetic resonance imaging. (Right panel) Functional architecture based upon the conditional estimates shown alongside their connections, with the percent confidence that they exceeded threshold in brackets. The most interesting aspects of this architecture involve the role of motion and attention in exerting bilinear effects. Critically, the influence of motion is to enable connections from V1 to the motion-sensitive area V5. The influence of attention is to enable backward connections from the inferior frontal gyrus (IFG) to the superior parietal cortex (SPC). Furthermore, attention increases the influence of SPC on the V5. Dotted arrows connecting regions represent significant bilinear affects in the absence of a significant intrinsic coupling. (Left panel) Fitted responses based upon the conditional estimates and the adjusted data are shown for each region in the DCM. The insert (upper left) shows the location of the regions. The fMRI data were from a study in which subjects viewed identical stimuli (visual motion subtended by radially moving dots) under different attentional manipulations of the task (detection of velocity changes) (Büchel & Friston 1997). The data were acquired from a normal subject at 2 Tesla, using a whole-body MRI system, equipped with a head volume coil. Contiguous multislice T2∗ -weighted fMRI images were obtained with a gradient echo-planar sequence (TE = 40 ms, TR = 3.22 s, matrix size = 64 × 64 × 32, voxel size 3 × 3 × 3 mm). Each subject had four consecutive 100-scan sessions comprising a series of 10-scan blocks under five different conditions D F A F N F A F N S. The first condition (D) was a dummy condition to allow for magnetic saturation effects. F (fixation) corresponds to a low-level baseline where the subjects viewed a fixation point at the center of a screen. In condition A (attention), subjects viewed 250 dots moving radially from the center at 4.7◦ per second and were asked to detect changes in radial velocity. In condition N (no attention), the subjects were asked simply to view the moving dots. In condition S (stationary), subjects viewed stationary dots. The order of A and N was swapped for the last two sessions. In all conditions, subjects fixated the center of the screen. There were no speed changes during scanning. No overt response was required in any condition.

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The central ideal behind dynamic causal modeling is to treat the brain as a deterministic nonlinear dynamic system that is subject to inputs and produces outputs. Effective connectivity is parameterized in terms of coupling among unobserved brain states (e.g., neuronal activity in different regions). The objective is to estimate these parameters by perturbing the system and measuring the response. This is in contradistinction to established methods for estimating effective connectivity from neurophysiological time-series, which include structural equation modeling (Büchel & Friston 1997, McIntosh & Gonzalez-Lima 1994) and models based on multivariate auto-regressive processes (Goebel et al. 2003, Harrison et al. 2003). In these models, there is no designed perturbation and the inputs are treated as unknown and stochastic. Furthermore, the inputs are assumed to express themselves instantaneously such that, at the point of observation, the change in states will be zero. From Equation 7, in the absence of bilinear effects, we have


STRUCTURAL EQUATION MODELING

x˙ = 0 = Ax + Cu x = −A−1 Cu.

(8)

This is the regression equation used in SEM where A = A − I and A contains the off-diagonal connections among regions. The key point here is that A is estimated by assuming u is some random innovation with known covariance. This is not tenable for designed experiments when u represents carefully structured experimental inputs. Although SEM and related autoregressive techniques are useful for establishing dependence among regional responses, these techniques are not a surrogate for informed causal models based on the underlying dynamics of these responses.

CONCLUSION In this article, we reviewed the main models that underpin image analysis and touched briefly on ways of assessing specialization and integration in the brain. The key principles of functional brain architectures were used to motivate the various models considered. These can be regarded as a succession of modeling endeavors, drawing more and more on our understanding of how brain-imaging signals are generated, both in terms of biophysics and the underlying neuronal interactions. We have seen how hierarchical linear observation models encode the treatments effects elicited by experimental design. General linear models based on convolution models imply an underlying dynamic input-state-output system. The form of these systems can be used to constrain convolution models and explore some of their simpler nonlinear properties. By creating observation models based on an explicit forward model of neuronal interactions, one can now start to model and assess interactions among distributed cortical areas and make inferences about

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coupling at the neuronal level. The next few years will probably see an increasing realism in the dynamic causal models introduced above (see Horwitz et al. 2001). Attempts already have been made to use plausible models of neuronal ensemble to estimate network parameters of evoked responses in EEG (David & Friston 2003). These endeavors are likely to encompass fMRI signals in the near future, enabling the conjoint modeling, or fusion, of different modalities and the marriage of computational neuroscience with the modeling of brain responses.


ACKNOWLEDGMENTS The Wellcome Trust funded this work. I would like to thank all of my colleagues at the Wellcome Department of Imaging Neuroscience for their conceptual input. The Annual Review of Psychology is online at http://psych.annualreviews.org

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I, et al. 1995. Visualization of neural networks using saliency maps. IEEE Int. Conf. Neural Netw., pp. 2085–90. Perth, Austr. Nichols TE, Holmes AP. 2002. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15:1–25 Passingham RE, Stephan KE, Kotter R. 2002. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3:606– 16 Petersen SE, Fox PT, Posner MI, Mintun M, Raichle ME. 1989. Positron emission tomographic studies of the processing of single words. J. Cogn. Neurosci. 1:153–70 Phillips CG, Zeki S, Barlow HB. 1984. Localisation of function in the cerebral cortex: past, present, and future. Brain 107:327–61 Price CJ, Friston KJ. 1997. Cognitive conjunction: a new approach to brain activation experiments. NeuroImage 5:261–70 Price CJ, Wise RJS, Ramsay S, Friston KJ, Howard D, et al. 1992. Regional response differences within the human auditory cortex when listening to words. Neurosci. Lett. 146:179–82 Sychra JJ, Bandettini PA, Bhattacharya N, Lin Q. 1994. Synthetic images by subspace transforms. I. Principal component images and related filters. Med. Phys. 21:193–201 Talairach P, Tournoux J. 1988. A Stereotactic Coplanar Atlas of the Human Brain. Stuttgart: Thieme Worsley KJ, Evans AC, Marrett S, Neelin P. 1992. A three-dimensional statistical analysis for rCBF activation studies in human brain. J. Cereb. Blood Flow Metab. 12:900– 18 Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC. 1996. A unified statistical approach or determining significant signals in images of cerebral activation. Hum. Brain Mapp. 4:58–73 Zeki S. 1990. The motion pathways of the visual cortex. In Vision: Coding and Efficiency, ed. C Blakemore, pp. 321–45. London: Cambridge Univ. Press

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BRAIN ORGANIZATION FOR MUSIC PROCESSING Isabelle Peretz


Department of Psychology, University of Montreal, Montreal, Quebec H3C 3J7; email: [email protected]

Robert J. Zatorre Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4; email: [email protected]

Key Words auditory cognitive neuroscience, neural correlates, musical disorders, neuroimaging ■ Abstract Research on how the brain processes music is emerging as a rich and stimulating area of investigation of perception, memory, emotion, and performance. Results emanating from both lesion studies and neuroimaging techniques are reviewed and integrated for each of these musical functions. We focus our attention on the common core of musical abilities shared by musicians and nonmusicians alike. Hence, the effect of musical training on brain plasticity is examined in a separate section, after a review of the available data regarding music playing and reading skills that are typically cultivated by musicians. Finally, we address a currently debated issue regarding the putative existence of music-specific neural networks. Unfortunately, due to scarcity of research on the macrostructure of music organization and on cultural differences, the musical material under focus is at the level of the musical phrase, as typically used in Western popular music.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MUSIC PERCEPTION AND RECOGNITION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pitch and Time Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pitch Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MUSIC PERFORMANCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Singing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Music Playing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sight-Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MUSIC SPECIFICITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0089$14.00

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INTRODUCTION Music processing has fascinated neuroscientists for more than a century (Critchley & Henson 1977). Yet it is only over the last decade that music processing has become an area of intense and systematic study, as illustrated by three special issues recently published on the cognitive neuroscience of music (Annals of the New York Academy of Sciences issues 930 in 2001 and 999 in 2003, and Nature Neuroscience issue 6, 2003). Two forces have driven this scientific effort. First, music offers a unique opportunity to better understand the organization of the human brain. For example, only a minority of individuals become proficient musicians through explicit tutoring. This particularity in the distribution of acquired skills confers to music a privileged role in the exploration of the nature and extent of brain plasticity. The other major motivation is that the study of brain organization provides a unique tool to reveal the inner working of music processing. For instance, brain anomalies may reveal to what extent and at what level music processing recruits neural networks that are distinct from those involved in other auditory-vocal functions, such as language. Although these two major approaches for exploring the brain principles that underlie music processing may not always converge, it is hoped that the combined effort gives rise to a better understanding of the neurobiological roots of one of the most characteristic traits of humans, namely music.

MUSIC PERCEPTION AND RECOGNITION A sound reaching the eardrum sets into motion a complex cascade of mechanical, chemical, and neural events in the cochlea, brain stem, midbrain nuclei, and cortex that eventually—but rapidly—results in a percept. The task of auditory cognitive neuroscience is to figure out how this happens. Musical sounds and all other sounds share most of the processing stages throughout the auditory neuraxis. However, as we discuss below, evidence points to a degree of functional segregation in the processing of music, which may in part be related to the important role played by pitch processing within music. In a broader context, the functional system that handles music processing must solve a similar computational problem to that faced by any perceptual system: It must generate internal representations of any given input, permitting the stimulus to be segregated from its background, analyzed along several dimensions, recognized, and possibly acted upon. Importantly, the nature of the representations eventually generated by this system need to be relatively abstract, in the sense that they must be insensitive to superficial variations in stimulus features (loudness, reverberation, spectral filtering, etc.). In other words, perceptual constancy must be maintained. Indeed, music is an excellent medium to study this process, as the Gestaltists realized long ago, because music relies on relations between elements, rather than on absolute values of elements (the most obvious example being a tune, which is defined not by the pitches of its constituent tones, but by the arrangement of the intervals between the pitches).

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Pitch and Time Relations Musical pitch-based (melodic) and time-based (temporal) relations traditionally are treated separately, both empirically and theoretically (Justus & Bharucha 2002, Krumhansl 2000). Their processing independence has been questioned, however. For example, Jones & Boltz (1989) have argued that perception, attention, and memory for pitch relations are inherently rhythmical. By this view, listeners treat melody and rhythm as a unified dimension. Nevertheless, the neuropsychological literature is more consistent with the traditional view, by which melodic and temporal structures are processed independently. Brain damage can interfere with the discrimination of pitch relations while sparing the accurate interpretation of time relations (Ayotte et al. 2000, Liégeois-Chauvel et al. 1998, Peretz 1990, Peretz & Kolinsky 1993, Piccirilli et al. 2000, Vignolo 2003). Conversely, rhythmic discrimination of musical events can be impaired while extraction of pitch content is spared (Di Pietro et al. 2004, Peretz 1990). This double dissociation between melodic and temporal processing has also been observed in singing and sight-reading (see below). Hence, the evidence suggests that these two dimensions involve the operation of separable neural subsystems. To our knowledge, neuroimaging techniques have not yet corroborated this dissociation. On the contrary, Griffiths et al. (1999) obtained similar activation patterns for pitch and time discrimination tasks. However, similarity of activation does not entail that the underlying operations are similar, or that the recruited brain areas are critical to performing a given task. This general point always must be taken into account in integrating lesion and neuroimaging findings.

Pitch Relations The right temporal neocortex plays a particularly important role in the computation of pitch relations. The initial evidence came from studies of patients with focal brain excisions (Milner 1962), which showed that (a) right temporal damage entailed a greater deficit than left, and (b) such impairment could not be explained by a deficit in simple pitch discrimination. Subsequent studies have generally supported these findings (Liégeois-Chauvel et al. 1998, Zatorre 1985). In addition, however, deficits in certain aspects of pitch processing have been noted specifically after damage to the right anterolateral part of Heschl’s gyrus. Patients with such damage have difficulty perceiving the pitch of the missing fundamental (Zatorre 1988) and have an increased threshold for determining the direction of pitch change (Johnsrude et al. 2000). Thus, this area of right auditory cortex seems to play a particular role in the analysis of pitch information (Tramo et al. 2002). Converging evidence is now beginning to accumulate from neuroimaging studies to support the view that analysis of pitch changes may involve areas of posterior secondary cortex. Several investigators have found activation in this location when comparing complex tones containing frequency or amplitude modulation, or spectral changes, to static tones (Hall et al. 2002, Hart et al. 2003, Thivard et al. 2000). In many cases, manipulations of fine-grained pitch lead to greater response from right

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auditory regions (Zatorre & Belin 2001, Zatorre et al. 2002). Using isochronous melodies made with iterated noise bursts, Patterson et al. (2002) observed similar results, but activity increases in anterior areas were also found (Griffiths et al. 1998). Warren et al. (2003) additionally report that posterior regions are involved in processing pitch height whereas anterior regions are important for pitch chroma. In summary, the data are quite consistent in implicating the right secondary auditory cortex region in operations related to processing relationships between pitch elements as they change over time, especially if the pitch changes are small. One plausible interpretation of these findings is that they represent an early stage of melodic analysis, beyond simple pitch extraction, where interval and/or contour information is processed. This interpretation is consistent with data from electroencephalography and magnetoencephalography (MEG) recordings indicating that the auditory cortex responds to such pitch relations even in the absence of attention (Tervaniemi 2003). The contribution of contour (defined by pitch directions) and intervals (defined by frequency ratios between successive notes) to melodic perception and recognition also can be distinguished in brain-damaged patients (Peretz 2001). Taken together, the results converge to show that when listeners rely on contour representation to discriminate melodies, the right superior temporal gyrus plays a critical role. When contour cues are not available and interval information is required, both the right and left temporal structures appear to be involved (Ayotte et al. 2000, Liégeois-Chauvel et al. 1998, Peretz 1990, Vignolo 2003). This cooperation of the hemispheres in the integration of contour with interval information in melodies is echoed by the pattern of coherence observed dynamically with MEG while neurologically intact people listen to melodylike sequences (Patel & Balaban 2000). Such integration may occur very early in development. Balaban et al. (1998) have shown that the same left-right brain asymmetry in the processing of interval and contour cues in melodies is already present in infancy. In the adult, extraction of both contour and intervals does not require attention either, as electrical brain responses suggest (Tervaniemi 2003, Trainor et al. 2002). Pitch relations do not define only direction and interval sizes in tonal music; they also evoke a particular scale. A musical scale refers to the use of a small subset of pitches (typically between five and seven) in a given piece. The scale tones are not equivalent and are organized around a central tone, called the tonic. A hierarchy of importance or stability exists among the other scale tones, with the fifth and the third scale tones more closely related to the tonic than to the other scale tones. Substantial empirical evidence demonstrates that listeners use scale structure in melodies for perception and memory, albeit in an implicit manner (Tillmann et al. 2000). Despite the central importance of this tonal knowledge to the encoding of pitch relations in a melodic context, it has been little explored with neuropsychological methods. Yet, this processing component appears isolable both functionally and neuroanatomically. Brain damage can disrupt the normal intervention of tonal knowledge in melodic processing (Fran¸ce` s et al. 1973), while sparing perception

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of intervals and contour (Peretz 1993). In such cases, the patient is no longer able to distinguish tonal from atonal music nor to judge melodic closure properly and suffers from a severe reduction in pitch memory. The use of scale structure in melody processing can also be indexed by electrical brain responses such as event-related potentials time-locked to tones that deviate from the expected scale degree (Besson & Fa¨ıta 1995, Besson et al. 1998) and, more surprisingly, by model-fitting of the haemodynamic responses obtained with functional magnetic resonance imaging (fMRI; Janata et al. 2002). Hence, the evidence points to the existence of neural networks that are specialized for the processing of scale structure in melodies. Their localization, however, remains to be determined. Up to this point, we have considered pitch relations between sequentially presented tones, but pitch relations also exist in simultaneously presented tones, as in chords. The pitch relations in chord sequences are governed by harmonic principles that are similar to those described earlier for the tonal hierarchy of scale tones. That is, different chords are differently related to one another, with one chord acting as the reference: the triadic chord that builds on the first degree of the scale (the tonic). Listeners have assimilated these principles via passive exposure to samples of Western music (Bigand 2003). These harmonic principles influence chord processing automatically even when more veridical information about the chord progression has been made explicitly available (Justus & Bharucha 2001). Hence, perception of pitch relations in chords seems to operate like the perception of the tonal hierarchy among successive tones, although the acquisition of harmonic hierarchy appears to emerge later in development (Trainor & Trehub 1992). By the age of five, the degree of harmonic appropriateness of chord progression appears assimilated, as electric brain responses indicate (Koelsch et al. 2003). Processing of harmonic relations rarely has been studied in brain-damaged populations. The only study, to our knowledge, in which chord processing has been examined showed sparing of the ability to generate expectancies from chord processing after bilateral lesion of the auditory cortex (Tramo et al. 1990). However, the patient suffered from a deficit in pitch processing, with a bias to judge welltuned chords as out of tune (Tramo et al. 2003). In contrast, detection of harmonic violations has been studied with diverse neuroimaging techniques. Deviations from harmonic expectancies elicit robust event-related potentials (Regnault et al. 2001). Their neural generators appear to be located in the inferior frontal areas (the frontal operculum) on both sides of the brain (which corresponds to Broca’s area on the left side; Maess et al. 2001). Involvement of the same areas has also been found in two recent fMRI studies of harmonic expectancies (Koelsch et al. 2002, Tillmann et al. 2003). Thus, the data point to bilateral involvement of the inferior frontal regions in detecting deviations from harmonic expectancies. Finally, it should be pointed out that both harmonic relations between chords and tonal relations between tones might be rooted in pitch consonance (or dissonance). Consonant intervals typically are expressed in terms of simple frequency ratios, such as the octave (2:1) and the perfect fifth (3:2), whereas dissonant intervals are related by complex ratios, such as the minor second (16:15). Despite the

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saliency of dissonance and its initial account in terms of the poor spatial resolution of the basilar membrane (Plomp & Levelt 1965), its functional origins and neural consequences are still open questions (Tramo et al. 2003). Current evidence suggests that dissonance might be further computed bilaterally in the superior temporal gyri by specialized mechanisms. Assemblies of auditory neuron populations in Heschl’s gyrus exhibit phase-locked activity for dissonant chords but not for consonant chords, as measured with implanted electrodes in both humans and monkeys (Fishman et al. 2001). Such cortical responses to dissonance can be disrupted by bilateral lesion of the auditory cortex, resulting in a loss of sensitivity to dissonance (Peretz et al. 2001). A remaining question for the future is to determine at what stage in auditory processing (e.g., at the level of acoustical analysis and/or of tonal encoding of pitch) the computation of the perceptual attribute of dissonance is critical to the perceptual organization of music. This question often is framed in terms of a distinction between sensory and musical dissonance that would reflect built-in auditory constrains and learned associations, respectively (Krumhansl 2000, Schellenberg & Trehub 1994).

Time Relations Two types of time relations are fundamental to the temporal organization, or “rhythm,” of musical sequences: the segmentation of an ongoing sequence into temporal groups of events based on their durational values, and the extraction of an underlying temporal regularity or beat (Fraisse 1982). Beat perception leads to the perception of a metrical organization corresponding to periodic alternation between strong and weak beats (the strong beats generally correspond to the spontaneous tapping of the foot). Grouping and regularity are conceived as hierarchically organized by certain researchers (Povel & Essens 1985), while others (Drake 1998, Lerdahl & Jackendoff 1983) conceive these relations as the result of distinct processing components. The available neuropsychological evidence supports the latter view, in showing functional dissociations between grouping and regularity. Ibbotson & Morton (1981) provided initial support by showing that subjects more easily tapped a rhythmic pattern with their right hand and the beat with their left hand than the other way around. These findings suggest that the right hemisphere better handles meter, whereas grouping would rely essentially on the left. Further support for the separation of the two types of organization mediating temporal pattern processing has been provided by the study of two brain-damaged patients who, after lesions of the right temporal auditory cortex, could no longer tap the beat or generate a steady pulse (Fries & Swihart 1990, Wilson et al. 2002). In contrast, both patients were able to discriminate or reproduce irregular temporal sequences. Neuroimaging data also suggest that metrical rhythms may be processed differently than nonmetrical rhythms, engaging different frontal and cerebellar mechanisms (Sakai et al. 1999). All of these studies have used tapping tasks. In studies that assessed more perceptual aspects of temporal processing, convergent

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evidence for dissociating meter from grouping was also found (Liégeois-Chauvel et al. 1998, Peretz 1990). Patients who have sustained an excision in either their left or right temporal lobe have been found to discriminate rhythmic patterns normally, but fail on meter evaluation after a right-sided lesion of the anterior part of the superior temporal gyrus (Liégeois-Chauvel et al. 1998). Conversely, impairments in rhythmic discrimination that spare meter evaluation can also be observed in brain-damaged patients after a left-sided lesion (Di Pietro et al. 2004). Hence, the data are generally consistent with the manual asymmetries observed in normal tapping (Ibbotson & Morton 1981) in pointing to the left hemisphere for temporal grouping (Di Pietro et al. 2004, Vignolo 2003) and to the right temporal auditory cortex for meter (Liégeois-Chauvel et al. 1998, Penhune et al. 1999, Wilson et al. 2002). The fact that essentially all the results coming from production tasks have parallels in perceptual tasks suggests a strong motor component to the mental representation of musical rhythm. Indeed, data from both lesion and neuroimaging studies have shown the participation of the cerebellum and/or basal ganglia as a possible central mechanism controlling motor and perceptual timing (Janata & Grafton 2003). Patients with cerebellar damage have increased variability in motor timing, as well as decrements in discrimination of auditory intervals (Ivry & Keele 1989). Further, patients with damage to the lateral cerebellar hemispheres showed increased variability in the timing component of the tapping task, in contrast to patients with damage to medial cerebellar regions who showed increased variability on the motor implementation component of the task (Ivry et al. 1988). The contribution of the lateral cerebellar hemispheres to timing has been corroborated by recent neuroimaging studies examining reproduction of rhythmic sequences (Penhune & Doyon 2002, Penhune et al. 1998) and perceptual monitoring of auditory and visual rhythmic sequences (Schubotz et al. 2000). These studies converge on the conclusion that a supramodal cerebellar timing system is involved in processing temporally organized events. Other fMRI studies have produced evidence for the possible involvement of the basal ganglia in both motor and perceptual timing (Harrington et al. 1998; Rao et al. 1997, 2001). Finally, several studies have pointed to the involvement of motor cortical areas in rhythm perception and production, including the supplementary motor area, premotor cortex, and parietal cortex (Halsband et al. 1993).

Memory Adding to the complexity—and interest—of studying the neural correlates of musical processing is the fact that music, like all sounds, unfolds over time. Thus, the auditory cognitive system must depend to a large degree on mechanisms that allow a stimulus to be maintained on-line to be able to relate one element in a sequence to another that occurs later. These working memory mechanisms apply broadly to many types of processes. As applied to pitch processing, cognitive studies of working memory suggest that there may be dissociable systems for maintenance of pitch information over short periods as compared to speech information (Deutsch 1970, Semal et al. 1996). Similarly, lesion studies focusing on working memory

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for pitch materials have implicated the right auditory cortex (Zatorre & Samson 1991), which is not surprising because a disruption in perceptual processing would likely lead to difficulty in maintaining a perceptual trace over a length of time. In addition to the participation of auditory cortex to working memory, both lesion and neuroimaging studies have found a role for frontal cortical areas (Gaab et al. 2003, Holcomb et al. 1998, Zatorre et al. 1994). In particular, dorsolateral and inferior frontal areas are most often recruited when working memory load is high (Griffiths et al. 1999, Zatorre et al. 1994). These studies largely converge on the idea that working memory for tones engages interactions between frontal cortical and posterior temporal areas, as is true for other domains. In general, auditory cortical regions are more strongly recruited during high-load conditions that require active rehearsal (Gaab et al. 2003, Zatorre et al. 1994), such as retaining a pitch in memory while other tones are presented, and may also be related to auditory-motor interactions (Hickok et al. 2003). These findings fit within a broader context in which working memory for pitch may be seen as a specialized subsystem within the framework of general working memory (Marin & Perry 1999). The contribution of memory to music processing is crucial not only because music unfolds over long periods of time but also because music is highly structured along multiple principles that require the contribution of different sources of knowledge. In the present review, however, we limit our attention to the memory component that enables recognition and mental representation (imagery) of a familiar tune. To enable recognition of a given tune, melodic and time relations must be mapped onto a stored long-term representation that contains invariant properties of the musical selection. As for words in language, the process of music recognition requires access and selection of potential candidates in a perceptual memory system (Dalla Bella et al. 2003). This musical memory is a perceptual representation system that is conceived as representing information about the form and structure of events, and not the meaning or other associative properties. Music is by essence perceptually driven. Unlike speech, music is not associated with a fixed semantic system, although it may convey meaning through other systems, such as emotional analysis (reviewed below) and associative memories (to retrieve contextual information, such as the title of a piece, the name of the singer, or its genre). Associative memories are probably the locus of the semantic priming effects recently observed between music and words with event-related brain potentials (Koelsch et al. 2004). Perceptual memories of familiar tunes must be relatively abstract in order to allow recognition despite transposition to a different register (Dowling & Fujitani 1971), change in instrumentation (Radvansky et al. 1995), and change in tempo (Warren et al. 1991). The stored representations can nonetheless preserve some surface features, such as absolute pitch and precise tempo (Halpern 1988, 1989; Levitin 1994; Levitin & Cook 1996). This duality between abstract and surface representations is congruent with our intuitions as to the role of memory in music listening. On the one hand, most listeners will not remember every detail of a musical segment but instead will follow the piece by a process of abstraction

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and organization, remembering its “gist” (Dowling & Harwood 1986, Large et al. 1995). On the other hand, appreciation of interpretation requires the consideration of surface characteristics that are unique to a particular rendition (Raffman 1993). Thus, both surface and structural features may be contained in the stored representations and fit with the role and definition of the perceptual representation systems that are posited in other perceptual domains. Neuropsychological support for a perceptually based memory system for music derives mainly from lesion studies. In effect, persistent loss of recognition abilities for music can occur despite normal or near-normal perceptual processing of musical input (Eustache et al. 1990, Peretz 1996). Above all, such memory loss can be limited to music. For instance, an amusic patient with bilateral damage to the auditory cortex was normal at recognizing and memorizing spoken lyrics, whereas she performed at chance when required to recognize or to relearn the corresponding melody (played without lyrics). The deficit was selective because the patient had no difficulties with other nonmusical auditory materials, such as voices and animal cries, and had no memory impairment for visual stimuli (Peretz 1996). Hence, the memory failures were not only modality-specific but also musicspecific. A milder dissociation between melodies and speech sounds has also been reported in patients with focal lesions of the medial temporal lobe (Samson & Zatorre 1992). A lesion to either medial temporal region led to initial difficulties in learning the melodies; after right-sided lesions, retention of melodies was affected more severely and selectively over time. The right temporal structures appear less critically involved when recognition of highly familiar tunes is considered. Difficulties in recognizing familiar melodies tend to occur after a surgery to either superior temporal region (Ayotte et al. 2000). Moreover, the participation of left inferior temporal and frontal areas for recognizing familiar music has been pointed out in neuroimaging studies (Platel et al. 1997, 2003). These findings do not imply that familiar tunes are processed differently from novel melodies. Familiar melodies are associated to a series of extramusical and extraexperimental events that may contribute to recognition. For instance, song melodies (played without lyrics) automatically trigger the lyrics with which they are typically paired (Peretz et al. 2004b). The presence of these associate memories may even confer an advantage in the case of lesion. As uncovered by Steinke et al. (2001), brain damage can impair recognition of instrumental music but spare recognition of song melodies. The principles along which the stored representations of music can be impaired, when partially damaged, are potentially instructive with respect to the structure of their internal organization. Such degradation remains, however, to be observed and systematically studied. One way to probe the nature of the representations that are stored in memory is to study musical imagery. Imagery refers here to the subjective experience of being able to imagine music or musical attributes in the absence of real sound input. By applying behavioral methods developed by Halpern (1988) to patients with focal auditory cortex lesions, it was possible to demonstrate that perceptual deficits and imagery deficits are found in common, following damage to right auditory

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cortical areas (Zatorre & Halpern 1993), suggesting that imagery requires access to perceptual mechanisms that are involved in processing melodies. This general conclusion has been supported in subsequent work using functional neuroimaging, which consistently indicates that secondary auditory cortices are recruited during a variety of tasks that require imagery or rehearsal of melodies (Halpern & Zatorre 1999, Zatorre et al. 1996), including sequences of tones (Penhune et al. 1998, Rao et al. 1997, Yoo et al. 2001) and isolated tones (Halpern et al. 2004). Further evidence comes from electrophysiological measures showing that the scalp topography elicited by imagining the continuation of a melody is similar to the electrical activity elicited by a real tone (Janata 2001). These demonstrations of auditory cortex activity in the absence of an acoustical stimulus, or at least not driven solely by external input, support the contention that perceptual mechanisms within auditory areas are responsible for the subjective experience of imagery. Retrieval processes from long-term representations, such as might occur when generating a familiar tune, tend to engage inferior frontal regions (Halpern & Zatorre 1999, Zatorre et al. 1996) in accord with many studies showing the importance of these mechanisms for memory retrieval in general (Nyberg & Tulving 1996). Additional neural mechanisms related to motor processes may also be involved in musical imagery. In particular, the supplementary motor area has often been identified in musical imagery studies (Halpern & Zatorre 1999, Halpern et al. 2004, Zatorre et al. 1996), which may relate to subvocalization and motor imagery (Lotze et al. 1999).

Emotion Music experience is not limited to perception and memory. Music experience is intimately related to its emotional appeal (Juslin & Sloboda 2001). Although the study of music as a means to express and induce emotions is a recent endeavor in neurosciences, a few studies (Blood et al. 1999, Peretz et al. 1998, Schmidt & Trainor 2001) have already highlighted important facts about music, emotions, and the brain. More specifically, these studies have suggested that the system for the analysis of emotional expression is neurally isolable (Peretz & Gagnon 1999, Peretz et al. 1998). Recognition of the emotional tone in music can be spared by brain damage while recognition of music identity is impaired (Peretz & Gagnon 1999), indicating that emotion recognition may rely on perceptual determinants that play little role in identity recognition. For example, the mode (major or minor) in which the music is written and the tempo (fast or slow) at which it is played can convey the happy-sad distinction (e.g., Peretz et al. 1998). In contrast, mode and tempo are not perceptually relevant for recognition; a piece of music can be easily recognized despite changes in mode and tempo (Halpern et al. 1998). Thus, the analysis of emotion expression may take as input emotion-specific musical features, as derived from pitch and time relation computations. In other words, emotional analysis could be mediated by a common (perceptual) cortical relay, suggesting no direct access to subcortical, limbic structures (Peretz et al. 2001). It would make sense for affective responses elicited by music to be cortically mediated, since there is a large cultural learning component to musically induced

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emotion. Yet it is possible to elicit consistent affective responses, albeit not necessarily to the same stimuli across people. One such response that has been studied with neuroimaging techniques is the “chills” effect, which many people report as a particularly pleasant or even euphoric musical experience. This effect has been documented behaviorally (Panksepp 1995) and can be elicited relatively reliably. Moreover, this and other emotions are associated with objective physiological markers (Goldstein 1980, Krumhansl 1997). Blood & Zatorre (2001) reported that while people experienced musical chills, cerebral blood flow changes occurred in several brain areas, including the dorsal midbrain, ventral striatum (which contains the nucleus accumbens), insula, and orbitofrontal cortex. Some of these regions have previously been implicated in response to highly rewarding or motivationally important stimuli, including food (Small 2001) and drugs of abuse (Breiter et al. 1997). Thus, under certain circumstances, music can access neural substrates that are primarily associated with biologically significant stimuli. Whether music is unique in this respect remains to be seen; it may be one of a class of human constructs that elicit pleasure by co-opting ancient neural systems via inputs from neocortex. In this respect, music may serve as an excellent paradigm to explore the interactions between neocortically mediated cognitive processes and subcortically mediated affective responses.

MUSIC PERFORMANCE Music performance includes a variety of tasks, such as playing or singing welllearned pieces from memory, sight-reading, and improvisation. They all combine rapid motor skills and relatively elaborate cognitive operations in addition to the perceptual, memory, and emotion components described above. In order to account for production, one can add to this core system a visual-to-musical conversion subsystem for sight-reading (Cappelletti et al. 2000) and a motor planning component for singing and for playing (Peretz & Coltheart 2003). Such a tight coupling between input and output processes is reflected in the involuntary motor activity observed in pianists as they listen to well-trained music (Haueisen 2001), which may reflect a so-called mirror system important for imitation and learning (Rizzolatti & Arbib 1998). However, there is very little neuropsychological research bearing on this issue. Part of this situation is due to the limited number of proficient musicians who have sustained focal brain damage and to methodological difficulties in studying motor behavior with neuroimaging techniques. Yet, singing is not the privilege of musicians. Singing is the most widespread mode of musical expression; hence, it has the potential to shed light on how the brain orchestrates music production in general.

Singing Infants spontaneously sing around the age of one. By the age of five, children have a large repertoire of songs of their own culture, and they display singing

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abilities that will remain qualitatively unchanged in adulthood (Dowling 1999). Thus, even without much practice, the ordinary adult seems to be endowed with the basic abilities that are necessary to sing simple songs. This conclusion has a long history in clinical neurology because researchers frequently observe nonfluent aphasics who can hardly speak intelligibly but who can produce recognizable songs (Yamadori et al. 1977). Similarly, transcranial magnetic stimulation to the left frontal cortex can temporarily produce speech arrest without interfering with singing in normal subjects (Stewart et al. 2001). These observations point to a neural dissociation between speech and music performance that is supported by the detailed analysis of aphasics’ production errors. Hébert and colleagues (Hébert et al. 2003, Peretz et al. 2004a) have shown that patients produce as few intelligible words in speaking as in singing, when tested in comparable conditions. Hence, the results indicate that verbal production, be it sung or spoken, is mediated by the same (impaired) language output system and that this speech route is distinct from the (spared) melodic output system. Conversely, amusic individuals can be normal at speaking while being unable to sing (Ayotte et al. 2002, Peretz et al. 1994). This distinction between singing and speaking is consistent with recent PET studies (Jeffries et al. 2003, Perry et al. 1999) that observed relative increases in activity during singing (versus speaking or listening) in bilateral motor structures, with predominance in the right hemisphere, particularly for premotor, insular, and auditory regions. Thus, in the vocal mode of expression, music can be dissociated from speech, as is also true in perception (see Music Specificity below). Similarly, as in perception studies, it has been found that singing pitch relations can be maintained while temporal relations are lost and vice versa; the melody can be impaired while the rhythm is intact (Alcock et al. 2000). Loss of melody, in both perception and production, is associated with a right-hemisphere lesion, whereas the loss of rhythm, in both perception and production, is related to a lesion of left-hemispheric structures. These findings are consistent with the notion that the melodic and temporal processing subsystems can function relatively independently of one another in music processing and that these support both perception and production.

Music Playing The study of musical playing can also yield insights into the mental representations used to plan the execution of music. Generally, a planned sequence is segmented in small units such as phrases. With practice, these units become larger, future events are more strongly anticipated, and expressive features of the performance are enhanced. The performer’s task is to highlight the structure of the musical piece and its emotional content through the complex programming of finely coordinated motor movements (Gabrielson 1999, Palmer 1997). Unfortunately, the contribution of cognitive neuroscience to the understanding of musical playing skills lags behind its cognitive study. Lesion studies are either anecdotal or sketchy regarding the nature of the expressive disorder (McFarland & Fortin 1982). Several neuroimaging

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studies (Kristeva et al. 2003, Lotze et al. 2003) have attempted to identify the neural correlates of musical performance but this has proven to be a difficult task due to its complex nature. Some of these findings highlight brain activity in certain areas (such as bilateral frontal opercular regions) when both executing and imagining music, which is consistent with the perceptual imagery research reviewed above.


Sight-Reading The cognitive neuroscience of sight-reading is more informative. It has been known for some time that sight-reading of musical notation is a distinct ability because it is neurally and functionally separable from reading words and Arabic numerals. Brain damage can selectively impair reading of musical notation while sparing reading of other kinds of symbols (Brust 1980, Cappelletti et al. 2000, Horikoshi et al. 1997). Moreover, the musical reading disorder can be observed in relative isolation because playing, singing, and musical memory can be well preserved (e.g., Cappelletti et al. 2000). The reverse situation has also been described. Brain damage can impair the ability to read letters and words while sparing music reading (Signoret et al. 1987). The lesions responsible for music alexia are located in the left hemispheric structures (Midorikawa et al. 2003, Schön et al. 2001). However, results of a recent fMRI study indicate the right occipital-temporal region is critically involved in deciphering pitch notation on a keyboard (Schön et al. 2002). The fact that sight-reading of musical notation has been examined as a unitary function might explain in part this conflicting information regarding the neural correlates of music reading. Music sight-reading calls for the simultaneous and sequential processing of a vast amount of information in a very brief time for immediate use. This task requires, at the very least, interpretation of the pitch and duration of the notes (written on the two staves of a piano score) in the context of the prespecified key signature and meter, detection of familiar patterns, anticipation of what the music should sound like, and generation of a performance plan suited for motor translation. This sketchy componential task analysis illustrates the number of operations that are involved in music sight-reading and that in principle can be distinguished neuropsychologically. The need for further fractionation of sight-reading skills finds support in the recurrent observation that after brain damage pitch and time relations are dissociable in reading musical notation, as seen in both perception and singing. Some patients are still able to convert printed information into a rhythmical pattern but are no longer able to decipher pitch-related information (see, e.g., Brust 1980, Fasanaro et al. 1990) and vice versa: Pitch reading can be preserved while rhythm reading is impaired (Midorikawa et al. 2003). This dissociation needs to be studied in more detail because it may arise as a result of damage at different levels in the decoding of musical notation. It can occur as early as when the music score is visually inspected because pitch and duration are represented differently in print (duration is determined by the shape of the note itself, whereas pitch is indicated by the spatial position on the stave). This dissociation may also result from a difficulty occurring

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at a later stage, when the reader imagines what the written code sounds like (Schön & Besson 2002). Moreover, there are different routes to achieve a proper interpretation of the pitch code. One route involves naming, which is learned by association between a spatial location on the stave and a name (Do, ré, mi or A, B, C; see below). Another involves some form of procedural memory, which is the consequence of learning to associate a notated pitch to a motor gesture (key press). However, most musicians are able to sight-read the same score in different modalities by singing and playing different musical instruments, and hence are likely to use a more abstract, modality-independent representation system of written scores. In sum, there are different ways to read musical notation. Moreover, this is a skill that is highly automated and specialized compared to what novices may learn (Stewart et al. 2003), an issue we discuss next.

Training Even if everyone engages in some sort of musical activity in everyday life, it is generally limited in time and effort. In contrast, a few individuals become proficient musicians through extensive practice from an early age and often with the help of explicit tutoring. The fact that musical training is not uniformly and systematically imposed in current educational curricula makes this natural variety of musically acquired skills a formidable laboratory in which to study the effects of training on brain functioning. Musicians represent a unique model in which to study plastic changes in the human brain (Münte et al. 2002). As suggested by animal research, experience can shape the size of cortical networks either by expansion or by reduction, depending on stimuli and on the structural levels examined (i.e., synaptic or macroscopic; see Münte et al. 2002 for a review). Hence, one would expect to find evidence of size differences in certain regions of the musician’s brain compared to the brain of an untrained person. The prime areas to look for differences are the motor areas. Indeed, there is clear evidence that the motor cortex of musicians is enhanced structurally (Gaser & Schlaug 2003) and functionally (Krings et al. 2000). Anatomical changes also have been seen in other motor-related structures, including cerebellum and corpus callosum (Schlaug 2003). In a seminal study, Elbert et al. (1995) investigated somatosensory-evoked magnetic fields in string players. Source analysis revealed that the cortical representation of the digits of the left hand (the fingering hand, especially for its fifth digit) was larger in musicians than in nonmusicians. In the case of the right hand, in which no independent movements of the fingers are required in string players, no differences were found between musicians and nonmusicians. Moreover, the cortical reorganization of the representation of the fingers was more pronounced in musicians who had begun their musical training at an early age. However, an investigation of sequential finger movements with fMRI found a reduction rather than an increase in activity in motor areas when comparing professional pianists to nonmusicians (Hund-Georgiadis & von Cramon 1999).

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In general, there is no doubt that a complex interplay exists between structural changes that may accompany prolonged behavioral performance and neural responses that underlie that performance. A greater volume of tissue may reflect a reorganization, which in turn may manifest itself as recruitment of fewer neurons, or differently synchronized firing patterns, or different effective connectivity with other regions under different circumstances. Each of these possibilities would have quite different consequences for functional measures; different functional measures are sensitive to different parameters of neuronal function. Thus, we are far from understanding in detail the nature of the reorganization associated with musical training. Yet, the study of musical training effects is a unique paradigm to achieve this understanding. A few fascinating training effects are worth reporting in some detail. One of these is the fact that functional and morphological brain changes as a function of musical training are not limited to motor control. Researchers have identified several auditory brain areas that differ between musicians and nonmusicians. For example, Pantev and colleagues (Pantev et al. 1989), using MEG, have shown that brain responses to piano tones were 25% larger in musicians than in nonmusicians. This effect appears to be more pronounced for tones from each musician’s own type of instrument (Pantev et al. 2003, Tervaniemi 2003), strongly implying a use-dependent plasticity phenomenon. However, using a similar MEG technique, Schneider and collaborators (Schneider et al. 2002) found that both the early activity evoked by pure tones and the gray matter volume of the anteromedial portion of Heschl’s gyrus were more than 100% larger in professional musicians than in nonmusicians. Pure tones do not exist in the environment and hence may not account for the observed plasticity effects. In fact, the functional and the morphological differences were related to musical aptitude, suggesting influence of innate determinants. These findings reopen the debate about whether the observed brain differences between musicians and nonmusicians arise from genetic or other predispositions (or talent) as well as from practice and experience. Absolute pitch (AP) is another domain in which the issues of training or exposure, development, and innate predispositions arise (Takeuchi & Hulse 1993, Ward 1999, Zatorre 2003). The principal feature that distinguishes an AP possessor from anyone else is that he or she can identify any pitch by its musical note name without reference to any other sound. In other words, with AP, one has a fixed associative memory for a large number of pitch categories (as many as 70, according to Ward 1999); nonpossessors of AP—nonmusicians and most musicians—also have fixed categories available (Levitin 1994), but the precision is an order of magnitude lower. Because AP is not a universally expressed trait, other predispositions must be at play. One possibility is that genetic factors may be involved. The evidence for this idea is growing (Baharloo et al. 2000, Gregersen et al. 2000). The sibling recurrence rate, for example, is quite high (on the order of 8% to 15%). Thus, AP likely depends on some innate neural substrate (genetically determined or not) in interaction with a certain environmental input at a certain time during neural development.

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Several studies have probed the differences in patterns of brain activity in AP possessors and musicians who do not have AP. One of the earliest demonstrations reported that AP listeners showed an absent or reduced electrical-evoked potential component thought to index updating of working memory (Klein et al. 1984, Wayman et al. 1992). Thus, a typical listener will show an electrophysiological response to a pitch change, indicating that some type of “on-line” memory system has been refreshed, but AP possessors do not show this effect. A related phenomenon is that an area of the right frontal cortex believed to be important for monitoring pitch information in working memory is more active in musicians lacking AP than in those who have AP (Zatorre et al. 1998). Thus, instead of requiring continuous maintenance of a sensory trace, AP possessors appear to use their categorical representation of tones in such tasks. The posterior dorsolateral frontal cortex also responds differentially in AP possessors as compared to control musicians (Zatorre et al. 1998). This area, concerned with establishing and maintaining conditional associations in memory (Petrides 1995), is a logical candidate area for the link between a pitch and its label in AP subjects. These findings help to demystify AP somewhat, by demonstrating that working memory and associative learning aspects of AP draw on well-understood neural resources. However, this still does not explain why some people are able to form the pitch categories in the first place and others are not. Part of the answer to this issue has been proposed to lie in the function of subcortical nuclei that are important for periodicity coding, such as the inferior colliculus (Langner et al. 2002) The presence of functional differences between AP and non-AP people leads to the question of whether brain anatomy might not also differ. Several studies have now shown that there are indeed significant differences in the degree of lateral asymmetry of auditory cortical areas (Schlaug et al. 1995, Zatorre et al. 1998). However, it remains uncertain how to interpret this effect: Whereas initial indications were that AP subjects showed relative enhancement of left-hemisphere structures, the latest evidence suggests that instead it may be a relative reduction in volume of right-hemisphere structures that accounts for the difference (Keenan et al. 2001). A simple explanation based on size may not be sufficient because there are likely complex interactions between gross morphological features and the underlying cortical function. One way to study the neural correlates of musical training while controlling for complex interactions between innate and environmental factors is to train novices to learn to play music. For example, training on the piano for two hours a day over five days leads to increased excitability of the cortical motor areas that control the contralateral finger muscles of the hand, as measured by applying transcranial magnetic stimulation. Interestingly, mental practice of the motor tasks leads to similar but less-pronounced effects (Pascual-Leone 2001). This type of research allows control for the short- and long-term effects of training on the nature of the plastic changes in the brain. However, this sort of study should be undertaken longitudinally to explore the interaction between plasticity and brain development.

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All of these studies demonstrate quantitative neural changes associated with musical training. Musicians appear to recruit more neural tissue or to use it more efficiently than do nonmusicians. An important question is to what extent training also modifies the way music is processed and organized in the brain. For instance, it has been widely argued that musicians might be more prone to adopt an analytical mode of processing music (Bever & Chiarello 1974). However, the use of such a strategy is not exclusive to musicians. As seen earlier, nonmusicians are able to consider local interval sizes when required by the task (Peretz & Morais 1987), and they do so automatically (Trainor et al. 2002). Moreover, musicians’ approach of musical material is not confined to an analytic mode; they can flexibly use either contour or interval information, depending on the structure of the stimuli (Peretz & Babai 1992). More generally, when musical abilities are assessed indirectly, more similarities than differences are noted between musicians and nonmusicians (Bigand 2003).

MUSIC SPECIFICITY As we have seen, a vast network of regions located in both the left and right hemispheres of the brain, with an overall right-sided asymmetry for pitch-based processing, is recruited by musical activities. This should not come as a surprise since musical activities are numerous, complex, and diverse (Tramo 2001). However, finding such a wide distributed network of brain regions raises the issue of which of these brain areas is dedicated to music processing. Some of these regions might not only overlap but might also share processing components with other functions, such as those involved in language. This issue of music and language specificity has a long history in neurology (Henschen 1924) and has been recently reopened by the observation that the detection of harmonic deviations activates Broca’s area (Koelsch et al. 2002). This research suggests that the mechanisms underlying syntactic processing are shared between music and language (Levitin & Menon 2003, Patel 2003). However, there are problems with this conclusion that we address in the present review, because the question of domain-specificity is important from both an evolutionary (Hauser et al. 2002) and an empirical perspective. First, we should keep in mind that Broca’s area is a vast brain region that can easily accommodate more than one distinct processing network (Marcus et al. 2003). Second, the degree of anatomical proximity of activation maxima that should count as reactivation of the same region in another task is not straightforward, particularly when activation is not limited to Broca’s area but involves the right hemisphere homologous region (Maess et al. 2001) and when the cognitive domains (music and language) are studied separately in different laboratories. Clearly, further comparison is needed between music and language in the same experimental setting, using similar tasks that are matched (or manipulated) for attentional resources (Shallice 2003).

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Indeed, comparisons across domains have generally been more consistent with the idea that musical abilities are subserved, at least in part, by neural networks that are dedicated to their respective processing. As seen previously, singing, memory, and sight-reading are all musical activities that are functionally and neuroanatomically dissociable from analogous activities that involve speech. The evidence rests on both lesion and neuroimaging studies. The strongest support for the existence of processing components whose operation is specific to the processing of music comes from the study of auditory disorders. Selective impairments and sparing of music recognition abilities can occur after brain damage. As recently reviewed in Peretz & Coltheart (2003), patients may suffer from recognition failures that affect the musical domain exclusively. Such patients can no longer recognize melodies (presented without words) that were highly familiar to them prior to the onset of their brain damage. In contrast, they are normal at recognizing spoken lyrics (and spoken words, in general), familiar voices, and other environmental sounds (such as animal cries). Similarly, there are individuals who suffer from lifelong difficulties with music but can recognize the lyrics of familiar songs even though they are unable to recognize the accompanying tune. Conversely, one can find people in whom brain damage has impaired the ability to recognize spoken words while sparing the ability to recognize music. Hence, the evidence points to the existence of distinct processing modules for music and speech. Music specificity may concern different processing components. Some components might be perceptual prerequisites for music processing (Peretz & Hyde 2003), while some processing components appear to be genuinely specialized for music (Peretz & Morais 1989). Still other components conceivably could be involved in the processing not only of music but also of speech (Patel et al. 1998). One important question for the future is to determine which other processing components are uniquely involved in music and which components are not. This would provide clues as to the roots of brain specialization for music processing and hence the roots of musicality in general.

CONCLUSIONS The work presented in this review constitutes a selection of what currently is known (and is not known) about how musical functions are organized in the brain. As pointed out, a number of musical processes have not yet been considered in neuropsychology. Yet, we trust that the accumulated knowledge on how and where short musical phrases are perceived, recognized, imagined, appreciated, and sung will provide the building blocks of a broader and more complete neurofunctional account of music processing. One such fundamental principle concerns the distinction between pitch-based and time-based mechanisms. As seen in both perception and performance, the musical processing of pitch and rhythm appears to result from the operation of largely distinct neural mechanisms. Extraction of musical pitch relations seems to depend on a series of operations that predominantly involve the right auditory

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cortex, whereas extraction of musical time relations recruits more widespread and bilateral neural networks. Obviously, music processing cannot be ascribed wholly to one cerebral hemisphere. However, as we have argued elsewhere (Peretz & Hyde 2003, Zatorre et al. 2002), it might be the case that right-sided neural mechanisms play a determinant role. Pitch-based mechanisms might be rooted in the specialization of the right auditory cortex for spectral resolution (Zatorre et al. 2002) and be instrumental in the normal development of musical competence (Peretz et al. 2002). These conclusions also assume that music processing is mapped onto the human brain with a certain consistency. If there were no consistency, understanding the relations between music and its neural correlates would be impossible. However, as discussed above, people differ in their musical training or experience, and perhaps also in aptitude or talent; these differences manifest themselves at various levels, and understanding the interplay between brain processes, environmental inputs, innate factors, and development will be one of the major challenges for our field. It should also be considered that the mapping between processing components and neural networks might not be one-to-one. The fact that the processing framework can account for the musical disorders exhibited by brain-damaged patients suggests that this mapping is feasible. Indeed, it is conceivable that some music processing components lack neuroanatomical separability. In that case, the neural substrates of the components would be intermingled with the neural systems devoted to the processing of other complex patterns. If so, brain damage could never affect just one processing component while sparing all other aspects. If the process under study possesses the property of neural separability, as we have described here for most processing components, then we expect to find corresponding isolable neural areas in the brain. Thus, we believe that the evidence is solid enough to envisage a proper mapping between processes and neural networks. Above all, we believe that convergence between lesion and neuroimaging data is the optimal strategy to build a sound model of how the brain processes music.

ACKNOWLEDGMENTS This chapter was written while both authors were supported by grants from the Canadian Institute of Health Research and the Natural Science and Engineering Research Council of Canada. The Annual Review of Psychology is online at http://psych.annualreviews.org

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BRAIN ORGANIZATION FOR MUSIC PROCESSING Gaab N, Gaser C, Zaehle T, Jäncke L, Schlaug G. 2003. Functional anatomy of pitch memory—an fMRI study with sparse temporal sampling. NeuroImage 19:1417–26 Gabrielson A. 1999. The performance of music. See Deutsch 1999, pp. 501–602 Gaser C, Schlaug G. 2003. Brain structures differ between musicians and non-musicians. J. Neurosci. 23:9240–45 Goldstein A. 1980. Thrills in response to music and other stimuli. Physiol. Psychol. 8:126– 29 Gregersen PK, Kowalsky E, Kohn N, Marvin EW. 2000. Early childhood music education and predisposition to absolute pitch: teasing apart genes and environment. Am. J. Hum. Genet. 98:280–82 Griffiths TD, Beuchel C, Frackowiak RSJ, Patterson RD. 1998. Analysis of temporal structure in sound by the human brain. Nat. Neurosci. 1:422–27 Griffiths TD, Johnsrude I, Dean JL, Green GGR. 1999. A common neural substrate for the analysis of pitch and duration pattern in segmented sound? NeuroReport 10:3825–30 Hall DA, Johnsrude I, Haggard MP, Palmer AR, Akeroyd MA, Summerfield AQ. 2002. Spectral and temporal processing in human auditory cortex. Cereb. Cortex 12:140–49 Halpern AR. 1988. Mental scanning in auditory imagery for songs. J. Exp. Psychol.: Learn. Mem. Cogn. 14:434–43 Halpern AR. 1989. Memory for the absolute pitch of familiar songs. Mem. Cogn. 17:572– 81 Halpern AR, Bartlett D, Dowling W. 1998. Perception of mode, rhythm, and contour in unfamiliar melodies: effects of age and experience. Music Percept. 15:335–56 Halpern AR, Zatorre R. 1999. When that tune runs through your head: a PET investigation of auditory imagery for familiar melodies. Cereb. Cortex 9:697–704 Halpern AR, Zatorre R, Bouffard M, Johnson JA. 2004. Behavioral and neural correlates of perceived and imagined musical timbre. Neuropsychologia. In press Halsband U, Tanji J, Freund H-J. 1993. The

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role of premotor cortex and the supplementary motor area in the temporal control of movement in man. Brain 116:243–46 Harrington DL, Haaland K, Hermanowicz N. 1998. Temporal processing in the basal ganglia. Neuropsychology 12:3–12 Hart HC, Palmer AR, Hall DA. 2003. Amplitude and frequency-modulated stimuli activate common regions of human auditory cortex. Cereb. Cortex 13:773–81 Haueisen JK, Knösche T. 2001. Involuntary motor activity in pianists evoked by music perception. J. Cogn. Neurosci. 13:786–92 Hauser M, Chomsky N, Fitch WT. 2002. The faculty of language: What is it, who has it, and how did it evolve? Science 298:1569–79 Hébert S, Racette A, Gagnon L, Peretz I. 2003. Revisiting the dissociation between singing and speaking in expressive aphasia. Brain 126:1838–50 Henschen SE. 1924. On the function of the right hemisphere of the brain in relation to the left in speech, music and calculation. Brain 44:110–23 Hickok G, Buchsbaum B, Humphries C, Muftuler T. 2003. Auditory-motor interaction revealed by fMRI: speech, music, and working memory in area SPT. J. Cogn. Neurosci. 15:673–82 Holcomb H, Medoff D, Caudill P, Zhao Z, Lahti A, et al. 1998. Cerebral blood flow relationships associated with difficult tone recognition task in trained normal volunteers. Cereb. Cortex 8:534–42 Horikoshi T, Asari Y, Watanabe A, Nagaseki Y, Nukui H, et al. 1997. Music alexia in a patient with mild pure alexia: disturbed visual perception of nonverbal meaningful figures. Cortex 33:187–94 Hund-Georgiadis M, von Cramon DY. 1999. Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals. Exp. Brain Res. 125:417–25 Ibbotson NR, Morton J. 1981. Rhythm and dominance. Cognition 9:125–38 Ivry RB, Keele S. 1989. Timing functions of the cerebellum. J. Cogn. Neurosci. 1:136–52

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Peretz I, Ayotte J, Zatorre RJ, Mehler J, Ahad P, et al. 2002. Congenital amusia: a disorder of fine-grained pitch discrimination. Neuron 33:185–91 Peretz I, Babai M. 1992. The role of contour and intervals in the recognition of melody parts: evidence from cerebral asymmetries in musicians. Neuropsychologia 30:277–92 Peretz I, Blood AJ, Penhune V, Zatorre R. 2001. Cortical deafness to dissonance. Brain 124:928–40 Peretz I, Coltheart M. 2003. Modularity of music processing. Nat. Neurosci. 6:688–91 Peretz I, Gagnon L. 1999. Dissociation between recognition and emotional judgment for melodies. Neurocase 5:21–30 Peretz I, Gagnon L, Bouchard B. 1998. Music and emotion: perceptual determinants, immediacy, and isolation after brain damage. Cognition 68:111–41 Peretz I, Gagnon L, Macoir J, Hébert S. 2004a. Singing in the brain: insights from cognitive neuropsychology. Music Percept. 21:373–90 Peretz I, Hyde K. 2003. What is specific to music processing? Insights from congenital amusia. Trends Cogn. Sci. 7:362–67 Peretz I, Kolinsky R. 1993. Boundaries of separability between melody and rhythm in music discrimination: a neuropsychological perspective. Q. J. Exp. Psychol. A 46:301–25 Peretz I, Kolinsky R, Tramo M, Labrecque R, Hublet C, et al. 1994. Functional dissociations following bilateral lesions of auditory cortex. Brain 117:1283–301 Peretz I, Morais J. 1987. Analytic processing in the classification of melodies as same or different. Neuropsychologia 25:645–52 Peretz I, Morais J. 1989. Music and modularity. Contemp. Music Rev. 4:277–91 Peretz I, Radeau M, Arguin M. 2004b. Two-way interactions between music and language: evidence from priming recognition of tune and lyrics in familiar songs. Mem. Cogn. 32:142– 52 Peretz I, Zatorre R. 2003. The Cognitive Neuroscience of Music. Oxford: Oxford Univ. Press Perry DW, Zatorre RJ, Petrides M, Alivisatos B, Meyer E, Evans AC. 1999. Localization

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BRAIN ORGANIZATION FOR MUSIC PROCESSING Schellenberg E, Trehub SE. 1994. Frequency ratios and the perception of tone patterns. Psychon. Bull. Rev. 1:191–201 Schlaug G. 2003. The brain of musicians. See Peretz & Zatorre 2003, pp. 366–81 Schlaug G, Jancke L, Huang Y, Steinmetz H. 1995. In vivo evidence of structural brain asymmetry in musicians. Science 267:699– 701 Schmidt LA, Trainor LJ. 2001. Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cogn. Emot. 15:487–500 Schneider P, Scherg M, Dosch G, Specht H, Gutschalk A. 2002. Morphology of Heschl’s gyrus reflects enhanced activation in the auditory cortex of musicians. Nat. Neurosci. 5:688–94 Schön D, Anton J-L, Roth M, Besson M. 2002. An fMRI study of music sight-reading. NeuroReport 13:2285–89 Schön D, Besson M. 2002. Processing pitch and duration in music reading: a RT-ERP study. Neuropsychologia 40:868–78 Schön D, Semenza C, Denes G. 2001. Naming of musical notes: a selective deficit in one musical clef. Cortex 37:407–21 Schubotz RI, Friederici AD, von Cramon DY. 2000. Time perception and motor timing: a common cortical and subcortical basis revealed by fMRI. Neuroimage 11:1–12 Semal C, Demany L, Ueda K, Halle P-A. 1996. Speech versus nonspeech in pitch memory. J. Acoust. Soc. Am. 100:1132–40 Shallice T. 2003. Functional imaging and neuropsychology findings: How can they be linked? NeuroImage 20:S146–54 Signoret JL, van Eeckhout P, Poncet M, Castaigne P. 1987. Aphasia without amusia in a blind organist. Verbal alexia-agraphia without musical alexia-agraphia in braille. Rev. Neurol. (Paris) 143:172–81 Small DM, Zatorre R, Dagher A, Jones-Gotman M. 2001. Brain activity related to eating chocolate: from pleasure to aversion. Brain 124:1720–33 Steinke WR, Cuddy LL, Jakobson LS. 2001. Dissociations among functional subsystems

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VESTIBULAR, PROPRIOCEPTIVE, AND HAPTIC CONTRIBUTIONS TO SPATIAL ORIENTATION Annu. Rev. Psychol. 2005.56:115-147. Downloaded from arjournals.annualreviews.org by Ball State University on 01/05/09. For personal use only.

James R. Lackner and Paul DiZio Ashton Graybiel Spatial Orientation Laboratory, Brandeis University, Waltham, Massachusetts 02454; email: [email protected]

Key Words semicircular canals, otoliths, muscle spindles, somatosensation, artificial gravity ■ Abstract The control and perception of body orientation and motion are subserved by multiple sensory and motor mechanisms ranging from relatively simple, peripheral mechanisms to complex ones involving the highest levels of cognitive function and sensory-motor integration. Vestibular contributions to body orientation and to spatial localization of auditory and visual stimuli have long been recognized. These contributions are reviewed here along with new insights relating to sensory-motor calibration of the body gained from space flight, parabolic flight, and artificial gravity environments. Recently recognized contributions of proprioceptive and somatosensory signals to the appreciation of body orientation and configuration are described. New techniques for stabilizing posture by means of haptic touch and for studying and modeling postural mechanisms are reviewed. Path integration, place cells, and head direction cells are described along with implications for using immersive virtual environments for training geographic spatial knowledge of real environments.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VESTIBULAR RECEPTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VESTIBULAR REFLEXES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vestibulo-Ocular Reflexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vestibulo-Spinal Reflexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Loss of Vestibular Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EFFECT OF UNUSUAL PATTERNS OF ACCELERATION ON ORIENTATION AND SENSORY LOCALIZATION . . . . . . . . . . . . . . . . . . . . . Oculogyral and Audiogyral Illusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oculogravic, Audiogravic, and Somatogravic Illusions . . . . . . . . . . . . . . . . . . . . . . PROPRIOCEPTIVE AND SOMATOSENSORY CONTRIBUTIONS TO ORIENTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muscle Spindles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Somatosensation and Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haptic Stabilization of Posture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0115$14.00

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MODELS OF ORIENTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internal Models and Cognitive Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tri-Axis Model of Spatial Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FALLING IN THE ELDERLY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experimental Tests and Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MOTION SICKNESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Incidence and Eliciting Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Space Motion Sickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ARTIFICIAL GRAVITY ENVIRONMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coriolis Forces and Disruption of Movement Control . . . . . . . . . . . . . . . . . . . . . . . Adaptive Accommodations and Aftereffects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Self-Generated Coriolis Forces in Everyday Behavior . . . . . . . . . . . . . . . . . . . . . . . Coriolis Forces and Motion Sickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PATH INTEGRATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Place Cells and Head Direction Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . COMPLEXITY OF SPATIAL REPRESENTATIONS: IMPLICATIONS FOR IMMERSIVE VIRTUAL ENVIRONMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . GLOSSARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION Thirty years have elapsed since the Annual Review of Psychology last included a chapter on the vestibular system. In that period, enormous progress has been made. Space flight became commonplace with the development of the U.S. space shuttle program, the Russian Mir space station, and the International Space Station. Now planning is taking place for a manned mission to Mars. A new panorama of information concerning human spatial orientation and performance in weightless and unusual force environments has emerged. In addition, important contributions of somatosensation and proprioception to orientation have been recognized as well as a contribution of cognitive factors. Falling in the elderly has become a major national and international health problem with the burgeoning numbers of older individuals. The present review provides a basic summary of vestibular function and then focuses on these new discoveries and problems.

VESTIBULAR RECEPTORS The bilaterally symmetric labyrinths of the vertebrate consist on either side of three orthogonally oriented semicircular canals and two otolith organs.1 Each semicircular canal is a skull-fixed cartilaginous ring filled with a fluid called endolymph. 1

For definitions of terms, see Glossary at end of text.

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A gelatinous, elastic membrane, the cupula, spans the cross-section of each canal within an enlargement known as the ampulla. The cupulae encapsulate the cilia of hair cell receptors whose somas are embedded in the canal walls. Rotary acceleration of the head in the plane of a semicircular canal causes the endolymph to lag relative to the canal wall because of its inertia and low viscosity, and the resulting endolymphatic pressure deflects the cupula–hair cell complex. Bilateral parallel pairs of semicircular canals are symmetric with respect to ampulla location and are activated in push-pull fashion. Head angular acceleration causes deflection either toward or away from the ampulla, increasing or decreasing the afferent discharge rate, respectively, relative to the high tonic discharge rate when the head is stationary. Over the frequency range of natural head movements, the outputs of the canals are proportional to head angular velocity owing to the viscoelastic properties of the cupula-endolymph system. Some of the experimental procedures described below involve use of a rotating chair to create a step-like onset of head rotation followed by prolonged, constant velocity motion. Cupula-endolymph dynamics are such that the rotating chair evokes canal discharge that shows a direction and velocity-dependent sudden rise followed by a decay back to resting level. The otolith organs by contrast are responsive to linear acceleration, be it of gravitational or inertial origin. These organs consist of aggregates of high specificgravity crystals, or otoconia, embedded in a gelatinous membrane that also encapsulates the cilia of many hair cells whose bodies are fixed relative to the skull. Linear acceleration displaces the roughly planar otoconial masses relative to the skull, thereby bending the embedded hair cells. Hair cells collectively have a wide distribution of morphological orientations, resulting in their receptor potentials being tuned to different directions of acceleration in a plane. The utricular otolith organs are oriented roughly in the head’s coronal plane and the saccular otoliths in the sagittal plane. Different classes of hair cells and their associated primary afferent fibers of the semicircular canals and otoliths segregate angular and linear accelerations into low- and high-frequency bands. There are also efferent pathways from the CNS to the end organs, but the function of these pathways remains largely unknown. The vestibular system is illustrated in Figure 1. For a detailed review of vestibular anatomy and physiology, see Wilson & Melvill Jones (1979). These specialized receptor systems participate in a variety of postural and oculomotor reflexes, and in the perception of body motion and orientation. Direct and indirect projections of the canal and otolith receptors to the vestibular nuclei, the “vomiting centers” in the pontine reticular formation (Miller & Wilson 1983), the cerebellum, the oculomotor nuclei, and the spinal cord have been systematically charted. Recently, vestibular projections to parietal and temporal cortical areas and hippocampus (Guldin & Grusser 1998, Kahane et al. 2003, Wenzel et al. 1996) have been identified that may underlie vestibular influences on perceived orientation and self-motion.

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Figure 1 (A) Schematic illustration of the vestibular end organs. For clarity, the semicircular canals and the otolithic maculae are shown larger than life size and unilaterally, although both sets of organs are actually present on each side. The arrows associated with the semicircular canals indicate the head rotation vectors for which individual canals are selective; the arrows on the otolith organs are a reminder that individual hair cells on each macula are attuned to different linear acceleration directions. (B) A block diagram of connections from the vestibular organs to some important brain regions: PCRF is the parvicellular reticular formation, the electrical stimulation of which triggers vomiting; PIVC is the parieto-insular vestibular cortex; TPSVC is the temporo-perisylvian vestibular cortex.

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VESTIBULAR REFLEXES


Vestibulo-Ocular Reflexes Vestibulo-ocular reflexes and vestibulocollic reflexes are elicited both by angular and linear acceleration. During gaze shifts in which the eyes and head are oriented to a new target position, the eyes generally reach the target by a rapid saccadic movement followed by a head turn toward the target. The eyes are maintained on the target position during the head turn by a vestibulo-ocular reflex that drives the eyes in the opposite direction to the head at the same rate (see Figure 2). The vestibulocollic reflex stabilizes the head in the final position if additional passive whole-body motions are imposed. In the dark, during exposure to passive angular acceleration of the whole body, the vestibulo-ocular reflex produces a nystagmoid pattern of eye movements with a slow phase component compensatory for the direction of acceleration (“field-holding reflex”) interrupted by rapid saccades that reposition the eyes. When vision is allowed, the resulting optokinetic stimulation reinforces the vestibular compensation. During constant velocity rotation in the dark, the semicircular canal signals decay to baseline and the nystagmoid eye movements will gradually abate, but if vision is allowed the resulting optokinetic stimulation will continue to drive the eyes. The vestibulo-ocular reflex has a three-dimensional structure (Cohen et al. 1999) that can be adaptively remapped (Dulac et al. 1995). Vestibulo-ocular reflex adaptation can be measured by comparing ocular responses to body oscillation in darkness before and after exposure to body oscillation relative to a rich visual scene. For example, a few minutes exposure to left-right rotation in synchrony with a head-fixed visual scene results in a change in gain of the vestibulo-ocular reflex such that the magnitude of compensatory eye movements in darkness is reduced. Furthermore, left-right rotation in synchrony with a visual scene oscillating up and down ultimately leads to a geometrical reorganization of the vestibulo-ocular reflex such that left-right oscillation of the body in darkness elicits vertical eye movements (Schultheis & Robinson 1981). Physiological pathways with as few as three neurons can mediate these reflexes, but higher-level inputs modulate them according to current goals. For example, a subclass of neurons in the vestibular nucleus faithfully conveys the primary afferent signals they receive about head motion in space during passive but not active head rotations (Gdowski & McCrea 1999, Roy & Cullen 1998). This task-dependent processing is based on an efferent copy of the head-to-torso rotation command (Roy & Cullen 2001). Compensatory eye movements induced by linear acceleration include ocular counterrolling of the eyes during static roll tilt of the body (Miller & Graybiel 1962), the doll’s eye reflex (Ebenholtz & Shebilske 1975) during head pitch, and elevations and depressions of the eyes during exposure to decreases and increases in background gravitoinertial acceleration (GIA) level in the z axis of the upright head (Whiteside 1961) and to linear translations of the body (Jongkees & Philipszoon 1962). The otolith organs are responsive to GIA, the resultant of gravitational and inertial accelerations acting on the body. Under normal static conditions, GIA

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Figure 2 Schematic illustration of the circuit for an idealized unidimensional angular vestibulo-ocular reflex. A leftward head turn produces ampulopetal flow of endolymph in the ipsilateral semicircular canal (SCC) and increases the receptor potential (+sign); ampulofugal flow and depolarization (−sign) occur on the right side. Primary afferent fibers whose cell bodies are in Scarpa’s ganglia (SG) have high tonic firing rates that are increased on the ipsilateral side and reduced on the contralateral side. Excitatory synapses (filled circles) cause parallel increases and decreases in the ipsilateral and contralateral vestibular nuclei (VN) and para-pontine reticular formation (PPRF), respectively. Rightward eye movements result from a combination of ipsilateral connections to the oculomotor nuclei (III) that innervate the medial rectus muscles and contralateral excitatory input to the abducens nuclei (VI) that innervate the lateral rectus muscles. Circuitry involved in adaptation is not included.

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magnitude simply corresponds to 1 g (9.8 m/s2), the magnitude of earth gravity. Eye movements evoked by linear acceleration are rarely fully compensatory, e.g., the gain of ocular counterrolling and of the linear vestibulo-ocular reflex is about 0.1 to 0.15 (Paige & Tomko 1991). However, the linear vestibulo-ocular reflex shows sophisticated coordination with other orienting movements. For example, when an individual is undergoing left-right translation in darkness, the amplitude of lateral eye movements rapidly increases when the eyes converge to a nearer distance, as would be required to prevent visual slip of near objects if any were visible (Telford et al. 1997).

Vestibulo-Spinal Reflexes Vestibular influences on postural control include modulation of the postural tone of the body and the antigravity reflexes that are key to maintaining without conscious effort the configuration and stance of the body vis-à-vis gravity (see Wilson & Peterson 1978, Wilson et al. 1995 for reviews). Such reflexes related to standing and balance interact in a synergistic fashion with reflexes related to the neck (tonic neck reflexes), the limbs, and the overall disposition of forces on the body (see Roberts 1978 for a review).

Loss of Vestibular Function Progressive loss of vestibular function owing to a degenerative process may be largely unnoticed by an individual, unlike loss of other sensory functions such as hearing or vision, because of adaptive changes that occur to maintain function. The neck proprioceptive contribution to compensatory eye movements during gaze shifts, for example, increases and largely compensates for the loss of the vestibuloocular reflex (Bronstein & Hood 1986). Individuals with total loss of function who under normal circumstances are “fully compensated” in terms of balance and locomotion, nevertheless experience difficulty on uneven terrain or soft surfaces, e.g., sand, and under conditions of low light levels or darkness. Thresholds for perception of angular or linear acceleration are greatly increased but perception of body orientation relative to gravity may be largely intact, especially for conditions of self-support and active maintenance of posture. The preservation of accurate perception of body orientation despite loss of vestibular function is based on somatosensory, proprioceptive, efferent, and visual signals (Graybiel et al. 1968, Niven & Graybiel 1953). Loss of labyrinthine function confers total immunity to motion sickness.

EFFECT OF UNUSUAL PATTERNS OF ACCELERATION ON ORIENTATION AND SENSORY LOCALIZATION Changes in visual and auditory localization and in perceived self-orientation are elicited by unusual patterns of vestibular stimulation. Many of these effects were initially described by Mach (1875).

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Oculogyral and Audiogyral Illusions Exposure to constant angular acceleration in a rotating chair produces changes in both visual and auditory localization (Clark & Graybiel 1949a, Graybiel & Hupp 1946). If a head-fixed visual target is being viewed in an otherwise dark chamber, it will be seen as moving with the body’s changing apparent position in space but leading the body as well in the direction of acceleration. The time course and magnitude of this illusion, known as the oculogyral illusion, is related to the discharge dynamics of the semicircular canals, as described in the Vestibular Receptors section. The occurrence of the oculogyral illusion is related to the partial or complete suppression by visual fixation of the involuntary eye movements that otherwise would occur. The fixation “hold signal” to override the covert vestibular nystagmus and average shift in eye position (Schlagfeld displacement) is misrepresented as an eye deviation, thus causing an error in body relative visual localization (Whiteside et al. 1965). In the dark, a head-fixed auditory target will be heard to displace relative to the body in the direction opposite angular acceleration. The contrasting directions of the audiogyral illusion and of the oculogyral illusion have yet to be adequately explained. Figure 3 illustrates the oculogyral and audiogyral illusions.

Oculogravic, Audiogravic, and Somatogravic Illusions Exposure to unusual patterns of GIA also leads to changes in sensory localization, in apparent self-orientation, and in apparent vehicle orientation. The oculogravic illusion is illustrated in Figure 4. During exposure to an increase in magnitude and a tilt of the GIA vector, a subject seated in a slow rotation room facing the direction of rotation, with left ear toward the center of the room, will experience rightward body tilt and see a luminous line as having displaced clockwise. If the room lights are on, the magnitude of the apparent body tilt will be decreased but a clockwise displacement of the entire room will be seen. Similar mislocalizations

Figure 3 Illustration of the oculogyral and audiogyral illusions. The large arrow indicates the direction of head acceleration. The unfilled icons represent the actual locations of a loudspeaker and a target light fixed in the head midline (dotted line). The gray icons and arrows represent the perceived locations and motions, respectively, of the sound and light source.

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Figure 4 (A) Illustration of the gravitoinertial acceleration (GIA) experienced on a rotating room by a subject who is facing the direction of rotation, with left ear toward the center of the room, when the centrifuge acceleration is 1 g. The GIA magnitude and direction are determined by the vector sum of gravity and centrifugal force (ω2r). (B) The somatogravic illusion refers to the apparent body tilt the subject experiences. (C) The oculogravic illusion. The subject perceives an externally presented, earthhorizontal line as tilted. If the room lights are on, the magnitude of the apparent body tilt will be decreased but a clockwise displacement of the entire room will be seen.

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of auditory targets occur. The apparent body tilt is known as the somatogravic illusion (Clark & Graybiel 1949b), and the visual and auditory mislocalizations are referred to as the oculogravic (Clark & Graybiel 1949c, Graybiel 1952) and audiogravic illusions (Graybiel & Niven 1951). These effects have sometimes been described as not illusions at all but simply accurate reflections of body and visual orientation in relation to a new vertical corresponding to the GIA orientation as sensed by the otolith organs, which cannot distinguish between inertial and gravitational accelerations (Howard & Templeton 1966). Recent experiments indicate a more complex mechanism (DiZio et al. 2001). Studies of visual and auditory localization have been carried out in which, during exposure to altered GIA direction and magnitude, subjects were required to adjust a visual or an auditory target so that it was perceived to remain in their body midline. In these circumstances, target position had to be displaced by about 15% of the GIA displacement and in the same direction in order to still be seen or heard in the midline of the head. Localization of somatosensory stimulation on the forehead was similarly affected (unpublished observation). Coincident with the altered sensory spatial localizations was a reorientation of the apparent median plane of the head. A joystick that was kept aligned with the apparent midline of the body was displaced in the direction opposite the rotation of the GIA. The time course and magnitude of this shift exactly paralleled the shifts in visual, auditory, and somatosensory localization but were of opposite sign. These localization changes are embedded in the framework of simultaneous changes in apparent orientation of the body with respect to external space. The overall pattern of remapping of sensory localization coupled with a shift in the apparent body midline suggests that a common reference frame for bodyrelative localization and for localization of the body with respect to external space has been modified (see also Lewald & Karnath 2000). A potentially related finding is the observation that vestibular stimulation can alter the pattern of egocentric, unilateral spatial neglect experienced by some patients with brain injury (Vallar et al. 1990). The underlying physiological bases for these modulations remain uncertain, but temporal and parietal cortices contain projections from the relevant sensory modalities, receive vestibular projections, and are implicated in the perception of body orientation in space (Bottini et al. 2001). Thus, they represent natural sites for participation in the observed changes.

PROPRIOCEPTIVE AND SOMATOSENSORY CONTRIBUTIONS TO ORIENTATION Muscle Spindles The otolith organs of the inner ear provide information about the orientation of the head to the GIA. However, the ongoing sense of orientation includes the orientation of the entire body, and all of its links and segments, relative to gravity. The sense of the relative configuration of the body is commonly referred to as the body

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schema. Joint receptors within the capsules of the joints were long thought to serve as potentiometers providing a fiducial representation of joint position that could contribute to an overall mapping of body configuration. However, with the advent of joint replacement surgery, it was found that accurate position sense was retained even in the absence of joint receptors (Grigg et al. 1973). About the same time, it was found that position sense was influenced by the muscle spindle fibers that are interspersed in parallel with the extrafusal muscle fibers that do the actual work of muscle contraction (cf. Matthews 1972). This arrangement means the total length of the muscle spindles is a function of overall muscle length; however, the length of their sensory region innervated by primary and secondary receptor endings is modulated by intrafusal muscle fibers innervated by gamma motoneurons in the spinal cord. The extrafusal muscle fibers are activated by the alpha motoneurons of the spinal cord. The spindle sensory signal is interrelated to the ongoing pattern of gamma and alpha activation and other signals about body loading to compute the angle and rate of change of the joint controlled by the muscle. This normal relationship can be distorted by vibrating a muscle mechanically, circa 100–120 Hz, to activate artificially its muscle spindle primary and secondary receptor endings (Hagbarth & Eklund 1966). In this circumstance, the heightened discharge is centrally interpreted as lengthening of the vibrated muscle and is referred to the joint controlled by the muscle. For example, vibration of the biceps brachii of the arm leads to the forearm feeling more extended than it actually is (Goodwin et al. 1972). When postural muscles are vibrated, various illusions of body motion can be elicited. For example, simultaneous vibration of the Achilles tendons of a standing subject restrained in position will cause the subject to experience forward pivoting in pitch about the ankles. The subject, if in total darkness, will exhibit nystagmoid eye movements with the slow phase compensatory for the direction of apparent self-displacement. If a visual target is presented for the subject to fixate, it will be seen to move in the direction of apparent self-motion and to displace ahead of the subject in the same direction (Lackner & Levine 1979). Thus, the visual target motion has the same characteristics as the oculogyral illusion described above, and a similar physiological explanation in terms of suppression of involuntary eye movements can account for its properties. Vibration of neck muscles leads to illusions of head rotation and displacement (Karnath et al. 1994, Lackner & Graybiel 1974, Lackner & Levine 1979). In fact, with vibration of the appropriate skeletal muscles, apparent motion and displacement of the body or its segments can be elicited in virtually any desired configuration (Lackner 1988). If visual or auditory targets are present, their positions also are remapped in the direction of apparent body motion and displacement. For example, when a small target light is attached to the hand and illusory motion of the restrained forearm is elicited by vibration of the biceps brachii or triceps muscles, the target light will be seen to displace physically in the direction of the apparent motion of the hand. This phenomenon is known as the oculobrachial illusion (Lackner & Levine 1978).

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Muscle spindle signals can be more important than vestibular cues in their influence on perceived orientation in altered gravitoinertial force environments. Subjects who move about during exposure to increases or decreases in background force level, for example during parabolic flight, experience instability of the aircraft deck and misperceive their own motion (Lackner & Graybiel 1981). When a deep knee bend is made in a greater than 1 g force level, the weight of the body is more than normal and the antigravity muscles that are undergoing eccentric contraction lengthen slightly more than for a lowering movement of the body in 1 g; in addition, an unusually high level of spindle discharge for the level of alphagamma motoneuronal activity likely is present. The nervous system “attributes” this unexpected lengthening to the floor moving up under the feet. With repeated deep knee bends, self-motion perception gradually becomes more accurate, and the aircraft again is perceived as stable under the feet. On return to a normal 1 g environment, aftereffects are experienced, with opposite signs. Astronauts on return to earth experience the same effects as individuals adapted to 1 g who are exposed to 1.8 g. The ground seems to move upward under their feet when they lower their body in a deep knee bend.

Somatosensation and Orientation Proprioceptive information about limb configuration combined with somatosensory information about hand or limb contact with the body itself and with external objects is a key factor in calibration of the apparent dimensions of the body and of its relationship to external space. For example, if a subject is grasping his nose with his fingers and the biceps brachii muscle of the arm is vibrated, an illusion of arm extension will be elicited and the subject may feel his nose elongate in Pinocchio fashion (Lackner 1988). If the subject is seated with hands on the waist and arms akimbo, bilateral vibration of the triceps brachii muscles will elicit the illusion of the hands approaching one another and of the waist shrinking to become wasp-like. Such interactions indicate that the calibration of the body, in terms of its spatial dimensions, may be achieved by interaction with the hands. Control of the hands and their represented size can be calibrated by interacting with the external environment. Visual direction and the direction of regard also can be calibrated through sight of the hands. Somatosensory stimulation influences perception of the upright and the control of posture. Receptors in the soles of the feet are important in the control of posture (Kavounoudias et al. 1998). For example, as the body leans forward, regions of the feet that are more anterior receive the most mechanical pressure in supporting the body against gravity. During passive stance, this provides a mapping of body orientation to the upright. Diabetic patients with peripheral sensory neuropathies have increased postural sway in part because of degradation of these foot-related signals. Postural control can be enhanced by the use of shoe insoles that vibrate to stimulate the plantar receptors, making it easier for the central nervous system to detect sway changes (Collins et al. 2003). Vests with tactor vibrators also have been employed with mild success to enhance balance by providing tactile stimulation

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of the torso dependent on body orientation to the vertical (Kentala et al. 2003). Such systems also have been proposed for use in signaling body orientation in helicopters and high-performance aircraft (Rupert 2000).


Haptic Stabilization of Posture Light touch cues from the hand have a powerful stabilizing influence on posture when a surface is touched with the index finger at mechanically nonsupportive force levels. In blindfolded individuals, finger contact reduces mean sway amplitude by about 50%. Even when normal sight of the surroundings is present, balance is further stabilized by light fingertip contact (Holden et al. 1994, Jeka & Lackner 1994). Labyrinthine-defective patients who cannot stand heel-to-toe with eyes closed for more than a few seconds can stand stably indefinitely when allowed light touch (Lackner et al. 1999). Finger stabilization of posture occurs in patients with diabetic neuropathy, cerebellar disease, alcoholism, and in fact, in all patient groups tested to date. If unbeknownst to an individual, the touched surface is oscillated, postural entrainment occurs. The person will be made to sway at frequencies and amplitudes above normal detection thresholds and not be aware of it while the oscillating surface will be perceived as stationary (Jeka et al. 1997). Even when the test subject is aware that the surface can oscillate, postural entrainment will occur, albeit of lesser amplitude. However, in this circumstance, the subject will perceive the touched surface as moving and in trials in which the surface is stationary, it nevertheless may be perceived to be moving. This latter fact emphasizes the importance of cognitive knowledge and assumptions (i.e., “top-down effects”) about the environment for whether unseen touched objects will be perceived to be stationary or not. The time course of postural stabilization by fingertip contact is remarkably rapid. When the finger is dropped to contact a surface, it will be stabilized on the surface within 100 milliseconds and a downward trend in body mean sway amplitude will be detectible within the next 100 milliseconds. By contrast, visual stabilization of posture by turning on the lights takes three or four times longer to begin and still longer to complete (JR Lackner, E Rabin, & P DiZio, submitted; Rabin et al. 2004). Touch signals from the hand also completely override the otherwise destabilizing effects of tonic vibration reflexes in leg muscles (Lackner et al. 2000). In addition, the illusions of self-displacement and aircraft displacement that occur with whole-body movements during exposure to increased or decreased background force levels in parabolic flight (see above) are virtually abolished by light contact of the hand with a stationary surface (Lackner & DiZio 1993). The postural stabilization conferred by light touch seems to reflect a form of “precision touch” analogous to precision grip. When an object is held between the index finger and thumb and lifted, the forces at the fingertip are “automatically adjusted” to generate appropriate grip and load forces. The latency of grip adjustments, about 85 ms, is well below conscious reaction times, and reflects the

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activity of a somatosensory-motor cortical control loop (Johansson & Westling 1984). During stance with maintained finger contact, the changes in the body’s force at the fingertip phase lead changes in the body center of pressure and center of mass by about 300 ms. With fingertip contact, inverted pendulum sway of the body is maintained.


MODELS OF ORIENTATION Physical sensors such as the otolith organs cannot distinguish gravitational from inertial accelerations. However, for humans, other forms of information that can disambiguate situations generally are available. One important factor is the control of the body itself in terms of the forces necessary to maintain balance or body support, and whether the body is actively moving. For example, when a person walks in a tight circle or makes a sharp turn, quite high centripetal forces are generated but body orientation is not misperceived (Imai et al. 2001). When the same forces are passively applied to the body, the same tilt of the GIA will lead to apparent roll tilt of the body (Miller & Graybiel 1966).

Internal Models and Cognitive Factors The otolith organs have “regular” and “irregular” receptor units with different firing patterns (Fernandez et al. 1972). The regular fibers have discharges that are tuned to different static orientations of the body relative to gravity and that maintain a steady-state discharge virtually indefinitely. By contrast, irregular fibers respond to changes in acceleration and in position, but adapt over time. Regular units show a less-prominent dynamic modulation. Such frequency-dependent segregation of otolith afferent firing has been proposed as a way to distinguish gravity from inertial accelerations, especially for frequency domains that exceed those of voluntary head movements (Mayne 1974). Investigators have also proposed that the CNS uses internal models to discriminate inertia and gravity based on representations of the body’s dynamics and on available semicircular canal as well as otolith input (Merfeld et al. 1999). Cognitive information also usually is available about the environment and its properties, such as whether the body is in a vehicle of some sort.

Tri-Axis Model of Spatial Orientation A recent tri-axis spatial orientation model has been developed based on the hypothesis that the vestibular system evolved in a 1 g, Earth gravity context and that 1 g is taken as a standard relative to which all shear and deformation patterns of the otolith membranes are interpreted (Bortolami et al. 2004). The model incorporates the known structural features of the otolith organs and includes “cross talk” between the pitch and roll axes. This cross talk occurs because of the complex three-dimensional organization of the otolith membranes and their resulting displacements to shear forces (Grant & Best 1987). It accurately predicts both the

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errors in spatial localization of the vertical that occur for different static orientations of the body in yaw, pitch, and roll relative to gravity, and the potential elicitation of inversion illusions in weightless conditions. A surprising prediction of the model is that when the recumbent body is in different static yaw orientations with respect to gravity, the level of resultant GIA will have negligible influences on perceived body orientation to the vertical. This prediction results from calculating yaw orientation from signals related to the pitch and roll axes and using a 1 g gravity standard to interpret these shear signals. Parabolic flight experiments have recently validated the model’s predictions. The model uses somatosensory and proprioceptive cues to distinguish up and down, which is consistent with the important influence of touch and contact cues on spatial orientation in weightless conditions.

FALLING IN THE ELDERLY Falls in the elderly have become a major health problem as longevity has increased (Maki et al. 1987). In the United States, the average survival time for a faller who has fractured a hip is about two years. Many factors contribute to falls, including reduced visual acuity, diminished vestibular function, lessened somatosensory and proprioceptive acuity, reduced muscle strength, and medications (Brown et al. 1999, Chen et al. 1996, Draganich et al. 2001, Klein et al. 1998, Lajoie et al. 1993, Leipzig et al. 1999, McIllroy & Maki 1996, Shumway-Cook et al. 1997, Thelen et al. 1996). Tripping on obstacles and when changing direction is also commonplace (Blake et al. 1988, Campbell et al. 1990, Overstall et al. 1977, Ruberstein et al. 1988). Individuals who are in the weightless phase of parabolic flight maneuvers and astronauts who are in orbital flight are actually in a state of continuous free fall; nevertheless, they do not perceive themselves to be falling (Lackner 1992). This means that otolith signals per se do not determine whether one experiences falling but that additional sensory and cognitive factors must contribute.

Experimental Tests and Models A number of studies have investigated the role of surface features, obstacles in the walking path (Persad et al. 1995), sudden changes in direction (Mbourou et al. 2003), and visual factors (Judge et al. 1995) in predisposing individuals to lose balance during locomotion. These studies highlight the relative importance of various external factors in triggering loss of balance. Other studies have evaluated how various perturbations of the stance surface and of the visual surround affect postural recovery (Luchies et al. 1994, Pai & Patton 1997). Still others have explored the ability to recover from artificially induced forward leans of the body (Thelen et al. 1997). From these investigations, age- and gender-related changes in somatosensory, proprioceptive, and vestibular function, and muscular strength have been identified that may contribute to falling in the elderly.

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Mathematical models of falling have been developed using an inverted pendulum representation of the body. The most advanced of these accurately reproduces the body’s response to a Hold and Release perturbation (Bortolami et al. 2003). The individual being tested actively resists a force applied to his or her chest that is unpredictably released. For some tens of milliseconds, the body is physically in a state analogous to free fall, pivoting about the ankles. After a latency of about 50 milliseconds, compensatory postural adjustments are made to arrest falling and the body undergoes damped oscillations over 10 seconds before settling. Video recordings of the body multilink response and recovery from the perturbation, and electromyographic recordings from the postural muscles involved in the recovery, serve as observational input providing a description of the body’s response. Fitting these data to a multilink inverted pendulum model allows computation of the stiffness and damping at the ankles, knees, hips, and neck, along with the recovery time to resettling. The patterning and magnitudes of muscle activation allow a snapshot of the underlying neurophysiological processes mediating recovery. It appears that postural responses to Hold and Release involve one-time, stable adjustment of stiffness and damping after a variable latency. The identified stiffness and damping parameters, as well as the latency, vary with visual and tactile sensory cues and with background GIA. The Hold and Release paradigm and model provide a useful clinical tool for experimentally studying falling and for characterizing the deficits in motor control of people prone to falling. A linear, four-link inverted pendulum model is sufficient to characterize postural recovery in patients with labyrinthine loss, those with somatosensory loss, and those with cerebellar lesions. For normal, healthy subjects only a two-link model is necessary to adequately describe recovery of balance.

MOTION SICKNESS Motion sickness has been a persistent accompaniment of exposure to vehicles of virtually any sort: cars, trains, boats, aircraft, camels, swings, and spacecraft. The acceleration patterns in terms of amplitudes, frequencies, and durations that are most provocative are relatively well known. Many theories have been proposed to account for motion sickness, including overstimulation of the vestibular system, reaction to a perceived poison, and sensory conflict (cf. Reason 1970). None is fully satisfactory nor has adequate predictive power (e.g., the ability to distinguish which sensory conflict situations will be nauseogenic and which will not). The most certain factor known with regard to motion sickness under terrestrial conditions is that to date subjects without functioning labyrinths have not been made motion sick despite sometimes heroic efforts to do so (Graybiel 1969, Johnson et al. 1962).

Incidence and Eliciting Factors It initially was thought that there were two general categories of responders to motion sickness, those expressing primarily “head symptoms,” and those expressing

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primarily “gut symptoms.” Head symptoms include headache, drowsiness, and eyestrain, whereas gut symptoms include stomach awareness, discomfort, nausea, and vomiting. It now is recognized that a person’s response to a particular form of motion depends on his or her individual susceptibility to that motion as well as to the relative intensity of the provocative stimulation. For example, making tilting head movements during constant velocity, passive body rotation at 3 rpm will elicit mainly head symptoms, whereas making tilting head movement at 30 rpm will rapidly induce nausea and vomiting for most subjects.

Space Motion Sickness Space motion sickness, now often referred to as the space adaptation syndrome to recognize the complex pattern of changes associated with exposure to weightlessness, is an operational problem in space travel. Symptoms may develop in some astronauts and cosmonauts as early as the first hour in flight, but generally within the first day. Nearly 70% of all space travelers are affected to some extent during the first 72 hours (Jennings 1997). Pitch and roll head movements are significant etiological factors in eliciting the space adaptation syndrome (Lackner & Graybiel 1984, 1986, 1987). After return to earth, a variety of disturbances of posture, gait, and motor control are present until readaptation to earth gravity occurs. Symptoms of motion sickness may redevelop postflight as well. Astronauts generally are less susceptible in subsequent space flights and experience less severe re-entry disturbances and motion sickness as well. Severe cases of space motion sickness can be treated with anti-motion-sickness drug injections of promethazine (Graybiel & Lackner 1987, Lackner & Graybiel 1994). It is unknown whether labyrinthine-defective subjects would be insusceptible to space motion sickness. However, they would not be immune to the postural, sensory-motor, and locomotory disturbances that occur after return to Earth. Adaptation to weightlessness involves not just adaptation of vestibularly mediated reflexive and orientation effects, but also accommodation of the entire postural and muscular control system of the body to a radically different force environment. This means that the control of body-relative movements, body movements in relation to the surroundings, and object manipulation and use involves a remapping of the motor commands to the musculature necessary to bring about the desired actions. Under terrestrial conditions, alterations in the normal motor control patterns necessary to stabilize the head or to achieve particular movement goals can elicit symptoms of motion sickness, postural disturbances, and sensory-motor performance disruptions analogous to those occurring in space flight and after re-entry. The manipulations evoking such impairments involve changing the apparent weight and inertia of the body segments, decreasing them to mimic weightless conditions and increasing them to simulate re-entry disturbances (Lackner & DiZio 1989). Such changes are independent of vestibular function per se and point to the important role of sensory-motor control and calibration of the body to the ongoing force

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environment. Motion sickness elicited in vehicles tends to be exacerbated by active maintenance of posture or by locomotion within the vehicle. Fully passive exposure with the body recumbent or not supporting itself tends to be least provocative (Lackner et al. 1991). Control over the onset and intensity of passive body motion also decreases susceptibility, e.g., the driver of a car rarely experiences carsickness even when passengers have been made quite ill.


ARTIFICIAL GRAVITY ENVIRONMENTS For long-duration space missions lasting many months, or even several years such as a manned mission to Mars, artificial gravity produced by rotation may be necessary to reduce or prevent the loss of bone mineral content and muscle deconditioning induced by mechanical unloading of the musculoskeletal system in weightless conditions (cf. Lackner & DiZio 2000, 2004; Young 1999 for reviews).

Coriolis Forces and Disruption of Movement Control “Artificial gravity” is the centripetal force imposed by a rotating vehicle that keeps objects within it moving in a curvilinear path. Centripetal force is proportional to the radius of rotation (r) times the velocity of rotation (in radians) squared. Accordingly, approximately 1.1 g of artificial gravity would be produced by a vehicle 1000 m in radius rotating at 1 rpm and by one 10 m in diameter rotating at 10 rpm. The latter situation is much more difficult to adapt to because when a person moves in a rotating environment, inertial Coriolis forces are generated on his or her body. These forces are larger the higher the velocity of rotation. For example, when a forward-reaching movement is made during exposure to counterclockwise rotation, a rightward Coriolis force will be generated on the arm proportional to the mass of the arm (m), the linear velocity of the arm relative to the rotating vehicle (v), and the angular velocity of rotation (ω): Fcor = −2m (ω × v) (see Figure 5). This means that for a given movement of the body or a part of the body with velocity, v, the Coriolis force is linearly dependent on the angular velocity of vehicle rotation, rotation at 10 rpm will generate a tenfold greater Coriolis force than rotation at 1 rpm. Vestibular function is one of the key factors affecting the ability of humans to adapt to rotating artificial gravity environments. For example, when a pitch head movement is made out of the plane of rotation, an unusual pattern of stimulation of the semicircular canals will result. One pair of semicircular canals will be brought into the plane of rotation and signal head rotation in the direction of vehicle rotation; simultaneously, another pair will be brought out of the plane of rotation losing angular momentum and consequently signaling head rotation in the opposite direction to that of the vehicle’s motion. The final pair will accurately signal the forward pitch of the head. The otolith organs will also be transiently stimulated by the Coriolis force generated by the head movement (see Figure 6). This bizarre pattern of vestibular activity leads to complex orientation illusions, destabilizes

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Figure 5 (A) Illustration of a subject in the center of a counterclockwise rotating room (ω) reaching forward to a target. A rightward Coriolis force, FCor, is generated while the arm is moving forward at velocity v. (B) A top view of the fingertip path shows that reaches are straight to the target before rotation starts, deviate to the right during rotation, return to baseline after additional reaches, and show mirror-image aftereffects when rotations stops. (C) Plots of the endpoints and curvatures of individual movements show that they return to baseline within 10 to 15 trials after rotation starts and stops.

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Figure 6 (A) Schematic showing the coordinate system of a set of angular rate sensors attached to the head for a movement in which the subject nods the head forward and returns immediately to the upright position (dotted arrows). (B) Traces from the angular rate sensors when the head movement is made in a normal stationary environment. (C) Traces recorded for a comparable pitch head movement made during 10 rpm counterclockwise rotation. Prior to the movement, the yaw axis of the head is rotating at the speed of the room; pitching the head forward reduces the yaw rate, and returning it to the upright restores it. The roll axis of the head comes into the room rotation plane during pitch forward and out of the plane during pitch up. The semicircular canals that are fixed to the head pick up these cross-coupled angular accelerations, and the subject will experience a tumbling sensation involving illusory yaw and roll motion in addition to the veridical pitch of the head.

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eye movement control, and if repeated can rapidly lead to motion sickness (except in individuals without labyrinthine function). Studies of artificial gravity in slow rotation rooms in the 1960s suggested that 5 rpm was the highest rate of rotation at which people could fully adapt to the vestibular stimulation encountered (Clark & Graybiel 1961; Fregly & Kennedy 1965; Graybiel et al. 1960, 1965; Guedry et al. 1962, 1964; Kennedy & Graybiel 1962). Subsequently, this value became the “accepted” upper velocity limit for space flight artificial gravity environments. In these early studies, posture, locomotion, and arm movement control were also disrupted. On cessation of rotation, normal subjects, but not vestibular-loss subjects, experienced a recurrence of symptoms of motion sickness. Both the normal and labyrinthine-defective subjects experienced postrotation disturbances of posture, gait, and arm movement control. These aftereffects reflect the fact that motor control as well as vestibular function is affected in rotating environments.

Adaptive Accommodations and Aftereffects Recent studies of adaptation to artificial gravity environments, however, have demonstrated that head and arm and leg movement control as well as locomotion can be adapted quite rapidly to rotational velocities of 10–25 rpm if the same movement is attempted repeatedly (Bouyer et al. 2003; DiZio & Lackner 1995, 1997, 2003; Lackner & DiZio 1994, 1998). In this circumstance, movement paths soon become straight again and movement endpoints accurate. On cessation of rotation, mirror-image errors in movement control occur, indicating that the nervous system has constructed a model of the Coriolis forces expected to be forthcoming and planned appropriate compensatory innervations to null their effect on movement paths. The persistence of this compensation even when no longer appropriate leads to the appearance of mirror-image aftereffects on cessation of vehicle rotation. When adaptation to a rotating environment is complete, the Coriolis forces associated with movements no longer are consciously perceived. Although still present during movements, these forces become perceptually transparent. On cessation of rotation when movements are made again, a new “force” is perceived that is felt to act in the direction opposite to the original forces felt during rotation. The internally generated compensations for Coriolis forces that are no longer appropriate and that deviate movement paths and endpoints postrotation thus are perceived as external forces deviating the arm. On Earth, the forces generated by support of the body against gravity and of body movements relative to gravity are also largely transparent. They are not felt or perceptually registered even though they are significantly above thresholds for detection. For example, to raise one’s arm it is necessary to counter the torques on the arm owing to the force of gravity. Nevertheless, the effort associated with the movement feels virtually the same even though the force generated to bring about the movement is varying greatly. Similarly, if balance is shifted from two feet to one while standing, hardly any force change on the sole of the remaining stance

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foot will be felt even though the contact force has doubled (Lackner & Graybiel 1982, 1984). For a 180 lb individual it will increase by 90 lb to 180 lb, a huge change. Such observations mean that the body is dynamically tuned to its force environment, and movements within it feel virtually effortless.


Self-Generated Coriolis Forces in Everyday Behavior The ability to adapt very rapidly and almost effortlessly to Coriolis force perturbations of head, arm, and leg movement trajectories in rotating environments was initially surprising given the restricted adaptation seen in earlier studies. However, natural locomotion generates significant linear and angular accelerations that are compensated for by automatic adjustments of the eyes and head (Imai et al. 2001). Recently, it has been shown that Coriolis forces are commonly encountered in our everyday activities (Pigeon et al. 2003a). When a natural turn-and-reach movement is made to an external object, the trunk reaches peak velocities much higher (e.g., 150–200◦ /s) than those ever contemplated for use in artificial gravity environments (e.g., 60–90◦ /s). Because turn-and-reach movements are not segmented into turn and then reach, the peak velocities of the trunk movement and of the forward velocity of the arm occur close together in time. As a consequence, very large Coriolis forces are generated on the reaching arm. Nevertheless, reaching accuracy is preserved and is independent of the peak velocity of the trunk and of whether visual feedback is available. This preservation of reaching accuracy means that the nervous system anticipates the Coriolis forces that will be generated, and it institutes appropriate anticipatory compensatory forces to eliminate their deflecting influence on movement paths. Head and leg movements out of the plane of rotation are also common during voluntary trunk rotation and are also made accurately. Such accuracy indicates that compensation for self-generated Coriolis forces is a typical aspect of everyday motor control. It is not surprising, therefore, that individuals are able to adapt to the Coriolis forces associated with movements in passively rotating artificial gravity environments because these forces are much smaller than those generated during their everyday activities. In other words, the body when turning constitutes its own artificial gravity environment from the perspective of eye, head, arm, leg, and torso movement control. An examination of the relative straightness of arm movements and of their velocity profiles during natural turn-and-reach movements supports the view that they are planned in relation to external spatial coordinates rather than joint or intrinsic coordinates. Arm trajectories are straight and their velocity profiles are single-peaked in relation to external space, but are curved with multiple velocity peaks when plotted in relation to joint or torso relative coordinates (Pigeon et al. 2003b).

Coriolis Forces and Motion Sickness Making pitch head movement during passive rotation is highly nauseogenic and elicits a complex, illusory tumbling sensation because it generates Coriolis

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cross-coupled stimulation (CCS) of the semicircular canals (Johnson et al. 1951), as shown in Figure 6. The puzzling finding from space flight experiments is that CCS is not nauseogenic in orbital flight (Graybiel et al. 1977). These investigators were motivated by the desire to ascertain whether the limits of tolerable rotation that had been found on Earth, where CCS occurs in a 1 g force background, predict what would occur in space, where the background, artificial gravity level (centripetal force) will likely be less than 1 g. This question was and still is highly relevant for developing a feasible artificial gravity countermeasure for prolonged space flight because the severity of the side effects of rotation (motion sickness, disorientation, and disruption of movement control) as a function of background force level is a major factor that must be considered in choosing the radius and rotation rate. Many attempts to understand the variable provocativeness of CCS have focused on putative sensory conflicts (e.g., Guedry & Benson 1978). In such theories, conflict occurs when CCS elicits semicircular canal signals encoding an off-vertical tumbling axis that is in contrast with a nonchanging otolith signal from the physically nontumbling head. Such a conflict is not possible in space flight where there is no background GIA whose direction can be encoded by the otolith afferent signals. Parabolic flight experiments have provided an alternate perspective on this problem. The duration and intensity of perceived tumbling are augmented in 1.8 g and almost eliminated in 0 g (as is motion sickness, confirming the space flight results) following the same CSS stimulation of the semicircular canals in both force backgrounds (DiZio et al. 1987). This pattern suggests that semicircular canal afferent signals encoding head velocity may be centrally integrated to produce a representation of body displacement, and that this integration process is GIA-dependent. This possibility has been confirmed recently in parabolic flight experiments. Blindfolded subjects continuously indicated their subjective self-displacement relative to external space by pointing to a fixed location with a joystick while they were exposed to seminatural, brief, suprathreshold tilts about their yaw axis. In 1 g and 1.8 g, pointing responses were nearly constant relative to the aircraft, but in zero gravity, the stick was always kept in a body-fixed orientation. Subjects verbally confirmed that they were aware of being moved in all force backgrounds but that the movement was not associated with any perceived displacement in 0 g. This means that the lack of motion sickness elicited by CCS in zero gravity may be due to a lack of internally represented body displacement. These recent findings also add to the debate about how three-dimensional spatial orientation is represented and remembered, which is surveyed in the following section.

PATH INTEGRATION Place Cells and Head Direction Cells The classical observations of Beritoff (1965) suggested that vestibular afferent signals during passive transport of the body could be integrated to generate a representation of the body’s path through space and of its new position in relation

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to the start position. These results have led to renewed interest because of the finding of “place cells” in the hippocampus that are active when an animal is in a specific location within a familiar environment (O’Keefe & Dostrovsky 1971) and of “head direction cells” that seem to code the direction in which the head is oriented with respect to extrinsic spatial coordinates (Taube et al. 1990). Numerous experiments have demonstrated that during passive transport, spatial memory and knowledge of the trajectory and of the spatial location within the environment are maintained (Berthoz et al. 1987; Bloomberg et al. 1991; Israel et al. 1993, 1997; Mittelstaedt 1980). This is manifested in the ability to orient the head and body toward the starting position or to memorized targets, or to recapitulate the path the body has traveled. Vestibular inputs may be sufficient for such path integration because neuronal recordings show that hippocampal cells maintain their spatial tuning even during passive transport in darkness (Gavrilov et al. 1998). However, vestibular inputs are not necessary for updating spatial orientation because humans without functioning labyrinths show path integration during active walking (Glassauer et al. 1994). Path integration is also exhibited by insects lacking a vestibular system (see Wehner 2003 for a review). These creatures must rely, in the absence of vision, on monitoring motor outputs and sensory feedback to generate spatial “representations.”

COMPLEXITY OF SPATIAL REPRESENTATIONS: IMPLICATIONS FOR IMMERSIVE VIRTUAL ENVIRONMENTS One of the most promising applications of virtual environment technology is training users to build cognitive maps of environments (Durlach et al. 2000). Through visual displays and clever “locomotion interfaces,” a user can “navigate” a large virtual space without moving around at all or while moving in very limited fashion. An example of the former would be using a joystick to navigate a virtual suite of rooms shown on a desktop display; an example of the latter would be a pilot trainee viewing a visual model of terrain he or she is “overflying” in a simulator that reproduces a portion of the inertial cues that would be present in a real aircraft. Realistic experiences can be generated in these situations, and subjects seem to acquire survey knowledge (Ruddle et al. 1997) of the virtual environment without normal vestibular inputs. An individual is considered to have survey knowledge when he or she remembers enough of the environmental layout to draw a map that indicates distances between landmarks and to give directions to destinations along novel routes (Siegel & White 1975). Acquiring survey knowledge in virtual environments is slower than in the real world (Witmer et al. 1996). It is not clear yet whether this decrement is due to lack of fidelity of the visually rendered virtual world or to the lack of normal inertial cues, including vestibular cues. However, one study has shown that spatial learning is not hindered when inertial cues are reduced or distorted (Waller et al. 2003).

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Rotating room studies have demonstrated that the spatial representation and recognition of environments is dependent on the means of normally accessing them and that the subenvironment is embedded in a broader spatial representation of the spatial context (Lackner & DiZio 1998). As a consequence, entry into a particular site, e.g., a room from a direction never before experienced, can lead to a sense of unfamiliarity and strangeness in an environment that when entered from the familiar entrance seems familiar and natural. Such observations mean that the cognitive representation of spatial environments is quite complex and is influenced by the modes of access. This has especial significance for recent developments in virtual environment technology. For example, it has been proposed that immersive virtual environments can be used to train knowledge of a real environment. This would make it possible, for example, for firefighters not to have to train in a real environment (e.g., an aircraft carrier) to know its spatial layout intimately. Training from the virtual to the real environment, however, may not transfer unless the “access” into the virtual environment is from modes of entry that are possible in the real environment. ACKNOWLEDGMENT This work has been partially supported by Air Force Office of Scientific Research grant F49620110171 and NASA grants NAG9-1483, NAG9-1263, and NAG91466.

GLOSSARY Afferent discharge rate – Primary afferent neurons of the mammalian vestibular system relay information from hair cells to the central nervous system. They encode head angular velocity and linear acceleration via a rate code, in terms of the number of action potentials per second. Ampulla – An enlargement in the toroidal ring of each semicircular canal, within which resides the sensory apparatus, the cupula/hair cell complex. Cupula – A gelatinous membrane inside the ampulla of each semicircular canal. The inertial tendency of the endolymph to lag head rotary acceleration displaces the cupula and deflects the ensheathed cilia of hair cells affixed to the ampulla base. The elasticity of the cupula causes recoil to the resting position following angular acceleration of the head. Doll’s eye reflex – Reflexive elevation or depression of the eyes relative to the head in the direction opposite to the pitch angle of the body. Eccentric contraction – Active production of tension in a muscle while it is being lengthened. For example, on Earth lowering the body during a deep knee bend involves eccentric contraction of the quadriceps muscle group. Efferent copy – A duplicate of a command signal innervating muscles. For example, efferent copies of signals from brainstem nuclei to eye muscles are

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thought to be involved in parcellation of visual signals into components due to self-motion and object motion. Endolymph – The fluid that fills the semicircular canals and bathes the otolith organs. Its mass and viscosity are major factors governing the encoding of head angular velocity by the afferent signal from the semicircular canals. Gain – The ratio of the output of a controlled system to its input. For example, the ratio of eye roll (output) to body tilt (input) in ocular counterrolling is about −0.1. Gravitational acceleration – Acceleration due to the force of gravity. An object at rest on the Earth’s surface is being accelerated by gravity toward the center of the earth and is simultaneously accelerated in the opposite direction by the ground. Gravitoinertial force – The vector resultant of gravitational and inertial force. The otoliths encode acceleration due to gravitoinertial forces. Einstein’s equivalence principle states that no simple physical sensor can detect whether its acceleration is due to inertial or gravitational force. A very active research topic concerns how multiple sensory inputs are processed to yield separate senses of tilt relative to gravity and inertial motion. Hair cell – Goblet-shaped sensory neuron with a bundle of cilia of graded length protruding from the cell body. Maximum depolarization or hyperpolarization is produced when the bundle is bent toward or away, respectively, from its apex, and graded responses are evoked by intermediate directions of deflection. Deflection of large arrays of hair cells is the basis of the afferent response of the semicircular canals and otolith organs (as well as other sensory systems). Haptic perception – A type of perception derived from active touch, in contrast to the passive impressions of external stimuli impressed on the skin. Inertial acceleration – Acceleration relative to a fixed- or constant-velocity frame of reference (such as the earth) by an external force to the object. The semicircular canals are stimulated by angular inertial acceleration and do not respond to gravity. Inversion illusion – The experience of oneself and/or the entire seen and felt environment being upside down relative to the subjective vertical. It is often elicited upon exposure to weightless conditions. Muscle spindle – A specialized receptor in muscle tissue producing afferent signals modulated by muscle length and rate of change in length. Its responses are modulated by the gamma motor system. Nystagmus – Named for a sawtooth pattern involving quick jumps in one direction alternating with slower drifts in the other. Nystagmoid eye movements involve a “field fixing” pattern in which slow eye drifts compensate for selfmotion in one direction alternate with rapid recentering movements. Ocular counterrolling – Reflexive rotation of the eyes about the naso-occipital

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axis in the direction opposite lateral body tilt about a parallel axis. Otolith signals related to body tilt are a major influence on this reflex. Optokinetic stimulation – Unidirectional motion of all or part of the external visual array. Otolith organ – Nonauditory sensory organ in the vertebrate inner ear responsible for transduction of linear acceleration and gravity. The otoliths include masses of dense crystals (otoconia) embedded in a gelatinous membrane anchored to the cilia of a skull-fixed array of hair cells. The otoconia are displaced opposite to linear accelerations of the head, thereby deflecting the hair cells and modulating their membrane potential. Different classes of spatially and temporally tuned hair cells are hypothesized to be crucial for distinguishing the inertial and gravitational parts of the net linear acceleration. Parabolic flight – Aircraft flown in a roller-coaster path to produce alternating periods of high gravitoinertial force during the climbs, and rapid transitions to weightlessness, zero gravity, during the pushover at the top of each parabola. Path integration – Knowledge of one’s current position and heading relative to a position with which one is no longer in sensory contact. It is derived from temporal accumulation of sensory and motor signals about movement path through space. Proprioception – The sense of position and motion of one’s body segments, derived from central processing of efferent signals as well as afferent signals from muscles, tendons, joints, and skin. Some investigators include vision and audition as contributing potential proprioceptive information. Push-pull – A traditional control theory name for the manner in which angular head acceleration in a given plane is encoded by the discharge rate of primary vestibular afferent neurons. The afferent neurons have a high resting discharge rate (about 100 imp/sec), which is increased during head angular acceleration in one direction and decreased by acceleration in the opposite direction. Receptor potential – Graded changes in voltage across the cell body membrane of vestibular hair cells, proportional to deflection of the ciliary bundle. Saccules – Bilateral pair of otolith organs anatomically arranged to respond to vertical-plane linear accelerations. Semicircular canals – Nonauditory sensory organs in the vertebrate inner ear that are stimulated by angular acceleration of the head and encode angular velocity of natural movements. The name describes the structure: three approximately orthogonal, fluid-filled toroidal tubes on either side of the head. The mechanism of the sensory response is the deflection of hair cells inside the canal by the inertial lag of the fluid during head acceleration. Spatial neglect – Decreased probability of detection of stimuli in a circumscribed region of extracorporeal space. It can be partial or complete, and can involve multiple sensory modalities.

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Utricles – Bilateral pairs of otolith organs anatomically arranged to respond to horizontal plane linear accelerations when the head is upright. The Annual Review of Psychology is online at http://psych.annualreviews.org


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Psychological Evidence at the Dawn of the Law’s Scientific Age, David L. Faigman and John Monahan

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HUMAN CATEGORY LEARNING F. Gregory Ashby


Department of Psychology, University of California, Santa Barbara, California 93106; email: [email protected]

W. Todd Maddox Department of Psychology, University of Texas, Austin, Texas 78712; email: [email protected]

Key Words exemplar, prototype, decision bound, multiple systems, striatum ■ Abstract Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular—rulebased tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EARLY CATEGORY-LEARNING THEORIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CATEGORY-LEARNING TASKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RULE-BASED TASKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neuropsychological Patient Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neuroimaging Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theories of Rule-Based Category Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . INFORMATION-INTEGRATION TASKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Category-Learning Limits in Information-Integration Tasks . . . . . . . . . . . . . . . . . . Neuropsychological Patient Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neuroimaging Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theories of Information-Integration Category Learning . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0149$14.00

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DISSOCIATIONS BETWEEN RULE-BASED AND INFORMATION-INTEGRATION CATEGORY LEARNING . . . . . . . . . . . . . . . . . PROTOTYPE DISTORTION TASKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neuropsychological Patient Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neuroimaging Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theories of Prototype Distortion Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WEATHER PREDICTION TASK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Task and Individual Differences Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neuropsychological Patient and Neuroimaging Studies . . . . . . . . . . . . . . . . . . . . . . CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION Is the plant edible or poisonous? Is the person friend or foe? Was the sound made by a predator or by the wind? All organisms assign objects and events in the environment to separate classes or categories. This allows them to respond differently, for example, to nutrients and poisons, and to predators and prey. Any species lacking this ability would quickly become extinct. Given the important role that categorization plays in our day-to-day lives, it is not surprising that there is a huge and old literature on the perceptual, cognitive, and neurobiological processes that mediate this vital skill. This article surveys that literature, with an emphasis on discoveries and developments during the past 10 years. The past decade has seen exciting and profound changes in categorization research. Two important new themes have fundamentally changed the field. First, there has been huge attention, both theoretical and empirical, devoted to the question of whether human category learning is mediated by a single system, or by multiple, qualitatively distinct learning systems. Second, the categorization field has whole-heartedly embraced the cognitive neuroscience revolution. Not only has the past 10 years seen a massive increase in neuropsychological and neuroimaging research on category learning, but this new knowledge also has permeated into, and significantly sharpened, the core theories in the field. These twin themes— multiple systems and cognitive neuroscience—organize and motivate much of the present review. The categorization literature is enormous, and no single article could survey it all. Thus, we focus on a restricted subset of the entire literature. In particular, this article focuses on how humans learn perceptual categories. By restricting ourselves in this way, we must necessarily ignore a number of large and interesting subliteratures. First, we do not discuss category learning in nonhuman animals (Smith et al. 2004). The animal literature is especially relevant to understanding the neural basis of human category learning. Unfortunately, however, this literature is fractionated, largely because, within the behavioral neuroscience literature at least, relatively few papers address animal categorization per se. Rather, the relevant results come from a wide variety of tasks and phenomena (e.g., discrimination learning, memory systems, long-term potentiation). For this reason, perhaps, we know of no

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recent comprehensive review (although, for reviews of the behavioral literature, see Vauclair 2002 or the 2002 special issue of the Journal of the Experimental Analysis of Behavior). Second, our focus on learning prevents us from considering the categorization behavior of highly experienced experts. This distinction is important because there is good evidence that the neural mechanisms and pathways that mediate the learning of new categories are different from the neural structures that mediate the representation of highly learned categories. For example, many neuropsychological groups that are impaired in category learning (e.g., frontal patients and Parkinson’s disease patients) do not lose old, familiar categories (e.g., fruits and tools). Similarly, there is no evidence that people who lose a familiar category (i.e., who develop a category-specific agnosia) develop any general category-learning deficits. Readers interested in the representation of highly learned categories are referred to any of several excellent reviews of the category representation literature (e.g., Cree & McRae 2003, Humphreys & Forde 2001, Joseph 2001). Third, we discuss how people learn new categories, but not how they use this new information in other cognitive tasks. For example, people use categorical information to make inferences about unobserved features of a stimulus, to facilitate decision making, and to problem solve (e.g., Lewandowsky et al. 2002, Markman & Ross 2003). Finally, our focus is on perceptual categories rather than concepts. By “perceptual category,” we mean a collection of similar objects belonging to the same group. Although the term “concept” often is used interchangeably with category, we refer to a concept as a group of related ideas. For example, the set of all X rays displaying a tumor forms a perceptual category, whereas the many varieties of religious experience form a concept. Although many of the results reviewed below are relevant to understanding both perceptual categorization and concept formation, the representation of categories and concepts is likely different, and a review of both literatures is beyond the scope of this article. Readers interested in concepts are referred to any of several excellent recent reviews (e.g., Barsalou 2003, Murphy 2002). If the goal is to study category learning rather than category representation, then it is necessary to present subjects with unfamiliar categories and observe their behavior during the period when their ability to assign stimuli to these categories rises from chance to some stable level. In experiments with adults, the prevailing method of ensuring unfamiliarity is for the experimenter to create new, arbitrary categories of objects (so-called “artificial categories”). In the past, little attention was paid to the manner in which these arbitrary categories were created. However, much recent evidence suggests some dramatic differences in the way people learn such categories, depending on exactly how the categories are constructed. In fact, these differences are so profound that we take the unusual step of organizing all the research that we review by the type of task that was used. Toward this end, we focus on four different kinds of category learning tasks—rule-based tasks, information-integration tasks, prototype distortion tasks, and the so-called weather

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prediction task. The next section briefly reviews some important early categorylearning theories. The third section describes the four basic tasks, and then sections four through eight review results from each of these tasks. Finally, we close with some general conclusions.


EARLY CATEGORY-LEARNING THEORIES Many theories of human category learning have been proposed. The early theories virtually all assumed that humans have a single category-learning system that they use to learn all types of categories (for an exception, see Brooks 1978). A few of these theories are still important in the sense that they continue to motivate significant amounts of new research. This section briefly introduces the most important of these theories. Other sources should be consulted for a more complete discussion, and for a more thorough review of category-learning theories (e.g., Ashby & Maddox 1998, Estes 1994, Smith & Medin 1981). Prototype theory assumes that category learning is equivalent to learning the category prototype. When an unfamiliar stimulus is then encountered, it is assigned to the category with the most similar prototype (Homa et al. 1981; Posner & Keele 1968, 1970; Reed 1972; Rosch 1973, 1975; Smith & Minda 1998). Exemplar theory assumes that category learning is a process of learning about the exemplars that belong to the category. When an unfamiliar stimulus is encountered, its similarity is computed to the memory representation of every previously seen exemplar from each potentially relevant category. The stimulus is then assigned to the category for which the sum of these similarities is greatest (Brooks 1978; Estes 1986, 1994; Hintzman 1986; Lamberts 2000; Medin & Schaffer 1978; Nosofsky 1986). Decision bound theory assumes subjects partition the stimulus space into response regions. When presented with an unfamiliar stimulus, the subject determines which region the percept is in, and then emits the associated response. The partition between regions associated with competing responses is called the decision bound. Category learning is the process of either learning the decision bound or else of learning the regions associated with each response (Ashby & Gott 1988, Ashby & Townsend 1986, Maddox & Ashby 1993).

CATEGORY-LEARNING TASKS As mentioned above, this article focuses on four different kinds of category learning tasks. Rule-based tasks are those in which the categories can be learned via some explicit reasoning process. Frequently, the rule that maximizes accuracy (i.e., the optimal strategy) is easy to describe verbally (Ashby et al. 1998). In the most common applications, only one stimulus dimension is relevant, and the subject’s task is to discover this relevant dimension and then to map the different

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dimensional values to the relevant categories. However, there is no requirement that the rule that maximizes accuracy (i.e., the optimal rule) in rule-based tasks is one-dimensional. For example, a conjunction rule (e.g., respond A if the stimulus is small on dimension x and small on dimension y) is a rule-based task because it is easy to describe verbally. Information-integration category learning tasks are those in which accuracy is maximized only if information from two or more stimulus components (or dimensions) is integrated at some predecisional stage (Ashby & Gott 1988). Perceptual integration could take many forms—from computing a weighted linear combination of the dimensional values to treating the stimulus as a gestalt. In many cases, the optimal strategy in information-integration tasks is difficult or impossible to describe verbally (Ashby et al. 1998). Real-world examples of informationintegration tasks are common. For example, deciding whether an X ray shows a tumor requires years of training, and expert radiologists are only partially successful at describing their categorization strategies. Examples of rule-based and information-integration categories that might be used in experimental research are shown in Figure 1. In both cases, the two contrasting categories are composed of circular sine-wave gratings (i.e., disks in which luminance varies sinusoidally). Examples are shown in Figure 1a. The disks are all of equal diameter, but they differ in spatial frequency (i.e., the frequency of the sine wave) and (sine-wave) orientation. Each symbol in Figures 1b and 1c denotes the spatial frequency and orientation of a sine-wave grating. Category A exemplars are denoted by pluses and category B exemplars are denoted by circles. In each condition, there are two distinct categories that do not overlap, so perfect accuracy is possible. Also shown in Figures 1b and 1c are the decision bounds that maximize categorization accuracy. In the rule-based task (Figure 1b), the optimal bound, denoted by the vertical line in Figure 1b, requires observers to attend to spatial frequency and ignore orientation. This bound has a simple verbal description: “Respond A if the bars are thick and B if they are thin.” In the information-integration task (Figure 1c), which was generated by rotating the rule-based categories by 45◦ , equal attention must be allocated to both stimulus dimensions. In this task, there is no simple verbal description of the optimal decision bound. Note that we use the word “rule” more narrowly than is common in the psychological literature, where it is often used to refer to any strategy from an explicit reasoning process to any algorithm that can be expressed formally. In particular, we define “rule-based strategy” narrowly to refer specifically to an explicit reasoning process. Note that according to this criterion, there is no limit on the complexity of the optimal rule in rule-based tasks. However, as the complexity of the optimal rule increases, its salience decreases and it becomes less likely that observers will learn the associated categories through an explicit reasoning process. Thus, the boundary is fuzzy between rule-based and information-integration tasks. Tasks in which the optimal rule is one-dimensional are unambiguously rule-based (at least with separable stimulus dimensions), and tasks in which the optimal rule is

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significantly more complex than a conjunction rule almost never are rule-based. In between, the classification is not so clear-cut. It is also important to emphasize that the terms “rule-based” and “informationintegration” make no assumptions about how people learn these different category structures in any particular application. For example, there is evidence that pigeons can learn both types of category structures (Herbranson et al. 1999), but no one would claim that they learn rule-based categories via an explicit reasoning process. The question of how people learn rule-based and information-integration categories is strictly empirical. As such, this particular classification of categorization tasks is useful only because there are many interesting empirical dissociations between the two tasks (e.g., Ashby et al. 1999, 2002, 2003; Ashby & Waldron 1999; Maddox et al. 2003). Prototype distortion tasks are a third type of category-learning task in which each category is created by first constructing a category prototype (Posner & Keele 1968, 1970). The other exemplars of the category are then created by randomly distorting the prototype. In the most popular prototype distortion task, each stimulus is a random pattern of (often nine) dots. One pattern is selected as the category prototype and then the other category exemplars are created by randomly perturbing the location of each dot in the prototype. Examples are shown in Figure 2. The final category-learning task we consider is the so-called weather prediction task. Stimuli in this task are tarot cards; each displays a unique geometric pattern. The subject’s task is to decide if the particular constellation of cards that is shown signals “rain” or “sun.” The actual outcome is determined by a probabilistic rule based on the individual cards.

Figure 2

Stimuli that might be used in a prototype distortion category-learning task.

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RULE-BASED TASKS


Introduction As mentioned above, rule-based tasks are those in which it is easy for subjects to describe the optimal strategy verbally. In general, several conditions must be met before a verbal description is possible. First, a semantic label must correspond to each of the stimulus properties that are relevant to the decision. In the Figure 1b rule-based task, the critical stimulus feature has the semantic label “width.” Second, the subject must be able to attend selectively to each relevant stimulus property. For example, it is possible to verbalize a rule such as “Respond A if the saturation of the color patch is high, respond B if saturation is low.” Even so, people are not good at attending selectively to saturation and ignoring irrelevant variations in hue and brightness, so it is unlikely that people would spontaneously experiment with such rules. In the selective attention literature, a stimulus feature that can be attended to selectively is said to be separable from the other stimulus features, whereas features for which it is difficult or impossible to attend selectively are said to be integral. A large and old literature is devoted to this topic (Ashby & Maddox 1994, Ashby & Townsend 1986, Garner 1974, Lockhead 1966, Shepard 1964). The third critical property necessary for easy verbalization is that the rule for combining information from the relevant stimulus features is itself verbalizable. In general, this requires that separate decisions are first made about the level of each feature, and then these separate decisions are combined using logical operations, such as “and” and “or.” Perhaps the most obvious example is a conjunction rule of the sort: “Respond A if the bars are wide and the orientation is steep; otherwise respond B.” Note that to apply this rule the subject must first decide if the bars are narrow or wide and if the orientation is shallow or steep. Next, the outcomes of these two decisions are combined using the word “and.” Thus, information from the various relevant stimulus dimensions is combined after decisions are first made about each dimension. This is in contrast to information-integration tasks in which the raw perceptual information from the relevant stimulus dimensions is combined before any decisions are made. Other rule-based strategies that require combining decisions from separate dimensions include disjunctive and exclusive-or rules. There is no doubt that healthy adults can learn these rules without much difficulty, at least if they are given regular feedback about their response accuracy (e.g., Salatas & Bourne 1974). However, it is also quite clear that such combination rules are much less salient than simple one-dimensional rules, in the sense that people rarely experiment with such rules unless compelled to do so by the feedback they receive (Alfonso-Reese 1996, Ashby et al. 1998). Virtually all category-learning tasks used in neuropsychological assessment are rule-based, including the widely known Wisconsin Card Sorting Test (WCST; Heaton 1981). Stimuli in the WCST are cards containing geometric patterns that vary in color, shape, and symbol number, and in all cases, the correct categorization

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rule is one-dimensional (and easy to describe verbally). Perseverative errors on the WCST are a classic symptom of frontal dysfunction (e.g., Kimberg et al. 1997).


Neuropsychological Patient Data Although categorization has been studied in many different neuropsychological groups, the most extensive data come primarily from studies with three different groups: (a) patients with frontal lobe lesions, (b) patients suffering from a disease of the basal ganglia, typically either Parkinson’s or Huntington’s disease, and (c) patients with amnesia. Within this latter group, the most theoretically interesting are those whose amnesia was caused by damage to the medial temporal lobes. In almost all cases, however, studies with amnesiacs include a wide variety of patients, typically including some with Korsakoff’s syndrome and some with medial temporal lobe amnesia. One characteristic feature of the neuropsychological literature on category learning is its inconsistency. For each major patient group, some studies report deficits and some do not. However, as we will see, when the existing studies are partitioned according to the type of task that was used, the discrepancies largely disappear. As mentioned above, perseverative responding on the WCST is among the most classic of all signs of frontal damage. Not surprisingly then, many studies have shown that frontal patients are impaired at rule-based category learning (see, e.g., Kimberg et al. 1997, Robinson et al. 1980). Another group with well-known deficits in rule-based category learning is Parkinson’s disease patients (e.g., Ashby et al. 2003, Brown & Marsden 1988, Cools et al. 1984, Downes et al. 1989). Although later in the disease Parkinson’s patients have frontal damage (primarily the result of cell death in the ventral tegmental area), the disease mainly targets the basal ganglia. The region most affected appears to be the head of the caudate nucleus (van Domburg & ten Donkelaar 1991), which is reciprocally connected to the prefrontal cortex. Thus, the rule-based category-learning deficits of frontal and Parkinson’s disease patients are consistent with the hypothesis that rule-based category learning is mediated, in part, by frontal-striatal circuits (Ashby et al. 1998). In contrast to frontal and basal ganglia disease patients, several studies have reported that amnesiacs with medial temporal lobe damage are normal in rulebased category learning (Janowsky et al. 1989, Leng & Parkin 1988). An obvious possibility is that many rule-based tasks are simple enough (e.g., the WCST) that working memory is sufficient for subjects to keep track of which alternative rules they have tested and rejected. If so, then a natural prediction is that medial temporal lobe amnesiacs should be impaired in complex rule-based tasks (e.g., when the optimal rule is disjunctive).

Neuroimaging Data A number of neuroimaging studies have used the WCST or a rule-based task similar to the WCST. All of these have reported task-related activation in prefrontal cortex,

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most have reported activation in the head of the caudate nucleus, and at least one has reported task-related activation in the anterior cingulate (Konishi et al. 1999, Lombardi et al. 1999, Rao et al. 1997, Rogers et al. 2000, Volz et al. 1997). Converging evidence for the hypothesis that these are important structures in rulebased category learning comes from several sources. First are the many studies that have implicated these structures as key components of executive attention (Posner & Petersen 1990) and working memory (Goldman-Rakic 1987, 1995), both of which are likely to be critically important to the explicit processes of rule formation and testing that are assumed to mediate rule-based category learning. Second, a recent neuroimaging study identified the (dorsal) anterior cingulate as the site of hypothesis generation in a rule-based category-learning task (Elliott et al. 1999). Third, lesion studies in rats implicate the dorsal caudate nucleus in rule switching (Winocur & Eskes 1998). Fourth, of course, are the neuropsychological data reviewed above, which show that patient groups with damage to any of these structures are impaired in rule-based tasks.

Theories of Rule-Based Category Learning Theories of rule-based category learning can be classified according to whether they assume that learning in rule-based tasks is not fundamentally different from other category-learning tasks, or whether they assume that rule-based learning is special. Most of the attempts to account for the results of rule-based category learning with a single system model have been by exemplar theorists. According to exemplar theory, rule-based tasks, in general, are no different from any other type of category-learning task. However, one-dimensional rules, like the one depicted in Figure 1b, are unique in that they encourage selective attention to a single dimension, which in turn dramatically affects the stimulus-exemplar similarity computations (Kruschke 1992, Nosofsky 1991, Nosofsky et al. 1989). In particular, increasing attention to a dimension will serve to increase the perceived differences on that dimension. As a result, the perceived separation between the Figure 1b rule-based categories will tend to be greater than the perceived separation between the Figure 1c information-integration categories. It is in this way that exemplar theory is able to account for the difficulty differences between the two tasks. The earliest theory that applies directly to rule-based tasks is the so-called classical theory of categorization (e.g., Bruner et al. 1956, Smith & Medin 1981), which assumes that every category is represented by a set of necessary and sufficient features. When a stimulus is presented for categorization, the subject is assumed to retrieve the feature list of the relevant categories and then test whether the stimulus features match one of these feature lists. This theory accounts for performance in many rule-based tasks. For example, category A in Figure 1b is defined by the necessary and sufficient feature “thick bars.” Similarly, the conjunction rule: “Respond A if the bars are thick and the orientation is shallow” is equivalent to the necessary and sufficient features “thick bars and shallow orientation.” On the

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other hand, in some rule-based tasks the optimal rule cannot be expressed as a set of necessary and sufficient conditions. For example, given appropriate feedback, subjects can learn disjunctive-or rules such as: “Respond A if the bars are thick and the orientation is shallow or if the bars are thin and the orientation is steep” (Salatas & Bourne 1974). Thus, it was recognized long ago that the classical theory is incomplete, even for the restricted set of rule-based tasks (e.g., Ashby & Maddox 1998, Smith & Medin 1981). As originally proposed, classical theory was meant to apply to all categorization tasks. There have been several attempts to modernize the theory. Each of these attempts, however, has assumed that rule-based category learning is only one of several category-learning systems that humans have available (Ashby et al. 1998, Brooks 1978, Erickson & Kruschke 1998, Nosofsky et al. 1994b). The rule-based components of these various models are similar. The model that perhaps is best developed, and the only one with a neuropsychological basis, was proposed by Ashby et al. (1998) as part of the COVIS (COmpetition between Verbal and Implicit Systems) model of category learning (which also includes a procedural learning component). The COVIS explicit system assumes that rule-based category learning is mediated primarily by an explicit, hypothesis-testing system that depends heavily on working memory and executive attention. The idea is that candidate rules are stored in working memory during the time they are being tested. COVIS assumes that subjects will continue to use the active rule until feedback or other evidence disconfirms its validity. At this point, a new rule must be instantiated. COVIS assumes that activating a new rule requires two separate processes. First, a new candidate rule must be identified or selected, and second, attention must be switched from the old rule to the new rule. The probability that any given rule will be instantiated is determined by its reinforcement history (which determines its overall salience), the tendency of the subject to select novel hypotheses (which is assumed to depend on cortical dopamine levels; Ashby et al. 1999), and the tendency of the subject to perseverate (which is assumed to depend on basal ganglia dopamine levels). Ashby and his colleagues proposed, and presented evidence in support of the hypothesis, that the selection operation is mediated cortically, by the anterior cingulate and possibly also by the prefrontal cortex, and that switching is mediated by the head of the caudate nucleus. A review of this evidence is beyond the scope of this article (see Ashby et al. 1998, 1999).

INFORMATION-INTEGRATION TASKS As mentioned above, information-integration category-learning tasks are defined as those in which accuracy is maximized only if information from two or more stimulus components (or dimensions) is integrated at some predecisional stage. Typically, the optimal rule is difficult or impossible to describe verbally (Ashby et al. 1998).

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Category-Learning Limits in Information-Integration Tasks An important theoretical and practical question is whether there are limits on the complexity of information-integration category structures that can be learned. One of the first research efforts to address this question focused on comparing learning in linearly and nonlinearly separable categories. A pair of categories is linearly separable if optimal performance can be achieved by making category decisions based on the magnitude of a linear combination of dimensional values (or equivalently, if a linear decision bound is optimal). Categories are nonlinearly separable if optimal performance depends on a nonlinear combination of dimensional values (i.e., if the optimal bound is nonlinear). Prototype theory predicts that nonlinearly separable category structures should be impossible to learn, at least if each category contains only a single prototype (Ashby & Gott 1988), whereas exemplar models predict no consistent advantage for linearly or nonlinearly separable categories. Medin & Schwanenflugel (1981) compared linearly and nonlinearly separable category learning using a small number of stimuli constructed from binary-valued dimensions and found no advantage for linearly separable structures. Ashby and colleagues (Ashby & Gott 1988; Ashby & Maddox 1990, 1992) compared linearly and nonlinearly separable category learning using a large number of stimuli constructed from continuous-valued dimensions. They found a consistent advantage for linearly separable categories, with nonlinearly separable category learning often requiring numerous experimental sessions. Despite their greater difficulty, however, the fact that people can learn nonlinearly separable categories effectively falsifies the standard prototype-theory account of information-integration category learning. Even so, there is evidence that early in learning people may abstract prototypes in some information-integration tasks (Minda & Smith 2001; Smith & Minda 1998, 2002). McKinley & Nosofsky (1995) examined category learning when the categories were composed of a large number of unique exemplars sampled from mixtures of bivariate normal distributions. Although some subjects were able to learn the categories fairly well, a large number failed to learn even after seeing nearly 4000 exemplars over a full week of training. These data present a challenge to exemplar theory because after such extensive training, exemplar theory predicts nearly optimal performance, no matter what the category structures (Ashby & AlfonsoReese 1995). In a related study, Ashby et al. (2001) compared two-category learning where the optimal decision bound was quadratic, with four-category learning where the optimal bound separating each pair of categories was quadratic. As in previous studies (e.g., Ashby & Maddox 1992), learning was good in the twocategory case, and the best fitting model assumed subjects used quadratic bounds. In the four-category case, however, learning was worse, and the best fitting model assumed subjects used suboptimal linear bounds to separate each pair of categories. Taken together, these two data sets suggest that there is an upper bound on the complexity of information-integration category structures that can be learned (in a reasonable amount of time) and that this upper bound is greater than a single quadratic curve but less than a set of such curves.

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Neuropsychological Patient Data Over the past several years, a number of studies of information-integration category learning have been conducted in brain-damaged populations. The focus has been on patients with medial temporal lobe amnesia or striatal damage (e.g., patients with Parkinson’s disease or Huntington’s disease). Filoteo et al. (2001b) tested the ability of amnesiacs to learn a highly nonlinear information-integration rule when the categories were normally distributed and a large number of unique stimuli were sampled from each category. Over the full 600 trials of the experiment, the performance of amnesiacs and controls was equivalent. One patient and one control returned for a second session on the following day. During the first block of trials on the second day, the amnesiac and control again showed equivalent performance, and in fact, performance during the first block of the second session was slightly better than during the final block of trials from the first session. Some researchers have argued that amnesiacs learn categorization rules using working or short-term memory processes (Nosofsky & Zaki 1998, Palmeri & Flanery 1999). The day 2 results of Filoteo et al. (2001b) argue against this possibility. Instead, these findings indicate that the categorization rule was retained over the one-day delay period and argue strongly against the hypothesis that working memory or explicit declarative memory mediates information-integration category learning. Filoteo et al. (2001a) and Maddox & Filoteo (2001) tested the ability of Huntington’s disease and Parkinson’s disease patients to learn the same category structures used by Filoteo et al. (2001b). Over the full 600 trials of the experiment, both patient groups showed a consistent performance decrement, suggesting an involvement of the striatum in nonlinear information-integration category learning. On the other hand, Ashby et al. (2003) found that Parkinson’s disease patients learned as well as an age-matched control group in an information-integration task with linearly separable categories. More recently, Filoteo et al. (2004) compared the ability of Parkinson’s disease patients to learn a linear and a nonlinear information-integration rule. The linear results replicated the Ashby et al. (2003) results—that is, the Parkinson’s disease patients were not impaired in learning linearly separable categories. On the other hand, the same patients were impaired in the nonlinear condition, but only later in training. Thus, these studies suggest that Parkinson’s disease subjects are impaired in information-integration tasks, but only if the category structures are complex (as, e.g., when the categories are nonlinearly separable).

Neuroimaging Data To date, only one neuroimaging study of information-integration category learning has been conducted (although see the section on the weather prediction task). Seger & Cincotta (2002) reported significant striatal and lateral occipital activation by a group of subjects who had already had extensive training in the task.

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Theories of Information-Integration Category Learning There are a number of successful theories of information-integration category learning. These can be classified into two types: parametric and nonparametric (see Ashby & Alfonso-Reese 1995 for a detailed discussion). Parametric classifiers assume either that the categories have a specific type of structure (e.g., normal distributions) or that the categorization boundary has a specific functional form (e.g., linear). Nonparametric classifiers make no assumptions about category structure or categorization boundaries. Simple prototype models (Reed 1972, Smith & Medin 1981) are parametric because they assume a linear decision bound (Ashby & Gott 1988). For this reason, as mentioned above, they can be rejected as a general theory of informationintegration category learning because humans can learn nonlinear decision bounds (see, e.g., Ashby & Maddox 1992, Medin & Schwanenflugel 1981). Exemplar models are nonparametric because they assume that every exemplar presented is stored in memory along with the appropriate category label. Because all category information is retained (although perhaps in a degraded form), exemplar models predict that, under very general conditions, subjects should eventually respond almost optimally, no matter how complex the categories (Ashby & Alfonso-Reese 1995). For this reason, despite the enormous success of this class of models, the failure of subjects to respond optimally in complex informationintegration tasks (e.g., Ashby et al. 2001, McKinley & Nosofsky 1995) suggests that exemplar models may be too powerful. In addition, the only current neurobiological hypotheses about the exemplar-memory process attach a critical role to the hippocampus (Pickering 1997). As such, the finding that medial temporal lobe amnesiacs are relatively normal at information-integration category learning is problematic for the hypothesis that exemplar theory is adequate as a general theory of categorization in information-integration tasks. Decision bound models can be parametric or nonparametric depending on whether they assume subjects learn decision bounds (parametric) or assign responses to regions (nonparametric). In this latter case, the bound is simply the partition between regions associated with contrasting responses. Ashby & Waldron (1999) conducted a critical test of whether category learning in information-integration tasks is parametric or nonparametric. Previous research (reviewed above) showed that people can learn either linear or nonlinear (e.g., quadratic) information-integration decision bounds. If humans are parametric classifiers, then some categorical information must signal whether they should use a linear bound or a nonlinear bound. Ashby & Waldron (1999) constructed categories in which all statistical information that could be readily estimated (e.g., means, variances, and covariances) signaled that a parametric classifier should use a linear decision bound, but for which the optimal bound was quadratic. In a second condition, the statistical information signaled that a nonlinear bound should be used, but the optimal bound was linear. All known parametric classifiers predict that subjects will use a decision bound of the wrong type in these two conditions.

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Yet, the data of every subject (who did not use a rule-based strategy) were best fit by a bound of the optimal type. These results provide strong evidence against all known parametric classifiers, including prototype models, and decision bound models that assume people learn decision bounds. At the same time, the Ashby & Waldron (1999) results support nonparametric classifiers, such as exemplar models and decision bound models that assume people learn to assign responses to regions of perceptual space. As a model of the latter type, Ashby & Waldron (1999) proposed a nonparametric decision bound model called the striatal pattern classifier. Other nonparametric decision-bound-type models include Anderson’s (1991) rational model, and Love et al.’s (2004) SUSTAIN (Supervised and Unsupervised Stratified Adaptive Incremental Network) model. Each of these models can be loosely described as multiple prototype (or cluster) models because perceptually similar category exemplars tend to be grouped or clustered together. For the most part, all three models can account for the observed complexity limits on the learning of information-integration category structures, but the rational and SUSTAIN models make no attempt to account for the neuropsychological and neuroimaging data, and neither proposes a neurobiological account of information-integration category learning. The striatal pattern classifier, on the other hand, offers a computational model of the procedural-learning-based system proposed in COVIS, and it proposes a neurobiological interpretation. In brief, the model assumes that information-integration category learning is mediated primarily within the tail of the caudate nucleus (for visual stimuli). Two key neurophysiological features make this system a good candidate for information-integration category learning. First, all of visual cortex (except area V1) projects directly to the tail of the caudate, and these projections are characterized by massive convergence (i.e., of approximately 10,000 to 1; Wilson 1995). This convergence causes the decision space represented in the caudate nucleus to have much lower resolution than the perceptual space represented in visual cortex, and is assumed to account for the complexity limits on information-integration category learning reviewed above. Second, the tail of the caudate receives dopaminergic input from the substantia nigra that is widely thought to serve as a reward-mediated feedback signal (e.g., Schultz 1992, Wickens 1993). The idea is that an unexpected reward causes dopamine to be released from the substantia nigra into the tail of the caudate, and that the presence of this dopamine strengthens recently active synapses. The next section reviews a number of empirical results that are thought to be directly attributable to unique features of this feedback system. The striatal pattern classifier is consistent with much of the neuropsychological and neuroimaging data reviewed above. For example, the model predicts that amnesiacs should show normal information-integration category learning, whereas patients with striatal damage (Parkinson’s and Huntington’s patients) should be impaired. In addition, the model predicts that there should be striatal activation during information-integration category learning. In addition, although speculative at this point, it seems reasonable to suppose that the learning of complex nonlinear

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information-integration category structures requires a higher resolution in the tailof-the-caudate decision space than the learning of simpler linear informationintegration category structures. Because the tail of the caudate is dysfunctional in Parkinson’s disease, the model makes the natural prediction that Parkinson’s disease patients should be especially impaired in nonlinear information-integration category learning. Each of these predictions was supported by the data reviewed above.

DISSOCIATIONS BETWEEN RULE-BASED AND INFORMATION-INTEGRATION CATEGORY LEARNING In a seminal study, Shepard et al. (1961; Shepard & Chang 1963) examined category learning in six tasks constructed from different stimulus-category assignments of the same 8 three-dimensional binary-valued stimuli (for replications and extensions, see Nosofsky et al. 1994a, Nosofsky & Palmeri 1996, Smith et al. 2004). These included rule-based, information-integration, and unstructured (memorization) tasks. Results showed that the one-dimensional task was easiest to learn and the unstructured, memorization task was the most difficult, with the other tasks (including the information-integration task) of intermediate difficulty. One weakness of this study is that the structural properties of the categories, such as within-category coherence and between-category discriminability, were not controlled. More recently, a number of studies have compared rule-based and information-integration category learning in a variety of settings where these and other structural properties are controlled. Collectively, these data offer a serious challenge to single-system models. Many of the studies in question were motivated by the COVIS model of category learning (Ashby et al. 1998). As outlined earlier, COVIS assumes that learning in rule-based tasks is dominated by an explicit, hypothesis-testing system that uses working memory and executive attention and is mediated primarily by the anterior cingulate, the prefrontal cortex, and the head of the caudate nucleus. In contrast, learning in information-integration tasks is assumed to be dominated by an implicit procedural-learning-based system, which is mediated largely within the tail of the caudate nucleus (Ashby et al. 1998, Ashby & Ell 2001, Willingham 1998). A series of studies attempted to dissociate the processes involved in rule-based and structurally equivalent information-integration category learning by introducing simple experimental manipulations that are predicted by COVIS to affect processing in the procedural-learning system but not the hypothesis-testing system, or vice versa. These tests focused on predictions that the two putative systems would be affected differently by manipulations of the nature and timing of feedback, by changes in the locations of the response keys, and by adding additional demands on working memory and executive attention. The remainder of this section briefly reviews these studies (for a more detailed review, see Maddox & Ashby 2004).

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Because the COVIS hypothesis-testing system is under conscious control and has full access to working memory and executive attention, the nature and timing of the feedback signal should not be critical for rule-based category learning. In contrast, a procedural-learning system that is mediated within the tail of the caudate nucleus would not be accessible to conscious awareness and is far removed from working memory.1 As a result, it would depend more heavily on the nature and timing of the feedback. Several studies tested these predictions. First, observational training was found to be equally effective to traditional feedback training with rulebased categories, but with information-integration categories, a distinct advantage occurred for feedback training (Ashby et al. 2002). During observational training, subjects are informed of the category membership of each stimulus just before it appears, whereas during feedback training, the stimulus is presented and then the category label is shown immediately after the subject responds. Second, some rulebased categories can be learned without feedback of any kind, whereas there is no evidence that information-integration categories can be learned without feedback (Ashby et al. 1999). Third, delaying the feedback by as little as 2.5 seconds after the response significantly interferes with information-integration category learning, but delays as long as 10 seconds have no effect on rule-based learning (Maddox et al.). A second set of studies tested the prediction that information-integration category learning is mediated largely by a form of procedural learning. The quintessential paradigm for studying procedural learning is the serial reaction time task (Nissen & Bullemer 1987), in which subjects press keys as quickly as possible in response to stimuli that appear in various locations on the screen. A large response time improvement is observed when the stimulus sequence is repeated, even when subjects are unaware that a sequence exists. Willingham et al. (2000) showed that changing the location of the response keys interferes with serial reaction time learning, but that changing the fingers that push the keys does not. Thus, if procedural learning is used in information-integration tasks, then switching the locations of the response keys should interfere with learning, but switching the fingers that depress the keys should not. In fact, Ashby et al. (2003) reported evidence that directly supported this prediction. They also reported that neither manipulation had any effect on rule-based category learning. Maddox et al. (2004) reported a similar sensitivity of information-integration category learning to response location. On half the trials, subjects responded “Yes” or “No” depending on whether the stimulus belonged to category A, and on half the trials they responded “Yes” or “No” depending on whether the stimulus belonged to category B. Thus, there was no consistent mapping of category label to response position. Compared to a standard 1

Crick & Koch (1990, 1995, 1998) offered a cognitive neuroscience theory of conscious awareness that states one can have conscious awareness only of activity in brain areas that project directly to the prefrontal cortex. The caudate nucleus does not project to the prefrontal cortex (it first projects through the globus pallidus and then the thalamus), so the Crick-Koch hypothesis predicts that we are not aware of activity within the caudate nucleus.

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control condition, learning was impaired with the information-integration categories, but not with the rule-based categories. These results provide the first direct evidence of procedural learning in perceptual categorization and suggest that the hypothesis-testing system learns abstract category labels, whereas the procedurallearning system learns response positions (for other examples of response effects in categorization, see Barsalou et al. 2003). A third set of studies tested the prediction that rule-based category learning requires working memory and executive attention, both to select and apply the correct rule and to interpret and process the feedback signal. First, Waldron & Ashby (2001) showed that rule-based category learning was disrupted more than information-integration category learning by the simultaneous performance of a task that required working memory and executive attention (a numerical Stroop task). In a second related study, Maddox et al. (2004) required subjects to alternate categorization trials with trials of a classic memory-scanning task (Sternberg 1966). In one condition, a short delay followed categorization and a long delay followed memory scanning, whereas these delays were reversed in the other condition. Learning of the information-integration categories was unaffected by the location of the short delay, whereas rule-based category learning was significantly worse when the short delay followed categorization. This result supports the hypothesis that feedback processing requires attention and effort in rule-based categorization, but not in information-integration category learning.

PROTOTYPE DISTORTION TASKS In prototype distortion tasks, the category exemplars are created by randomly distorting a single category prototype. As mentioned above, the most widely known example uses a constellation of dots (often 7 or 9) as the category prototype (see Figure 2 for an example), and the other category members are created by randomly perturbing the spatial location of each dot. These random dot stimuli and categories have been used in dozens of studies (e.g., Homa et al. 1979, 1981; Posner & Keele 1968, 1970; Shin & Nosofsky 1992; Smith & Minda 2002). Two different types of prototype distortion tasks are popular—(A, B) and (A, not A). In an (A, B) task, subjects are presented a series of exemplars that are each from some category A or from a contrasting category B. The task of the subject is to respond with the correct category label on each trial (i.e., “A” or “B”). An important feature of (A, B) tasks is that the stimuli associated with both responses each have a coherent structure—that is, they each have a central prototypical member around which the other category members cluster. In an (A, not A) task, on the other hand, there is a single central category A and subjects are presented with stimuli that are either exemplars from category A or random patterns that do not belong to category A. The subject’s task is to respond “Yes” or “No” depending on whether the presented stimulus was or was not a member of category A. In an (A, not A)

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task, the category A members have a coherent structure, but the stimuli associated with the “not A” (or “No”) response do not. Historically, prototype distortion tasks have been run both in (A, B) form and in (A, not A) form, although (A, not A) tasks are most common.


Neuropsychological Patient Data Prototype distortion tasks are particularly important because the neuropsychological patient data are profoundly different from those in rule-based or informationintegration tasks. In particular, a variety of patients groups that are known to have deficits in rule-based and information-integration tasks show apparently normal prototype distortion learning, at least in (A, not A) designs. This includes patients with Parkinson’s disease (Reber & Squire 1999), schizophrenia (Keri et al. 2001), or Alzheimer’s disease (Sinha 1999, although see Keri et al. 1999). Normal (A, not A) performance has also been shown in patients with amnesia (Knowlton & Squire 1993, Kolodny 1994, Squire & Knowlton 1995). These results must be interpreted with caution, however, because several studies have shown that if category A is created from low-level distortions of the category A prototype, then healthy young adults can learn in (A, not A) tasks without any feedback (i.e., training) at all (Homa & Cultice 1984, Palmeri & Flanery 1999). Thus, it is not yet clear that all these patient groups would learn normally in a difficult (A, not A) task (i.e., one that requires feedback for optimal performance). At least two studies have compared (A, not A) and (A, B) prototype distortion learning on the same patients—and both studies report the same striking dissociation. Specifically, Sinha (1999) reported normal (A, not A) performance in Alzheimer’s disease patients, but impaired (A, B) performance, and Zaki et al. (2003) reported this same pattern of results with amnesiacs. Sinha (1999) also reported deficits in (A, B) prototype distortion learning in patients with amnesia.

Neuroimaging Data A handful of neuroimaging studies have used prototype distortion tasks. When interpreting these results, it is vital to consider whether an (A, B) or (A, not A) task was used. As in the purely behavioral studies, the most popular choice has been the (A, not A) task. All of these studies have reported learning-related changes in occipital cortex (Aizenstein et al. 2000; Reber et al. 1998a,b)—in general, reduced occipital activation was found in response to category A exemplars, although Aizenstein et al. (2000) found this reduction only under implicit learning conditions. When subjects were given explicit instructions to learn the A category, increased occipital activation was observed. Studies that have used (A, B) tasks have reported quite different results. Seger et al. (2000) did report categorization-related activation in occipital cortex, but they also found significant learning-related changes in prefrontal and parietal cortices. Vogels et al. (2002) reported results from a hybrid task in which subjects were to respond “A,” “B,” or “Neither.” Thus, stimuli were created from distortions of

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an A prototype or a B prototype, or were just random patterns. Like Seger et al. (2000), Vogels et al. (2002) found prefrontal and parietal activation (although in different foci). However, they also reported task-related activation in orbitofrontal cortex and the neostriatum, and they failed to find any task-related activation in occipital cortex.

Theories of Prototype Distortion Learning The most recent debates between prototype and exemplar theories have focused on prototype distortion tasks. Prototype theory assumes a category is represented as a prototype, and that stimuli are categorized by comparing them to the prototypes of each contrasting category (Homa et al. 1981; Posner & Keele 1968; Reed 1972; Rosch 1973, 1975; Smith & Minda 2001). This theory seems ideally suited to prototype distortion tasks where all category members are simple distortions of a central prototype. Indeed, early results seemed to support this prediction. For example, performance is generally better on the prototype and on exemplars similar to the prototype than on distortions, even when subjects are trained on the distortions but not on the prototype (Homa et al. 1979, 1981; Posner & Keele 1970; Strange et al. 1970). Even so, exemplar theorists showed that exemplar theory was also compatible with these results, and they argued that exemplar theory provides at least as good an account of data from prototype distortion tasks as does prototype theory (Hintzman 1986, Hintzman & Ludlam 1980, Shin & Nosofsky 1992). Smith & Minda (2001) identified a critical test between exemplar and prototype accounts of prototype distortion data. Consider a space with a dimension for each stimulus component. In the case of the dot patterns there would thus be two dimensions for each dot—one to identify the horizontal position of the dot and one to identify the vertical position. In this space, each category exemplar is identified by a single point, and the category prototype is represented by a point in the center of the cloud of points denoting all exemplars of the category. Smith & Minda’s (2001) analogy was to the solar system, with the sun representing the category prototype and the planets the category members that were created by distorting the prototype. Now consider the probability of responding “A” in an (A, not A) task and how this probability changes with the position of the stimulus in the dot pattern space. According to prototype theory, the probability of responding “A” is completely determined by the similarity, or equivalently the distance, between the stimulus and the category prototype (i.e., the sun in the solar system analogy). According to exemplar theory, however, the probability of responding “A” depends on the (sum of the) similarities between the stimulus and all of the category A exemplars, or equivalently on the distances between the stimulus point and all the planets in the category A solar system. When the stimulus is outside of the category A cluster or solar system, then prototype and exemplar theories both predict that the probability of responding “A” will increase sharply as the stimulus moves toward the category A prototype,


EXEMPLAR VERSUS PROTOTYPE THEORIES

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because the distance between the stimulus and the sun is decreasing as are the distances between the stimulus and each planet. The critical difference between prototype and exemplar theories emerges when the stimulus first enters the category A solar system. For example, as a meteor passes Pluto and enters our solar system, it is still moving closer to the sun and to the planets nearest to the sun, but it is now moving away from Pluto. The closer it moves toward the sun the greater this effect—that is, it is always moving steadily nearer the sun, but as it approaches the sun it begins moving away from more planets and moving nearer to fewer planets. As a result, prototype theory predicts that the probability of responding “A” continually increases as the similarity between the stimulus and the category A prototype is increased. However, exemplar theory predicts that this probability gradient will begin to flatten as the stimulus moves inside the category A cluster. Smith & Minda (2001, 2002; Smith 2002) examined these probabilityof-responding-“A” profiles for a number of new and previously published studies and showed that they were steeper than predicted by exemplar theory, but were in general agreement with the predictions of prototype theory. The neuroimaging results showing learningrelated changes in visual cortex in (A, not A) prototype distortion tasks motivated several proposals that the perceptual representation memory system contributes to learning under these conditions (Ashby & Casale 2002, Reber & Squire 1999). The idea is that performance may be mediated, at least in part, by perceptual learning within visual cortex. If such visual cortical perceptual learning is important in prototype distortion tasks, then it likely will have different effects in (A, not A) and (A, B) tasks. Consider first an (A, not A) task. The category A prototype will induce a graded pattern of activation throughout visual cortex. One particular cell (or small group of cells) will fire most rapidly to the presentation of this pattern. Call this cell A. A lowlevel distortion of the category A prototype will be visually similar to the prototype and therefore will likely also cause cell A to fire. Thus, cell A will repeatedly fire throughout training on the category A exemplars. Perceptual learning is thought to occur any time repeated presentations of the same stimulus occur during some relatively brief time interval (Dosher & Lu 1999). As a result, perceptual learning will cause the magnitude of the cell A response to increase throughout training. In contrast, the stimuli associated with the “not A” response will be visually dissimilar to the category A prototype and therefore will be unlikely to cause cell A to fire. During the transfer or testing phase of the experiment, the subject can use the increased sensitivity of cell A to respond accurately. In particular, stimuli from category A are likely to lead to an enhanced visual response compared to stimuli that do not belong to category A. Thus, to respond with above chance accuracy, subjects need only respond “A” to any stimulus that elicits an enhanced visual response. Note that one could interpret cell A as encoding the representation of the category prototype, and thus, this type of perceptual learning could be interpreted as a neuropsychological basis of prototype theory.

PERCEPTUAL LEARNING THEORIES

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Next, consider an (A, B) task. In this case, there will be some cell A maximally tuned to the category A prototype, but there will be some other cell B that is tuned to the category B prototype. During training, every presented stimulus is a distortion of either the category A or category B prototype, so it is likely that either cell A or B will fire on many trials. During the testing phase, all stimuli are again from either category A or B, and so stimuli from both categories will be equally likely to elicit an enhanced visual response. As a result, the mere existence of an enhanced visual response will not help subjects decide whether to respond “A” or “B.” The conclusion, therefore, is that perceptual learning could greatly assist in (A, not A) tasks but, by itself, it would be of little help in (A, B) tasks. This is not to say that learning in (A, B) prototype distortion tasks is impossible; only that other learning systems must be used. For example, in low-distortion (A, B) tasks, the enhanced prototype responses caused by perceptual learning might facilitate an explicit memorization strategy in which subjects memorize the A and B prototype patterns and their associated responses. Much of the cognitive neuroscience data reviewed above supports these predictions. First, neuroimaging results of (A, not A) prototype distortion tasks consistently report learning-related activation in visual cortex (Aizenstein et al. 2000; Reber et al. 1998a,b). Second, neuroimaging results of (A, B) tasks have sometimes failed to find such occipital activation (Vogels et al. 2002), and they have consistently reported task-related activation in prefrontal cortex that is not seen in (A, not A) tasks (Seger et al. 2000, Vogels et al. 2002). Third, a variety of neuropsychological studies show normal (A, not A) prototype distortion learning in patient groups that are impaired in rule-based or information-integration category learning (e.g., schizophrenics, Keri et al. 2001; Parkinson’s disease patients, Reber & Squire 1999; Alzheimer’s disease patients, Sinha 1999), and in amnesiacs (Knowlton & Squire 1993). Fourth, amnesiacs and patients with Alzheimer’s disease are impaired in (A, B) prototype distortion learning (Sinha 1999, Zaki et al. 2003). The prefrontal activation in (A, B) tasks, and the impaired learning of amnesiacs, suggest that learning in (A, B) prototype distortion tasks may be mediated by explicit reasoning strategies and/or by explicit memorization.

WEATHER PREDICTION TASK An important distinction in category-learning experiments is whether category membership is deterministic or probabilistic. In deterministic tasks, each stimulus is unambiguously a member of one category (i.e., optimal performance is perfect), whereas in probabilistic tasks, at least some stimuli are probabilistically associated with the contrasting categories. For example, on one trial in a probabilistic classification task a particular stimulus might belong to category A but on the next trial the same stimulus might belong to category B. Obviously, in such tasks, perfect performance is impossible. Although most category-learning studies have used deterministic tasks, probabilistic classification also has a long history (e.g.,

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Ashby & Gott 1988; Ashby & Maddox 1990, 1992; Estes et al. 1989; Gluck & Bower 1988; Kubovy & Healy 1977). One popular probabilistic classification task, which is used extensively in cognitive neuroscience, is the so-called weather prediction task (Eldridge et al. 2002; Knowlton et al. 1994, 1996a,b; Reber et al. 1996; Reber & Squire 1999). On each trial of this task, subjects are shown one, two, or three of four possible tarot cards and are asked to indicate whether the presented constellation signals rain or sun. Each card is labeled with a unique, and highly discriminable, geometric pattern. Fourteen of the 16 possible card combinations are used (the no cards and four card patterns are excluded) and each combination is probabilistically associated with the two outcomes. In the original version of the task, the highest possible accuracy was 76% (Knowlton et al. 1994). Interestingly though, a single-cue strategy in which the subject gives one response if one card is present and the other response if that same card is absent yields an accuracy of 75% correct. Knowlton et al. (1994) reported that performance of a control group increased from approximately 50% to 65% correct during the first 50 trials and continued to improve to approximately 75% correct after 350 trials.

Task and Individual Differences Analysis In order to relate results from the weather prediction task to the other results reviewed in this article, it is important to determine its relationship to the other tasks we have discussed. Because the optimal strategy requires information-integration across cues and is nonverbalizable, the weather prediction task is technically an information-integration task. On the other hand, a single-cue strategy results in nearly optimal performance (75% for a single cue versus 76% for the optimal strategy), so nearly optimal accuracy does not rule out simple rule-based strategies. In addition, because the task uses only a few highly distinct exemplars, explicit memorization is also a plausible strategy. Because a variety of different strategies are all about equally effective, we might expect more individual differences in results obtained with the weather prediction task than with the other tasks we have considered. This possibility makes it especially important to determine what strategy each subject is using before interpreting his or her data. Gluck et al. (2002) provided a “strategy” analysis of data collected in the weather prediction task to address this issue. In one study, they simply asked subjects what strategy they used after the experiment was over. Although many of the protocols lacked detail, those with sufficient detail generally fell into one of three types: (a) single-cue strategies in which responding was based on the presence or absence of one card (as in a rule-based task), (b) multiple-cue learning (as in an informationintegration task), or (c) singleton learning in which correct responses to the singlecard patterns were memorized, and guessing occurred for the remaining patterns. Based on these self-reports, Gluck et al. (2002) developed a model-based analysis to identify each subject’s strategy in two follow-up studies. Based on a full 200-trial session, 90% (Experiment 1) and 80% (Experiment 2) of the subjects used

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a singleton strategy (explicit memorization) to learn the categories. When broken down into 50-trial blocks, a shift from singleton toward multiple-cue strategies was observed (although the proportion of subjects using a multiple-cue strategy was still well below 0.5). Interestingly, there was little correspondence between the strategies that subjects self-reported in their protocols and the strategies identified by the modeling approach. Thus, although originally designed as an informationintegration task, these results suggest that subjects adopt a variety of different strategies in the weather prediction task, and the evidence indicates that the most popular choice may be explicit memorization.

Neuropsychological Patient and Neuroimaging Studies The weather prediction task has been used to study category learning in a number of patient groups. In one of the first such studies, Knowlton et al. (1994) found that amnesiacs performed as well as healthy controls during the first 50 trials of learning, but with extended training, amnesiacs showed a learning deficit relative to healthy controls. Declarative memory is impaired in amnesia, so amnesiacs are impaired in explicit memorization strategies. Healthy controls are not, however, so one interpretation of the late training deficit is that controls begin memorizing and the amnesiacs do not (Knowlton et al. 1994; see also Gluck et al. 1996). Unlike amnesiacs, patients with Parkinson’s or Huntington’s disease show learning deficits in the weather prediction task during the first 50 trials that continue throughout training (Knowlton et al. 1996a,b). The weather prediction task has also been used to examine category learning in patients suffering from Alzheimer’s disease or schizophrenia. Patients in the early stages of Alzheimer’s disease are similar to amnesiacs in the sense that both show anterograde amnesia due to neurodegenerative processes in the medial temporal lobes. As one might predict given this similarity, Alzheimer’s patients show intact performance during early trials of the weather prediction task, similar to that seen in amnesia (Eldridge et al. 2002). Schizophrenics exhibit marked abnormalities in executive function and explicit memory. Even so, their performance on the weather prediction task is within normal ranges (Keri et al. 2000). Neuroimaging studies of the weather prediction task indicate that the medial temporal lobes are active early in learning, and gradually become deactivated as learning progresses (Poldrack et al. 2001). This deactivation is mirrored by a simultaneous activation of the basal ganglia. Specifically, early in learning the basal ganglia are inactive, and gradually become more active as learning progresses. The weather prediction task has provided a useful tool for studying classification learning and has offered some important insights into the neurobiology of category learning. Even so, the fact that nearly optimal performance can be achieved by a variety of different strategies (e.g., information-integration, rule-based, explicit memorization) makes it difficult to draw strong inferences from data collected with this task. Although the strategies approach developed by Gluck et al. (2002) helps identify the type of strategy that subjects are using (however, see Shohamy

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et al. 2004), a better alternative might be to use one of the other tasks discussed in this article that are not so susceptible to identifiability problems.


CONCLUSIONS The results reviewed in this article offer important lessons. First, when interpreting a category-learning result, it is critical to consider carefully the specific task that was used. For example, Parkinson’s disease patients are normal in (A, not A) prototype distortion tasks, they are mildly impaired in information-integration category learning, and they are profoundly impaired in rule-based categorization. Several studies support each of these conclusions, but without specifying the task, these studies would appear to just catalog a confusing set of contradictory results. Second, although the issue is far from resolved, the results presented here make a strong case that human category learning is mediated by multiple qualitatively distinct systems. To a large extent, it is also becoming clear that this issue—of whether there are one or more category-learning systems—is tied to the historically older issue of whether there are one or more memory systems. Learning is, by definition, the process of laying down some sort of memory trace, and there is certainly no reason to suspect that any of the separate memory systems that have been hypothesized are incapable of storing memories about categories. Although research efforts to resolve this debate will continue, other work is already attacking the next set of questions. For example, the next decade will likely see a flurry of research activity directed at determining the conditions under which the various systems contribute to category learning, at determining how the different systems interact, and at fleshing out their underlying neurobiology. ACKNOWLEDGMENTS Preparation of this article was supported by Public Health Service Grants MH3760 and MH59196. We thank Michael Casale and David Smith for their helpful comments. The Annual Review of Psychology is online at http://psych.annualreviews.org

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Figure 1 (a) Three stimuli that might be used in a perceptual categorization experiment. Each stimulus is a circular sine-wave grating that varies across trials in spatial frequency and orientation. (b) A plot of stimuli that might be used in a rule-based category-learning task. Each plus denotes the spatial frequency and orientation of an exemplar from category A and each circle denotes a category B exemplar. The vertical line is the optimal category boundary. (c) A plot of stimuli that might be used in an information-integration category-learning task.

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Annu. Rev. Psychol. 2005. 56:179–206 doi: 10.1146/annurev.psych.55.090902.141409 c 2005 by Annual Reviews. All rights reserved Copyright

PAVLOVIAN CONDITIONING: A Functional Perspective


Michael Domjan Department of Psychology, University of Texas at Austin, Austin, Texas 78712; email: [email protected]

Key Words naturalistic conditioned stimuli, ecological learning, object learning, conditioned modifications of unconditioned behavior, adaptive significance of learning ■ Abstract From a functional perspective, Pavlovian conditioning involves learning about conditioned stimuli (CSs) that have a pre-existing relation to an unconditioned stimulus (US) rather than learning about arbitrary or neutral CSs. In addition, the most important product of learning involves changes in how the organism responds to the US, not in how it responds to the CS, because the US is the more biologically relevant stimulus. These concepts are illustrated using examples from a variety of behavioral and physiological situations including caloric intake and digestion, breast feeding, poison-avoidance learning, eyeblink conditioning, sexual conditioning, fear conditioning, aggression, and drug tolerance and sensitization.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Natural Learning Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditioned Modifications of the Unconditioned Response . . . . . . . . . . . . . . . . . . . LEARNING WITH ECOLOGICALLY RELEVANT CONDITIONED STIMULI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Poison-Avoidance Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Taste-Odor Potentiation and Contrablocking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caloric Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sexual Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fear Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maternal Nursing and Infant Suckling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONDITIONED MODIFICATIONS OF RESPONSES TO THE UNCONDITIONED STIMULUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditioned Modification of the Eyeblink Response . . . . . . . . . . . . . . . . . . . . . . . . Sexual Behavior and Reproductive Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aggression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maternal Nursing and Infant Suckling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0179$14.00

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DOMJAN Fear-Potentiated Startle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditioned Hypoalgesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digestion and Feeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditioning and Drug Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Drug Conditioning and Sensitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SUMMARY AND CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION Pavlovian conditioning is one of the oldest and most extensively studied learning paradigms. The paradigm basically involves two stimuli. The unconditioned stimulus (US) elicits vigorous responding without any special prior training, or unconditionally. Because of that, the US originally was labeled the unconditional stimulus (Gantt 1966). In contrast, the conditioned stimulus (CS) elicits little more than an orienting response at first. The effectiveness of the CS depends, or is conditional, upon its pairings with the unconditioned stimulus. Hence, the CS originally was called the conditional stimulus. Learning is identified by the emergence of new responses to the CS, called conditioned responses or CRs. Because the development of conditioned responding depends on the pairing of the CS and US, the learning is considered to involve the establishment of an association between the CS and the US. This has made Pavlovian conditioning a favorite paradigm for the study of associative learning. Staddon (1983), for example, characterized Pavlovian conditioning as “the prototype for all signal learning” (p. 103). The associative tradition encouraged investigators to use conditioned stimuli that are initially unrelated to, or arbitrary, with respect to the US. In fact, the initial independence of the CS and US has been incorporated into the definition of Pavlovian conditioning. Authors have characterized the CS as “arbitrary” (Bower & Hilgard 1981, p. 49) or “neutral” (Anderson 1995, p. 10; Papini 2002, p. 491; Shettleworth 1998, p. 109; Staddon 1983, p. 102) with respect to the US. The view that Pavlovian conditioning involves learning about neutral or arbitrary cues that come to elicit conditioned behavior has provided a great deal of information about associative mechanisms. Parallel to, but in the shadows of, the associative tradition, a functional perspective on Pavlovian conditioning also has been developed (Domjan et al. 2000; Hollis 1982, 1997). The functional perspective is encouraged by the fact that Pavlovian conditioning has been demonstrated in a wide range of species and response systems (Turkkan 1989). The prevalence of Pavlovian conditioning suggests it is an adaptive trait that readily occurs under natural circumstances and serves to promote reproductive fitness, directly and/or indirectly. These are the key assumptions of a functional perspective (Dukas 1998; Hollis 1982, 1997; Shettleworth 1983, 1994).

Natural Learning Paradigms If Pavlovian conditioning is an adaptive trait, it presumably occurs under natural circumstances. However, outside the laboratory, the hand of an experimenter is not

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available to make sure that occurrences of a conditioned stimulus are reliably paired with presentations of a US. Rather, the CS-US pairings that are necessary for Pavlovian conditioning have to be features of the natural environment. For that to be the case, the CS has to be naturally related to the US instead of being an arbitrary cue or a neutral stimulus. An arbitrary CS may coincide with a US occasionally under natural circumstances, but such accidental pairings are bound to be rare. In addition, an accidental pairing will be preceded and followed by unpaired CS and US encounters, which will undermine the development of conditioned responding (e.g., Benedict & Ayres 1971, Rescorla 2000). Thus, although CS-US associations reside in the nervous system, they no doubt reflect physical relationships between conditioned and unconditioned stimuli that exist in the natural environment of the organism. A pre-existing relation between the CS and the US can take several different forms. One possibility is that the CS is a stimulus early in the causal chain of events that leads to the US (Dickinson 1980, Staddon 1988). Another possibility is that the CS is a feature of the US that initially does not elicit the unconditioned response but comes to do so after repeated encounters with the US. This was the case in the first documented demonstration of Pavlovian conditioning, which was conducted by S.G. Vul’fson in Pavlov’s laboratory (Boakes 1984, Todes 1997). Vul’fson repeatedly presented various substances to dogs (sand, dry food, wet food, or sour water) and measured the quantity and quality of saliva elicited by each. After one of these substances had been placed in the dog’s mouth several times, Vul’fson noticed that the dog would salivate when it was “teased” by having the substance presented at a distance. In Vul’fson’s demonstration, the US was the sand or dry food in the mouth. The CS was the sight and/or smell of the US at a distance. Notice that the CS was not unrelated to the CS at the outset of training. Rather, the CS and the US were different features of the same object (sand, for example). In the absence of experimental intervention, having a CS and a US that are different features of the same object helps to make sure that the CS will occur with the kind of contingent and temporal relation to the US that will result in the establishment of an association. This type of pre-existing relation between the CS and US is probably a feature of most naturally occurring instances of Pavlovian conditioning and therefore has to be carefully considered in a functional analysis. Therefore, I review instances in which the CS is a natural precursor of the US, to see whether these examples of Pavlovian conditioning have any unique properties.

Conditioned Modifications of the Unconditioned Response In addition to fashioning laboratory experiments that better mimic natural conditions, a functional approach requires focusing on aspects of behavior that are of potential adaptive significance. From a functional perspective, the important task for the organism is to interact effectively with the unconditioned stimuli it encounters. By definition, USs are of great biological significance (Pavlov 1927). In contrast, conditioned stimuli are important only because of their relation to a US. If the US does not occur, a response to the CS is a useless “false start.”

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Conditioned responses are functionally significant only to the extent that they facilitate the subject’s interactions with the US. The significance of conditioned responses in facilitating the subject’s interactions with a US was brought into focus in several important papers by Hollis (1982, 1990, 1997). However, the implications of this claim have not been fully appreciated. If the primary function of Pavlovian conditioning is to improve how an organism interacts with the US, then the critical behavioral consequence that one should measure in studies of classical conditioning is how responses to the US change as a function of learning. Accordingly, I review various lines of evidence showing how Pavlovian conditioning can modify responses to unconditioned stimuli.

LEARNING WITH ECOLOGICALLY RELEVANT CONDITIONED STIMULI Definitions of classical conditioning that call for selecting a conditioned stimulus that is “neutral” or “arbitrary” imply that the choice of a CS is of little consequence for the outcome of conditioning experiments. That is hardly the case. The choice of the CS can make a big difference. One prominent parameter is CS intensity. Generally, more intense conditioned stimuli produce faster learning and more vigorous conditioned responding (e.g., Kamin 1965). The conditioned stimulus can also determine the nature of the conditioned response (Holland 1984). In this section, I review evidence that conditioning effects also vary as a function of the ecological relevance of the CS.

Poison-Avoidance Learning One of the most dramatic examples of learning influenced by the conditioned stimulus was discovered in the course of research on poison-avoidance learning. The ingestion of a poisonous food involves first tasting the food and then swallowing and digesting it. The poisonous effects become prominent as the food becomes absorbed in the body. This sequence of events results in the learning of an aversion to the taste of the food (Garcia et al. 1974, Rozin & Kalat 1971, Rzoska 1953). Taste-aversion learning has been modeled in the laboratory by permitting rats to drink an innocuous flavored solution (e.g., saccharin) and then injecting them with something that makes them sick (e.g., lithium chloride). By experimentally controlling the exposure to the CS and the US, investigators have been able to examine how the learning is influenced by the nature of the CS and the effects of the CS-US interval. Taste aversion learning can occur in a single trial, even if the US is delayed several hours after the CS (Garcia et al. 1966, Smith & Roll 1967). However, the aversion learning occurs much more readily if the CS is a novel taste than if the CS is an auditory and/or visual cue (Domjan & Wilson 1972, Garcia & Koelling 1966). Furthermore, the long-delay learning of taste aversions is at least in part due to the specificity of the learning to taste cues (Revusky 1977).

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For organisms that hold food in their front paws, the aversive consequence of eating poisonous food is reliably preceded not only by taste cues but also by the olfactory and tactile cues of the poisonous food as it is held in preparation for eating. Aversions also can develop to these tactile and olfactory features of the food (Domjan 1973, Domjan & Hanlon 1982). However, food-related tactile and olfactory cues have to be distinguished from the tactile and olfactory cues that are encountered during the course of locomotion or other noningestive activities. Studies have shown that at least in the case of odor aversion learning, the sensations provided by the motor movements involved in eating and drinking facilitate this discrimination (Domjan 1973).

Taste-Odor Potentiation and Contrablocking The taste and odor features of ingested food are closely related because as one gets close enough to a food to taste it, one invariably encounters the food’s odor. This relationship appears to result in some unusual learning effects. When two conditioned stimuli are presented simultaneously and paired with a US, the typical outcome (called overshadowing) is that the presence of one CS interferes with the conditioning of the other (e.g., Kamin 1969). In contrast to overshadowing, when a taste and an odor are presented together prior to illness, the presence of the taste sometimes facilitates rather than interferes with the conditioning of the odor (Bouton et al. 1986; Rusiniak et al. 1982a,b). This phenomenon is called potentiation. Another important compound cue effect is “blocking.” In the blocking design, subjects are conditioned first to asymptote with one CS. A second CS is then added to the first one, as conditioning trials are continued. The interesting result is that the presence of the initially conditioned CS interferes with (or blocks) the conditioning of the added CS. The blocking effect was demonstrated initially in fear conditioning in rats with light and noise conditioned stimuli (Kamin 1969) and became a keystone phenomenon that inspired numerous major theories of learning. However, an effect opposite blocking (contrablocking) occurs if taste and odor cues are used. When an odor cue is added to a previously conditioned taste stimulus (or a taste cue is added to a previously conditioned odor), aversion conditioning of the added stimulus is facilitated rather than blocked (Batsell et al. 2001, Batson & Batsell 2000).

Caloric Conditioning Nutritious foods provide caloric repletion rather than poisoning as the postingestive consequence. Tastes can become associated with caloric repletion, with the outcome that subjects increase their preference for the associated flavor (Fedorchak 1997, Sclafani 1997). The caloric substances may be mixed with the CS flavor or intubated directly into the stomach. The resultant flavor preferences have been characterized as highly resistant to extinction (Fedorchak 1997, Mehiel 1991), but it is not clear to what the resistance to extinction was compared. Interestingly, a caloric

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conditioned preference is sensitive to changes in hunger, with higher preferences evident when the subject is food deprived as compared with being nondeprived (e.g., Capaldi et al. 1994). This latter finding suggests that stimulus-stimulus, or S-S, learning mechanisms mediate caloric conditioning.


Sexual Conditioning The effects of environmental regularities have been also examined in sexual conditioning with male Japanese quail (Domjan et al. 2004). Sexual conditioning is conducted by presenting a CS shortly before providing the male with access to a female. If a localized stimulus (e.g., a light) serves as the CS, the male quickly comes to approach the CS as the conditioned response (Domjan et al. 1986). Japanese quail are ground birds that live in grassy areas (Schwartz & Schwartz 1949). When a male initially detects a female, he is likely to see just a part of her body, perhaps her head sticking up through the grass. By approaching these limited visual cues, the male will get close to the female and may get a chance to copulate with her. This sequence of events may be modeled in the laboratory by presenting a CS that includes the taxidermically prepared head of a female (see Cusato & Domjan 1998, Figure 1), and following that with access to a live female with whom the male may copulate. Studies have shown that such a naturalistic CS elicits only modest approach behavior unconditionally. However, if the CS is paired with access to a live female, conditioned approach behavior significantly increases (Köksal et al. 1994). In addition, the males also come to grab and attempt copulations with the naturalistic CS (Cusato & Domjan 1998). Such conditioned copulatory responses did not develop in subjects that were conditioned with an arbitrary CS that had the same size and shape but lacked female head cues. Additional comparisons revealed a constellation of learning effects that differentiated the naturalistic CS from the more conventional arbitrary CS. The naturalistic CS was resistant to blocking (Köksal et al. 1994) and failed to show extinction (Krause et al. 2003). It was also resistant to increases in the CS-US interval (Akins 2000), resulted in stronger second-order conditioning (Cusato & Domjan 2001), and showed a sensitization rather than a habituation effect with repeated unreinforced exposures (Cusato & Domjan 1998).

Fear Conditioning Fear conditioning typically is investigated using experimental procedures in which an auditory or visual CS is presented to laboratory rats shortly before a brief foot shock. Outside the laboratory, however, fear conditioning is likely to cues that are natural precursors of an aversive event. Such precursors are easy to identify in predator-prey interactions. For example, the sight and sound of a rattlesnake preying on a rabbit is a natural precursor of the biting attack. An extensive series of experiments have examined the conditioning of fear to the sight of a snake in

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¨ monkeys and people (Ohman & Mineka 2001, 2003). In both species, stronger fear conditioning was found when the CS was provided by visual cues of a snake rather than visual cues of flowers or mushrooms. However, the flower and mushroom stimuli were just as effective as the sight of a snake in appetitive conditioning procedures (EW Cook et al. 1986, M Cook & Mineka 1990). This selective advantage of the snake stimuli in fear conditioning was also evident when the stimuli were presented to human participants without their conscious awareness ¨ (Ohman & Soares 1998). The evolutionary basis of selective conditioning of snake stimuli is further supported by evidence that snake cues are more effective in human fear conditioning than are visual cues provided by a gun (EW Cook et al. 1986). Selective associations in fear conditioning also have been demonstrated in pigeons, where auditory cues have been found to be more effective as a CS for shock, whereas visual cues are more effective as a signal for food (LoLordo 1979). In addition, auditory-shock associations are resistant to the blocking effect (LoLordo et al. 1982). The presentation of a previously conditioned CS does not interfere with the development of a tone-shock association. Thus, as in sexual conditioning (Köksal et al. 1994), a naturalistic CS is less susceptible to blocking.

Maternal Nursing and Infant Suckling Another situation in which the natural course of events results in the reliable pairing of conditioned and unconditioned stimuli is provided by the interactions between mother and infant that occur during the course of nursing. The unconditioned stimulus for the milk letdown and milk ejection reflex is suckling stimulation of the breast. Olfactory and other cues from the infant typically precede suckling stimulation, and these cues can become conditioned to elicit the release of oxytocin and prolactin, hormones that stimulate milk letdown and ejection (Fuchs et al. 1987; Grosvenor & Mena 1972, 1974; McNeilly et al. 1983). Correspondingly, various exteroceptive cues provided by the mother before a nursing episode can become conditioned to elicit suckling on the part of the infant (Blass 1990, Blass et al. 1984). Research has clearly demonstrated that such conditioned endocrine and suckling responses can develop. However, in this line of research specific experiments have not been conducted to see if naturalistic conditioned stimuli are more effective than arbitrary cues.

Discussion The evidence reviewed above suggests that learning with ecologically relevant stimuli often proceeds differently from learning with arbitrary cues. In foodaversion learning, sexual conditioning, and fear conditioning, the use of an ecologically relevant CS resulted in acquisition that was more robust and was resistant to the blocking effect. In food aversion learning and sexual conditioning, the learning also occurred over longer CS-US delays. These and the other contrasts with

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conventional conditioning phenomena that were described suggest that efforts to understand how Pavlovian conditioning occurs in the natural environment have to consider the role of pre-existing relations between CSs and USs.


CONDITIONED STIMULUS INTENSITY, SALIENCE, AND BIOLOGICAL SIGNIFICANCE

One possible explanation for the more robust learning effects that were observed when an ecologically relevant CS was used in food-aversion learning, sexual conditioning, and fear conditioning is that the naturalistic CSs were more intense or salient than the corresponding arbitrary cues. Consistent with this possibility, several investigators have reported that a target CS is less likely to be blocked by a previously conditioned cue if the target CS is of higher intensity (Feldman 1975, Hall et al. 1997, Miller & Matute 1996). However, stimulus salience or intensity is not likely to be the primary dimension that distinguishes naturalistic CSs from arbitrary cues. In studies of food aversion learning, taste cues were more effective than audiovisual cues only if poisoning was used as the US. When shock served as the US, the relative effectiveness of taste versus audiovisual cues was reversed (Domjan & Wilson 1972, Garcia & Koelling 1966). Similarly, in studies of fear conditioning in pigeons, auditory cues were more effective than visual cues only if the US was shock—not if the US was food (LoLordo 1979). In studies with monkeys and people, the flower and mushroom stimuli that were not effective in fear conditioning worked well when appetitive conditioning was conducted (EW Cook et al. 1986, M Cook & Mineka 1990). Another way to characterize the difference between a naturalistic CS and an arbitrary cue is that the former is of greater biological significance. Gunther et al. (1997) defined biological significance by the vigor of responding (conditioned or unconditioned) that is elicited by a stimulus at the outset of a training procedure. Consistent with this interpretation, the naturalistic CS used in the sexual conditioning experiments elicited more responding even in the absence of conditioning than did the arbitrary CS (Cusato & Domjan 1998, Köksal et al. 1994), and this may have been responsible for the resistance to blocking, extinction, and increases in the CS-US interval that was observed. Stimuli of greater biological significance also have been shown to be more resistant to blocking, overshadowing, relative validity, and degraded contingency manipulations in fear conditioning (Miller & Matute 1996, Oberling et al. 2000). In many of the examples described above, the pre-existing relation between CS and US was provided by the fact that the stimuli were different features of the same object. Food-aversion learning and caloric conditioning involve learning about different features of ingested food. The CS feature is the taste of the food, and the US feature is its aversive or caloric postingestional consequence. One may also conceptualize the sexual conditioning with a naturalistic CS as an instance of object learning. The naturalistic CS included the taxidermically prepared head of a female quail, whereas the US was copulatory access to a live female. The partial female visual cues that were provided by the CS were just one

OBJECT LEARNING

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feature of the US, which included additional visual, auditory, and tactile stimuli, as well as movement cues produced by the female’s behavior. In fear conditioning, visual cues provided by a snake were paired with an aver¨ sive US. In the laboratory experiments (Ohman & Mineka 2001, 2003), the aversive US was not a snakebite but a shock or the sight of a demonstrator monkey behaving fearfully. However, the laboratory procedures presumably activated processes that evolved to deal with encounters with dangerous snakes. In the conditioning of oxytocin and prolactin release, the CS and US were both provided by the infant. Suckling on the part of the infant provided the US, and cues provided by the infant before nipple attachment served as the CS. Correspondingly, different features of the mother provided the CS and the US in conditioning infant suckling. In this case, orosensory stimuli of the nipple served as the US and maternal cues preceding access to the nipple served as the CS. Object learning or part-whole associations also have been examined in the context of drug conditioning (Cepeda-Benito & Short 1997, Greeley et al. 1984, Kim et al. 1999) In these experiments, a small dose of a drug was given as a CS for a larger subsequent dose. For control subjects, the two drug doses were administered in an unpaired fashion. Test trials revealed that the small drug dose came to activate a conditioned compensatory response (see Conditioning and Drug Tolerance section below). However, further research is required to determine whether such drug CSs have properties different from more arbitrary CSs in these situations. Learning that involves associating different features of an object is no doubt widespread. Organisms have to learn about all sorts of objects to successfully navigate their environment. We can recognize something as being a chair even if we only see a small part of it because we have learned a constellation of associations involving different features of chairs. The prevalence of object learning makes object learning a useful heuristic for identifying CS-US pairings in the natural environment. If a US object has multiple features, only some of which elicit behavior unconditionally, the initially ineffective features of the object may come to elicit responding as well through Pavlovian conditioning. Whether the concept of object learning helps to explain some of the special properties of learning with ecologically relevant stimuli remains to be seen. The more rapid and robust learning that was observed with ecologically relevant stimuli in food-aversion learning, sexual conditioning, and fear conditioning is yet to be ¨ satisfactorily explained. Ohman & Mineka (2001, 2003) favor an evolutionary account of the special efficacy of snake cues in fear conditioning. An evolutionary explanation also was offered for the special efficacy of taste cues in food aversion learning (Garcia et al. 1974). Evolutionary explanations leave open the question ¨ of proximate mechanism. Both Ohman & Mineka (2001) and Garcia et al. (1974) offered neurophysiological proximate hypotheses. Behavioral mechanisms may also promote object learning. When the CS and the US are different features of the same object, the two events are likely to covary more closely and may have more stimulus elements in common. Stricter covariation and greater similarity can both promote CS-US associations (e.g., Rescorla & Furrow 1977, Testa 1975).

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CONDITIONED MODIFICATIONS OF RESPONSES TO THE UNCONDITIONED STIMULUS An associationist approach to classical conditioning emphasizes the learning of an association between the CS and the US. Once this association has been learned, presentation of the CS activates a representation of the US (Rescorla 1988) or a representation of what significant biological event is about to occur. However, it was clear even from the research in Pavlov’s laboratory that subjects learn not only what will occur but also when it will happen. Pavlov (1927) noted that when an extended CS-US interval is used, the CR becomes delayed as training progresses, until it occurs close to the actual time of US delivery. (This phenomenon was termed “inhibition of delay.”) Subsequent research has confirmed that subjects encode not only CS-US associations but also precise information about the temporal relation between the CS and the US (Blaisdell et al. 1998, Denniston et al. 1998, Savastano et al. 1998), and that conditioned responses can be timed beautifully to occur just when the US is about to be presented (Kehoe et al. 1989). If subjects timed their CRs perfectly, the CR would occur exactly when the US was delivered, and no behavior would be evident during the CS-US interval. This has encouraged the use of test trials to measure conditioned behavior. However, such test trials miss the critical function of Pavlovian conditioning, which is to permit the subject to respond to the US more effectively. From a functional perspective, the most important consequence of learning is how the subject’s interactions with the US change as a function of having that US preceded or signaled by a conditioned stimulus. Such conditioned modifications of the UR have been documented in several situations.

Conditioned Modification of the Eyeblink Response Irritation of the eye elicits a blink unconditionally. The eyeblink reflex has been a popular response system for the study of classical conditioning (Gormezano et al. 1983). Furthermore, one of the first demonstrations of conditioned modification of the UR was in eyeblink conditioning. Testing human participants, Kimble & Ost (1961) found that a CS that had been paired with an airpuff US not only elicits a blink as a conditioned response, but the magnitude of the blink response to the airpuff is attenuated by prior presentation of the CS. This phenomenon was called “conditioned diminution of the UR.” Subsequent investigators did not always replicate the conditioned diminution effect and also reported enhanced responding to the US following exposure to a CS on occasion. A more systematic examination of the parameters of the conditioned diminution effect suggested that a critical variable is the intensity of the US. Conditioned facilitation of the UR is more likely with low US intensities, and conditioned diminution of the UR is likely with higher US intensities (Donegan & Wagner 1987). Thus, a CS may attenuate or enhance unconditioned responding under different parametric conditions.

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Contrasting conditioned modifications of the UR also have been observed in conditioning with pharmacological unconditioned stimuli (see below).


Sexual Behavior and Reproductive Success As is the case in other domains of Pavlovian conditioning, the emphasis in studies of sexual conditioning has been on conditioned responses that develop to cues that are predictive of sexual reinforcement (Domjan & Holloway 1998). However, a growing body of evidence indicates that a sexually conditioned CS can also facilitate responding to the US in a sexual situation. Exposure to a sexually conditioned stimulus decreases the latency of rats to ejaculate during copulations with a female (Zamble et al. 1985), and decreases the latency of male quail to initiate copulation with a female (Domjan et al. 1986). Furthermore, this decrease in copulatory latency helps to determine the outcome of sexual competition. When two male quail receive access to a single female, the male that is able to predict the sexual encounter because of a CS is able to copulate with the female first (Gutiérrez & Domjan 1996). In studies with the blue gourami fish (Trichogaster trichopterus), males that encountered a female after exposure to a sexually conditioned light stimulus showed reduced levels of aggression and more frequent courtship appeasement action patterns in response to the female (Hollis et al. 1989). In other studies, male quail were found to be more responsive to minimal female cues if these cues were presented in a context that was previously paired with access to a female (Hilliard et al. 1997). Other evidence of conditioned modifications of unconditioned behavior is evident in female quail presented with a conditioned stimulus that signals the impending introduction of a male (Gutiérrez & Domjan 1997). The females show no response to a sexually conditioned stimulus but the CS makes them more receptive once they encounter a male. The ultimate standard for evidence of functional significance is reproductive success. If a behavioral trait is of adaptive significance, individuals with that behavioral trait should produce greater numbers of offspring. Sexual behavior is one of the few systems in which the contributions of learning to reproductive outcomes can be measured directly. In the first study involving direct measurement of reproductive outcome, Hollis et al. (1997) permitted male blue gourami to copulate and tend eggs after exposure to a sexually conditioned stimulus. Males that encountered a female after exposure to a sexually conditioned stimulus showed less aggression toward the female, more nest-building behavior, more clasping behavior, and shorter latencies to spawn. Most importantly, sexual encounters that were preceded by a conditioned stimulus yielded more than 10 times as many offspring as unsignaled encounters. Sexual conditioning has been also found to increase reproductive parameters in quail. Male quail release greater numbers of sperm following exposure to a sexually conditioned stimulus as compared with a control condition (Domjan et al. 1998). More recently, Adkins-Regan & MacKillolp (2003) found that the sexual conditioning of either the male or the female increases the number of fertilized eggs

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that are produced following a copulatory interaction. Increased numbers of fertilized eggs also were found in a subsequent study (Mahometa & Domjan 2004), but only when both the male and the female were able to anticipate a sexual encounter. In addition, Mahometa & Domjan (2004) showed that as with the blue gourami, the greater fertilization success is correlated with changes in how the males and females react to each other. Exposure to a sexually conditioned stimulus increases the female’s receptivity and the efficiency of the male’s copulatory behavior.


Aggression Agonistic behavior contributes to reproductive success through the defense of important resources, such as food, territory, nesting sites, or potential mates (Poole 1985). The unconditioned stimulus for aggression is the presence of a rival or intruder male. In many studies of the conditioning of agonistic behavior, the emphasis has been on the development of aggressive responses to a conditioned stimulus that was paired with a territorial intruder (e.g., Jenkins & Rowland 1996, Thompson & Sturm 1965). However, Hollis (1984, 1990) found that such Pavlovian conditioning also increases the effectiveness with which a territorial male fights with an intruder. Male blue gourami for which an aggressive encounter was preceded by a previously conditioned light CS made significantly more bite and tail-beating responses than did subjects in a control group that previously had the CS unpaired with the US. Interestingly, the success that paired subjects experienced in their test encounter with the intruder also made these males more effective combatants in an unsignaled fight conducted two weeks later (Hollis et al. 1995). Thus, the increase in US effectiveness that was initially stimulated by the presentation of the CS was long-lasting and persisted when the US subsequently was presented in the absence of the CS.

Maternal Nursing and Infant Suckling As described above, another social situation that readily lends itself to conditioning effects involves the interactions between mother and infant that occur during the course of nursing. Although the emphasis in this area of research has been on the development of conditioned endocrine and suckling responses to conditioned stimuli, these conditioned stimuli may also enhance the effectiveness of an unconditioned stimulus. Typically, the unconditioned stimulus for oxytocin secretion and milk letdown is suckling stimulation provided by the infant. However, other aspects of the infant can serve as CSs and elicit these as conditioned responses (Fuchs et al. 1987; Grosvenor & Mena 1972, 1974; McNeilly et al. 1983). Furthermore, the CS cues provided by the infant may also enhance responses to the suckling US. In a recent study involving dairy cows, for example, Tancin et al. (2001) found that the presence of a mother’s calf can increase oxytocin release and milk yield in response to standard unconditioned stimulation of the teats by a milking machine. These effects were more prominent in multiparous cows that presumably had a more extensive conditioning history involving the pairing of a calf with tactile stimulation of the teats.

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Fear-Potentiated Startle One paradigm in which the focus has always been how the conditioned stimulus modifies responses to an unconditioned stimulus is knows as the fear-potentiated startle. The startle response is a defensive response that can be readily elicited in the laboratory by a brief loud noise (Hoffman & Ison 1980). In this system, the brief loud noise is the unconditioned stimulus and the startle response is the unconditioned response. Brown et al. (1951) demonstrated that conditioned fear elicited by an audiovisual stimulus enhances the startle response elicited by a brief loud noise in rats. The parameters of this fear-potentiated startle procedure have since been examined in detail (e.g., Walker & Davis 2002) and the paradigm has become a highly productive technique for investigating the neural and pharmacological mechanisms of fear and defensive behavior (Davis 1997, Fendt & Fanselow 1999, Hijzen et al. 1995). Most of the research on fearpotentiated startle has been conducted with laboratory rats. However, the procedure has been also extended to rhesus monkeys (Winslow et al. 2002). Fearpotentiated startle has been also studied with human participants, using the eyeblink response as an index of startle, and shock or the threat of shock paired with a CS to elicit fear (Ameli et al. 2001, Grillon & Davis 1997, Riba et al. 2001).

Conditioned Hypoalgesia Aversive conditioning can also lead to changes in pain elicited by an aversive unconditioned stimulus. This phenomenon was initially labeled “conditioned analgesia,” but because one cannot be certain that pain is eliminated entirely by exposure to a conditioned stimulus, a more conservative term for the effect is “conditioned hypoalgesia.” In one study (Fanselow & Baackes 1982), for example, rats received shock in a distinctive experimental chamber and were then tested for conditioned fear and pain sensitivity either in the same context or in a different context that had not been paired with shock. Conditioned fear was measured in terms of time spent freezing, and pain sensitivity was measured by recording recuperative responses to having an irritant (a small dose of formalin) injected into one of the hind paws. Subjects tested in the shock chamber showed extensive freezing but little reactivity to the painful formalin injection. In contrast, subjects tested in the alternative context showed very little freezing and substantial levels of recuperative behavior induced by the formalin injections. Similar context-elicited hypoalgesia has been obtained with cold-water swimming and exposure to carbon dioxide as the aversive US (Blustein et al. 1997, Mongeluzi et al. 1996). Conditioned hypoalgesia also has been observed with discrete conditioned stimuli (Illich & Grau 1991, Matzel & Miller 1987). Conditioned hypoalgesia exhibits many of the properties of other conditioning effects, including extinction (Fanselow 1984, Matzel et al. 1988), blocking (Ross 1985), latent inhibition (Maier & Watkins 1991), conditioned inhibition (Wiertelak et al. 1992), and second-order conditioning (Ross 1986).

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The hypoalgesia elicited by the shock-associated context in the study by Fanselow & Baackes (1982) was reversed by treatment with the opiate antagonist naltrexone, suggesting that the reaction was mediated by the release of endogenous opiates (Matzel & Miller 1987). The conditioned hypoalgesia and conditioned freezing are also attenuated by benzodiazepines (Fanselow & Helstetter 1988). Conditioned hypoalgesia has important implications for the mechanisms of aversive conditioning. As Fanselow (1991) noted, “analgesia may act as a negative feedback loop that regulates conditioning” (p. 79). During the course of acquisition, the development of conditioned hypoalgesia will reduce the effectiveness of the US and thereby limit the development of conditioned fear. Conditioned hypoalgesia may also be responsible for the blocking effect (Fanselow 1998). Consistent with these predictions, treatment with naloxone increases fear conditioning (Fanselow 1981, Westbrook et al. 1991), reduces the blocking effect (Schull 1979), and also attenuates the US pre-exposure effect, which is a form of blocking in which the context serves as the previously conditioned stimulus (Matzel et al. 1988).

Digestion and Feeding The first unconditioned stimulus used in studies of Pavlovian conditioning was food. If an important function of Pavlovian conditioning is to modify how the organism interacts with the unconditioned stimulus, evidence of this should be available with food USs. It was recognized early on that the conditioned salivation that occurred in anticipation of dry meat powder can be helpful in digesting the food, and salivation in anticipation of an irritant in the mouth (e.g., a weak acidic solution) can serve to dilute the aversive stimulus. More recently, Woods and his colleagues have argued that such anticipatory responses are critical for the efficient digestion of large meals (Woods 1991, Woods & Ramsay 2000, Woods & Seeley 2002, Woods & Strubbe 1994). Food intake triggers major physiological adjustments involved in the digestion, absorption, and storage of the energy source. Woods has compared eating a large meal to suffering a major physiological assault (Woods 1991). In addition to stimulating the secretion of digestive hormones and enzymes, eating causes the release of a cascade of stress hormones, including adrenocorticotropic hormone, epinephrine, and norepinephrine. Pavlovian conditioning serves to mitigate the disruptive effects of eating by mobilizing the secretion of digestive hormones and enzymes before the food reaches the gut. As Woods noted, “by successfully anticipating the ingestion of food, animals can make appropriate compensatory responses and hence lessen the impact of eating upon the body” (Woods 1991, p. 492). The importance of Pavlovian processes in digestion is clearly illustrated by how insulin secretion is regulated. Insulin is required for the transfer of nutrients from the blood to target tissues and is released by the pancreas in response to elevated serum levels of carbohydrates, fats, and proteins. However, insulin is often released before ingested nutrients are absorbed into the circulatory system.

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The taste or smell of food can trigger the release of insulin long before the food is digested and enters the bloodstream. This has been labeled “cephalic insulin” because it is mediated by brain mechanisms rather than signals originating in the gut. Cephalic insulin secretions also occur in anticipation of predictable large meals and in response to conditioned stimuli that precede meals (Woods et al. 1977). Anticipatory insulin secretion also can be conditioned with injections of insulin rather than meals serving as the US (Woods & Kulkosky 1976). Cephalic insulin and conditioned insulin secretions are responses to cues that precede the gut stimuli that ordinarily serve as the US for insulin secretion. Hence, these are conditioned responses rather than modified unconditioned responses. However, as Woods & Strubbe (1994) have pointed out, “the increment in cephalic insulin coming when it does circumvents the need for a much greater postprandial insulin response” (p. 149). Measurements of the insulin response consequent to the ingestion of a meal would provide decisive evidence on this point. One interesting implication of these mechanisms is that subjects who are able to anticipate a meal should be able to tolerate the stresses of eating more effectively. Consistent with this prediction, rats eat more following a signal for feeding (Zamble 1973). In the study by Zamble, visual and auditory stimuli served as the conditioned stimulus predictive of a meal. Temporal cues can also serve as conditioned stimuli if meals are provided at fixed intervals (e.g., once a day). Interestingly, rats fed at the same time each day eat less if their usual feeding is delayed so that it does not occur in conjunction with the usual temporal CSs (Bousfield & Elliott 1934). This is a remarkable finding because delaying a feeding increases food deprivation. As Woods and his colleagues have pointed out, the Pavlovian approach to the analysis of eating provides a perspective that contrasts with more traditional negative feedback models. Both approaches start with the axiom that ingestion serves to provide needed nutrients and is part of a homeostatic regulatory system. According to classic negative feedback models, organisms monitor an aspect of energy balance (levels of glucose or lipids, for example), and ingestion is initiated when a physiological index of energy balance indicates a deficit. Food intake then rectifies this deficit. The Pavlovian approach suggests that physiological antecedents of meals such as a drop in blood sugar or a decrease in metabolic rate do not trigger eating but help to process the impending meal more effectively (Woods & Seeley 2002, Woods & Strubbe 1994).

Conditioning and Drug Tolerance The administration of a psychoactive drug also may be viewed as creating a major physiological disturbance, whose anticipation may permit the recruitment of processes to deal more effectively with the drug insult. Thus, the types of conditioning mechanisms that facilitate the digestion of food are potentially also relevant to coping with drug experiences (Woods & Ramsay 2000). That drugs may act as unconditioned stimuli was evident to Pavlov, whose associates observed salivation

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and other conditioned responses in dogs that were exposed to cues that reliably preceded the administration of morphine and apomorphine (Pavlov 1927, pp. 35– 37). However, as in his studies with food, Pavlov emphasized the development of responses to conditioned stimuli that did not have much impact before being paired with the drug administrations. How the presentation of a drug-conditioned stimulus may alter the organism’s reactions to the drug itself was not considered until later in the twentieth century, when investigators became interested in the role of Pavlovian conditioning in drug tolerance and sensitization (Eikelboom & Stewart 1982; Siegel 1989, 1991; Siegel et al. 2000; Woods & Ramsay 2000; Young & Goudie 1994). Drug tolerance refers to a decrease in a measured drug effect that is frequently observed with repeated administrations of the drug. Learning has been implicated in drug tolerance in part because drug tolerance can last as long as a year (Cochin & Kornetsky 1964). However, the most significant feature of drug tolerance that has encouraged a learning interpretation is its situation specificity. Drug tolerance is most readily observed when the circumstances of drug administration during a test trial are the same as the circumstances that existed during prior drug treatments. If the context or cues in the presence of which the drug was previously administered are altered, drug tolerance is reduced or abolished. The situation specificity of drug tolerance has been demonstrated with a variety of drugs, including opiates (Siegel 1991), ethanol (Lê et al. 1979), nicotine (Cepeda-Benito et al. 2000), pentobarbital (Cappell et al. 1981), haloperidol (Poulos & Hinson 1982), and benzodiazepines (King et al. 1987). Other features of learning evident in drug tolerance include extinction (Siegel et al. 1980); external inhibition (Poulos et al. 1988); latent inhibition (Goodison & Siegel 1995); inhibitory conditioning (Fanselow & German 1982); stimulus generalization and loss of stimulus control over time (Feinberg & Riccio 1990); and sensory preconditioning, blocking, and overshadowing (Dafters & Bach 1985, Dafters et al. 1983). Pavlovian analyses of drug tolerance have emphasized how physiological and behavioral responses to a drug are attenuated by the presence of drug-predictive cues that become conditioned by repeated drug administrations. Efforts to understand why a drug-conditioned stimulus contributes to tolerance have involved examining conditioned responses elicited by the CS in the absence of the drug itself. For drugs that show the development of tolerance, the CS generally elicits physiological and behavioral changes that are opposite to or compensate for the drug effects. Such drug-compensatory CRs have been observed with a variety of drugs, including opiates (Grisel et al. 1994, Krank et al. 1981), ethanol (Larson & Siegel 1998), and caffeine (Andrews et al. 1998, Rozin et al. 1984). The Pavlovian analysis of drug tolerance is based on homeostatic regulatory concepts. It assumes that the administration of a drug creates physiological disturbances that in turn activate compensatory changes that serve to attenuate those perturbations. Initially, the compensatory adjustments occur only as delayed unconditioned responses to the drug. However, as conditioning proceeds, the unconditioned compensatory responses also come to be activated by the drug-predictive CSs and

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thereby contribute to the attenuated drug effects that are observed (Dworkin 1993, Eikelboom & Stewart 1982). The Pavlovian analysis of drug tolerance is well supported by a large body of evidence and has been extended to analyses of drug abuse and treatment (Siegel et al. 2000, Siegel & Ramos 2002). When originally formulated, the model focused on exteroceptive drug-predictive cues. More recently, the model has been extended to also incorporate internal cues involved in the initiation of an episode of drug self-administration (Donny et al. 1995) as well as internal cues characteristic of the onset of a drug experience (Kim et al. 1999). These extensions help explain why drug tolerance is more evident if the drug is self-administered and why some cases of long-term tolerance are not context specific (Siegel et al. 2000, Siegel & Ramos 2002).

Drug Conditioning and Sensitization Although the Pavlovian model of drug tolerance has enjoyed wide success, it is limited to physiological systems that involve some form of homeostatic regulation. Systems in which the physiological changes induced by a drug do not activate compensatory unconditioned adjustments are not predicted to show conditioned tolerance. Such systems may in fact show the opposite outcome, namely sensitization. Sensitization is an increase in the impact of a drug that occurs with repeated drug administrations. Although research on the contributions of Pavlovian conditioning to drug sensitization is not as extensive as research on conditioned drug tolerance, Pavlovian processes have been implicated in drug sensitization as well (Stewart 1992). Furthermore, Pavlovian sensitization is assumed to play an important role in models of drug abuse. In particular, the development of drug craving has been attributed to a context-specific drug sensitization process (Robinson & Berridge 1993, 2000). Anagnostaras & Robinson (1996), for example, demonstrated sensitization of locomotor behavior in rats elicited by amphetamine. The rats were first given 10 injections of a fairly high dose of amphetamine (3 mg/kg) before being tested with one of several amphetamine doses. Sensitization developed in the context in which the drug was administered but was not observed if the subjects were tested with amphetamine in a different context. Furthermore, extinction of the contextual cues attenuated the drug sensitization effect (see also Drew & Glick 1988, Hinson & Poulos 1981, Post et al. 1981, Terelli & Terry 1999).

Discussion The emphasis on research concerned with CS-induced modifications of responding to the unconditioned stimulus has been on documenting such effects and building a case that such effects obey conventional laws of associative learning. The mechanisms of such effects have garnered much less attention. Perhaps the simplest way to explain such effects is in terms of the summation of responses to the CS and the US. According to the summation model, certain conditioned responses

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come to be elicited by the CS. If the US is presented right after the CS, the responses observed during the US period represent the summation of delayed CRs and responses directly elicited by the US. The summation model has been used to explain conditioned drug tolerance (e.g., Siegel 1989). In this explanation, the CS is presumed to elicit a drug-compensatory conditioned response, which serves to attenuate the effects of the drug that is administered in the CS context. Consistent with this interpretation, in many instances of conditioned tolerance, the response elicited by the drug-conditioned CS is opposite the initial physiological disturbance caused by the drug itself. The summation model has also been used to explain conditioned modifications of eyeblink responding to a US (Donegan & Wagner 1987), and the model readily lends itself to explanations of UR modification in nursing, suckling, and feeding, although it has not been precisely tested in those situations. Although the summation model may explain some instances of conditioned modifications of responding to the US, it cannot serve as a general account of such effects. In particular, the summation model is not readily applicable to instances in which responses to the CS are qualitatively different from the responses that are elicited by the US. The fear-potentiated startle paradigm is a case in point. Here the conditioned response is a conditioned suppression or freezing response to a relatively long CS. In contrast, the startle US is a much shorter (e.g., 500 msec) stimulus that elicits a vigorous startle reaction. The summation model is also difficult to reconcile with instances of enhanced sexual and aggressive behavior. Many of the sexual and aggressive responses that occur in the presence of a conspecific sexual partner or intruder are not observed when a conditioned stimulus is presented because the CS typically does not provide supportive stimulation needed for various copulatory and combative action patterns. A simple summative model has also been brought into question by evidence that conditioned modifications of unconditioned behavior do not always correspond to conditioned responses elicited by a CS. In their studies of conditioned amphetamine sensitization, for example, Anagnostaras & Robinson (1996) observed increased locomotion as a CR when they administered saline in the amphetamine-paired context. However, the size and timing of this CR was not adequate to fully account for the sensitized amphetamine response that was observed in the same context. As an alternative to the summation model, Anagnostaras & Robinson (1996) proposed that conditioned stimuli modify unconditioned responding through an occasion-setting mechanism whereby the CS sets the occasion for the US (Holland 1992, Rescorla 1985, Schmajuk & Holland 1998). However, it is unclear how occasion setting might account for CS-induced modifications of responding to the US, since this mechanism deals with interactions between target and modulating CSs rather than interactions between conditioned and unconditioned stimuli. A more promising alternative to the summation model rests on the common observation that the fundamental outcome of Pavlovian conditioning is that the CS comes to activate a representation of the US (Rescorla 1988). This activation of the

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US representation prior to the occurrence of the US presumably allows for more effective interactions with the US once the US arrives. Hollis (1982) referred to this kind of process as “prefiguring.” Prefiguring or anticipatory activation of the US representation may alter responses to the US in different ways in different response systems. Possible mechanisms include reductions in the threshold for eliciting unconditioned behavior and/or changes in the perception of the US. Additional research is required to document how these mechanisms might operate.


SUMMARY AND CONCLUSION Pavlovian conditioning is typically described as a form of learning in which a neutral or arbitrary CS becomes associated with US, and as a consequence the CS comes to elicit a conditioned response. This description emphasizes that the CS is unrelated to the US at the outset of conditioning and that learning is best measured by the development of new responses to the CS. These features accurately characterize many laboratory studies of Pavlovian conditioning (especially those conducted in the associationist tradition) but fail to capture the critical features of Pavlovian conditioning from a functional perspective. A functional perspective assumes that Pavlovian conditioning is an adaptive trait that occurs under natural circumstances. This simple claim has two important implications, one relevant to the conditioned stimulus, and the other relevant to the conditioned response. For Pavlovian conditioning to occur in the ecological niche of an organism, CS-US pairings must be a feature of that environment. For that to be the case, the CS cannot be unrelated or arbitrary with respect to that US. Rather, there must be a pre-existing ecological relation between the CS and the US. Thus, a functional approach to Pavlovian conditioning rejects the common characterization that Pavlovian conditioning involves learning to associate a neutral or arbitrary CS with a US. In addition to focusing on how learning might occur in the natural environment, a functional approach directs us to focus on behavioral consequences of Pavlovian conditioning that are of adaptive significance. The common characterization of Pavlovian conditioning emphasizes how organisms learn new responses to the CS. However, starting with Pavlov himself, researchers have widely acknowledged that conditioned stimuli are not particularly important in their own right. Therefore, the adaptive significance of Pavlovian conditioning probably does not rest with how an organism’s interactions with the CS are improved by learning. From a functional perspective, the critical task for the organism is to cope with the unconditioned stimulus, which is of much greater biological import. Therefore, a functional perspective directs our attention to how an organism’s responses to the US are changed by Pavlovian conditioning. Evidence of learning with conditioned stimuli that are natural precursors of the US is available from studies of poison avoidance, food intake, sexual behavior,

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fear and defensive behavior, and maternal nursing and infant suckling. Although the details have not been documented in all of these learning situations, the studies have provided provocative evidence that learning with naturalistic stimuli proceeds differently from learning with arbitrary cues. In particular, learning with naturalistic stimuli is more rapid, more resistant to increases in the CS-US interval, and more resistant to the blocking effect. These findings suggest that the phenomena of Pavlovian conditioning may differ for naturalistic as contrasted with arbitrary conditioned stimuli. The mechanisms mediating these learning effects may not be distinctive, but considerable additional research is required to understand how conventional learning mechanisms may produce some of the special learning effects that have been documented with naturalistic CSs. Although conventional descriptions of Pavlovian conditioning emphasize the development of conditioned responses to the CS, numerous studies have shown that conditioning also alters how organisms react to, and interact with, the unconditioned stimulus. Evidence of conditioned modifications of responding to the US is available from studies of eyeblink conditioning; sexual, aggressive, and maternal behavior; fear conditioning; feeding and digestion; and drug conditioning. These conditioned modifications of responding to the US improve the efficacy of the organism’s interactions with the US and reduce the disruptive effects of encountering the US. If these changes in responding to the US are of adaptive significance, they should also be correlated with increased reproductive fitness. In most of the behavior systems examined, adaptive significance is inferred from parameters that are presumed to be related to reproductive fitness. Adaptive significance can be demonstrated more explicitly in the sexual behavior system, which permits direct measurement of reproductive fitness. Consistent with a functional perspective, recent studies have shown that Pavlovian conditioning enhances sperm output and various aspects of sexual behavior, and increases the number of offspring that result from a sexual interaction. Breland & Breland (1961) warned more than 40 years ago that common laboratory paradigms for the study of learning might not accurately reflect learning in natural ecosystems. Although this warning was voiced in relation to operant and instrumental conditioning, it was soon generalized to include Pavlovian and other forms of learning (Hinde & Stevenson-Hinde 1973, Seligman & Hager 1972). Initially the development of a vibrant functional approach to the study of Pavlovian conditioning was hampered by inadequate and difficult methods of discovery (Domjan & Galef 1983). The behavior systems approach developed by Timberlake and his associates (e.g., Timberlake 2001) represents a promising solution to these difficulties. The issues reviewed in the present chapter are theoretically agnostic and complementary to the behavior systems approach. They emphasize two important factors that are central to a functional approach to Pavlovian conditioning: (a) the use of conditioned stimuli that are natural precursors of a US, and (b) the measurement of changes in behavior directed toward the US rather than the CS as the primary behavioral manifestation of Pavlovian conditioning.

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ACKNOWLEDGMENT Preparation of this review was supported by grant MH39940 from the National Institute of Mental Health. The Annual Review of Psychology is online at http://psych.annualreviews.org


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ence approach to delayed learning. In Learning Mechanisms in Food Selection, ed. LM Barker, MR Best, M Domjan, pp. 319–66. Waco, TX: Baylor Univ. Press Riba J, Rodriguez-Fornells A, Urbano G, Morte A, Antonijoan R, Barbanoj MJ. 2001. Differential effects of alprazolam on the baseline and fear-potentiated startle reflex in humans: a dose-response study. Psychopharmacology 157:358–67 Robinson TE, Berridge KC. 1993. The neural basis of drug craving: an incentivesensitization theory of addiction. Brain Res. Rev. 18:247–91 Robinson TE, Berridge KC. 2000. The psychology and neurobiology of addiction: an incentive-sensitization view. Addiction 95(Suppl. 2):S91–117 Ross RT. 1985. Blocking and unblocking of conditioned analgesia. Learn. Motiv. 16: 173–89 Ross RT. 1986. Pavlovian second-order conditioned analgesia. J. Exp. Psychol.: Anim. Behav. Process. 12:32–39 Rozin P, Kalat JW. 1971. Specific hungers and poison avoidance as adaptive specializations of learning. Psychol. Rev. 78:459– 86 Rozin P, Reff D, Mark M, Shull J. 1984. Conditioned responses in human tolerance to caffeine. Bull. Psychon. Soc. 22:117–20 Rusiniak KW, Palmerino CC, Garcia J. 1982a. Potentiation of odor by taste in rats: tests of some nonassociative factors. J. Comp. Physiol. Psychol. 96:775–80 Rusiniak KW, Palmerino CC, Rice AG, Forthman DL, Garcia J. 1982b. Flavor-illness aversions: potentiation of odor by taste with toxin but not shock in rats . J. Comp. Physiol. Psychol. 96:527–39 Rzoska J. 1953. Bait shyness, a study in rat behaviour. Br. J. Anim. Behav. 1:128–35 Savastano HI, Hua Y, Barnet RC, Miller RR. 1998. Temporal coding in Pavlovian conditioning: Hall-Pearce negative transfer. Q. J. Exp. Psychol. 51:139–53 Schmajuk NA, Holland PC, eds. 1998. Occasion Setting: Associative Learning and

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THE NEUROSCIENCE OF MAMMALIAN ASSOCIATIVE LEARNING Annu. Rev. Psychol. 2005.56:207-234. Downloaded from arjournals.annualreviews.org by Ball State University on 01/05/09. For personal use only.

Michael S. Fanselow Department of Psychology and Brain Research Institute, University of California, Los Angeles, Los Angeles, California 90095-1563; email: [email protected]

Andrew M. Poulos Neuroscience Program, University of Southern California, Los Angeles, California 90089-2520; email: [email protected]

Key Words Pavlovian conditioning, amygdala, hippocampus, cerebellum, fear, eyeblink ■ Abstract Mammalian associative learning is organized into separate anatomically defined functional systems. We illustrate the organization of two of these systems, Pavlovian fear conditioning and Pavlovian eyeblink conditioning, by describing studies using mutant mice, brain stimulation and recording, brain lesions and direct pharmacological manipulations of specific brain regions. The amygdala serves as the neuroanatomical hub of the former, whereas the cerebellum is the hub of the latter. Pathways that carry information about signals for biologically important events arrive at these hubs by circuitry that depends on stimulus modality and complexity. Within the amygdala and cerebellum, neural plasticity occurs because of convergence of these stimuli and the biologically important information they predict. This neural plasticity is the physical basis of associative memory formation, and although the intracellular mechanisms of plasticity within these structures share some similarities, they differ significantly. The last Annual Review of Psychology article to specifically tackle the question of mammalian associative learning (Lavond et al. 1993) persuasively argued that identifiable “essential” circuits encode memories formed during associative learning. The next dozen years saw breathtaking progress not only in detailing those essential circuits but also in identifying the essential processes occurring at the synapses (e.g., Bi & Poo 2001, Martinez & Derrick 1996) and within the neurons (e.g., Malinow & Malenka 2002, Murthy & De Camilli 2003) that make up those circuits. In this chapter, we describe the orientation that the neuroscience of learning has taken and review some of the progress made within that orientation.

CONTENTS CORE QUESTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 TECHNIQUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 MODEL SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 0066-4308/05/0203-0207$14.00

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Fear Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Eyeblink Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225


CORE QUESTIONS Three core questions dominate the neuroscience of associative learning. At the systems level we ask, what are the brain circuits that mediate between environmental stimuli and acquired behavior? Then, within those circuits, what synapses must undergo modification for learning to occur? The third question is decidedly more molecular: What intracellular events occur at those sites of critical plasticity to confer the necessary changes in the synaptic efficacy that underlies the formation of a new memory? The preponderance of research on these issues has used Pavlovian conditioning preparations, where dependent relationships between two stimuli result in altered reactions to those stimuli. The behavioral change of interest is to a stimulus that provokes new behavior because receiving a biologically significant stimulus is conditional upon its presence. The stimulus that provokes new behavior is the conditional stimulus (CS) because the reaction to it is conditional upon experiencing a relationship with the biologically significant stimulus. Because behavior is evoked by the biologically relevant stimulus independent of a conditional relationship with another stimulus, it is called an unconditional stimulus (US). A fundamental truth, recognized by neuroscientists of learning for some time, is that the US and the type of reaction it causes determine what neural circuits and what sites of plasticity mediate particular changes in behavior. One implication of this is that there is not a single mechanism for associative learning. Rather, there are many separate systems of associative learning, and although these systems may share some characteristics, they are just as likely to be different from one another. It also means that for analytic purposes it is critical to focus on specific model systems to understand the neural basis of learning. We have chosen two model systems, Pavlovian conditioning of the eyeblink response and Pavlovian conditional fear, as the model systems for the subject of this review. In eyeblink conditioning, a relatively neutral stimulus such as a tone becomes a CS because it is paired with a US such as an air puff or mild shock delivered near the eye to produce an eyeblink. In fear conditioning, a tone CS also may be used, but it is paired with a more threatening US such as foot shock. These are the appropriate models to focus on for several reasons. For both of these systems, staggering progress has been made on each of the three questions raised above. Additionally, the two employ almost completely nonoverlapping circuits. Finally, the behavioral phenomenology of these systems is very different. Fear conditioning is rapidly learned and fear responses are relatively diffuse in timing and topography. Eyeblink is slowly learned, but is characterized by precise timing of a very exact behavioral topography. Additionally, in eyeblink conditioning, the conditional response (CR) to the CS is a near-replica of the unconditional response (UR) to the US; both CR and UR are an eyeblink. In fear conditioning, the CR has

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little topographical relationship to the UR, except that it is defensive. For example, a rat or a mouse will show a vigorous burst of activity in response to the shock, but the CR used in most neuroscientific studies of fear is a complete suppression of activity called freezing (Fanselow 1980).


TECHNIQUES The use of genetically engineered mice has taken on an increased role in the arsenal of techniques used to understand the neural basis of learning. These molecular genetic techniques are unique in their ability to target specific proteins and eliminate, overexpress, or alter their composition. These tools have become increasingly sophisticated, such that tissue selectivity and temporal control of the mutations are becoming available. However, traditional techniques such as lesions, pharmacological manipulations, and electrophysiological stimulation and recording remain important contributors to our understanding. It needs to be recognized that there is no one superior technique; rather, the greatest analytical power derives from combining various approaches. We try to represent that in this review.

MODEL SYSTEMS Fear Conditioning The neural hub for fear conditioning is the amygdala, which is composed of several nuclei in the anterior portion of the medial temporal lobe. Sensory stimuli corresponding to potential CSs arrive at what Swanson & Petrovich (1998) refer to as the frontotemporal amygdala (FTA) because it interconnects frontal and temporal cortices. This region corresponds to the almond-shaped region for which the amygdala originally derived its name, and often is referred to as the basolateral complex because it contains the lateral and basal nuclei. CS information from the thalamus, hippocampus, and several cortical regions reaches the FTA via glutamatergic projections (i.e., neurons releasing the excitatory neurotransmitter glutamate; see Figure 1). The most straightforward and best-characterized CS pathway is a direct projection from the medial geniculate nucleus of the thalamus to the dorsal portion of the lateral nucleus of the amygdala (LeDoux et al. 1991). This pathway carries auditory information to the amygdala and is sufficient for delay conditioning to simple auditory CSs, where tone onset occurs briefly before US onset and CS termination is coincident with either US onset or termination (Romanski & LeDoux 1992). When the CS requires greater processing, polysynaptic routes to the FTA become necessary and the amygdala receives CS information from cortex. For example, the apparatus or context cues present at the time of shock reach the basolateral amygdala via the ventral angular bundle after processing by the hippocampus and entorhinal cortex (Anagnostaras et al. 1999, Maren & Fanselow 1995) and also reach the lateral amygdala from the perirhinal and postrhinal cortex (Amaral et al. 1992). Although damage to the

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Figure 1 The basic circuit for contextual fear conditioning organized according to various types of conditional stimuli at the top and conditional fear responses at the bottom. LA refers to the lateral nucleus of the amygdala and BLA refers to the basolateral nucleus of the amygdala. The BLA and the LA are parts of the frontotemporal amygdala and comprise the neuroanatomical hub for learned fear.


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ventral angular bundle, perirhinal cortex, and postrhinal cortex produces deficits in context conditioning, it has little effect on delay conditioning to a simple auditory cue (Bucci et al. 2000, Burwell et al. 2004, Maren & Fanselow 1995). Interestingly, when the auditory stimulus is similar to the ultrasonic distress call of a rat, perirhinal lesions will attenuate conditioning (Lindquist et al. 2004), again indicating that stimulus complexity determines whether the monosynaptic projections from the thalamus will be sufficient to inform the FTA about the presence of the CS. One can increase the complexity of the fear conditioning procedure to an auditory cue simply by having CS termination occur briefly before US onset. Pavlov called this procedure trace conditioning because he believed a trace of the CS must remain in the nervous systems to bridge the gap between the CS and US. Like context conditioning, trace fear conditioning requires the hippocampus (McEchron et al. 1998, Quinn et al. 2002). However, lesions of the anterior cingulate cortex reduce trace conditioning but do not affect context conditioning (Han et al. 2003). Additionally, using gene expression as a marker for cellular activity, trace conditioning caused greater activation of the anterior cingulate cortex than delay conditioning with the same stimuli. Thus, the anterior cingulate may play the bridging role to which Pavlov referred. Figure 1 groups various types of CS information with the regions that contribute to processing this information. Each of these regions contains monosynaptic projections to the FTA (Amaral et al. 1992). Information about the reinforcing US arrives at the amygdala from a number of routes, all of which appear to be sufficient, but none appear to be necessary. Pain information arrives at the FTA directly from the posterior thalamus as well as via the insular cortex (Brunzell & Kim 2001, Jasmin et al. 2004, Lanuza et al. 2004, Shi & Davis 1999). However, although combined lesions of both of these forebrain routes attenuate conditioning to a tone, context conditioning proceeds normally (Brunzell & Kim 2001). Pain information from subcortical structures such as the parabrachial nucleus, nucleus of the solitary tract, and even the dorsal horn of the spine reach the central nucleus of the amygdala (Benarroch 2001, Burstein & Potrebic 1993, Gauriau & Bernard 2002). However, there do not seem to be any major monosynaptic projections from the central nucleus to the FTA, at least in the rat (DeOlmos et al. 1985). The primary output of the FTA for the generation of conditional fear responses is the central nucleus of the amygdala, which projects to a wide range of regions responsible for emotional responses (see Figure 1). Autonomic reactions are triggered by direct projections to regions of the brainstem and hypothalamus responsible for controlling autonomic function (Kapp et al. 1979, LeDoux et al. 1988). Central nucleus projections to the brainstem modulate several simple reflexes, including the auditory startle response (Canli & Brown 1996, Rosen & Davis 1990, Rosen et al. 1991). Projections to the hypothalamus lead to an elevation of activity in the pituitary-adrenal axis (Feldman & Weidenfeld 1998). Defensive responses are triggered by projections to the periaqueductal gray, which also trigger an endogenous opioid-mediated analgesic state (Fanselow 1991). This analgesia is of particular importance because it provides a negative feedback regulation of the

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ascending painful information that supports fear conditioning (Fanselow 1998). This regulation serves to keep the level of fear appropriate to the level of threat and directs fear toward the best predictors of aversive outcomes. Not all conditional fear–relevant behaviors are controlled via the central nucleus because termination of a fear CS still serves as a reinforcer in rats with central nucleus lesions (Amorapanth et al. 2000, Killcross et al. 1997). It is possible that fear modulates instrumental behavior via projections from the basolateral nucleus to the ventral striatum (French & Totterdell 2003). Some anxiety-related responses are mediated by projections from the FTA to the bed nucleus of the stria terminalis, but it is not clear if these projections play a role in normal fear conditioning (Walker et al. 2003). Fear not only produces behavior, it affects cognitive activity as well. The FTA has extensive projections to the frontal and temporal lobes; for example, all the cortical-FTA projections in Figure 1 are reciprocal (Amaral et al. 1992). The influence of the central nucleus is certainly not entirely descending, either. It can generate cortical arousal through its control of the ascending cholinergic projections from the basal forebrain (Jolkkonen et al. 2002). Together, this array of ascending and descending projections generates the diffuse set of behavioral manifestations referred to as a fear state. An assumption of neuroscientific theories of associative learning is that convergence of CS and US information onto particular cells leads to changes in synaptic strength at the synapses mediating the CS input to those commonly activated cells. This synaptic plasticity is the mechanism that underlies association formation. Learning is the induction of these synaptic changes and the presence of memory depends on the stability of these changes. Long-term potentiation (LTP) induced by activation of the N-methyl-D-aspartate (NMDA) type of glutamate receptor is the most heavily emphasized form of plasticity for fear conditioning. This long-lasting form of synaptic strengthening was first discovered in the hippocampus and can be induced when glutamate activity at initially “weak” synapses is paired with stimulation that causes the cell to spike (Bliss & Gardner-Medwin 1973, Bliss & Lomo 1973). Such a dual pattern is necessary for NMDA receptor activation and the ensuing calcium influx at the ion channel of this receptor leads to strengthening at those NMDA receptor-containing synapses (Collingridge et al. 1983, Malinow & Miller 1986). This is an attractive model for Pavlovian conditioning because a CS-generated glutamatergic input that at first weakly activates a synapse will be potentiated if the US causes the cell to fire within a temporally limited window. Thus, the cells that participate in this plasticity must receive both CS and US inputs. It is clear that individual cells within the lateral nucleus respond to tones that can serve as an auditory CS and shocks that can serve as a US (Romanski et al. 1993). Furthermore, electrical stimulation of auditory input to the FTA (medial geniculate to lateral nucleus) supports long-term plasticity (Clugnet & LeDoux 1990). Indeed, LTP induction in this pathway produced by electrical stimulation

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increases the FTA’s response to a tone (Rogan et al. 1997). Following fear conditioning, cells within the amygdala show increased firing to the CS, suggesting that the CS input has been potentiated following conditioning (Quirk et al. 1997). Finally, McKernan & Shinnick-Galagher (1997) compared brain slices containing the auditory pathway from the auditory thalamus to the lateral nucleus taken from fear-conditioned and control animals. Stimulation of this projection caused greater activation of the amygdala in trained animals, suggesting that fear conditioning induces potentiation of this pathway. Thus, there is a very convincing case that auditory fear conditioning normally supports long-term potentiation of the pathway carrying simple tone information from the medial geniculate nucleus to the lateral amygdala. There is also good evidence that the pathways described above carrying information from the cortex to the FTA and from the hippocampus to the FTA are capable of long-term potentiation (Chapman et al. 1990, Maren & Fanselow 1995, Yaniv et al. 2000). Because administration of NMDA antagonists to the amygdala prevents the acquisition of fear conditioning, it is highly likely that LTP in the FTA supports changes necessary for fear conditioning (Fanselow & Kim 1994, Miserendino et al. 1990). If LTP is a mechanism of memory formation and LTP in the FTA is normally critical for fear conditioning, then the FTA is presumably encoding some important aspect of the fear-conditioning experience. This must be a general aspect of the experience because the FTA is equally important for fear of simple and complex conditional stimuli (Gale et al. 2004). Because the level of fear is highly dependent on the emotional significance of the US, the memory for emotional significance is one possible candidate for the information that the amygdala has encoded. One method that has proven successful in probing the content of associative memory, particularly for the memory of a US, is the reinforcer revaluation technique. For example, Rescorla (1974) gave rats pairings of tone and weak shock such that the tone alone would provoke a low level of fear. In one group of rats, Rescorla changed how the rats valued the memory of shock by giving them a series of strong shocks without the tone. These rats then behaved as if the tone had been paired with a strong shock. Since the tone-shock association should not be strengthened because of receiving shock by itself, the changes in behavior must be related to changes in the memory for shock. Variants of this procedure have been used to probe the content of associations in a range of situations (Dayan & Balleine 2002). If the FTA is encoding the emotional significance of the US, amygdala activity should be important during memory revaluation, and this appears to be the case. If the amygdala is inactivated only during the memory-inflating strong shocks, rats respond to the tone at levels appropriate to the weak shock with which it was paired while amygdala activity was normal and not at the inflated level (Fanselow & Gale 2003). That is, inactivation of the amygdala during memory revaluation prevents revaluation. FTA lesions made after training eliminate conditional responding even if the memory is allowed to consolidate for a period that approaches the adult life span of the rat (17 months), which suggests that the amygdala permanently encodes the emotional significance of the shock reinforcer (Gale et al. 2004).

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Of course, for conditioning you must have not only a representation of emotional significance but also a representation of the stimuli that are associated with that emotional significance. During the course of conditioning to a tone, changes in the medial geniculate cells code the auditory signal (Edeline & Weinberger 1992). Individual cells respond differentially to tones of different frequencies, and during conditioning, many of these cells retune so that the frequency specificity shifts in the direction of the tone paired with shock. These changes in the representation of the tone appear to depend on the amygdala because inactivation of the amygdala prevents the plasticity in the medial geniculate (Maren et al. 2001, Poremba & Gabriel 2001). Individual cells in the auditory cortex also show shifts in their preferred tuning toward the frequency of reinforced CS (Edeline et al. 1993). NMDA-dependent activity plays a role in the learning about the contextual CS associated with shock as NMDA antagonists injected into the hippocampus or genetic deletion of NMDA receptors from the CA1 region of the hippocampus interfere with contextual fear conditioning (Shimizu et al. 2000, Young et al. 1994). Indeed, genetic manipulations that enhance NMDA receptor function can enhance contextual fear learning (Tang et al. 1999). This learning seems to be specifically about contexts because blocking NMDA antagonists only during a time where the animal is exploring a context eliminates the benefits of that exploration (Stote & Fanselow 2004). Inactivating the amygdala at the time of exploration also prevents the benefits of exploration (Huff & Rudy 2004), similar to the effect of amygdala inactivation on retuning of auditory-responsive neurons in the thalamus. This is somewhat surprising, given that there is no emotion-provoking shock during the exploration period; however, this may reflect emotional arousal due to novelty or handling by the experimenter. During fear conditioning, theta rhythm activity generated by a tone paired with shock synchronizes in hippocampus and amygdala (Seidenbecher et al. 2003). Additionally, hippocampal neurons will differentially respond to tones that were paired, as opposed to not paired, with shock, but only in their place fields (Moita et al. 2003). Thus, it is clear that fear conditioning represents a rich interplay between the structures that encode the emotional, signaling, and contextual aspects of the learning. Questions about plasticity for Pavlovian conditioning typically focus on those related to sensory inputs as opposed to response outputs. This is no doubt because Pavlovian conditioning is thought of as learning about stimulus relationships, which results in preprogrammed responses to new stimuli. However, there is no a priori reason to assume that synaptic strengthening cannot play a role at the other relays in the circuit mediating between environment and behavior. A suggestion that there is also significant plasticity within the response-generation pathways comes from analyses of development of fear systems. The ability to condition fear to different CS appears at different ages, as does the ability to express fear in different responses (Hunt & Campbell 1997). Fear-induced freezing occurs at an earlier age than fear-induced potentiation of the startle reflex (Hunt 1999). Fan & Richardson (2002) used odor-shock pairings to train rats at an age when they were old enough to express fear in terms of odor avoidance, but too young to express fear

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in terms of potentiation of the startle response. The intriguing result is that if the rats were not tested until they were old enough to express potentiated startle, they showed avoidance but not enhanced startle. The finding that animals only express behaviors available at the time of training suggests that some component of the plasticity occurs within the response-generation machinery. Thus, it seems that in fear conditioning, important plasticity occurs in the pathways corresponding to CS, US, and CR. All of this plasticity must be rapidly induced because significant fear conditioning occurs with a single pairing of CS and US. If CS and US convergence in the FTA causes potentiation of the glutamatergic synapses activated by the CS, changes must be occurring within the presynaptic neuron, the postsynaptic neuron, or both. The majority of what we know about the intracellular events that support synaptic strengthening comes from studies of long-term potentiation in rodent hippocampal neurons or from studies of gill and siphon withdrawal reflexes of the marine mollusk aplysia (Pittenger & Kandel 2003). There has been considerable debate within each of these two domains as to whether presynaptic or postsynaptic changes are of primary importance, although the hippocampal work has tended to emphasize postsynaptic processes and the aplysia work has emphasized presynaptic processes (Antonov et al. 2003, Lisman 2003, Roberts & Glanzman 2003). Presynaptic changes could take the form of greater neurotransmitter release per action potential arriving at the relevant synaptic terminals. Postsynaptic changes typically take the form of changes that make the postsynaptic cell more responsive to a fixed amount of neurotransmitter release. This could happen through restructuring the postsynaptic region to be more responsive to glutamate, such as the insertion of more of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)type glutamate receptors that mediate the majority of excitatory glutamatergic transmission (Isaac et al. 1995, Liao et al. 1995). Obviously, such changes are not logically exclusive. Rather, coordinated changes in both pre- and postsynaptic regions would be synergistic, and there is some evidence that postsynaptic activity may trigger retrograde messengers that affect their presynaptic contacts (Boehring & Snyder 2003). Finally, some forms of plasticity result in increased synaptic contacts through the growth of new dendritic spines (Muller et al. 2002, Trachtenberg et al. 2002). Thus, we have a rich field of candidates for learning-related cellular changes in the FTA. Determination of which changes contribute to learning requires indirect methods because it is not feasible at this time to identify, in vivo, whether or not neurotransmitter release or receptor density has changed at the individual synapses responsible for learning. The first suggestion that increased transmitter release may be critical to fear-related plasticity in the FTA was made by Maren & Fanselow (1995). They used an in vivo LTP preparation that stimulated the ventral angular bundle, which carries information from the hippocampus and entorhinal cortex to the basolateral nucleus. High-frequency stimulation of this pathway induced a form of long-term potentiation that reduced paired-pulse facilitation. Paired-pulse


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facilitation is an enhanced postsynaptic response to the second of two closely spaced electrical stimulations of the presynaptic neuron. This observation of an interaction between paired-pulse facilitation and FTA-LTP is critical because pairedpulse facilitation results from increased neurotransmitter release by the presynaptic terminal. The idea is that if neurotransmitter release is already optimized by LTP, then additional manipulations designed to enhance neurotransmitter release, such as paired-pulse facilitation, should produce little effect. Like acquisition of fear conditioning, induction of the LTP was prevented by application of an NMDA antagonist (Maren & Fanselow 1995). These researchers went on to confirm that either severing the ventral angular bundle with electrolytic lesions or killing the cells in the region of the FTA that received these projections with a neurotoxin prevented contextual fear conditioning, providing evidence that this pathway participates in contextual fear conditioning. The pattern of results suggests that the acquisition of fear depends on postsynaptic activation of NMDA receptors, which in turn sets in motion processes that lead to increased neurotransmitter release by the presynaptic neuron. Subsequently, Huang & Kandel (1998) replicated the interaction of FTA LTP and paired-pulse facilitation using an in vitro slice preparation where stimulation of the external capsule caused LTP in the lateral nucleus. The external capsule contains projections from the auditory cortex to the lateral amygdala, which suggests that in this pathway, too, expression of potentiation involves increased neurotransmitter release by the presynaptic neuron. Again consistent with the earlier report, NMDA receptors were critical for induction of LTP. Additionally, because injecting a calcium chelator into the postsynaptic neuron blocked LTP, calcium influx through the NMDA receptor appears to be critical for the postsynaptic contribution to the LTP. Paired-pulse facilitation and LTP were attenuated by blocking protein kinase A (PKA) activity in both the presynaptic and postsynaptic cells. However, blocking PKA in the postsynaptic cell alone did not affect LTP. Thus, the increased transmitter release caused by LTP depends on presynaptic activation of PKA (Huang & Kandel 1998). Brain slices containing the auditory pathway from either the thalamus (McKernan & Shinnick-Gallagher 1997) or cortex (Tsvetkov et al. 2002) to the lateral amygdala taken from fear-conditioned animals show enhanced neurotransmission accompanied by an increased probability of neurotransmitter release, which suggests that presynaptic changes are generally involved in the formation of fear memories. The finding that infusion of PKA inhibitors into the amygdala blocks acquisition of long- but not short-term memory for conditional fear is consistent with the hypothesis that PKA-regulated presynaptic changes mediate fear learning (Goosens et al. 2000, Schafe & LeDoux 2000). Induction of the presynaptic changes mediating learning requires NMDAreceptor activation of the postsynaptic neuron. At this point, there is reasonable detail about the postsynaptic cascade within the FTA that contributes to the formation of memory, and these events are roughly parallel to the postsynaptic events that contribute to plasticity in the CA1 region of the hippocampus (see, e.g., Lynch 2004, Pittenger & Kandel 2003). The influx of calcium ions through the NMDA

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receptor results in persistent activation of calcium-calmodulin-dependent protein kinases (CaMK). Genetically induced deletions of one type of CaMK (CaMKII) impair several forms of learning in mice, although much of the work has focused on hippocampus-dependent learning (e.g., Silva 2003). Mayford et al. (1996) targeted a mutation to a specific amino acid within the CaMKII molecule that interfered with its normal activation. Mice bearing this mutation in a manner restricted to amygdala and striatal neurons had a deficit in fear conditioning. Similarly, genetic deletion of CaMKIV appears to produce selective deficits in fear learning and FTA-LTP (Wei et al. 2002). Both auditory fear conditioning and LTP-inducing electrical stimulation of the medial geniculate-amygdala pathway cause activation and movement of CaMKII into dendritic spines of lateral nucleus neurons (Rodrigues et al. 2004). Electron and light microscopy confirmed that this activated CaMKII is associated with postsynaptic NMDA receptors located in spines that synapse with the terminals of neurons that originate in the medial geniculate. Rodrigues et al. also found that infusions of pharmacological inhibitors of CaMKII into the FTA blocked both fear conditioning and LTP. Activation of CamK is known to activate cyclic-AMP response element-binding protein (CREB), which is a regulator of gene expression and memory (Impey et al. 1998, Kida et al. 2002, Tully 1997). Along with a loss of fear conditioning, mice with a genetic deletion of CaMKIV show reduced CREB activation in the FTA (Wei et al. 2002). Increasing CREB expression in the FTA by introducing additional copies of the CREB gene carried by a virus enhances long-term memory for fear (Josselyn et al. 2001). Gene expression is further implicated because mRNA for the immediate early gene Zif268 is expressed specifically in the lateral amygdala following fear conditioning (Rosen et al. 1998). Blockade of the transcription of DNA to RNA within the amygdala prevents the acquisition of fear memories (Bailey et al. 1999), as does translation of RNA into protein (Schafe & LeDoux 2000). Some important issues have not been resolved. It is not known what these new proteins are or how they function to produce enduring changes in synaptic efficacy in the FTA. Protein synthesis in the postsynaptic cell seems necessary for the induction of the essential plasticity, but the critical changes in the expression of this plasticity appear to be presynaptic. Some combination of postsynaptic calcium influx, activation and movement of CamK into postsynaptic spines, and/or gene expression-mediated protein synthesis in the nucleus of the postsynaptic neuron initiates PKA-dependent presynaptic changes confined to the synapses that participated in the learning. Of course, this is such a fast-evolving field that these questions may well be answered by the time this review appears in print. As pointed out in the section above, fear conditioning involves plasticity at a number of sites. Here we have chosen to focus on the FTA because it is the site most relevant to the formation of new associations between the CS and US, and the presently available data make the strongest link between intracellular events and specific learned behaviors in vertebrate animals. In addition, with the exception of the hippocampus, little is known about the intracellular events mediating

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plasticity in these other regions. Certainly, the knowledge of the intracellular events mediating plasticity for the CA1 field of the hippocampus is more detailed than it is for the amygdala. Although much of the postsynaptic cascade is similar in the two, a critical mediator of hippocampal plasticity is the insertion of new AMPAtype glutamate receptors into the postsynaptic region (Isaac et al. 1995, Liao et al. 1995, Pickard et al. 2001). However, the current state of knowledge does not preclude such postsynaptic changes in the FTA, and there is certainly evidence for a presynaptic influence on hippocampal LTP (Lisman 2003). Indeed, one can be reasonably confident that the plasticity underlying learning in these structures will turn out to be a result of an active dialogue between the pre- and postsynaptic neurons. Generalization to humans Although it is impossible to perform equivalently detailed analysis in humans, to the extent that such work has been done the specifics are remarkably consistent. Bechara et al. (1995) examined a patient with UrbachWiethe disease, a condition that results in bilateral calcification and atrophy of the amygdala with little damage to any other medial temporal lobe structures. The patient had pronounced loss in delay fear conditioning assessed by autonomic arousal despite the fact that her memory for the details (declarative memory) of the conditioning session was completely intact. Consistent with the lesion data, functional magnetic resonance imaging (fMRI) shows enhanced blood flow to the amygdala during delay fear conditioning (LaBar et al. 1998), and this amygdala activation is highly correlated with the conditional fear response (Cheng et al. 2003). However, hippocampal activity is correlated more with the cognitive appreciation of the situation than with the emotional response (Cheng et al. 2003). Similarly, humans with hippocampal damage and an intact amygdala show normal fear conditioning with a loss of declarative memory for the conditioning situation (Bechara et al. 1995). Contextual fear conditioning has not been studied in humans. Knight et al. (2004) found that trace and delay CS produced similar activation of the human anterior cingulate; this contrasts with the differential activation found in mice. It is not clear if this is because the animal studies examined cellular markers of activity that were more precise (Han et al. 2003) or because the human studies trained trace, delay, and unpaired stimuli in the same subjects (Knight et al. 2004).

Eyeblink Conditioning The core neural component for eyeblink conditioning is the cerebellum, a brain structure located just caudal to the cerebral hemispheres and overlying the dorsal surface of the brainstem. The neuronal cell bodies of the cerebellum form a thick cortical layer that covers the underlying white matter (axons) and deep cerebellar nuclei. These nuclei, organized in medial-lateral orientation, consist of the fastigial, interpositus, and dentate nuclei. The relays of cerebellum are organized in highly regular manner, with well-defined sensory input and motor output pathways.

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Sensory information corresponding to potential tone or light CSs is relayed to the cerebellum by auditory and visual pathways primarily via pontine mossy fibers (see Figure 2). Mossy fibers form monosynaptic connections to neurons in the interpositus nucleus (IPN) and disynaptic connections to the Purkinje cells of the cerebellar cortex by way of granule cell parallel fibers. Damage to appropriate portions of the pontine nuclei can abolish CRs established to a tone CS while leaving conditioning to a visual CS, such as light, intact (Steinmetz et al. 1987). At the level of the IPN, single neurons receive CS information from multiple sensory modalities. For example, single cell recordings reveal IPN neurons that respond singly and in combination to both tone and light CSs and air puffs (Tracy et al. 2001), and appropriately directed lesions of the IPN prevent conditioning to all CS modalities tested (Ivkovich et al. 1993, Lavond et al. 1985, Steinmetz et al.

Figure 2 A simplified schematic of the putative neural circuitry essential for delay eyeblink conditioning. Arrows and fork terminals denote excitatory synapses; bar terminals denote inhibitory synapses. Solid ovals denote fibers (axons) and dotted ovals denote proposed sites of plasticity underlying eyeblink conditioning. A/V, auditory and visual pathways; CC, cerebellar cortex; cf, climbing fiber; CS, conditioned stimulus; CR/UR, conditioned response/unconditioned response; GC, granule cell; IO, inferior olive; IPN, interpositus nucleus; mf, mossy fibers; MN, motor nuclei; PC, Purkinje cell; pf, parallel fiber; PN, pontine nucleus; RN, red nucleus; RF, reticular formation; TN, trigeminal nucleus; US, unconditioned stimulus. (Modified from Christian & Thompson 2003.)

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1992, Thompson & Krupa 1994, Yeo et al. 1985). Substitution of peripheral CSs with direct electrical stimulation of either the pontine nucleus or IPN serves as a very powerful CS, producing conditioning that is more rapidly acquired than to light or tone (Poulos & Thompson 2004, Steinmetz et al. 1986, Tracy et al. 1998). Somatosensory information corresponding to an air-puff or mild eye-shock US arrives at the cerebellum primarily by climbing fibers from the inferior olive (see Figure 2). This essential US pathway includes the trigeminal nucleus, which in turn innervates the inferior olive, which sends axonal processes to the cerebellum via climbing fibers. The climbing fibers and their collaterals synapse on Purkinje cells and IPN neurons of the cerebellum and utilize glutamate and CRH as primary neurotransmitters (Ito 2002). Prior to training, lesions of the inferior olive prevent eyeblink conditioning, whereas similar lesions following conditioning result in extinction with continued paired training (McCormick et al. 1985). Conversely, electrical stimulation of the inferior olive that elicits behavioral responses when used as a US produces conditioning at a rate, magnitude, and topography similar to peripheral USs (Mauk et al. 1986). The IPN is not only a primary site of CS-US convergence, but also a primary cerebellar motor output responsible for the generation of the conditional eyeblink response. The IPN, like the motor cortex, contains a somatotopic representation of the entire body (Schultz et al. 1979). For example, in the rabbit, direct electrical stimulation of the medial aspects of IPN elicits movements in the lower trunk and hind limb areas, whereas stimulation of lateral portions of IPN evokes movements in the head area that include eyelid closure (McCormick et al. 1983). This eyelid region of the IPN sends heavy projections to the contralateral magnocellular red nucleus. From the red nucleus, projections to a set of motor nuclei trigger the expression of the conditional eyeblink response. The same motor nuclei also receive direct and indirect projections from the trigeminal nucleus, responsible for producing the unconditional eyeblink reflex. Under simple delay conditioning procedures, the cerebellum and its associated brainstem regions are necessary and sufficient for the acquisition and expression of conditional eyeblink responses (Figure 2). However, damage to the medial septum, a primary source of cholinergic projections to the hippocampus, retards the rate of delay conditioning (Berry & Thompson 1979). Conversely, intraseptal injections of scopolamine, an acetylcholine receptor antagonist that suppresses hippocampal functioning, also slow eyeblink conditioning (Salvatierra & Berry 1989, Solomon & Gottfried 1981). However, if the hippocampus is lesioned first, scopolamine no longer impairs learning (Solomon et al. 1983). Thus, for delay eyeblink conditioning, a functionally compromised hippocampus is more detrimental to learning than is the absence of the hippocampus. As similarly described in fear conditioning, the introduction of a stimulus-free period between the CS and US requires the hippocampus. Lesions of the hippocampus, which produce no discernable effects on delay conditioning, markedly impair trace eyeblink conditioning (Beylin

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et al. 2001, Solomon et al. 1986). In trace conditioning, lesions made one month following training do not impair performance of trace CRs (Kim et al. 1995) in a manner that parallels hippocampus-dependent consolidation of context fear (Kim & Fanselow 1992). Anatomical evidence suggests that interactions between the cerebellum and hippocampus occur indirectly, with the hippocampus modulating CS and US input by interacting with the pontine nucleus and inferior olive (Lee & Kim 2004). In eyeblink conditioning, it is quite clear that both neurons of IPN and Purkinje cells of the cerebellar cortex receive CS and US inputs. Therefore, it is conceivable that plasticity at one or both sites mediates the formation and storage of eyeblink memory traces. Furthermore, anatomical evidence reveals that both cerebellar regions send and receive reciprocal connections among each other (see Figure 2). For this reason, determining the relative contributions of IPN and cerebellar cortex has been difficult. Initial findings by McCormick et al. (1981) demonstrated that electrolytic lesions of the dentate-IPN region and large aspirations of cerebellar cortex completely abolished all expression of conditional eyeblink responses without affecting the UR. Later studies revealed that lesions of the anterior lateral IPN as small as 1 mm3 are sufficient to completely abolish CRs (Lavond et al. 1984). Further, temporary inactivation methods demonstrate that inactivation of the IPN during training completely prevents learning, as evidenced by the lack of CRs following inactivation (Clark et al. 1992; Krupa & Thompson 1995, 1997). In contrast, learning is not prevented by inactivation of an efferent pathway or its target, the red nucleus, which completely abolishes CR expression (Clark & Lavond 1993, Krupa & Thompson 1995, Krupa et al. 1993). These results suggest that the essential plasticity cannot occur efferent to the IPN and that essential memory trace (or traces) is established and maintained in the cerebellum. Single- and multiple-unit recordings in the IPN show learning-related unit activity, also found in the cerebellar cortex, that precedes and predicts occurrence of the behavioral CR (Berthier & Moore 1986, 1990; Gould & Steinmetz 1994; King et al. 2001; McCormick & Thompson 1984; Rogers et al. 2001). Moreover, lesions or temporary inactivation of the IPN (Thompson & Krupa 1994) abolish neuronal recording of conditioning-related unit activity in many regions of the essential eyeblink circuitry (see Figure 2). Removal of cerebellar cortical tissue has been reported to result in varying effects in eyeblink conditioning. Although it has been reported that lesions of cerebellar cortex prevent the acquisition of conditional eyeblink responses (Garcia et al. 1999), all other studies to date report either no effect or impairments in acquisition, retention, and/or CR timing (Lavond & Steinmetz 1989, Logan et al. 1994, McCormick & Thompson 1983, Perrett et al. 1993). Perhaps the clearest interpretation of these lesion studies is provided by Purkinje cell degeneration (PCD) mice. Several weeks after birth of these mice, Purkinje cells, the sole output of the cerebellar cortex, completely degenerate. PCD mice acquire eyeblink


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conditioning at a slower rate and lower level than wild-type mice, but they do learn (Chen et al. 1996). Further, CRs expressed tended to have shorter peak latencies. Recent work by Bao et al. (2002) suggests that CR expression and timing may be completely dissociable and that memory traces for eyeblink CRs may be encoded in a functionally distinct manner in both the cerebellar cortex and IPN. In their study, rabbits displaying well-timed CRs were given sequential IPN application of the gamma-aminobutyric acid (GABAA) agonist muscimol and the GABAA antagonist picrotoxin, which resulted in the expression of reduced onset and peak CR latencies poorly timed to the US. Such results are consistent with studies by Mauk and associates using high concentrations of picrotoxin (Garcia & Mauk 1998, Medina et al. 2001). This disruption of CR timing following blockade of cortical outputs suggests that the memory traces for learned timing and for basic associative eyeblink memory may be expressed within two distinct sites of plasticity, the cerebellar cortex and IPN, respectively. Early cerebellar learning theories proposed plasticity within the cerebellar cortex, specifically at the parallel fiber-Purkinje cell (PF-PC) synapses, as a general mechanism of motor learning (Albus 1971, Marr 1969). Albus (1971) further hypothesized that the high tonic-firing rate of Purkinje cells resulting in sustained GABAergic inhibition of the IPN could be released by a decrease in PF-PC synaptic strength. Empirical evidence for this theory came with the discovery that long-term depression (LTD) resulting from simultaneous low-frequency stimulation of parallel fibers and climbing fibers could result in persistent decreases in PF-PC synaptic efficacy (Ito & Kano 1982). However, under such parameters of simultaneous CS and US presentation, eyeblink conditioning does not develop. Further work by Chen & Thompson (1995) demonstrated that under specific in vitro conditions that did not block GABA receptors, LTD could be induced by repeated parallel fiber and climbing fiber stimulation separated by 250 msec, matching the optimal eyeblink conditioning interstimulus intervals. In contrast, simultaneous stimulation of parallel and climbing fibers can elicit LTD in the presence of the GABA antagonist biccuculline. Indeed, in the intact animal, direct electrical stimulation of parallel fibers as a CS and climbing fibers as a US produces appropriately timed eyeblink conditioning (Shinkman et al. 1996). Recent investigations both in vivo and in vitro have begun to examine plasticity specific to the IPN and its synapses. One attractive mechanism of memory formation in eyeblink conditioning is long-term plasticity of the mossy fiber-IPN synapse. Racine (1986) previously reported that tetanic electrical stimulation of mossy fibers induces LTP of the mossy fiber-IPN synapse. Perhaps the most compelling evidence of conditioning-specific synaptic plasticity comes from recent findings by Kleim and colleagues (2002) using unbiased stereological synapse quantification methods. In this study, rats trained in delay eyeblink conditioning exhibited an increased number of excitatory IPN synapses compared to unpaired and untrained controls. Moreover, because the expression of CRs is driven solely by the CS, increases in excitatory IPN synapses are likely to occur along the CS

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pathway, specifically at mossy fiber-IPN synapses. There was no increase in inhibitory synapses in the IPN (presumably from Purkinje axons). A form of nonsynaptic plasticity also has been implicated in cerebellar learning. Aizenman & Linden (2000) showed that direct high-frequency stimulation of deep nuclear neurons of the cerebellum under reduced levels of inhibition resulted in persistent increases in maximum firing rate. Interestingly, IPN electrical stimulation to eyeblink thresholds are markedly reduced following eyeblink conditioning, suggesting possible conditioning-related increases in IPN excitability (Poulos et al. 2002). Conversely, infusion of NMDA antagonist APV, in the IPN, which in vitro blocks the induction of intrinsic excitability (Aizenman & Linden 2000), selectively impairs conditioning but not expression of eyeblink CRs (Chen & Steinmetz 2000a). In eyeblink conditioning the combination of evidence from several levels of analysis strongly suggests that the development and maintenance of the adaptive (well-timed) associative memory (CRs) are mediated by plasticity occurring in at least two cerebellar regions, the cortex and IPN. Moreover, the forms of plasticity that contribute to learning have yet to be directly identified in vivo; therefore, it is imperative to employ molecular and genetic methods to isolate key components of plasticity and learning. At the level of the IPN, if an increase in synaptic strength is a mechanism of the primary CSUS memory, then alterations of intracellular mediators of LTP or synaptogenesis should compromise conditioning. Further, if depression of synaptic function in the cortex mediates the appropriate timing of the associative memory, utilizing methods to manipulate expression of key molecular components of cerebellar cortical LTD should affect conditional responding and timing. The demonstration that eyeblink conditioning is associated with an increase in the number of IPN excitatory synapses prompts important questions as to possible mechanisms of synapse formation in the adult central nervous system that may mediate memory formation. Almost certainly any physical or chemical changes in neurons involve alterations in gene expression and/or protein structure. Gomi et al. (1999) demonstrated that inhibition of RNA synthesis in the IPN profoundly impairs learning but not the expression of eyeblink CRs. Conversely, inhibition of protein synthesis similarly reduces the rate of conditioning (Bracha et al. 1998). Gomi et al. (1999) further identified a kinase whose expression increased in the IPN with eyeblink conditioning. Isolation of cDNA and sequence analysis revealed that the expressed gene was KKIAMRE kinase, a member of the CDC2-related and mitogen-activated protein (MAP) family. Both inhibitors of protein kinases and specific MAP kinase p38 markedly impair learning but not CR expression (Chen & Steinmetz 2000b, Zhen et al. 2001). Evidence of synaptogenesis, or gene transcription and translation, does not preclude a role for mossy fiber-IPN LTP as a mechanism of learning, such that LTP may be an antecedent or act in concert with synaptogenesis to promote memory formation. Like amygdaladependent fear conditioning and hippocampal-dependent maze learning, application of an NMDA receptor antagonist, which prevents learning and amygdala and

INTRACELLULAR MEDIATORS OF PLASTICITY

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hippocampal LTP, markedly impairs eyeblink conditioning (Chen & Steinmetz 2000a). A reduction in PF-PC synaptic strength at multiple and spatially distinct synapses is an attractive model that could promote the appropriate temporal release of IPN inhibition and hence expression of appropriately timed conditional eyeblink responses. Indeed, single-unit recordings of Purkinje cells reveal decreases and increases in firing rates that immediately precede the expression of well-timed CRs (King et al. 2001). The requirements for eyeblink conditioning, like those of PF-PC LTD, are typically associative. Both can be induced by parallel fiber and Purkinje cell stimulation. To date, molecular mechanisms of cerebellar LTD are more clearly understood. The induction of cerebellar LTD is initially triggered by activation of mGluR1 metabotropic and AMPA receptors. Mutant mice deficient in mGluR1 and impaired in the induction of LTD are also impaired in eyeblink conditioning (Aiba et al. 1994). Further, blockade of cerebellar cortical AMPA receptors, which block the induction of LTD (Wang & Linden 2000), also has been reported to impair the acquisition and expression of CRs (Attwell et al. 1999, 2001). Following activation of glutamate receptors, transient activation of protein kinase C (PKC) is essential. Transgenic mice with Purkinje cell–specific inhibition of PKC, which prevents the induction of LTD, show impairments in eyeblink conditioning (Goossens et al. 2001, Koekkoek et al. 2003). Conversely, eyeblink conditioning results in an increase in membrane-bound PKC specific to cerebellar cortical tissue (Freeman et al. 1998). Further, inhibition of nitric oxide, which blocks the induction of LTD (Shibuki & Okada 1991), results in attenuated eyeblink acquisition and learning-related neural activity in the IPN (Allen & Steinmetz 1996). Expression of PF-PC LTD is expressed postsynaptically, possibly as a reduction in the number of functional AMPA receptors produced by endocytosis. Interestingly, quantitative autoradiography reveals eyeblink conditioning– related decreases in (3H) AMPA binding to synaptic subpopulations of cerebellar cortical AMPA receptors (Hauge et al. 1998). Additional support for the LTD hypothesis comes from mice deficient in glial fibrillary acidic protein; these mice show normal excitatory synaptic transmission and have no observable motor deficits, but are markedly deficient in PF-PC LTD and have impaired eyeblink conditioning (Shibuki et al. 1996). It is likely that the eyeblink conditioning results in plasticity at a number of sites. Here we have chosen to focus primarily on two critical sites of plasticity, the IPN and cerebellar cortex. It should be noted that within each of these regions, synaptic plasticity at sites other than PF-PC and mossy fiber-IPN synapses has been identified and discussion of such sites and mediators of plasticity and their potential contributions to eyeblink conditioning has been reviewed by Hansel et al. (2001). However, these potential mechanisms have yet to be tested in eyeblink conditioning. As pointed out above, much of the research in cerebellar synaptic plasticity has focused on PF-PC LTD. However, because most of the evidence suggests that eyeblink conditioning can occur independent of the cerebellar cortex, it seems likely that elucidating the molecular mechanisms of IPN plasticity (LTP

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and/or synaptogenesis) may yield for the first time a cellular substrate of cerebellar memory. Generalization to humans The results obtained in animal studies of eyeblink conditioning correspond closely to the evidence available in human eyeblink conditioning. The combination of lesion, neuroimaging, and observation in amnesic patients confirms that the cerebellum plays a critical role in eyeblink conditioning in humans as well. Positron emission tomography in humans reveals changes in glucose metabolism in the cerebellum correlated with eyeblink conditioning (Blaxton et al. 1996, Logan & Grafton 1995, Molchan et al. 1994, Schreurs et al. 1997). Moreover, fMRI shows increases in cerebellar cortex and deep nuclear activity over the course of eyeblink conditioning (Lemieux & Woodruff-Pak 2000). In addition, both positron emission tomography and fMRI reveal activation of the medial temporal lobe, which includes the hippocampus (Lemieux & WoodruffPak 2000, McIntosh & Schreurs 2000). In amnesic patients suffering from bilateral medial temporal lobe damage, trace conditioning under a long trace interval is markedly impaired, whereas delay eyeblink conditioning is normal (Clark & Squire 1998, McGlinchey-Berroth et al. 1997, McGlinchey-Berroth 2000). However, patients with bilateral as well as with unilateral cerebellar lesions ipsilateral to the trained eye are severely impaired in delay and trace eyeblink conditioning (Daum et al. 1993, Lye et al. 1988, Solomon et al. 1989, Topka et al. 1993). In sum, across a wide spectrum of mammals including humans the cerebellum is critically for the acquisition, retention, and expression of conditional eyeblink responses.

CONCLUSIONS Different types of learning and memory must be analyzed as separate systems that evolved to solve specific functions. Emotional memory, examined through Pavlovian fear conditioning, and specific motor responses, examined through Pavlovian eyeblink conditioning, arise from largely different neural circuits. Even the specific mechanisms that produce memory at the cellular level are different. We also need to recognize that there are a few important similarities. At the circuit level, both systems require input from similar cortical structures when conditioning becomes more complex. The hippocampal contribution to trace conditioning is the clearest example. Additionally, a negative feedback circuit regulates learning in both systems, although the specific circuit is very different (Fanselow 1998). In terms of plasticity, there is considerable overlap in the specific molecules that regulate synaptic plasticity, although these molecules may play different roles in the different forms of learning. Two emerging findings are critical. The first finding is that the mechanisms of learning involve a coordinated interplay between pre- and postsynaptic regions, and the second is that learning and gene expression are so intimately related that the old nature-nurture distinction is of little value.

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The Annual Review of Psychology is online at http://psych.annualreviews.org


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BEHAVIORAL INHIBITION: Linking Biology and


Behavior within a Developmental Framework Nathan A. Fox,1 Heather A. Henderson,2 Peter J. Marshall,3 Kate E. Nichols,1 and Melissa M. Ghera1 1

Department of Human Development, University of Maryland, College Park, Maryland 20742; 2Department of Psychology, University of Miami, Coral Gables, Florida 33149; 3Department of Psychology, Temple University, Philadelphia, Pennsylvania 19122; email: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]

Key Words amygdala, fear, anxiety, attention, temperament, social anxiety ■ Abstract Behavioral inhibition refers to a temperament or style of reacting that some infants and young children exhibit when confronted with novel situations or unfamiliar adults or peers. Research on behavioral inhibition has examined the link between this set of behaviors to the neural systems involved in the experience and expression of fear. There are strong parallels between the physiology of behaviorally inhibited children and the activation of physiological systems associated with conditioned and unconditioned fear. Research has examined which caregiving behaviors support the frequency of behavioral inhibition across development, and work on the interface of cognitive processes and behavioral inhibition reveal both how certain cognitive processes moderate behavioral inhibition and how this temperament affects the development of cognition. This research has taken place within a context of the possibility that stable behavioral inhibition may be a risk factor for psychopathology, particularly anxiety disorders in older children. The current chapter reviews these areas of research and provides an integrative account of the broad impact of behavioral inhibition research.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THE BIOLOGY OF BEHAVIORAL INHIBITION . . . . . . . . . . . . . . . . . . . . . . . . . . . Heart Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cortisol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electroencephalogram, Event-Related Potentials, and Functional Magnetic Resonance Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONTINUITY AND DISCONTINUITY IN BEHAVIORAL INHIBITION OVER DEVELOPMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Longitudinal Studies of Behavioral Inhibition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role of Caregiving Behaviors and Environments . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0235$14.00

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COGNITION-EMOTION INTERACTIONS IN BEHAVIORAL INHIBITION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Effects of Emotion on Attentional Orienting . . . . . . . . . . . . . . . . . . . . . . . . . . . Voluntary Attentional Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attention and the Development of Social Cognition in Behaviorally Inhibited Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . BEHAVIORAL INHIBITION AND PSYCHOPATHOLOGY . . . . . . . . . . . . . . . . . . FUTURE DIRECTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EPILOGUE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION Behavioral inhibition to the unfamiliar refers to “the child’s initial behavioral reactions to unfamiliar people, objects, and contexts, or challenging situations” (Kagan et al. 1985, p. 53). The initial research reports on behavioral inhibition (Garcia-Coll et al. 1984, Kagan et al. 1988) described a group of toddlers who, by both parent report and observation in the laboratory, avoided unfamiliar events and people. When confronted with such challenges, these children ceased their play behavior and withdrew to the proximity of their caregivers. They remained vigilant of their surroundings during these situations and rarely approached novel objects or unfamiliar people. Our goal in this chapter is to provide a broad overview of the work on behavioral inhibition. After a brief introduction, we begin with a review of the research identifying the biological underpinnings of behavioral inhibition. We next review the longitudinal studies of behavioral inhibition outlining the findings on continuity and discontinuity and identifying factors both within the child and in the environment that may affect these different developmental trajectories. In the third section, we focus on the role of attentional processes as an example of a within-child factor that contributes to patterns of reactivity and regulation among behaviorally inhibited children. Our fourth section details the findings from the developmental psychopathology literature on relations between behavioral inhibition and the heightened risk for general and specific anxiety disorders and more global problems in adjustment. In the final section of this review, we suggest future directions for the study of behavioral inhibition. Research on behavioral inhibition has in many ways provided a model of interdisciplinary integration for other areas of developmental psychology that are now at the forefront of psychological science. Among these areas are the study of links between basic neuroscience and emotional development, examinations of the mutual influences of affect and cognition on behavior, and the identification of precursors to psychopathology in early childhood. There are a number of reasons why the work on behavioral inhibition has been successful in creating these links. First, unlike most previous research on temperament, this work has relied less on questionnaire data (e.g., parent report of temperament) and more on behavioral

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description. The focus on behavioral observation and clear descriptions allowed scientists to identify certain responses (e.g., freezing, avoidance) that are similar to those described in animal models of fear or anxiety. The ability to relate behavioral descriptions of humans to descriptions of other animals provided an important initial link between temperament and the neurosciences. A second reason why the work on behavioral inhibition has had such a broad impact in psychology is the emphasis placed on the categorical nature of extreme temperamental behavioral inhibition, particularly by Kagan and colleagues (e.g., Kagan et al. 1984). Kagan appealed to notions in biology and medicine where categories serve an important function in identifying a species or a specific disease state (Kagan 1994). Correct or not, the idea of a categorical trait with its own unique biology and behavioral constellation forged a link with biologists and neuroscientists. Kagan’s reading of the then current work in behavioral neuroscience, including the studies of LeDoux and Davis (e.g., Davis 1986, LeDoux et al. 1988) enhanced his interest in describing the underlying biology of behavioral inhibition. In separate research programs, LeDoux and Davis focused on the amygdala as the brain structure responsible for the enhancement of fear conditioning and the potentiation of fear behaviors (Davis 1992, LeDoux et al. 1988). Building on the psychophysiological data that he and his colleagues had collected, Kagan suggested that individual differences in behavioral inhibition were the result, in part, of an overactive amygdala, creating an enhanced fear response to novelty and unfamiliarity. This attempt to bridge the behavior-neuroscience gap came at a time when the work of Davis and LeDoux was receiving widespread attention, and it facilitated a broader discussion of the ways in which the interplay of biology and behavior could be understood over time and within the context of human development. The initial research on behavioral inhibition was also inherently developmental in approach and theory. Two key observations were highlighted in longitudinal studies of behavioral inhibition. First, across development, children developed a greater repertoire of behaviors in response to novel social situations. Second, while the behavioral manifestation changed somewhat over development, there was significant preservation of individual differences in inhibition. That is, behaviorally inhibited children displayed marked continuity in their distinctive pattern of responding to unfamiliar social and nonsocial stimuli. At the same time, variations in the developmental trajectories of behaviorally inhibited children necessitated broadening the model to include both endogenous and exogenous factors that might influence these different developmental paths, and merited examination of the contextual factors and cognitive processes that may mediate the expression of behavioral inhibition as children get older. A primary source of hypotheses regarding factors influencing different developmental trajectories for behaviorally inhibited children is the model of temperament postulated by Rothbart and colleagues. Rothbart’s model proposed two components: reactivity and self-regulation (Rothbart & Derryberry 1981). Reactivity reflects the infant’s or child’s physiological and behavioral responses to sensory stimuli, and is assessed in terms of the latency and intensity of responding. In the context

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of behavioral inhibition, individual differences in reactivity are reflected by differences in the strength of the disposition to express particular discrete emotions to novel or unfamiliar stimuli or challenging situations. Regarding regulation, Rothbart’s argument is twofold. First, certain cognitive processes such as voluntary attentional control and response inhibition that develop over infancy and early childhood serve to modulate reactive responses. Rothbart refers to these processes under the broad label of “effortful control.” Second, the child’s style of reactivity may influence the manner in which these processes emerge. Thus, temperament reflects individual differences not only in reactivity, but also in the manner in which effortful control processes are shaped by that reactivity (Derryberry & Rothbart 1997). There are many implications of Rothbart’s model of temperament for the study of behavioral inhibition. Individual differences in reactions to novelty may be identified early in the first year of life and may be described with respect to the disposition to express negative affect (fear or distress) when presented with novel, unfamiliar, or challenging events. Across early childhood, self-regulatory processes (voluntary attention, inhibitory control) will be influenced by this pattern of negative reactivity to novelty and may become biased to focus on negative affect or prevent disengagement from novel, unfamiliar, and often threatening stimuli. The manner in which the development of attention and other executive-function processes underlying effortful control either heighten or dampen the expression of behavioral inhibition in older children is thus of great interest. A further area of study to which research on behavioral inhibition has contributed is the role of early temperament as a risk factor for the development of psychopathology. A number of studies have suggested that by middle to late childhood, socially withdrawn children are rejected and victimized by their peers (Boivin et al. 1995, Hanish & Guerra 2000). As well, by middle childhood, these children are more likely to report feelings of loneliness, low self-esteem, and even depression or anxiety (Hymel et al. 1993, Rubin 1993). Behavioral inhibition may be an antecedent of social withdrawal, which may lead to peer rejection, which in turn may exacerbate inhibited or isolated behavior. Recent evidence has found an increased prevalence of diagnosed anxiety disorders among children identified as behaviorally inhibited (Rosenbaum et al. 1993, Schwartz et al. 1999). Thus, the research on behavioral inhibition has been of great interest to child psychiatrists and clinical child psychologists as they identify early risk factors associated with the onset of anxiety and mood disorders in children.

THE BIOLOGY OF BEHAVIORAL INHIBITION The contrast in behavioral reactions to novelty of inhibited and uninhibited children has been proposed to arise from variation in the excitability of neural circuits of the limbic system (Kagan & Snidman 1991). In particular, this model focuses on the amygdala, which has been implicated in the generation of fear (Davis 1992, 1998). Increased activity of the amygdala (especially the central nucleus)

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would be expected to result in increased activity across response systems that have extensive connections with the central nucleus (see Marshall & Stevenson-Hinde 2001). The work examining this supposition is described below for the cardiac and neuroendocrine response systems, as well as for certain aspects of cortical processing.


Heart Rate One prediction of the above model is that inhibited children should show consistently lower heart period (HP), corresponding to higher heart rate, and larger decreases in HP (i.e., heart rate acceleration) in response to unfamiliarity, compared with uninhibited children (Kagan 1994). Indeed, HP during quiet or active tasks was significantly negatively correlated with behavioral inhibition at assessments in toddlerhood up to 7.5 years of age (Kagan et al. 1984, Kagan et al. 1988, Reznick et al. 1986). In addition, inhibited children tended to show larger decreases in HP to stressors compared with uninhibited children (Kagan et al. 1988). Low baseline HP was associated with increased behavioral inhibition in an unselected sample of more than one thousand 3-year-old Mauritanian children (Scarpa et al. 1997). Other studies have found mixed results: Marshall & Stevenson-Hinde (1998) found no significant relation between behavioral inhibition and HP in a group of 4.5-year-olds who had been selected for high or low levels of inhibition. However, HP at 4.5 years predicted which of the children would remain inhibited when assessed 2.5 years later at 7 years of age: HP at 4.5 years was significantly lower for highly inhibited children who remained highly inhibited at 7 years, compared with those children who became less inhibited. In an unselected sample of 2-year-olds, Calkins & Fox (1992) found that behavioral inhibition was unrelated to baseline levels of HP, which suggests that large sample sizes or a focus on extremes via the use of selected samples are more likely to yield associations between behavioral inhibition and HP.

Cortisol A perceived threat may activate the hypothalamic-pituitary-adrenal system, with the secretion of the stress hormone cortisol as one of the products of this activation. Salivary cortisol levels have been studied in relation to various aspects of child temperament, including behavioral inhibition (Stansbury & Gunnar 1994). Evidence for an association between cortisol levels and inhibited behavior has been mixed: Some researchers have found that high baseline cortisol levels are associated with behavioral inhibition (e.g., Kagan et al. 1987, Schmidt et al. 1997), but other studies examining the relations between behavioral inhibition and changes in adrenocortical activity in response to stress have been more equivocal. For instance, De Haan and colleagues (De Haan et al. 1998) found home cortisol levels to be associated with more anxious, internalizing behavior in 2-year-olds, but found that an increased cortisol response to starting preschool was associated with more assertive, angry, and aggressive behavior rather than with socially inhibited or anxious

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behavior. Gunnar (1994) suggested that one reason inhibited children might not show elevated cortisol reactivity during such transitions is that unlike less fearful children, inhibited children tend to avoid the kinds of social and physical activities that would elicit elevations in cortisol. In addition, adrenocortical activity may not necessarily map onto fear-related constructs, but instead cortisol levels may be related to the maintenance or failure of coping strategies. Nachmias et al. (1996) examined cortisol responses of inhibited and uninhibited 18-month-olds to the Ainsworth Strange Situation as well as to a challenging coping episode. Infants who were highly inhibited and insecurely attached showed larger cortisol responses to the Strange Situation and the challenging coping episode compared with children who were highly inhibited but securely attached. The cortisol increase for inhibited-insecure infants was greater than that for the uninhibited infants, whether securely or insecurely attached. In this sense, mothers in secure dyads who have inhibited children may support their children’s strategies for coping with an unfamiliar and/or stressful situation.

Electroencephalogram, Event-Related Potentials, and Functional Magnetic Resonance Imaging A number of studies have examined the relation of behavioral inhibition to electrophysiological measures derived from the electroencephalogram (EEG). The majority of work in this domain has focused on hemispheric asymmetries in EEG activation, although event-related potentials (ERPs) more recently have been used to probe electrophysiological responses to stimuli in inhibited infants and children. In addition, a recent study has incorporated the use of functional magnetic resonance imaging (fMRI) into the study of inhibited temperament. Several studies have related individual differences in approach or withdrawal behaviors in infancy and childhood to patterns of asymmetrical activation in EEG signals recorded over the frontal region of the brain. Fox (1991, 1994) and Davidson (1992) argue that the functional significance of frontal EEG asymmetry may be conceptualized in terms of motivational systems of approach and withdrawal. In this perspective, the left frontal region promotes appetitive, approach-directed emotional responses, while the right frontal region promotes withdrawal-directed responses to perceived aversive stimuli. Hemispheric asymmetries in EEG alpha band activity have been used to probe individual differences in the relative activation of these motivational systems. The use of EEG alpha power in this respect is driven by the fact that when sensory cortex receives incoming stimuli, EEG alpha power over the same part of the cortex is decreased in amplitude (desynchronized). In this sense, alpha power has been used as a proxy for cortical activation, being inversely related to cortical activation (see Marshall et al. 2002). A “right frontal” pattern of EEG asymmetry refers to a pattern of decreased alpha power in electrodes over the right frontal region, relative to the homologous electrodes in the left hemisphere. This right frontal pattern has been taken to indicate activation of the motivational system associated with withdrawal. Indeed, infants who

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displayed a pattern of stable right frontal EEG asymmetry across the first two years of life tended to be more inhibited at both 14 and 24 months of age compared with infants who exhibited a pattern of stable left frontal EEG (Fox et al. 1994). Infants who went on to be consistently inhibited up to 4 years of age exhibited greater right frontal EEG asymmetry at 9 and 14 months of age than infants who were to become less inhibited (Fox et al. 2001). In addition, Calkins et al. (1996) found that infants who were selected at 4 months of age for high frequencies of motor behavior and negative affect in response to novel visual and auditory stimuli tended to show right frontal EEG asymmetry at 9 months of age. They also were more behaviorally inhibited at 14 months of age compared with infants who showed either high positive affect or low levels of either positive or negative reactivity at 4 months of age. The best predictor of the tendency to be socially reticent with unfamiliar peers in 4-year-old children was the combination of both temperamental negative affect and right frontal EEG asymmetry (Henderson et al. 2001). Another recent study examined EEG asymmetry in a sample of 10- to 12-yearolds who had been followed since 4 months of age. Children who had exhibited high levels of behavioral inhibition to the unfamiliar in laboratory assessments at ages 14 and 21 months and who had shown high levels of emotional and motor reactivity at 4 months of age were more likely to show right frontal asymmetry in the late-childhood assessment (McManis et al. 2002). In addition to relations with baseline EEG asymmetry, behavioral inhibition is associated with changes in EEG asymmetry during tasks designed to elicit an anxious state. For example, Schmidt et al. (1999) examined changes in the EEG in inhibited and uninhibited children over a task in which the participants had to mentally prepare for giving a speech about their most embarrassing moments. In the end, they were not required to give such a speech, but the EEG patterns during anticipation of this event differed significantly between inhibited and uninhibited children. Relative to the uninhibited children, the inhibited children showed an increase in activation over the right frontal region over the course of the anticipation period. Interestingly, the EEG changes also were coupled with an increase in heart rate over the same period in the inhibited children. We recently examined EEG asymmetries in relation to two different forms of nonsocial behavior in preschoolers during a play session with unfamiliar peers: social reticence and solitary-passive behavior (Henderson et al. 2004). In contrast to solitary-passive children, who occupy themselves with exploratory and constructive activities such as drawing and working on puzzles while in the company of unfamiliar peers, reticent children remain visually focused and oriented toward other children, yet do not join them in their activities. In this sense, reticence is related to behavioral inhibition: Indeed there is evidence for continuity between behavioral inhibition in toddlerhood and reticence in the preschool years (e.g., Rubin et al. 2002). Both reticent and solitary-passive children showed a pattern of resting right frontal EEG asymmetry, which suggests that these different forms of solitude may share a common withdrawal motivation. However, other physiological and

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behavioral evidence from this study suggested that reticence is associated with a particularly aroused, vigilant physiological profile. For instance, reticent children showed a pattern of increased generalized EEG activation (decreased alpha-band power) across the scalp, which is consistent with Eysenck’s model of increased generalized arousal in introverted individuals (Eysenck & Eysenck 1985), due to tonic differences in the ascending reticular activating system. It is also consistent with animal studies demonstrating that generalized cortical activation can reflect activity in the central nucleus of the amygdala (e.g., Kapp et al. 1994). One promising line of research, which also uses electrophysiological techniques, is the examination of central nervous system responses to stimulation as indexed by ERPs. ERP techniques have the benefit of superlative temporal precision and can give insight into the nature and timing of mental events such as novelty detection and orienting. One relatively recent development in studies of behavioral inhibition is the use of auditory ERPs to examine individual differences in stimulus processing in inhibited and uninhibited children. One question of interest in this respect is the ability of descending projections from limbic structures such as the amygdala to influence stimulus processing at early stages of transmission and stimulus processing. Woodward and colleagues (Woodward et al. 2001) examined the brainstem auditory evoked response in 10- to 12-year-old children who had previously been selected on the basis of high or low affective reactivity in infancy. The high-reactive infants, who had gone on to become more inhibited young children, had a higher amplitude of a certain component of the brainstem auditory evoked response thought to originate in the inferior colliculus. Woodward et al. (2001) interpreted this finding as indicating that projections from the amygdala to the inferior colliculus are more excitable in children with an inhibited, fearful temperament. However, the precise origin of these effects has yet to be elucidated. In a similar vein, we recently examined the mismatch negativity (MMN) in the auditory ERP of both socially withdrawn and more outgoing control children aged 7 to 12 years (Bar-Haim et al. 2003). The MMN indexes a change-detection mechanism in primary auditory cortex, and it is elicited using auditory “oddball” paradigms without specific task demands (Picton et al. 2000). The MMN usually is derived from a comparison of the ERPs to infrequent “deviant” and frequent “standard” auditory stimuli, although its characteristics in terms of morphology and latency vary over infancy and childhood (Cheour et al. 2001). We found that socially withdrawn children have reduced MMN amplitude compared with more outgoing children. Such individual differences in sensory processing either could be a consequence of “top-down” influences by higher affective centers such as the amygdala, or may reflect “bottom-up” differences in early processing that may feed forward to affect the later processing and evaluation of sensory information. Other recent work on auditory ERP responses to novelty in infancy suggests that temperamentally different infants may show different degrees of electrophysiological reactivity to novelty. Infants who have a temperamental tendency to respond to stimulation with high levels of positive affect also show an enhanced ERP response to complex novel stimuli that are interspersed in a train of repetitive tonal

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“standard” stimuli, but do not show elements of orienting to stimuli that are only mildly deviant from the standards. In contrast, temperamentally high-negative infants respond with an indication of an orienting response to the mildly deviant stimuli, but with a reduced response to the complex novel stimuli (PJ Marshall, MG Hardin, & NA Fox, in preparation). One explanation for these group differences is that the high-negative and high-positive infants may have differing optimal levels of novelty at which engagement with a novel stimulus is promoted (Berlyne 1960). Although the use of functional neuroimaging techniques such as positron emission tomography (PET) and fMRI in infants and young children presents specific ethical and practical challenges, there is increasing interest in using these techniques, especially fMRI, to probe brain responses to sensory stimulation in older children. One recent study used fMRI to directly assess amygdala activity in response to novel face stimuli in young adults who had been classified as inhibited or uninhibited in the second year of life in Kagan’s longitudinal studies (Schwartz et al. 2003). Participants were familiarized with a set of faces, and then were exposed to a stimulus set that interspersed novel, previously unseen faces among the previously seen, familiar faces. Analyses of the fMRI signal revealed that the adults who as toddlers had been more inhibited showed increased bilateral activation of the amygdala in response to the novel faces compared to adults who initially had been categorized as uninhibited. Although the contemporaneous behavioral profiles of the adults were not assessed, this study provides intriguing evidence of continuity in the reactivity of the physiological systems proposed to underlie an inhibited temperament.

CONTINUITY AND DISCONTINUITY IN BEHAVIORAL INHIBITION OVER DEVELOPMENT Behavioral inhibition and shyness are among the most stable individual differences in the personality development literature, with continuities found throughout early childhood, middle childhood, and adulthood. Despite this relatively high degree of continuity across samples, examination of individual differences in patterns of behavior over time reveals that many children show markedly changed patterns of behavior across the course of childhood. Understanding these patterns of continuity and change has become a vital focus of behavioral inhibition research.

Longitudinal Studies of Behavioral Inhibition More than four decades ago, Kagan focused on the temperamental quality of fearfulness, noting that it showed a remarkably high degree of continuity from toddlerhood through adulthood (Kagan & Moss 1962). Using data from the Fels longitudinal study, Kagan & Moss (1962) reported that the behavioral tendency to express fear and avoidant behaviors to novelty and challenge in the first three years

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of life showed the highest degree of continuity through adolescence compared to all other behavioral tendencies. Into young adulthood, a remarkable number of the children displaying fearful behaviors at younger ages were described as introverted. In Kagan’s later studies, two cohorts of toddlers were selected based on their extreme reactions to novelty in order to try to tap into these individual differences (e.g., Kagan et al. 1984). These children were followed up later in childhood, when they were observed in play sessions with an unfamiliar peer, and in different unfamiliar situations including a “risk room” in which children’s willingness to engage in mild risk-taking behaviors was measured. The major finding from these longitudinal studies was that the expression of behavioral inhibition, although elicited in different contexts, showed a moderate degree of continuity. Inhibited toddlers who were quiet and restrained tended to be quiet, cautious, and socially reticent children at 7.5 years of age. Conversely, uninhibited toddlers who were social remained talkative and interacted easily with unfamiliar adults and children at 7.5 years (Kagan et al. 1988). The behavioral inhibition index at 7 years correlated significantly with indices of behavioral inhibition at 21 months (Pearson r = 0.67), 4 years (r = 0.54), and 5.5 years (r = 0.57) (Kagan et al. 1988). Other researchers have noted that continuity in behavioral inhibition is lower in samples that are unselected for early patterns of reactivity compared to the selected samples reported on in this original work by Kagan and colleagues. For instance, Stevenson-Hinde & Shouldice (1995) found lower consistency in inhibition toward an unfamiliar adult in an unselected sample from 4.5 to 7 years of age (Pearson r = 0.24) than in a selected sample over the same age range (r = 0.46; Marshall & Stevenson-Hinde 1998). Fox and colleagues (Fox et al. 2001) found similar results in a study of an extreme group of infants who displayed high negative affect and motor reactivity in response to unfamiliar visual and auditory stimuli at 4 months and who were followed up from 9 to 48 months. Approximately half of the infants in this high negative reactivity group continued to show high levels of behavioral inhibition through 24 months of age, and approximately one third of these infants continued to show extreme social reticence during interactions with unfamiliar peers at 48 months (Fox et al. 2001). In the Australian Temperament Project, Sanson and colleagues studied nearly 500 randomly selected infants who were assessed initially at 4 to 8 months of age and followed until they were 5 to 6 years old (Sanson et al. 1996). Maternal reports of shyness showed moderate continuity from infancy to childhood, with continuity being higher at older ages. In addition, similar to Kagan et al. (1988), they found that children at the behavioral extremes showed the greatest degree of continuity in behavior over time. These results have also been replicated in both European and Asian samples. Broberg et al. (1990) used maternal reports and observational measures of temperament in a study of firstborn children in Sweden. An inhibition composite was created that aggregated maternal reports of fear, observer-rated peer noninvolvement in play, and the reversed observer-rated sociability with a stranger score.

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Toddlers who were inhibited at 16 months were more likely to be inhibited at 28 and 40 months of age than were uninhibited toddlers. In another unselected Swedish sample, inhibition ratings showed continuity from 21 months to 6 years only in children at the behavioral extremes (Kerr et al. 1994). Reports of continuity have also been found in Chinese studies of temperament (Zhengyan et al. 2003) and in Asendorpf’s (1994) longitudinal study of behavioral inhibition in German children from 4 to 10 years. In a study of Mauritanian children, Scarpa et al. (1995) found that inhibited children at age 3 and age 8 had higher inhibition scores at age 8 and age 11, using questionnaire measures and brief observational ratings. The set of studies finding moderate continuity of inhibition across early and middle childhood has led researchers to assess whether such continuity follows into adulthood. Caspi and colleagues (Caspi et al. 1989) identified shy and reserved children using archival data from the Berkeley Guidance Study (Eichorn 1981). Although there was evidence for continuity of shyness into adulthood, the psychosocial outcomes associated with shyness differed by sex. Shy girls were more likely to follow conventional patterns of marriage, homemaking, and motherhood whereas shy boys were more likely to delay marriage, parenthood, and stable careers, and attain less achievement in their careers. Adults who were previously shy children described themselves as nonassertive and overcontrolled; they reported experiencing few positive emotions and had little desire to influence others. An informant who knew them well described them as less affiliative and less interested in engaging in their surroundings (Caspi et al. 2003). A similar profile of characteristics was reported in young adults who had been behaviorally inhibited as 8- to 12-year-olds (Gest 1997). These young adults reported a less active social life and were less likely to move away from family; men reported experiencing greater emotional distress and negative emotionality. These studies and others have established that behavioral inhibition shows at least moderate continuity across childhood and to a certain extent is also associated with aspects of adult personality. However, given the multitude of influences on development, detailed models are needed that allow for the investigation of specific factors affecting continuity and discontinuity in temperamental tendencies over time. As such, there has been much theoretical discussion regarding the moderating effects of environmental factors on the associations between temperament and outcomes (e.g., Bates 2001, Rothbart & Bates 1998). To date, however, there have been few empirical investigations of these moderating effects. In the following sections, we discuss the possible moderating effects of parenting behaviors and nonparental care environments on patterns of continuity and change in behavioral inhibition.

The Role of Caregiving Behaviors and Environments In early childhood, the most salient environmental influence on the child is the caregiving environment. However, only a few studies have investigated the specific parenting behaviors or styles associated with continuity and discontinuity

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in behavioral inhibition. The parenting characteristics that have been examined in relation to continuity and discontinuity are acceptance, warmth, sensitivity, responsiveness, control, and overprotection (Park et al. 1997, Wood et al. 2003). Some evidence suggests that parental sensitivity can reduce emotional negativity and perhaps behavioral inhibition by enhancing feelings of self-worth. For example, infants who became less negative from 3 to 9 months of age had interaction patterns with their mothers at 3 months that were more complementary and harmonious (Belsky et al. 1991). Similar findings were observed in a study involving preterm infants (Washington et al. 1986). In contrast to these findings suggesting that sensitive parenting functions to reduce negative reactivity in temperamentally prone infants, Kagan (1994) hypothesized that sensitive parenting behaviors may increase negative reactivity in temperamentally distress-prone infants. He suggested that a firm parenting style including limit setting might help the child cope and lead to reduced inhibition. Consistent with this hypothesis, Park and colleagues (Park et al. 1997) found that more intrusive parenting by mothers across the second and third year led to less inhibited behavior at age 3, after controlling for early emotionality. Less inhibited behavior was also more likely when fathers were less sensitive and affectionate in the second year, and more intrusive and expressive of negative affect in the third year. Park et al. (1997) suggest that sensitive parenting sends the message to the child that it is fine to be who you are, while negative and intrusive controlling behavior sends the message to change. Because Park et al. used global ratings of parenting, it is not possible to discern the specific circumstances or contexts surrounding the parenting behavior. Under certain circumstances, pushing a child to control his anxieties may actually reflect a sensitive awareness instead of a controlling intrusive parenting style (Park et al. 1997). Rubin and colleagues (Rubin et al. 2002) investigated whether the interaction of parenting behaviors and behavioral inhibition at age 2 years explained child characteristics at 4 years of age, either directly or through the moderation of earlier inhibition. A maternal parenting style that consists of overly warm, intrusive, unresponsive, and derisive behavior moderated the concurrent association between shyness and behavioral inhibition at 2 years (Rubin et al. 1997). These associations remained two years later when children were reassessed at 4 years of age (Rubin et al. 2002). Inhibition at 2 years only predicted reticence with unfamiliar peers at 4 years when mothers behaved in a psychologically controlling or derisive manner. Wood and colleagues (Wood et al. 2003) suggest two heuristics for guiding research focused on refining and extending models of parenting and childhood anxiety, including clarifying issues of timing and direction of effects. One heuristic, based on the work of Rubin and colleagues, suggests that parents who are solicitous and overresponsive (in situations in which the child does not need help) may reinforce child anxiety or shyness by rewarding their child’s initial signs of anxiety or distress with parental warmth, and by preventing the child from using and developing self-regulatory skills. In contrast, parents who encourage children to engage in social activities may help prevent their child from developing greater

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distress related to social interactions. The other heuristic suggested involves ideas of control and mastery: Children whose parents provide them with opportunities to excel and master their environment are less likely to feel dependent on their parents. The ideas set forth in these heuristics are not inconsistent with the idea that sensitive parenting leads to reduced negative affect. Sensitive and responsive parents are aware that sensitivity and responsiveness needs to change with development and they will encourage their children to explore their world because they are sensitive and responsive to their child’s burgeoning independence. The studies by Park and colleagues, Rubin and colleagues, and others are a good first step to understanding what types of parenting behaviors are associated with maintaining inhibition. Nonparental caregiving may also influence patterns of continuity and discontinuity in behavioral inhibition. Fox and colleagues (Fox et al. 2001) found that infants who showed high negative emotionality at 4 months were more likely to change their behavior and become less inhibited over toddlerhood when they were placed in nonparental caregiving environments for at least 10 hours per week. They suggested several ways that the caregiving environment could lead to different patterns of continuity and discontinuity. For example, children in nonparental care may receive less responsive and sensitive caregiving and/or they may gain more experience interacting with unfamiliar peers at an earlier age compared to their peers who remained inhibited over time. Ahnert & Lamb (2003) have noted that mothers may be less sensitive than care providers in quality day care settings when interacting one-on-one with a child. Even though children may be competing for caregiver attention and be limited in the amount of time they have to interact with the caregiver, this does not necessarily affect the quality of the relationship formed or the quality of the care. The personality of parents who choose to keep their infant at home may explain the higher degree of continuity because a parent’s own anxious or fearful personality may lead to both the decision to keep a child at home and to an overprotective parenting style that contributes to the continuity of inhibition. Alternatively, a working mother may have stress at work above and beyond the stress related to responsibilities in her home, and this stress may affect her parenting style and interactions with her child. Some evidence suggests that mothers of children in childcare took longer to respond to their toddlers’ signals of distress compared to mothers who stay at home. Other observations suggest that toddlers in daycare display more negative behaviors when interacting with their parents outside of daycare (Nelson & Garduque 1991). Taken together, these may explain some of the observed differences in continuity and discontinuity of behavioral inhibition between young children who are in the exclusive care of their parents versus those who spend time in nonparental care. Children who stay at home may be more likely to receive parenting that is more overcontrolling and oversolicitous, whereas children who go to daycare may be more likely to receive parenting that fosters independence. Yet another reason experiences in out-of-home care could promote discontinuity is that children in out-of-home care may simply gain experience and practice

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interacting with unfamiliar people, therefore reducing their fears. Furman et al. (1979) found that socially withdrawn preschoolers who were given the opportunity to interact with other preschoolers showed an increase in the amount of peer interactions they engaged in compared to preschoolers who were not given an opportunity to interact with others. Greco & Morris (2001) reviewed peerbased interventions for childhood shyness and related behaviors and found that peer-mediated and peer-pairing approaches seem to be effective in treating internalizing behaviors that are related to low social status. Experience with other children combined with mother-child conversations about peers may be especially associated with children’s competence. Laird et al. (1994) studied mother-child conversations about the child’s peers and found that children’s competence was associated with the frequencies of conversations, maternal advice giving, and the presence of emotional themes in conversations.

COGNITION-EMOTION INTERACTIONS IN BEHAVIORAL INHIBITION The tendency to display behavioral inhibition is associated with the experience and expression of moderately high levels of negative emotions including fear, anxiety, and distress (Eisenberg & Fabes 1992). Thus, one of the biggest challenges for children with a history of behavioral inhibition is to learn to modulate or regulate these relatively intense emotional reactions. At both behavioral and neural levels of analysis, a gradual transfer of control over regulation has been described in which infants’ behaviors and emotions are initially governed by their reactive tendencies, thus making them relatively dependent on external sources, such as their caregivers, for regulation (Kopp 1982, Rothbart & Derryberry 1981). Based on an anatomical argument, Panksepp (1998) paralleled the views of Rothbart and Kopp that early in development, infant emotional responses are governed primarily by upward controls from the limbic system. However, with age and cortical development, cognitive control capacities (i.e., response inhibition and attentional control) increase, allowing for greater downward or top-down control over initial reaction tendencies (Panksepp 1998, Rueda et al. 2004).

The Effects of Emotion on Attentional Orienting One of the adaptive functions of the behavioral inhibition system is that it functions to increase vigilance and orienting to environmental cues that could be indicative of threat (LeDoux 2000). This is an example of a bottom-up influence of emotional biases on information processing, where emotional tendencies guide the direction and patterns of engagement/disengagement of attention (Ochsner & Gross 2004). Individual differences in sensory orienting are attributed to the functioning of the posterior attention system, which includes the superior parietal cortex, the

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temporal parietal junction, frontal eye fields, and superior colliculus (Corbetta & Shulman 2002). The posterior attention system is functional early in life, and relates to individual differences in orienting responses to novelty, early attentional persistence, duration and latency of orienting, and early state control (Derryberry & Rothbart 1997, Eisenberg et al. 2004). Related to the study of fear and behavioral inhibition, numerous studies have evaluated the effect of extraversion and neuroticism, in combination with anxiety, on various aspects of cognitive functioning. Such studies generally adopt a bottom-up approach to studying emotion-cognitive interactions, as it is hypothesized that individual differences in emotional tendencies will influence the quality of functioning or the ability to implement specific cognitive functions, including attention focusing and shifting. It has long been known that anxiety and stressful states promote attentional narrowing (Easterbrook 1959). Specifically, it has been theorized that attention selectivity facilitates early processing of potential threat, influencing subsequent cognitive and emotional processing (Mathews 1990). However, the exact mechanisms and the direction of these emotion-attention relations are still unclear. Beck et al. (1985) noted that emotional dysfunctions co-occur with activation of a cognitive schema biased toward mood-congruent information. This biased information processing, brought about by anxiety-related schema, leads to better encoding of threat-related information. Consistent with this, when anxious individuals perform an emotional Stroop task, they tend to take longer to report the color of threatening words compared to nonthreatening words. This pattern of performance is thought to be due to increased attention to the threatening word itself, thereby leading to distraction from the color-naming task (Williams et al. 1996). Studies of individual differences on a dot-probe paradigm have led to similar conclusions. Specifically, anxious individuals are quick to detect targets in threatening word locations, possibly due to attention being drawn covertly to the location of the threatening word (MacLeod & Mathews 1988, Wells & Matthews 1994). In detailed analyses of the processes of attentional engagement and disengagement in anxious individuals, recent studies have found that compared to nonanxious individuals, anxious individuals may not attend more quickly or intensely to threatening stimuli, but rather have difficulty disengaging their attention from such stimuli (Derryberry & Reed 1994; E. Fox et al. 2001, 2002). Derryberry and Reed hypothesized that previous findings of differences in performance on emotional Stroop and dot-probe tasks can be attributed to the fact that anxious individuals cannot disengage from the threatening stimuli to attend to other aspects of the task (i.e., word or color), thereby perpetuating the attentional bias to negatively biased, threatening cues (see Derryberry & Reed 2002). Thus, children with a bias toward anxious, fearful reaction tendencies face the challenge of learning to intentionally control their attention at several different levels including the shifting of visual attention away from distressing stimuli and situations and cognitively reappraising stimuli and situations that elicit distress.

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Voluntary Attentional Control Whereas individual differences in attentional orienting have been related to the functioning of the posterior attention system, voluntary control of attention is considered a higher-order cognitive process associated with the functioning of a more anterior system of attention including the anterior cingulate cortex and the lateral prefrontal cortex (Posner & DiGirolamo 2000). The emergence of voluntary control over attention, and specifically the ability to flexibly focus and shift attention, contributes to the developing system of behaviors or responses that allow for greater self-regulation of thought, behavior, and emotion (Posner & Rothbart 1998). The relation between the flexible and intentional employment of attention and the regulation of emotion is apparent early in life and characterizes many of the early interactions that take place between caregivers and infants. During adult-infant interactions, adults engage and disengage infants’ attention in order to manage the infants’ levels of arousal. States of engaged attention between infants and their caregivers tend to be associated with play, states of joy, and general states of positive affect (Gottman et al. 1997). In addition to engaging infants’ attention, adults also tend to be sensitive to infants’ needs to disengage their attention in order to dampen or reduce levels of arousal. When attention is engaged, arousal is heightened, and by disengaging attention, adults give infants the opportunity to dampen their levels of arousal. Early on, infants learn to use such strategies to manage their own levels of arousal. When parents respond contingently to their infants’ needs to disengage and re-engage interactions, infants learn about the efficacy of attentional control as a means of self-regulation (Gottman et al. 1997). The relation between attentional control and self-regulation is supported by the fact that these individual differences have been associated with differences in temperamental reactivity and regulation (Johnson et al. 1991, Rothbart et al. 1994). These findings suggest that the development of attentional control over the first several years of life may provide children with an important source of regulation over their reactive temperamental tendencies. For temperamentally fearful children, the shifting of attention to a different aspect of a situation, or distracting oneself, may provide an effective means of regulating emotional distress. Our observations suggest that disengaging attention from aspects of unfamiliar situations may be particularly challenging for behaviorally inhibited children. In unfamiliar social situations, temperamentally fearful children face the competing challenges of managing feelings of anxiety and interacting with peers. These challenges are particularly salient for a subtype of socially withdrawn children referred to as socially reticent, who spend their time primarily watching other children when put in a group of unfamiliar peers (Coplan et al. 1994, Henderson et al. 2004). These children tend to hover on the fringe of social activity, display behavioral signs of anxiety, carefully watch the other children, and remain unengaged in any other activities. These children appear to be unable to disengage their attention from the other children and make few attempts to join group activities. This fixation appears to be not only ineffective in reducing wariness, but it also

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may increase wariness over the course of the play period. These observations are consistent with others’ hypotheses that across the life span, individuals who are biased toward the experience of high levels of negative emotion, including fear and anxiety, and who are unable to employ attentional mechanisms in order to regulate their emotions, would be easily overwhelmed by negative emotion (Derryberry & Rothbart 1988; Eisenberg et al. 1998; Rothbart et al. 1992, 2004). Aspects of attentional control that contribute to the regulation of negative emotional states include attention shifting, attention focusing, and cognitively manipulating one’s assessment of a situation (Eisenberg et al. 1998, Ochsner & Gross 2004). Shifting attention away from a fear-inducing stimulus, or cognitively reframing environmental stimuli that might be interpreted as distressing, appear particularly important for the regulation of shyness (Eisenberg et al. 1998). Adults who report high levels of shyness also report being poor at shifting and focusing attention (Eisenberg et al. 1995). Similarly, in children, Eisenberg et al. (1998) found that teachers’ reports of shyness are negatively correlated with attention shifting at school. In adult populations, it has been found that anxious individuals with good attentional control were better able to shift their attention away from threatening stimuli compared with anxious individuals with poor attentional control, thereby showing that skilled control of voluntary attention may provide anxious individuals with an important form of self-regulation (Derryberry & Reed 2002). Similarly, for children, Eisenberg et al. (1998) reported a significant interaction effect between shyness and attention shifting in the prediction of internalizing emotions, such that internalizing emotions were positively related to parent reports of shyness, but only for children who were relatively low in attention shifting. Shyness was not predictive of internalizing emotions for children who were moderate or high in attention-shifting abilities. Therefore, the ability to shift attention may moderate the associations between shyness and the development of social anxiety and more global internalizing difficulties.

Attention and the Development of Social Cognition in Behaviorally Inhibited Children Social-cognitive models of social adjustment emphasize the cognitive processes or mental steps children engage in before enacting a social behavior. These steps are (a) the encoding of situational cues, (b) the representation and interpretation of those cues, (c) a mental search for possible responses to the situation, and (d) the selection of a response. Individual differences in the processing of information at any of these stages are believed to provide a mechanism through which individual differences in social adjustment develop (Dodge 1989, Ladd & Mize 1983, Rubin & Krasnor 1986). Given the effects of temperament or emotional biases on the direction and control of children’s attention, over time these biases may influence several stages of social information processing, including the encoding and interpretation of social and emotional cues. In turn, differences in social

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information processing may affect patterns of continuity and discontinuity in behavioral inhibition across childhood and into adolescence. Behaviorally inhibited children interact with others in relatively ineffective ways, and others’ reactions to them likely influence their social cognitions over time. Although behaviorally inhibited children tend to use nonassertive strategies during interactions with peers, they are more likely than are their peers to have their requests refused (Rubin & Borwick 1984). Such findings suggest that in early childhood, social withdrawal is associated with the experience of poor peer relationships (e.g., Rubin 1985). Over time, the experience of social failure may influence children’s perceptions of social situations and their attributions with regard to social success and failure. Specifically, children may begin to interpret otherwise ambiguous social situations as threatening and believe that poor social outcomes are a result of internal causes (Goetz & Dweck 1980, Rubin et al. 1998). The relations between emotional biases and social information processing have been demonstrated in studies of clinical populations. Emotional disorders including depression, anxiety, and phobias are associated with cognitive biases, or the selective processing of emotion-relevant information (Mineka & Tomarken 1989). Attentional biases, as reviewed above, appear to affect the nature of the information that initially is attended to among anxious children and adults. However, emotions, and anxiety in particular, appear to influence later stages of information processing as well, including the interpretation of otherwise ambiguous situations. Specifically, anxious children and adults tend to interpret ambiguous stimuli or situations as disproportionately negative or threatening (Vasey & MacLeod 2001). For example, in a sample of 7- to 9-year-old children, self-reported trait anxiety was positively associated with the number of threatening interpretations children gave for a series of homophones with both a threatening and neutral meaning (Hadwin et al. 1997). Beyond the interpretation of single words or images, anxiety is associated with more global interpretive biases in the context of scenarios involving social interactions. Negative or anxious social schemas have been evaluated by presenting children or adults with ambiguous social scenarios and asking for their interpretations. For example, Chorpita et al. (1996) assessed 9- to 13-year-old children’s interpretations and behavioral plans regarding ambiguous situations. Children’s trait anxiety scores significantly predicted the total proportion of both anxious interpretations and anxious plans. Similarly, socially anxious adolescents predict more socially threatening outcomes for hypothetical social scenarios compared to their nonanxious peers (Magnusdottir & Smari 1999).

BEHAVIORAL INHIBITION AND PSYCHOPATHOLOGY The pattern of anxious behaviors, social withdrawal, negative affect, and low selfesteem, all reported in the developmental literature as characteristic of behavioral inhibition, are symptoms also used to diagnose certain anxiety and mood

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disorders. A good deal of work on the relation between behavioral inhibition and psychopathology has been done by Biederman, Rosenbaum, and their colleagues. In their first study, Rosenbaum et al. (1988) assessed 56 Caucasian children ages 2 to 7 whose parents were undergoing treatment for one of three possible diagnoses: (a) panic disorder with or without agoraphobia (PD-AG), (b) comorbid PD with major depressive disorder (PD-MDD), or (c) major depressive disorder alone (MDD). These groups were compared to a “control” group of children whose parents did not have PD or MDD. This control group consisted of siblings of children in treatment for attention deficit disorder or children of parents in treatment for other disorders (i.e., tobacco dependence, obesity, or generalized anxiety disorder). Researchers found that children of parents with PD, with or without comorbid MDD, had longer latencies to speak and fewer spontaneous comments than did children from the control group, whereas children of MDD parents did not differ from either group. Results also indicated a higher incidence of behavioral inhibition in children of parents with PD-AG, with or without MDD, compared to children from the control group. The authors suggested that the increased prevalence of behavioral inhibition in children of parents with PD (i.e., anxiety disorders) suggests a familial or biological tie between psychopathology and inhibition. Additional studies by this research group examined the prevalence and familial loadings of psychopathology in children with and without behavioral inhibition in nonclinical samples. Biederman et al. (1990) used a subset of the sample from their 1988 study whose parents were being treated for PD-AG (classified as the Massachusetts General Hospital high-risk sample) and compared these children to inhibited and uninhibited Caucasian children (ages 7 to 8 years) from Kagan’s longitudinal study of behavioral inhibition. Both groups were also compared to healthy controls (ages 4 to 10 years) who were not classified into temperament categories. Results revealed that behaviorally inhibited children in the Massachusetts General Hospital high-risk sample were significantly more likely to have multiple diagnoses (>4), two or more anxiety disorders, overanxious disorder, and oppositional disorder when compared to healthy controls, although comparisons to not-inhibited children failed to reach statistical significance. Additionally, although not statistically significant, rates of major depression and attention deficit disorder were also higher in inhibited children as compared to not-inhibited children or healthy controls. In Kagan’s sample that had been selected for comparison as a nonclinical group, rates of oppositional disorder were found to be significantly lower in inhibited children as compared to uninhibited children, whereas rates of phobic disorder were higher in inhibited versus uninhibited children. Based on these results, the authors concluded that behavioral inhibition was associated with childhood anxiety disorders. Other studies also have found links between inhibition in adolescents and young adults and various disorders. Using behavioral assessments, Schwartz et al. (1999) assessed a group of adolescents who had been previously classified as inhibited or uninhibited at 2 years of age. At 13 years of age, participants were observed in a laboratory battery of physiological, behavioral, and cognitive procedures.

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Participants were also interviewed directly by a psychologist to assess past and current anxiety symptoms. No differences were found between inhibited and uninhibited teens with respect to specific fears, separation anxiety, or performance anxiety, although the two groups did differ in respect to social anxiety: Sixtyone percent of teens who had been inhibited as toddlers reported social anxiety symptoms, compared to 27% of adolescents who had been uninhibited earlier in life. Additional studies have relied on self-report measures in an attempt to further elucidate the relationship between inhibition and psychopathology. The Australian Temperament Project data revealed associations between shyness and internalizing problems at 5 to 6 years (Sanson et al. 1996) and anxiety at 13 to 14 years of age (Prior et al. 2000). In the latter study, participants who were rated as shy from infancy onward had a higher incidence of later anxiety problems, with persistently shy children being at increased risk compared to children who were never shy or whose shyness rating had changed over time. Forty-two percent of children rated at the higher end on a shyness scale had anxiety problems in adolescence, compared to 11% who never were rated as shy. Muris et al. (1999; see also Muris et al. 2001) conducted a study that examined the relation between self-reported behavioral inhibition and psychopathology symptoms in high school students. Adolescents who rated themselves high on inhibition also reported increased symptoms of anxiety, worry, and depression. In a similar study, Hayward et al. (1998) administered a retrospective self-report of inhibition (in childhood) to high school students. Results indicated social avoidance in childhood to be predictive of the onset of social phobia during high school, but unrelated to depression. Fearfulness in childhood also was found to increase the risk for later diagnoses of social phobia and depression. Research has demonstrated that stable behavioral inhibition is a risk factor in the development of anxiety disorders. Existing research has focused on the link between inhibition and the development of anxiety; however, the relation between inhibition and externalizing behaviors has not been as extensively investigated. It has been hypothesized that behavioral inhibition may actually have a protective effect against externalizing disorders (Hirshfeld-Becker et al. 2003, Kerr et al. 1997). Indeed, not only does anxiety protect children from becoming delinquent adolescents (Tremblay et al. 1994), but behavioral inhibition also has been proposed to be a protective factor against the development of delinquency (Kerr et al. 1997).

FUTURE DIRECTIONS With the approach of 20 years of study of behavioral inhibition, a number of issues present challenges to current researchers in this area. These include enhanced characterization of the early manifestations of the inhibited temperament pattern, an increased understanding of the role of cognitive processes in moderating

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behavioral inhibition, and an expanded understanding of the manner in which behavioral inhibition is displayed in older children and adolescents. This latter point is critical for understanding possible mechanisms to the etiology of psychopathology among behaviorally inhibited children. Indeed, the boundary between risk and disorder may be apparent in the behavior of behaviorally inhibited older children. The assessment of behavioral inhibition has differed for infants, young children, and older children. The infant temperament pattern has been identified by presenting novel auditory and visual stimuli to infants and selecting those infants who reflect the top 10% to 15% of the population in motor arousal and negative affect to these stimuli. Behavioral inhibition has been assessed in toddlers by observing their reactions to novel objects (e.g., a moving robot or car) or unfamiliar adults. Aggregate measures of inhibition have included wariness and avoidance of both the nonsocial novel objects and social situations. As children get older, there has been a shift in focus of the assessment, with a greater emphasis on the child’s response to unfamiliar peers. Indeed, much of the work on behavioral inhibition in preschool and older children has been in situations with unfamiliar (and sometimes familiar) peers. Only one study examined the difference in response to novel social and nonsocial stimuli in young children (Rubin et al. 1997). There is a need to characterize the specific stimuli that challenge the young child and elicit behavioral inhibition reliably (Stevenson-Hinde 1989). It may be that there are different subgroups of children: some who show overall avoidance or wariness to any novel event (social or nonsocial) and others who show responses to a distinct class of stimuli. These fine distinctions will be helpful in understanding and characterizing early in life the multiple trajectories that behavioral inhibition may take over the life span. There has been a growing recognition of the importance of the role of specific cognitive processes in the control or modulation of emotion. The interface of cognition and emotion is salient for understanding the different developmental trajectories seen with behaviorally inhibited children. For example, there is great interest in the manner in which both inhibitory control and attentional processes facilitate emotion control. Recent work by Henderson (2004) demonstrates the manner in which inhibitory control interacts with child temperament in a somewhat counterintuitive fashion. She found that behaviorally inhibited children who demonstrated heightened inhibitory control in a delay task were more likely to exhibit heightened social reticence than those inhibited children who did not demonstrate heightened inhibitory control. Because inhibitory control is often viewed as a positive facilitative process for emotion regulation, these results at first appear counter to expectations. Upon further reflection, though, they signal the manner in which certain cognitive processes may actually enhance temperamental dispositions. Future studies of the interaction of temperament and these cognitive processes will further elucidate the importance of certain individual differences in understanding social behavior. Most of the work in behavioral inhibition has focused on young or school-age children. However, children who are avoidant of social interaction may be at risk

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for social isolation during the critical years of adolescence. The consequences of this social isolation may result in increased feelings of loneliness, low self-worth, and perhaps the emergence of behavior patterns leading to psychopathology. At the moment, there are preciously few data on the role of temperament as a predictor of adolescent psychological development. Such studies would be highly informative for understanding the dramatic increase in anxiety and mood disorders found during this period of development. Finally, the importance of understanding the role that culture plays in behavioral inhibition has become increasingly recognized. The notion that there might be differences in the distribution of certain temperaments as a function of ethnicity, or that certain cultures might hold different perceptions of these temperamental dispositions, only recently has been explored. For example, Rubin (1998) has suggested that behavioral inhibition in North American and Western European countries is different from behavioral inhibition in Chinese culture because the cultural meaning of shyness and the social responses to children with shyness differ in the two areas. Chen et al. (1995, 1999) found that inhibited behavior is related to positive adolescent adjustment, including teacher-assessed competence, academic achievement, and leadership, and that behavioral inhibition is not seen as maladaptive in China. In a related study, in which toddlers from China and Canada were observed, Chen et al. (1998) found that Chinese children were more inhibited than Canadian children. They used observational methods that focused on the child’s latencies to approach novel objects and unfamiliar individuals, and durations of behaviors such as the child’s proximity to his or her mother. Differences in parenting behaviors have been used to explain some of the differences in behavioral inhibition between cultures. Parenting practices and childrearing beliefs are important factors that may mediate cultural influences on child development (Super & Harkness 1986). Chen et al. (1998) found that in the Chinese sample, mothers’ warmth and acceptance was positively associated with inhibition, and maternal rejection and punishment was negatively associated with inhibition. The opposite was found in the Canadian sample. Rubin (1998) suggests that the meaning associated with a social behavior is a function of the context where the behavior is produced. If a behavior is perceived as maladaptive in a culture, then parents will discourage its development, while encouraging the development of adaptive behaviors.

EPILOGUE The study of behavioral inhibition in some ways is like a complex puzzle. Each of the pieces by themselves tells an interesting story, but together they provide a broad view of the phenomenon. Study of behavioral inhibition has ranged from understanding the underlying biology of the fear system, to the effects of this temperament on parenting behavior, and to research on the development of certain forms of psychopathology. Each piece of this puzzle or area related to this

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phenomenon has provided rich data on the psychology of behavioral inhibition. Ultimately, however, the union of these pieces will afford the greatest explanatory power. The Annual Review of Psychology is online at http://psych.annualreviews.org


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Annu. Rev. Psychol. 2005. 56:263–86 doi: 10.1146/annurev.psych.56.091103.070208 c 2005 by Annual Reviews. All rights reserved Copyright First published online as a Review in Advance on July 21, 2004

HUMAN DEVELOPMENT: Biological and Genetic Processes


Irving I. Gottesman Department of Psychiatry and Department of Psychology, University of Minnesota, Minneapolis, Minnesota 55454; email: [email protected]

Daniel R. Hanson Departments of Psychiatry and Psychology, University of Minnesota, Minneapolis, and Veterans Administration Hospital, Minneapolis, Minnesota 55417; email: [email protected]

Key Words adaptive systems, endophenotypes, CNS plasticity, schizophrenia, autism ■ Abstract Adaptation is a central organizing principle throughout biology, whether we are studying species, populations, or individuals. Adaptation in biological systems occurs in response to molar and molecular environments. Thus, we would predict that genetic systems and nervous systems would be dynamic (cybernetic) in contrast to previous conceptualizations with genes and brains fixed in form and function. Questions of nature versus nurture are meaningless, and we must turn to epigenetics—the way in which biology and experience work together to enhance adaptation throughout thick and thin. Defining endophenotypes—road markers that bring us closer to the biological origins of the developmental journey—facilitates our understanding of adaptive or maladaptive processes. For human behavioral disorders such as schizophrenia and autism, the inherent plasticity of the nervous system requires a systems approach to incorporate all of the myriad epigenetic factors that can influence such outcomes. CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . COMPLEX ADAPTIVE SYSTEMS IN HUMAN DEVELOPMENT . . . . . . . . . . . . The Meaning of “Gene-Environment Interaction” . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Endophenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generation and Degeneration as a Continuum of Development . . . . . . . . . . . . . . . . BUILDING THE BRAIN FROM THE GENES UP . . . . . . . . . . . . . . . . . . . . . . . . . . . Cellular Differentiation and Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Angiogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PLASTICITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EXAMPLE: AUTISM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0263$14.00

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Twin Data Paradoxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phenotypes and Endophenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EXAMPLE: OFFSPRING OF SCHIZOPHRENIC PARENTS, CHILDHOOD ONSET CASES, AND COTWINS AS “MEDIA” FOR GROWING ENDOPHENOTYPES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Systems Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prospective High-Risk Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Candidate Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clues from Childhood Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSIONS AND A GLIMPSE AT THE FUTURE . . . . . . . . . . . . . . . . . . . . . . CONFLICT OF INTEREST STATEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION Newton’s laws of physics and Einstein’s theories of relativity anchor the physical sciences. Biological sciences have a parallel organizing principle in the concept of adaptation (Holland 1975). Adaptation can be viewed from many perspectives, including adaptation of species, of populations, or of individuals. The time scale for evolutionary adaptation of species occurs over a very long period, measured in multiple generations. During this process, genotypes (DNA) are altered through mutation followed by natural selection to reconfigure the architectures of species. Populations adapt on an intermediate time scale that is often within one, or a few, life spans. Genetic diversity reflected in population individual differences allows for deployment of different partially heritable skill sets for different challenges. For example, human populations have both created and then adapted to the changes from a largely agrarian existence to a complex industrial world within one or two life spans. Individuals adapt over short time frames measured in fractions of a life span. This ability to adapt quickly is an evolutionarily derived trait. We humans, with opposable thumbs, hands freed by upright locomotion, expanded forebrains, and skills with symbolic language, are the most adaptable of all creatures. This chapter is about adaptation and how, in large part, our genetic and biological systems foster adaptation in health and detract from it in illness. By understanding the ramifications of the concept of adaptation in biological systems, most of the details of biological development and behavioral function can be derived (Turkheimer 1998, 2004). In so doing, we quickly confront two old dogmas and hope to teach them new tricks. First, if we accept the idea that our genomes have been “designed” by evolution to maximize adaptability, we could not conclude that genetically mediated traits are fixed and immutable. On the contrary, if our genomes are tuned to maximize adaptability, we would expect genetic factors to play an important role in change even within the individual and over short time frames. The idea that genetic factors influencing behavior are fixed stems mostly from the study of human genetic diseases such as the inborn errors of metabolism and chromosomal anomalies. However, these are examples of broken genes, which cannot be generalized to genes functioning via “rheostatic” control. For example, phenylketonuria (PKU), a recessive disorder producing one kind of mental retardation, is due to a dysfunctional genotype for phenylalanine

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hydroxylase. In the absence of normal function, people cannot metabolize ubiquitous dietary phenylalanine, leading to high concentrations that damage the brain. However, the normal or even half-normal genotype allows the individual to adapt to widely varying amounts of phenylalanine, thus preventing toxic levels even with massive ingestion. The expression of any one gene is embedded within a biological system influenced by a multitude of other genetic and environmental influences; concepts of gene regulation (expression) and epigenesis are now essential for understanding development (Carey 2003, Petronis 2004). The classic Mendelian disorders involve genes that are so broken as to overshadow ancillary modifiers. Such dramatic errors of nature divert us from appreciating the subtle, complex, and dynamic nature of biological systems (Dipple & McCabe 2000) that allows them to be adaptive and self-regulating. Second, it has been a long-held belief that the central nervous system is hardwired and cannot be changed easily by the time we reach adulthood. From the perspective of adaptability, this would make no sense because we continue to learn, change, and adapt throughout the life span. The brain does change with experience and the underlying physiology is guided by genetic factors (Grossman et al. 2003, Insel & Fernald 2004, Kennedy et al. 2003, Weaver et al. 2002). Just as observations of broken genes lead to the erroneous conclusion that genetic effects are unmodifiable, the observations that broken brains (e.g., stroke, trauma) do not heal has led to the belief that the central nervous system (CNS) is a fixed structure. However, in the intact brain, synaptic connections and neuronal circuits are shaped continuously to enhance adaptation. Everything that is genetic is biological, but not all things biological are genetic. Having stated that our genomes and CNS are designed to promote adaptation, we must then consider to what are the adaptations responding. The obvious answer is the environment. However, a global concept such as environment is not helpful. In the PKU example, environmental factors could mean the difference between normal IQ and mental retardation. But, by “environment,” we are not referring to air quality, climate, economic class, amount of parental affection, or hours spent watching Sesame Street. All of these factors may have an impact on a child with PKU, but the trait-relevant environment is the amount of phenylalanine in the diet (Meehl 1977). Thus, the outcomes for a person with the PKU mutation (genotype) are determined to a large degree by diet (trait-relevant environment), mediated through the biology of phenylalanine blood levels (endophenotype) that affect the eventual IQ (phenotype).

COMPLEX ADAPTIVE SYSTEMS IN HUMAN DEVELOPMENT The Meaning of “Gene-Environment Interaction” The phrase “gene-environment interaction” is used in a variety of ways and with a variety of meanings (Carey 2003, pp. 291–297; Rutter & Silberg 2002). A few words of clarification are offered as a prelude to the discussion following about

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epigenesis. The G × E interaction concept originated from early quantitative genetics (Falconer 1960), especially agricultural genetics, and represents, in a strict sense, an interaction effect in an analysis of variance. As such, G × E interaction means that different genotypes respond differently to different environments. As a quick example, suppose you took some rice seeds and wheat seeds (the different genotypes) and planted some of each in wet conditions and some in dry conditions (the different environments). In the wet conditions, the rice grew vigorously but the wheat drowned and failed. In the dry condition, the wheat thrived but the rice withered. Another way to phrase the gene environment interaction concept is to indicate that both genes and environment make a difference for the development of some trait. Height is a simple example: A person’s height depends on genes promoting height as well as on good nutrition. No matter what your genotype, good nutrition will help individuals grow to their full potential. However, no matter what the genotype, good nutrition always works in the same direction to enhance height. This may be thought of as coaction, but it is not G × E interaction in the strict analysis-of-variance sense. The study of human development does not allow the easy quantification and manipulation of genetic and environmental variables in the same manner as agricultural genetics, thus limiting our abilities to assess G × E interaction in the analysis-of-variance sense. Simple additive models that suggest the phenotype is the sum of environmental and genetic effects do not conform to biological realities (cf. Meaney 2001). The additive model reduces human development to a simple recipe, but there is more to it than adding two parts genes to three parts environment plus a pinch of luck. Turkheimer and colleagues (Turkheimer et al. 2003) demonstrate that socioeconomic status modified the heritability of IQ in a nonlinear fashion such that, in impoverished families, 60% of the variance in IQ was attributed to shared environment, and genetic effects were negligible; in affluent families the reverse was true. Thus, the concept of gene-environment interaction, however defined, is difficult to apply in studies of human development (Caspi et al. 2002, 2003; Gunnar 2003; Kagan 2003). A further limitation of simple G × E interaction models arises from the fact that gene expression is dynamic over time. This is illustrated by studies of the effects of caloric restriction on longevity. When mice were placed on restricted diets, there was a rapid change in the expression of genes associated with longevity, including genes involved in metabolism, signal transduction, stress response, and inflammation (Dhahbi et al. 2004). To introduce a dimension of time into human developmental models and to allow for changes in both the environment and the expressed genotype, the concept of epigenesis is rapidly supplementing the ideas of gene-environment interaction.

Epigenesis The term epigenesis originated with embryological theories suggesting that complex organisms originate from undifferentiated cells, and the term has been broadly

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defined to include all the forces that lead to the phenotypic expression of an individual’s genotype (Petronis 2000, 2004; Waddington 1957). Gottesman & Shields (1972) transduced this concept of epigenesis into human behavioral genetics in the early 1970s, with later elaboration (Gottesman et al. 1982). The definition of epigenetic continues to evolve and, to many molecular biologists, the term refers to the mechanisms by which cells change form or function and then transmit that form or function to future cells in that cell line (Jablonka & Lamb 2002, Jaenisch & Bird 2003, Morange 2002). Examples include transformation of an undifferentiated embryo cell into a liver cell or transformation of a normal liver cell into a cancerous cell. Once a cell type acquires a new form through selective gene expression and environmental influences, that cell, through cell division, transmits that acquired characteristic to future cells in the lineage. The previously spurned concept of the inheritance of acquired characteristics is resurfacing at the molecular level (Varmuza 2003). The best-studied mechanisms for the epigenetic regulation of mammalian gene expression involve the addition of a methyl group to cytosine that, along with adenine, thiamine, and guanine, forms the four-letter alphabet of DNA. This methylation of cytosine changes the configuration of the DNA such that the genetic information encoded in that area cannot be read and is nullified (Jaenisch & Bird 2003, Jones & Takai 2001)—the gene essentially is turned off. Conversely, removing DNA methylation allows the gene to be expressed. The variety of factors that influence DNA methylation is huge and includes such things as developmental processes, diet, viral infections, aging, and chance. Failure of methylation systems leads to clinical syndromes, such as Rett syndrome, that involve mental retardation, autistic-like behaviors, and other neurodevelopmental anomalies in girls (Shahbazian & Huda 2002). The impact of prenatal and early postnatal nutrition on the adult development of type 2 diabetes, cardiovascular disease, obesity, and cancer are also thought to be mediated by epigenetic factors mediated by DNA methylation (Waterland & Jirtle 2004). Such epigenetic mechanisms may explain why maternal behavior toward young offspring affects the size of the offspring’s hippocampus in adulthood, depending on the offspring’s genotypes (Weaver et al. 2002). More speculatively, epigenetic theorizing is being applied to the development of schizophrenia (Petronis et al. 2003) and depression (Caspi et al. 2003, Charney & Manji 2004). Although not approaching a biochemical analysis, even traits such as talent are being rethought in epigenetic terms (Simonton 1999). Epigenetic perspectives grapple with the complexities of how multiple genetic factors and multiple environmental factors become integrated over time through dynamic, often nonlinear, sometimes nonreversible, processes to produce behaviorally relevant endophenotypes and phenotypes. How an embryonic cell differentiates into a liver cell while a genetically identical cell in the same embryo develops into a neuron is an epigenetic question. Identical twins discordant for a given trait or disease provide other examples of epigenetic processes (Cannon et al. 2002, Pol et al. 2004, van Erp et al. 2004). Diverse reviews of epigenetic concepts relevant to human development are available (Gottesman & Gould 2003, Nijhout 2003, Petronis 2000, Petronis et al.

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2003, Sing et al. 2003). A stellar example of a systems biology approach to studying epigenesis is in the research mapping the developmental sequences in the sea urchin from fertilized egg onward (Davidson et al. 2002). Epigenetic thinking builds on the notion that only a small fraction of our DNA codes for structures (proteins, enzymes, etc.) while, in keeping with the central theme of adaptation, the majority of our DNA codes for regulatory processes. In response to transduced environmental stimuli, genes are turned on or off as the organism proceeds through life. At any time, any one genotype may have a wide array of potential phenotypes. The actual phenotype will depend on the influence of the individual’s other genes and on the specific contexts of environments experienced among a wide range of possible environments. Which environment is experienced may be stochastic (luck) or may be a function of the individual’s past phenotypes. Indeed, an individual’s phenotype (which is partially a result of his or her genotype) may lead the individual to select environments, thereby establishing a correlation between genotype and environment (Carey 2003). The array of possible outcomes could be plotted, in theory, in multidimensional space, as functions of genotypes, environments, and time. The plot would produce an undulating surface that would represent the phenotype for that unique combination of genotype, environment, and time. Such a surface has been referred to as a reaction surface (Gottesman & Gould 2003, Sing et al. 2003) or phenotypic surface (Nijhout 2003); these articles provide informative graphics. Figure 1 provides such an example (Manji et al. 2003) applied to the ontogenesis of schizophrenia with provision for the changing reaction surface and a threshold, suggested endophenotypes, some already connected to candidate genes, and a dimension of environmental inputs (harmful versus protective), all “bathed” in epigenetic influences.

Endophenotypes One of the primary obstacles to progress in connecting the genotypic contributors to many human phenotypes is that the traits submitted to genetic analyses lack biological meaning. It is a long road from genotype through epigenetic pathways to ultimate phenotype, as seen above. When we study the product of this process that may have encompassed decades, we often have too much “pheno” and not enough “geno” to make sense of the trait. What is needed is some kind of intermediate trait that sits closer to the genotype in the developmental scheme. In spite of the best efforts to improve the reliability of psychiatric classification, the diagnoses in the official nomenclatures are still syndromal and lack validating pathophysiological markers. Traits such as IQ and personality have demonstrable genetic effects, but efforts to understand the genetic component suffer from the lack of any intermediary connection between the behavior and the biological underpinnings. The missing links have been referred to as “endophenotypes” (Gottesman & Gould 2003). Alternative concepts with similar but different meanings include “biological markers,” “intermediate phenotypes,” “risk factors,” “vulnerability markers,” and “subclinical traits.” Attempts to develop those that are genetically mediated endophenotypes would require that:

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1. Endophenotypes would be associated with the trait in the population. 2. The endophenotype would be demonstrably heritable. 3. The endophenotype is present whether the trait/disease is or is not present (e.g., vulnerability marker). 4. Within families, endophenotype and trait cosegregate (but not perfectly; see 3 above). 5. The endophenotype found in families with the trait (especially an illness) is found in nonaffected family members at a higher rate than in the general population. An instructive example comes from cardiology and the long QT syndrome. It was known that phenotypes including syncope, ventricular arrhythmias, and sudden death aggregated in families. The common denominator turned out to be QT elongation on electrocardiogram. Using QT elongation as the endophenotype, and by excluding or including family members with this finding, genetic linkage studies were successful in identifying the associated genes (Keating et al. 1991, Keating & Sanguinetti 2001). Putative endophenotypes for schizophrenia include, but are not limited to, those shown in Figure 1. Current strategies for identifying behavioral phenotypes such as psychiatric diagnoses, cognitive abilities, personality traits, or special talents all lack a biological “handle” to submit to genetic analysis. A search for endophenotypes will move us closer to establishing the biological underpinnings of these traits. Returning to the criteria for an endophenotype, we can observe that most implicate features that are closer to the phenotype end of the genotype-to-phenotype pathway. What will we do when we finally traverse the entire phenotype-to-gene pathway and discover the genetic contributors to the trait under study? More than 40 years ago, in a remarkably prescient anticipation of epigenetic and endophenotype thinking, Paul Meehl (1962) emphasized a perspective closer to the genotype end of the pathway. Writing about schizophrenia, he cautioned that knowing “specific (genetic) etiology” does not imply any of the following (p. 828): 1. The etiological factor always, or even usually, produces clinical illness. 2. If illness occurs, the particular form and content of symptoms is derivable by reference to the specific etiology alone. 3. The course of the illness can be influenced materially only by the procedures directed against the specific etiology. 4. All persons who share the specific etiology will have closely similar histories, symptoms, and course. 5. The largest single contributor to symptom variance is the specific etiology. Meehl offered these cautions not to suggest hopeless complexity but to challenge us to rethink our received paradigms (Hanson 2004) and to underscore the importance of epigenetic perspectives.

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Generation and Degeneration as a Continuum of Development Concepts of epigenesis and endophenotypes help us climb out of our paradigm ruts toward understanding disease states. When things go wrong in the course of our lives, we tend to classify the cause of the problem either as a phenomenon of faulty assembly (developmental models) or of breakdown after normal function (degenerative models). Degenerative models of adaptive failure imply that after a period of normal development, the organism, or one of its parts, takes an unhappy turn in life trajectory and begins to disintegrate. This, of course, describes the eventual outcome for all life forms and is a biological restatement of the second law of thermodynamics. Because degeneration is (eventually) universal, stating that an illness is degenerative is not particularly enlightening. It would be helpful to determine when in the life course the degeneration begins, and how. Answers to the “when” and “how” questions would describe the degenerative process in developmental terms. Developmental models of adaptive dysfunction (Grossman et al. 2003) implicate early brain development as setting the stage for the future (Monk et al. 2001, Webb et al. 2001). The proponents of the developmental models further argue that the perturbations of development are limited to the early times of development and are not continuous. Without this qualifier, developmental models are indistinguishable from degenerative models where the degeneration starts early in the life span (Lewis & Levitt 2002). The early abnormalities are not necessarily the cause of adaptive failure, but instead create a vulnerable risk state for future dysfunction. Consequently, there must be factors later in life that convert the vulnerability to an actuality. These additional factors are presumed to somehow damage development in such a way that dysfunction becomes manifest (cf. Sing et al. 1994). To gain a complete understanding of the syndrome, we must return to the questions of what happens and when. Following this line of reasoning, the distinction between degenerative and developmental models blurs. In fact, a medical-behavioral condition can be both developmental and degenerative, as exemplified by Down syndrome (Head & Lott 2004, Kornberg et al. 1990, Opitz & Gilbert-Barness 1990). Individuals with trisomy 21 exhibit a number of developmental anomalies, including cardiac malformations, abnormal dermatoglyphics, skeletal changes, and muscular hypotonia. As infants with trisomy 21 mature, they exhibit mental retardation. By about age 50, these individuals invariably develop Alzheimer-like CNS degenerative changes. Given the above cited evidence that prenatal and neonatal nutritional deficiencies (developmental) lead to adult diseases such as cancer and heart disease (usually thought of as degenerative), we need to redirect our thinking away from developmental versus degenerative dichotomies just as we have moved away from the nature versus nature mind-sets (Gottesman 2001). In his book Unheard Cry for Meaning, Viktor Frankl (1978) suggested we are not fully developed until we die—we continue to change right up to the last moment of life. Generation and degeneration go hand in hand as we traverse our own epigenetic landscapes.

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Cellular Differentiation and Migration Our brains can be simplified schematically into three primary components: neurons, glia, and the vascular system. Conventionally, the neuron has been the target of attention in the study of human behavior. This is especially apparent in the various neuronal transmission theories of mental illness where there has been a preponderant focus on dopaminergic function in schizophrenia, and serotonergic and noradrenergic transmission in affective (mood) disorders (Schatzberg & Nemeroff 2004). However, neurons comprise only about 50% of brain volume. Glial cells, which comprise close to the additional 50%, are receiving increasing attention for their possible role in behavioral disorders (Moises et al. 2002). A third component of the CNS, the vascular system, comprises only about 0.1% of total brain volume, but this small component forms the “blood brain barrier” and plays a vital role in regulation of brain metabolism (Schusta & Boado 2002). Development of the brain starts in the primordial inner layer of the neural tube with totipotent stem cells that differentiate into neurons and glial cells (Monk et al. 2001, Webb et al. 2001). Brain development proceeds through proliferation and migration of neurons and glia responding to epigenetic regulation of a large spectrum of genes coding for growth factors. Some neuroblasts move by radial migration, building tissue from the inner depths to the outer layers of cortex (Mehler & Kessler 1999, Nadarajah & Parnavelas 2002). In radial migration, neurons formed in proliferative tissues zones move perpendicular to the brain surface following radially oriented glial fibers. Tangential migration connects brain components across regions when neurons moving parallel to the brain surface following precursor neurons. A multitude of genetic factors guides the migratory processes. Mutations in these genes prevent normal migration and have behavioral consequences (Nadarajah & Parnavelas 2002, Taylor et al. 2004). One of the migration modulators, reelin, has received particular attention in relation to autism and schizophrenia. Glial cells continue to play important roles in regulation and repair in the adult brain (Jessen & Richardson 2001, Levine et al. 2001). Additionally, astroglia cells act as intermediaries between neurons and the vascular system. The cell bodies have two arms; one reaches out and embraces the neuron while the other reaches out to the microcapillaries and is interspersed with cells of the capillary endothelium.

Angiogenesis The brain does not work well if deprived of adequate blood flow. Because the CNS has virtually no reserves of energy and cannot function on anaerobic metabolism, the brain requires constant and precise delivery of glucose and oxygen. Developmentally, the CNS vascular system originates from mesodermic capillary endothelial cells that migrate into developing neuroexoderm under the influence of

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neuron-derived trophic factors such as vascular endothelial growth factor (Risau et al. 1998) and erythropoietin (Sasaki 2003), both produced by astroglia. Rather than being a passive conduit, the CNS vascular system is the most precisely managed and complex fluid dynamic system known. Regulation of cerebral blood flow (Kety & Schmidt 1948) is managed primarily by a partnership between astrocytic glial cells (Coyle & Schwarcz 2002, Haydon 2001, Kurosinski & Gotz 2002) and capillary endothelium (Abott 2002, Kety & Schmidt 1948, Medhora et al. 2001, Paulson 2002, Virgintino et al. 2002, Yoder 2002, Zonta et al. 2003). Astrocytes sense local neuronal metabolic activity and adjust blood flow as needed. Cerebral vessels change diameter in response to vasoactive substances released by astrocytes activated by glutamate receptors, serotonin (Cohen et al. 1999), acetylcholine (Elhusseiny et al. 1999), and dopamine (Bacic 1991, Favard 1990). When neuronal activation of discrete areas is sustained over longer periods, vasoactive substances stimulate angiogenesis (blood vessel formation), resulting in capillary density increases (Harder et al. 2002) and thus enhancing local neuronal circuitry. Conversely, a decrease in capillary density is likely to reduce the functional capacity of brain areas so affected (Harder et al. 2002). Consequently, capillary beds in the cortex are not distributed uniformly (Cavaglia et al. 2001). Close relationships exist among local neuronal activity, density of capillary bed, and the distribution of valve-like flow control structures (Harrison et al. 2002). The growing awareness of the dynamic circulation of the CNS vascular flow has important consequences for studying CNS metabolic activity. Imaging studies (e.g., fMRI, positron emission tomography) assume the vascular flow is a constant, so a measured change in cerebral blood flow is attributed to reduction in neural activity. However, we must consider reversals of the causal arrow, with the possibility that a primary vascular disease leads to the deranged cell metabolism, as is being considered in Alzheimer disease (Borroni & Akkawi 2002, Preston & Steart 2003).

PLASTICITY Psychologist D.O. Hebb postulated more than a half century ago that experience modifies cortical connections (Hebb 1947, 1949), yet the adult brain has been primarily viewed as a fixed structure (Grossman et al. 2003, Webb et al. 2001). Recent developments indicate Hebb was correct and that the brain is constantly changing in response to experience. The changes in synaptic connections and recruitment of expanded representational areas devoted to a particular function are referred to as plasticity. Plastic changes are associated with learning/memory, skill acquisition, recovery from injury, and even addiction. A myriad of factors influence brain plasticity, including pre- and postnatal experience, genes, drugs, hormones, maturation/aging, diet, disease, stress, and trauma (Kolb et al. 2003). Details of the molecular mechanisms of CNS plasticity are beyond the scope of this chapter; we limit ourselves to a few examples. Hodge & Boakye (2001) and Johansson (2000) review details about molecular mechanism. Thompson &

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Nelson (2001), Shonkoff (2003), and Grossman et al. (2003) summarize many of the social-political ramifications of the growing understanding of brain plasticity. Much of the recent progress in understanding brain plasticity in humans has been made possible by advances such as fMRI (Casey 2002), which has been used to demonstrate and map plasticity, as well as to partial out effects due to maturation, per se, versus experience/practice. Some of the most detailed research on plasticity comes from studies of musicians; their skill development involves unlearning of existing synaptic connections and establishment of new sensory-motor-memory-affective connections (cf. Peretz & Zatorre 2005). The virtuoso performance involves memorizing the music at the motor level, coordinating tactile and auditory sensory inputs, and adding an emotional interpretation that transcends the mechanical reproduction of notes. However, remodeling the brain to achieve such feats involves risks as well. Guitar players, for example, are subject to dystonic movements of their hands that, alternatively, are described as an overuse syndrome. Pascual-Leon (2001) has demonstrated with fMRI that musicians with motor dystonias showed significantly greater activation of contralateral sensorimotor cortex and conspicuous bilateral underactivation of premotor areas. The authors suggest that extensive practice of coordinated hand motions in which fingers function as a single unit might induce changes in sensory-motor field representation of the hand with blurring of the normal separation of each digit. Another illustration of the downside of CNS plasticity comes from addiction research (Ujike et al. 2002). Behavioral sensitization to addicting drugs arises from structural modification of neural networks. Repeated exposure to amphetamines and cocaine alter the cytoarchitecture of the nucleus accumbens and frontal cortex by increasing the length of dendrites and the density of dendritic spines. These changes are regulated by a host of genetic factors regulating synaptogenesis, including genes for neurite sprouting, neuritic elongation, and cell division regulators throughout the brain. The tenacity of addictions is then explained by the difficulty of remodeling these drug-induced structural changes or, possibly, by long-term alteration in gene expression supporting the CNS-mediated behavioral sensitization to the drugs. Responses to stress, including the development of depression and stress syndromes such as posttraumatic stress disorder, are attributed to failures in CNS plasticity, more so in predisposed persons. Chronic stress is implicated in CNS signal transduction cascades that normally allow neuronal plasticity. Chronic stress damages a wide variety of plasticity modulators and, at the biochemical level, causes a reduction in expression of genes associated with synaptic plasticity, resulting in diminished frontal cortical activity (Kuipers et al. 2003). Assembling and maintaining the brain involves a sometimes choreographed and sometimes extemporaneous dance among the partners of neuron, glia, vascular supply, and experience. Damage to the brain interrupts this performance. Repair may be more an issue of probabilities than of potentials. Damage does not mean that the dancers will never dance again. However, the probability of exactly

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repeating the prior performance is extremely low. Efforts focused on CNS repair are increasingly using epigenetic strategies to modulate gene expression in tissues combined with intensive rehabilitative experiences. It is too early to talk about gene transplants.


EXAMPLE: AUTISM Some 30 years ago, we (Hanson & Gottesman 1976) reviewed the scant literature on the genetics of autism and early onset schizophrenia and concluded that autism was not connected genetically to schizophrenia, an opinion that has been sustained (Gousse et al. 2003). We also found little in the extant literature providing convincing evidence for genetic factors in autism. Over the ensuing years, this opinion has been overruled so that it is common to find opinions in the literature that autism is strongly genetic (Muhle et al. 2004). Within the Annual Reviews series, there are numerous comprehensive reviews of genetic factors in autism (Cowan et al. 2002, Plomin & McGuffin 2003, Veenstra-VanderWeele et al. 2004) and it would be redundant to repeat them. Instead, we will take an epigenetic perspective to examine the consensus evidence and highlight topics needing more research.

Epidemiology Published prevalence rates for autism and autism spectrum disorders have skyrocketed from early estimates of 1 case in 10,000 to rates as high as 60 in 10,000 (Fombonne 2003). This rise in a few decades is much too fast to be blamed on genomic changes. If the increase is real and anywhere near this dramatic, it would implicate a dramatic rise in the environmental contributors that could involve epigenetic regulation of genetic factors. Continuing public expressions of concerns that agents used to immunize infants could be such a factor cannot be substantiated by careful epidemiology (DeStefano 2002, DeStefano & Thompson 2004). An analysis of pervasive developmental disorders in Israeli residents compared to immigrants to Israel from Ethiopia found much lower rates in the Ethiopians, which could point to environmental factors in the industrialized setting compared to nonindustrial environments; barriers to migration of Ethiopian families with PDD children is an alternate explanation (Kamer et al. 2004). The consensus explanation for the increased rate of autism-like disorders is that the changes are most likely due to broader definitions of the illness and improved case finding (Fombonne 2003, Gernsbacher et al. 2004, Lingam et al. 2003, Wing & Potter 2002). One epidemiological finding that remains undisputed is the higher rate of autism (299.00 in the DSM) in males as compared to females, a factor of about 4:1. The difference may even be greater as broader definitions of the disorder are applied (Veenstra-VanderWeele et al. 2004). Despite common knowledge of this fact, gender is rarely taken into consideration in genetic analyses of autism spectrum disorders. Countless linkage studies implicate loci on autosomes (especially chromosomes 2, 3, and 7). However, autosomes are freely exchanged between the

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genders and could not account for the gender differences in prevalence of autism. If autosomal factors were involved, then gender-related epigenetic differences, possibly hormonal, would have to be invoked to explain the gender differences. Alternatively, genes on the X chromosome could explain the gender differences if they were expressed as partial Mendelian recessive traits. Genetic modeling for X-linkage would lead to testable hypotheses. Alternatively, multifactorial threshold models of transmissions would lead to the prediction that the lower frequency group (females in this case) would have higher loadings of the risk factors, thus their siblings would have higher risk compared to the siblings of affected males. Future genetic studies will benefit from capitalizing on the clues provided by the apparent different expressivity in girls versus boys. Simply invoking the notion that males appear more vulnerable to development insults is not a sufficient response because there still must be detailed explanation for the vulnerability.

Twin Data Paradoxes Much of the support for a genetic component in autism came from the study of twins (see reviews cited in the Epidemiology section above). The twin data consistently show high concordance rates in identical twins (monozygotic, MZ) in the range of 60%–90%. By contrast, fraternal twins (dizygotic, DZ) have a low concordance rate, typically close to zero and ranging up to 10% with large standard errors, given the rarity of such samples. Fraternal twins are genetically no more similar than ordinary siblings, and the DZ twin concordance falls in the same ballpark rate of 4%–5% as siblings of autistic people also being affected. Two issues arise. First, for twin studies to be valid, the trait under study cannot be an outcome simply of the twinning process. Twin pregnancies are considered high risk and are associated with increased rates of a wide range of developmental disorders, including mental retardation and cerebral palsy. Two recent reports indicate that autism is more prevalent in twins, suggesting the twin method may not be completely valid for this trait (Betancur et al. 2002, Greenberg et al. 2001). Counterarguments suggest diagnosis inflation and that sampling artifacts could explain the perceived increased rate of twins with autism (Visscher 2002). Other studies fail to find increased twinning among people with autism (Hallmayer et al. 2002). Patience is called for as the debate goes on (Hodge et al. 2002) and we await the definitive answer from population-based international collaborative studies of autism in twins following the precedents applied to the study of cerebral palsy (Petterson et al. 1993, Scher et al. 2002) and the discovery of how environmental factors such as infection, in combination with inherited variations in response to infection, can lead to cerebral palsy (Gibson et al. 2003, Nelson & Willoughby 2000). The very high concordance rates for autism in MZ twins in contrast to very low concordance rates in DZ twins gives rise to very high heritability estimates for autism when these twin rates are plugged into standard formulas for computing heritability from twin data (Carey 2003). At the same time, the low DZ concordance rates (often zero) and low sibling risk rates suggest that autism has low recurrence risks or transmissibility. As a counterpoint, consider a trait like

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Huntington disease. This also rare autosomal dominant disorder is little modified by environmental factors (highly genetic), and the disorder conveys risks of 50% to DZ twins or siblings (highly transmissible). For autism, the high heritability contrasted with the low transmissibility creates dissonance in genetic theorizing, especially given the “background noise” of 3%–5% rates of serious developmental disabilities in the general population. A simple explanation is that genes really are not so important after all. Alternatively, epistasis (gene x gene interaction within and across loci) could be invoked—it is more the combination of genes than the specific genes that holds the key to this illness. Another possibility is that an epigenetic or chance change in the germ line leads to autism (Keller & Persico 2003). Once we figure out the heritability versus transmissibility issue, we will then have to understand why it affects males more than females.

Phenotypes and Endophenotypes Autism is certainly heterogeneous, etiologically and clinically. Known genetic conditions with autism-like characteristics include syndromes of tuberous sclerosis, fragile-X, and Prader-Willi, Angelman, Rett, and various chromosomal abnormalities, to name a few (Gillberg & Coleman 2000). Individuals with the syndrome may or may not have evidence of in utero growth retardation, EEG abnormalities or epilepsy, profound (IQ < 50) intellectual impairment, increased head growth in infancy (Courchesne et al. 2003, Lainhart 2003), altered immune systems (Licinio et al. 2002, Lipkin & Hornig 2003), structural brain abnormalities on MRI (Brambilla et al. 2003), and altered neuroarchitecture (Casanova et al. 2002, 2003). Developmental characteristics such as these may point to endophenotypes that are more suitable for genetic analysis than are the phenotypes based on overt behavioral syndromes utilized by our current clinical diagnoses (Baird et al. 2003). While we laud the efforts to further refine the behaviorally based assessment strategies (Constantino et al. 2004), it is our bias to look for classification strategies that incorporate biological as well as behavioral characteristics. The use of eye tracking technology to assess social visual pursuit in autism (Klin et al. 2002) is one such example. Returning to our earlier comments on adaptation, we can view behavior itself as an evolutionary strategy to maximize adaptability. However, the resulting variability of human behavior may mean that strictly behavioral phenotypes are too imprecise to lead us to the neurobiological and genetic underpinnings of disorders such as autism.

EXAMPLE: OFFSPRING OF SCHIZOPHRENIC PARENTS, CHILDHOOD ONSET CASES, AND COTWINS AS “MEDIA” FOR GROWING ENDOPHENOTYPES Systems Biology Our next exemplars maintain a focus on the biological and genetic processes that affect developmental psychopathology by examining recent research on the

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precursors to adult schizophrenia, childhood onset schizophrenia, and pervasive developmental disorders (but not autism, already discussed above). We have tried to select research that emphasizes a systems biological strategy (Sing et al. 1994, Zerba et al. 2002)—everything involving brain and behavior is connected to everything else—to integrate across the regions of Figure 1. Excellent reviews complement our selections (Cowan et al. 2002, Erlenmeyer-Kimling 2000, Kennedy et al. 2003, Lewis et al. 2003, Walker et al. 2004). Keeping track of the “gene of the month” in regard to the psychoses is a full-time occupation that can be mentioned only briefly. Contrary to some jaundiced views of such enterprises, replications of candidate genes and new candidates are here to stay; a good two-dozen candidate genes, each of modest impact on total liability to developing schizophrenia in their respective populations, deserve attention and following up (Lewis et al. 2003, McGuffin 2004, Moises et al. 2004, Owen et al. 2004, Sklar 2002).

Prospective High-Risk Studies Prospective longitudinal studies of the offspring born to schizophrenic parents have been a staple in the search for antecedent traits and endophenotypes related to schizophrenia even though they may require 30 or more years of intensive efforts. The high-risk strategy exemplifies a systems approach by the breadth of variables studied including genetics, birth and pregnancy complications, and a host of behavioral variables followed developmentally over decades. The strategy (Pearson & Kley 1957) was suggested with the foreknowledge that 10% or so of such children in such cohorts would go on to develop the illness, thus permitting the observation of illness precursors unconfounded by the effects of the illness per se. Adoption strategies reinforced the continuing efforts by showing that rearing by ill parents was not essential for the dire outcomes (Ingram & Kety 2000, Tienari et al. 2003), and left open the question as to the role of other contributors to liability that were not genetic (cf. Murray et al. 2003). The theme of the endophenotype strategy (Glahn et al. 2004, Gottesman & Gould 2003, Lenox et al. 2002) embedded within a systems biology context is straightforward: deconstruct the disease phenotype into its precursors and correlates not available to the naked eye or ear (neurocognitive tasks, measured personality indicators, fMRI, neuronal growth factors, etc.), and focus on those that are heritable with implied genes and their polymorphisms as distal causes in the complex genes-to-behaviors pathways. Identify the polymorphisms and then explore their expression over the life course, sensitive to the agents of epigenetics. The process may also begin at the genotype level, reversing the plan of research, and starting with those genes now being implicated by whole genome scans that rely on patients and controls without involving relatives (Lewis et al. 2003, Owen et al. 2004). Many of the genes identified do not yet have known disease polymorphisms or CNS-impacting functions, as it is still early in the game (Merikangas & Risch 2003, Varmus 2002). The New York High-Risk Project systematically sampled 358 children ages 7 to 12, starting in 1971, whose mothers or fathers were in one of three groups:

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schizophrenics or affectively ill who had been admitted to a state hospital, and community controls without psychiatric diagnoses or treatment. Erlenmeyer-Kimling (2000), after Herculean efforts, followed these offspring through seven rounds of testing until the late 1990s (when the mean age of the offspring was the early thirties), by which time 15%, 7%, and 1% of each of the respective risk groups had developed a schizophrenia-related psychosis (only Sample A, the first half, is reported here). Because their data were gathered prospectively, the researchers could look back to see which neuropsychological indicators (cf. Meehl 1962), if any, would have predicted the outcomes. Briefly, three sets of indicators, when configured, yielded noteworthy results. Using a criterion of failure on all three indicators in early childhood—attentional deviance index, verbal short-term or working memory index, and impairment (subtle) in gross motor skills index—the sensitivity of the battery correctly predicted for 50% of the offspring of schizophrenics, with a false positive rate of 10.4%. The battery was 100% accurate in not predicting any child in the other two groups as a future sufferer from a schizophreniarelated psychosis. Thus, a foundation has been laid for carrying out the remaining steps in the larger strategy, with some of the evidence for success illustrated in Figure 1.

Candidate Genes The COMT gene on the long or q arm of chromosome 22 at band 11 has attracted considerable attention as a candidate gene for schizophrenia given (a) its involvement in coding for a dopamine enzyme and its functional mutations, (b) its overlap with the gene deletion leading to velocardiofacial syndrome or DiGeorge syndrome, wherein excess diagnoses of schizophrenia are seen in some samples (Ivanov et al. 2003), and (c) research programs connecting COMT polymorphisms to measures of frontal lobe function such as working memory in patients (Egan et al. 2001, Malhotra et al. 2002). The genetic association with schizophrenia is equivocal in that a box score shows four positive and four negative results (Owen et al. 2004). Studies of mice with a deletion in the equivalent region of their genome provide interesting and encouraging leads (Maynard et al. 2003). The mix of data requiring integration to this COMT story includes data from studies of unaffected relatives of schizophrenic patients (Goldberg et al. 2003) and of normal children. Diamond et al. (2004) looked at COMT polymorphisms in 39 children under age 12 (mean = 9) who had been tested with a variant of the Stroop test and two control tasks. Their results showed that only some of the supposed prefrontal cortex cognitive functions were sensitive to the dopamine levels inferred from the genotypes, adding to the complexity of what is being tested in the brain–behavior relationship. The story and the validity of genes in the 22 q region, as with all of the other promising candidates already in hand and about to be reported, leave many hurdles left to be traversed successfully; many will fail the tests and others will open windows to neighboring genes that were not even suspected beforehand (Mowry et al. 2004).

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Clues from Childhood Schizophrenia Childhood onset schizophrenia (COS) cases, although rare, are considered especially valuable for casting light on the biology and genetic processes of adult onset cases. Criteria are not different for COS, but the criteria are met before the thirteenth birthday. Cases of COS appear to be more severe and more homogeneous, with more familiality for schizophrenia spectrum disorders (Asarnow et al. 2001, Hanson & Gottesman 1976, Nicholson et al. 2003). We would expect COS to have a larger dose of the genes that predispose to schizophrenia as well as to have biology that, if different, cannot be attributed to the wear and tear that confound the phenotypes of adult cases. Lingering problems about the diagnostic overlaps in COS are a challenge to unraveling etiologies. Psychosis not otherwise specified (NOS), pervasive developmental disorder (PDD), and numerous organic diseases of the CNS have symptoms that overlap one another (Sporn et al. 2003, 2004). An important observation made on follow-up of psychotic children with an initial diagnosis of psychosis NOS was that 13 of 27 were diagnosed as bipolar disorder (Addington et al. 2004). In that same study, G72—a gene at 13q33.2—was found to be significantly associated with both COS and early onset bipolar cases; the gene is one of those already with replicated positive results in adult cases of schizophrenia (Owen et al. 2004). Sporn et al. (2003) have observed a likely biological marker in 60 COS cases; a “striking progressive reduction in cortical gray matter volume” was detected with MRI at the follow-up time into adolescence of the COS cases. Altered cortical thickness and surface morphology possibly due to aberrant cellular migration patterns may provide another marker (White et al. 2003). With the growing interest in discovering endophenotypes and the availability of advanced imaging techniques has come a renewed interest in studying identical and fraternal twins with the new equipment. The findings are too new to be integrated into this review, but they can be expected to provide important milestones on the research road map in Figure 1 (Cannon et al. 2002, Pol et al. 2004, Torrey et al. 1994).

CONCLUSIONS AND A GLIMPSE AT THE FUTURE The charge of responsibly overviewing—while being limited to a finite number of words—a “planet” in the solar system of the planet Psychology is both a daunting and enlightening task: daunting because conscientious and highly motivated scientists have produced too many important, and less important, advances since the subject was last reviewed; enlightening because it compels opening one’s mind to the fact that our planet is so enmeshed with others in the system that no one or even three topical Annual Reviews chapters could accomplish the mission of summarizing selected and relevant nuggets of wisdom. These points are made obvious by what is present and even more so by what has been omitted from our cited literature. We hope we will be accused of stretching your mind muscles but not of spraining them if our reach sometimes exceeded our grasp. Consider each citation as a seed for turning on an Internet search engine with a mission of

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incorporating biology and genetics into your own research program—you will be pleasantly surprised by the planets you encounter and what they are doing there that is relevant to your own work. Incorporation followed by integration will take place, we predict, and you will discover the benefits of hybrid vigor for yourselves.


ACKNOWLEDGMENTS Preparation of this chapter was supported in part by a grant from the Stanley Foundation to DRH. We dedicate this chapter to the memory of Paul E. Meehl (1920–2003). His far-ranging intellect and his self-described interdisciplinary mind (“I am more of a knowledge-absorber, knowledge-integrator, and knowledge-transmitter”) taught us to search widely for answers to questions posed by science and humanity. Paul Meehl’s autobiography can be found in A History of Psychology in Autobiography, 1989, ed. G. Lindzey, volume 3, pp. 337–389, published by Stanford University Press, Stanford, California.

CONFLICT OF INTEREST STATEMENT IIG and DRH know of no conflicts of interest related to this manuscript. The Annual Review of Psychology is online at http://psych.annualreviews.org

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Figure 1 Sketch of a systems biology approach toward explaining a complex behavior that incorporates dynamic interplay among candidate genes and gene regions, a sampling of endophenotypes (solid lines indicate those downstream of real genes), and the scope of preand postnatal environmental and epigenetic influences (harmful versus protective) over the course of development. Question marks indicate gaps in knowledge; p and q indicate regions of interest localized by research to specific numbered chromosomes; QTL = quantitative trait locus. Two planes intersect the reaction surface for the liability to developing schizophrenia over time, demarcating levels above which clinical diagnoses are warranted (cf. Gottesman & Gould 2003, Manji et al. 2003 for details). Copyright 2003 by I.I. Gottesman (used with permission).

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THE PSYCHOLOGY AND NEUROBIOLOGY OF SUICIDAL BEHAVIOR Thomas E. Joiner Jr., Jessica S. Brown, and LaRicka R. Wingate Psychology Department, Florida State University, Tallahassee, Florida 32306-1270; email: [email protected], [email protected], [email protected]

Key Words genetics, serotonergic dysregulation, psychological risk ■ Abstract Suicide is a leading cause of death, but it is not well understood or well researched. Our purpose in this review is to summarize extant knowledge on neurobiological and psychological factors involved in suicide, with specific goals of identifying areas particularly in need of future research and of articulating an initial agenda that may guide future research. We conclude that from both neurobiological and psychological perspectives, extant research findings converge on the view that two general categories of risk for suicide can be identified: (a) dysregulated impulse control; and (b) propensity to intense psychological pain (e.g., social isolation, hopelessness), often in the context of mental disorders, especially mood disorders. Each of these categories of risk is underlain at least to some degree by specific genetic and neurobiological factors; these factors in general are not well characterized, though there is emerging consensus that most if not all reside in or affect the serotonergic system. We encourage future theorizing that is conceptually precise, as well as epistemically broad, about the specific preconditions of serious suicidal behavior, explaining the daunting array of suicide-related facts from the molecular to the cultural level. CONTENTS THE NEUROBIOLOGY OF SUICIDAL BEHAVIOR . . . . . . . . . . . . . . . . . . . . . . . . Family History and Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abnormalities in the Serotonergic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Brain Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSYCHOLOGICAL AND CLINICAL RISK FACTORS FOR SUICIDALITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mental Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hopelessness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Isolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impulsivity and Aggression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ineffectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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THE NEUROBIOLOGY OF SUICIDAL BEHAVIOR


Of the leading causes of death, there is little doubt that suicide is the least well understood and the least well researched. It is the cause of death for more than half a million people a year worldwide, and agonizes many more. Our purpose in this review is to summarize extant knowledge on neurobiological and psychological factors involved in suicide, with specific goals of identifying areas particularly in need of future research and of articulating an initial agenda that may guide future research.

Family History and Genetics Initial studies attempting to determine the genetic contribution to suicidal behavior were conducted with twin samples. Roy (1992) reviewed four of the early twin studies and found that overall, 13.2% of the monozygotic (MZ) twin pairs were concordant for death by suicide as compared to only 0.7% of the dizygotic (DZ) twin pairs, a significant difference. Roy & Segal (2001) conducted two more twin studies and summed those with the previous four to find a MZ concordance rate of 18.5%, as compared to a DZ concordance rate of 0.7%. With such studies having clearly indicated that there is some genetic component to suicidality, more recent studies have sought to clarify the role of genetics in suicide by evaluating variables that may account for this association. One study assessed 3401 female twins in Missouri, and identified a concordance rate for suicide attempts of 25% for MZ twins and 12.8% for DZ twins. Based on these results, they estimated the additive genetic effects to be 48%, the unique environmental factors to be 44%, and shared environmental effects to be 8% of the contribution to suicide attempts. When controlling for other psychopathology, they found major depression, childhood physical abuse, social phobia, African-American ethnicity, alcohol dependence, and conduct disorder to be significant predictors of suicide attempts, whereas alcohol abuse, generalized anxiety disorder, simple phobia, and parental education did not predict suicide attempts (Glowinski et al. 2001). A similar study conducted on 5995 adult twins in Australia yielded similar results. This study examined three categories: the presence of suicidal thoughts; persistent thoughts of suicide, a plan to commit suicide, or a minor suicide attempt; and serious suicide attempts. In all categories, concordance was higher for MZ than for DZ twins. In comparison to the earlier studies, the concordance rates for serious attempts were 23.1% in MZ twins and 0% in DZ twins. Estimates of heritability of these types of suicidality were 43% for any ideation, 44% for persistent thoughts/plan/minor attempt, and 55% for a serious attempt, comparable to the estimate of the prior study. When controlling for other psychopathology, a history of major depressive disorder, conduct disorder, alcohol dependence, and panic disorder, each increased risk of persistent thoughts/plan/minor attempt in both men and women. In women, risk of a serious attempt was increased by a history of major depressive disorder, panic disorder, social phobia, and childhood sexual abuse.

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Risk of a serious attempt in men was increased by a history of major depressive disorder, conduct disorder, panic disorder, and childhood sexual abuse. However, when controlling for these psychiatric and psychosocial variables, the cotwin’s history of suicidal thoughts was a significant predictor of both increased serious attempts and persistent thoughts/plan/minor attempts in MZ twins, indicating a genetic link to suicide beyond the contribution of mental illness (Statham et al. 1998). This is a key point about the relation of mental disorders to suicidality— mental disorders, though very important in understanding suicidality, do not fully explain it (and a simplistic view of the association between mental disorders and suicidality does not explain why most people with mental disorders do not attempt, or die by, suicide). Finally, a study on 6744 adult male twins in Missouri also identified a similar pattern of results (Fu et al. 2002). This study examined suicidal ideation as well as suicide attempts and calculated a heritability estimate for suicide attempts of 30%, lower than the two prior studies. It identified nearly identical psychiatric risk factors: History of major depressive disorder, adult antisocial personality disorder, post-traumatic stress disorder, panic disorder, and drug and alcohol dependence predicted both suicidal ideation and attempt when controlling for psychiatric history. Childhood conduct disorder was also a significant predictor of suicidal ideation, but unlike in the two prior studies, suicide attempts were not a significant predictor. Like the Statham et al. study (1998), the Fu et al. (2002) study also evaluated the role of cotwin ideation or attempt while controlling for other psychiatric and psychosocial risk factors. Here, too, genetic risk remained even when various mental disorders were controlled. Also, for MZ twins, risk of both suicidal ideation and attempt increased regardless of whether the cotwin endorsed ideation or attempt. However, in DZ twins, a more specific pattern emerged: A cotwin’s suicidal ideation increased risk of ideation only, and a cotwin’s suicidal attempt increased risk of attempt only. This suggests a broader effect on various forms of suicidality among MZ twins, and a more specific effect among DZ twins. Further evidence of the role of genetics in suicide is shown through family studies. One early study (Egeland & Sussex 1985) examined the Old Order Amish over a 100-year period. During this time 26 people died by suicide, the majority of whom came from only four families. Interestingly, while these four families also had a high genetic loading for affective disorders, other families had a similarly high loading for affective disorders but no suicides, again consistent with the argument that an independent genetic component to suicide exists. In examining familial risk factors for individuals who died by suicide, a family history of suicide contributed about a twofold increase in risk, even when controlling for family psychiatric history, which is also a significant predictor (Qin et al. ˚ 2003, Runeson & Asberg 2003). These results also add support to the argument that there is a genetic contribution to suicide independent of the genetic contribu˚ tion to mental illness. Interestingly, Runeson & Asberg (2003) found no gender differences in the relation of family history to personal risk for suicide, whereas

FAMILY STUDIES

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Qin et al. (2003) found that a family history of suicide increases risk for suicide in females more than in males. Clearly, further research on the interaction between gender and family history of suicide is necessary. Research on the parent-child transmission of suicide risk has also been utilized to identify risk factors for suicide attempts. One study found a sixfold increase in risk for suicide attempt in children of suicide attempters versus nonattempters. Additionally, 82% of the offspring who attempted suicide also had a mood disorder. A history of sexual abuse and increased impulsive aggression in both the parent and child also increased risk for suicide in the children of suicide attempters (Brent et al. 2002). This same group conducted another study (Brent et al. 2003), which divided the parents into three groups: those who had attempted suicide and had a sibling who had attempted suicide (highest genetic loading), those who had attempted suicide but none of whose siblings had attempted suicide (moderate genetic loading), and those who had not attempted suicide and whose siblings had not attempted suicide (low genetic loading). As expected, offspring with the highest genetic loading had the highest risk for suicide attempt, those with a moderate genetic loading had a moderate risk for suicide attempt, and offspring with a low genetic loading had the lowest risk for suicide attempt; greater genetic loading was associated with earlier age of first suicide attempt in offspring. Similar to their prior study, impulsive aggression predicted both familial transmission of suicide attempt and earlier age at first attempt, though in contrast to the earlier study, history of physical or sexual abuse did not (Brent et al. 2003). These two studies of parent-child transmission of suicidal behavior illustrate the important role of impulsive aggression as a mediator, but contribute mixed results regarding the role of childhood abuse in risk for suicidal behavior. In recent years, one gene that has received much attention is the serotonin transporter gene. The serotonin transporter (5-HTT) maintains control over the availability of serotonin in the synaptic cleft. In humans, 5-HTT is encoded by one gene (SLC6A4), located on chromosome 17q12. The transcriptional control region of this gene, denoted 5-HTTLPR, has been identified as having a polymorphism consisting of a 44 base pair insertion or deletion. These two alleles have been called the long (l) and short (s) (Lesch et al. 1996). These two alleles combine in individuals to form three different genotypes—the homozygous short (s/s), homozygous long (l/l), and heterozygous (s/l). Studies to date examining the link between these various genotypes and suicidality have generally shown mixed results. A recent study that followed 103 suicide attempters over the course of a year found that having the s allele increased the risk for subsequent suicide attempt, and that the frequency of the s/s genotype rose as the number of suicide attempts rose. Additionally, subjects carrying the s/s genotype had significantly higher scores on a measure of impulsivity (Courtet et al. 2004). Joiner et al. (2002a) reported that those with a significant family history of suicide were more likely to have the s/s genotype than were those without a family history. Mann et al.’s (2000) postmortem study found that short alleles were more

STUDIES OF CANDIDATE GENES

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common among suicide victims than among others, but this difference did not reach statistical significance. However, a recent meta-analysis (Brown & Joiner, in preparation) has indicated that suicide completers are significantly less likely to carry the s allele than are controls. Suicide attempters showed no significant genotype differences from controls. These apparently contradictory findings highlight the need for further research on this gene as it relates to suicidality. In particular, the serotonin transporter gene’s potential link to impulsivity warrants further investigation, as impulsivity appears to be involved in risk for suicide (a point that will be expanded on below). Perhaps the most commonly studied gene with relation to suicidality is the tryptophan hydroxylase (TPH) gene. TPH is the rate-limiting enzyme in the synthesis of serotonin, making it an obvious candidate for speculation regarding suicide. This gene is located on chromosome 11q7, and two polymorphisms in particular have been studied: A218C and A779C. A meta-analysis of the association between the A218C polymorphism and suicidal behavior (combined attempted and completed suicides) found that presence of the 218A allele was significantly related to increased risk for suicide (Rujescu et al. 2003a). Other studies (e.g., Bennett et al. 2000, Pooley et al. 2003) have examined the A779C polymorphism and its relationship to suicide, with mixed results. One study (Nielsen et al. 1998) evaluated suicidality in male violent offenders and classified offenses as impulsive or nonimpulsive. Interestingly, suicidal impulsive offenders were more likely to carry the 779C allele, but suicidal nonimpulsive offenders were less likely to carry the 779C allele. The A218C polymorphism and the A779C polymorphism have been shown to be linked, such that almost all individuals have the same genotype for both polymorphisms (Kunugi et al. 1999). This suggests that the results regarding the A218C polymorphism likely hold true for the A779C polymorphism as well, and vice versa. Currently, the implications of these results are unclear, given that these two polymorphisms do not seem to have any functional influence on TPH gene transcription. Research is needed to identify the possible mechanisms of this gene’s influence on suicidality as well as its potential moderating effect of impulsivity. A third serotonergic gene that has been examined in relation to suicide is the 5-HT2A receptor gene. A polymorphism has been identified on chromosome 13q14.1–14.2 and has been labeled T102C. This gene has come into question based on findings that suggest abnormalities in the 5-HT2A receptors in suicidality (discussed below). However, the polymorphism in question has shown no functional relationship with the receptor, with one study (Du et al. 1999) showing no association between genotype of the T102C polymorphism and 5-HT2A receptor density. Given these results, it is not surprising that most studies to date have found no association between the T102C polymorphism and suicidality (see Arango et al. 2003 for a review). However, these findings should not be taken to mean that the 5-HT2A receptor gene has no effect on suicidality. It is more accurate to say that we have not yet identified the polymorphism that regulates the effect of the 5-HT2A receptor gene on suicidal behavior.

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Finally, one gene that has only recently been studied with regard to suicide is the catechol-O-methyltransferase (COMT) gene. The COMT enzyme is responsible for degradation of catecholamines (dopamine, epinephrine, and norepinephrine). A polymorphism on chromosome 22q11 codes for COMT activity and is composed of two alleles, the H allele and the L allele, which trigger high or low COMT activity, respectively. As with most other research on candidate genes, results have been mixed. One study (Russ et al. 2000) identified no difference in COMT genotype between patients at high risk for suicide and controls. However, other studies have suggested that the COMT gene is associated only with violent suicide. In one sample (Rujescu et al. 2003b), the L allele was more frequent in violent suicide attempters versus nonviolent attempters and nonattempters. The nonviolent suicide attempters and nonattempters showed no difference in COMT genotype. A similar study (Nolan et al. 2000) stratified the results by gender and found that the L allele was more frequent in males with a history of suicide attempts than in females. Furthermore, males who carried the L allele were more likely to have made violent suicide attempts and more attempts overall, but this relationship did not hold for females. Clearly, additional studies must be conducted to replicate these results, which suggest an interesting gender difference in suicide with regard to the COMT gene. The results also suggest that the COMT gene is linked to violent behaviors. Though recent studies have not evaluated the COMT gene with regard to impulsivity, this is an important area for future research. To summarize, twin and family studies of suicidality have clearly shown a genetic component to suicidal behavior. Current research estimates the genetic contribution to suicidality to be between 30% and 50%. This genetic risk for suicidality appears to be partly independent of risk for mental illness and other psychological stressors. With advances in the study of the human genome and readily available procedures for genotyping, several candidate genes for the transmission of suicide risk have been identified. The serotonin transporter gene, the TPH gene, and the COMT gene have all shown links to suicidal behavior. The effects that these genes may have on impulsive and violent behaviors have only begun to be assessed and are a promising area of future research, as they may be the mechanisms through which the genetic risk is expressed. It is also important to note that suicidal behavior is not simple enough to be caused by any one gene, and haplotype analysis—the analysis of the effects of multiple genotypes in combination—may help to differentiate levels of genetic risk.

Abnormalities in the Serotonergic System To date, one of the most well replicated findings in suicidal behavior is decreased levels of 5-hydroxyindoleacetic acid (5-HIAA), a major metabolite of serotonin, in the cerebrospinal fluid (CSF) of suicidal individuals. A meta-analysis comparing levels of 5-HIAA as well as homovanillic acid (HVA, a dopamine metabolite) and 4-hydroxy-3-methoxyphenyl glycol (MHPG, a metabolite of norepinephrine) found consistent evidence for

PERIPHERAL MEASURES OF SEROTONIN FUNCTION

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lowered 5-HIAA in suicide attempters and completers but no evidence for consistent changes in HVA or MHPG, indicating specificity to the serotonergic system (Lester 1995). A subsequent review came to similar conclusions and indicated that low levels of 5-HIAA in suicide attempters are predictive of subsequent attempts ˚ (Asberg 1997). Given the robust findings regarding decreased 5-HIAA in the CSF of suicide attempters and completers, recent research has begun to investigate possible moderators of this relationship. Some research (Mann & Malone 1997, Placidi et al. 2001) has divided suicide attempters based on the lethality of their attempts and has found that levels of 5-HIAA are significantly lower in high-lethality versus lowlethality attempters and that levels of HVA and MHPG do not vary by group. Other research (Cremniter et al. 1999) has evaluated impulsivity as a potential moderator. In violent suicide attempters, those who were identified as having high impulsivity had significantly lower 5-HIAA levels than did nonimpulsive attempters and controls. When evaluating serotonin in blood samples, plasma levels of 5-HIAA were lower in impulsive suicide attempters than in nonimpulsive attempters and controls (Spreux-Varoquaux et al. 2001). Taken together, these more recent studies suggest that lethality and impulsivity may serve as a link between low 5-HIAA and suicidal behavior. One study (Nielsen et al. 1994) examined the association between CSF 5HIAA levels and the TPH gene in impulsive alcoholic offenders. Individuals who carried the 779C allele were more likely to have lower 5-HIAA concentrations than individuals who did not. As mentioned previously, impulsive offenders with a suicide history were also more likely to carry the 779C allele, again supporting the link between low 5-HIAA and increased impulsivity and suicidality. Another mechanism for documenting abnormalities in the serotonin system is by administration of a fenfluramine challenge. This task is useful in that it stimulates serotonin release and inhibits reuptake, and the degree to which it stimulates serotonin can be measured by levels of prolactin. Results have generally shown a decreased prolactin response in suicide attempters versus depressed patients and controls, indicating less serotonergic activity in suicidality specifically (Coccaro et al. 1989, Corrêa et al. 2000, Duval et al. 2001). However, this difference has not always been found (Prochazka et al. 2000). Similar to the 5-HIAA studies, lethality and impulsivity have been evaluated as moderators of this relationship. Not surprisingly, higher-lethality suicide attempters show decreased prefrontal cortex functioning as compared to lowlethality attempters, particularly after fenfluramine challenge (Oquendo et al. 2003). These high-lethality attempters also show decreased prolactin response to the fenfluramine challenge as compared to low-lethality attempters (Corrêa et al. 2000, Malone et al. 1996). Blunted prolactin response to the fenfluramine challenge is also seen in impulsive (Coccaro et al. 1989, Dolan et al. 2001, Sher et al. 2003) and aggressive individuals (Coccaro et al. 1989, Sher et al. 2003) as

FENFLURAMINE CHALLENGE STUDIES

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compared to controls. One study (Malone et al. 1996) examined the interrelationships between suicide attempt lethality, Diagnostic and Statistical Manual (DSM) Cluster B personality disorders (characterized by erratic and impulsive behaviors), and fenfluramine-induced prolactin response. Again, the evidence to date suggests that hypoactivity of the serotonin system may influence aggression, impulsivity, and the lethality of suicide attempts. Another method of evaluating the serotonin system is by postmortem analysis of the brains of individuals who have died by suicide. This area of research is not as clearly defined as the CSF 5-HIAA and fenfluramine literature, as some have found no difference in serotonin transporter binding between suicide victims and control subjects (Arango et al. 2001, Du et al. 1999). However, another study (Mann et al. 2000) found decreased serotonin transporter binding in the ventral prefrontal cortex that was specific to suicide as compared to major depression. Findings also indicated that abnormalities in the ventral prefrontal cortex are associated with disinhibition and impulsivity. Clearly, further research—either in vivo using imaging techniques or by postmortem analysis—regarding serotonin transporter binding in suicidal individuals is necessary. Similar techniques have also been used to study serotonin receptor activity in suicidal individuals. Studies of the serotonin 2A receptor have indicated increased binding in suicide victims as compared with controls (Du et al. 1999, Turecki et al. 1999). Results for the serotonin 1A receptor, however, are less consistent. Increased serotonin 1A receptors have been seen in the dorsal raphe nucleus in suicide victims as compared with controls (Stockmeier et al. 1998). It has also been reported (Arango et al. 2001) that the receptor density in the dorsal and median raphe nucleus is equivalent between suicide victims and controls, but that the binding capacity is decreased in suicide victims. Again, this area of research is still in the preliminary stages and clear differentiation between suicidal individuals and controls regarding serotonin receptors has not yet been identified.


TRANSPORTER AND RECEPTOR BINDING ABNORMALITIES

Other Brain Systems Thus far, research has primarily focused on the role of the serotonin system in suicidal behaviors. As reported in the CSF studies, metabolites of dopamine and norepinephrine are generally no different in these groups than in others (see Placidi et al. 2001 for a review). However, it has been suggested that other brain systems, such as the hypothalamic-pituitary-adrenal (HPA) axis, may be involved in suicidal behavior. One way to measure HPA axis activity is by administering the dexamethasone suppression test (DST) and measuring cortisol response. Nonsuppression of cortisol in response to the DST indicates hyperactivity of the HPA axis. A meta-analysis has indicated that nonsuppression of cortisol in response to the DST may be predictive of later death by suicide (Lester 1992). While some more recent studies have not found an association between cortisol response to the DST and suicidality (Pitchot et al. 1995, 2003), others have shown that nonsuppressors show greater suicidality and severity of depression (Westrin & Niméus 2003).

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One study (Coryell & Schlesser 2001) followed a group of patients over 15 years and found that those with nonsuppression of cortisol at baseline went on to have a 14-fold greater risk of death by suicide than those who did suppress cortisol output in response to the DST. These studies suggest that hyperactivity of the HPA system may be involved in suicidal behavior, though the evidence is not conclusive. In summary, neurobiological research to date has clearly shown that there are serotonergic differences in suicidal individuals as compared to others. These differences appear to be specific to serotonin and may not involve other neurotransmitters. Studies of CSF and fenfluramine challenge responses indicate decreased serotonergic function in suicide attempters and completers, and point to impulsivity, aggression, and lethality as being influenced by this serotonin hypoactivity. More specific biological mechanisms underlying this hypoactivity are not yet clear, as there is no conclusive evidence for abnormalities in either the serotonin transporter or serotonin receptors in the brains of those who die by suicide. The twin and family genetic studies also implicate impulsivity as a factor, as well as mood and anxiety disorders; they further document that the genetic contribution to suicide exists beyond these factors. Continued research in this area is clearly necessary. Research has also implicated the HPA axis in suicidality, and further research on the nature of this system and its link to suicidal behavior promises to be an exciting new avenue of inquiry.

PSYCHOLOGICAL AND CLINICAL RISK FACTORS FOR SUICIDALITY Mental Disorders The presence of a psychiatric disorder and particularly major depressive disorder is a well-established risk factor for suicide across all age groups (Prigerson et al. 2003). Approximately 90% of completed suicides have a diagnosable psychiatric disorder at the time of death (Bertolote 2003, Cheng 1995, Conwell et al. 1996, Henriksson et al. 1993, Vijayakumar & Rajkumar 1999). A number of other psychiatric disorders have also been repeatedly and specifically linked to suicide. The specific disorders with strong empirical support for a relation to suicide include mood disorders, borderline personality disorder (BPD), disruptive behavior disorders, alcohol and drug use disorders, anxiety disorders, anorexia nervosa, and schizophrenia. Depression is a long-established risk factor for suicide (see, e.g., Brent et al. 1988, Driessen et al. 1998, Friedman et al. 1983, Kessler et al. 1999, McGlashan 1986, Preuss et al. 2002, Yen et al. 2003), and mood disorders are the disorders most frequently associated with suicide and suicide ideation and discussed as risk factors for the presence of suicidality (Chioqueta & Stiles 2003, Hawton 1987). The majority of the studies that evaluate the relation of psychiatric disorders to suicide report major depression as the most significant diagnosis related to suicide (Asnis et al. 1993, Chioqueta & Stiles 2003, Isometsa et al. 1996, Spalletta et al. 1996). Overall, the lifetime risk for suicide in depressed patients is estimated at 2.2%

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(Bostwick & Pankratz 2000, Papakostas et al. 2003). In a study of published cases specifically examining the diagnosis of people who died by suicide, Bertolote et al. (2003) reported that 53.7% of those who died by suicide were diagnosed with depression. This finding is consistent with Lonnquest (2000), who found a diagnosis of depression ranging from 29% to 88% in his review of psychological autopsy studies. It has been suggested that patients with depressive disorders have a suicide risk 60% to 70% higher than the general population (Khan 2002). In addition to depression, the rates of suicide have also been found to be substantial for other mood disorders, especially bipolar disorder (Chen & Dilsaver 1996; e.g., Chioqueta & Stiles 2003, Vieta et al. 1997), and perhaps dysthymia (Angst 1995, Chioqueta & Stiles 2003, Hintikka et al. 1998, Isometsa et al. 1996). People diagnosed with bipolar disorder represent a diagnostic group of especially high suicide risk (Goodwin & Jamison 1990). Epidemiologic studies have found that 29% of bipolar disorder patients admit to at least one suicide attempt in their lifetime (Chen & Dilsaver 1996). Previous studies have identified bipolar disorder in 10% to 15% (Arato et al. 1988, Beautrais et al. 1996, Rihmer et al. 1995) and up to 47% (Rihmer et al. 1990) of suicide victims or suicide attempters. In their review of studies of bipolar disorder Balazs et al. (2003) reported that suicidal behavior is more frequent in bipolar II than in bipolar I disorder (Dunner et al. 1976, Endicott et al. 1985, Rihmer et al. 1990, Rihmer & Pestality 1999), attempts are more common in bipolar than in unipolar depression patients (Bulik et al. 1990, Dunner et al. 1976, Endicott et al. 1985, Rihmer et al. 1990, Rihmer & Pestality 1999), and bipolar patients are overrepresented among suicide victims (Rihmer et al. 1995). The relation of dysthymia to suicide is less clear and studies have reported mixed results. Chioqueta & Stiles (2003) studied suicide risk in outpatients with specific mood and anxiety disorders and found that both a major depressive episode and bipolar disorder, but not dysthymia, were significantly associated with higher levels of suicidal ideation. The data supported an interaction effect in which higher levels of anxiety in patients with dysthymia were associated with more suicidal ideation. Mood disorders have long been established as a risk factor for suicidality and particular emphasis and study have been placed on the relationship between depression and suicide. An obvious candidate for a neurobiological explanation of the association is dysregulated serotonergic functioning, which characterizes both depression and suicidality (Papakostas et al. 2003). From a psychological standpoint, hopelessness may be a mediator, in that hopelessness is clearly associated with both depression and suicidality, perhaps causally (as will be expanded on below). Specific personality disorders have also been associated with suicidality. A diagnosis of a personality disorder is found in 9% to 28% of completed suicides and the significance of a personality disorder as a risk factor for attempted suicide is even greater, with rates reported as high as 55% among attempters (Soloff et al. 1994). BPD is frequently associated with suicide and is a long-established risk factor for suicide (see, e.g., Friedman et al. 1983, McGlashan 1986, Yen

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et al. 2003). BPD is characterized by affective instability, interpersonal storminess, a diffuse sense of self, and behavioral impulsivity, including self-harm. BPD is described as among the most lethal of all psychiatric disorders, with the usual mechanism of death being suicide (Gunderson 1984, Keel et al. 2003). Approximately 50% of BPD patients have made a minimum of one very severe suicide attempt (Gunderson 1984); and among patients with this syndrome, an average of more than three lifetime suicide attempts has been documented (Soloff et al. 1994). Findings from the Collaborative Longitudinal Personality Disorders Study (Yen et al. 2003) report that BPD was the strongest predictor of a suicide attempt. In this study, a majority (77.6%) of those who attempted suicide met criteria for BPD, and conversely, 20.5% of borderline participants had made a suicide attempt during the two-year study interval (Yen et al. 2003). Further, a history of a previous suicide attempt among BPD patients is a stronger predictor of completed suicides than for any other diagnostic group (e.g., in 65% of suicides among BPD patients a prior attempt had been made, versus 33% among patients with major depression; Stone et al. 1987). The most significant link between suicide and BPD may be the instances of repeated self-injury characterized by the disorder. People diagnosed with this disorder will commonly engage in repeated self-harm behavior involving self-cutting, scratching, hitting, and swallowing harmful objects. Through repeated self-injury, people with BPD become practiced regarding suicidal behavior, and thus may lose fear regarding suicide and become competent about suicide, and as a consequence, engage in increasingly dangerous self-harm (Joiner 2002). Another mechanism connecting BPD to suicidality may be emotion dysregulation. Many have hypothesized that when people with BPD engage in self-harm behaviors they do not actually intend to die; rather, they are attempting to regulate their emotions (Kemperman et al. 1997, Yen et al. 2003). Linehan (e.g., 1993) has emphasized emotion dysregulation as a core problem in and cause of suicidal behavior. The interpersonal strains associated with emotional dysregulation are likely to contribute to feelings of disconnection and ineffectiveness, which are significant predictors of suicidal behavior (Joiner 2002). Dialectical behavior therapy is an empirically supported therapy for BPD in which the main ingredient of the therapy is the reduction of self-injury. This is accomplished by teaching skills to regulate emotion. Antisocial personality disorder, and its youth analogue, conduct disorder, are associated with suicidality. These disorders are characterized as a long-standing pattern of aggressive behavior and reckless and impulsive disregard for others as well as disregard for rules and norms. Substantial evidence exists for a relation between one aspect of antisociality—antisocial deviance—and suicidal acts, but not between another aspect of antisociality—unemotional callousness—and suicidality (Verona 2001; Verona et al. 2001, 2004). One mechanism explaining the link between antisocial personality disorder and suicide may be the combination of aggression and negative emotionality (e.g., anger). There is a heightened risk for suicidal behavior among individuals manifesting reactive aggressiveness,

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persistent criminality, and antisocial personality disorder (Verona 2001). Additionally, a history of violent crime and juvenile delinquency has been associated with suicide attempts and completions in adulthood (Bland et al. 1998, Ivanoff & Jang 1991, Marcus & Alcabes 1993). Aggression is discussed further as an independent risk factor for suicide below. Another possible explanation for the relationship between antisocial personality disorder and conduct disorder and suicide may be the cumulative effects of repeated instances of self-harm, both direct and indirect (e.g., through recklessness). Repeated self-harm is related to higher risk for eventual suicide (Ivanoff 1992, Marcus & Alcabes 1993). Antisocial personalities characterized by undercontrolled behaviors would be at a higher risk for suicide because their recklessness gives them opportunity to habituate to pain and injury, a potentially key process in serious suicidal behavior (Joiner 2002). Of course, impulsivity may be another mechanism for the relationship between suicide and Cluster B personality disorders such as BPD and antisocial personality disorder. Impulsive personality characteristics are a well-documented risk factor for serious suicidality (see, e.g., Apter et al. 1993). Soloff et al. (1994) suggested that many suicide attempts in patients with BPD arise from a background of anger or impulsivity (“impulsive-aggression”), which may represent a primary behavioral dyscontrol in this disorder. Aggression, impulsivity, and antisocial traits have been associated with suicidal behavior in many studies (Soloff et al. 1994). As was noted above, there is converging evidence that the serotonergic system is a neurobiological substrate underlying the association between impulsivity, aggression, and suicidality. Substance use disorders confer risk for suicidality as well (American Psychiatric Association 1994, Borges et al. 2000, Harris & Barraclough 1997, Miles 1977, Pokorny 1983). Koller et al. (2002) report that approximately 30% to 40% of male suicide attempters and 15% to 20% of female suicide attempters have alcohol-abuse or alcohol-dependence diagnoses (Rygnestadt et al. 1992), and approximately 7% to 8% of alcohol-dependent subjects die by suicide (Inskip et al. 1998). The relative lifetime risk for suicide in alcoholics is seven times higher than that of the general population (Gorwood 2001, Soyka et al. 1993). Darke & Ross (1997) report that in all studies they reviewed, the prevalence of attempted suicide among those with substance use disorders is many orders of magnitude greater than that of community samples. In studies of heroin users specifically, the lifetime prevalence of attempted suicide was reported at 35% (Vingoe et al. 1999) and 40% (Darke & Ross 2001). High levels of polydrug use have also been associated with attempted suicide (Murphy et al. 1983, Rossow & Lauritzen 1999). The number of drug classes used is a stronger predictor of suicidal behavior than is any individual drug class (Borges et al. 2000). Empirical evidence suggests that the risk is often associated with comorbidity with other disorders. Prigerson et al. (2003) states that there is a well-established suicide risk of comorbid diagnosis with substance abuse disorders in the general population. In his review of studies of death by suicide, Bertolote et al. (2003) found that comorbidity of mood disorder with

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substance-related disorder was the most frequently found multiple diagnosis. Suicide risks associated with substance abuse and associated dual diagnoses among differing age groups are less well understood (Prigerson et al. 2003). Koller et al. (2002) found that alcohol-dependent subjects with a history of suicide attempts have more aggressive and impulsive traits, and suggest that these traits may be significant factors in the pathogenesis of suicide attempts in alcoholics, largely independent of the suicide attempt method used. Additionally, it is important to note the confluence of general suicide risk factors in those who abuse drugs. Darke & Ross (1997) describe the social profile of heroine users (and likely other drug abusers) as one of predominant unemployment, low educational levels, social isolation, repeated incarceration, and high rates of parental alcoholism, general psychopathology, and divorce (Darke & Ross 2001; Harlow 1990; Johnsson & Fridell 1997; Marx et al. 1994; Murphy et al. 1983; Rossow & Lauritzen 1999, 2001; Segest et al. 1990; Tunving 1998). Given their widespread exposure to suicide risk factors, it is not surprising that the rates of both completed and attempted suicide are many times those observed in the general community (Darke & Ross 1997). Symptoms of anxiety disorders have been implicated in serious suicidality, though findings on this point appear to be mixed. Data linking anxiety and suicide risk have been described as unclear (Khan et al. 2002, Placidi et al. 2000) and controversial (Cox et al. 1994). Noyes (1991) reported that suicide rates for those with an anxiety disorder range from 6% to 60%. An analysis of the Food and Drug Administration database of treatment outcome studies found a significant association between anxiety disorders and suicide (Khan et al. 2002). This study reported a suicide risk higher than described in many previous studies, and regardless of the type of anxiety disorder with which the subject was diagnosed, reported a high risk of suicide. Fawcett and colleagues have repeatedly shown that severe anxious agitation is an important sign of acute suicide risk (see, e.g., Busch et al. 2003), and others have reported the highest amount of suicidal ideation was associated with co-occurring major depression and anxiety disorder (Bartles et al. 2002). However, in a study by Beck et al. (1991), no patients with panic disorder, and 1.3% with panic disorder and agoraphobia, reported they had made a suicide attempt. In a study of suicide risk in outpatients with specific mood and anxiety disorders (Chioqueta & Stiles 2003), none of the anxiety disorders were associated with increased levels of suicide risk, and specific phobia was actually associated with significantly lower levels of suicidal ideation. Of all the anxiety disorders, panic disorder probably has received the most attention with regard to associations with suicidality, and indeed, according to some researchers there does seem to be a significant association between panic disorder and suicidal symptoms (Noyes 1991, Weissman et al. 1989). It is possible, though, that the connection may be explained partly through comorbidity with mood disorders (Cox et al. 1994, Schmidt et al. 2000). The findings regarding panic disorder are mixed as are the findings regarding anxiety in general (Yen et al. 2003); some studies have not found associations between panic disorder and suicidality. Chioqueta & Stiles (2003) reported that panic disorder, whether

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or not associated with agoraphobia, was not found to be associated with either hopelessness (a significant predictor of suicide) or suicidal ideation. Their findings were in accordance with Overbeek et al.’s (1998) study, which did not find higher suicide risk for the panic-disordered patients, even though they found higher levels of hopelessness in comparison with the control group, and with other more recent prospective studies in which panic disorder was not associated with an increased risk of suicide attempt (Brown et al. 2000, Placidi et al. 2000, Warshaw et al. 2000). The diagnosis of schizophrenia has long been associated with an increased risk for suicide and suicide-related behaviors. It has been estimated that up to 13% of patients with schizophrenia will die of suicide, a rate comparable to that of mood disorder patients, and more than 20 times higher than in the general population (Allebeck 1989, Altamura et al. 2003, Black 1988, Meltzer 2003, Meltzer et al. 2000). As many as 40% of people diagnosed with schizophrenia spectrum disorders attempt suicide at some time in their lives (Axelsson & Lagerkvist-Briggs 1992, Meltzer & Fatemi 1995, Meltzer et al. 2000, Planasky & Johnston 1971). Many studies have investigated clinical variables linking schizophrenia to suicide. Not surprisingly, depressive symptoms in patients with schizophrenia are one of the most reported symptom sets related to suicidal behavior (Cohen et al. 1990, Heila et al. 1997, Mann et al. 1999, Potkin et al. 2003, Radomsky et al. 1999, Rossau & Montensen 1997, Roy & Draper 1995, Tandon & Jibson 2003; but not in Allebeck et al. 1987 or Young et al. 1998). In addition to depressive symptoms, Altamura et al. (2003) described many other variables related to suicide in schizophrenic patients, including male gender, a younger age, unemployment, a positive family history for suicidal attempts, comorbid substance abuse, and the lack of a supportive environment (Black & Winokur 1988, Breier & Astrachan 1984, Caldwell & Gottesman 1990, Siris 2001). Positive symptoms have also been associated with an increased risk for suicide (Addington & Addington 1992, Amador et al. 1996, Falloon & Talbot 1981, Fenton et al. 1997, Kaplan & Harrow 1999; Messias et al. 2001, Miller & Chabrier 1988). However, others have found that the presence of delusions did not increase the risk for suicide attempts (Grunenbaum et al. 2001). Meltzer (2003) reported five variables that were most predictive of suicidality among schizophrenic people, including number of lifetime suicide attempts, number of hospitalizations within the last three years, current or lifetime substance abuse, depression, and the severity of Parkinsonism. The relationship between hopelessness and suicide has been demonstrated in schizophrenic patients (Murphy 1983). In a study of patients’ charts, hopelessness accounted for the relationship between depression and suicide in a group of schizophrenic inpatients who eventually died by suicide (Drake & Cotton 1986). Joiner et al. (2001) provided data relating a particular aspect of depressive symptoms (self-hatred) that combines with schizophrenia to encourage suicidality. Their findings were consistent with Bleuler’s (1911/1987) suggestions that self-hate may be one aspect of depressive symptoms that takes on an especially pernicious quality in people with schizophrenia-spectrum symptoms. The relation

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of self-hate to suicidal symptoms was stronger among those with schizophreniaspectrum symptoms than among others; this finding was specific to self-hate, and did not apply to depression in general (Joiner et al. 2001). Finally, there is evidence that anorexia nervosa is associated with high rates of death by suicide. Herzog et al. (2000) followed 246 eating-disordered women (some with anorexia nervosa, some with bulimia nervosa) for more than 10 years. Seven died, three from suicide. All of those who died were anorexic; no bulimic women died. The risk of death by suicide among the anorexic participants was 58 times the expected rate (see also Keel et al. 2003). Up until this point, we have focused on the specific relationships between psychiatric disorders and suicide. Much research supports these relationships; however, as has been noted, research has focused on other specific factors and their relationship to suicide. In addition to the several disorders that have been repeatedly linked with suicide, other factors such as hopelessness, social isolation, aggression, and ineffectiveness have been consistently linked to suicide.

Hopelessness Hopelessness is significant in the etiology and maintenance of depression, and is a significant predictor of suicide. Hopelessness is defined as “a system of cognitive schemas whose common denominator is negative expectations about the future” (Beck et al. 1974, p. 864), or a “lowered expectation of obtaining certain goals and a diminished belief in the likelihood of achieving success” (Melges & Bowlby 1969). Beck (1963) considered hopelessness to be the mechanism, or the key factor, in the relationship between depression and suicide. Hopelessness has been found to correlate better with suicidal ideation than depression in prospective studies (Beck et al. 1974, Chioqueta & Stiles 2003, Silver et al. 1971, Wetzel 1976, Wetzel et al. 1980), and is a better predictor of suicide than of depression in some studies (Beck et al. 1985, 1990). Empirical research strongly supports the central role of hopelessness in suicidal ideation (Bedrosian & Beck 1979, Chioqueta & Stiles 2003, Dyer & Kreitman 1984, Minkoff et al. 1973, Nekanda-Trepka et al. 1983). Hopelessness is arguably the best predictor overall of suicide completions in clinical populations (Steer et al. 1993). The development of hopelessness is often related to interpersonal factors (PerezSmith et al. 2002). Joiner’s (2002) Interpersonal-Psychological Theory of Attempted and Completed Suicide hypothesizes that suicidal people are specifically hopeless about feelings of being a burden on others and of failed belongingness. Concepts like burdensomeness and failed belongingness are interpersonal, and there is evidence that interpersonal factors are among the strongest predictors of serious suicidality.

Social Isolation Social isolation—a state in which interpersonal contacts and relationships are disrupted or nonexistent (Trout 1980)—has been consistently related to suicidal

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behavior. Many studies cite Durkheim (1897) as the first to propose and study the theory that suicide results in part from failure in social integration. It has been suggested that the fact that those who die by suicide experience isolation and withdrawal before their deaths is among the clearest in all the literature on suicide (Trout 1980). Studies of adolescents found that those with histories of suicide attempt often chose not to seek support from others (Greholt et al. 2000), and did not tell anyone what they were thinking during the period of suicidal ideation (Negron et al. 1997). In a study of suicidal and death ideation in older primary care patients, fewer social supports were associated with greater overall ideation (Bartles et al. 2002). Darke & Ross (1997) suggested that indices of social isolation and disadvantage were associated with risk of suicide (Appleby et al. 1999, Beautrais et al. 1996, Hassan 1995, Shepherd & Barraclough 1980). Baumeister & Leary (1995) proposed that the need to belong is a fundamental human motive, and they provided many diverse lines of empirical support for their model. Baumeister & Leary (1995) argued that the need to belong to valued groups or relationships is a powerful, fundamental, and extremely pervasive motivation. When this need is thwarted, numerous negative effects on health, adjustment, and well-being have been documented. Joiner’s (2002) theory of suicide suggests that this need to be social is so powerful that, when satisfied, it can prevent suicide; however, when the need for social connection is thwarted, risk for suicide is increased. In addition to social isolation in the general population, isolation has been studied specifically in correctional settings. According to Felthous (1997), several authorities have argued from a clinical or theoretical viewpoint that isolation can adversely affect an inmate’s mental state and increase the likelihood of suicide. Research shows that a large percentage of inmates who killed themselves while incarcerated were in isolation, and two of every three jail suicides occurred in isolation (Felthous 1997, Hayes & Kajdan 1981). In their study of an incarcerated group, Anderson et al. (2000) found that isolation presented an increased suicide risk, and the incidence of psychiatric disorders developed in the prison was significantly higher in prisoners in solitary confinement. There is not only support for the fact that social isolation is detrimental to mental health, but data suggest that the presence of a social network is a protective factor against suicide. In their study of risk and protective factors for suicidal behavior in abused African American women, Kaslow et al. (2002) found that social support was a significant protective factor associated with nonattempter status. Hoyer & Lund (1993) studied nearly one million women in Norway and reported that women with six or more children had one-fifth the risk of death by suicide as compared to other women. These findings support Durkheim’s (1897) hypothesis that parenthood is of great importance when it comes to suicide prevention in married women. Hoyer & Lund (1993) also found the highest rates of suicide in single women. This is consistent with statistics compiled by McIntosh (2002) that indicate the following suicide rates in the United States in 1999: divorced—32.7 per 100,000, widowed—19.7 per 100,000, single—17.8 per 100,000, married—10.6 per 100,000.

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Impulsivity and Aggression As has been touched on above, lifetime aggression and impulsivity are known correlates of suicidal behavior (Mann et al. 1999). Conner et al. (2003) explain that no matter how aggression is defined (as a psychiatric diagnosis, psychological construct, or overt violent behavior), it confers risk for suicide. Reactive aggression (Dodge & Coie 1987, Vitiello & Stoff 1997), characterized by proneness to reflexive anger in the context of aversive events, particularly perceived interpersonal threat, has been proposed as a diathesis for suicide risk, with acute risk occurring in the context of psychiatric illness (Conner et al. 2003, Mann et al. 1999). Several studies focusing on violence and suicide found clear support for their association. In a study that compared 50 persons attempting suicide with 50 nonsuicidal psychiatric patients and with 50 nonpsychiatric control patients attending a heart clinic, suicidal patients had experienced an array of violent episodes to a significantly higher degree than either control group (Whitlock & Broadhurst 1969). Similar results were found in a study of coroners’ statistics in 32 inner-city boroughs in London (Kennedy et al. 1999). Rates for suicide, violence, and homicide were highly correlated with one another. The more violence and homicide occurred in a given borough, the higher the suicide rate. Conner et al. (2001) reported on a representative survey of next-of-kin and other respondents close to people who had died by suicide and by other means in the previous year. Findings suggest that those who had died by suicide had more frequently threatened and attempted violence in the previous year, as compared to accident victim controls. Brent et al. (1994) found that a lifetime history of aggression differentiated adolescent suicide victims from matched controls, even after controlling for differences in psychopathology between suicides and controls. Other factors also point to an association of experience with violence and related suicidality. As Conner et al. (2003) pointed out, prison inmates are at increased risk for suicide compared to community dwellers, and inmates completing suicide are more likely to have been incarcerated for manslaughter or murder as compared to other prisoners (DuRand et al. 1995).

Ineffectiveness The view that ineffectiveness is painful has informed and been informed by prominent theories of psychopathology, including those related to the learned helplessness model (Abramson et al. 1978, 1989). Extending this perspective to include the particularly painful aspects of personal ineffectiveness affecting others is consistent with work on the possible roles of shame and guilt in psychopathology generally (Tangney et al. 1992) and suicidality in particular (Hastings et al. 2000). Factors such as unemployment, low income, physical illness, and less education can make it more difficult to be an effective agent of change in one’s environment. These factors likely make it more difficult to negotiate tasks of daily living, obtain help from others, and cope with stressors. If individuals are able to obtain material resources, they may be somewhat protected against suicide (Kaslow et al. 2002);

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if not, this can lead to feelings of hopelessness and feelings that one is a burden on others. To perceive oneself as ineffective is painful, and it is even more painful to perceive oneself as so ineffective that loved ones are threatened and burdened. Joiner’s (2002) suicide theory proposes that feelings of ineffectiveness contribute to the desire for suicide, and that feeling ineffective to the degree that others are burdened is among the strongest sources of all for the desire for suicide. To our knowledge, four studies have directly assessed perceived burdensomeness in suicidality. Brown et al. (1999) conducted a questionnaire study of college students, and found the predicted correlation between feeling a burden on kin and suicidality. Burdensomeness stood out as a unique and specific predictor of suiciderelated symptoms even when key variables were controlled. Joiner et al. (2002b) trained raters to evaluate suicide notes regarding the following dimensions: perceived burdensomeness, hopelessness, and generalized emotional pain. Unknown to the raters, half of the notes were from people who died by suicide, and half were from people who attempted suicide and survived. In correlation/regression analyses in which predictors were controlled for each other, the notes from those who died by suicide contained more perceived burdensomeness than did notes from attempters; no effects were found regarding hopelessness and emotional pain. A second study in the Joiner et al. (2002b) report took a similar approach, except that all notes were from those who died by suicide, and perceived burdensomeness, hopelessness, and generalized emotional pain were used as predictors of lethality of suicide method (e.g., self-inflicted gunshot wound was viewed as relatively more lethal than overdose). Here again, perceived burdensomeness was a significant predictor of lethality, whereas hopelessness and generalized emotional pain were not. DeCatanzaro (1995) conducted a survey on reproductive behavior, quality of family contacts, and suicidal ideation on several hundred community participants, as well as on five high-suicide-risk groups (e.g., general psychiatric patients, incarcerated psychiatric patients). Within each of these samples, of all the many variables assessed, perceived burdensomeness toward family and social isolation were especially correlated with suicidal ideation. Although not direct tests of the burdensomeness view of suicidality, several other studies have reported results consistent with this perspective. For example, Brown et al. (2002) reported that genuine suicide attempts were often characterized by a desire to make others better off, whereas nonsuicidal self-injury was often characterized by desires to express anger or punish oneself. As in other studies (e.g., two studies from Joiner et al. 2002b), burdensomeness emerged as a key variable even as compared to other powerful correlates of suicidality (see also Filiberti et al. 2001, Magne-Ingvar & Oejehagen 1999, Motto & Bostrom 1990).

CONCLUSIONS From both neurobiological and psychological perspectives, extant research findings converge on the view that two general categories of risk for suicide can be identified: (a) dysregulated impulse control; and (b) propensity to intense

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psychological pain (e.g., social isolation, hopelessness), often in the context of mental disorders, especially mood disorders. Each of these categories of risk is underlain at least to some degree by specific genetic and neurobiological factors; these factors in general are not well characterized, though there is emerging consensus that most, if not all, reside in or affect the serotonergic system. The specific ways in which these categories of risk are translated into actual suicidal behavior is not clear, and is a main frontier for future research. In this connection, we would counsel against simplistic theorizing of the sort that one factor or class of factors is a main cause of suicide. For example, the evidence is clear that mental disorders comprise a significant risk for suicide. Yet, the genetic contribution to suicide exists beyond the effects of mental disorders, and the majority of people with mental disorders do not attempt or die by suicide. Similarly, impulsivity is a documented risk factor for suicidal behavior, leading some to imagine that “impulsive suicide”—that is, spur-of-the-moment death by suicide—is common. Yet, it is exceedingly difficult to document a case like this, where it is clear that spur-of-the-moment processes were operative apart from more long-standing processes. We therefore believe that a full understanding of suicide will involve multivariate, interactive models that are constrained to account for the facts summarized in this review. Joiner’s (2002) interpersonal-psychological theory is an attempt at this—the theory contends that people gradually acquire the ability to enact lethal self-injury through prior experience with self-injury (which in turn is encouraged by impulsive behavior underlain by serotonergic dysregulation). The theory further asserts that this ability is not acted upon unless the desire for death is instantiated by a strong sense of perceived burdensomeness coupled with a sense of failed belongingness; these two factors are relevant to why hopelessness, social isolation, and the mental disorders of which they are associated features comprise a clear risk for suicidal behavior. This model is presented as a tentative example of the kind of theorizing that will be necessary to be simultaneously conceptually precise about the specific preconditions of serious suicidal behavior as well as epistemically broad, explaining the daunting array of suicide-related facts at levels ranging from the molecular to the cultural. The Annual Review of Psychology is online at http://psych.annualreviews.org

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AUTISM IN INFANCY AND EARLY CHILDHOOD Fred Volkmar, Kasia Chawarska, and Ami Klin


Child Study Center, Yale University School of Medicine, New Haven, Connecticut 06520; email: [email protected], [email protected], [email protected]

Key Words psychopathology, pervasive developmental disorders, joint attention ■ Abstract Although initially described as an inborn disorder of affective contact, information on autism as it exists in infants has been limited. Delays in diagnosis, lack of information about the condition, and reliance on retrospective research strategies have been problematic. An awareness of the increased risk for siblings is now allowing the development of new, prospective approaches. Consistent with Kanner’s original hypothesis, the available information strongly suggests a fundamental difficulty in the earliest social processes, which, in turn, impacts many other areas of development. New approaches to screening have lowered the age of initial diagnosis; this presents new challenges for early intervention. Directions for future research are highlighted. CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DEVELOPMENT OF AUTISM AS A DIAGNOSTIC CONCEPT . . . . . . . . . . . . . . . AUTISM IN THE FIRST YEAR OF LIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age and Type of Onset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical Presentation in the First Year of Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . AUTISM: AGES 1 TO 3 YEARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DIAGNOSTIC ISSUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Categorical Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dimensional Approaches to Diagnosis and Checklists . . . . . . . . . . . . . . . . . . . . . . . Clinical Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DEVELOPMENTAL FEATURES OF AUTISM IN THE FIRST TWO YEARS OF LIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Functioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Communicative Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cognitive Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ASSESSMENT AND INTERVENTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SUMMARY AND FUTURE DIRECTIONS FOR RESEARCH . . . . . . . . . . . . . . . . .

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INTRODUCTION Although first described by Kanner in 1943 as an inborn disorder of “affective contact,” information on autism in infants and very young children is limited. Delays in diagnosis were common and a diagnosis often was not made until age 0066-4308/05/0203-0315$14.00

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4 (Siegel et al. 1988). This has changed recently as public awareness of autism and the importance of early diagnosis have increased (Natl. Res. Counc. 2001). A decade ago, information about the early development of autism was based on parent reports (Cohen et al. 1986) or retrospective review of home movies or videotapes (Osterling & Dawson 1994); over the past decade a few longitudinal studies of young children with autism have begun to appear (Lord 1995). As part of its 10-year plan for autism research, the National Institute of Mental Health has set the ambitious goal of actually reducing the frequency of autism in school-aged children through early diagnosis and intervention—a goal that would have seemed unthinkable a decade ago. In this chapter, we summarize what is known about autism as it exists in the first years of life, including the onset of the condition and behavioral and developmental features as well as approaches to screening. We review some aspects of assessment and intervention and conclude with a discussion of the limitations of current knowledge and areas important for the future.

DEVELOPMENT OF AUTISM AS A DIAGNOSTIC CONCEPT Kanner’s original paper (1943) emphasized that children with autism were born without the usual predisposition to be social. He grounded his description developmentally by citing the early emergence of social interest, an interest that we now know is present from birth (Chawarska & Volkmar 2004). Although his description was remarkably accurate in some aspects, it was modified based on clinical experience and research. For example, he initially believed children with autism had normal intelligence, but it is now clear that although markedly uneven profiles of ability are observed, most individuals with strictly defined autism function, overall, in the intellectually deficient range. Kanner’s impression that children with autism did not exhibit other medical conditions was modified because a high risk for developing seizures became apparent as cases were followed (Volkmar & Nelson 1990). It also became clear that autism has a very strong genetic basis, with siblings having a 20- to 50-fold increase in their risk for developing autism (Rutter et al. 1997, Volkmar et al. 2004b). The original definition of autism has evolved over time. Kanner emphasized two features: the “autism” and a group of unusual behaviors he subsumed under the terms “insistence of sameness” or “resistance to change”; the latter include unusual movements and mannerisms as well as a literal difficulty in dealing with novelty. Early work on autism was impeded by controversies over the validity of the condition, but by 1980 autism was officially recognized and included in a new class of disorder—Pervasive Developmental Disorder (PDD). Over the years other, apparently related, diagnostic concepts were proposed. Probably the most common and, somewhat paradoxically, the least studied of these conditions is Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS)—a category used for children with some features of autism (Volkmar et al. 2004a). The validity of these conditions apart from autism remains the topic of much interest and research; the primary focus of this review is on strictly defined autism.

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A series of epidemiological studies have been conducted and generally suggest rates of autism between 1 in 500 and 1 in 1000 children; children with difficulties falling within the broader PDD category are probably three or four times as common (Fombonne 1999). Of the approximately 30 epidemiological studies, recent ones have tended to report higher rates of the disorder, although changes in diagnosis, increased awareness, and other factors complicate our understanding of this increase (Fombonne 1999, Wing & Potter 2002). Rates of autism in boys are three to four times higher than in girls (Fombonne 1999, Lord et al. 2002). In addition to the high risk for developing seizures and the strong genetic basis of the condition, a host of other research findings strongly implicates neurobiological factors in pathogenesis of the condition (Volkmar et al. 2004b).

AUTISM IN THE FIRST YEAR OF LIFE Age and Type of Onset Parents report a range in the age at which they are first concerned about their child’s development, with about 90% recognizing abnormality by 24 months (De Giacomo & Fombonne 1998). While speech delays or worries about hearing are common, other early concerns may be that the child is “too good” or is highly irritable (Stone & Lemanek 1990). Unfortunately, the specificity of these problems to autism remains unclear. Most of the available work is based on parent report. Prospective data are needed, although the interpretation of such data is complicated given the fast pace of change and the various meanings of the presence (or absence) of a behavior at specific time points (Lord & Risi 2000). For example, preintentional use of gestures such as reach-and-grasp motions in pursuit of an object is seen before age 9 months, but it usually becomes quickly synchronized to eye contact and indicates emerging intentional communication (Bates et al. 1979). After, but not before, the one-year developmental level a lack of conventional gestures such as nodding or pointing would be a source of concern. To complicate the task even further, early symptoms change over time (Kanner 1968, Lord 1995). Finally, effects of situation, novelty, task demands, and unusual patterns of environmental responsivity may result in considerable variability in the child’s presentation, particularly in infants and young children (Natl. Res. Counc. 2001). The phenomenon of reported regression is another potential problem. Various studies (Kobayashi & Murata 1998, Rogers & DiLalla 1990, Tuchman & Rapin 1997, Volkmar et al. 1985) have documented parental reports of regression in 20% to 40% of cases. Unfortunately, various terms—which overlap to some degree— are used, e.g., regressive autism, setback autism, and developmental stagnation. These terms do, however, speak to some aspects of the underlying phenomenon, i.e., in some children there may be a gradual or more rapid loss of language and/or social skills (Kobayashi & Murata 1998, Rogers & DiLala 1990, Tuchman & Rapin 1997). In other cases, the problem seems to be one not so much of loss as of a failure to make progress, e.g., the child seems to say one or two words but then

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language does not progress (Sipersein & Volkmar 2004). Finally, in some rare cases the child progresses normally for several years (usually three to four) with normal language, cognitive, and self-care skills, and then gradually or abruptly loses these abilities and begins to exhibit more classic features of autism. The term childhood disintegrative disorder is used in such instances and available data suggest an even worse outcome than for more typical autism (Volkmar & Rutter 1995). The relationships, if any, between these various subtypes and their validity as clinical phenomena remain controversial. For example, Osterling et al. (2002) found minimal differences between regressive and nonregressive autism, whereas Rogers & DiLalla (1990) have noted poorer outcome in the regression group. The latter group may also be more likely to exhibit dysmorphic features (Lainhart et al. 2002). Thus, consistent with Kanner’s original (1943) impression, autism does appear to be a very early onset disorder but there are a small number of cases where regression is reported. It is possible such cases represent a specific subtype or potentially different diagnostic group, but the issue remains unresolved.

Clinical Presentation in the First Year of Life Kanner’s original (1943) report emphasized unusual social development. Subsequent research has refined his initial impression in various ways, e.g., infants with autism may have limited eye contact and diminished overall social responsiveness (Maestro et al. 2002, Sparling 1991). They also may be less likely to engage in motor or vocal imitation and to exhibit problems in arousal or unusual sensory responses (Dawson et al. 2000). The lack of comparative or normative data often presents difficulties for interpreting these findings, although in one study (Klin et al. 1992) preschool children were not exhibiting social behavior that normally would be expected prior to age 1 year. For example, the children with autism failed to assume anticipatory postures, reach for familiar persons, show interest in children other than peers, and engage in simple social interaction games. In addition to parent report data, the retrospective analysis of home movies or videotapes provides another potential research resource. Such research has generally confirmed the early emergence of developmental differences in infants with autism (Adrien et al. 1992, Maestro et al. 1999, Osterling & Dawson 1994, Osterling et al. 2002). For example, Maestro et al. (2002) examined early videotapes of infants later diagnosed with autism as compared to age-matched typically developing infants; the infants who went on to be diagnosed with autism showed less visual attention to social stimuli, they smiled less frequently, vocalized less, and they engaged less in object exploration. However, the infants did not differ in terms of repetitive behaviors. In a study of 8- to 10-month-old infants, Werner et al. (2000) reported that those subsequently diagnosed with autism were less likely to respond to their own name, a result similar to that of Osterling & Dawson (1994) in their study of 12-month-olds. The use of a typically developing comparison

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group limits some of these findings, i.e., differences observed may reflect general developmental delay rather than the effects of autism per se. A more stringent approach includes the use of a developmentally delayed comparison group. Using a range of behaviors, Osterling et al. (2002) compared 12month-old infants with autism to infants with mental retardation only: Significant differences were found in orienting to name and looking at people, using gestures, looking at objects held by people, and performing repetitive actions. Although these data are limited in various respects, early signs of autism appear to include—as Kanner would have predicted—a lack of social interest in the first months of life, with reduced levels of social engagement and social-communicative interchanges; differences in the nonsocial areas are much less striking. By ages 6 to 12 months, differences become more pronounced in the communicative area, including a general lack of orientation toward verbalization in general and to their own name in particular. Infants with autism are less interested in people at a time when most infants begin to more fully integrate object exploration with social interaction and become more clearly intentional (Bates et al. 1979). On the other hand, some behaviors frequently reported by parents have not so clearly emerged as areas of difference using videotaped analyses, e.g., difficulties in arousal regulation. Such problems may be less specific to autism; alternatively, the failure to find differences may have more to do with the nature of the available videotaped materials.

AUTISM: AGES 1 TO 3 YEARS The quantity and quality of available research rises dramatically with respect to toddlers. In part this reflects the fact that it is around this time that parents are more likely to seek evaluation (De Giacomo & Fombonne 1998). In addition to grossly apparent developmental delays (e.g., failure to speak), unusual behaviors may prompt concern and may begin to include stereotyped motor mannerisms (e.g., hand flapping) or idiosyncratic uses of materials (e.g., spinning things) (Wimpory et al. 2000). Parental report has been used to compare children with autism to those with both typical development and developmental delay. By age 30 months, differences from typical peers are readily apparent, and social deviance, problems in communication, and unusual responses to the nonsocial environment are striking (Ornitz et al. 1977). Parent report data have identified various behaviors that differentiate young children with autism from those with developmental delay. These include both person-to-person behaviors (anticipatory postures, turn taking, intensity of eye contact) and behaviors in which an object is the focus of joint interest (joint attentional skills such as pointing to materials, following a point of another person, or giving objects) (Wimpory et al. 2000). Limited affective engagement and unusual sensory behaviors are also observed (Hoshino et al. 1982).

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By this developmental period, it becomes feasible to employ contemporaneous parent report and direct observation. For example, in a prospective questionnaire study, Dahlgren & Gillberg (1989) reported that a number of items discriminated groups of children with autism (below age 3) from those with typical development or developmental delay. Difficulties in the area of social development, unusual gaze, abnormal play, and lack of responsivity to speech were observed. Cox et al. (1999) followed a group of 20-month-old infants prospectively; the infants were high-, medium-, or low-risk for autism as identified through a screening using the Checklist for Autism in Toddlers (CHAT) (Baron-Cohen et al. 1992). At 20 months, behaviors that discriminated the children included range of facial expression, use of conventional gesture, and pointing to indicate interest. A follow-up study was conducted at 42 months, and additional items were noted to differentiate the group, e.g., desire for shared enjoyment, imaginative play, offering comfort, and nodding; none of the repetitive, stereotyped behaviors differentiated the groups. Lord (1995) followed 2-year-olds evaluated for a possible diagnosis of autism. A standard set of assessments was employed, including the Autism Diagnostic Interview (ADI) (Le Couteur et al. 1989), with items specifically selected as relevant to children under 2 years of age. After 12 to 15 months, the children were re-evaluated using the same measures. Items that differentiated the groups again included social activities (seeking shared enjoyment, social reciprocity, use of another person as a tool, interest in other children), and communicative tasks (attending to voice, pointing, using and understanding gesture). Two behaviors, directing attention and attention to voice, correctly identified 82% of children. By age 3, finger mannerisms, attention to voice, pointing, and use of the other person’s body were, in addition, able to correctly classify all subjects. These results suggest significant changes between the second and third birthday, with higher levels of more “typical” autistic behaviors present by age 3; conversely, some of the behaviors suggestive of autism at age 2 substantially decreased by age 3 in the developmentally delayed comparison group (Lord 1996, Lord & Pickles 1996). This growing body of research illustrates important areas where findings converge, but it also suffers from a number of significant limitations. The available data suggest that for many infants, differences may begin to become apparent in the first months of life; however, these can be subtle and current data have important limitations. For example, in retrospective studies, parents may be more accurate in reporting negative, rather than positive, symptoms (Stone et al. 1994) and may have difficulties in noting some of the deficits in play and joint attention that seem more frequent between the second and third birthday (Charman et al. 2001). Videotaped materials provide important advantages, but they suffer from the potential disadvantages intrinsic in selective taping, e.g., lack of evidence for areas of difference in what parents chose to record. Comparisons across studies can be difficult due to differences in method, comparison group, sample selection, and so forth. As diagnostic methods for infants become more robust and as prospective studies are undertaken, it may be possible to more adequately address these concerns.

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DIAGNOSTIC ISSUES


A growing body of work has emphasized the importance of early case detection and intervention for improving long-term outcome (Natl. Res. Counc. 2001). Although early signs of autism are apparent in many cases before age 12 months, diagnosis of infants and very young children is quite complex. Several approaches have been utilized (see Lord & Corsello 2004 and Volkmar et al. 2004b for reviews).

Categorical Approaches Categorical approaches to autism are exemplified in systems such as the Diagnostic and Statistical Manual (DSM; Am. Psychiatric Assoc. 1994) and International Statistical Classification of Diseases and Related Health Problems (ICD; WHO 1992). These have the advantages of official recognition and a long history of facilitating research and clinical work; in many ways, they also serve as the conceptual basis for dimensional approaches (Volkmar et al. 2004a). In his original paper, Kanner (1943) emphasized two essential features—the autism as well as the entire set of behaviors he subsumed under the term “insistence on sameness.” Early progress in the field was delayed because of a lack of explicit definition and confusion about continuities with other disorders (particularly childhood schizophrenia). During the 1970s, a consensus emerged on the validity of autism, and diagnostic guidelines that were more formal were proposed. Rutter (1978) emphasized the early onset of the disorder and the characteristic problems in social development and communication (not just due to associated mental retardation), along with the presence of unusual behaviors of the type Kanner had conceptualized as “insistence on sameness.” His approach was highly influential in the definition that appeared in DSM-III (Am. Psychiatric Assoc. 1980), where autism was first granted official recognition as a category of disorder. Over the years, various changes and modifications have been made in the DSM and ICD definitions of autism. The original DSM-III definition focused on the “infantile” aspects of infantile autism, e.g., delineating the presumed initial presentation of the disorder; developmental issues in syndrome change were not satisfactorily addressed. This concern led to major changes in the revision of DSM-III, wherein developmental issues were addressed in the guidelines for the disorder although with the price of over-diagnosis of the condition in more intellectually disabled individuals (Volkmar et al. 1994). The current system, DSM-IV (Am. Psychiatric Assoc. 1994), was developed based on an international study done in collaboration with the developers of ICD-10; these two systems are now essentially the same. The current definition of autism is historically continuous with Kanner’s original work and with Rutter’s subsequent modifications. In this system, autism is defined on the basis of problems in the areas of social development, communication and play, and restricted and stereotyped interests (with social factors being given slightly more weight); by definition autism must have its onset by age 3 years. This system has a reasonable balance of sensitivity and

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specificity over the IQ range (Volkmar et al. 1994). In addition to autism, it includes a number of other specifically defined disorders, including Asperger’s disorder, Rett’s disorder, and childhood disintegrative disorder as well as the “subthreshold” PDD-NOS. The data used to develop this definition included a substantial cohort of 3- to 5-year-olds, but few children under age 3. By ages 3 to 5 the definition appears to work reasonably well, although for infants and young children the data on its sensitivity and specificity are much more limited. As the work on early development suggests, two kinds of problems are particularly likely. Some children at age 2 exhibit the social and communication-play problems typical of autism but do not yet exhibit the unusual stereotyped movements or other behaviors in the third category of disturbance; many of these children go on to do so before their third birthday. Less frequently, children below age 3 may appear to exhibit all of the features required for a diagnosis of autism but then lose these features as they mature (Lord 1995). Furthermore, Stone and colleagues (1999) note that some of the DSM-IV criteria clearly are not as applicable to very young children, e.g., criteria involving peer relationships and conversational skills. At least one attempt has been made to provide an alternative categorical diagnosis system for autism and related disorder specifically for infants and young children (Natl. Cent. Clinical Infant Prog. 1994), although this system suffers from a number of problems and lacks substantive empirical basis. A somewhat different approach to the issue of categorical diagnosis is employed in instruments like the Autism Diagnostic Interview-Revised (ADI-R) (Lord et al. 1994) and the Autism Diagnostic Observation Schedule (ADOS) (Lord et al. 2000)—a semistructured interview with parents and direct child assessment, respectively. Both instruments were developed to be keyed to categorical diagnostic criteria and emerged as the most widely used diagnostic instruments for research purposes (see Lord & Corsello 2004). The ADI-R works well for children over 4 years of age but much less so for younger children (Cox et al. 1999, Lord 1995, Stone et al. 1999). Lord (1995) noted that among 2-year-olds this instrument overdiagnosed among the more mentally handicapped and conversely under-diagnosed among the more intellectually able (sensitivity and specificity both approximately 0.50). Cox and colleagues (1999) reported a similar result in a study of 20-montholds. Some modification in the scoring rules increased the sensitivity of the instrument, but low specificity remained problematic. By age 42 months, sensitivity was improved but the tendency to over-diagnose autism in children with severe cognitive delay remained (Cox et al. 1999). In contrast to the ADI-R, the ADOS employs observation of and interaction with the child. DiLavore et al. (1995) used an early version of this instrument to compare young children with autism (age range 38 to 51 months) to children with developmental delay (ages 2 to 3 years) and noted several areas of differences between the groups, e.g., use of social smile, sharing pleasure, eye contact, giving to others, response to name, use of gesture, differentiation of parent from examiner, and levels of stereotyped behaviors. These results are similar to those reported by Stone et al. (1994).

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Dimensional Approaches to Diagnosis and Checklists Dimensional diagnostic approaches offer many potential advantages, particularly for infants and young children. Their use offers better approaches in dealing with developmental change and is not necessarily incompatible with categorical approaches (see Lord & Corsello 2004). These instruments also have some intrinsic limitations. The focus on highly deviant behavior creates some challenges for these instruments, e.g., items may be more difficult to develop, and behaviors of interest may be low in frequency or difficult to rate reliably. Other problems include the way the instrument addresses (if at all) the issue of history and change, the nature of the intended informant (parent report, teacher report), and the format (interview versus direct observation). Low-frequency but highly significant diagnostic behaviors may not be observed during direct assessment; on the other hand, employing parent or teacher report raises other issues in terms of reliability, potential effects of informant knowledge and sophistication, and so forth. One approach might be to combine multiple informants or sources of information, although this creates additional difficulties (Offord et al. 1996). In terms of item or scale development, differences may exist in the extent to which the absence of specific skills is emphasized versus the presence of highly unusual behaviors. For example, are the widely recognized difficulties in eye contact in early social engagement better conceptualized as a lack of normal social engagement or as the presence of high abnormal patterns of functioning? For some items/scales, there may be strong correlates with age or cognitive development (Tadevosyan-Leyfer et al. 2003). To complicate the situation further, the presence of deviant behaviors may be differentially correlated with the absence of more developmentally appropriate behaviors. Attention to both delay and deviance is required. In observational approaches, the absence of a specific behavior may be much more difficult to interpret than its presence (Lord & Corsello 2004). Despite these concerns, a number of dimensional rating scales and checklists have been developed. These instruments differ in many ways, with intended uses ranging from diagnosis and diagnostic assessment to population screening. These approaches have had a significant role in research as well as in clinical work, although there is—with some noteworthy exceptions centering particularly around screening—a dearth of information on their use in infants and very young children. For example, the Childhood Autism Rating Scale (CARS) (Schopler et al. 1980) has been widely employed in the assessment of older children; it rates the child along a series of dimensions of severity of autism. In Lord’s (1995) study, CARS and the ADI-R were in general agreement with each other and with the diagnoses of clinicians. However, both CARS and the ADI-R were less accurate for children 2 years of age or younger, with CARS over-diagnosing the condition. In contrast to instruments like the ADI-R and CARS, another line of work has centered on development of brief rating scales/checklists that might be used more for screening than for definitive diagnosis. The development of such instruments is particularly important for prospective studies (e.g., of newborn siblings at risk for

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autism) and for epidemiological studies. Development of screening instruments entails consideration of a somewhat different set of concerns and constraints (see Aylward et al. 1997, Coonrod & Stone 2004). Level 1 screeners are intended to identify children at risk for more general disability; such instruments are frequently used in general medical (pediatric) practice and are usually concerned with the potential identification of a broad range of developmental problems. In contrast, level 2 screeners focus more specifically on differentiating children at risk for autism from those with other difficulties, such as overall cognitive delay or language impairment. Level 2 screeners are more detailed and focused and more frequently used in specialized settings. The Checklist for Autism in Toddlers is a Level 1 screener that combines parent report and observation of the child at 18 months (Baron-Cohen et al. 1992, 1996, 2000), and samples a small number of items thought to suggest autism. Results from the original study (from which children with severe delays were excluded) suggested high levels of specificity but low sensitivity: subsequent modifications have been undertaken (Baird et al. 2000, Scambler et al. 2001). Robins and colleagues (2001) attempted to address some of the concerns about the original CHAT in the modified CHAT (M-CHAT); it employs 23 items in a screener designed for 2-year-olds. In their report, the instrument appeared to have reasonably good sensitivity and specificity. Predictors of autism included items related to joint attention, social engagement, and communication, consistent with earlier reports of symptoms typical of autism in 2-year-olds (Baron-Cohen et al. 1996, Lord 1995). This instrument is still in development; rates of false negative and false positive cases remain to be determined. Second stage screening tools have also been developed. The Pervasive Developmental Disorders Screening Test (PDDST) is a parent report measure for children under age 6 years with different versions for different levels of screening (Siegel & Hayer 1999). Early results appeared promising but they have not yet been published in a peer-reviewed journal. The Screening Tool for Autism in Two-Year-Olds (STAT) (Stone et al. 2000) is designed for children from ages 2 to 3 years. It includes 12 pass/fail items that address aspects of communication, social engagement, and play; administration is in a play-like setting and takes about 20 minutes. Early results were encouraging but again the instrument needs to be evaluated in larger, and community-based, samples. Other Level 2 screeners include the Autism Behavior Checklist (ABC) (Krug et al. 1980) and the Gilliam Autism Rating Scale (GARS) (Gilliam 1995). The ABC is a 57-item checklist with items differentially weighted depending on the strength of their association with autism in a large sample of individuals (from age 18 months to 35 years). Although it is quickly administered and widely used, there are some concerns about its psychometric properties, and its usefulness in infants and very young children remains unclear. The GARS is a 56-item behavior checklist designed to screen for autism in individuals from ages 3 to 22; it can be rapidly completed with little training. Its utility in infants and young children

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remains to be established; there is some concern about its sensitivity with preschool children (South et al. 2002).


Clinical Diagnosis To date the most robust method to diagnose autism in infants and very young children remains the use of experienced clinicians (Adrien et al. 1992, Cox et al. 1999, Lord 1995, Stone et al. 1999). The clinical diagnosis of autism is highly stable, with 72% to 87% of cases retaining the diagnosis at follow-up (see Klin et al. 2004 for discussion). It presumably is the case that experienced clinicians draw upon a much richer range of knowledge than that embodied in diagnostic guidelines or criteria (Klin et al. 2000). At present clinical diagnosis continues to be the “gold standard” in diagnosis of autism spectrum disorders in infants, toddlers, and preschoolers (Lord 1995, Stone et al. 1999). Development of better (categorical and dimensional) approaches specific to infants and very young children is an important research priority and one that is essential in understanding the boundaries of the disorder in relation to broader autism spectrum conditions.

DEVELOPMENTAL FEATURES OF AUTISM IN THE FIRST TWO YEARS OF LIFE Social Functioning Social difficulties are the single most powerful predictor of diagnosis for older individuals with autism (Siegel et al. 1989, Volkmar et al. 1994); this likely is true for infants as well. Preschool children with autism often fail to demonstrate social skills typically present in the first months of life (Klin et al. 1992). Eye contact is limited as is overall social engagement and responsivity (Dawson et al. 2000, Sparling 1991). Difficulties in the area of joint attention are striking (Mundy et al. 1990, Sigman & Ruskin 1999); such behaviors are central in development of communicative and social-cognitive abilities (Tomasello 1995). Over time, both initiation and response to bids for joint attention do increase in children with autism (Leekam et al. 2000), but are severely compromised in natural contexts (Klin et al. 2002a,b). The pattern of acquisition of these skills in autism may be atypical (Carpenter et al. 2002, Klin et al. 2003). For typically developing children, imitation and play are important for symbolic (Piaget 1952a,b; Vygotsky 1990) and social-cognitive development (Trevarthen & Aitken 2001)1. Older children with autism consistently have difficulties in imitation 1

It is important to distinguish between true imitation, which entails an aspect of intentionality as well as the translation of observed movements of others into motor output from echopraxia, and simple mimicry, which occurs without an appreciation of the person’s goal (Tomasello 1995).

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(Hobson & Lee 1999, Loveland et al. 1994, Smith & Bryson 1994), and these difficulties are present by at least the second year of life (Charman et al. 1997, Dawson et al. 1998, Roeyers et al. 1998). Levels of imitation positively correlate with language levels (Sigman & Ungerer 1984). Play activities in the typical child progress from simple object exploration to functional object use to pretend play. Although the evidence on this issue is limited, in the first months of life strong differences are not observed between infants with autism and typical or delayed peers (Baranek 1999, Maestro et al. 2002, Osterling et al. 2002). But by 9 to 12 months, abnormalities are evident (Baranek 1999) and become progressively more deviant due to higher levels of perseveration (Osterling et al. 2002). Although early functional play routines may be observed (Charman & Baron-Cohen 1997), by around age 2 years differences from typically developing peers are striking: play is less purposeful, less symbolic, and less developmentally complex (McDonough et al. 1997, Mundy et al. 1986, Sigman & Ruskin 1999, Stone et al. 1990). By the end of the first year of life, typical infants have developed strong patterns of attachment; suck behaviors maintain the proximity of the child to the caregiver while also facilitating exploration (Rutter 1995). Children with autism do form attachment to parents (Capps et al. 1994, Rogers et al. 1993, Shapiro et al. 1987, Sigman & Mundy 1989, Sigman & Ungerer 1984) and differentially respond to familiar and unfamiliar individuals (Landry & Loveland 1989, Sigman & Ungerer 1984). However, the quality of attachment behaviors may be unusual (Rogers et al. 1993). Among younger children with autism, attachments to unusual objects are relatively common (Volkmar et al. 1994), but the significance of this is unclear.

Communicative Development Concerns about the child’s speech and communicative development are among the most frequent initial presenting complaints (De Giacommo & Frombonne 1998). Even before they begin to produce spoken language, patterns of sound production in children with autism are abnormal (Wetherby et al. 1989) as is vocal quality (Sheinkopf et al. 2000), a likely precursor of the noteworthy deficits in intonation and vocal quality seen later (Shriberg et al. 2001). The development of nonverbal communicative abilities is intimately involved for typical children in the development of conventional communicative abilities and marks the beginning of intentional communication (Bates et al. 1979). However, very young children with autism communicate less frequently than matched developmentally delayed children (Stone et al. 1997, Wetherby et al. 1989). They are less likely to use contact and conventional gestures but are more likely to use highly unconventional gestures such as manipulating their conversational partner’s hand to obtain objects (Stone et al. 1997). Children with autism have difficulties with both the expression and production of affective responses, and the range, frequency, and integration of affective

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displays are unusual (Ricks & Wing 1975, Snow et al. 1987, Yirmiya et al. 1989). Children with autism are less likely to look at an apparently distressed adult (Sigman et al. 1992) and they have difficulties imitating facial displays of emotion (Loveland et al. 1994).


Cognitive Development Assessment of infants and very young children with autism present particular challenges (Klin et al. 2004) given the inherent limitations of instruments in this age group and the particular difficulties posed for assessment of children with autism. As developmental tests sample a broader range of skills, i.e., as the child becomes somewhat older, delays typically become more apparent in tasks that require language-based problem solving, symbolic thinking, or aspects of social interaction; tasks that are less verbal, e.g., visual matching, may be closer to ageexpected levels (Klin et al. 2004). One consistent observation made first by Kanner (1943) concerns the emergence of noteworthy discrepancies across developmental areas. This developmental decalage has its origin in early childhood (Sigman & Ungerer 1981), and it not only persists over time but it often becomes even more striking (Ehlers et al. 1997, Freeman et al. 1988, Klin et al. 1995). Differences in sensorimotor development do not seem to be syndrome-specific (Cox et al. 1999, Dawson et al. 2002b, Morgan et al. 1989, Sigman & Ungerer 1984). Although studies of older children have demonstrated difficulties in executive functioning (Pennington & Ozonoff 1996), studies with younger children generally have not revealed syndrome-specific differences; in some instances preschool children with autism perform better than matched developmentally delayed controls on tasks (Dawson et al. 2002b, Griffith et al. 1999). Attentional abnormalities in older children with autism are well documented. Difficulties in autism have to do with selective attention, with problems in attending to multiple aspects of stimuli and with auditory stimuli in particular (Burack et al. 1997). Infants later diagnosed with autism show that they attend less to people than to objects as compared to both typically developing and delayed controls (Baranek 1999, Maestro et al. 2002, Osterling et al. 2002); this is also true after the second year of life (Dawson et al. 1998). Selective social attention is particularly impaired, e.g., during free play 20-month-olds with autism were more likely to look at objects than at people than were either typical or delayed peers (Swettenham et al. 1998). Recent work employing new approaches to the study of social attention has shown that older individuals with autism exhibit markedly deviant patterns of attention to people and, particularly, to faces as compared to developmentally matched controls (Klin et al. 2002a,b). Similar findings are emerging with very young children with autism (Klin et al. 2003). Figure 1 shows a toddler with autism viewing a videotape of a popular children’s program: Rather than focusing on either the large purple character or the two children, the child’s point of regard is on the intersection of several corners and the rainbow backdrop. Various attempts have been made to account for these early differences in social-visual attention. The problem might have to do with avoiding complex

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visual stimuli, e.g., faces (Swettenham et al. 1998), or with avoiding unpredictable and variable social stimuli (Dawson & Lewy 1989). A third hypothesis posits the fundamental problem as a lack of social motivation and salience (Dawson et al. 2002a, Klin 1991). In a study of automatic attentional cueing (by either eye movement or nonbiological movement) in 2-year-olds, the visual attention of the children with autism could be cued by directional changes in eye movement, although cue-specific differences also suggested different underlying strategies (Chawarska et al. 2003). Data that clarify the nature of these differences are critically needed.

ASSESSMENT AND INTERVENTION Multiple aspects of the child’s development must be assessed, usually necessitating the involvement of a range of professionals with attendant issues of coordination and communication. Additionally, diagnostic issues can be complex and sometimes are clarified only with time. Translation of assessment results into practical implications for intervention is critical, as is involvement of parents. Developmental assessment is a crucial aspect of this process (Klin et al. 2004); it helps to frame subsequent observations and can provide crucial information on intervention strategies. Speech-language-communication assessment is also vital. Delay and deviance in the area of communicative ability is universal in autism and is one of the most central areas for intervention; hence, speech-language-communication assessment is a core component of the diagnostic process (Wetherby & Prizant 2000). Assessment of adaptive skills is also critical; the inability to translate what the child can do in a structured testing situation into the real world is a universal problem and one with important implications for treatment (Carter et al. 1998, Sparrow et al. 1984). The final diagnostic process should aim at the integration of all available information, including historical information, the child’s current developmental levels, behavioral strengths, and vulnerabilities. A growing number of assessment instruments for infants and young children are now available (see Klin et al. 2004 for a review), and, as noted previously, a number of instruments have been developed specifically to aid in the diagnosis of autism in young children. A large body of available evidence strongly supports the importance of intensive educational and behavioral interventions in children with autism. The recent National Research Council (2001) report has summarized the range of programs around the country with demonstrated efficacy in the treatment of children with autism. Although there are some differences in these programs, there are also many areas of similarity. Areas of difference include aspects of theoretical orientation, the degree to which it is the child or the adult who sets the learning/teaching agenda, and the degree to which the curriculum is guided by developmental principles. Areas of general agreement include the use of highly structured approaches to teaching, intensive involvement with the child, and a general focus on “learning to learn” challenges, i.e., basic abilities to participate in and benefit from instruction (Natl. Res. Counc. 2001).

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Applied Behavior Analysis (ABA) has been the most widely studied treatment method, although there has been an increasing move to more eclectic models drawing from a range of other effective methods as well (Natl. Res. Counc. 2001, Smith 2000). Some attempts have been made to tailor treatments to presumed “core” deficits (Kasari 2002); differential response to treatment has now been included in some newer neuropsychological models of the condition (Mundy 2003). Some reasonably well-designed controlled treatment studies have now appeared (Drew et al. 2002, Eikeseth et al. 2002). However, even for well-established treatments like ABA, the results of recent randomized control trials (Smith et al. 2000) have not been as dramatic as those initially reported (Lovass & Smith 1988); it sometimes proves difficult for families to maintain the intensity recommended by some intervention programs. There is some suggestion that the age at which treatment begins may be an important factor (Harris & Handleman 2000). Available treatment studies have generally focused on preschool or school-age children, and few studies have directly addressed the issues of intervention in infancy. This problem will become more critical as early diagnosis improves (Volkmar et al. 2004). Various factors appear to be central in successful intervention programs (Howlin 2000). Children with less classical autism may respond better than those with more strictly defined autism, and children with better cognitive abilities or higher levels of engagement may respond more positively (see Drew et al. 2002, Kasari et al. 2001, Koegel et al. 1999, Rogers 2000, Siller & Sigman 2002, Volkmar et al. 2004c, Whalen & Schreibman 2003, Wolery & Garfinkle 2002). For all treatments, generalization of skills learned across settings is critical (Hwang & Hughes 2000, Strain & Hoyson 2000). Unfortunately, even though earlier detection and intensive intervention have led to an overall improvement in outcome, some children fail to do well even with excellent intervention; the study of such children may help clarify how treatments can be more individually tailored to the child. Although there has been a growing interest in teaching approaches for social skills, much of this literature has focused on somewhat older children. In addition, much of the information on effective treatments has emerged through single-subject designs. There is a critical need for research that addresses issues of treatment mechanisms and moderators as well as individual differences in response to treatment(s) (Paul 2003).

SUMMARY AND FUTURE DIRECTIONS FOR RESEARCH Although autism was first described 60 years ago as a disorder with onset in infancy, our knowledge about infants with the condition remains limited. Until recently most of the available information was derived from retrospective reports or videotapes. Knowledge of the strong genetic basis of autism, heightened media and public attention, and an awareness of the importance of early diagnosis and treatment have focused increased attention on autism in the first months of life. Research findings to date have supported and refined Kanner’s original (1943)

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hypothesis that the problem is indeed fundamentally a social one, with studies consistently revealing early deficits in joint attention, gaze and eye contact, and early emerging social interest and routines. These difficulties appear to then have important implications for emerging skills in other areas. To date a major limitation of the research on infants with autism has been the reliance on retrospective approaches. The development of better screening methods and the study of high-risk populations, e.g., siblings of those with autism, now provides us with the opportunity to design prospective studies. The results of such studies will further help us refine current theories of both the psychological and neurobiological basis of autism and to address issues of the broader spectrum of autism-related disorders as well. Results will have important implications for understanding the role of social factors in other aspects of development and the potential for allowing us to develop even better behaviorally and biologically based approaches to screening and early diagnosis. A focus on specific processes is critically needed, e.g., how deficits in joint attention become entwined with other attentional, communicative, and cognitive processes (Volkmar et al. 2004b). Advances in assessment and methodology may help extend work from older and more able individuals to the first years, if not the first months, of life. The increasing numbers of infants and very young children identified as at risk will present important new challenges for service delivery and research on early intervention. ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the National Institute of Child Health and Human Development (grants 1PO1HD35482-01, 5-P01-HD03008, and R01-HD042127-02) and the National Institute of Mental Health (STAART grant U54-MH066494). The support of the National Alliance of Autism Research, the Simons Foundation, and the Doris Duke Charitable Foundation is gratefully acknowledged. The authors also thank Lori Klein and Amanda Merz for their help in the preparation of this chapter. The Annual Review of Psychology is online at http://psych.annualreviews.org

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Volkmar FR, Rutter M. 1995. Childhood disintegrative disorder: results of the DSM-IV autism field trial. J. Am. Acad. Child Adolesc. Psychiatry 34:1092–95 Volkmar FR, Stier DM, Cohen DJ. 1985. Age of recognition of pervasive developmental disorder. Am. J. Psychiatry 142:1450–52 Vygotsky L. 1990. Imagination and creativity in childhood. Sov. Psychol. 28:84–96 Werner E, Dawson G, Osterling J, Dinno N. 2000. Brief report: recognition of autism spectrum disorder before one year of age: a retrospective study based on home videotapes. J. Autism Dev. Disord. 30:157– 62 Wetherby AM, Prizant BM, eds. 2000. Autism Spectrum Disorders: A Transactional Developmental Perspective. Baltimore, MD: Brookes. 422 pp. Wetherby AM, Yonclas DG, Bryan AA. 1989. Communicative profiles of preschool children with handicaps: implications for early identification. J. Speech Hear. Disord. 54:148–58 Whalen C, Schreibman L. 2003. Joint attention training for children with autism using behavior modification procedures. J. Child Psychol. Psychiatry 44:456–68 Wimpory DC, Hobson RP, Williams JM, Nash S. 2000. Are infants with autism socially engaged? A study of recent retrospective parental reports. J. Autism Dev. Disord. 30: 525–36 Wing L, Potter D. 2002. The epidemiology of autistic spectrum disorders: Is the prevalence rising? Ment. Retard. Dev. Disabil. Res. Rev. 8:151–61 Wolery M, Garfinkle AN. 2002. Measures in intervention research with young children who have autism. J. Autism Dev. Disord. 32:463– 78 World Health Organization. 1994. Diagnostic Criteria for Research. Geneva: WHO Yirmiya N, Kasari C, Sigman M, Mundy P. 1989. Facial expressions of affect in autistic, mentally retarded and normal children. J. Child Psychol. Psychiatry 30:725–35

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YOUTH PSYCHOTHERAPY OUTCOME RESEARCH: A Review and Critique of the Evidence Base


John R. Weisz Judge Baker Children’s Center, Harvard University, Boston, Massachusetts 02120-3225; email: [email protected]

Amanda Jensen Doss Department of Educational Psychology, Texas A & M University, College Station, Texas 77843-4225

Kristin M. Hawley Department of Psychological Sciences, University of Missouri, Columbia, Missouri 65211

Key Words children, adolescents, psychotherapy, mental health, treatment ■ Abstract Over the past four decades, researchers have produced extensive evidence on psychotherapy for youth mental health problems and disorders. The evidence often has been evaluated through narrative reviews and through meta-analyses assessing the magnitude of treatment effects, but methodological analysis addressing the character and quality of the evidence base itself is an important complement, needed to place treatment effects in perspective and to suggest directions for future research. We carried out such an analysis, focusing on all the methodologically acceptable published randomized trials our search identified involving treatment of anxiety, depression, ADHD and related conditions, and conduct-related problems and disorders. The 236 studies tested 383 treatments and included 427 treatment-control comparisons, spanning the years 1962 through 2002. The analysis revealed considerable breadth, diversity, and rigor in the measurement approaches used to assess participant characteristics and treatment outcomes. However, reporting on important sample characteristics (e.g., ethnicity) showed major gaps, and more than half the studies failed to use well-standardized procedures to ensure appropriate sample selection. Because sample sizes left most studies underpowered, and procedures to enhance treatment fidelity were generally weak, many of the treatments investigated may not have received fair tests. Studies were particularly weak in clinical representativeness of their samples, therapists, and settings, suggesting a need for increased emphasis on external validity in youth treatment research.

CONTENTS INTRODUCTION: BRIEF HISTORY OF YOUTH PSYCHOTHERAPY AND OUTCOME RESEARCH . . . . . . . . . . . . . . . . . . . . . . . . 338 0066-4308/05/0203-0337$14.00

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PREVIOUS NARRATIVE AND QUANTITATIVE REVIEWS . . . . . . . . . . . . . . . . . QUANTITATIVE METHODOLOGICAL ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . Focusing on Four Common Problem/Disorder Clusters . . . . . . . . . . . . . . . . . . . . . . Characteristics of Study Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment of Diagnoses, Target Problems, and Intervention Outcomes . . . . . . . . Treatments Tested and Control Conditions to Which They Have Been Compared . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical Representativeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . METHODS EMPLOYED IN THE REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Search Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coding Procedures and Intercoder Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RESULTS: DESCRIBING THE EVIDENCE BASE . . . . . . . . . . . . . . . . . . . . . . . . . . Study Samples: Demographic and Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . Procedures Used to Identify Youth Problems and to Assess Outcomes . . . . . . . . . . Informants and Technology Employed in Assessment . . . . . . . . . . . . . . . . . . . . . . . Types of Treatment Tested in the Evidence Base . . . . . . . . . . . . . . . . . . . . . . . . . . . Treatment Characteristics: Dose, Format, Participants, Homework, Integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Treatment Groups and Control Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical Representativeness of the Evidence Base . . . . . . . . . . . . . . . . . . . . . . . . . . OVERVIEW AND CRITIQUE OF THE EVIDENCE BASE, AND SUGGESTIONS FOR IMPROVING FUTURE RESEARCH . . . . . . . . . . . . . . . . . . CONCLUDING COMMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION: BRIEF HISTORY OF YOUTH PSYCHOTHERAPY AND OUTCOME RESEARCH The term “psychotherapy” refers to an array of nonmedical interventions designed to alleviate nonnormative psychological distress, reduce maladaptive behavior, or increase deficient adaptive behavior through counseling, interaction, a training program, or a predetermined treatment plan. One likely source of this practice is the ancient tradition of helping by listening, discussing, and questioning (see Plato’s Apology). An early practitioner, Socrates, developed both a method and a thesis that arguably set a pattern for some modern forms of psychotherapy. His approach, later called the Socratic method, involved questioning others to prompt examination of their beliefs and bring them closer to truth. His “midwife thesis,” the notion that the philosopher’s role was to deliver the truth that is already within others, much like the midwife delivers the baby that is within the mother, is not far from the view many modern therapists have of their own professional roles. By asking others to tell him what they thought, rather than telling them what to think, Socrates sought to reach the rational soul or psyche of those he talked with. The term psyche denoted the mind, inner nature, and capacity for feeling, desire, and reasoning, and was the precursor to the word psychology. Finally, Socrates maintained that thoughts and outward behavior are closely connected, presaging a tenet of many modern therapies.

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Just when psychotherapy became a career track is not clear, but a case can be made for the era when Sigmund Freud (1856–1939) launched psychoanalysis. Important themes in this work were the notions that early experience can be critical, and that even children may be appropriate candidates for intervention. These points were illustrated by Freud’s (1909) treatment of a boy (“Little Hans”) who was afraid of horses by consulting with the boy’s father. Freud’s daughter Anna (1895– 1982) later became a prominent child analyst, as did many others in the first half of the twentieth century. Other models and methods propelled the acceleration of child psychotherapy through the century, including a radically different behavioral approach. Mary Cover Jones (1924), for example, used modeling and gradual exposure to help a two-year-old, Peter, overcome fear of a white rabbit. This work helped to launch a remarkable burgeoning of behavioral psychotherapies for young people, complementing psychoanalysis and humanistic treatments. By the late twentieth century, child and adolescent psychotherapy had expanded remarkably in the variety of its forms and the extent of its reach. The growth of psychotherapy prompted increasing curiosity about its impact. Although psychotherapy research developed later and more slowly than psychotherapy practice, studies began to accumulate. Eysenck (1952) reviewed studies of adult psychotherapy and concluded that the evidence did not show it to be effective. A few years later, Levitt (1957, 1963) reviewed studies that included children and adolescents and concluded that rates of improvement in the youth were about the same with or without treatment. These early reviews were influential, but the studies they relied on were not rigorous by today’s methodological standards. Subsequent research has grown stronger and much more plentiful (Durlak et al. 1995, Kazdin 2000). Many of the studies now in the evidence base meet the standards of randomized clinical trials, and their focus has sharpened over the decades, shifting from early studies of unspecified “treatment” for often vaguely defined youth problems to tests of rather well-articulated therapies targeting specific patterns of dysfunction. In sum, thanks to several historical developments, we are now in a position to profit from a large and increasingly rigorous body of evidence on youth psychotherapies and their effects.

PREVIOUS NARRATIVE AND QUANTITATIVE REVIEWS The evidence on youth psychotherapies is examined periodically through both narrative and quantitative reviews of outcome findings. Narrative reviews (e.g., Kazdin 2000, Shirk & Russell 1996) can bring the perspectives of thoughtful experts to bear on what the outcome findings show, and can identify the strengths and limitations of those findings. As an example, Kazdin’s (2000) scrutiny of the research on youth treatment led to a detailed critique of what has and has not been learned, and a list of specific recommendations for improving the yield of youth outcome trials. As another example, Shirk & Russell (1996) focused their narrative review partly on the question of what treatment research has told us about

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pathogenic processes and change processes in treatment. This review, too, led to a proposed new framework for individualizing and implementing youth treatments. Another approach to reviewing outcome research is the quantitative review of treatment effects, an approach called meta-analysis. Meta-analysts apply a common effect size metric to sets of studies, to gauge the magnitude of treatment benefit (or harm) for entire bodies of evidence or selected subsets. Four published meta-analyses of the youth treatment outcome literature have been particularly broad in their inclusion criteria, encompassing studies of diverse problems and disorders and varied treatments (Casey & Berman 1985; Kazdin et al. 1990; Weisz et al. 1987, 1995). Across these four meta-analyses, mean unweighted effect sizes were all 0.71 or higher, indicating that the average treated youth scored better than more than three fourths of control group youths on outcome measures at the end of treatment. Effect sizes are more modest, but still positive and substantial, when weighting is introduced to adjust for sample size (Weisz et al. 1995). Meta-analytic findings also suggest that treatment effects show specificity—i.e., effects are larger on measures of the problems actually targeted in treatment than on measures of other mental health outcomes not targeted—and that the effects hold up well after the end of treatment, at least for the five- to six-month follow-up period that is common in youth outcome research (Weisz et al. 1995).

QUANTITATIVE METHODOLOGICAL ANALYSIS A third method of taking stock of the field is quantitative methodological analysis. In this approach, the focus shifts to the nature and quality of the evidence base from which narrative and quantitative reviewers derive their findings and conclusions. One goal is to lend perspective to those findings and conclusions. As an example, conclusions about the potency of treatment programs in comparison to control groups are best evaluated in light of information about what those treatment programs were, how faithfully they were carried out, to whom they were delivered, and with what strength, as well as information about the kinds of control groups to which they were compared. Summary statements about outcome findings are also best framed by an understanding of the kinds of outcome measures used, who provided the information gathered via those measures, and the extent to which the measures and those providing them were apt to be free of bias. More broadly, examining the evidence base may provide insights into the lay of our land in youth treatment outcome research, and whether investigators are gathering the kinds of information most needed to understand and improve mental health care for young people. In one example of quantitative methodological analysis, Kazdin et al. (1990) reviewed published studies of psychotherapy for youngsters aged 4–18. In another example, Durlak et al. (1995) reviewed psychotherapy studies with children aged 13 and younger. By examining features of their respective study sets, Kazdin et al. and Durlak et al. were able to identify important characteristics of the evidence base. They noted, for example, that the research disproportionately involved tests

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of behavioral and cognitive-behavioral procedures, with significant underrepresentation of other treatment approaches that are widely practiced. In addition, the reviewers noted that outcome studies in general focused primarily on testing techniques, with scant attention to nontechnique variables (such as youth or family characteristics) that might have a substantial impact on treatment outcome. Through their observations about the structure and content of the treatment outcome studies in their collection, these investigators characterized the evidence base upon which so many conclusions of so many reviewers had rested, and in the process influenced the direction of future youth outcome research. Because Kazdin and colleagues (1990) reviewed studies published between 1970 and 1988, and the Durlak et al. (1995) review encompassed studies from more than a decade ago, the content of the evidence base is now likely to have changed significantly. In this chapter, we report the results of our own methodological analysis, focused on studies published from 1963 through 2002. In addition to our expanded time range, we encompass the adolescent years not included by Durlak et al., and we introduce new inclusion requirements not previously employed (e.g., requiring random assignment to treatment and control groups) to ensure the methodological rigor of studies. Another distinguishing feature of our approach is that we focus attention on four particularly important clusters of youth dysfunction, as discussed in the following section.

Focusing on Four Common Problem/Disorder Clusters Youth psychotherapy is used to address diverse problems and disorders that cause emotional distress, interfere with daily living, undermine the development of adaptive skills, or threaten the well-being of others. The concerns addressed may include, for example, enuresis, Tourette’s syndrome, anorexia nervosa, fire setting, and trichotillomania. Encompassing every treated condition within a quantitative methodological review could pose a risk that conclusions lack clear referents within the treatment outcome literature. To reduce this risk, we chose to focus on four broad problem/disorder clusters that appear to account for a very high percentage of youth referrals for mental health care in the United States (see, e.g., Jensen & Weisz 2002). The four clusters are: ■

Anxiety-related problems and disorders (e.g., social phobia, generalized anxiety disorder)

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Depression-related problems and disorders (e.g., dysthymic disorder, major depressive disorder)

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Attentional problems, impulsivity, and attention deficit/hyperactivity disorder (ADHD)

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Conduct-related problems and disorders (e.g., oppositional defiant disorder, conduct disorder)

With a focus on these four clusters, we examined the treatment outcome database in an effort to address several issues of significance for the field.

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Characteristics of Study Samples We examined both the size and the demographic characteristics of outcome study samples. One aim was to determine whether the sample sizes generally employed have generated sufficient power to detect treatment effects. Another aim was to assess the range of human characteristics tapped in the studies and thus ascertain whether certain groups of young people are missing. We also hoped to learn about the quality of reporting on participant characteristics and the extent to which researchers have been conscientious about clearly characterizing important dimensions of human variability (e.g., ethnicity) in their samples. Attention to such dimensions is relevant to our ability to identify moderators of treatment outcome and to judge how representative the clinical trial samples are of those populations seen in clinical service settings.

Assessment of Diagnoses, Target Problems, and Intervention Outcomes We examined the process by which investigators identified youths as appropriate for their treatment programs. In the process, we hoped to learn how much confidence to place in the fit of youth treatments to the problems addressed by those treatments. To shed light on the degree of rigor and objectivity used in assessing treatment effects, we also noted the measurement approaches used, focusing on both the informants who provided outcome information and the measurement technology employed. Another focus in our examination of outcome assessment was breadth of coverage. Several investigators (e.g., Hoagwood et al. 1996, Kazdin 2000, Weisz 2004) have argued that youth treatment research needs to examine intervention impact in ways that go beyond assessment of the specifically targeted youth symptoms and diagnoses. To assess the extent to which this has happened in research to date, we examined how much attention has been given to nontarget youth outcomes (e.g., reduced depressive symptoms in treatment for ADHD), real-world functioning by the youths (e.g., school performance), “environmental impact” (e.g., reduced depressive symptoms in parents of children treated for conduct problems), and client satisfaction with treatment.

Treatments Tested and Control Conditions to Which They Have Been Compared We also examined the array of treatments tested. We sought to learn the extent to which they focused on youths themselves versus others in the youths’ environments who might play key roles in family, school, or community. In addition, we noted the theoretical orientations represented by the tested treatments. We have been concerned over the years (e.g., Weisz et al. 1987, Weisz & Hawley 1998) that the treatment models tested most often in research are not the models most widely used in practice, and that this results in a very limited picture of how well the most

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widely used interventions work. We generated data on the extent to which this problem is evident in a particularly comprehensive collection of methodologically sound studies focused on four of the most clinically significant problem clusters. In addition, we took steps to characterize the treatments with respect to their strength or “dose,” and the formats most commonly employed. This characterization included the extent to which youths, parents, entire families, and teachers have been included in treatment sessions, and the steps taken to ensure skill building by youths (i.e., the extent to which therapeutic homework has been assigned) and to ensure the faithful delivery of treatment programs by therapists. All these elements speak to the likely potency of the treatments as delivered and tested. Because the potency measurement of a treatment depends in part on the control condition to which it is compared, we also examined the kinds of control groups employed in treatment trials.

Clinical Representativeness Finally, a long-standing concern of ours (Weisz & Hawley 1998, Weisz et al. 2004) has been that a significant proportion of treatment outcome research may involve youths, therapists, and settings that are rather different from those of everyday clinical practice, and that this difference may limit the relevance of the research to practice. The evidence base available at the time this review was conducted provided an opportunity to assess the validity of these concerns for research on four of the most common forms of youth dysfunction targeted in everyday clinical care.

METHODS EMPLOYED IN THE REVIEW To provide the most reliable and relevant evidence bearing on the questions noted above, we sought to identify treatment outcome studies meeting uniform standards that were important for our particular purposes; this made our study collection different from those used in previous reviews. Here we describe the search process, the inclusion criteria we applied, and the coding system used to characterize the studies.

Literature Search Procedures To identify relevant studies, we used a variety of sources. First, we searched standard computerized databases beginning in 1965 and eventually continuing through the end of 2002. We used PsycInfo, employing 21 psychotherapy-related key terms (e.g., psychother-, counseling, treatment) derived from previous youth psychotherapy meta-analyses (Weisz et al. 1987, 1995), and we used MEDLINE via PubMed, the principal bibliographic database of the National Library of Medicine. PubMed uses a controlled vocabulary indexing system (MeSH) that provides a consistent way to retrieve citations from publishers who may use different key words for the same concepts. We used “mental disorders,” with the following search limits: clinical trial, child (3–18 years), published in English, and human subjects.

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The MeSH descriptor, “mental disorders,” encompasses all related subject terms more specific than this broad heading (e.g., anxiety, mood disorders, mental disorders, ADHD). In addition to these database searches, we surveyed published reviews and meta-analyses of the youth psychotherapy and pharmacotherapy literature to identify studies not found in PsycInfo or MEDLINE (e.g., Casey & Berman 1985, Compton et al. 2002, Durlak et al. 1991, Dush et al. 1989, Farmer et al. 2002, Prout & DeMartino 1986). We also followed reference trails of reviewed studies, and we screened studies suggested by investigators in the field. We obtained copies of all published youth treatment outcome studies identified by the procedures noted above. To ensure some level of quality control, we selected only studies that had been subjected to peer review. In most cases, this meant that the studies had been published in peer-reviewed journals; we excluded unpublished manuscripts, book chapters (except in the rare cases where serious peer review was evident), and unpublished dissertations. Applying this criterion meant that our ultimate collection of studies would not fully represent all research that has been carried out (see McLeod & Weisz 2004). However, our goal was to characterize the outcome research that met our minimum criteria for methodological soundness (e.g., random assignment) and that had been judged acceptable in professional peer review and made available to the field through publication. We included all studies that met inclusion criteria regardless of treatment outcome. To be included in the review, studies were required to be tests of psychotherapy, defined as any intervention designed to alleviate nonnormative psychological distress or reduce maladaptive behavior through counseling, interaction, a training program, or a predetermined treatment plan. Studies were also required to: ■

Include comparison of psychotherapy to a control group (waitlist, no treatment, placebo, or other process intended not to be an active treatment)

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Involve prospective design and random assignment of subjects to treatment and control conditions

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Use a sample within the 3- to 18-year range

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Use participants selected for having psychological problems or maladaptive behavior (within the four problem clusters noted above)

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Include a posttreatment assessment of the psychological problem(s) or maladaptive behavior for which participants were selected and treated

To ensure a focus on comparison of psychotherapy to a control group, we selected for review only those studies in which participants in the groups to be compared were not taking psychotropic medications.

Coding Procedures and Intercoder Reliability After we identified studies, we coded characteristics of their samples, settings, treatments, treatment providers, design characteristics, and types of outcomes assessed. To establish interrater reliability for these codes, project coders (one

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postdoctoral fellow who served as the master coder and two clinical psychology graduate students) coded 30 randomly selected studies. To compute reliability, each graduate student coder’s results were compared to those of the master coder, with kappa (k) statistics computed for categorical codes and Pearson correlation coefficients (r) computed for continuous codes. The codes used and the corresponding intercoder reliabilities are detailed below. We coded study samples for (a) size of treatment and control groups (mean r = 0.99), (b) age at treatment onset (mean r = 0.99), (c) gender (percentage of sample that was male: mean r = 0.99), (d ) ethnicity (percentage of sample that was Caucasian, African American, Latino Hispanic, Asian Pacific, other; mean r = 0.93), (e) recruitment source (recruited, clinically referred, court-referred; mean k = 0.71). We also coded for the location where treatment was administered (western, midwestern, southern, or eastern United States, Caribbean/U.S. territory, international; mean k = 0.87).


PARTICIPANT/SAMPLE/LOCATION INFORMATION

We classified the problems targeted in treatment into four types: (a) anxiety, fears, and shyness; (b) depression; (c) impulsivity and ADHD-related problems; and (d ) oppositional-conduct problems (mean k = 0.91).

TARGET PROBLEM INFORMATION

We coded the investigators’ procedures for assessing and identifying participants’ problems. The coding included diagnosis given for target problem (whether participants met diagnostic criteria, via the prevailing diagnostic system, for a disorder falling within the target problem domain; mean k = 0.79) and clinical cutoff met for target problem (i.e., whether participants met a clinical cutoff on a standardized continuous measure, such as a self-report symptom questionnaire; mean k = 0.56).

PROBLEM ASSESSMENT INFORMATION

Each randomly assigned treatment or control group was coded according to treatment or control technique. Coders classified study treatment groups with reference to the primary participant or target of the intervention (e.g., child, family, parent, teacher) and the theoretical orientation (e.g., operant, respondent- or exposure-based, cognitive-behavioral, psychodynamic, client-centered, systems-based, eclectic, other). Coders classified control groups into (a) no therapy/waitlist, psychotherapy placebo (active condition designed to control for nonspecific effects), (b) medication placebo (sugar pill or equivalent), and (c) case monitoring or management (e.g., standard, custodial, educational, or judicial). Interrater reliability for the classification of treatment and control conditions resulted in a mean k of 0.75 (treatment and control group designations were assigned categorically as a single code).

TREATMENT AND CONTROL METHODS USED

Treatment conditions were coded for format by noting (a) whether sessions were individual or group (mean k = 0.80), (b) whether or not clients received homework (mean k = 0.67), and

FORMAT, NATURE, AND INTENSITY OF TREATMENT

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(c) which form(s) of treatment contact were involved (i.e., contact between target youth and therapist, youth’s parent(s) and therapist, youth’s family and therapist, youth’s teacher and therapist) (mean k = 0.60). In addition, treatment dose was coded in terms of (a) total number of sessions (mean r = 0.99), (b) total weeks of treatment (mean r = 0.98), (c) average length of sessions (mean r = 0.99), and (d ) total hours in treatment (mean r = 1.00). We coded (a) whether investigators arranged for pretherapy training in the specific therapy techniques that would be used (mean k = 0.94), (b) whether adherence checks were used (mean k = 0.74), and (c) whether a treatment manual or equivalent documentation was employed (mean k = 0.71).


EFFORTS TO ENSURE TREATMENT INTEGRITY

The setting where treatment took place was coded to indicate if it was (a) a typical clinical service setting (e.g., community clinic versus a university lab office), (b) a typical research setting (e.g., university lab office, school), or (c) a correctional setting (e.g., prison); some settings were difficult to classify because of incomplete reporting in the article (mean k = 0.53).

TREATMENT SETTING

We coded whether treatments were provided by therapists of the following primary vocations: practicing mental health clinician, graduate student or researcher, or paraprofessional (neither a researcher nor a mental health clinician, e.g., teacher); therapist vocation was sometimes difficult to classify because of incomplete reporting in the article (mean k = 0.63).

THERAPIST PRIMARY VOCATION

We coded outcome measures with regard to the domain the measure assessed, including symptoms or diagnosis, functioning, environmental impact of treatment (e.g., parent self-esteem or mood), and satisfaction with treatment (mean k = 0.83). If the measure was in the domain of symptoms or diagnosis, we further coded whether it assessed the target problem for which the sample had been selected and treated (mean k = 0.89).

OUTCOME MEASURE CHARACTERISTICS

To gauge the methodological character and strength of the measures employed, we coded each measure in each study for (a) measurement technology (self-report, other report, objective behavior count, independent life event data) (mean k = 0.87), and (b) source or reporter (target child, parent/guardian, sibling, peer, teacher, therapist, other observer), (mean k = 0.89). We also coded for whether the source or reporter was aware of the target youth’s treatment status at the time the outcome measure was obtained (i.e., blindness of source = yes/no) (mean k = 0.67). In addition, we coded for whether the subject of the rating (generally the child) was aware that a measure was being taken and was reasonably able to influence the

METHODOLOGICAL CHARACTER AND QUALITY OF THE MEASURES

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results of that rating (blindness of subject = yes/no; e.g., observer coding of child behavior during a five-minute assessment task would be coded as yes, assuming that the child was aware of the assessment; observer coding of naturally occurring child behavior every day at recess over two weeks would be coded as no) (mean k = 0.76).


RESULTS: DESCRIBING THE EVIDENCE BASE Although our initial search led to a pool of more than 3000 studies focused on treatment, the methodology of many of the studies was not strong enough to support credible inferences about treatment effects. Application of our methodological criteria (e.g., random assignment required) reduced the pool to 236 methodologically acceptable studies, which had tested 383 different treatments and included 427 treatment-control comparisons. This total included 82 studies in the anxiety domain, testing 137 anxiety treatments and employing 162 treatment-control comparisons; 18 studies in the depression domain, testing 27 depression treatments and employing 28 treatment-control comparisons; 40 studies in the ADHD domain, testing 74 different treatments and employing 84 treatment-control comparisons; and 96 studies in the conduct problem domain, testing 145 different treatments for conduct problems and employing 153 treatment-control comparisons. Across the full set of studies, year of publication spanned four decades, from 1962 to 2002, with a median publication year of 1986; 39% of the studies were published in the 1990s or later. The anxiety studies were published from 1967 to 2002; median year was 1985; 43% were published in the 1990s or later. The depression studies were published from 1986 to 2002; median was year 1997; 89% were published in the 1990s or later. ADHD studies spanned the years 1968 to 2001; median was year 1981; only 20% were published in the 1990s or later. Finally, for conduct studies, publication ranged from 1963 to 2001; median year 1986; 33% published in the 1990s or later.

Study Samples: Demographic and Characteristics Table 1 summarizes demographic characteristics of the study samples, with data presented for the four problem clusters and the total collection of studies. The table shows that mean sample age was lowest for ADHD and highest for depression studies, perhaps reflecting early the detection of ADHD as children enter educational settings and the fact that base rates of depression are low in childhood but increase with the transition to adolescence. Samples were predominantly male for the two externalizing clusters, and predominantly female for anxiety and depression. Studies in which race and ethnicity were reported indicated that Caucasian youths were the majority sampled in all four problem clusters, particularly in the case of ADHD. Substantial percentages of black youths were sampled across the four clusters as well, but there was much more limited representation of other

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Demographic characteristics of treatment study samples∗ Anxiety (N = 82)

Depression (N = 18)

Mean age in years Studies not reporting

10.17 1.22

13.87 0

Boys in study Studies not reporting

44.31 20.73

Ethnicity Caucasian Black Latino Asian Other Studies not reporting

Conduct (N = 96)

All studies (N = 236)

8.53 0

10.53 2.08

10.30 1.27

40.76 0

85.63 17.50

75.65 11.46

64.03 14.83

56.14 24.38 6.20 2.88 7.97 68.29

57.48 19.98 9.63 3.16 6.22 33.33

82.80 12.59 0.00 0.00 1.61 75.00

50.88 33.04 4.66 0.14 10.96 51.04

56.56 26.88 5.18 1.25 8.56 59.75

Income/SES Studies reporting income Reporting SES Studies not reporting

0 0 82.93

0 0 88.89

2.50 2.50 77.50

11.46 6.25 61.46

5.08 2.97 72.46

Geographic region Studies in United States International studies Studies not reporting

58.52 36.59 4.88

61.11 16.67 22.22

75.00 12.50 12.50

73.97 18.75 7.29

67.80 23.73 8.47

∗

ADHD (N = 40)

All values (except mean age) are noted in percentages.

ADHD, attention deficit/hyperactivity disorder; SES, socioeconomic status.

ethnic groups. Although many in the field are greatly interested in the income and socioeconomic status (SES) of the samples, we are not able to provide any useful substantive summary because reporting was absent in most cases and erratic across studies that did provide such information (with some reports of income, some of SES, others of the percentage of family members receiving public assistance, etc.). The data on geographic region make it clear that the youth treatment evidence base is largely an American product, with more than two thirds of all the studies conducted in the United States. The most “international” of the four domains is anxiety, for which more than one third of the evidence comes from other countries—particularly Australia, Canada, Israel, the Netherlands, and the United Kingdom. In general, a striking feature of Table 1 is the fact that so much information needed to characterize and understand the youths and families targeted in treatment is simply absent from the written reports. As an example, 60% of all the articles failed to include any report on the race or ethnicity of their samples, and more than 70% failed to provide any information on family income or socioeconomic status.

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Procedures Used to Identify Youth Problems and to Assess Outcomes Table 2 summarizes the procedures used to identify youths as having the diagnoses or problems that were to be targeted in studies. Formal Diagnostic and Statistical Manual (DSM) or International Classification of Diseases (ICD) diagnoses were used to select some or all of the sample in approximately one fourth of all studies; diagnoses were most often used in depression studies, least often in conduct studies. However, even within the minority of studies that did use DSM diagnoses, fewer than half used standardized diagnostic procedures, raising questions about the validity of the diagnoses for which some of the youngsters were selected and treated. An alternate, nondiagnostic approach to problem identification is the use of clinical cutoff scores on standardized continuous measures of psychopathology. Although a strong case can be made for the value of this sample selection strategy, it has been used even less than categorical diagnosis. More than 80% of studies failed to demonstrate that even some of their sample met inclusion criteria based TABLE 2

Measurement: I. Identification of youth problems and kinds of measures obtained∗ All Anxiety Depression ADHD Conduct studies

Mean number target problems in measures/study

5.39

4.56

5.60

4.62

5.05

25.61 2.44 71.95 82.60

50.00 0 50.00 22.22

32.50 0 67.50 38.50

8.33 6.25 85.42 21.40

21.61 3.39 75.00 49.15

6.10 1.22 92.68 68.29

22.22 11.11 66.67 27.78

30.00 2.50 67.50 50.00

14.58 2.08 83.33 73.96

14.83 2.54 82.63 64.41

Mean number of nontarget problem 3.26 in measures/study Studies w/nontarget symptom measures 75.61 Studies w/functioning measures 32.93 Studies w/environment impact measures 1.22 Studies w/client satisfaction measures 4.88

8.83

4.90

10.16

6.77

94.44 11.11 5.56 11.11

55.00 22.50 2.50 2.50

86.46 30.21 8.33 11.46

77.97 28.39 4.66 7.63

Target problem confirmed via DSM diagnosis? Studies w/DSM for full sample Studies w/DSM for partial sample Studies w/DSM for none of sample Among studies using DSM, total using standardized procedures Target problem confirmed via clinical cutoff on standardized continuous measure? Studies w/cutoff for full sample Studies w/cutoff for partial sample Studies w/cutoff for none of sample Studies not employing DSM or cutoff

∗

All values (except mean number of target and nontarget problems) are noted in percentages.

ADHD, attention deficit/hyperactivity disorder; DSM, Diagnostic & Statistical Manual.

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on a clinical cutoff. Combining both sets of codes revealed that more than 60% of all studies failed to employ either approach for even a portion of the sample. Given the low rates of sample selection via formal diagnosis and via clinical cutoffs, how did most study authors identify youths for participation in their clinical trials? In fact, most studies identified participants based on teacher or parent reports on nonstandard measures or questionnaires, or via responses to an advertisement or to researcher requests for referrals of youths with particular kinds of problems; when DSM diagnoses were used, they were most often assigned by clinicians or researchers using clinical judgment and other nonstandardized procedures of unknown validity. The one domain in which sample selection was carried out more carefully and systematically was depression; 72% of depression studies used either standardized DSM diagnostic procedures or clinical cutoffs on standardized measures. This may reflect the fact that the depression literature includes more recent studies than the other areas (as discussed previously), thus sample selection profited from the more rigorous methodological standards of the era. Also provided in Table 2 is information about the number and types of measures used in the evidence base. In contrast to the rather limited use of formal diagnosis and even clinical cutoffs to identify study samples, the evidence shows extensive and diverse measurement of participants. Across the four problem domains, studies have averaged approximately five different measures of target problems and approximately seven different measures of nontarget problems; thus, the average study has obtained twelve participant measures. By our inclusion criteria for the review, 100% of the studies included at least one measure of the target problem. In addition, though, 78% of all studies included at least one measure of nontarget problems or symptoms (e.g., a measure of depressed mood or self-esteem obtained in a study of ADHD treatment), 28% of all studies included at least one functioning measure (e.g., school grades or disciplinary incidents), 8% included at least one consumer satisfaction measure, and 5% included at least one measure of the environmental impact of treatment, broadly construed (e.g., maternal depression or parenting stress assessed in a study testing behavioral parent training for child conduct problems). Clearly, the range in types and content of measurement is broad across studies. Equally clearly, though, questions such as how would the participants evaluate the treatment and what impact did treatment have on everyday functioning in school or at home have not been of primary interest to most treatment researchers over the years.

Informants and Technology Employed in Assessment Table 3 lists the informants used, and the measurement technology employed, in assessing participants and their treatment outcome. It is evident from the table that the youths themselves are highly favored as informants when anxiety and depression are the treatment targets, but are much less likely to be informants in studies focused on ADHD and conduct problems. Because treatment of ADHD and conduct problems focuses on outwardly observable behavior, the treated youths’ own

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YOUTH PSYCHOTHERAPY RESEARCH TABLE 3

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Measurement: II. Sources of information and measurement technology∗


All Anxiety Depression ADHD Conduct studies Sources of information in measures used Studies using treated youth as reporter Studies using parent as reporter Studies using sibling as reporter Studies using peers as reporters Studies using teachers as reporters Studies using therapist as reporter Studies using other reporters/observers Measurement technology Studies using self-report Studies using other report Studies using behavior counts Of these, total using observer ratings Of these, total using blind raters Of these, total in which youth was not aware of or able to influence rating Studies using independent life event data ∗

76.83 28.05 0 1.22 13.41 9.76 71.95

94.44 55.56 0 0.00 5.56 5.56 77.78

37.50 25.00 0 2.50 37.50 5.00 87.50

47.92 35.42 0 4.17 42.71 6.25 79.17

59.75 32.63 0 2.54 28.81 7.63 77.97

75.61 43.90 69.51 85.97 46.94 30.61

94.44 83.33 38.89 85.70 33.33 100.00

35.00 50.00 90.00 94.44 38.24 20.59

64.58 80.21 51.04 95.92 46.81 78.72

65.68 62.71 63.14 76.56 44.12 47.79

8.54

5.56

2.50

26.04

14.41

All values are noted in percentages.

reports may be less critical to assessment than in cases of anxiety and depression, for which treatment focuses on internal processes that are hidden from view. Parents often have been tapped for their perspective, particularly in depression studies, whereas teachers were used as reporters most often in studies focused on the two externalizing clusters. Table 3 also shows heavy reliance on “other reporters,” which are generally clinical assessors and trained observers. Interestingly, in none of the 236 studies did we find any reliance on what might be a particularly well-informed source of information on treated youths: their siblings. A second aspect of Table 3 is its summary of the kinds of measurement technology employed in youth treatment research. Use of informants’ self-reports and collateral reports by others, typically accomplished via questionnaires and interviews, has been common. In addition, to an encouraging extent, there has been substantial use of what appear to be more objective approaches, including behavior counts and behavioral observation (e.g., heart rate, performance on a skill test, observer recordings of out-of-seat behavior and prosocial behavior), and independent life event data (e.g., arrests, school attendance records). Enthusiasm about this measurement approach is tempered somewhat by other data in the table: Of those studies that included behavioral observation, fewer than half reported the use of raters who were blind to the treatment condition of the youths they observed; this was the case across all studies and within each of the four problem clusters.

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Moreover, with the exception of depression studies, a substantial proportion of the studies using behavioral observation did so with youths who knew they were being observed and who could have intentionally influenced the ratings.


Types of Treatment Tested in the Evidence Base Table 4 summarizes the kinds of treatments that have been tested to date. It is clear that youth-focused treatments have been much more common than parentand family-focused treatments, even for the externalizing problems and disorders for which parent training programs are so well known. The table also makes it clear that behavioral or “learning-based” treatments are included in 8 to 10 times as many studies as insight-based approaches, broadly construed. The fact that insight-based methods are so poorly represented in the treatment of anxiety is especially interesting in light of the important historical role such treatments have had in relation to anxiety (e.g., Freud 1909). Of course, this historical emphasis was on the use of such methods, not on their scientific study. By contrast, the development of behavioral interventions was closely linked to the experimental tradition, and that linkage is reflected, no doubt, in the extensive representation of behavioral methods within the youth treatment outcome research literature, as shown in Table 4.

Treatment Characteristics: Dose, Format, Participants, Homework, Integrity In addition to the theoretical models and therapeutic procedures associated with tested treatments, it is useful to examine treatment strength or “dose,” the format and participants involved, and other aspects related to the intensity and care with which interventions were carried out. Table 5 provides information relevant to these issues. The upper portion of the table shows that the average treatment involved 11 to 12 one-hour sessions, spanning 8 to 9 weeks, and totaling about 13 hours of contact. The lengthiest treatments have tended to be those addressing conduct problems and depression. Data on treatment formats show that individual treatment has been more common than group treatment for ADHD, but that the reverse has been true for depression and conduct problems. Youths themselves are by far the most frequent participants in treatment (included within treatment sessions in 83% of studies), but parents often have been involved (25%), as have entire families (18%), and, more rarely, teachers (8%). Table 5 also outlines the steps taken to build mastery by treated individuals and to support faithful delivery of treatments by therapists. Client homework assignments have been a part of treatment in a majority of studies focused on anxiety (65%), depression (83%), and conduct problems (59%), but less so in ADHD treatment trials (30%); only 40% of the full study set reported any use of homework. Data on the steps taken to foster treatment integrity revealed mixed support. Only 52% of studies reported the use of a treatment manual, but an additional 33% described treatments that we rated as “structured”—e.g., outlining the content covered in

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TABLE 4

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Theoretical orientation and type of treatment provided∗


All Anxiety Depression ADHD Conduct studies Child-focused treatments Studies w/learning-based approaches Studies w/operant treatment Studies w/respondent treatment Studies w/modeling treatment Studies w/CBT treatment Studies w/social skills treatment Studies w/multiple or other approaches Studies w/insight-based approaches Studies w/client-centered treatment Studies w/psychodynamic treatment Studies w/gestalt treatment Studies w/multiple or other approaches Studies w/eclectic approaches

90.24 3.66 25.61 13.41 43.90 4.88 13.41

77.78 0 11.11 5.56 66.67 11.11 0

90.00 7.50 15.00 15.00 52.50 2.50 17.50

45.83 9.38 2.08 0 26.04 8.33 5.21

71.19 6.36 13.14 7.63 39.83 6.36 9.75

7.32 7.32 0 0 1.22

11.11 0 0 0 11.11

2.50 2.50 0 0 0

10.42 4.17 4.17 0 2.18

8.05 4.66 1.69 0 2.12

9.38

4.66

1.22

5.56

0

Parent-focused treatments Studies w/learning-based approaches Studies w/insight-based approaches Studies w/eclectic approaches

1.22 0 0

0 0 0

7.50 0 0

18.75 0 2.08

9.32 0 0.85

Family-focused treatments Studies w/learning-based approaches Studies w/insight-based approaches Studies w/systems-based approaches Studies w/eclectic approaches

2.44 0 0 0

5.56 5.56 0 0

0 0 0 0

10.42 2.08 1.04 0

5.51 1.27 0.42 0

Teacher-focused treatments Studies w/eclectic approaches

0

0

0

1.04

0.42

4.17 0 1.04 1.04 1.04 1.04 5.21

5.93 2.12 2.12 0.42 0.42 0.85 2.12

Multiple target/multisystem treatments Studies w/child + parent Studies w/child + family Studies w/child + parent + family Studies w/child + teacher Studies w/child + parent + teacher Studies w/parent + teacher Studies w/child + parent + family + teacher ∗

4.88 3.66 3.66 0 0 0 0

11.11 5.56 5.56 0 0 0 0


ADHD, attention deficit/hyperactivity disorder; CBT, cognitive behavior therapy.

10.00 2.50 0 0 0 2.50 0

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Treatment characteristics: dose, format, participants, homework, integrity∗


All Anxiety Depression ADHD Conduct studies Treatment dose Mean number of sessions Mean number of weeks Mean length of sessions in minutes Mean total hours of treatment

9.61 6.69 53.01 9.96

12.26 9.47 67.53 14.33

9.24 5.79 42.15 8.59

14.38 11.53 64.98 17.61

11.44 8.58 57.35 12.92

Format of treatment sessions Studies using individual sessions Studies using group sessions Studies using individual + group Studies not reporting format

45.12 45.12 6.10 6.10

33.33 61.11 11.11 16.67

62.50 22.50 7.50 12.50

31.25 52.08 7.29 18.75

41.53 45.34 7.20 13.14

Treatment participants Studies involving youths in sessions Studies involving parents in sessions Studies involving families in sessions Studies involving teachers in sessions Studies w/any homework assigned

97.56 17.07 14.63 4.88 64.63

88.89 27.78 27.78 5.56 83.33

90.00 17.50 7.50 10.00 30.00

66.67 33.33 23.96 10.42 59.38

83.05 24.58 18.22 8.05 40.25

Steps to support treatment integrity Studies w/pretreatment therapist training Studies w/supervision/adherence checks Studies using treatment manuals Studies reporting structured treatments

26.83 24.39 47.56 35.37

66.67 66.67 72.22 16.67

22.50 32.50 52.50 45.00

34.38 32.29 52.08 29.17

32.20 32.20 52.12 33.05

∗

All values (except those listed under Treatment dose) are noted in percentages.

ADHD, attention deficit/hyperactivity disorder.

sessions. Only 32% of the studies noted any pretreatment training for therapists, and only 32% noted any use of supervision procedures or adherence checks. This is another area where depression studies as a group surpassed studies in the other three areas; 67% of depression studies employed pretreatment training, 67% used supervision procedures or adherence checks, and 89% used either treatment manuals or structured treatments. It is possible that treatment adherence support was higher than we could detect, if some authors simply failed to note these aspects of their procedure. Nevertheless, the evidence presented in the articles we reviewed raises concerns about the level of fidelity of actual treatments to the protocols tested. This, in turn, suggests the possibility that some of the interventions delivered may have been weaker than, or at least different from, what the treatment developers and researchers intended.

Treatment Groups and Control Groups Table 6 shows that mean sample size across all trials in our review was 22 for treatment groups and 21 for control groups. The highest means, 30 and 31

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TABLE 6

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Treatment and control groups∗ Anxiety

Depression

ADHD

Conduct

All studies

Mean sample size of treatment groups

18.23

30.41

12.38

26.31

21.95

Mean sample size of control groups

16.78

31.41

11.66

24.36

20.62

Types of control groups Studies using no treatment/waitlist Studies using attention/placebo Studies using medication placebo Studies using standard case management

64.63 39.02 0 4.88

77.78 27.78 0 0

42.50 70.00 0 0

64.58 29.17 0 14.58

61.86 39.41 0 7.63

∗

All values (except mean sample sizes) are noted in percentages.

respectively, for treatment and control groups in depression trials, still fall well below the standard of 50 cases per treatment/control group noted by Chambless & Hollon (1998; based on Cohen 1988). Table 6 also shows that the most common type of control group employed in youth trials is waitlist or no treatment. The one exception to this rule is the research base on ADHD treatment. For ADHD studies alone, control groups involving attention and/or placebo interventions were actually more common than no treatment or waitlist control groups (70% versus 43%).

Clinical Representativeness of the Evidence Base Table 7 shows the extent to which the treatment research is congruent with clinical practice along three different dimensions. In general, levels of clinical representativeness in the evidence base are low. About 13% of the study samples were actually treatment-seeking or clinically referred youth; about 19% of the studies employed at least one practicing clinician (although for many of these studies, practitioners were a minority of the therapists used); only about 4% of the studies provided treatment within a clinical service setting. Clinical representativeness was highest, across all three dimensions, for studies treating depression and conduct problems. Anxiety studies were least representative of clinical practice conditions. Perhaps the most complete picture of representativeness is presented by the figures at the bottom of the table, which show that only about 1% of the total study set was rated as representative across all three dimensions.

OVERVIEW AND CRITIQUE OF THE EVIDENCE BASE, AND SUGGESTIONS FOR IMPROVING FUTURE RESEARCH This methodological review of the evidence base has highlighted several trends, and some important limitations, in youth outcome research, with respect to four broad problem clusters.

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Clinical representativeness of the studies: youths, therapists, and settings∗


All Anxiety Depression ADHD Conduct studies How youths were enrolled in the study Recruited, not treatment-seeking Treatment-seeking, clinic-referred Required via court/justice system Studies not reporting

90.24 3.66 1.22 4.88

77.78 16.67 0 5.56

87.50 12.50 0 0

60.42 19.79 17.71 2.08

76.69 12.71 7.63 2.97

Who provided the treatment With any researchers/grads With any paraprofessionals With any practicing clinicians Studies not reporting

57.32 20.73 1.22 28.05

47.06 11.11 55.56 11.11

45.00 12.50 10.00 40.00

38.54 22.92 30.21 19.79

47.21 19.49 18.64 25.42

Setting where treatment took place Research settings Clinical service settings Correctional settings Studies not reporting

50.00 2.44 1.22 46.34

44.44 5.56 0 50.00

42.50 0 0 55.00

48.96 7.29 7.29 37.50

47.88 4.24 3.39 44.49

38.89 50.00 5.56 5.56

77.50 22.50 0 0

55.21 34.38 8.33 2.08

70.76 24.15 3.81 1.27

Representativeness sum (youths, therapists, and settings) Reporting no representativeness factors 92.68 Reporting one representativeness factor 7.32 Reporting two representativeness factors 0 Reporting all three representativeness 0 factors ∗


From an historical perspective, a kind of era effect is evident. The year of publication data we presented showed that controlled research on problems within the anxiety, ADHD, and conduct clusters borders on the ancient, with randomized trials in each domain found as early as the mid 1960s. By contrast, the first published randomized trial for youth depression appeared two decades later, in 1986, and the median year of publication for depression trials was 1997. This trend may suggest something about the power of theories to dictate targets of study. As Hammen & Rudolph (1996) note, early psychoanalytic theory held that depression in children was an impossibility because the superego was not sufficiently well developed to direct aggression against the self (see also Rochlin 1959). Even in the late 1970s, writers not associated with psychoanalytic theory also argued against the existence of depression in children (Lefkowitz & Burton 1978). In the early 1980s, views shifted, and evidence was assembled that appeared to support the existence of a depression syndrome in children and adolescents (see Cantwell & Carlson 1983). With the relatively recent “discovery” of depression in young people, treatment research has begun, but it may be decades before there is a substantial body of work. Interestingly, however,

HISTORICAL ERAS AND THE INFLUENCE OF THEORIES

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one effect of the delayed start in the depression domain appears to be that the methodology used in depression studies as a whole is superior to that in other domains in several important respects, as we noted above. This review also illustrates the fact that high levels of interest, theory, and literature devoted to a particular theoretical perspective on dysfunction do not automatically translate into high levels of intervention research. Consider anxiety, for example. It has the oldest history of theoretical and clinical attention of any of the youth problems (see, e.g., Freud 1909), with a great deal of the early work reflecting psychoanalytic thinking and other psychodynamic models. Yet, we came up empty in our search for randomized clinical trials of psychodynamic treatments for anxiety in youth. Clearly, theory and research do not invariably stimulate one another. On the other hand, when theory is closely linked to research, or when research is embedded in the value system of a theory, the picture may be very different. This may help to explain the overwhelming number of randomized trials testing learning-based treatments, in contrast to the much lower numbers for other theoretical orientations. Although this review yielded a substantive picture of treatment research, one particularly striking finding was the very spotty sample description across studies. Indeed, some potentially important information that would have been readily available at the time studies were done is now lost, with no opportunity for recovery. As an example, race and ethnicity were not reported for the samples in about 60% of the studies. Gaps of this sort can hamper efforts to understand how well treatments work across a range of human characteristics. Experts in minority mental health have expressed serious concerns about whether evidence from ethnic minority groups has in fact supported evidence-based treatments. The minority supplement to the U.S. Surgeon General’s report on mental health (U.S. Department of Health and Human Services 2001) noted that the ethnicity of a substantial percentage of participants was not identified in treatment outcome studies. Our figures on the youth treatment research literature support this concern: 60% of the published studies in our review failed to provide ethnicity data. Chambless et al. (1996), reporting on the work of a task force on empirically supported treatments, commented that they did not know of any psychotherapy treatment research that met basic criteria for demonstrating treatment efficacy for minority populations. Our review suggests that this strong conclusion may not (any longer) hold for youth treatment research. The percentages of studies in our pool that did report sample ethnicity point to substantial representation of African American youth (28%), and at least some representation of Latino youth (5%). However, because of the many studies not reporting ethnicity at all, it is difficult to know whether those figures are representative of most outcome research. A similar point can be made about socioeconomic factors. Some 72% of the studies provided no information of any kind about income or SES, thus limiting our ability to determine whether findings are relevant across the socioeconomic spectrum.

ASSESSMENT AND DOCUMENTATION OF SAMPLE CHARACTERISTICS

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A closely related concern is the need to test moderators of treatment outcome. Many have stressed the need to identify moderators and to define the range within which beneficial treatments work, and have noted the failure of most studies to take this step (e.g., Durlak et al. 1995, Kazdin 2000, Kazdin et al. 1990, Weisz 2004). Our review suggests that the critical first step toward moderator assessment—i.e., collecting information on participant characteristics that might moderate effects— has not been taken in a remarkably large percentage of studies for some of the potential moderators that might be considered prime suspects and in which there is considerable societal interest (see previous paragraph). A related problem noted in our review is the high percentage of studies in which no data were reported for important variables such as who carried out the treatment and in what setting the treatment took place. Given the potential value of all these types of information to the field, and the lost opportunities for moderator assessment once data sets are no longer available, it seems desirable to encourage greater consistency in the kinds of information required by journals prior to acceptance of manuscripts for publication. If journal editors could agree on the contents, a simple checklist of information that must be included in the manuscript seems a relatively simple step to add to the review process (e.g., the Consolidation of the Standards of Reporting Trials statement for medication trials, Moher et al. 2001). IDENTIFICATION OF PROBLEMS AND DISORDERS TO BE TARGETED IN TREATMENT PARTICIPANTS Turning to the procedures investigators used in their studies, we

begin with one of the earliest and most critical steps in a clinical trial: determining whether potential participants do in fact have the problem or diagnosis that is to be targeted in treatment. Our figures (see Table 2) indicate that 75% of all the studies did not confirm their participant selection by obtaining a formal diagnosis for any individual in their sample, and that even among those that did take this step, about half did not use a reliable, standardized diagnostic assessment procedure. Instead, many used unstructured, nonstandardized approaches, including diagnoses based on clinician judgment, an approach that has been found to show little agreement with standardized diagnostic assessment (see, e.g., Jensen & Weisz 2002). Of course, a very reasonable alternative to diagnosis—a preferred alternative, in some respects—is the use of a cutoff on some standardized continuous symptom or problem measure, but our data show that more than 80% of studies did not take that step either. Given that more than 60% of studies did not report either a diagnostic procedure or a cutoff procedure for sample identification, it appears that we lack a precise picture of the target disorders or problems, or their severity, in most of the outcome evidence base. Indeed, it seems possible that a significant number of the youths treated did not actually meet criteria for the conditions targeted in the treatment. The significance of this procedural gap in so many youth treatment outcome studies is highlighted by Chambless & Hollon’s (1998) comment that “. . . if psychological treatment outcome research is to be informative, researchers must have clearly defined the population for which the treatment was designed and tested.

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Thus, we do not ask whether a treatment is efficacious; rather, we ask whether it is efficacious for a specific problem or population” (p. 9). As Chambless & Hollon’s comment implies, showing that a particular treatment had beneficial effects in a treatment trial may be of limited value if individuals in the trial were selected (a) via procedures of unknown validity, and (b) in ways that cannot be replicated in future applications. In the search for evidence-based treatments, a critical task is designing research in ways that permit matching of treatments to the groups for whom they have been shown to work. This matching process does not appear to be well supported in the evidence base to date. Closely linked to the process of identifying youths who fit study criteria is the process of building a sample with adequate power to detect treatment effects. Chambless & Hollon (1998), citing Cohen (1988), have noted that for investigators to achieve 80% power to detect a medium difference between two groups in a treatment trial, sample size needs to be about 50 participants per condition. By these standards, the average youth psychotherapy trial has been substantially underpowered. As Table 6 shows, mean sample size across all trials in our review was 22 for treatment groups and 21 for control groups.

RESEARCH DESIGN: SAMPLE SIZE

Beyond statistical power, another factor that is closely linked to prospects for finding treatment effects is the type of treatment-control comparison investigators create in their studies. Our data (Table 6) show that the most common form of treatment-control group comparison employed in youth trials is the weakest experimentally: active treatment versus inactive waitlist or no treatment. Such passive control groups control only for the passage of time. Active control groups that control for time, attention, and nonspecific therapy/relationship factors were evident in fewer than 40% of the studies we reviewed, and standard case management as a control condition appeared in fewer than 8%. The one exception to this rather poor report card on treatment-control comparisons was the research base on ADHD treatment. For ADHD studies alone, active control groups involving attention and/or placebo interventions were more common than no treatment or waitlist control groups (70% versus 43%). It is possible, though speculative, that this reflects the close attention paid by ADHD treatment researchers to psychopharmacology trials (e.g., of stimulant medication), which must meet Food and Drug Administration standards; such trials often involve active treatment versus medication placebo conditions with participants blind to their condition.

RESEARCH DESIGN: NATURE OF TREATMENT-CONTROL COMPARISONS

RESEARCH DESIGN: NATURE OF THE TREATMENTS TESTED AND SUPPORT FOR TREATMENT INTEGRITY Our findings on the nature of the treatments that are compared

to control conditions do nothing to mitigate the concerns of previous reviewers (e.g., Durlak et al. 1995, Kazdin et al. 1990) that behavioral and cognitivebehavioral treatments are disproportionately represented in the youth clinical trials literature. In studies featuring individual youth-focused treatments, 71% involved

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learning-based interventions whereas only 8% involved insight-based approaches, broadly construed, and 9% involved eclectic approaches. A similar disproportion was evident in studies featuring parent-focused treatments and in studies employing family-focused treatments. Clearly, much ground remains to be covered in the future if the evidence base is to shed light on the impact of nonbehavioral interventions, which are in fact much more widely practiced outside research settings than the behavioral approaches that are so extensively tested. Our review also characterizes the tested treatments in ways other than their theoretical basis. For example, we established that mean treatment duration was about 11 sessions, spanning about 9 weeks, and entailing about 13 hours of contact. These numbers varied considerably across the different problem clusters, with ADHD treatment averaging about 9 hours and conduct problem treatment about 18. Overall, use of group sessions (employed in 45% of studies) now appears to be as common as use of individual sessions (42%), and youths are the most common participants (involved in 83% of studies), with parents a distant second (25%). With so much attention given in current literature to the use of structured treatment procedures that involve participant homework (see, e.g., Chambless et al. 1996, Weisz 2004), it was interesting to note that only 40% of studies included any mention of homework assignments. Finally, because the validity of any test of a treatment depends significantly on the faithfulness with which the treatment is delivered, we looked for evidence of fidelity support in study procedures. The results were not encouraging. Only 52% of studies reported using any form of a treatment manual, only 32% reported any pretraining of therapists, and only 32% reported either therapist supervision or adherence checks. Of course, this pattern could reflect incomplete reporting in some studies. However, it may also suggest that some of the research base entails tests of treatment models that were only weakly or erratically implemented by study therapists. MEASUREMENT AND OUTCOME ASSESSMENT: INFORMANTS AND TECHNOLOGY EMPLOYED A somewhat more encouraging picture emerged from our focus on mea-

surement. Although the youths participating in studies are popular as informants (used in 60% of all studies), significant percentages of the studies derived information from parents (33%), teachers (29%), and others (78%), including clinician raters and trained observers. The frequency with which self-report measures were used (66%) was almost matched by the frequencies for other-report measures (63%), objective behavior counts (63%), and behavioral observations by raters (58%, although the raters were blind to treatment condition of the youth in only 44% of the studies). In general, measurement strategies appear to draw from a healthy diversity of informants and to entail some use of respectable strategies for minimizing subjectivity and bias. A significant challenge for treatment research is identification of the range and limits of treatment impact.

RANGE OF OUTCOMES ASSESSED FOLLOWING TREATMENT

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To meet this challenge may require outcome assessment that goes beyond the specific problems and disorders targeted by treatments. Indeed, in a conceptual model proposed by Hoagwood et al. (1996), a case is made for outcome assessment that includes not only target problems/symptoms and disorders, but also measures of such dimensions as real-world functioning, consumer perspectives, and impact on individuals and systems that relate to the treated youth. Given our criteria for the review, 100% of the studies included at least one measure of the target problem, symptoms, or disorder (we excluded many studies that did not measure what they set out to treat). In addition, 78% of all studies included at least one measure of nontarget problems or symptoms (e.g., a measure of depressed mood or self-esteem obtained in a study of ADHD treatment), 28% of all studies included at least one functioning measure (e.g., school grades or disciplinary incidents), 8% included at least one consumer satisfaction measure, and 5% included at least one measure of the environmental impact of treatment, broadly construed (e.g., parenting stress, assessed in a study testing behavioral parent training for child conduct problems). These numbers suggest that outcome assessment that transcends problems, symptoms, and diagnoses is not unheard of in the literature, but that such assessment is also not common practice.

A final issue, about which our group has written frequently, is the clinical representativeness of study procedures. Our concern has been that a capacity to develop treatments that work well in actual clinical practice, and to determine how well they work in practice, may depend in part on the extent to which the characteristics of clinical trials resemble the characteristics of practice. Of course, clinical representativeness can involve multiple dimensions. Our analysis in this review has focused on three dimensions that seem particularly important—i.e., clinical representativeness of the youths sampled, the therapists who provide treatment, and the settings in which treatment is provided. Our findings suggest that the research base is not very clinically representative with respect to any of these three dimensions considered separately, and only 1% of the studies showed representativeness across all three dimensions—i.e., including at least some clinically referred children, some practicing clinicians, and some treatment in a clinical service setting. These limits on external validity of the evidence base illustrate the concern that has led to a proposed new model of intervention development and testing. This “deployment-focused model” (Weisz 2004, Weisz et al. 2004) acknowledges the need for initial efficacy evidence to establish the potential for treatment benefit. However, the model calls for such initial efficacy evidence to be followed in short order by research directed increasingly toward the kinds of individuals, interveners, and settings for which the intervention program is ultimately intended. The current state of the evidence base suggests that research conducted without reference to this model has used participants, therapists, and settings that depart markedly from those most germane to clinical practice.

CLINICAL REPRESENTATIVENESS OF STUDIES

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CONCLUDING COMMENT In conclusion, we stress that the evidence base shows some notable strengths but also some significant limitations. Our hope is that through periodic efforts to take stock of that base, investigators may identify ways in which refinement can contribute to the complementary challenges of improved intervention research and improved treatments for youths and families.


ACKNOWLEDGMENTS We are grateful to Amie Bettencourt, Vickie Chang, Brian Chu, Jen Durham, Samantha Fordwood, Dan Fulford, Eunie Jung, and Robin Weersing for the important roles they played in this project, and to the John D. and Catherine T. MacArthur Foundation for its support of the project. The authors were also supported by the National Institutes of Mental Health (R01 MH 547347 and R01 MH 068806 to JRW, NRSA F31 MH65811 to AJD, and NRSA F31 MH12853 to KMH), by San Diego State University’s Oscar Kaplan Postdoctoral Fellowship (KMH), and by the Department of Health and Human Services, Health Resources and Services Administration (Training Grant 1 D40 HP00017-01 to AJD), for which we express our thanks. The Annual Review of Psychology is online at http://psych.annualreviews.org

LITERATURE CITED Cantwell DP, Carlson GA, eds. 1983. Affective Disorders in Childhood and Adolescence New York: Spectrum Casey RJ, Berman JS. 1985. The outcome of psychotherapy with children. Psychol. Bull. 98:388–400 Chambless DL, Hollon SD. 1998. Defining empirically supported therapies. J. Consult. Clin. Psychol. 66(1):7–18 Chambless DL, Sanderson WC, Shohan V, Bennett Johnson S, Pope KS, et al. 1996. An update on empirically validated therapies. Clin. Psychol. 49:5–18 Compton SN, Burns BJ, Egger HL, Robertson E. 2002. Review of the evidence base for treatment of childhood psychopathology: internalizing disorders. J. Consult. Clin. Psychol. 70(6):1240–66 Durlak JA, Fuhrman T, Lampman C. 1991. Effectiveness of cognitive-behavior therapy for

maladapting children: a meta-analysis. Psychol. Bull. 110(2):204–14 Durlak JA, Wells AM, Cotten JK, Johnson S. 1995. Analysis of selected methodological issues in child psychotherapy research. J. Clin. Child Psychol. 24(2):141–48 Dush DM, Hirt ML, Schroeder HE. 1989. Selfstatement modification in the treatment of child behavior disorders: a meta-analysis. Psychol. Bull. 106(1):97–106 Eysenck HJ. 1952. The effects of psychotherapy: an evaluation. J. Consult. Psychol. 16:319–24 Farmer EMZ, Compton SN, Burns JB, Robertson E. 2002. Review of the evidence base for treatment of childhood psychopathology: externalizing disorders. J. Consult. Clin. Psychol. 70(6):1267–302 Freud S. 1955. Analysis of phobia in a five-yearold boy. In Standard Editions of the Complete

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YOUTH PSYCHOTHERAPY RESEARCH Psychological Works of Sigmund Freud, Vol. 10, pp. 3–149. London: Hogarth Hammen C, Rudolph KD. 1996. Childhood depression. In Child Psychopathology, ed. EJ Mash, RA Barkley, pp. 153–95. New York: Guilford Hoagwood K, Jensen PS, Petti T, Burns BJ. 1996. Outcomes of mental health care for children and adolescents: I. A comprehensive conceptual model. J. Am. Acad. Child Adolesc. Psychiatry 35(8):1055–63 Jensen AL, Weisz JR. 2002. Assessing match and mismatch between practitioner-generated and standardized interview-generated diagnoses for clinic-referred children and adolescents. J. Consult. Clin. Psychol. 70(1): 158–68 Jones MC. 1924. A laboratory study of fear: the case of Peter. Pedagog. Semin. 31:308–15 Kazdin AE. 2000. Psychotherapy for Children and Adolescents: Directions for Research and Practice. Oxford, UK: Oxford Univ. Press Kazdin AE, Bass D, Ayers WA, Rodgers A. 1990. Empirical and clinical focus of child and adolescent psychotherapy research. J. Consult. Clin. Psychol. 58(6):729–40 Lefkowitz M, Burton N. 1978. Childhood depression: a critique of the concept. Psychol. Bull. 85:716–26 Levitt EE. 1957. The results of psychotherapy with children: an evaluation. J. Consult. Clin. Psychol. 21:189–96 Levitt EE. 1963. Psychotherapy with children: a further evaluation. Behav. Res. Ther. 60:326– 29 McLeod BD, Weisz JR. 2004. Using dissertations to examine potential bias in child and adolescent clinical trials. J. Consult. Clin. Psychol. 72:235–5l Moher D, Schulz KF, Altmann DG. 2001. The CONSORT statement: revised recommendations for improving the quality of reports of parallel group randomized trials. BMC Med. Res. Method. 1:2

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Prout HT, DeMartino RA. 1986. A metaanalysis of school-based studies of psychotherapy. J. Sch. Psychol. 24(3):285– 92 Rochlin G. 1959. The loss complex. J. Am. Psychoanal. Assoc. 7:299–316 Shirk SR, Russell RL. 1996. Change Processes in Child Psychotherapy: Revitalizing Treatment and Research. New York: Guilford US Department of Health and Human Services. 2001. Mental Health: Culture, Race, and Ethnicity—A Supplement to Mental Health: A Report of the Surgeon General. Rockville, MD: US Dept. Health Hum. Serv., Subst. Abuse Mental Health Serv. Admin., Cent. Mental Health Serv. Weisz JR. 2004. Psychotherapy for Children and Adolescents: Evidence-Based Treatments and Case Examples. Cambridge, UK: Cambridge Univ. Press Weisz JR, Hawley KM. 1998. Finding, evaluating, refining, and applying empirically supported treatments for children and adolescents. J. Clin. Child Psychol. 27(2):206– 16 Weisz JR, Jensen AL, McLeod BD. 2004. Milestones and methods in the development and dissemination of child and adolescent psychotherapies: review, commentary, and a new deployment-focused model. In Psychosocial Treatments for Child and Adolescent Disorders: Empirically Based Strategies for Clinical Practice, ed. ED Hibbs, PS Jensen. Washington, DC: Am. Psychol. Assoc. 2nd ed. In press Weisz JR, Weiss B, Alicke MD, Klotz ML. 1987. Effectiveness of psychotherapy with children and adolescents: a meta-analysis for clinicians. J. Consult. Clin. Psychol. 55(4): 542–49 Weisz JR, Weiss B, Han SS, Granger DA, Morton T. 1995. Effects of psychotherapy with children and adolescents revisited: a metaanalysis of treatment outcome studies. Psychol. Bull. 117(3):450–68

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PROSOCIAL BEHAVIOR: Multilevel Perspectives Louis A. Penner


Karmanos Cancer Institute/Family Medicine, Wayne State University, Detroit, Michigan 40202, and Research Center for Group Dynamics, University of Michigan, Ann Arbor, Michigan 48109; email: [email protected]

John F. Dovidio Psychology Department, University of Connecticut, Storrs, Connecticut 06269; email: [email protected]

Jane A. Piliavin Department of Sociology, University of Wisconsin, Madison, Wisconsin 53706; email: [email protected]

David A. Schroeder University of Arkansas, Fayetteville, Arkansas 72701; email: [email protected]

Key Words altruism, cooperation, helping ■ Abstract Current research on prosocial behavior covers a broad and diverse range of phenomena. We argue that this large research literature can be best organized and understood from a multilevel perspective. We identify three levels of analysis of prosocial behavior: (a) the “meso” level—the study of helper-recipient dyads in the context of a specific situation; (b) the micro level—the study of the origins of prosocial tendencies and the sources of variation in these tendencies; and (c) the macro level—the study of prosocial actions that occur within the context of groups and large organizations. We present research at each level and discuss similarities and differences across levels. Finally, we consider ways in which theory and research at these three levels of analysis might be combined in future intra- and interdisciplinary research on prosocial behavior.

CONTENTS PROSOCIAL BEHAVIOR: MULTILEVEL PERSPECTIVES . . . . . . . . . . . . . . . . . . Meso Level of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Micro Level of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Macro Level of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Within- and Between-Group Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FUTURE DIRECTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integrative Understanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prosocial Behavior and Ongoing Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0365$14.00

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PROSOCIAL BEHAVIOR: MULTILEVEL PERSPECTIVES This chapter provides a comprehensive overview of current theory and research on prosocial behavior among humans. Prosocial behavior represents a broad category of acts that are defined by some significant segment of society and/or one’s social group as generally beneficial to other people. Attention to prosocial behavior in psychology originated with McDougall (1908), who argued that prosocial behavior is the result of “tender emotions” created by the parental instinct, but most current research has its roots in lay and scientific reactions to the nonresponsive bystanders in the brutal murder of Katherine “Kitty” Genovese in 1964. Since then, it has evolved to encompass a broad range of biological, motivational, cognitive, and social processes (see Dovidio & Penner 2001 and recent Annual Review of Psychology articles by Caporael 2001 and Eisenberg 2000). In light of these recent and continuing developments, we believe that it is time to examine prosocial behavior from a multilevel perspective that recognizes the diverse influences that promote actions for the benefit of others, considers the variety of ways in which prosocial behavior can be manifested, and explicates both the common and the unique processes that underlie prosocial acts across the different levels of analysis. Our organization differs in many respects from that found in chapters on prosocial behavior in many social psychology textbooks and social psychology handbooks (e.g., Batson 1998), as well as in related works in sociology (e.g., Piliavin & Charng 1990). Specifically, we examine prosocial behavior from three distinct, but related, levels of analysis: micro, meso, and macro. Research at the micro level of analysis is primarily concerned with the origins of prosocial tendencies in humans (e.g., neural or evolutionary bases) and the etiology of individual differences in these tendencies. The meso level of analysis refers to studying the behaviors of helper-recipient dyads within the context of a specific situation; helping at this level has been the traditional focus of psychological work on prosocial behavior (see Dovidio & Penner 2001). The macro level of analysis focuses on prosocial actions that occur within the context of groups and large organizations (e.g., volunteering, cooperation). The chapter concludes by briefly considering future directions and questions that remain to be answered about prosocial behavior at each of the three levels. We begin our review by revisiting the original research questions that first spawned interest in helping behavior.

Meso Level of Analysis Research at the meso level of analysis examines helping at the interpersonal level: one person helping another. Because this has been the traditional focus of research on helping in social psychology and relatively extensive reviews on this topic are available (e.g., Schroeder et al. 1995), we consider this level of prosocial behavior first to establish a benchmark from which to extend our presentation, but are relatively brief in our coverage.

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Much of the work at the meso level of analysis, particularly from the mid 1960s until the early 1980s, investigated when people would help in emergency and nonemergency situations. Later research and theory, in the 1980s and 1990s, considered why people help, examining processes that motivated prosocial action. The most recent developments in the field have expanded the scope of this perspective to examine nonconscious and intergroup influences on helping. To organize the large number of research findings that were accumulating in the 1960s and 1970s, general frameworks were developed that modeled the decision process that determines whether individuals will intervene. The first of these, Latané & Darley’s (1970) decision model of bystander intervention, proposed that whether or not a person renders aid depends upon the outcomes of a series of prior decisions that involve recognizing the situation as one requiring assistance, deciding to take personal responsibility, and deciding how to help. Although the model was initially developed to understand how people respond in emergencies requiring immediate assistance, aspects of the model have been successfully applied to many other situations, ranging from preventing someone from driving drunk to making a decision about whether to donate a kidney to a relative (Schroeder et al. 1995). The cost-reward analysis of helping (Piliavin et al. 1981) assumed an economic view of human behavior—people are motivated to maximize their rewards and to minimize their costs. From this perspective, people are relatively rational and primarily concerned about their self-interest. In an emergency, potential helpers analyze the circumstances, weigh the probable costs and rewards of alternative courses of action, and then arrive at a decision that will result in the best personal outcome for them. Research findings are consistent with the central tenet of the cost-reward approach. Situational factors that make bystander interventions more likely to occur include those that decrease the net costs of helping (e.g., by framing helping as an opportunity for personal development; Perlow & Weeks 2002), increase potential rewards of helping (e.g., by enhancing mood; Gueguen & De Gail 2003), or increase the costs of not helping (e.g., by inducing guilt or shame for inaction) (Dovidio et al. 1991).


WHEN PEOPLE HELP

Although these approaches effectively modeled whether people would help in a given situation, research in the 1980s and 1990s moved to the question of why people engage in prosocial behavior. In general, approaches to the question of why people help focused on three types of mechanisms: (a) learning, (b) social and personal standards, and (c) arousal and affect. The learning explanation applied general principles from learning theories, particularly operant conditioning and social learning, to the acquisition of helping skills and of beliefs about why these skills should be used to benefit others (Grusec et al. 2002). Socialization experiences (Staub 2002) and developmental factors (Eisenberg & Fabes 1991) received considerable attention within this framework. The social and personal standards approach emphasized how norms such as social responsibility

WHY PEOPLE HELP

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and reciprocity (Dovidio 1984) can promote helping as people strive to maintain positive self-images or achieve their ideals (Schwartz & Howard 1982) and fulfill personal needs (Omoto & Snyder 1995). This perspective contributed to the shift in emphasis from spontaneous, single-encounter helping to longer term, sustained prosocial behaviors such as volunteering, and thus contributed to the emergence of the macro level of analysis. Arousal and affect approaches recognized the important role that emotion plays in motivating prosocial action. Affect is a fundamental element of many potential helping situations. People are aroused by the distress of others; this reaction appears even among very young children and occurs across cultures (see Eisenberg & Fabes 1991). Moreover, arousal and affect theories generally shared a guiding principle with learning theory that people are motivated to behave in ways that help them attain some goal—improving the person’s own situation (egoistic motivation) or, in some cases, improving the welfare of another person (altruistic motivation). Although most researchers agree that empathic arousal is fundamental to many kinds of helping (Davis 1994), there is much less agreement about the nature of this emotion and how it actually motivates people to help. Empathic arousal may produce different emotions. In severe emergency situations, bystanders may become upset and distressed (Piliavin et al. 1981); in less critical, less intense problem situations, observers may feel sad (Cialdini et al. 1987), tense (Hornstein 1982), or concerned and compassionate (Batson 1991). How arousal is interpreted can shape the nature of prosocial motivation. Feeling upset, personally distressed, guilty, or sad produces egoistically motivated helping with the goal of relieving one’s own negative emotional state (Batson 1991, Cialdini et al. 1997, Piliavin et al. 1981). Feelings of empathic concern, such as sympathy and compassion, arouse altruistic motivation with the primary goal of improving the welfare of the person in need (Batson 1991). Although there is continuing debate about the role of feelings of “oneness” with the recipient of help, self-other merging, and negative self-directed emotions as potential factors underlying some of the empathy-altruism findings (see Maner et al. 2002), the preponderance of evidence indicates that, at least under some specific conditions, altruism can occur among humans (Batson 1998). Over the years, research at the meso level has evolved in two new directions. One involves processes related to the micro level of analysis, whereas the other is more closely related to the macro level. We consider these developments in the next section. The work more closely related to the micro level has built on significant recent interest in implicit cognition and how processes outside conscious awareness can influence behavior. This work has examined the effects of implicit cognitions on helping. For example, van Baaren et al. (2004) and Garcia et al. (2002) have found that a wide variety of primes can affect the likelihood that a person will offer help. The line of research more related to the macro level analysis uses theories of intra- and intergroup behavior to investigate how perceived group memberships

NONCONSCIOUS AND INTERGROUP INFLUENCES

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influence helping. An extensive body of research based on these theories has consistently revealed a strong favoritism bias toward members of one’s own group as opposed to members of other groups (Hewstone et al. 2002, Mullen et al. 1992). Hornstein and his colleagues (e.g., Flippen et al. 1996) have demonstrated that the effects of common group membership increases helping beyond the dyadic effects of interpersonal similarity or attraction. They proposed that factors such as similarity or common fate might give rise to a sense of “we-ness”—a sense of belonging to a common group. This sense of we-ness (analogous to self-other merging) facilitates empathy, which, in turn, leads to more prosocial behaviors. Emotional appeals reflecting a person’s need for assistance that emphasize ingroup status can also effectively increase helping (Vaes et al. 2002). In a direct test of the influence of social categories on helping, Dovidio et al. (1997) found that inducing a “common group identity” (Gaertner & Dovidio 2000) increased helping toward others formerly perceived as outgroup members. Although factors associated with interpersonal helping, such as liking and empathy, were related to helping, only social recategorization as members of a common group fully mediated the effect of the manipulation on helping. Recent research by Sturmer et al. (2004) demonstrated further evidence of the distinction between personal and group processes in helping. They found that because attachment to the person in need is an important factor in the arousal of empathic concern, empathy is a stronger factor determining helping a member of the ingroup than a member of the outgroup. However, interpersonal factors, such as attraction, are stronger predictors of helping for an outgroup member than for an ingroup member (for whom attraction is often depersonalized).

Micro Level of Analysis Whereas much of the work at the meso level of analysis was stimulated by the question of why people often do not help others in need (Darley &Latané 1968), scholars who studied the origins of prosocial tendencies and individual differences in those tendencies were initially puzzled by the fact that a behavior they thought should not occur (i.e., helping another person at some sacrifice to oneself) occurred quite frequently. Answers as to why this should be have involved evolutionary theory, biological and genetic bases of action, developmental processes, and personality factors. Whereas social psychologists working at the meso level have defined altruism in terms of motivation, evolutionary theorists have defined it in terms of consequences. Contemporary neo-Darwinian models of evolution, which define evolutionary success as the survival of one’s genes in subsequent generations, generally agree that prosocial tendencies exist in humans because of (a) genetically based predispositions to act prosocially, and (b) the evolutionary success of people who displayed such predispositions (see Barrett et al. 2002, Buss 2003, Dawkins 1989). The three evolutionary processes or mechanisms most

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commonly proposed to explain why prosocial acts lead to evolutionary success are kin selection, reciprocal altruism, and group selection. Kin selection Kin selection is based on the premise that what matters in evolution is not individual fitness, but inclusive fitness, which is the successful transmission of one’s genes from all sources to the next generation (Hamilton 1964). As a consequence, there is an evolutionary benefit in terms of inclusive fitness to those who regularly help their relatives. Much of the empirical work on kin selection has focused on the relationship between “relatedness” (i.e., the percentage of genes two individuals share) and willingness to help. Several studies have shown that humans are more inclined to help relatives than unrelated individuals (see Barrett et al. 2002). Data that are even more persuasive have come from studies in which predictions derived from kin selection were essentially “pitted” against social norms and rules for helping. For example, Burnstein et al. (1994) found, consistent with kin selection theory but contrary to the norm of social responsibility, both Americans and Japanese reporting that in a “life-or-death” circumstance (e.g., saving someone from a fire) they would be more likely to help healthy relatives (who presumably were more likely to reproduce) than nonhealthy relatives. Thus, in this simulation participants helped their kin in a manner that maximized their own inclusive fitness (see also Wang 2002). Other researchers have made more elaborate and refined predictions based on how helping of relatives would affect inclusive fitness. For example, Euler & Weitzel (1996) found that paternal certainty (the likelihood that a putative descendant is actually related to you) caused maternal grandparents to invest considerably more than paternal ones in their grandchildren. Webster (2003) replicated this finding, but also found that the effects of paternal certainty were greatest when the benefactor had limited resources. Other research on kin selection has focused on learning more about the proximal mechanisms that are responsible for kin selection—how the presumed genetic tendencies are translated into behaviors. Korchmaros & Kenny (2001) demonstrated that emotional closeness partially mediated the effects of genetic relatedness on willingness to help. Kruger (2003) replicated the effect of kinship on helping intentions but found that proximal mechanisms, such as empathic concern and a sense of oneness with the target, did not mediate this relationship. Thus, the search for the proximal mechanisms that make people more likely to help relatives continues. Reciprocal altruism The concept of reciprocal altruism was proposed to explain the evolutionary advantages of helping unrelated individuals. According to Trivers (1971), humans derive some evolutionary benefit from helping unrelated others if this favor is repaid in kind. Systematic investigations of reciprocal altruism as an explanatory mechanism in human prosocial behavior have been less frequent and explicit than have investigations concerned with kin selection. Some of this

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research has examined how strangers play zero-sum Prisoner’s Dilemma games and found, for example, that reciprocal or “tit-for-tat” strategies, in which people respond in kind to their partner’s choice on the previous trial, produce greater payoffs for the players than any other strategy (Axelrod 1984). Other indirect evidence in support of the notion that reciprocal altruism is a genetic expression is the fact that the norm of reciprocity (Gouldner 1960) apparently exists in every culture in the world (Schroeder et al. 1995), and reciprocity does provide benefits that might add to a person’s evolutionary success. For example, people are more likely to help those who offer help (Boster et al. 2001), and offering help increases one’s status and reputation among members of one’s community (Wedekind & Braithwaite 2002). At least among males, higher status has been associated with greater desirability as a mating partner (Buss 2003). Some researchers have postulated that the reproductive advantages afforded by high status may also explain phenomena such as strong reciprocity or altruistic punishment— cooperation with cooperating others and punishment of noncooperators—even when the costs of cooperating and/or punishing are not likely to be recouped (Gintis et al. 2003). Costly-signaling theory (Grafen 1990) also uses status gains to explain another seemingly counterintuitive phenomenon—people providing large benefits to others when they know these actions will not be reciprocated. McAndrew (2002) proposes that conspicuous displays of unreciprocated generosity may provide information to others about the benefactors that will enhance perceptions of their reputation and status within the group, because only people with considerable resources would engage in such “generous” behaviors. Although these explanations of the origins of altruism are widely accepted, they are not without their critics. Caporael (2001), for example, has noted there are other, less male-oriented, explanations of the evolutionary gains that prosocial acts may provide. One of these is considered next. Group selection The final mechanism proposed to explain the evolutionary benefits of altruism is group level selection (Sober & Wilson 1999). The group-level selection position argues that if two groups are in direct competition with one another, the group with a larger number of altruists (i.e., people willing to sacrifice themselves for the group) will have an advantage over a group comprised mainly of selfish individuals. Thus, the altruistic group would dominate the selfish group and derive a reproductive advantage over them. At a population level, the number of phenotypic (and presumably genotypic) altruists would therefore increase relative to selfish individuals. Although early versions of group selection theory placed it somewhat in opposition to individual-level selection theories (e.g., kin selection), more recent versions allow selection to occur at both levels (multilevel selection theories; McAndrew 2002). Group selection theory has yet to receive much direct empirical support. However, as noted above, Caporael (2001) has provided a persuasive argument for its role in the evolution of altruism among humans.

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Although evolutionary theory is controversial and has its critics (e.g., see Rachlin 2002), it has generated considerable research in this area and has stimulated other productive directions of inquiry. In particular, the proposal that prosocial tendencies are passed from generation to generation via genes has two specific implications. The first is that there must be some physiological or neurological processes that facilitate prosocial behaviors. The second is that at least some of the processes that facilitate prosocial responses are inherited. We consider recent theory and research relevant to these implications in the next section. There have been some recent attempts to explain the neuroanatomy and neurochemistry of prosocial actions. Although the explanatory models differ in many respects, they share the underlying assumption that in most instances people do not reflexively act prosocially, but rather that some physiologically based affective or motivational state precedes prosocial actions. The models then attempt to specify the processes or mechanisms responsible for these states. Buck (1999, 2002), for example, focused on biological “affects”—feelings and desires that have an innate neurochemical basis—and posited that there are prosocial and selfish kinds of affects. He suggests that the left hemisphere is more strongly associated with prosocial emotions and feelings than is the right. He argues that these emotions facilitate positive communication among members of a species and result in cooperative exchanges that confer an evolutionary advantage to the exchange partners. Buck thus posits, “communicative genes (rather than kin selection or reciprocal altruism) underlie genuine altruism” (Buck 2002, p. 742). However, Buck’s ideas have been strongly criticized by Gray (2002), who argued that there is relatively little empirical support for the proposal that hemispheric differences are reflected in prosocial and selfish affects. Rather, Gray claims that brain systems related to approach and withdrawal tendencies might provide a more parsimonious explanation of the hemispheric differences in affects. Other attempts to understand the biological mechanisms that underlie prosocial actions have focused on a more specific affective process, empathy—the ability to discern and vicariously experience the emotional state of another being. It generally is agreed that empathic responses precede many (but certainly not all) prosocial acts. Empathic responses are found in most species and are present among human infants shortly after birth (Preston & de Waal 2002), suggesting that such responses are innate. Recently, Preston & de Waal (2002) have put forth a more elaborate model of the neuroanatomy of empathic responses. In their perception action model, they propose that if one attends to another person’s “state,” this automatically activates one’s “representations of that state,” which, in turn, automatically “primes or generates the associated autonomic and somatic responses, unless inhibited (by learning or experience)” (Preston & de Waal 2002, p. 4). Like Buck, Preston & de Waal question the assumption that kin selection and reciprocal altruism are the core processes responsible for the evolution of prosocial tendencies. Instead, they


BIOLOGICAL AND GENETIC BASES OF PROSOCIAL ACTIONS

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argue that because humans spend so much time with their relatives and people who have mutual interests with them, the evolutionary benefits of helping them are the result of a “highly adaptive nervous system organization” (p. 6). That is, the predisposition to experience empathy drives helping and other prosocial actions. This perspective is controversial, criticized for its assumptions about the neuroanatomy and neurochemistry that underlie empathic responses (e.g., Eslinger et al. 2002), its biologically reductionistic orientation (e.g., Bard 2002), and its oversimplification of complex social processes (e.g., Eisenberg 2002). Although Buck’s and Preston & de Waal’s frameworks have yet to subjected to direct empirical tests (e.g., neural imaging), these theories offer intriguing hints of how the architecture of the human nervous system can predispose people toward prosocial actions and further implicate the genetic basis of prosocial behavior. Although there is no singular “altruistic gene,” there are certain affective and behavioral capacities or predispositions that are products of certain complex combinations of genes. There has been work on the heritability of tendencies associated with prosocial actions and the relative roles of genetic and environmental influences on prosocial tendencies. With respect to heritability, most of the research has used some variant of twin methodologies to estimate the percentage of differences among people in empathy that could be attributed to genetic influences. The various studies agree that heritability plays some role in the variability among people with respect to at least self-reported empathy (e.g., Davis et al. 1994), but the estimates vary widely, from well over 60% to less than 30%. On the other hand, Krueger et al. (2001) found little evidence of a genetic cause of differences in selfreported prosocial behaviors, but they did not study empathy or other prosocial thoughts/feelings. Developmental psychology, with its interest in the origins and the causes of individual differences in prosocial responses to others, represents an important component of the micro level of analysis of prosocial behaviors. In the past 10 to 15 years, developmental theories regarding prosocial tendencies have moved from a rather strong environmental bias toward models that focus on the interplay between biologically based tendencies and socialization experiences. A great deal of attention has focused on temperament, which refers to broad ways of responding to one’s environment that appear very early in life and are assumed to have a very strong genetic component. No one seriously argues that there is such a thing as a prosocial temperament; rather it has been proposed that these affective and behavioral dispositions interact with other classes of variables to produce individual differences in prosocial tendencies. This perspective has found its clearest expression in the work of Eisenberg (e.g., Eisenberg et al. 2000). For example, Eisenberg has focused on how emotionality interacts with other variables to affect empathy and other prosocial responses in children. Children who are inclined toward positive emotionality tend to be more prosocial. Whether children inclined toward negative emotionality are more or less prosocial depends on the specific kind of negative emotions they are feeling

DEVELOPMENT OF PROSOCIAL TENDENCIES

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and their ability to regulate their emotions. Eisenberg’s work suggests that children may actually show an increase in prosocial responses when they are sad or anxious if they have the ability not to let these emotions overwhelm them. However, if they cannot regulate these emotions, sadness and anxiety will result in decreases in prosocial actions (e.g., Grusec et al. 2002). The shift to more biological models of the origins of differences in prosocial tendencies has not led developmentalists to discount environmental factors entirely. For example, Eisenberg et al. (2000) and Zhou et al. (2002) reported that children whose mothers are empathic, warm, good at perspective taking, and comforting are themselves highly empathic. Further, developmental psychology pays substantial attention to socialization processes in their explanations of the growth of prosocial responses as people mature (e.g., Grusec 1991). For example, in a longitudinal study, Hastings et al. (2000) found that an authoritative parenting style was associated with more prosocial behaviors two years later. Among the micro-level analyses of prosocial behaviors, the developmental one is the first to explicitly address (a) the interplay of “nature” and “nurture” in the development of these behaviors, and (b) the development of individual differences in prosocial tendencies. Given these emphases, it should not be surprising that many developmentalists have attempted to identify stable personal characteristics associated with differences in prosocial thoughts, feelings, and actions. This leads us to the last major area in the micro level of analyses of prosocial behaviors, the influence of personality. The idea that children display stable differences in empathy and other prosocial tendencies has been widely accepted for a fairly long time. A more recent perspective, however, is that these tendencies are relatively stable across a person’s life. Studies have revealed that there is longterm consistency in temperament and personality in general (Caspi et al. 2003) and in prosocial dispositions in particular. For example, Eisenberg et al. (2002) found stability in prosocial dispositions across five years in a sample of young adults, and they found that these dispositions were related to ratings of empathy and prosocial responding taken when the adults were young children. Atkins et al. (2004) used a “type” approach to classify children as resilient, overcontrolled, and uncontrolled, and found that resilient children were more likely to be volunteers ten years later than were the other two types. Additional research has focused on other personal attributes and their relationship to prosocial behaviors. Graziano & Eisenberg (1997) and others (e.g., Ashton et al. 1998) have argued that variability in the Agreeableness dimension from the Big Five theory of personality might result in differences in people’s propensity to act prosocially. Consistent with this proposition, Graziano et al. (2004) found that Agreeableness interacted with situational variables (e.g., ingroup versus outgroup status) to affect prosocial behaviors in three different experimental situations. Other personality traits strongly associated with Agreeableness also have been shown to correlate with prosocial actions. For example, Davis and colleagues found that

PERSONALITY AND PROSOCIAL BEHAVIOR

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dispositional differences in empathic tendencies manifest themselves in differences in prosocial behaviors such as donating and volunteering (Davis 1994, Davis et al. 1999). Building on prior work on the personality correlates of helping, Penner and his associates (Penner et al. 1995) have focused their attention on traits they believe comprise the “prosocial personality.” Factor analyses of these traits suggest that there are two dimensions to the prosocial personality. The first concerns prosocial thoughts and feelings, such as a sense of responsibility and a tendency to experience cognitive and affective empathy (“other-oriented empathy”). This first factor correlates substantially with measures of Agreeableness (Graziano et al. 2004, Penner et al. 1995) and dispositional empathy, and it overlaps substantially with the collection of personality traits Eisenberg and her associates (2002) believe to be associated with prosocial behaviors. The second factor is the self-perception that one is a helpful and competent individual (Helpfulness). At a conceptual level, the two factors are quite consistent with the Oliners’ (1988) description of individuals who rescued Jews during the time of the holocaust and with Colby & Damon’s (1992) description of 23 individuals whom they identified as lifelong altruists or moral exemplars. Penner and others have demonstrated significant associations between these clusters of prosocial dispositions and prosocial actions, ranging from interpersonal helping to volunteering to helpful actions directed toward coworkers and the organizations for which people worked (e.g., Borman et al. 2002, Eisenberg et al. 2002, Penner 2002, Unger & Thumuluri 1997). These findings suggest there may well be a constellation of traits that form a prosocial personality that is consistently related to a broad range of prosocial behaviors. However, it is still not clear why these attributes lead to prosocial responses.

Macro Level of Analysis We begin the section on the macro level by discussing prosocial behaviors performed by individuals within an organizational context, with our major focus on volunteering and related behaviors. The second part of this section explores the dynamics of cooperation, in which the outcomes of individuals are mutually interdependent and assistance to others has direct implications for personal and collective outcomes. Volunteering involves prosocial action in an organizational context, which is planned and that continues for an extended period (Penner 2002). It differs from interpersonal helping in many ways, but the most important of these is that, relative to interpersonal helping, volunteering is less likely to result from a sense of personal obligation (Omoto & Snyder 1995). That is, whereas most acts of interpersonal helping involve a sense of personal obligation to a particular person, volunteering to work for a charity or service organization is typically not motivated by such considerations. Given this aspect of volunteering and the fact

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that it usually begins with a thoughtful decision to join and contribute to an organization, it is not surprising that research on the reasons why people volunteer has taken a very different tack than has research on interpersonal helping. We begin with research on the decision to volunteer. The 2000 issue of the Annual Review of Sociology contained a contribution solely concerned with volunteering (Wilson 2000). Not surprisingly, Wilson and other sociologists focus attention on the social institutions and demographic factors that are associated with volunteering. With regard to social institutions, the family and religious organizations play important roles in the initial decision to volunteer. With regard to the former, people are more likely to volunteer and commit more strongly to these activities if their parents have also been volunteers (Piliavin 2004a, Sundeen & Raskoff 1995). Turning to religion, among both youths and adults, there is a positive association between affiliation with some religion and being a volunteer (Lam 2002, Reed & Selbee 2000, Uslaner 2002). Although volunteering for one’s church, synagogue, or mosque is the most common form of volunteering, at least in the United States (Independent Sector 2002, U.S. Department of Labor 2003), people who identify more strongly with an organized religion also have a higher incidence of other types of volunteer activities (Penner 2002, Piliavin 2004a). Volunteering is also strongly associated with level of education and income (Independent Sector 2002). One possible explanation of these findings is that better educated, wealthier people have more free time to donate because they are less constrained by their jobs [e.g., salaried, rather than hourly, employees (Wilson & Musick 1997b)]. The problem with this explanation, however, is that in the United States, the United Kingdom, Canada, and Australia, volunteerism is substantially more common among employed than unemployed individuals (Independent Sector 2002, Institute for Volunteering Research 1997, Reed & Selbee 2000, Volunteering Australia 2003). Thus, the relationship between economic status and volunteering cannot simply be explained by the amount of free time, but may involve other factors, such as more awareness of the problems of others, greater empathy for their distress, and an expectation of greater effectiveness (Wilson 2000). Another explanation of these findings is that people at upper socioeconomic levels, especially those who also are active members of some religion, are better integrated into and involved with their communities than are people at the lower levels. More active involvement and participation in the civic and social activities of one’s community, as well as more positive moral and civic attitudes, have been shown to be strongly associated with volunteering in both the United States and Canada (Hart et al. 2002, Reed & Selbee 2000). The more involved people are in their community, the more likely they are to be asked to volunteer and then to agree when asked. Sex and ethnicity also are related to volunteering. In North America, women are somewhat more likely to be volunteers than are men (Independent Sector 2002, Reed & Selbee 2000), but this difference is not found in Europe or Australia


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(Volunteering Australia 2003, Wilson 2000). However, across these specific locations there are sex differences in the kinds of work volunteers do. For example, women are less likely to be found in leadership positions (Wilson 2000). In the United States, members of ethnic minorities (e.g., African Americans and Hispanic/Latino Americans) historically have been less likely to volunteer than have European Americans, although the percentage of African Americans who reported volunteering increased dramatically in the last Independent Sector survey (Independent Sector 2002). Part of the reason for these ethnic differences is probably social exclusion (Ferree et al. 1998). Moreover, when education, income, and other socioeconomic factors are controlled, these racial/ethnic differences largely disappear (Latting 1990). Psychologists who study why people volunteer have, not surprisingly, focused, much more than have sociologists, on how the personality and needs of potential volunteers and the social situations they confront affect volunteering. For example, Davis et al. (1999) found an association between dispositional empathy and willingness to engage in certain kinds of volunteer activities. Both Penner and his associates (e.g., Penner 2002) and Hart and his associates (Atkins et al. 2004, Hart et al. 2004) have demonstrated that a cluster of personality dispositions (which include empathy) plays a significant role in the decision to volunteer. Recently, Hart and his associates (2004) have proposed an innovative model that attempts to integrate both sociological and psychological approaches to volunteering. Using data from a national survey, they presented evidence that both personality factors and social structures (e.g., family, culture) played roles in the incidence of volunteering, but this relationship was mediated by intrapersonal cognitive processes (e.g., attitudes, identity, commitment to ideals) and the richness of people’s social networks. Similarly, Wilson & Musick (1997a) presented a model in which both volunteering and informal helping are predicted from demographic variables and various kinds of “capital,” including “human capital” (education, income, functional health), “social capital” (number of children, social interaction patterns), and “cultural capital” (religiosity and valuing helping). Volunteering can be a long-term activity, and thus the processes relating to the maintenance of the activity need to be considered. There have been two major theoretical models of what factors sustain volunteering over an extended period of time: Omoto & Snyder’s (1995, 2002) volunteer process model, and the role identity model proposed by Piliavin and her associates (Grube & Piliavin 2000, Piliavin & Callero 1991). The models agree on many points (e.g., the inclusion of organizational variables) and are not really “competitors” for the best explanation of sustained volunteer activities, but they diverge somewhat in both focus and emphasis. Omoto & Snyder’s model appears to give greater attention to intrapersonal variables (specifically motives), whereas the Piliavin et al. model is more concerned with social roles and the social context in which volunteering occurs.

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Omoto & Snyder’s volunteer process model sees sustained volunteerism as being primarily determined by the extent to which there is a match between the motives or needs that originally led the person to volunteer and that person’s actual experiences as a volunteer. Snyder and his associates and other researchers (e.g., Independent Sector 2002, Institute for Volunteering Research 2002, Omoto & Snyder 2002, Reed & Selbee 2000, Snyder et al. 2000) have found that volunteers claim that they were, at least initially, most motivated by other-oriented or prosocial motives. However, additional evidence suggests that volunteering can also be motivated by less selfless motives, such as advancing one’s career or developing social relationships (Clary et al. 1998, Clary & Snyder 1999). Omoto & Snyder’s model also posits that prosocial dispositions, social support for the volunteer’s activities, satisfaction with the volunteer experience, and integration with the organization play important roles in sustained volunteering. Consistent with this framework, personal motives, social and organizational support, and satisfaction with the volunteer experience are critical factors in sustaining volunteer activity (Kiviniemi et al. 2002, Penner & Finkelstein 1998, Vecina 2001). The two key constructs in Piliavin et al’s (2002) role identity model are perceived expectations (i.e., beliefs about how significant others feel about the person’s behavior) and role identity [i.e., the extent to which a particular role (e.g., being a volunteer) becomes part of the person’s personal identity (Grube & Piliavin 2000)]. The model posits that perceived expectations lead to becoming a volunteer, but organizational variables (e.g., prestige of the service organization) and the experiences and behaviors associated with actually volunteering facilitate the development of a volunteer role identity, which is the immediate precursor of sustained volunteering. Grube & Piliavin (2000) have conducted the most direct test of this model and found that perceived expectations and organizational attributes (e.g., prestige) were associated with the development of a volunteer role identity, and this identity was significantly associated with intentions to continue volunteering and number of hours devoted to the organization (see also Penner & Finkelstein 1998). Models of sustained volunteerism have been successfully applied to organizational citizenship behavior (OCB). An OCB is a voluntary “extrarole” behavior; that is, it is not part of the worker’s formal job requirements, and there is no explicit or formal demand that the worker engage in OCB (Brief & Weiss 2002). Rioux & Penner (2001) found that OCB motives were significantly associated with self and peer ratings of OCB, and an OCB role identity has been shown to be significantly related to self, peer, and supervisor ratings of a target person’s level of OCB (Finkelstein & Penner 2004, Krueger 2004). Another potential extension of the volunteering models has been suggested by Piliavin et al. (2002), who considered how role identities might influence another kind of prosocial behavior, “principled organizational dissent”—efforts by an organization’s employee to protest or change current organizational policies or

RELATED ORGANIZATIONAL BEHAVIORS

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practices because they violate some personal standard of justice or honesty. The clearest example of a principled organizational dissent might be whistle-blowing by a government official or organizational employee. Grube et al. (2004), for example, found that nurses were more likely to report healthcare errors when they had a strong role identity, when they felt more valued, and when observed error rates were high. Volunteering, civic service, and OCB are all strongly encouraged and praised because they are intended to improve the well being of other individuals and of society in general. Recently some researchers have begun to investigate whether there are effects of these prosocial actions on the people who perform them. At the micro level of analysis, researchers have generally assumed the benefits to a helper for prosocial action in terms of increased inclusive fitness, higher status, and certain reproductive advantages. At the meso level of analysis, there traditionally has been more interest in the causes of helping than in its consequences. Thus, there were relatively few meso-level studies concerned with the personal consequences of helping for the helper. In general, successful helping is experienced positively, in part because it can relieve a negative affective state caused by witnessing another’s problem and thus is a reinforcing event (Piliavin et al. 1981). Unsuccessful attempts at helping can have an adverse effect (Rosen et al. 1987). There is considerably more research at the macro level about the possible benefits of prosocial actions to the person who engages in them. Much of this research has used survey methodologies, and therefore one must be cautious about inferring specific causes and effects. Researchers have expected that volunteering by adolescents will benefit the children by enhancing self-esteem and psychological well-being, improving social and career skills, and increasing prosocial attitudes, values, and identities. Further, volunteering should reduce the incidence of dangerous and antisocial behaviors and increase community participation as adults. Although there is some support for a positive impact on self-esteem (Yogev & Ronen 1982) and academic achievement (Osguthorpe & Scruggs 1986), overall support is mixed. Some research finds that service learning (i.e., educational programs that involve volunteering) positively affects personal efficacy, self-esteem, and confidence (Giles & Eyler 1994, Yates & Youniss 1996), but Lee (1997) found no such effects among first-year college students. High levels of volunteering among adolescents appear to lead to the development of prosocial attitudes, values, and identities, and a greater probability of volunteering when they become adults (Astin et al. 1999, Reidel 2002), which is consistent with the assumption that volunteering has a positive developmental impact (Hansen et al. 2003, Johnson et al. 1998). There is also evidence for the impact of adolescent volunteering on dangerous and antisocial behaviors. Several well-controlled cross-sectional and longitudinal studies provide evidence that high school students who engage in community service are less likely to smoke marijuana, abuse alcohol, perform

THE POTENTIAL CONSEQUENCES OF VOLUNTEERING

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poorly in school, become pregnant, commit delinquent acts, or be arrested (Barber et al. 2001, Eccles & Barber 1999, Moore & Allen 1996, Uggen & Janikula 1999, Youniss et al. 1997). The other major line of research on the consequences of volunteering has focused on whether it affects the psychological and physical health of adult volunteers. Thoits & Hewitt (2001) carried out analyses showing that the amount of volunteering in 1986 was positively related to volunteers’ well-being and psychological and physical health in 1989. Using the same data set, Musick & Wilson (2003) found that volunteering was related to a subsequent decrease in depression, but only among adults over 65. Studies have also revealed greater self-reported well-being among elderly volunteers (Morrow-Howell et al. 2003). Some of the most impressive data from studies of elderly volunteers concern the relation of volunteering to physical health and mortality. For example, both Moen et al. (1992) and Oman et al. (1999) have followed adult volunteers over many years and found that they were healthier and lived longer than nonvolunteer groups (also see Brown et al. 2003). These striking findings beg the question of what processes or mechanisms underlie these effects. Midlarsky (1991) proposed that helping others may benefit the helper because it distracts one from one’s own troubles, enhances a sense of value in one’s life, improves self-evaluations, increases positive moods, and causes social integration. Oman et al. (1999) suggested that several of Midlarsky’s proposed mechanisms could influence the body through psycho-neuro-immunologic pathways, thus reducing mortality in aging populations. Consistent with this reasoning, Oman et al. found that the negative relationship between volunteering and mortality got stronger with increasing age. In the same vein, the positive psychological and physical outcomes associated with volunteering appear to be greatest for those who are otherwise socially isolated (Musick & Wilson 2003, Musick et al. 1999, Piliavin 2004b). If alienation from society is indeed unhealthy, then volunteering may serve to reduce this alienation. Finally, volunteering could also contribute to a sense of control and efficacy, which can provide protection against morbidity and mortality (see Rodin & Langer 1977), possibly by improving functioning of the autoimmune system (Wiedenfeld et al. 1990). In summary, volunteering is a form of prosocial behavior that can extend across time. It provides considerable benefits to others, even when the specific recipients of aid are personally unidentifiable. Volunteering also can have significant psychological and physical benefits for the volunteer. In the next section we examine another form of prosocial behavior at the macro level, one in which people are mutually interdependent. Cooperation differs from the kinds of prosocial actions discussed thus far in the following respect. In interpersonal helping (of the kind studied primarily at the meso level) and volunteering, the parties involved are typically not equal partners (that is, one person needs assistance and the other possesses the resources necessary to provide it). In contrast, cooperation involves two or more people coming together as partners to work

COOPERATION AND GROUP-LEVEL PROSOCIAL ACTION

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interdependently toward a common goal that will benefit all involved. Much of the current research on cooperation has been conducted in the context of social dilemmas (e.g., Komorita & Parks 1999, Weber et al. 2004). All social dilemmas have two fundamental characteristics: (a) each individual receives a higher payoff for not doing (i.e., defecting from) what is in the group’s best interest, and (b) all individuals are better off if they cooperate than if they defect. We consider below some of the factors that influence cooperation in social dilemmas. Individual differences Whereas research on individual differences in helping has examined a large number of personality variables, research on individual differences in reactions to social dilemmas has primarily focused on one set of characteristics—an individual’s social value orientation. The four most consistently identified social value orientations are: altruists, cooperators, individualists, and competitors (Liebrand et al. 1986). People who hold one of these four main social value orientations differ fundamentally in their preferences for allocating resources to themselves and others. For example, people classified as having a “prosocial” orientation (i.e., altruistic and cooperative) show greater concern for the common good than do individualists and competitors (“proself” orientation; van Lange et al. 1997). Prosocial motivations Batson has extended his work on the effects of prosocial and altruistic motivations from interpersonal helping to cooperation in a Prisoner’s Dilemma. Batson & Moran (1999) found that participants induced to experience empathic concern for their partner were more likely to cooperate than were those in a control condition. In another study, Batson & Ahmad (2001) found that people who experienced empathic concern tended to show high levels of cooperative responses even when they knew that their partner had already made a competitive choice. However, Batson et al. (1995) also reported that targeted help in a social dilemma might be given to a specific individual with whom a person has empathized, even when that help compromises cooperative action that would have benefited a larger group. Social influence Several variables that affect helping at the meso level also affect cooperation. For example, just as the presence of others inhibits bystander intervention in emergencies (Latané & Nida 1981), anonymity increases the incidence of free riding—that is, attempting to benefit from the group’s actions without personally contributing to the public good (Kerr & MacCoun 1985). Not surprisingly, overt communication among group members consistently promotes cooperation (Dawes 1988) by reducing the impact of pluralistic ignorance, coordinating actions of those involved, and helping in the development of closer personal relationships. Kerr et al. (1997) found that communicating a public commitment to cooperate led to greater subsequent cooperation, even if the person to whom the original commitment was made would not know if the commitment had been fulfilled (also see Kerr 1995, 1999).

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Social identity As noted in the section on meso-level helping, people consistently favor ingroup members over both outgroup members and those not identified by group membership. This is true even when the basis of categorization is random and would be nonconsequential outside of a laboratory context. Moreover, people are more likely to be cooperative and exercise more personal restraint when using endangered common resources if the resources are being shared with ingroup members rather than with outgroup members (Van Vugt et al. 2000). Furthermore, they work harder for and are more loyal to groups they identify as their ingroup (Van Vugt & Hart 2004). In society-level social dilemma situations, the impact of an individual’s identity with a group and the associated acceptance of the norms and values of those superordinate entities (including governmental agencies) has also been shown to be important for increasing a wide variety of cooperative actions, ranging from participation in social activism and protests (Klandersman 2000) to organizational citizenship (Cropanzano & Byrne 2000). The process of social categorization of people as ingroup and outgroup members is alterable, and even though people may continue to view others in terms of group membership, the perceptions of group boundaries may change as people become aware of higher-level categories (e.g., nations) that are more inclusive of lower-level ones (e.g., cities or towns). The process of changing perceptions of group boundaries, known as recategorization, has been the focus of considerable research in the common ingroup identity model (Gaertner et al. 2000). According to this model, intergroup bias and conflict can be reduced by factors that transform participants’ representations of memberships from two groups to one more inclusive group. As we noted earlier, recategorization of former outgroup members as members of a common ingroup increases helping. One explanation of why people may be more cooperative when common group membership is emphasized is that fairness and procedural justice increase in importance relative to personal outcomes (DeCremer & Tyler 2004). Thus, there are many parallels in the processes underlying actions in which one person helps another and in which a person responds prosocially toward a collective entity, through either volunteering or cooperation. However, research on cooperation has also identified emergent phenomena that occur uniquely in interdependent relations and do not have parallels in other kinds of prosocial actions.

Within- and Between-Group Cooperation Unlike helping, which is a unilateral and individual action, cooperation often involves relationships within and between groups. Thus, the within- and betweengroup distinction is a more basic and complex one in cooperation than in helping and involves processes—some quite fundamental—that are not necessarily key determinants of helping. One of the most important of these factors is trust (Kramer 1999). Because trust often is based on an expectation of reciprocity (Yamagishi & Kiyonari 2000), it has special significance in situations of interdependence (Chaudhuri et al. 2002). Establishing a reputation as being trustworthy can be a critical factor for eliciting cooperation in future interactions (e.g., Milinski et al.

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2001). Because ingroup members are attributed more positive characteristics than are outgroup members (Mullen et al. 1992), they are viewed as more distinctly individual than are outgroup members (Mullen & Hu 1989) and are seen as more similar to the self. Thus, people are generally more trusting of ingroup than of outgroup members (Turner et al. 1987) and are more likely to dismiss and forgive negative actions of ingroup than of outgroup members. Trust is also a critical factor in intergroup relations—that is, in interactions between groups. However, trust between groups is more difficult to achieve than trust between individuals. Insko, Schopler, and their colleagues have demonstrated a fundamental individual-group discontinuity effect in which groups are less trusting and less trustworthy in their interactions and exchanges with other groups than are individuals (Insko et al. 2001). In part as a consequence of greater distrust between groups than between individuals (Insko et al. 2004), groups are substantially less cooperative with one another than are individuals. Whereas replacing separate group identities with a common ingroup identity generally enhances prosocial interaction, maintaining separate group identities while simultaneously emphasizing common group membership at a more inclusive level can have conflicting effects. Hewstone & Brown (1986), for instance, have proposed that positive and generalizable intergroup consequences result when cooperative interactions between groups are introduced without redefining or degrading the original ingroup-outgroup categorization. However, others have suggested that introducing a common, superordinate group identity can threaten existing social identities, thereby arousing needs for positive distinctiveness and differentiation and leading people to assert their own group’s standards as the standards for the superordinate group (Waldzus et al. 2003). These forces tend to reduce prosocial relations between groups. Thus, emphasizing or creating common identity tends to increase cooperation between individuals, but it may increase competition between groups (Insko et al. 2004).

FUTURE DIRECTIONS The study of prosocial behavior has a long history in psychology in general and in social psychology in particular. However, perhaps because of the maturity of the area, there has been a decline in research attention to many traditional aspects of prosocial activity, that is, dyadic helping at the meso level. Yet, the study of prosocial behavior still has much to contribute to psychology and other disciplines. Although it may still be valuable to refine current focused theories about when and why people offer help, we believe that the best way to maximize new contributions at this time is to adopt a more comprehensive perspective to prosocial behavior. Two promising ways to do this involve (a) developing an integrative understanding of how certain cognitive, neurological, and genetic processes and mechanisms affect prosocial behavior across the three levels of analysis; and (b) conceptualizing prosocial behavior as an element within ongoing interpersonal and intergroup relations.

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Integrative Understanding One way to develop a broader and more integrative understanding of prosocial behavior might be to focus more attention on the proximal causes of prosocial actions. The personal tendencies, motives, cost-reward calculations, and responsiveness to situational demands that have been demonstrated to be critical to prosocial responding are not always consciously accessible. Thus, work on implicit cognitive processes that immediately precede social behaviors (e.g., Greenwald et al. 2002) might be useful for developing a more comprehensive understanding of when and why people do or do not act prosocially in interpersonal as well as in interdependent and group contexts. This research might also add to an understanding of how the prosocial dispositions posited by evolutionary theorists are translated into actual prosocial behaviors. Understanding the link between distal evolutionary causes and proximal causes of prosocial actions may also be enhanced by introducing to this area new research techniques, such as the rapidly advancing technology of neural imaging, which could help illuminate the relation between brain functioning and facilitating processes, such as cognitive and emotional empathy. Psychologists’ perspective on prosocial behavior could also be broadened by applying the techniques and principles of behavioral genetics more directly to this topic, illustrating how the interplay between genetic factors, life experiences, and situational influences affect this kind of behavior (see Caspi et al. 2002).

Prosocial Behavior and Ongoing Relations A second strategy may be to widen the “lens” through which prosocial behavior is viewed. Specifically, rather than consider helping, cooperation, and volunteering as end points, these behaviors may be conceived of as parts of ongoing processes. For example, research should continue on the possibly positive mental and physical benefits of prosocial actions. [Some preliminary experimental work in this area suggests that interpersonal helping may facilitate cardiovascular recovery (SL Brown, K Johnson, & B Fredrickson, personal communication, May 17, 2004)]. Future work could also consider the ongoing contribution of prosocial actions to interpersonal and intergroup relations. For instance, prosocial behavior may be an integral component of forgiveness, which is an important contributor to stable relationships (Ripley & Worthington 2002), and a key element of reconciliation, which strengthens collective identity and action (de Waal 2000). Another avenue for future research is to explore the factors that sustain cooperation within, as well as between, groups. Recent works by DeCremer & Tyler (2004), Schroeder et al. (2003), and Tyler & Blader (2003) have investigated ways in which formal, justice-based procedural systems can serve to maintain intragroup harmony. This linkage of prosocial behavior and justice research could have important implications for public and organizational policy development. Finally, future work might consider how different forms of prosocial behavior can function quite differently in intergroup contexts. Whereas successful

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cooperation, which involves mutual coordination and trust, may be a critical element in reconciliation and social integration between groups, helping behavior can be a tool that promotes social differentiation. The act of helping can be used to establish or reinforce perceptions of status differences between helpers and recipients, and helping over time could create a sense of lower status, dependency, and powerlessness among recipients (Nadler 2002, Nadler & Fisher 1986). Future research might thus focus on the effects of certain prosocial actions on social relations and stratification.

CONCLUSION The research presented in this chapter suggests that the study of prosocial behavior is still growing and expanding. The multilevel approach that we have taken allows the integration and synthesis of a broad range of questions, from ones relevant to evolutionary psychology, behavioral genetics, and neuroscience to those relevant to social psychology, developmental psychology, personality theory, industrial/organizational psychology, and the study of intra- and intergroup behavior. Because of its breadth, research on prosocial behavior is able to contribute to connections between psychology and other social science disciplines and can facilitate the development of interdisciplinary and multidisciplinary collaborations and perspectives for both theory and application. The Annual Review of Psychology is online at http://psych.annualreviews.org

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THE SOCIAL PSYCHOLOGY OF STIGMA Brenda Major and Laurie T. O’Brien


Department of Psychology, University of California, Santa Barbara, Santa Barbara, California 93105; email: [email protected], [email protected]

Key Words social identity, identity threat, stress and coping, stereotyping, prejudice, discrimination ■ Abstract This chapter addresses the psychological effects of social stigma. Stigma directly affects the stigmatized via mechanisms of discrimination, expectancy confirmation, and automatic stereotype activation, and indirectly via threats to personal and social identity. We review and organize recent theory and empirical research within an identity threat model of stigma. This model posits that situational cues, collective representations of one’s stigma status, and personal beliefs and motives shape appraisals of the significance of stigma-relevant situations for well-being. Identity threat results when stigma-relevant stressors are appraised as potentially harmful to one’s social identity and as exceeding one’s coping resources. Identity threat creates involuntary stress responses and motivates attempts at threat reduction through coping strategies. Stress responses and coping efforts affect important outcomes such as self-esteem, academic achievement, and health. Identity threat perspectives help to explain the tremendous variability across people, groups, and situations in responses to stigma.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCEPTUALIZING STIGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MECHANISMS OF STIGMATIZATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Negative Treatment and Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Expectancy Confirmation Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Automatic Stereotype Activation-Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stigma as Identity Threat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A MODEL OF STIGMA-INDUCED IDENTITY THREAT . . . . . . . . . . . . . . . . . . . . Collective Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Situational Cues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personal Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Identity Threat Appraisals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Involuntary Responses to Identity Threat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coping in Response to Threats to the Self . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OUTCOMES OF STIGMATIZATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Self-Esteem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Academic Achievement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0393$14.00

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Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411


INTRODUCTION Stigma is a powerful phenomenon with far-ranging effects on its targets (Crocker et al. 1998, Jones et al. 1984, Link & Phelan 2001). Stigma has been linked to poor mental health, physical illness, academic underachievement, infant mortality, low social status, poverty, and reduced access to housing, education, and jobs (Allison 1998, Braddock & McPartland 1987, Clark et al. 1999, Yinger 1994). Although psychologists have long been interested in the causes of stereotyping, prejudice, and discrimination, only recently have they focused in earnest on understanding the psychological effects of these processes. The roots of contemporary perspectives on stigma can be traced to Erving Goffman’s (1963) classic book Stigma: Notes on the Management of a Spoiled Identity. In the 1980s, theory and research began to challenge traditional perspectives on how stigma affects self-esteem (Crocker & Major 1989) and academic performance (Steele 1992, Steele & Aronson 1995), among other outcomes (Jones et al. 1984). This emphasis on the situational nature of stigma and the role of the self in responses to stigma stimulated an explosion of research. PsychInfo reveals a dramatic increase in the number of articles that mention stigma published in the period from 1965–1989 (N = 603) as compared to 1990–2004 (N = 2321). Reflecting the vibrancy of this growing field, review chapters on stigma and related processes appeared recently in the Handbook of Social Psychology (Crocker et al. 1998), Advances in Experimental Social Psychology (Major et al. 2002b, Steele et al. 2002), European Review of Social Psychology (Major et al. 2003b, Schmitt & Branscombe 2002, Stangor et al. 2003b), and the Annual Review of Sociology (Link & Phelan 2001). This chapter is the first on stigma to appear in the Annual Review of Psychology. Because of space restrictions, we were unable to review several important areas of research, such as the functions of stigma, the impact of stigma on social interaction, and the ways in which different characteristics of stigma (e.g., controllability, concealability, and entitativity) differentially influence psychological and behavioral reactions to those stigmas (see Crocker et al. 1998). We focus here on what stigma is, and how it affects the thoughts, feelings, behavior, and health of its targets.

CONCEPTUALIZING STIGMA According to Goffman (1963, p. 3), stigma is an attribute that extensively discredits an individual, reducing him or her “from a whole and usual person to a tainted, discounted one.” Crocker et al. (1998) proposed that stigmatization occurs when a person possesses (or is believed to possess) “some attribute or characteristic that

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conveys a social identity that is devalued in a particular social context” (p. 505). These definitions share the assumption that people who are stigmatized have (or are believed to have) an attribute that marks them as different and leads them to be devalued in the eyes of others. Stigmatizing marks may be visible or invisible, controllable or uncontrollable, and linked to appearance (e.g., a physical deformity), behavior (e.g., child abuser), or group membership (e.g., African American). Importantly, stigma is relationship- and context-specific; it does not reside in the person but in a social context. In stigmatization, “marks” become associated with “discrediting dispositions”—negative evaluations and stereotypes (Jones et al. 1984). These stereotypes and evaluations are generally widely shared and well known among members of a culture (Crocker et al. 1998, Steele 1997), and they become a basis for excluding or avoiding members of the stereotyped category (Leary & Schreindorfer 1998, Major & Eccleston 2004). Although both powerful and powerless groups may stereotype and negatively evaluate the other, because the former control access to resources, their beliefs are likely to prevail (Fiske 1993, Link & Phelan 2001). Furthermore, members of high-status and low-status groups enter situations with different understandings of the position of their group in the larger society. Thus, members of high-status and low-status groups are likely to respond in dramatically different ways to being the target of negative stereotypes and/or discrimination, even though the immediate situation seems the same (Branscombe 1998, Inzlicht & Ben-Zeev 2000, Schmitt & Branscombe 2002, Schmitt et al. 2002, Sekaquaptewa & Thompson 2002). Without reference to power, the stigma concept becomes overly broad. In short, stigma exists when labeling, negative stereotyping, exclusion, discrimination, and low status co-occur in a power situation that allows these processes to unfold (Link & Phelan 2001). Although each of these terms is often used interchangeably with stigma, stigma is a broader and more inclusive concept than any one of these processes. Most stigma scholars regard stigma as a social construction—a label attached by society—and point to variability across time and cultures in what attributes, behaviors, or groups are stigmatized (Crocker et al. 1998, Jones et al. 1984). Evolutionary scholars, however, point to commonality across cultures in what attributes are stigmatized. They propose that in order to avoid the potential pitfalls that accompany group living, humans have developed cognitive adaptations that cause them to exclude (stigmatize) people who possess (or are believed to possess) certain attributes. These are attributes that signal that (a) they are a poor partner for social exchange (e.g., a convict), (b) they might carry parasitic infection (e.g., a physical deformity), or (c) they are a member of an outgroup that can be exploited for ingroup gain (Kurzban & Leary 2001; see also Neuberg et al. 2000, Park et al. 2003). These three categories for exclusion map closely onto the categories of stigmatizing attributes proposed by Goffman (1963): blemishes of individual character, abominations of the body, and tribal stigma. Even if there are evolved mechanisms that precipitate exclusion of certain social categories across cultures, however, cultural beliefs can dictate which attributes within those categories are

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stigmatized and the specific content of the stereotypes that are attached to those attributes (Park et al. 2003). Nonetheless, stigmatized groups tend to be negatively stereotyped on the dimensions of competence and/or warmth in most cultures (Fiske 1998). Stereotyping people along these two dimensions may be functional; in order to survive, people need to know who is friend or foe (warmth) and who has higher status (competence).


MECHANISMS OF STIGMATIZATION In the following section, we describe four mechanisms by which stigma affects the stigmatized: (a) negative treatment and direct discrimination, (b) expectancy confirmation processes, (c) automatic stereotype activation, and (d) identity threat processes.

Negative Treatment and Discrimination By limiting access to important life domains, discrimination directly affects the social status, psychological well-being, and physical health of the stigmatized. Members of stigmatized groups are discriminated against in the housing market, workplace, educational settings, health care, and the criminal justice system (Crandall & Eshleman 2003; for an overview see Sidanius & Pratto 1999). They are even discriminated against in the family. The parents of heavyweight women, for example, are less likely to pay for their daughter’s college education than are the parents of average-weight women (Crandall 1995). Older adults are patronized by younger adults and discriminated against in the workplace (Nelson 2002). The low social status that results from discrimination may, in turn, engender further discrimination (Link & Phelan 2001). Accumulated institutional practices may also work to the disadvantage of the stigmatized even in the absence of individual prejudice or discrimination. For example, the practice of funding school districts through property taxes perpetuates educational disparities between whites and stigmatized ethnic minority groups in the United States (Sidanius & Pratto 1999).

Expectancy Confirmation Processes Stigma also affects the stigmatized via expectancy confirmation processes, or selffulfilling prophecies (Jussim et al. 2000). Perceivers’ negative stereotypes and expectations can lead them to behave toward stigmatized targets in ways that directly affect the targets’ thoughts, feelings, and behaviors. The targets’ behavior may then confirm the initial, erroneous, expectation (Darley & Fazio 1980, Deaux & Major 1987, Jussim et al. 2000) and even lead to expectancy-consistent changes in the targets’ self-perceptions (Fazio et al. 1981). The target need not be aware of others’ expectations, stereotypes, or prejudicial attitudes for this process to unfold (e.g., Snyder et al. 1977, Vorauer & Kumhyr 2001). Studies examining expectancy effects among actual stigmatized groups are rare. The few that have

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done so suggest that expectancy confirmation processes may be exacerbated when the targets actually are members of stigmatized groups (Harris et al. 1992, Jussim et al. 1996, McKown & Weinstein 2002).


Automatic Stereotype Activation-Behavior Stigma may also affect the stigmatized via the automatic activation of negative ingroup stereotypes. Dominant cultural stereotypes of groups in society are widely known (Steele 1997) and can affect behavior in the absence of discriminatory behavior on the part of others, and even when no other person is present in the immediate situation. Knowledge of cultural stereotypes may affect behavior through ideomotor processes (see Wheeler & Petty 2001 for a review). Because of associative linkages in memory between stereotypes and the behaviors they imply, activation of stereotypes can automatically lead to behavior that assimilates to the stereotype (Bargh et al. 1996, Dijksterhuis et al. 2000). For this process to occur, the person must be aware of the contents of the stereotype, the stereotype must be activated in a situation, and the stereotype must be applicable to the behavioral domain. Activating cultural stereotypes of stigmatized groups can produce stereotype-consistent behavior even among people who are not members of the group, as long as they are aware of the stereotype. For example, white Americans for whom the African American stereotype was activated performed more poorly on an intellectual task than did white Americans for whom this stereotype was not activated (Wheeler et al. 2001). Activating stereotypes of the stigmatized, however, is more likely to result in stereotype-consistent behavior among the stigmatized than the nonstigmatized for several reasons. First, self-relevant stereotypes are more likely to be chronically accessible than non-self-relevant stereotypes, resulting in a lowered threshold of activation for the former (Shih et al. 2002). Subliminally presented primes, for example, lead to stereotype-consistent behavior in the target group but not in nontarget groups (Levy 1996, Shih et al. 2002). Second, the same situation may prime negative stereotypes for the stigmatized, but not for the nonstigmatized. The mere act of indicating their race before taking a standardized test, for example, decreased test performance among African Americans but not among white Americans (Steele & Aronson 1995, Study 4). Finally, although the explanation is unclear, situations that activate negative stereotypes and harm performance among stigmatized group members sometimes elevate performance among members of nonstigmatized groups (Walton & Cohen 2003).

Stigma as Identity Threat Contemporary perspectives on stigma emphasize the extent to which stigma’s effects are mediated through targets’ understanding of how others view them, their interpretations of social contexts, and their motives and goals. These theories are “top down” in their emphasis on how people’s construals of their environment and self-relevant motives (e.g., self-esteem protection) shape their emotions, beliefs,

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and behavior. They are also “bottom up” in their assumption that construals emerge from experiences (direct or vicarious) with being a target of negative stereotypes and discrimination. These perspectives assume that stigma puts a person at risk of experiencing threats to his or her social identity. Crocker & Major (1989, Crocker et al. 1998), for example, hypothesized that stigmatization threatens self-esteem (personal and collective), and can lead to attributional ambiguity, i.e., uncertainty as to whether outcomes are due to one’s personal identity or social identity. Steele (1997, Steele & Aronson 1995) theorized that negative self-relevant group stereotypes can lead to stereotype threat, a situationally based fear that one will be judged on the basis of or confirm those stereotypes. Steele et al. (2002) hypothesized that cultural knowledge or situational cues that indicate one’s group is devalued, marginalized, and of low status lead to social identity threat, i.e., a threat to the aspect of self that is derived from membership in a devalued social group or category (Tajfel & Turner 1986). Identity threat theories dominate current research on stigma. In the following sections, we review research generated by these theories (for related reviews, see Crocker et al. 1998; Major et al. 2002b, 2003b; Schmitt & Branscombe 2002; Stangor et al. 2002; and Steele et al. 2002).

A MODEL OF STIGMA-INDUCED IDENTITY THREAT Figure 1 presents a model that integrates identity threat models of stigma (e.g., Crocker et al. 1998, Major et al. 2002b, Steele et al. 2002) with transactional models of stress and coping (e.g., Lazarus & Folkman 1984, Smith 1991). This model assumes that possessing a consensually devalued social identity (a stigma) increases one’s exposure to potentially stressful (identity-threatening) situations. Collective representations (box A), immediate situational cues (box B), and personal characteristics (box C) affect people’s appraisals of the significance of those situations for their well-being. Identity threat (box D) results when an individual

Figure 1

An identity-threat model of stigma.

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appraises the demands imposed by a stigma-relevant stressor as potentially harmful to his or her social identity, and as exceeding his or her resources to cope with those demands. Responses to identity threat can be involuntary (e.g., anxiety, increased vigilance, and working memory load) (box E) or voluntary (e.g., coping efforts; box F). Involuntary stress responses are emotional, cognitive, physiological, and behavioral responses that do not serve to regulate or modify stressful experiences. Voluntary responses, or coping, refer to conscious, volitional efforts to regulate emotion, cognition, behavior, physiology, and the environment in response to events or circumstances appraised as stressful (Compas et al. 1999). Both involuntary and voluntary responses can be distinguished from the outcomes of those responses, such as self-esteem, academic achievement, and health (box G). Although not drawn, this model is recursive, in that involuntary and voluntary responses to identity threat may furnish feedback that affects objective circumstances as well as subjective construals and appraisals of those circumstances. These may attenuate or exacerbate the effects of stigma. In the following section, we use this framework to organize and review recent literature on stigma.

Collective Representations Based on their prior experiences as well as their exposure to the dominant culture, members of stigmatized groups develop shared understandings of the dominant view of their stigmatized status in society (Crocker 1999, Crocker et al. 1998, Steele 1997). These collective representations include awareness that they are devalued in the eyes of others, knowledge of the dominant cultural stereotypes of their stigmatized identity, and recognition that they could be victims of discrimination (Crocker et al. 1998). Virtually all members of a culture, including members of stigmatized groups, are aware of cultural stereotypes, even if they do not personally endorse them (Steele 1997). By 10 years of age, most children are aware of cultural stereotypes of different groups in society, and children who are members of stigmatized groups are aware of cultural stereotypes at an even younger age (McKown & Weinstein 2003). Members of a culture also are aware of the dominant ideologies, or shared explanations, for why different groups occupy the status positions that they do (Jost & Banaji 1994, Sidanius & Pratto 1999). Collective representations influence how the stigmatized perceive and appraise stigma-relevant situations. Collective representations can affect the behavior of the stigmatized in the absence of obvious forms of discriminatory behavior on the part of others, and even when no other person is present in the immediate situation.

Situational Cues Situations differ in their social identity threat potential, i.e., in the extent to which they signal that one is at risk of being devalued, negatively stereotyped, or discriminated against because of one’s social identity (Steele et al. 2002). For abilitystigmatized groups, threatening situations include taking an ability diagnostic test (e.g., Spencer et al. 1999, Steele & Aronson 1995), being outnumbered by

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members of nonstigmatized groups (Ben-Zeev et al. 2004, Inzlicht & Good 2004, Sekaquaptewa & Thompson 2003), being taught by an instructor who is a member of a dominant outgroup (Marx & Roman 2002), being exposed to media images that reinforce negative stereotypes of one’s group (Davies et al. 2002), being asked to reveal a concealable stigma (Quinn et al. 2004), or overhearing that an evaluator is sexist (Major et al. 2003c). Because the collective representations that individuals bring to a situation shape its meaning, the same situation may be perceived and appraised differently by different individuals. For example, situational cues that increased the relevance of negative group stereotypes lead to stereotype threat effects (e.g., impaired performance) among children old enough to be aware of negative stereotypes about their group, but not among stigmatized children as yet unaware of group stereotypes (McKown & Weinstein 2003), and to impaired performance on a math test among women who believe that gender stereotypes about math ability are true, but not among women who reject these stereotypes (Schmader et al. 2004). Nonstigmatized and stigmatized groups in particular react very differently to the same local situation, in part because they differ in the collective representations they bring to the situation. It is important to note that perceptions of situations do not always correspond to objective events (see Crosby 1982, Feldman-Barrett & Swim 1998, Stangor et al. 2003a). Some individuals who are targets of objective discrimination, for example, fail to realize it, whereas other individuals believe they are victims of discrimination even when they are not (Major et al. 2002b, Stangor et al. 2003b). A variety of personal, situational, and structural factors determine whether people perceive themselves as targets of prejudice. For example, individuals are more likely to perceive discrimination (a) against their group as a whole than against themselves personally (Crosby 1982, Taylor et al. 1994), (b) when information is presented aggregated across members of a group than on a case-by-case basis (Crosby et al. 1989), and (c) when prejudice cues are clear rather than ambiguous (Major et al. 2003c).

Personal Characteristics Individual characteristics also influence how situations are perceived and appraised. Following, we describe several personal characteristics that have been the focus of research. Individuals differ in their chronic sensitivity to being stigmatized. People who expect to be treated on the basis of their group membership rather than their personal identity (Pinel 1999) and/or who are sensitive to rejection based on their group membership (Mendoza-Denton et al. 2002) are more vigilant for stigma-related threats, and are more likely to appraise stigma-relevant situations as threatening. For example, the higher people score on a measure of stigma-consciousness, the more likely they are to perceive themselves as targets

STIGMA SENSITIVITY

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of discrimination at both a personal and group level (Pinel 1999), the more they expect to be treated negatively by outgroup members (Pinel 2002), and the more attention they allocate to subliminally presented words that threaten their social identity (CR Kaiser, SB Vick, B Major, submitted, Study 2). African American students who scored high on a measure of race-rejection sensitivity prior to college were more likely than those who scored low on this measure to perceive negative race-related experiences and discrimination over the course of their first three weeks in college, felt more negatively toward their roommates and professors, and were less likely to feel accepted at college (Mendoza-Denton et al. 2002). Individuals who regard their stigmatized social identity as a central part of their self-identity are more likely to see themselves as targets of personal and group discrimination (e.g., Branscombe et al. 1999, Sellers & Shelton 2003), especially when prejudice cues are attributionally ambiguous (Major et al. 2003c, Operario & Fiske 2001). They are also more likely to appraise stigmarelevant events as self-relevant. Consequently, they report increased threat and lower self-esteem in response to perceived prejudice against the ingroup (McCoy & Major 2003) and perform more poorly in situations where the ingroup is negatively stereotyped (Schmader 2002).

GROUP IDENTIFICATION

Individuals who strongly identify with domains in which their group is negatively stereotyped are more likely to regard performance feedback in those domains as self-relevant, increasing their potential for experiencing identity threat (Aronson et al. 1999, Steele et al. 2002). For example, African American students who regarded their performance on intellectual tests as central to their self-regard reported lower self-esteem following poor performance feedback compared to those who were less domain identified (Major et al. 1998).

DOMAIN IDENTIFICATION

Individuals’ goals and motives also shape how they perceive and appraise situations. Two motives have been emphasized in the stigma literature. One is the motive to protect or enhance self-esteem. People are more likely to perceive an evaluator as sexist or racist if they receive negative than positive feedback from him or her (e.g., Crocker et al. 1991). People are also motivated to believe the system is just and that they are fairly treated (Jost & Major 2001, Jost et al. 2003, Major 1994). In the service of maintaining these beliefs, members of stigmatized groups may fail to see themselves as victims of prejudice even in the presence of prejudice cues. The more strongly members of stigmatized ethnic groups (Latino/a Americans, African Americans) believe in a just world and that any individual can get ahead regardless of group membership, the less likely they are to report that they personally, or members of their group, are targets of ethnic discrimination, the less likely they are to blame discrimination when a member of a higher status group (e.g., a European American) rejects them for a desirable role (Major et al. 2002a, Study 2), and the more threat and lower self-esteem they

GOALS AND MOTIVES

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report when they are confronted with prejudice against themselves or their group (B Major, CR Kaiser, SK McCoy, submitted).


Identity Threat Appraisals The centerpiece assumption of transactional stress and coping models is that events are appraised for their significance for well-being, and that the outcome of this appraisal process directs affective, cognitive, behavioral, and physiological responses to that event (Smith 1991). Appraisals include primary appraisals of the demands posed by a stressor (e.g., the extent to which it is perceived as self-relevant, dangerous, effortful, and creates uncertainty) and secondary appraisals of resources to cope with those demands (Lazarus & Folkman 1984). Threat results when the perceived demands of a self-relevant situation are appraised as exceeding one’s perceived resources to meet those demands. Challenge results when perceived coping resources are appraised as exceeding demands (Blascovich & Tomaka 1996). Stigma-induced identity threat results when an individual appraises the demands imposed by a stigma-relevant stressor as potentially harmful to his or her social identity, and as exceeding his or her resources to cope with those demands. This appraisal results from an interaction between perceived cues (affective or semantic) in the immediate situation that make stigma relevant to that situation, the collective representations that the individual brings to that situation, and individual characteristics. The appraisal process can be automatic, nonverbal, instantaneous, and occur outside of consciousness (Smith 1991). Although originally theorized to result from cognitive processing, appraisals also can result from affective (i.e., feeling) processing instigated by well-learned affective cues (e.g., a Ku Klux Klan emblem; Blascovich & Mendes 2000). Further, affective processing can occur independently of cognitive processing and, like cognitive processing, may occur below awareness (e.g., LeDoux 1996, Zajonc 2000). Stimuli presented below levels of awareness can elicit emotional reactions strong enough to drive judgment and behavior in the absence of any conscious feelings accompanying these reactions (Winkielman & Berridge 2004). Although we focus on how stigma may lead to appraisals of identity threat, stigma may also lead to identity challenge (e.g., O’Brien & Crandall 2003). For example, a woman could perceive herself as a potential target of sexism, yet not appraise this as a threat if she feels she has more than sufficient coping resources to meet the demand (Kaiser et al. 2004a). Such resources might include perceived control over important resources, the ability to limit exposure to others who are prejudiced, strong group identity, or dispositional optimism.

Involuntary Responses to Identity Threat Involuntary responses to identity threat include anxiety (Spencer et al. 1999), arousal (Ben-Zeev et al. 2004), and increased blood pressure (Blascovich et al. 2001). Identity threat has been linked to indirect measures of anxiety in the absence

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of self-reported anxiety (Blascovich et al. 2001, Bosson et al. 2004). For example, gay men who interacted with preschool children under conditions conducive to creating stereotype threat demonstrated increased nonverbal anxiety compared to unthreatened gay men, but the former did not report feeling more anxious on selfreport measures. Nonverbal anxiety, but not self-reported anxiety, mediated the effects of threat condition on participants’ child-care performance (Bosson et al. 2004). These latter findings strongly suggest that affective responses to identity threat may not be conscious and amenable to self-report measures. Like other types of stress, identity threat can also consume valuable cognitive resources (Klein & Boals 2001). Schmader & Johns (2003) found that manipulations of stereotype threat (e.g., describing a test as measuring quantitative or intellectual capacity) led to lower working memory capacity among individuals targeted by the stereotype (women and Latinos) while having no effect on individuals not targeted by the stereotype (men and whites). Furthermore, reductions in working memory capacity mediated the effects of the stereotype threat manipulation on performance. Identity threat may also engender automatic vigilance to threat-related stimuli. Automatic stimulus evaluation directs attention toward events that may have undesirable consequences for the perceiver (Pratto & John 1991). Previous experience with prejudice and discrimination can set the stage for members of stigmatized groups to use a “zero miss” signal detection strategy wherein injustice cues in the environment trigger vigilance for discrimination (Feldman-Barrett & Swim 1998). Women led to anticipate interacting with a sexist (versus a nonsexist) man allocated more attention to subliminally presented words that threatened their social identity, as did women who scored high (versus low) in stigma consciousness (CR Kaiser, SB Vick, & B Major, submitted). Automatic vigilance, however, is not an inevitable by-product of stigmatization. Some members of stigmatized groups screen out identity threat–relevant information at a nonconscious attentional level (CR Kaiser, SB Vick, & B Major, submitted; Miller & Kaiser 2001; Stangor et al. 2003a). Ironically, people who chronically expect and are vigilant for signs of discrimination may create the rejection they fear by communicating these expectancies to others. For example, when women high in stigma consciousness interacted with a male partner who they had been led to believe was sexist, they rated him especially critically; their ratings elicited negative evaluations from the male partner, in turn confirming the women’s belief that they would not like him (Pinel 2002). African American students who were high in sensitivity to race-based rejection prior to entering college had less-diverse friendships and felt less trust in their university at the end of their first year in college. They also reported decreased attendance at academic review sessions, increased anxiety about approaching instructors with academic problems, and decreased GPAs by the time they were college juniors (Mendoza-Denton et al. 2004).

Coping in Response to Threats to the Self People cope with stigma-induced identity threat in a variety of ways (see, e.g., Allport 1954, Miller & Kaiser 2001, Miller & Major 2000, Swim & Thomas

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2004). Some coping efforts are primarily problem focused (e.g., when an overweight person decides to go on a diet), whereas others are primarily emotion focused (e.g., restricting one’s comparisons to others who are also overweight), although some strategies may serve both goals (e.g., avoiding wearing a bathing suit). Coping strategies can also be characterized as engagement versus disengagement strategies, with the former reflecting approach or fight motivation, and the latter reflecting avoid or flight motivation (Miller 2004, Miller & Kaiser 2001). We focus here on three coping strategies addressed in recent research: (a) attributing negative events to discrimination (versus to the self), (b) disengaging self-esteem and effort from identity-threatening domains (versus engaging and striving in these domains), and (c) increasing identification with one’s stigmatized group (versus distancing oneself from the group). When members of stigmatized groups encounter negative outcomes, one way they may cope with the threat to their self-esteem is by blaming the outcome on discrimination rather than on themselves (Crocker & Major 1989, Major et al. 2002b). Blaming specific negative events on discrimination is conceptually and methodologically distinct from self-reports of experiencing pervasive discrimination. The latter confound attributional processes with frequency and severity of exposure to discrimination. Attributions to discrimination buffer self-esteem primarily when an individual has experienced a threat to an internal, stable aspect of the personal self (Major et al. 2002b). In such contexts, blaming the event on discrimination shifts blame from stable, unique aspects of the personal self to a more external cause—the prejudice of others—thereby protecting self-esteem (e.g., Major et al. 2003a). However, making an attribution to discrimination still implicates one’s social identity. Thus, making an attribution to discrimination is more harmful to self-esteem than is making a purely external attribution (Schmitt & Branscombe 2002). Attributions to discrimination also protect self-esteem more when prejudice is blatant rather than hidden or disguised. Attributing a rejection to discrimination is positively associated with self-esteem in the presence of blatant prejudice, but is negatively related to self-esteem in the absence of prejudice cues (Major et al. 2003c). Coping strategies simultaneously affect more than one outcome. Whereas making an attribution to discrimination may sometimes protect personal self-esteem, it may also interfere with accurate knowledge of one’s strengths and weaknesses (Aronson & Inzlicht 2004, Cohen et al. 1999). Furthermore, members of stigmatized groups who blame their failures on discrimination are socially derogated (Kaiser & Miller 2001). Thus, it is not surprising that members of stigmatized groups are more likely to blame negative outcomes on discrimination in private or when they are with other members of their ingroup than when they are in public settings with members of higher-status groups (Stangor et al. 2002). Furthermore, under public reporting conditions, stigmatized targets of discrimination are less likely to report discrimination than are similar others who view the same event (Sechrist et al. 2004). Thus, even when attributing negative events to discrimination

BLAMING DISCRIMINATION VERSUS BLAMING THE SELF

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may be warranted and beneficial to self-esteem, targets of discrimination may be unwilling to engage in this coping strategy. Another way in which the stigmatized may cope with identity threat is by withdrawing their efforts and/or disengaging their self-esteem from domains in which they are negatively stereotyped or fear being a target of discrimination (Keller & Dauenheimer 2003, Major & Schmader 1998, Major et al. 1998, Schmader et al. 2001, Steele 1997, Stone 2002). For example, Davies et al. (2002) demonstrated that women taking a difficult test who were exposed to negative gender stereotypes chose to answer fewer math questions and instead focused on answering questions related to verbal ability. Over time, individuals may disidentify with domains in which their group is negatively stereotyped or unfairly treated so that their performance in that domain is no longer important to their self-worth (Crocker & Major 1989, Steele 1997). For example, the more African American college students believed that differences in status between ethnic groups in America were unjust, the less likely they were to say that academic performance was an important part of their self-concepts (Schmader et al. 2001). Members of lower-status groups also are more likely to devalue domains if they are led to believe that status differences between their group and higher-status groups are unfair (Schmader et al. 2001). Although devaluing and withdrawing effort from domains in which one is negatively stereotyped and treated unjustly may protect self-esteem, it may come at the cost of success in those domains. An alternative way of coping with identity threat in socially valued domains is to compensate, or strive even harder to overcome obstacles (Allport 1954, Miller & Myers 1998). In a direct demonstration of enhanced striving, overweight women who believed their stigma might have a negative impact on an interaction (i.e., who thought that their partner could see them) compensated by bolstering their social skills compared to overweight women who thought they could not be seen by their partners (Miller et al. 1995). A dispositional preference to work harder to overcome obstacles, however, may be related to poor health, as in the positive relationship observed between “John Henryism” and hypertension among African Americans (James et al. 1983).


DISENGAGEMENT VERSUS STRIVING

Members of stigmatized groups may cope with identity threat by approaching, or identifying more closely with, their group (Allport 1954). Groups can provide emotional, informational, and instrumental support, social validation for one’s perceptions, social consensus for one’s attributions, and a sense of belonging. Group identification is positively correlated with self-esteem among stigmatized groups (e.g., Bat-Chava 1994, Rowley et al. 1998). Branscombe et al. (1999) proposed that group identification increases in response to perceived prejudice against the group and that this increase in group identification partially offsets the negative effects of perceiving pervasive prejudice on personal self-esteem. These predictions have been supported in correlational research with African Americans (Branscombe et al. 1999), older adults (Garstka

GROUP IDENTIFICATION VERSUS DISIDENTIFICATION

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et al. 2004), international students at an American university (Schmitt et al. 2003), and women (Schmitt et al. 2002). Further, customers in a piercing salon who read that prejudice existed against body piercers subsequently identified more strongly with that group than did customers who read that prejudice against body piercers was decreasing (Jetten et al. 2001). Although this evidence suggests that group identification may be an effective coping strategy in response to perceptions of prejudice against the group, it is important to define precisely what is meant by group identification (Ashmore et al. 2004). For example, the more women perceive pervasive discrimination against women, the more central they regard their gender to be in their self-concept, but the less proud they are to be a woman (Eccleston & Major 2004). Whereas highly identified group members may respond to threats to the group by increasing their identification with the group, members who are low in identification may cope by decreasing their identification even more (see Ellemers et al. 2002 for a review). After reading about pervasive discrimination toward their ethnic group, for example, Latino/a American students who had previously reported low levels of ethnic group identification identified even less with their ethnic group, whereas previously highly identified Latino/a American students identified even more strongly (McCoy & Major 2003).

OUTCOMES OF STIGMATIZATION Coping with stigma often involves trade-offs. Strategies used in the service of achieving one goal (protecting self-esteem) may inhibit attainment of other goals (academic achievement). Thus, it is important to look at multiple responses to and effects of stigmatization within the same study. Rarely, however, have stigma researchers done so. In the following section, we briefly focus on how stigma affects three important outcomes: self-esteem, academic achievement, and health.

Self-Esteem A number of empirical investigations of the relationship between stigma and selfesteem have been conducted over the past 15 years (e.g., Branscombe et al. 1999, Crocker et al. 1991, Quinn & Crocker 1999). Researchers typically measure personal (Twenge & Crocker 2002) and collective self-esteem (e.g., Crocker et al. 1994) with self-report measures. More recently, they have assessed personal and collective self-esteem with the implicit association test (IAT) (e.g., Nosek et al. 2002) and other indirect measures (Jost et al. 2002). Many classic perspectives on the effects of stigmatization assumed that the stigmatized internalize the negative view of them held by society at large (e.g., Cartwright 1950, Clark & Clark 1947). According to this view, levels of self-esteem in stigmatized groups should parallel the degree to which they are devalued by the culturally dominant group (Twenge & Crocker 2002). Members of nonstigmatized

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groups should have higher self-esteem than members of stigmatized groups, and among stigmatized groups, those who are more valued (e.g., Asian Americans) should have higher self-esteem than those who are less valued (e.g., blacks and Latinos in the United States). A meta-analysis of racial differences in self-report measures of personal self-esteem showed no support for this prediction. African Americans had higher self-esteem than did white Americans, who had higher selfesteem than did Latino Americans, who had higher self-esteem than did Asian Americans and Native Americans (Twenge & Crocker 2002). In contrast, the selfreported collective self-esteem of African, Latino, and Asian Americans is greater than or equal to the collective self-esteem of white Americans (Crocker et al. 1994). Research assessing self-esteem with indirect or implicit measures (e.g., racial preferences), however, tells a different story. One study suggests that blacks have the highest implicit personal self-esteem, followed by Latinos, whites, and Asians, although the differences between the groups were slight (Nosek et al. 2002). Several studies using indirect measures of collective self-esteem, however, show that whites demonstrate ingroup favoritism, whereas Latino, Asian, and African Americans demonstrate significant outgroup favoritism (favor whites) (Ashburn-Nardo et al. 2003, Jost et al. 2002, Nosek et al. 2002). The picture is no less complicated with regard to nonracial stigmas. For example, overweight women self-report lower personal self-esteem than do average-weight women (Miller & Downey 1999) and show lower collective self-esteem on implicit measures (Rudman et al. 2002). Younger adults and older adults have equivalent levels of personal self-esteem on both implicit and explicit measures; however, both groups favored younger adults on an implicit measure of collective self-esteem (Hummert et al. 2002). Women self-report lower levels of personal self-esteem than do men (Kling et al. 1999, Major et al. 1999), but score equal to men on implicit measures of personal self-esteem (Aidman & Carroll 2002, Greenwald & Farnham 2000, Nosek et al. 2002). In short, results are inconsistent. Some research supports an internalization perspective, but most does not. Part of the problem can be traced to measurement issues associated with both explicit (Greenwald et al. 2002) and implicit measures (Fazio & Olson 2003, Olson & Fazio 2003). Whereas self-report measures are susceptible to social desirability, implicit measures can sometimes be tainted by environmental or extrapersonal associations—culturally shared, but not necessarily personally endorsed, representations of groups (Karpinski & Hilton 2001, Olson & Fazio 2004). Measures such as the IAT may overestimate the extent of outgroup favoritism by stigmatized groups (Olson & Fazio 2004). However, measurement issues cannot tell the whole story, as the IAT has demonstrated predictive validity. For example, blacks who demonstrated outgroup favoritism on the IAT were also more likely to choose a white person over a black person for an interaction partner (Ashburn-Nardo et al. 2003). Rather than focusing on self-esteem differences between stigmatized and nonstigmatized groups, identity threat perspectives draw attention to variability in self-esteem within stigmatized groups, and even within the same individual across

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contexts. Self-esteem varies as a function of collective representations, situational cues, and personal characteristics (Crocker et al. 1991, 1993; Major et al. 2003b; Quinn & Crocker 1999). For example, experimentally manipulating perceptions of prejudice against their ethnic group led to higher threat appraisals and lower selfesteem (compared to a no-prejudice manipulation) among Latinos who strongly endorsed the ideology of meritocracy, but not among Latinos who rejected this ideology (B Major, CR Kaiser, SK McCoy, submitted). Exposure to pervasive sexism led to higher threat appraisals and lower self-esteem among pessimistic women but not among optimistic women (Kaiser et al. 2004a). In both of these studies, threat appraisals mediated the effect of situation (exposure to prejudice or not) and personal characteristics on explicit self-esteem. Research is needed to understand how collective representations, situational cues, and personal characteristics affect implicit measures of self-esteem among stigmatized groups.

Academic Achievement Members of stigmatized and nonstigmatized groups differ substantially in measures of academic achievement. Data compiled by the National Center for Education Statistics, for example, indicate that in 2001, black (10.9%) and Latino students (27%) were more likely than were white students (7.3%) in the United States to drop out of high school. Blacks (18%) and Latinos (11%) were also less likely than whites (33%) to earn bachelors degrees. Moreover, compared to whites, black, Latino, and Native American students had lower standardized test scores across all subjects and grade levels tested (National Center for Education Statistics 2004). Differences in performance among ethnic groups or between women and men often are attributed to various forms of discrimination (see Steele 1997). In countries around the world, children who are members of ethnically stigmatized groups receive a smaller proportion of public education funds than do children who are not members of stigmatized groups (for a review, see Sidanius & Pratto 1999). Parents have lower math expectations for girls than boys (Eccles et al. 1990), and are less likely to pay for their daughters’ college education if they are fat than if they are average weight (Crandall 1995). Although discrimination clearly contributes to achievement differences between stigmatized and nonstigmatized groups, this is not the full story (see Steele et al. 2002 for a review). Situational cues increase the extent to which academic performance situations are appraised as threatening to social identity, and involuntary and voluntary responses to this identity threat may depress academic performance. For example, situational cues signaling that a negative stereotype is relevant as a possible interpretation for one’s behavior (e.g., describing a test as diagnostic of ability, or as showing gender differences) impair the test performance of African Americans (Steele & Aronson 1995) and women (Spencer et al. 1999), respectively. Being outnumbered in a stereotyped environment also can harm performance of stigmatized groups (Inzlicht & Ben Zeev 2000). People who would be expected

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to appraise stereotype-relevant situations as threats to their social identity, such as people who identify highly with the relevant domain (Aronson et al. 1999) or stereotyped group (Schmader 2002), who are highly conscious of being stereotyped based on their stigma (Brown & Pinel 2002), and who have high testosterone levels (Josephs et al. 2003) are more likely to show performance decrements in stereotype-relevant situations. Stigma-induced identity threat resulting from activation of negative group stereotypes or fear of being a victim of prejudice can harm performance through involuntary stress responses such as anxiety and decrements in working memory capacity (Blascovich et al. 2001, Bosson et al. 2004, Schmader & Johns 2003). Emotion-focused coping efforts such as self-handicapping and withdrawing effort from negatively stereotyped domains can also impair performance (Davies et al. 2002, Keller 2002, McKown & Weinstein 2003). Other coping strategies, such as thinking of intelligence as malleable, or attributing one’s group’s past poor performances to situational factors, in contrast, can reduce the negative impact of social identity threat on performance (Good et al. 2003). Stigma-induced identity threat also can lead stigmatized groups to chronically disengage their self-esteem from intellectual tasks (Crocker et al. 1998; Steele 1992, 1997). Consistent with this notion, the correlation between self-esteem and academic achievement weakens in African American adolescents over time (Osborne 1995). Furthermore, whereas the self-esteem of European American students is affected by performance feedback on tests of intellectual ability, African American students’ self-esteem is not, suggesting that the latter may psychologically disengage their self-esteem from test feedback (Major et al. 1998). African Americans are particularly likely to disengage their self-esteem from performance feedback when their race is made salient (Major et al. 1998). Over time, disidentification from a domain may undermine African American students’ school achievement (Steele 1992, 1997). Cognitive strategies such as shaping people’s theories about intelligence may be a partial remedy to the problem of disidentification in stigmatized groups (Aronson et al. 2002).

Health Compared to the nonstigmatized, members of stigmatized groups are at a greater risk for mental and physical health problems, such as depression, hypertension, coronary heart disease, and stroke (American Heart Association 2003, Jackson et al. 1996, Krieger 1990, McEwen 2000). African Americans, for example, have shorter life expectancies, higher infant mortality, and more heart disease than do European Americans (Allison 1998, Flack et al. 1995). Discrimination directly affects the health of the stigmatized by exposing them to physical and social environments that are more toxic and by limiting their access to quality medical care and nutrition (Clark et al. 1999, Harrell 2000, Link & Phelan 2001). Stigma can also affect health indirectly via identity threat mechanisms. Threats to identity can initiate a cascade of negative cognitions and emotions as well as

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physiological threat responses, including elevated cortisol, increased blood pressure, and other cardiovascular responses (Blascovich et al. 2000, Chen & Matthews 2003, Dickerson & Kemeny 2004). Although these physiological responses can be adaptive in the short run, they have adverse health implications if repeatedly experienced over time (McEwen 2000). Subjective perceptions of stigmatization may be as important as objective exposure to discrimination in predicting adverse health-relevant outcomes among the stigmatized (Allison 1998, Finch et al. 2000, Harrell 2000). Subjective social status is positively related to health-related outcomes, even controlling for objective indicators of social status (Adler et al. 2000, Ostrove et al. 2000). Self-reported experiences of discrimination are positively correlated with psychological distress, and with self-reported physical health problems, such as hypertension, number of sick days, and chronic pain (e.g., Contrada et al. 2001, Diaz et al. 2001, Finch et al. 2000, Klonoff et al. 2000, Krieger 1990, Lewis et al. 2003, Swim et al. 2001, Taylor & Turner 2002, Williams et al. 1997). Self-reported experiences of discrimination also correlate positively with resting blood pressure levels among ethnic minority men (Krieger & Sidney 1996). Although these findings are consistent with the hypothesis that subjective experiences of discrimination harm mental and physical health, they are limited in several ways. First, most studies do not distinguish between objective exposure to negative events (e.g., being denied a loan) and subjective perceptions of discrimination (because one is black). Studies that attempted to disentangle these effects have produced mixed results (Kessler et al. 1999, Magley et al. 1999, Taylor & Turner 2002, Williams 1997). Second, because these studies are correlational, it is possible that negative emotion predicts perceptions of discrimination rather than the reverse (e.g., Sechrist et al. 2003). Third, none controlled for dispositional variables that might affect correlations between self-reported experiences with discrimination and self-reported psychological distress or health problems, such as individual differences in attributional style or rejection sensitivity. Thus, further research is needed to assess the effects of perceived discrimination on mental and physical health. Results of the few experiments that have assessed biological stress responses to acute discrimination-related stressors under controlled laboratory conditions are mixed. Some suggest that African Americans exposed to a discrimination stressor in the laboratory have significantly higher cardiovascular reactivity than do those exposed to a nondiscrimination stressor (e.g., Armstead et al. 1989, McNeilly et al. 1995), whereas others suggest that encounters with discrimination-related stressors do not produce greater blood pressure reactivity than do encounters with nondiscrimination stressors (e.g., Fang & Myers 2001). Collective representations and personal characteristics may interact with the immediate situation to affect threat appraisals and cardiovascular responses. African American women who reported being a target of interpersonal mistreatment in the past because of discrimination had higher baseline heart rate levels, and showed greater cardiac reactivity while giving a speech about a potentially discriminatory incident (but not while

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engaging in a nonsocial stress task) compared to African American women who either reported not being a target of mistreatment or who reported being a target of mistreatment but did not attribute it to discrimination (Guyll et al. 2001). Psychological factors that increase susceptibility to stigma-related identity threat may negatively affect health-related outcomes. Individuals who are chronically vigilant for stigma-related identity threats may appraise potentially stigmarelevant situations as threatening, leading to heightened physiological threat responses. HIV-positive gay men who were high in sensitivity to rejection based on their homosexuality showed more rapid advancement of HIV infection over a nine-year period than did those less sensitive to rejection based on their homosexuality (Cole et al. 1997). Furthermore, HIV progressed most rapidly among high rejection-sensitive men who were “out” (versus concealed), and thus who were more exposed to the risk of social rejection. Psychological factors associated with perceived resources, in contrast, may have beneficial effects on health by decreasing identity threat. As noted above, optimistic women are less threatened by and have less negative emotional reactions to prejudice than do pessimistic women (Kaiser et al. 2004a). Optimism (as opposed to pessimism) is positively related to mental and physical health in response to a variety of severe stressors (Chen & Matthews 2003, Scheier et al. 2001). The coping strategies used to deal with identity threat may also have implications for health.

CONCLUSIONS This chapter sought to integrate theory and research on the phenomenology and effects of social stigma—of being labeled, negatively stereotyped, excluded, discriminated against, and low in social status and power. Traditionally, members of stigmatized groups have been portrayed as passive victims of others’ negative stereotypes, prejudicial attitudes, and discriminatory behaviors. Research reviewed here demonstrates that stigma does have direct and insidious negative effects on the stigmatized via mechanisms of discrimination, expectancy confirmation, and automatic stereotype activation. Theory and research that takes the perspective of the stigmatized, however, illustrates that individual construals also play a key mediating role in responses to stigma. We organized recent theory and research within an identity threat model of stigma. This model posits that responses to stigma-relevant situations and circumstances are a function of cues in the immediate situation, collective representations of one’s stigma status, and individual characteristics. These combine to affect appraisals of the significance of the situation for well-being. Identity threat results when an individual appraises the demands imposed by a stigma-relevant stressor as potentially harmful to his or her social identity, and as exceeding his or her resources to cope with those demands. Identity threat leads to involuntary stress responses such as anxiety, vigilance to threat, and decreased working memory

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capacity, and motivates attempts at threat reduction through coping strategies such as blaming negative events on discrimination, identifying more closely with the threatened group, and disengaging self-esteem from threatening domains. These involuntary stress responses and voluntary coping efforts have implications for important outcomes such as self-esteem, academic achievement, and health. The identity threat perspective integrates research on the link between perceived discrimination and self-esteem with research on the link between stereotype threat and test performance. It also identifies holes in the literature and suggests several avenues for future research. First, little is known about the conditions that elicit vigilance for stigma-relevant threats in the environment as opposed to active suppression of such knowledge. Second, the significance and meaning of outgroup favoritism among stigmatized groups on implicit measures is unclear. Does this indicate lower implicit collective self-esteem among stigmatized groups or knowledge of cultural valuation of different groups? Third, researchers need to identify the conditions that lead the stigmatized to appraise stigma-relevant stressors as identity challenges rather than as identity threats and that lead to increased striving rather than withdrawal. Fourth, the mechanisms through which stigmainduced identity threat affects health need to be identified. Finally, evidence of trade-offs among different coping mechanisms points to a need for researchers to include multiple dependent variables (e.g., academic performance, self-esteem) in the same study. We close by noting that one of the major insights of a social psychological perspective on stigma is the tremendous variability across people, groups, and situations in responses to stigma. The emerging understanding of factors that make people resilient, as well as vulnerable, to stigma and identification of effective coping strategies for dealing with identity-threatening situations holds some promise for improving the predicament of the stigmatized. The Annual Review of Psychology is online at http://psych.annualreviews.org

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ed. JK Swim, C Stangor, pp. 219–41. San Diego, CA: Academic Major B, Spencer S, Schmader T, Wolfe C, Crocker J. 1998. Coping with negative stereotypes about intellectual performance: the role of psychological disengagement. Personal. Soc. Psychol. Bull. 24:34–50 Major B, Vick SB. 2004. The psychological impact of prejudice. In Reflecting on the Nature of Prejudice, ed. JF Dovidio, P Glick, L Rudman. Malden, MA: Blackwell. In press Marx DM, Roman JS. 2002. Female role models: protecting women’s math test performance. Personal. Soc. Psychol. Bull. 28: 1183–93 McCoy SK, Major B. 2003. Group identification moderates emotional responses to perceived prejudice. Personal. Soc. Psychol. Bull. 29:1005–17 McEwen BS. 1998. Stress, adaptation, and disease: allostasis and allostatic load. In Annals of the New York Academy of Sciences, ed. SM McCann, JM Lipton, vol. 840, pp. 33– 44. New York: NY Acad. Sci. McEwen BS. 2000. The neurobiology of stress: from serendipity to clinical relevance. Brain Res. 886:172–89 McKown C, Weinstein RS. 2002. Modeling the role of child ethnicity and gender in children’s differential responses to teacher expectations. J. Appl. Soc. Psychol. 32:159–84 McKown C, Weinstein RS. 2003. The development and consequences of stereotype consciousness in middle childhood. Child Dev. 74:498–515 McNeilly MD, Robinson EL, Anderson NB, Pieper CF, Shah A, et al. 1995. Effects of racist provocation and social support on cardiovascular reactivity in African American women. Int. J. Behav. Med. 2:321–38 Mendoza-Denton R, Page-Gould E, Pietrzak J. 2004. Mechanisms for coping with statusbased rejection expectations. See Levin & van Laar 2004. In press Mendoza-Denton R, Purdie VJ, Downey G, Davis A, Pietrzak J. 2002. Sensitivity to status-based rejection: implications for African American students’ college expe-

rience. J. Personal. Soc. Psychol. 83:896– 918 Miller CT. 2004. Social psychological perspectives on coping with stressors related to stigma. See Levin & van Laar 2004. In press Miller CT, Downey KT. 1999. A meta-analysis of heavyweight and self-esteem. Personal. Soc. Psychol. Rev. 3:68–84 Miller CT, Kaiser CR. 2001. A theoretical perspective on coping with stigma. J. Soc. Issues 57:73–92 Miller CT, Major B. 1998. Coping with stigma and prejudice. See Heatherton et al. 2000, pp. 243–72 Miller CT, Myers AM. 1998. Compensating for prejudice: how heavyweight people (and others) control outcomes despite prejudice. In Prejudice: The Target’s Perspective, ed. JK Swim, C Stangor, pp. 191–218. San Diego, CA: Academic Miller CT, Rothblum ED, Felicio D, Brand P. 1995. Compensating for stigma: obese and nonobese women’s reactions to being visible. Personal. Soc. Psychol. Bull. 21:1093–106 Miller LS. 1995. An American Imperative: Accelerating Minority Educational Advancement. New Haven, CT: Yale Univ. Press National Center for Education Statistics. 2004. The Condition of Education. Washington, DC: Inst. Educ. Sci., U.S. Dep. Educ. Nelson TD. 2000. Ageism: Stereotyping and Prejudice Against Older Persons. Cambridge, MA: MIT Press Neuberg SL, Smith DM, Asher T. 2000. Why people stigmatize: toward a biocultural framework. See Heatherton et al. 2000, pp. 31–61 Nosek BA, Banaji M, Greenwald AG. 2002. Harvesting implicit group attitudes and beliefs from a demonstration web site. Group Dyn. 6:101–15 O’Brien LT, Crandall CS. 2003. Stereotype threat and arousal: effects on women’s math performance. Personal. Soc. Psychol. Bull. 29:782–89 Olson MA, Fazio RH. 2003. Relationships between implicit measures of prejudice: What are we measuring? Psychol. Sci. 14:636–39

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ingroup bias as a function of group status. Soc. Cogn. 20:294–320 Scheier MF, Carver CS, Bridges MW. 2001. Optimism, pessimism, and psychological well-being. In Optimism and Pessimism: Implications for Theory, Research, and Practice, ed. EC Chang, pp. 189–216. Washington, DC: Am. Psychol. Assoc. Schmader T. 2002. Gender identification moderates stereotype threat effects on women’s math performance. J. Exp. Soc. Psychol. 38:194–201 Schmader T, Johns M. 2003. Converging evidence that stereotype threat reduces working memory capacity. J. Personal. Soc. Psychol. 84:440–52 Schmader T, Johns M, Barquissau M. 2004. The costs of accepting gender differences: the role of stereotype endorsement in women’s experience in the math domain. Sex Roles 50:835–50 Schmader T, Major B, Eccleston CP, McCoy SK. 2001. Devaluing domains in response to threatening intergroup comparisons: perceived legitimacy and the status value asymmetry. J. Personal. Soc. Psychol. 80:782–96 Schmitt MT, Branscombe NR. 2002. The meaning and consequences of perceived discrimination in disadvantaged and privileged social groups. In European Review of Social Psychology, ed. W Stroebe, M Hewstone, vol. 12, pp. 167–99. London: Psychol. Press Schmitt MT, Branscombe NR, Kobrynowicz D, Owen S. 2002. Perceiving discrimination against one’s gender group has different implications for well-being in women and men. Personal. Soc. Psychol. Bull. 28:197–210 Schmitt MT, Spears R, Branscombe NR. 2003. Constructing a minority group identity out of shared rejection: the case of international students. Eur. J. Soc. Psychol. 33:1–12 Sechrist GB, Swim JK, Mark, MM. 2003. Mood as information in making attributions to discrimination. Personal. Soc. Psychol. Bull. 29:524–531 Sechrist GB, Swim JK, Stangor C. 2004. When do the stigmatized make attributions to discrimination occurring to the self and others?

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The roles of self-presentation and need for control. J. Personal. Soc. Psychol. 87:111– 22 Sekaquaptewa D, Thompson M. 2002. The differential effects of solo status on members of high- and low-status groups. Personal. Soc. Psychol. Bull. 28:694–707 Sekaquaptewa D, Thompson M. 2003. Solo status, stereotype threat, and performance expectancies: their effects on women’s performance. J. Exp. Soc. Psychol. 39:68–74 Sellers RM, Shelton JN. 2003. The role of racial identity in perceived racial discrimination. J. Personal. Soc. Psychol. 84:1079–92 Shih M, Ambady N, Richeson JA, Fujita K, Heather G. 2002. Stereotype performance boosts: the impact of self-relevance and the manner of stereotype activation. J. Personal. Soc. Psychol. 83:638–47 Sidanius J, Pratto F. 1999. Social Dominance: An Intergroup Theory of Social Hierarchy and Oppression. New York: Cambridge Univ. Press Smith CA. 1991. The self, appraisal, and coping. In Handbook of Social and Clinical Psychology: The Health Perspective, ed. CR Snyder, DR Forsyth, pp. 116–37. Elmsford, NY: Pergamon Snyder M, Tanke ED, Berscheid E. 1977. Social perception and interpersonal behavior: on the self-fulfilling nature of social stereotypes. J. Personal. Soc. Psychol. 35:656–66 Spencer S, Steele CM, Quinn DM. 1999. Stereotype threat and women’s math performance. J. Exp. Soc. Psychol. 35:4–28 Stangor C, Swim JK, Van Allen KL, Sechrist GB. 2002. Reporting discrimination in public and private contexts. J. Personal. Soc. Psychol. 82:69–74 Stangor C, Swim JK, Sechrist GB. 2003a. Sensitivity to sexism and perceptions of reports about sexist events. Swiss J. Psychol. 48:251– 56 Stangor C, Swim JK, Sechrist GB, DeCoster J, Van Allen KL, Ottenbreit A. 2003b. Ask, Answer, and Announce: three stages in perceiving and responding to discrimination. In European Review of Social Psychology, ed.

W Stroebe, M Hewstone, vol. 14, pp. 277– 311. London: Psychol. Press Steele CM. 1992. Race and the schooling of African Americans. Atl. Mon. 269:68–78 Steele CM. 1997. A threat in the air: how stereotypes shape intellectual identity and performance. Am. Psychol. 52:613–29 Steele CM, Aronson J. 1995. Stereotype threat and the intellectual test performance of African Americans. J. Personal. Soc. Psychol. 69:797–811 Steele CM, Spencer SJ, Aronson J. 2002. Contending with group image: the psychology of stereotype and social identity threat. In Advances in Experimental Social Psychology, ed. MP Zanna, vol. 34, pp. 379–440. San Diego, CA: Academic Stone J. 2002. Battling doubt by avoiding practice: the effects of stereotype threat on selfhandicapping in white athletes. Personal. Soc. Psychol. Bull. 28:1167–78 Swim JK, Hyers LL, Cohen LL, Ferguson MJ. 2001. Everyday sexism: evidence for its incidence, nature, and psychological impact from three daily diary studies. J. Soc. Issues 57:31–53 Swim JK, Thomas MA. 2004. Responses to stigma: a synthesis. See Levin & van Laar 2004. In press Tajfel H, Turner JC. 1986. The social identity theory of intergroup behavior. In The Psychology of Intergroup Relations, ed. S Worchel, WG Austin, pp. 7–24. Chicago: Nelson-Hall Taylor DM, Wright SC, Porter LE. 1994. Dimensions of perceived discrimination: the personal/group discrimination discrepancy. In The Psychology of Prejudice: The Ontario Symposium, ed. MP Zanna, JM Olson, vol. 7, pp. 233–55. Hillsdale, NJ: Erlbaum Taylor J, Turner RJ. 2002. Perceived discrimination, social stress, and depression in the transition to adulthood: racial contrasts. Soc. Psychol. Q. 65:213–25 Twenge J, Crocker J. 2002. Race, ethnicity, and self-esteem: meta-analyses comparing whites, blacks, Hispanics, Asians, and Native Americans, including a commentary on

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THE SOCIAL PSYCHOLOGY OF STIGMA Gray-Little and Hafdahl (2000). Psychol. Bull. 128:371–408 Vorauer J, Kumhyr SM. 2001. Is this about you or me? Self- versus other-directed judgments and feelings in response to intergroup interaction. Personal. Soc. Psychol. Bull. 27:706– 19 Walton GM, Cohen GL. 2003. Stereotype life. J. Exp. Soc. Psychol. 39:456–67 Wheeler SC, Petty RE. 2001. The effects of stereotype activation on behavior: a review of possible mechanisms. Psychol. Bull. 127:797–826 Wheeler SC, Petty RE, Jarvis WBG. 2001. Think unto others: the self-destructive impact of negative racial stereotypes. J. Exp. Soc. Psychol. 37:173–80 Williams DR. 1997. Race and health: basic

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questions, emerging directions. Ann. Epidemiol. 7:322–33 Williams DR, Yu Y, Jackson J, Anderson N. 1997. Racial differences in physical and mental health: socioeconomic status, stress and discrimination. J. Health Psychol. 2:335– 51 Winkielman P, Berridge K. 2004. Unconscious emotion. Curr. Dir. Psychol. Sci. 13:120–23 Yinger JM. 1994. Ethnicity: Source of Strength? Source of Conflict? Albany, NY: State Univ. NY Press Zajonc RB. 2000. Feeling and thinking: closing the debate over the independence of affect. In Feeling and Thinking: The Role of Affect in Social Cognition. Studies in Emotion and Social Interaction, Second Series, pp. 31–58. New York: Cambridge Univ. Press

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PERSONALITY ARCHITECTURE: Within-Person Structures and Processes


Daniel Cervone Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607-7137; email: [email protected]

Key Words personality structures, personality dynamics, personality coherence ■ Abstract This chapter reviews theory and research on intraindividual personality structures and processes. Principles for modeling the architecture of personality, that is, the overall design and operating characteristics of intraindividual personality systems, are addressed. Research demonstrates that a focus on within-person structures and processes advances the understanding of two aspects of personality coherence: the functional relations among distinct elements of personality, and cross-situational coherence in personality functioning that results from interactions among enduring knowledge structures and dynamic appraisal processes. Also reviewed are recent conceptual and empirical advances, which demonstrate that the interindividual personality variables that summarize variability in the population are wholly insufficient for modeling intraindividual personality architecture.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Two Meanings of Personality “Structure” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . BETWEEN-PERSON TAXONOMIES AND WITHIN-PERSON SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Between-Person and Within-Person Constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . Taxonomic Versus Explanatory Constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . “Having” and “Doing” . . . or is it “Doing” and “Having”? . . . . . . . . . . . . . . . . . . . Personality and Individual Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PERSONALITY ARCHITECTURE: INTRAINDIVIDUAL PROCESSES AND STRUCTURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling Personality Architecture: On “Parts” and the Whole . . . . . . . . . . . . . . . . Meta-Theory: The Person as a Complex Dynamic Processing System . . . . . . . . . . Theory: Three Principles for Distinguishing Among Personality Structures and Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PERSONALITY COHERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Functional Coherence Among Personality Structures and Processes . . . . . . . . . . . . Cross-Situational Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0423$14.00

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INTRODUCTION In its earliest days, contributors to the Annual Review of Psychology treated personality psychology as a whole (Bronfenbrenner 1953, Eysenck 1952, Mackinnon 1951, Sears 1950). More recently, the series has cleaved the field in two, with the division resting on a distinction between personality processes (Mischel & Shoda 1998, Revelle 1995) and structures (Digman 1990, Wiggins & Pincus 1992).1 A thesis of the present contribution is that a division of labor is necessary but the process/structure distinction is not the right one. This conclusion is compelled by recent conceptual and empirical advances, which constitute the primary focus of the present chapter.

The Two Meanings of Personality “Structure” Anyone seeking to understand the scientific literature in personality psychology, including the import of recent advances, faces an obstacle: Writers refer to two different phenomena with the same term. One phenomenon is the within-person content and organization of personality systems. Psychologists from Freud (1923) to the present day (Magnusson & Torestad 1993, Mischel 2004) have provided scientific models of intra individual mental systems that underlie people’s distinctive patterns of experience and action. The other phenomenon is betweenperson variation in the population at large. Here, investigators summarize interindividual variations by identifying between-person categories or dimensions of population variation (e.g., Asendorpf 2003, Ashton et al. 2004, Saucier 2003). The obstacle to understanding involves the word “structure.” In its naturallanguage usage, it refers to the stable organization among the parts of a whole entity. If the whole entity being studied is the individual person and if the withinperson organization of personality systems is stable across time and place (e.g., Magnusson 2003, Mischel & Shoda 1998), then investigators who analyze intraindividual psychological systems can be said to be analyzing personality structure. If the entity being studied is the population and if investigators find that the dimensions of variation that characterize the population are stable across instruments, observers, and subpopulations (McCrae & Costa 2003), then these investigators, too, could be said to be analyzing structure. In practice, the term is employed to describe both endeavors, which confuses the field’s discourse. The fact that the term structure is used to refer to two distinct entities—organization among mental systems in the head of the individual and dimensions of between-person variation 1

Coverage of personality psychology in the Annual Review of Psychology does extend beyond chapters on personality structure versus process. It includes reviews of personality development (Caspi et al. 2005) and assessment (Butcher & Rouse 1996, Ozer & Reise 1994), plus numerous special topics. Also, rather than organizing their coverage around psychological systems, some writers have chosen to highlight theoretical paradigms (Funder 2001) or controversial issues (Pervin 1985) in the discipline. Nonetheless, the structure/process division has been the primary framework for organizing the core of the field’s activities.

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in the population at large—suggests that it is not optimal for organizing scientific advances. A superior organization would capitalize on the distinction between intraindividual and interindividual analyses. Numerous writers have explained that withinperson and between-person analyses, although they surely may inform one another, nonetheless constitute domains of inquiry that are conceptually distinct (e.g., Allport 1937, Asendorpf 2002, Block 1995, Epstein 1994, Murphy 1947, Revelle 1995, Rorer 1990). “Research at the level of the human individual requires its specific theoretical model, methodological tools, and research strategies” (Magnusson 2003, p. 4). The present chapter takes an intraindividual focus. We review recent work that seeks a coherent, synthetic account of intraindividual personality structures and dynamics, or “personality architecture” (Cervone 2004a). Before turning to questions of personality architecture, we consider the following possibility. There might be a lucky coincidence. Psychological constructs that describe interindividual variation in the population might, in principle, also capture intraindividual mental systems that explain the functioning of the individual person. Personality psychologists might be able to kill their two quarries with one stone. To many, this possibility appears quite plausible: The assumption that constructs identified in between-person research can be treated also as within-person structures is explicit in recent theorizing (McCrae 2005; McCrae & Costa 1996, 1999) and implicit in much of the field’s discourse. A critical feature of recent advances is that they completely undermine this assumption. In so doing, these advances motivate renewed attention to the topic that historically was the defining concern of personality psychology: the psychological life of the individual (e.g., Allport 1937, Stern 1935; see Lombardo & Foschi 2002, 2003).

BETWEEN-PERSON TAXONOMIES AND WITHIN-PERSON SYSTEMS Between-Person and Within-Person Constructs A converging set of advances underscores the distinction between interindividual and intraindividual analyses. One such development concerns scientific explanation. Borsboom et al. (2003) clarify the distinction between two explanatory tasks: (a) accounting for differences between persons and (b) explaining the experiences and actions of an individual person. Combined psychometric and philosophical analyses demonstrate that between-person analytical methods and psychological constructs do not substitute for an analysis of psychological structure at the level of the individual case. “Between-subjects models do not imply, test, or support causal accounts that are valid at the individual level” (Borsboom et al. 2003, p. 214). This does not mean that intraindividual and between-person analyses are always unconnected. Psychological constructs may sometimes prove to be locally homogenous (Borsboom et al. 2003), that is, to pertain not only to population variation but also

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to the individual persons in the population; the positive relation between performing extraverted acts and experiencing positive affect appears locally homogenous among U.S. college students (Fleeson et al. 2002). Of great importance, however, is that local homogeneity cannot be assumed. Constructs may prove to be locally irrelevant (Borsboom et al. 2003), that is, to capture between-person variability while not in any way providing an explanatory account of individual persons. Borsboom et al.’s (2003, p. 215) analysis reveals that variables “such as the factors in the Five Factor Model” are of this latter sort; they “[do] no work at the individual level” (p. 215), that is, “they should . . . not be conceptualized as explaining behavior at the level of the individual.” Five-factor constructs speak to betweenperson variability, not within-person causal dynamics (cf. Block 1995, Caprara & Cervone 2000, Cervone 1999, Lamiell 1997, Rorer 1990). One simple consideration pointing to this conclusion is that, when seeking causal explanation, one must at minimum identify factors that covary with the occurrences that are to be explained (e.g., Pearl 2000). However, in approaches such as five-factor theory, a person’s standing on personality dimensions is construed as a constant (e.g., Costa & McCrae 1994), which means that it does not covary with any within-individual occurrences or developmental processes and thus cannot be invoked as their cause (Borsboom et al. 2003). A complementary advance derives from psychometric simulation studies by Molenaar and colleagues (e.g., Molenaar et al. 2002). Results indicate that a factor structure that describes between-person variation in the population may fail to describe any individuals in that population. Pooling data from individuals may yield a simple between-person structure that does not capture the qualities of any of the individual persons (Molenaar et al. 2002). This demonstrates unequivocally that factor structures that summarize variations in a population cannot be assumed to capture psychological tendencies or structures at the level of the individual case. These simulation results are complemented by studies of actual individuals. When people’s behavioral tendencies are studied one person at a time, with data collected at multiple time points, the five-factor model that robustly captures betweenperson dimensions of variation is found in only a small minority of individual cases (Borkenau & Ostendorf 1998; also see Hamaker et al. 2004). An individual case illustrates the type of discrepancy between inter- and intraindividual results that occurs commonly. Borkenau & Ostendorf (1998) report an individual for whom the psychological characteristic “calm” had only a low loading on neuroticism, prototypical conscientiousness items (“industrious” and “responsible”) loaded highly on extraversion but not conscientiousness, and “domineering” (which has little to do with the definition of between-person conscientiousness) was the third highestloading conscientiousness item (Borkenau & Ostendorf 1998). These advances speak forcefully to a prominent theoretical position, namely, five-factor theory (McCrae & Costa 1996, 1999). This theory claims that the between-person latent variables identified in the analysis of the population are within-person structures possessed by all individuals; the factors are “universal

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raw material” (McCrae & Costa 1996, p. 66) that “define the individual’s potential and direction” (p. 68). Borsboom et al.’s work makes it plain that this interpretation is not an empirical finding but “an article of faith” (Borsboom et al. 2003, p. 206). Robust between-person findings provide no evidence that the between-person constructs correspond isomorphically to within-person mental systems. This fact fuels recent calls for an idiographic science of persons (Molenaar 2004; also see Lamiell 2003), that is, a science that would seek general principles that apply at the level of the individual by conducting research at the required level of analysis: the individual. This call is consistent with a growing zeitgeist in the behavioral and social sciences (see Shweder 1999, 2000) whose defining theme is that an understanding of persons requires careful analysis of concrete, contextualized cases (cf. Geertz 1973, Wittgenstein 1953).

Taxonomic Versus Explanatory Constructs A complementary point distinguishes two functions that scientific constructs serve: classification and explanation (Harré 1998). Classificatory constructs organize entities in a population but do not provide scientific explanation: “To say that a chimpanzee is a primate does not explain anything about its characteristics” (pp. 80–81). Taxonomic, classificatory constructs “are of the wrong logical type” to serve as explanations; in the domain of personality, “we need higher order dispositional constructs in talking about people, but their only function is classificatory” (Harré 1998, p. 80). Harré’s (1998) point seems obvious once stated. Yet it conflicts with common discourse in personality psychology. Prominent discussions involve “Big Five” personality variables. It can be difficult to discern whether writers intend these constructs to be treated merely as taxonomic classifications or also as intraindividual entities with causal force. For example, John & Srivastava (1999) treat the variables as a “taxonomy [that] provides descriptive concepts” (p. 130), with the variables differentiating “domains of individual differences” (p. 131) in the population. Yet they also posit that Big Five constructs “combine into coherent patterns within individuals” (p. 127). Whatever the empirical support for the latter contention,2 conceptually it is difficult to reconcile the statements. Taxonomic, classificatory variables generally are not construed as combining within the individuals being classified (at least if “combine” retains its normal meaning: to mix together, yielding a compound). A Linnaean taxonomy differentiates plants from animals; vertebrate animals from invertebrates; mammals from reptiles, etc. But when one gets down to individual cases, one will not find that the classificatory 2

The idea that Big Five variables combine into patterns within the individual is based on the possibility that interindividual-differences analyses may yield three distinct person types (see Caspi 1998). Empirical findings, however, do not replicate the person types consistently across samples and data sources (Asendorpf 2002, Costa et al. 2002, De Fruyt et al. 2002, Rammstedt et al. 2004).

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variables exist as unitary entities within the individual, such that they may combine intraindividually. There won’t be any synergistic admixture of vertebrateness, mammalness, and primateness in Harré’s chimp. The personality psychologist may combine between-person taxonomic variables in a statistical program, but this does not imply that the variables represent intraindividual structures that combine within the person. Treating between-person taxonomic variables as entities that combine within individuals might be reasonable if one expects that future research will lead to “an explication . . . of the descriptive taxonomy in causal and dynamic terms” (John & Srivastava 1999, p. 103), where the explication might identify withinperson causal systems that are distinctively defining of each Big Five variable. Yet this expectation, too, seems problematic. The descriptive taxonomy is a betweenpersons classification but the required causal dynamics are intraindividual and, as noted (Borsboom et al. 2003), between-person and intraindividual analyses may not correspond isomorphically. Furthermore, explicating the causal dynamics of an individual-differences dimension implies that the dynamics will explain why people have a given standing on the dimension. This, in turn, implies that people with the same score will share the same causal dynamics; otherwise, there can be no consistent explanation—no “bridge principles” (Bennett & Hacker, 2003)—linking causal dynamics to factor scores. Such bridge principles may be identified when individual-difference dimensions tap a unitary psychological system that is homogenous across persons, in other words, when there is only one way to obtain a given score on the dimension. But five-factor dimensions are explicitly multifaceted (Costa & McCrae 1992), encompassing a range of distinct (albeit intercorrelated in the population) tendencies. This opens the door to equifinality: Different individuals may obtain the same aggregate factor score for different underlying reasons. Equifinality, in turn, implies that there cannot be a singular causal-dynamic explication of persons’ standings on the dimension. Although the primary issues here are conceptual, it is noteworthy that, empirically, behavior-genetic analyses of individual differences in the endorsement of adjectives that are markers of the Big Five trait variables also question the notion that these variables correspond to unitary latent entities. For the majority of the variables (agreeableness, conscientiousness, and openness), the analysis of genetic and environmental sources of variance reveals “little coherent etiological structure” (Johnson & Krueger 2004), suggesting that the classificatory variables in fact do not correspond to unitary latent personality constructs.

“Having” and “Doing” . . . or is it “Doing” and “Having”? The distinction between intraindividual analyses and between-person taxonomic variables speaks to other prominent aspects of discourse in personality psychology. One involves the field’s “having” and “doing” sides (Cantor 1990). Specifically, a commonly accepted notion (e.g., Little 2004, McCrae & Costa 1996) is that

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(a) between-person taxonomic dimensions—“the major and stable dimensions on which people can be said to differ” (Cantor 1990, p. 735)—refer to qualities that people “have,” whereas (b) cognitive and affective systems of personality— the “schemas, tasks, and strategies” through which people “interpret life tasks . . . [envisage] alternative future selves, and [devise] cognitive strategies” (Cantor 1990, p. 735)—are what people “do” (Cantor 1990). Upon reflection, this formulation seems backward. Between-individual taxonomic constructs are not generally construed as qualities that individuals “have” (at least if “have” retains normal meaning: possess). A chimpanzee does not have a primate. It is a primate (Cervone 2004b). Conversely, knowledge structures through which people interpret events and plan courses of action can, in fact, be said to be things people “have”; Bennett & Hacker (2003) incisively explain that beliefs (e.g., about the self and life tasks) can rightly be said to be attributes of a person— i.e., qualities an individual has—and that these beliefs must figure centrally in an explanation of intentional action (also see Bandura, 2001). People of course have more than just beliefs; they also possess biological systems that do not represent features of the world yet causally contribute to affective experience and behavioral tendencies when activated by relevant contexts (Kagan 2003). These cognitive and affective systems are what the individual “has.” In contrast, dispositional constructs—e.g., “an individual’s standing on abstract dispositions of sociability or openness to experience and the like” (Cantor 1990, p. 735)—summarize things people tend, on average, to do (Buss & Craik 1983). They thus represent the field’s “doing” side. It is in this sense that the traditional having/doing discourse is backward. The failure to adhere to the between-/within- distinction also fosters misunderstanding of research programs. In a publication devoted to dynamical systems perspectives (Vallacher et al. 2002), Shoda and colleagues (2002, p. 324) declare their interest in “analyzing the structure of the person’s cognitive-affective processing system (Shoda & Mischel 1998)”; that is, the structure of personality within their theoretical model (Mischel & Shoda 1995). Paradoxically, Read & Miller (2002) criticize “Shoda and his colleagues” for “not currently focusing on trying to capture the structure of personality” (Read & Miller 2002, p. 367). These writers are using the word “structure” to refer to entirely different things. Shoda and colleagues are referring to intraindividual structure: the “stable organization or network of interrelations” (Mischel & Shoda 1998, p. 238) within a given individual’s personality system. Read & Miller (2002) are using the same word to refer to a different entity: the interindividual structure of “individual differences in personality and behavior” (p. 358) described by taxonomic systems such as “the Big Five analysis” (p. 357).

Personality and Individual Differences In sum, recent developments indicate that an optimal way to organize advances in personality psychology is to recognize the distinction between two endeavors:

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the development of interindividual taxonomic systems and the analysis of intraindividual personality structure and dynamics. These two inquiries surely inform one another. The point here is that they cannot substitute for one another. Three features of this conclusion should be noted. First, it does not represent the opinions of one particular “theoretical camp.” Distinguished investigators who themselves develop lexical individual-difference taxonomies fully recognize both the merits and limits of such inquiry. Saucier et al. (2000) explain, “[T]he study of different lexicons [of personality description] can lead to a useful and highly generalizable classification system for personality traits, but this classification system should not be reified into an explanatory one. A model of descriptions does not provide a model of causes, and the study of personality lexicons should not be equated with a study of personality” (p. 28). Second, an intraindividual focus does not indicate disinterest in individual differences. Instead, it represents a strategy for understanding them: a “bottom-up” strategy (Cervone 1997, 1999; cf. Eliasz & Klonowicz 2001, Zelli & Dodge 1999) in which psychological tendencies are explained by reference to specified underlying psychological systems rather than abstract, “high-level” dispositional constructs. A critical implication of this strategy is that the differences among individuals include but go beyond differences in average response tendencies (Cervone & Shoda 1999a). People differ not only in mean response tendencies but also in patterns of variability in response across contexts (Vansteelandt & Van Mechelen 2004), where the relevant contexts may vary idiosyncratically (Cervone 1997, 2004a; Shadel et al. 2000, 2004). Individuals differ not only in mean affective experience but also in rates of change in affect after exposure to emotionally significant stimuli (Hemenover 2003). Third, the ideas here are not new. In the early days of the Annual Review of Psychology, synthetic accounts of personality structures and processes (Bronfenbrenner 1953, Eysenck 1952, Mackinnon 1951, Sears 1950) appeared side by side with chapters on a distinct scientific task: the charting of individual differences (Anastasi 1953, Humphreys 1952, Thorndike 1950, Tyler 1951). The first editors of this series, in other words, already recognized that the best way to organize the field was by providing coherent accounts of personality processes and structures in the individual, and accompanying these with separate chapters on the assessment of individual differences in the population. There was wisdom in the ancients.

PERSONALITY ARCHITECTURE: INTRAINDIVIDUAL PROCESSES AND STRUCTURES Personality psychologists have devoted much effort to specifying a system of between-person constructs that summarizes individual differences comprehensively. Curiously, they recently have devoted lesser attention to an equally compelling task: specifying a system of within-person constructs that model intraindividual personality structures and processes comprehensively. This task is analogous to one faced by the cognitive psychologist (Anderson 1983). The

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personality psychologist must model the overall design and operating characteristics of personality systems, or the architecture of personality (Cervone 2004a). This effort is distinct from that of the cognitive scientist in that personality psychology is charged with explaining broad patterns of experience and social behavior, which inherently entails a more molar level of analysis than is prototypic of cognitive science.


Modeling Personality Architecture: On “Parts” and the Whole Historically, theorists who have modeled intraindividual personality architecture have done so by adopting a conceptual strategy that has been of incalculable value to the sciences. They have divided their target of investigation into distinct parts, or variables. In personality theory, the variables refer to qualitatively distinct personality subsystems, that is, functionally interrelated parts of the whole person. Theorists have drawn different distinctions among subsystems, yielding different personality theories (Pervin et al. 2005). Before we review recent efforts of this sort, it is valuable to consider the nature, costs, and benefits of this overall strategy. A basic goal for a personality theory is to explain individual experiences and actions, and enduring tendencies in experience and action. Scholars instruct that, when explaining human actions, one should attribute agency to the whole person, not isolated parts of persons (Bennett & Hacker 2003, Harré 1998). Indeed, because “a human being is a psychophysical unity” (Bennett & Hacker 2003, p. 3), the entire notion that persons have “parts” may be “at best a metaphor” (Harré 1998, p. 15). The widespread strategy of carving whole persons into discrete variables may reflect an overly zealous importation of physical sciences methods into the human sciences (Geertz 2000; Polkinghorne 1988; Taylor 1985a,b). Such concerns follow from the following consideration. A fundamental fact of personhood is that people construct meaning out of the situations they encounter. The “imposition of meaning on life is the major end and primary condition of human existence” (Geertz 1973, p. 434; cf. Shweder & Sullivan 1990, Markus 2004). The agent who constructs meaning is the whole person, not a subpart of the person (Bennett & Hacker 2003). Explanations of a person’s everyday social behavior, then, commonly “do not work by explaining the behavior of wholes in terms of the properties and behavior of their parts. Rather, they work by explaining the behavior of human beings by reference to the context in which they find themselves and to the reasoning they go through” (Bennett & Hacker 2003, p. 364). Dividing the individual into parts—discrete variables—thus appears to conflict with the overarching goal of explaining the experiences and actions of whole, coherent individuals (Cervone & Shoda 1999b, Cloninger 2004). Another caveat involves theory construction. In the world of reality, there really exist people who possess cognitive and affective systems and display distinctive behavioral tendencies. Personality exists. Yet theories about persons are social constructions. Theoretical models inevitably reflect, and are constrained by, the sociocultural and linguistic systems within which they arise (Gergen 2001).

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This set of concerns might make one chary of constructing a multivariable model of personality architecture. Yet such effort can be useful, and can be undertaken without naive assumptions about the ultimate truth of one’s theoretical system (see Kelly 1955). Giere (1999) explains that theories provide conceptual models of the real world. Models inevitably are imperfect. They cannot be considered “true.” Yet a model may capture important aspects of the world, yielding “realism without truth” (Giere 1999, p. 6). Proctor & Capaldi (2001) relatedly suggest that theories should be evaluated according to a pragmatic criterion: Does the theory provide a useful model of its target phenomenon? This is superior to a falsifiability criterion because, if all theories are limited (Giere 1999), all can be falsified in some respects, which could yield the rejecting of all theories, some of which may be useful. One can model personality architecture with this pragmatic criterion in mind. One also can formulate a multipart model of personality architecture while acknowledging that actions should be explained by reference to whole persons, not person parts. Taylor (1985a, Chapter 5) valuably delineates different aspects to the explanation of human action. One involves identifying psychological competencies that enable a person to understand and respond to the world. These competencies can be modeled by distinguishing among qualitatively distinct psychological subsystems (e.g., Bandura 1986, Mischel 1973). In addition, however, one may wish to explain the actions of a given person at a given time and place, where “explain” means to “make sense of” (Taylor 1985a, p. 27) the person’s actions. This requires that one understand the whole, coherent, contextualized person. Discursive (Harré & Van Langenhove 1998) and dialogical approaches (Hermans 2003) contribute to personality psychology by providing tools for such interpretation.

Meta-Theory: The Person as a Complex Dynamic Processing System Personality architecture has been analyzed at two conceptual levels. Metatheoretical analyses provide general principles for conceptualizing the nature of persons. Theoretical analyses specify structures and processes that might explain patterns of personality functioning at the level of the individual case. An encouraging sign for personality science is that at the level of meta-theory there is much consensus. It reflects developments in the sciences at large (Kauffman 1995, Taylor 2001, Waldrop 1992). Numerous writers view personality as a complex dynamical system (Fraley & Brumbaugh 2004, Nowak & Vallacher 1998, Read & Miller 2002, Shoda et al. 2002). A complex systems viewpoint has three implications of particular importance. One concerns explanation. A complex system’s overt pattern of behavior is not explained by inferring an internal structure that corresponds to the pattern of behavior to be explained (cf. Funder 1991), but by reference to interactions among multiple elements of the system, no one of which corresponds isomorphically to an overt response pattern. The second implication concerns the overt properties of the system that require explanation. These properties include not only the system’s

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average tendencies, but also dynamic variations in behavior (Nowak et al. 2002, Shoda et al. 2002). The third implication involves development. The development of a dynamical system is not prefigured, or preprogrammed. Development occurs via reciprocal interactions between the system and the environments that the system encounters. In the study of personality, this implies that developmental outcomes are not encoded in the genome, but result from dynamic person-environment transactions. Findings suggest that these transactions include agentic processes in which people contribute to the development of their own behavioral and affective tendencies (Caprara et al. 2003, 2004). Recent reviews of biological mechanisms in developmental dynamics by Lickliter & Honeycutt (2003a,b) provide compelling evidence of dynamic organism-environment interactions that occur throughout ontogenesis. The interactions include the environmental activation of genetic mechanisms that, in turn, contribute to the organism’s biological make-up (Gottlieb 1998, Lewontin 2000, Li 2003). IMPLICATIONS FOR NATIVIST EVOLUTIONARY PSYCHOLOGY AND FIVE-FACTOR THEORY Advances in systems analyses of person-environment interactions severely

challenge two conceptual frameworks. One is evolutionary psychology—specifically, that prominent subset of evolutionary views described (Heyes 2003) as human nativist evolutionary psychology (HNEP). This perspective is defined by the claim that the mind consists of large numbers of innate, domain-specific mental modules that are evolutionarily prespecified (Buss 1991, Pinker 1997, Tooby & Cosmides 1990). Lickliter & Honeycutt (2003a,b; cf. Buss & Reeve 2003, Tooby et al. 2003) review biological evidence that environmental factors stimulate gene activity, with the result that phenotypic outcomes are not preprogrammed in the genes but result from developmental dynamics that include organism-environment interactions. “To regard the phenotypic traits of an organism as simply the ‘manifestation’ or ‘expression’ of its genotype . . . ignores the known principles of both developmental biology and psychology” (Lickliter & Honeycutt, 2003a, p. 825). Relatedly, Heyes (2003) reviews evidence of four pathways through which cognitive systems develop, some of which involve environment-organism interactions during ontogeny that are not addressed in the traditional HNEP framework. Another challenge to HNEP is recent failures to replicate results that originally were foundational to this perspective. Purported gender differences in the events that trigger jealousy appear to reflect methodological artifacts (DeSteno et al. 2002). Previously reported gender differences in mate preferences are not found in reanalyses of the original data sets (Miller et al. 2002; also see Bussey & Bandura 1999, Wood & Eagly 2002). Furthermore, traditional HNEP fails even to address one of the most central issues in personality psychology: individual differences in the interpretation of ambiguous social encounters (Cervone 2000). Most encounters are open to multiple interpretations; a coworker, for example, might be interpreted as someone who could advance one’s position in a status hierarchy, cheat one in social exchange, or

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become a sexual mate. In the language of evolutionary psychology, the different interpretations indicate the presence of different evolutionarily significant domainspecific problems that purportedly activate different mental mechanisms (Pinker 1997). But before any of these domain-specific mechanisms can become active, the individual facing ambiguity must, in essence, figure out what domain they are in. This “figuring out” inherently must be performed by mechanisms that are domaingeneral, not domain-specific (Cervone 2000; cf. Fodor 2000, Mithen 1996). An evolutionary framework that posits only domain-specific mental modules provides no tools for addressing this issue, and thus is inherently inadequate as a foundation for analyzing personality architecture. The second theoretical framework is five-factor theory (McCrae & Costa 1996, 1999). Two unique claims distinguish this view. One is that between-person dimensions of variation identified “through studies of groups” correspond to “intrapsychic personality structure(s)” (McCrae 2005, p. 193). As already noted, this is a theoretical claim that receives no support whatsoever from the between-person analyses that are the empirical foundation of the model. The second claim is that personality structures are “influenced not at all by the environment” (McCrae et al. 2000, p. 175). Unlike dynamic systems views, five-factor theory is explicitly noninteractionist. The original basis for this claim was behavioral genetic evidence; substantial heritability of individual differences was taken as evidence that biology independently determines personality traits. But contemporary research on genetics and personality does not support the noninteractionist position of five-factor theorists. It contradicts it. In genetics and personality development, “everything is interactive . . . no arrows proceed uninterrupted from cause to effect” (Turkheimer 2000, p. 161). Geneticists recognize that organisms develop via interactions between the biological being and the environment in which it develops; genetic influences thus cannot be understood without considering the context in which development occurs (Grigorenko 2002). Recent findings vividly illustrate such interactions. Genetic contributions to individual differences in phenotypic outcomes vary as a function of social factors; heritability contributes substantially to variation in IQ in high but not low socioeconomic status groups (Turkeimer et al. 2003). Variations in genes that influence neurotransmitter systems affect psychological outcomes interactively; variation in a serotonin transporter gene only predicts depression among individuals who experience highly stressful environments (Caspi et al. 2003; also see Caspi et al. 2002). In addition to interactions, environments exert main effects. Alleviating poverty reduces childhood behavioral disorders (Costello et al. 2003). Media violence increases aggressive behavioral tendencies (Anderson et al. 2003). Sociocultural factors that vary from one historical birth cohort to another produce between-cohort variations in personality trait scores (Twenge 2002). Data also contradict the claim that people’s standing on individual-difference dimensions does not change across the course of life (Costa & McCrae 1994). Cross-sectional and longitudinal findings indicate significant degrees of life-course change (Foster et al. 2003; Helson et al. 2002a,b; Srivastava et al. 2003).

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In sum, of the two unique claims of five-factor theory (McCrae & Costa 1996, 1999), one is not based on evidence and the other is contradicted by evidence. The idea that personality can be treated as a complex dynamical system is found in various theoretical models, including Carver & Scheier’s (1998, 2002) control process approach, Cloninger’s (2003, 2004) theory of temperament, character, and personality coherence, and Kuhl and colleagues’ (2000, Kuhl & Koole 2004) personality systems interaction (PSI) theory. A particularly prominent systems formulation is the cognitive-affective personality systems (CAPS) model of Mischel & Shoda (1995, 1998). The CAPS model distinctively highlights three points. Two involve internal personality dynamics: Personality systems are understood not only in terms of the content of their cognitive and affective units but also the coherent organization of those units; and, second, the personality system functions in interaction with the social environment, with situational features activating internal dynamics in a manner that may vary idiosyncratically from one person to the next (Shoda & LeeTiernan 2002). The third point concerns overt expressions of the personality system. The CAPS model highlights the importance of variability in response; people display behavioral profiles in which variability in behavior distinctively characterizes the individual. Because a recent chapter (Mischel 2004) has reviewed research guided by the CAPS model, such work will not be reviewed here except to note a recent contribution by Vansteelandt & Van Mechelen (2004; also see Vansteelandt 1999, Vansteelandt & Van Mechelen 1998). They addressed the following issue. The original CAPS research methodology (e.g., Shoda et al. 1994) established that individuals display distinctive profiles of response but it did not provide tools to identify subgroups of individuals with similar profile shapes or systematic individual differences within subgroups. Vansteelandt & Van Mechelen (2004) present a two-stage modeling procedure for identifying such subgroups and variations. Applied to self-reports of anger, the method identified three distinct subgroups of individuals, plus within-subgroup variations that reflected differences in the hostile encoding of frustrating encounters (cf. Zelli & Dodge 1999). In addition to traditional empirical work, the systems viewpoint has fostered computer simulations of personality dynamics. Work by Nowak et al. (2002), as well as by Shoda et al. (2002), has shown how stable personality styles may result not only from internal personality dynamics but from dynamic interactions with interpersonal partners. A neural network model developed by Read & Miller (2002) demonstrates that a simulation of the individual that features approach and avoidance motivation systems plus agentic and communal goal systems is capable of “producing the Big 5 and other traits from personality dynamics” (p. 367). The present paper’s theme does raise a question about this latter result. Simulations of individual virtual personalities (“individual VP’s,” p. 366) yield “3 of the Big 5 dimensions” (Read & Miller 2002, p. 367), and the authors explain how the other two dimensions could be recovered in simulations of the individual, as well. But,


CONTEMPORARY SYSTEMS PERSPECTIVES

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as noted above, studies of real individual personalities do not consistently recover the Big Five at the level of the individual case (Borkenau & Ostendorf 1998), which raises the question of what, exactly, the computer model is simulating.


Theory: Three Principles for Distinguishing Among Personality Structures and Processes A systems approach such as the CAPS model is a “meta-theory” that facilitates theoretical analyses of how “constituent components and subsystems and processes [of personality] interact and exert their influences” (Mischel 2004, p. 13). To move from meta-theory to explanatory theory, then, one must specify these subsystems by distinguishing among intraindividual structures and processes in personality functioning. It is not difficult to draw conceptual distinctions among mental contents that contribute to personality functioning. Indeed, it is so easy that the ease itself is problematic. Nomenclature proliferates. Contemporary personality scientists refer to mental contents and states via a potpourri of terminology: schemas, expectations, strivings, orientations, strategies, competencies, intentions, motivations, moods, perceptions, regulatory mechanisms, etc.—plus their “self-” variants. The size and relative lack of organization of this lexicon confronts students of intraindividual personality architecture today with a problem analogous to that faced by students of interindividual personality taxonomies decades ago: a “Babel of concepts” (John 1990, p. 66), an absence of bedrock distinctions, and, more deeply, an absence of principles through which a set of comprehensive distinctions could be identified. In searching for a set of basic variables for modeling personality architecture, personality psychologists need not “go it alone.” Insights can be found not only throughout the interdisciplinary field of personality science (Cervone & Mischel 2002) but in the philosophy of mind. A recent theory (Cervone 2004a) capitalizes on such contributions in identifying three distinctions that are fundamental to modeling personality architecture—where “fundamental” is meant here to imply that any theoretical model that failed to recognize these distinctions would be a poor guide to the conceptualization and assessment of intraindividual personality systems. The first of these distinctions involves the notion of intentionality. This term refers to a particular property of mental contents, namely, that they are directed beyond themselves to objects in the world. If people have a belief, for example, “it must be a belief that such and such is the case” (Searle 1983, p. 1). It makes no sense to say that one is “just believing,” with no particular belief about the world in mind. In contrast, some mental contents do not have the quality of intentionality. These include feeling or “core affect” states (Russell 2003). It would make sense to say of someone that they are “just feeling” something (e.g., tired) at a given moment, without the feeling representing or being directed to something in the world (one need not be tired “about” or “of” something). A fundamental distinction in modeling personality architecture, then, differentiates mental contents

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that do have from those that do not have the quality of intentionality (Cervone 2004a). This distinction implicitly is recognized in various two-process models, such as Metcalfe & Mischel’s (1999) “hot-cool” approach and Cloninger’s (2003, 2004) distinction between “temperament” and “character” systems. It is consistent with LeDoux’s (1996) delineation of two neural pathways in the processing of emotional stimuli and with recent evidence of distinct neural systems involved in “hot” versus “cold” processes in empathic reactions of disgust (Wicker et al. 2003). Heyes’s (2003) analysis of phylogenetic and ontogenetic processes in cognitive evolution similarly includes a distinction between cognitive and noncognitive mechanisms. The distinction between intentional and nonintentional mental contents has two important implications. One concerns this chapter’s theme: the distinction between inter- and intraindividual analyses. When modeling intraindividual personality architecture, this distinction is fundamental. Yet in variable systems obtained by factor analyzing individual differences in the population, it does not even appear. As Cloninger (2003) has observed, “models of personality . . . derived by linear factor analysis do not distinguish temperament and character, and so confound relevant developmental phenomena in which these domains differ” (p. 162). Between-person variable systems clearly cannot function as models of withinperson architecture because they fail to capture even the simplest distinction that is required when modeling intraindividual systems. The second point concerns personality assessment, specifically, the distinction between global and contextualized assessment strategies (see, e.g., Cervone & Caprara 2001). Global assessments aggregate responses across situations to index overall response tendencies. Contextualized assessments tap responses in specific circumstances that are judged, on theoretical grounds, as relevant to the psychological system being assessed. The choice of global versus contextualized assessments commonly is discussed as if it were a stylistic preference. Theorists of different backgrounds are said to prefer different options. For example, Funder (2001) suggests that theorists interested in “the cognitive processes of the individual” have an “aversion” (p. 203) to cross-situational generality in personality functioning and therefore choose contextualized strategies. Such discussions of purported theoretical preference ignore the substantive connection between the phenomenon being assessed and the assessment strategy that is required. Investigators conceptualize and assess “the cognitive processes of the individual” (Funder 2001, p. 203) in a contextualized manner because the cognitive processes are themselves contextual. They have the quality of intentionality, that is, they inherently are directed to the physical and social contexts of the world. Empirically, it may prove to be the case that certain knowledge structures pertain to multiple social contexts and that certain patterns of thinking generalize broadly (Cervone 1997, 1999, 2004a). But this does not alter the fact that the cognitive content itself is directed to the contexts (including people) of the world. Ignoring context in assessment would misconstrue the phenomenon. Note that nonintentional systems may also require contextualized assessment. Investigators studying temperament (Kagan 2003) and hypothesized

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evolved mechanisms subserving social exchange (Stone et al. 2002, Sugiyama et al. 2002) employ contextualized research strategies because theory and evidence indicate that the aspects of personality architecture under study function contextually. The second principle for distinguishing among subsystems within the architecture of personality pertains to that large subset of mental contents that has the quality of intentionality. Searle (1983, 1998) proposes that such content can be distinguished according to the principle of “direction of fit.” It involves the relation, or fit, between a proposition and that aspect of the world that the proposition represents. Different “directions” are different relations between the contents of the mind and the properties of the world. Some propositions have “mind-toworld” direction of fit. To be true, the contents of the mind must fit to states of the world. These propositions generally are labeled “beliefs.” Other propositions have “world-to-mind” direction of fit. They are not true or false when formulated, but instead represent intentions to bring about a future state of the world that fulfills, or fits, the current mental content. Such propositions commonly are labeled “goals.” A third class of mental content is analytically distinct from both mind-to-world beliefs and world-to-mind goals. These cognitions represent criteria for judging the goodness or worth of an entity, or “evaluative standards.” Standards are distinct from beliefs in that they are not objectively true or false, and distinct from goals in that they do not necessarily entail a personal intention to attain a given future state (Cervone 2004a). Boldero & Francis (2002) review functional distinctions between goals and standards. One may valuably draw distinctions within each of these three classes of cognitive content. Bandura and colleagues distinguish between self-efficacy and outcome beliefs (Bandura 1997, Bandura & Locke 2003). Goal researchers differentiate among mastery, performance, approach, and avoidance goals (Grant & Dweck 2003, Elliot & McGregor 2001, Van Yperen 2003). Regarding evaluative standards, social-cognitive research supports a distinction between ought and ideal standards (Higgins 1987), which are associated with distinct forms of selfregulation (Higgins 1996a, Higgins & Spiegel 2004) and psychological distress (Mor & Winquist 2002), and neuroscientific findings support a distinction between automatic and consciously controlled evaluative judgments, which are differentially associated with activity in the medial prefrontal cortex (Cunningham et al. 2003). The direction-of-fit principle, then, yields distinctions that are necessary but not sufficient for modeling personality architecture. The third principle (Cervone 2004a) captures a feature central to virtually all personality theories: the distinction between process and structure variables (Pervin et al. 2005). Within the domain of mental contents with intentionality, structure and process can be distinguished by recognizing the distinction between knowledge and appraisal (Lazarus 1991, Lazarus & Smith 1988, Smith & Lazarus 1990). Knowledge is “our understanding of the way things are and work” (Lazarus 1991, p. 144), that is, enduring mental representations of the attributes of entities. Appraisals, in contrast, are not stored facts about the world, but dynamic

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evaluations of the meaning of encounters for oneself; appraisals gauge “the significance of what is happening for one’s personal well-being” (Lazarus 1991, p. 144). The knowledge/appraisal distinction is broadly significant to personality theory in that knowledge and appraisal mechanisms play qualitatively different roles in personality functioning. Knowledge is an enduring structural feature of personality. Appraisals are dynamic personality processes. People possess vast repertoires of knowledge, only a small subset of which is active, and thus potentially influential to appraisal processes, in a given setting (cf. Higgins 1996b, Markus & Wurf 1987). These three principles comprise an intraindividual model labeled a “knowledgeand-appraisal personality architecture” (KAPA; Cervone 2004a; also see Cervone 2004b, Cervone et al. 2004). In this model, the knowledge/appraisal and directionof-fit distinctions are crosscutting (because both knowledge structures and appraisal processes are intentional cognitions); their combination yields six classes of cognitively based personality variables (Cervone 2004a). The knowledge/appraisal distinction captures personality structures versus processes within the domain of intentional mental systems. Regarding nonintentional affective systems, an analogous distinction involves ongoing affective experience (a dynamic personality process) versus reactivity, or the ease with which biological systems that give rise to affective experience are activated (Kagan 2003, Rothbart et al. 2004). Thresholds of reactivity are highly stable over time, as indicated by the finding that variations in inhibited temperament at age 2 predict responses to novelty in adulthood (Schwartz et al. 2003), and thus constitute a stable structural feature of personality. A challenge is to identify the precise architecture of intraindividual affective systems in personality functioning. Much work indicates the importance of two systems: appetitive or approach-oriented, and aversion or avoidance-oriented systems (Cacioppo & Gardner 1999, Gable et al. 2003). However, research on temperament not only highlights additional dimensions of variation (Strelau 1998) but also suggests that temperament is best conceptualized in terms of a potentially large number of contextualized systems that vary categorically in the population, rather than a small number of global factors that vary dimensionally (Kagan 2003, Woodward et al. 2000; also see Schmidt & Fox 2002). The KAPA system (Cervone 2004a) is not the only recent effort to model intraindividual personality architecture. Cloninger (2004) suggests that a complete portrait of the individual must include not only temperament and character variables (see above), but also holistic, intuitive processes that enable achievements such as wisdom, creativity, and spiritual transcendence (cf. Baltes & Staudinger 2000, Emmons & Paloutzian 2003). These ideas add a conceptual level of analysis to the knowledge-and-appraisal framework outlined above. Kuhl and colleagues’ PSI theory (Kuhl 2000, Kuhl & Koole 2004), a development from Kuhl’s (1984) earlier action control framework, takes a functional approach to personality architecture. PSI theory distinguishes two functions through which people volitionally influence their actions: (a) self-control, or the inhibiting of impulses and distracting thoughts that might otherwise interfere with goal pursuit (cf. Wenzlaff & Wegner 2000); and (b) self-maintenance, or the pursuit of tasks that are congruent

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with one’s values, yielding states akin to flow (Csikszentmihalyi 1990) or selfdetermination (Ryan & Deci 2003) that entail holistic, intuitive modes of thinking. Recent PSI-based research indicates that negative affect impairs such intuitive processing (Baumann & Kuhl 2002, Bolte et al. 2003).

PERSONALITY COHERENCE


Functional Coherence Among Personality Structures and Processes The study of intraindividual personality architecture reformulates two goals of personality psychology. The first is to identify functional relations among personality structures and processes. One wishes to explain, in particular, how stable structures influence ongoing personality dynamics. Some approach this problem by seeking causal processes through which between-person factors influence within-person dynamics; “the agenda” is to “identify dynamic processes . . . that are differentially activated by the [given] factor” (McCrae & Costa 1999, p. 148). This approach is reasonable if one assumes that between-person factors correspond directly to within-person structures with causal force. But, as we have seen, this assumption is enormously problematic. By analogy, physical attractiveness may be a statistically robust between-person factor in ratings of physical features, but no biologist would search for the physiological processes that are activated by this factor. In an intraindividual model such as the KAPA system (Cervone 2004a), relating enduring personality structures to dynamic personality processes is straightforward. Personality structures include knowledge structures that vary in content and accessibility (e.g., Higgins 1996b) and biological systems of affect that vary in thresholds of reactivity (Kagan 2003). A wealth of theory and evidence—far too large to review in any single chapter—illuminates the interrelations among enduring cognitive and affective structures, and dynamic cognitive appraisal processes and affective experiences. Lines of research in social cognition (Higgins & Kruglanski 1996, Macrae & Bodenhausen 2000) identify mechanisms through which knowledge structures influence appraisal processes. Emotion research details the influence of appraisals on affective experience (Scherer et al. 2001). Conversely, much research reveals how affect influences cognition (Forgas 1995). These influences include the effect of emotions on cognitive appraisals (Lerner et al. 2003, Lerner & Keltner 2001); for example, negative affect causes people to invoke evaluative standards that are more stringent (Scott & Cervone 2002, Tillema et al. 2001; also see Meyer et al. 2004). Particularly clear evidence of the contribution of enduring knowledge structures to individuals’ coherent, distinctive personality dynamics comes from research on categorization processes. Robinson and colleagues (Robinson 2004, Robinson & Neighbors 2004) examine the relative speed with which individuals categorize lexical items as being evaluatively negative; this reaction time measure is an index of

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the cognitive accessibility of the knowledge that individuals employ when evaluating events. Reaction times predict self-reported negative emotional experience and somatic symptoms, as well as individuals’ appraisals of the valence and degree of threat present in their daily life circumstances (Robinson et al. 2004). Self-report measures of global interindividual constructs (extraversion and neuroticism) are found to be unrelated to the reaction-time index. The coherent relations among distinct personality structures and processes also are illuminated by research on self-regulation. For example, goals (construed as enduring knowledge structures; Kruglanski 1996) are dynamically related to strategies of goal pursuit, as evidenced by the fact that strategies prime goals (Shah & Kruglanski 2003). Enduring knowledge is systematically linked to experiences of value during goal pursuit; people value experiences and objects more highly when they pursue goals through strategies that are congruent with their chronic self-regulatory orientation, which can be assessed by tapping the accessibility of enduring self-representations (Higgins et al. 2003). Developmental research illuminates factors that contribute to the acquisition of self-knowledge and self-regulatory capacities (Harter 2003). Important factors include interpersonal contexts across which individuals may experience fluctuating self-views (Harter & Whitesell 2003). In the research cited in the two paragraphs above, investigators explore general mechanisms through which personality structures and processes are interrelated. For such purposes, nomothetic research strategies suffice. However, when one addresses questions of cognitive content—the belief systems of the individual and the specific circumstances that activate those beliefs—there may be much idiosyncrasy (Cervone 1997). Investigators thus employ idiographic methods (see Cervone 2004a, Cervone et al. 2001, Cervone & Shadel 2003). Grice (2002) has developed software for analyzing repertory grid data (Kelly 1955) idiographically. Findings indicate that roughly half the information about individuals that is uncovered through this technique would be lost if only nomothetic methods were employed (Grice 2004). Showers et al. (2004) use idiographically tailored stimuli to gauge the compartmentalization of self-knowledge, that is, the degree to which the individual organizes knowledge according to categories that contain exclusively positive or negative attributes. People with a highly integrative (rather than compartmentalized) system of self-knowledge are better able to maintain positive moods when faced with negative self-aspects (Showers et al. 2004). Shadel et al. (2004) combine idiographic assessments with experimental methods designed to test causal links from knowledge structures to self-appraisals. When self-knowledge is primed experimentally, people are faster to make self-judgments when appraising those particular circumstances to which the given aspect of knowledge is highly applicable (Shadel et al. 2004). Related findings derive from attachment research, where reaction-time results indicate that threat primes activate mental representations of attachment figures (Mikulincer et al. 2002). In sum, diverse lines of experimental research illuminate the functional relations among enduring mental structures and dynamic personality processes. Idiographic

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assessment methods tap the content and organization of cognitive personality structures at the level of the individual case and reveal how these knowledge structures causally influence appraisal processes. These synthetic accounts of intraindividual structures and processes would be obscured if one divided the field along structure/process lines.


Cross-Situational Coherence The second issue is cross-situational coherence, that is, the coherent interrelation among responses exhibited across distinct contexts. Historically, this topic has been central to the field (Mischel 1968). Unfortunately, most research on the topic has had a between-person rather than within-person focus. Investigators primarily have gauged whether individual differences with respect to a given personality construct are consistent across fixed sets of situations. No matter what the results of such research, with respect to questions of intraindividual personality architecture, findings inherently are equivocal. At the level of the individual, enduring personality structures may contribute to patterns of cross-situational coherence that are idiosyncratic, and thus that are overlooked in between-person, nomothetic methodologies. Research illustrates the point (Cervone 1997, 2004a). This work tested the hypothesis that schematic knowledge structures contribute to coherence in selfappraisals across potentially diverse situations, specifically, situations that a given individual links to his or her schematic self-knowledge. This possibility was addressed by combining open-ended assessments of self-schemas (Markus 1977) with independent assessments of those social circumstances that the individual believed were relevant to his or her schematic personality attributes. Subsequent to these measures, people’s appraisals of self-efficacy (Bandura 1997) for executing specific behaviors in concrete, well-specified situations were assessed. This methodology yielded robust patterns of cross-situational coherence in self-efficacy appraisal (Cervone 1997, 2004a). As compared to circumstances that were linked to negatively valenced self-knowledge, across situations linked to positively valenced self-schemas people exhibited substantially higher appraisals of self-efficacy. Of critical importance is that the patterns of cross-situational coherence that were observed (Cervone 2004a) commonly were of the sort that might be obscured in a nomothetic between-person analysis. People exhibited coherence across sets of situations and acts that violated the structure of traditional between-person trait categories. Participants sometimes grouped together contextualized acts that researchers conducting a between-person analysis would likely see as unrelated. For example, one individual indicated that her schematic attribute of being a “responsible” person manifests itself in a diverse set of acts such as saving money after having overspent or cheering up a friend who seems depressed. In a nomothetic between-person approach, these acts likely would be categorized as manifestations of different, independent traits (e.g., conscientiousness, agreeableness), with the result that the coherence among the acts for this particular individual would

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be missed. Other times, participants sharply discriminated between acts that, in a between-person analysis, typically would be grouped together. For example, one individual’s self-schemas included his being “skilled at public relations” and “shy” (Cervone & Shoda 1999a). He judged that seemingly similar circumstances that might both appear to be manifestations of nomothetically defined extraversion— e.g., telling a lively anecdote on a job interview, keeping up an animated conversation on a trip with a friend—were unrelated, in that they were relevant to one versus the other of his very different schematic attributes. Viewed through a nomothetic between-person lens, the variability in this individual’s responses to similar circumstances might be seen as uninterpretable “noise” (also see Mischel 2004, Mischel & Shoda 1995). By shifting our focus to a within-person level of analysis, and examining belief systems idiographically, the meaningful coherence among the individual’s responses became clear. An analysis of intraindividual personality architecture, then, has dual advantages for the study of cross-situational coherence in personality functioning. First, it enables one to identify patterns of coherence that might otherwise be overlooked. Second, it enables one not only to describe observed patterns of coherence but also to explain them in terms of functional relations among underlying personality structures and processes. The KAPA approach (Cervone 2004a) also speaks to the two aspects of explaining behavior noted earlier (Taylor 1985a). By distinguishing knowledge structures from appraisal processes and by invoking principles of knowledge activation (Higgins 1996b) that specify how the structures and processes are related, one identifies general psychological mechanisms that enable people to respond to diverse situations in a coherent manner. By assessing the content of belief systems idiographically, one obtains contextualized, interpretable portraits that begin to make sense of the ultimate target of investigation: the whole, coherent individual.

CONCLUSIONS This chapter has reviewed programs of theory and research that cannot be characterized as either structure approaches or process approaches to personality psychology because they are both. Meta-theoretical and theoretical analyses of personality architecture yield synthetic accounts of personality structures and dynamics. In the contemporary field, these analyses inform not only basic research efforts but also applications of personality science (Shadel 2004). When modeling personality architecture, the process/structure distinction remains useful. But when characterizing programs of research and theory, it is a misleading dichotomy that should be abandoned. We also have reviewed theoretical, psychometric, and empirical advances, which indicate that no single set of theoretical constructs is sufficient for addressing the two tasks of personality psychology: analyzing intraindividual structures and dynamics, and providing taxonomies of interindividual differences in the population. Between-person constructs derived from factor analyses of populations

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fail to capture even the most basic distinctions that are required to model intraindividual personality architecture. Personality psychology requires more than one set of conceptual tools. Some may lament this turn of events. Writers in the field commonly have longed for an integrative theory that would envelop all of the discipline’s activities. But things don’t always work out that way. Professional fields do not inevitably reach “an omega point were everything falls happily together” (Geertz 2000, p. 199). A discipline may encompass different problems that require different solutions. Recognizing the differences and incorporating them properly into professional reviews can only enhance everyone’s appreciation of the multifaceted field of personality science. ACKNOWLEDGMENTS The author thanks Daniele Artistico, Tracy L. Caldwell, Heather Orom, Walter Mischel, Sunyoung Oh, and Yuichi Shoda for their comments on drafts of this chapter. During the preparation of the chapter, the author was supported by grant DA14136 from the National Institute on Drug Abuse. The Annual Review of Psychology is online at http://psych.annualreviews.org

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PERSONALITY DEVELOPMENT: Stability and Change


Avshalom Caspi Social, Genetic and Developmental Psychiatry Research Center, Institute of Psychiatry, King’s College London, London, England SE5 8AF, and Department of Psychology, University of Wisconsin, Madison, Wisconsin 53706; email: [email protected]

Brent W. Roberts Department of Psychology, 603 East Daniel Street, University of Illinois, Urbana-Champaign, Champaign, Illinois 61820; email: [email protected]

Rebecca L. Shiner Department of Psychology, Colgate University, Hamilton, New York 13346; email: [email protected]

Key Words personality dynamics, temperament, longitudinal methods, individual differences ■ Abstract In this review, we evaluate four topics in the study of personality development where discernible progress has been made since 1995 (the last time the area of personality development was reviewed in this series). We (a) evaluate research about the structure of personality in childhood and in adulthood, with special attention to possible developmental changes in the lower-order components of broad traits; (b) summarize new directions in behavioral genetic studies of personality; (c) synthesize evidence from longitudinal studies to pinpoint where and when in the life course personality change is most likely to occur; and (d) document which personality traits influence social relationships, status attainment, and health, and the mechanisms by which these personality effects come about. In each of these four areas, we note gaps and identify priorities for further research. CONTENTS THE STRUCTURE OF PERSONALITY: DEVELOPMENTAL CONSIDERATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Perils and Promise of Studying Personality Structure Across the Life Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Developmental Taxonomy of Higher- and Lower-Order Traits . . . . . . . . . . . . . . Directions for Future Developmental Work on Personality Structure . . . . . . . . . . . THE ORIGINS OF INDIVIDUAL DIFFERENCES IN PERSONALITY: CONTRIBUTIONS FROM BEHAVIORAL GENETICS . . . . . . . . . . . . . . . . . . . . . New Directions in Behavioral Genetic Studies of Personality . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0453$14.00

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Behavioral Genetics in the Postgenomic Era: Opportunities for Advancing Psychosocial Research on Personality Development . . . . . . . . . . . . . . PATTERNS OF CONTINUITY AND CHANGE IN PERSONALITY TRAITS FROM CHILDHOOD TO OLD AGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Differential Continuity and Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mean-Level Continuity and Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principles of Personality Development in Adulthood . . . . . . . . . . . . . . . . . . . . . . . . PERSONALITY EFFECTS ON SOCIAL DEVELOPMENT . . . . . . . . . . . . . . . . . . . Cultivating Relationships: Friendships, Intimate Relationships, and Parenting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Striving and Achieving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Health Promotion and Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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THE STRUCTURE OF PERSONALITY: DEVELOPMENTAL CONSIDERATIONS Both child psychologists and adult personality researchers study individual differences, but historically the two groups have done so within different research traditions. Child psychologists have focused on temperament traits, the behavioral consistencies that appear early in life, that are frequently but not exclusively emotional in nature, and that have a presumed biological basis (Shiner 1998). Researchers studying adults have focused on personality traits, which encompass a broader range of individual differences in thinking, feeling, and behaving. The last decade has been a vibrant, productive period in the study of the links between early temperament and later personality (Graziano 2003). From the point of view of this emerging developmental science of personality, childhood temperament should be conceptualized with an eye toward adult personality structure, and adult personality should be understood in light of its childhood antecedents. The conceptual distinctions between temperament and personality traits have been challenged by recent empirical work demonstrating similarities between the two domains of individual differences (McCrae et al. 2000). Temperament traits by definition appear earlier, and they tend to be more narrow, lower-level traits. However, like temperament traits, nearly all personality traits show moderate genetic influence (Bouchard & Loehlin 2001), and individual differences in “personality traits” have been identified in nonhuman animals (Gosling 2001). Like personality traits, temperament traits are not immune from experience. Behavioral genetic studies have established that individual differences in temperament, measured even during the first few years of life, are only partially heritable and are influenced by environmental experiences (Emde & Hewitt 2001). Further, differences in the experience and expression of positive and negative emotions are at the heart of some of the most important temperament and personality traits (Rothbart et al. 2000, Watson 2000). Temperament and personality traits increasingly appear to be more alike than different.

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The Perils and Promise of Studying Personality Structure Across the Life Course One of the most challenging tasks in the study of personality across the life course has been to develop a taxonomy of traits: What are the most reliable patterns of covariation of traits across individuals? The elucidation of a taxonomy for youth has been particularly challenging because children’s maturation enables them to display an increasingly differentiated set of traits. Children develop rapidly from manifesting only a small number of emotions during early infancy—interest, contentment, and distress—to manifesting an expanded set of emotions—including joy, sadness, anger, fear, empathy, pride, shame, and guilt—by age 3 (Eisenberg 2000, Lewis 2000). The emotion-based individual differences children can display therefore change quickly in number and content during these years. Similarly rapid developments in motor skills, cognition, and language may at times make the attempt to develop a taxonomy of early individual differences seem like trying to hit a moving target. Despite the challenges inherent in mapping out temperament and personality structure across the life course, researchers have made substantial progress in elaborating taxonomies of individual differences in both childhood and adulthood. One of the earliest and still best known temperament models is the ThomasChess nine-trait structure (Thomas et al. 1963). Work that is more recent has identified limitations of this model. Factor analyses of questionnaires designed to measure the original dimensions have uncovered fewer than nine factors, and the original model obscured the fact that young children’s tendencies toward positive and negative affect are independent from each other (Rothbart & Bates 1998, Shiner & Caspi 2003). Current models of temperament in infancy and toddlerhood typically include the following six traits: activity level; positive emotions/pleasure; irritable distress/anger/frustration; fearful distress/withdrawal from new situations (including social situations); soothability; and attention span/persistence (Lemery et al. 1999, Rothbart & Bates 1998). In the last decade, adult personality researchers have moved toward increasing consensus about the higher-order structure of adult personality. Among the bestestablished models is the Five-Factor Model, and several three-factor models also have received support (John & Srivastava 1999). Although there are important differences among these various models, they overlap to a considerable degree. Consistent support has been found for the traits of Extraversion/Positive Emotionality, Neuroticism/Negative Emotionality, and Conscientiousness/Constraint, and additionally in the Five-Factor model, Agreeableness and Openness-to-Experience. Consensus about the structure of adult personality traits has important implications for developmental research: We now have greater clarity about the adult personality traits that developmental studies should be trying to predict over time. Developmental researchers have explored the possibility that childhood personality structure may share important similarities with adult personality structure, and there is now evidence that such is the case, from preschool age through

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adolescence. In a number of studies, the Big Five and Big Three traits have been obtained in factor analyses of parent and teacher ratings of children (summarized in Shiner & Caspi 2003), although the evidence for an Openness-to-Experience trait is somewhat weak. Although children exhibit traits that are remarkably similar to those seen in adults, researchers should remain attentive to developmental differences in the manifestations of these traits; for example, the traits may be less coherent earlier in childhood (Lamb et al. 2002). The structure of individual differences from age 2 to 8 years warrants special attention because developmental changes during this period are rapid and wide-ranging.

A Developmental Taxonomy of Higher- and Lower-Order Traits Although there is increasing consensus about the structure of personality at the level of higher-order, broad traits, there is little consensus about the lower-order traits subsumed within those superfactors (John & Srivastava 1999). The broad traits (e.g., extraversion) represent the most general dimensions of individual differences in personality; at successively lower levels are more specific traits (e.g., sociability, dominance) that, in turn, are composed of more specific responses (e.g., talkative, good at leading others). Personality research most frequently focuses on higherorder traits, but the lower-order traits may provide better prediction of behavioral outcomes (Paunonen & Ashton 2001). Below we provide a synopsis of recent work on the Big Five traits and their potential lower-order components in both children and adults. Developmental research provides a particularly rich source of information about the lower-order traits because these traits have been studied using a variety of methods, including observational studies and lab studies, in addition to the questionnaire studies that are more typical in adult personality research (Shiner 1998). Children and adults vary in their tendencies to be vigorously, actively, and surgently involved with the world around them. Extraverted individuals are outgoing, expressive, energetic, and dominant, whereas introverted individuals are quiet, inhibited, lethargic, and more content to follow others’ lead. What is the core feature of this trait? Recent theoretical and empirical work with adults has pinpointed three possible central features: the tendency to experience frequent positive moods (Fleeson et al. 2002), sensitivity to potential rewards (Lucas et al. 2000), and the tendency to evoke and enjoy social attention (Ashton et al. 2002). A complementary biological perspective suggests that Extraversion derives from a biological system promoting active approach and exploration of the environment (Depue & Collins 1999). Extraversion/Positive Emotionality (PEM) encompasses at least four lowerorder traits: social inhibition or shyness, sociability, dominance, and energy/activity level. Social inhibition or shyness reflects reluctance to act and feelings of discomfort in social encounters. Social inhibition can be identified as an individual

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difference from early in childhood and is predicted by earlier physiological and behavioral reactivity (Kagan 1998). By middle childhood, shyness is distinguishable from sociability, the preference to be with others rather than alone and to seek close relationships. Sociability may primarily tap elements of approach and positive emotionality, whereas shyness may be a more multidimensional trait combining elements of low approach, high negative emotionality, and high behavioral avoidance (Nigg 2000). A third component of extraversion is dominance, the tendency to be assertive and confident, to exert control over others, and to capture and enjoy others’ attention (Hawley 1999). Observed social status and dominance are related to extraversion in adults (and to low neuroticism in males; Anderson et al. 2001) and to well-being and positive emotions in chimpanzees (Weiss et al. 2002). Energy and activity level are aspects of extraversion that are easily observed among children (Rothbart et al. 2001); although adults are less motorically active, extraverted adults still manifest higher levels of energy. A high level of positive activity is typically associated with extraversion, but it is important to recognize that activity level may be related in some cases to impulsivity and poor behavioral control in young children. Just as children and adults vary in their predisposition toward positive emotions, they vary in their susceptibility to negative emotions (Rothbart et al. 2001, Watson 2000). All temperament and personality taxonomies include a trait that encompasses the tendency to experience the world as distressing or threatening. Children and adults who are high on Neuroticism/Negative Emotionality (NEM) are anxious, vulnerable to stress, guilt-prone, lacking in confidence, moody, angry, easily frustrated, and insecure in relationships; individuals low on this trait are emotionally stable and adaptable. Neuroticism is one of the most widely studied traits in the entire field of psychology. Even more may be known about this trait than previously recognized, in light of new evidence that Neuroticism may be part of an underlying dimension that includes self-esteem, locus of control, and generalized self-efficacy (Judge et al. 2002). A number of researchers have suggested that some aspects of this trait may be rooted in a biological system aimed at helping guard against potentially threatening or harmful situations (Rothbart et al. 2000, Watson et al. 1999). Neuroticism/NEM includes both anxious (or fearful) distress and irritable distress (Rothbart & Bates 1998, Shiner & Caspi 2003). These two separate dimensions of distress proneness are evident already in infancy. Anxious distress is inner-focused and, by childhood, includes tendencies toward anxiety, sadness, insecurity, and guilt. Big Five Neuroticism typically emphasizes this component. In contrast, irritable distress taps outer-directed hostility, anger, jealousy, frustration, and irritation; in children, such hostile distress is often evoked by limits set by adults. The distinction between inner-directed and outer-directed distress is similar to the distinction between internalizing and externalizing psychiatric disorders. Because anxious distress and irritable distress are likely to follow different developmental paths and predict different outcomes, these two lower-order traits should

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be investigated separately in many instances. In fact, international lexical studies of adult personality have provided evidence that these two aspects of distress proneness often appear on different factors (Peabody & DeRaad 2002, Saucier & Goldberg 2001). Children and adults vary widely in their capacities for behavioral and cognitive control. Conscientious individuals are responsible, attentive, careful, persistent, orderly, and planful; those low on this trait are irresponsible, unreliable, careless, and distractible. Individual differences in control may be related to biological differences in executive attentional systems that develop across early childhood and the early school years (Posner & Rothbart 2000). In fact, the ability to focus attention in infancy predicts control later in childhood (Kochanska et al. 2000), and adult capacity for effortful attention is associated with Big Five Conscientiousness (Rothbart et al. 2000). An ongoing debate about the nature of individual differences in self-control involves the question of how the high end of this trait should be conceptualized. High Conscientiousness has been variously seen as willingness to follow authority and conform to group norms (Hogan & Ones 1997) or as positive “engagement within task-related endeavours” (Ashton & Lee 2001, p. 346). Is it possible to be too constrained or inhibited, as Block & Block (1980) argued, or is high Conscientiousness a marker for positive strength of will? Further work is needed to clarify the underlying nature of this trait. Conscientiousness/Constraint includes at least six lower-order traits: self-control versus behavioral impulsivity, attention, achievement motivation, orderliness, responsibility, and conventionality. Self-control ranges from the tendency to be planful, cautious, and behaviorally controlled to the tendency to be incautious, careless (or carefree), and impulsive (Halverson et al. 2003, Kochanska et al. 2000). Attention taps the capacity to regulate attention by shifting mental sets, focusing attention, and persisting at tasks in the face of distraction. Although this trait is not prominent in adult personality models, it is an important trait in most temperament models in children (Shiner 1998). Individual differences in this trait are measured by many neuropsychological tests requiring a high level of focus (Nigg 2000). It is not clear why attention is not included in most adult personality models; perhaps differences in attention become subsumed under inhibitory control with age. Achievement motivation taps the tendency to strive for high standards and to pursue goals over time in a persistent, determined manner (Halverson et al. 2003). There is some evidence from lexical studies with adults that this trait may be a blend of high Conscientiousness and high Extraversion/Positive Emotionality (Peabody & De Raad 2002). Orderliness reflects a propensity to be neat, clean, and organized versus sloppy and disorderly (Roberts et al. 2004a). It forms the empirical core of most factor-analytically derived models of conscientiousness. Responsibility reflects a blend of conscientiousness and agreeableness and ranges from the tendency to be reliable and dependable to being undependable (Roberts et al. 2004a). Consistent with its conceptual overlap with psychopathy, this domain


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subsumes some of the strongest predictors of delinquency (Roberts et al. 2004b). Finally, conventionality taps the tendency to uphold traditions and societal norms and serves as one of the strongest predictors of avoiding risky behaviors such as excessive drug and alcohol consumption (Bogg & Roberts 2004). Agreeableness includes a variety of traits that foster congenial relationships with others (Graziano & Eisenberg 1997). Agreeable individuals are cooperative, considerate, empathic, generous, polite, and kind. Disagreeable individuals are aggressive, rude, spiteful, stubborn, cynical, and manipulative. In studies with both children and adults, Agreeableness also includes a willingness to accommodate others’ wishes. Agreeableness is linked with Neuroticism/NEM in that both traits measure aspects of anger proneness; however, Agreeableness taps the poor control of anger expressed through aggression, whereas Neuroticism/NEM taps individuals’ experiences of angry emotions (Martin et al. 2000). Agreeableness is also linked with Conscientiousness/Constraint, in that both traits tap aspects of inhibition versus disinhibition (Clark & Watson 1999). There may be good developmental reasons that Agreeableness is associated with both Neuroticism and Conscientiousness: Negative emotionality, self-control, and attention in childhood are all important predictors of later Agreeableness (Eisenberg et al. 2000, Laursen et al. 2002, Rubin et al. 2003). The two poles of Agreeableness—antagonism and prosocial tendencies—have been examined separately as distinct traits in many studies of children and adults. Antagonism ranges from the tendency to be peaceful and gentle to the tendency to be aggressive and hostile. By childhood, individuals differ significantly in their levels of physical aggression (Tremblay 2000) and relational aggression, which includes gossiping and social exclusion (Crick et al. 2001). Children also differ markedly in their levels of prosocial behavior, including their tendencies to be helpful, kind, considerate, generous, empathic, and nurturant (Eisenberg & Fabes 1998). Although antisocial and prosocial tendencies tend to covary negatively, they are not necessarily opposite ends of a single dimension (Graziano & Eisenberg 1997). Support for this view comes from research pointing to etiological distinctions between antisocial and prosocial behavior (Krueger et al. 2001): Whereas genetic influences account for a significant amount of the variance in antisocial tendencies, shared, family-wide environmental experiences also account for a significant amount of the variance in altruism. A third lower-order trait, cynicism/alienation, has been identified in adults and may be related to both Neuroticism/NEM and Agreeableness (Martin et al. 2000); this trait taps an individual’s tendency to mistrust others and to feel mistreated. A similar individual difference indexing alienation has been identified in research on social-information processing in youths (Crick & Dodge 1994).


AGREEABLENESS

Openness-to-Experience/Intellect is the most debated and least understood of the Big Five traits, yet it includes a number of potentially important traits (McCrae & Costa 1997). Openness (imaginative,

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creative, and aesthetically sensitive) and Intellect (quick to learn, clever, insightful) have each been proposed to be the core of this trait (John & Srivastava 1999); each of these traits may be a separate subcomponent of the higher-order trait. The two component traits can be measured in children as young as age 3 (Halverson et al. 2003), but in a number of studies with children and adolescents the higherorder trait is measured less reliably than the other Big Five traits (Shiner 1998). Openness-to-Experience/Intellect does not appear in temperament models, despite the fact that parents often use words from this domain of individual differences to describe their children (Mervielde et al. 1998). The developmental precursors of this trait are also unknown, but there is preliminary evidence for two possible temperamental bases. First, the tendency to seek stimulation and to explore new environments actively in early childhood predicts later academic achievement and IQ (Raine et al. 2002); these behaviors may also predict later Openness/Intellect. Second, orienting sensitivity, which includes the tendency to be sensitive to internal and external sensory stimulation, is concurrently related to Openness in adulthood (Rothbart et al. 2000).

Directions for Future Developmental Work on Personality Structure We suggest six ways to build on the current success of elucidating the structure of personality differences across the life course. First, much more work is needed to specify lower-order traits that can be identified in children and adults. There are a number of lower-order traits beyond the ones specified here that may turn out to be important; e.g., humility and integrity (Agreeableness), and talent (Openness). It may also be possible to distinguish among different negative emotions making up the Neuroticism/Negative Emotionality trait (e.g., sadness, anxiety, and fear). Second, we have emphasized advances in dimensional, or variable-centered, approaches to personality classification. These approaches are concerned with systematizing the enormous differences between individuals. In contrast, approaches that are typological, or person-centered, aim to develop a taxonomy not of personality variables but of personality types, and are concerned with the overall structure of personality dimensions within individuals. Efforts to classify people rather than variables are still in their early stages of development, although there appear to be striking regularities in the identification of at least three “types” of children: resilient, overcontrolled, and undercontrolled (Hart et al. 2003). However, the descriptive and predictive efficiency and utility of typological versus dimensional approaches awaits more explicit evaluation (Asendorpf 2003). Third, more creative measurement of individual differences, beyond the sole use of questionnaires, would benefit the fields of child development and personality psychology. For example, implicit measures have been used to assess anxiety and shyness in adults (Asendorpf et al. 2002, Egloff & Schmukle 2002); rather than directly inquiring about a person’s self-view of personality, these instruments

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measure indirectly an individual’s automatic associations between trait descriptors and the self. Physiological measures can parse groups of individuals into more homogeneous subtypes (Kagan et al. 2002). Puppet interviews have been used to assess self-views of traits in children as young as age 4 and could be used to measure a wide range of traits early in childhood (Measelle et al. 1998). Fourth, more research is needed about the cross-cultural generalizability of the taxonomic system reviewed here for children and adolescents. Cross-cultural studies of adult personality structure have been pursued vigorously over the past decade (Church 2001), but children have been left out of these studies. We do know that parents from the United States, China, and several European countries consider the Big Five traits to be important in describing their children (Kohnstamm et al. 1998), but we know little about the structure of youths’ individual differences in countries outside the United States and Europe. We also know little about when in the life course mean-level cross-cultural differences in personality emerge. Fifth, it will be important to chart the development of sex differences in mean levels of personality traits. Although there do not appear to be any sex differences in the structure of personality, there are some differences in the mean levels of personality traits (Costa et al. 2001). A deeper understanding of the causes of such sex differences will be important for explaining both personality development and the development of psychopathology (Rutter et al. 2003). Sixth, we concur with Funder’s (2001) conclusion that more data are needed about how personality traits are expressed through behavior in context. To achieve such understanding will require casting a wider nomological net that includes more detailed measurements of social, cognitive, and emotional processes. First, observational studies (of behavior and affect) can be applied to study how traits are manifest in different social relationships across the life course. Second, although social-cognitive and trait approaches to personality are often portrayed as antagonistic, they are perfectly complementary and mutually informative. By integrating social-cognitive constructs (e.g., mental representations, encoding processes) into research on traits, developmentalists can advance understanding of how traits are differently manifested at different ages. Third, the union of differential psychology and neuroscience may increasingly lead the way to a better understanding of personality differences in brain functions (Canli et al. 2001).

THE ORIGINS OF INDIVIDUAL DIFFERENCES IN PERSONALITY: CONTRIBUTIONS FROM BEHAVIORAL GENETICS Behavioral genetics is no longer an exotic research enterprise. In fact, large, worldwide registers of data on twins and their relatives are making it possible to advance understanding of both genetic and environmental influences on personality functioning across the life course (Boomsma et al. 2002).

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New Directions in Behavioral Genetic Studies of Personality Behavioral genetics research has uncovered increasingly reliable and robust evidence that genetic factors substantially influence personality traits. Bouchard & Loehlin (2001) provide a comprehensive review of this research, pointing to heritability estimates across the Big Five factors in the range of 0.50 ± 0.10. There are some fluctuations from study to study, but, in general (a) all five superfactors appear to be influenced by genetic factors to the same extent, and (b) genetic and environmental factors also affect individual differences in men’s and women’s personalities to the same extent. Several novel findings in behavioral genetics research on personality merit mention. First, measures of personality that incorporate multiple viewpoints or perspectives (e.g., by consolidating information from multiple reporters or across multiple situations) yield larger estimates of genetic influences (as well as smaller, but more reliable, estimates of nonshared environmental influences) than measures based on a single viewpoint (e.g., Arseneault et al. 2003, Phillips & Matheny 1997, Reimann et al. 1997). This finding has been uncovered in studies of young children, adolescents, and adults. It has been suggested that “consensus trait measures,” which eliminate specificities or idiosyncrasies in different viewpoints about a person, could be used to better identify both specific genes and specific experiences that are correlated with personality (Bouchard & Loehlin 2001). Second, behavioral genetic studies have been used to examine whether there is etiological differentiation within the narrow facets (or lower-order, primary traits) that make up broader personality superfactors (such as the Big Five). The question is whether higher-order dimensions, or superfactors, represent the best level of analysis for research in genetics. Analyses at the lower-order or primary-trait levels of the personality hierarchy offer additional, useful information about the origins of individual differences in personality. For example, analyses at the primary-trait level suggest that siblings resemble each other in their altruism and prosocial behavior (facets of Agreeableness) in part because of the rearing environments they share (Jang et al. 1998, Krueger et al. 2001), but analyses of the Agreeableness superfactor (which includes many other facets) may conceal this shared environmental influence. Earlier we noted that, for behavioral prediction, it is often a shortsighted strategy to rely exclusively on measures of broad superfactors. Likewise, such exclusive reliance may limit research into the etiology of personality differences. Third, quantitative genetic studies are widely seen as a necessary preliminary to identifying heritable phenotypes that can be usefully examined at the molecular genetic level (Martin et al. 1997). Carey (2003) provides a valuable introduction to methods and concepts in molecular genetics, and Plomin & Caspi (1999) discuss how personality researchers can use genes once they have been identified. But the problem has been to identify specific genes responsible for the genetic influence on personality. A recent meta-analysis of studies reporting data on associations between candidate genes and personality traits concluded there were few replicable associations (Munafo et al. 2003). Much of the initial excitement about research

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on molecular genetics and personality has given way to a more sober appreciation of the pitfalls (Benjamin et al. 2002). The search for genes for personality is difficult because, unlike classical singlegene disorders in which a single gene is necessary and sufficient to produce the disorder, there is no evidence for such major effects of genes for personality. For complex quantitative traits like personality, genetic influence is much more likely to involve multiple genes of varying but small effect sizes. Design (e.g., sample size), sampling (e.g., ethnic stratification), and measurement shortcomings are usually invoked to explain inconsistencies in results from studies of gene mapping and personality. It is also likely that genes do not directly encode for personality traits. In fact, it is now recognized that the heritability coefficient indexes not only the direct effects of genes but also effects of interactions between genes and environments (Rutter & Silberg 2002). Gene-environment interactions (GxE) occur when the effect on a person of exposure to a particular environment is conditional upon their genotype (or conversely, when environmental experiences moderate gene expression). Because interactions are independent of main effects, it is possible that even genome-wide scans of very large numbers of people whose personalities are carefully measured will fail to detect genes whose effects are conditional on environmental risk. Two studies suggest the possibility that some complex traits, instead of resulting from many genes of small effect, result from relatively fewer genes whose effects are conditional on exposure to environmental risk. The first study showed that maltreated children whose genotype conferred low levels of monoamine oxidase A (MAOA) expression more often developed conduct disorder, antisocial personality, or violence than children with a high-activity MAOA genotype (Caspi et al. 2002). The second study showed that individuals with one or two copies of the short allele of the 5-HTT promoter polymorphism exhibited more depressive symptoms, diagnosable depression, and suicidality following stressful life events than individuals homozygous for the long allele (Caspi et al. 2003). If replicated, and if more GxE are found to influence related phenotypes, this may improve methodology in genetics by focusing attention on theory-guided studies of candidate genes in the context of environmental risks. Measured genotypes and measured environments will increasingly be brought together to understand the processes by which they eventuate in personality phenotypes.

Behavioral Genetics in the Postgenomic Era: Opportunities for Advancing Psychosocial Research on Personality Development Will quantitative genetic studies become obsolete in the postgenomic era? No. They will continue to play an important role in research on personality development, for at least four reasons. First, quantitative genetic studies are ideally suited for identifying true environmental influences on personality development. During the 1990s, the assumption that “nurture” influences personality development came under fire. Traditional socialization studies, which could not separate environmental influences on personality development from their correlated genetic influences, were challenged by

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four empirical discoveries: (a) ostensible environmental measures are influenced by genetic factors (Plomin & Bergeman 1991); (b) parents’ heritable traits influence the environments they provide for their children (Plomin 1994); (c) children’s genetically influenced behavior affects how their parents rear them (Ge et al. 1996, O’Connor et al. 1998); and (d) for many personality measures, environmental influences do not seem to account for the similarity among persons growing up in the same family (Rowe 1994). Although nonbehavioral-genetic studies showed that certain rearing experiences were correlated with certain child outcomes, theories of causation based on findings from such designs were guilty of a fundamental logical error: mistaking correlation for causation (Scarr 1992). These challenges culminated in the assertion that children’s family environments and children’s developing psychological traits both arise from common genetic influences (Harris 1998). Some social scientists responded thoughtfully to this claim (e.g., Collins et al. 2000), but many also missed the point. The reason there is controversy in the first place about the importance of the family environment for personality development is that the data are not good enough to support anyone’s claims. To be more precise: The data are sufficiently ambiguous to support everyone’s claims. The responsible way to tackle the genetic challenge to socialization research is head on, by using genetically sensitive designs that can provide leverage in identifying environmental risks. Research designs that are able to disentangle genetic and environmental influences on behavior are characterized by two key features. First, they require studying multiple family members, not simply one person from each family. Second, the proportion of genes (and environments) shared among family members is known and can therefore be modeled. Twin and adoption studies are clearly suited to this type of environmental research, but so too are other large-scale research projects, such as the National Longitudinal Survey of Youth (Rodgers et al. 1994) and the Add Health Study (Duncan et al. 2001), that contain information about genetically related individuals in their samples. Environmentally informative genetic designs offer “strong” and “fair” tests of the effect of nurture on personality development. Such designs offer strong tests of the nurture hypothesis in that they use genetically informative samples to control for and disentangle genetic influences. The tests are fair in that behavioral geneticists are increasingly incorporating state-of-theart measurements of the environment into their research. To date, several genetic designs have been used to identify environmentally mediated effects of neighborhood/school (Cleveland 2003, Rose et al. 2003), family structure (Jaffee et al. 2003), and parenting (Jaffee et al. 2004) variables on children’s and adolescents’ personalities and behavioral problems. A second way in which quantitative genetic studies are proving generative is that they are being harnessed to better understand why children growing up in the same family are so different. Indeed, a striking finding from quantitative genetics is not that the environment does not matter, but that what environmental experiences tend to do is to create differences between children growing up in the same family (Plomin 1994). Progress in identifying specific experiences that make

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genetically related children different from each other has been slow (Turkheimer & Waldron 2000), leading one reviewer to offer the “gloomy prospect” that psychosocial researchers may never identify the systematic sources of differences between children growing up in the same family because these differences are most likely created by stochastic developmental processes (Turkheimer 2000). This is probably an overstatement, as increasingly stronger and unambiguous designs as well as improved measurement strategies are being used to identify specific environmental causes of personality differences between children growing up in the same family (Caspi et al. 2004, Reiss et al. 2000). Third, quantitative genetic studies of personality are increasingly used to ask questions about causal mechanisms (Rutter et al. 2001). This is partly in response to evidence that genetic factors influence people’s environments; i.e., measures that assess putative environmental experiences are saturated by genetic variance. Genetic influences have been identified on multiple aspects of the child-rearing environment (e.g., Wade & Kendler 2000) as well as on extrafamilial, peer environments beyond the family (Iervolino et al. 2002). A key issue for personality researchers is the extent to which personality contributes to genetic influences on measures of the environment. So far, genetic effects on the Big Five personality traits have been reported to explain genetic influences on life events (Saudino et al. 1997), and personality traits account for more than 30% of the genetic influence on divorce risk (Jockin et al. 1996). The main implication from these results is that associations between environmental measures and personality cannot be assumed to be caused environmentally, and in some instances the likely direction of effects is the other way around: Individuals are sometimes differentially exposed to environments (e.g., divorce) as a result of their genetically influenced personality traits. Fourth, although it is often said disparagingly that behavioral genetics is adevelopmental, recent findings in three different areas of research demonstrate that behavioral genetic methods are ideal for application to questions about agerelated changes in etiology, in relation to both normal and abnormal development. First, longitudinal research on cognitive development shows that the heritability of IQ increases from early childhood through late adolescence (Plomin et al. 1997). In this instance, one can think of a heritability estimate as an outcome variable. When it changes with age, this suggests that the balance of genetic versus environmental causal processes differs at successive developmental stages. In the case of IQ, the findings suggest that the effect on IQ of environmental factors shared by siblings dissipates with age, as each child increasingly seeks out environments that are correlated with their genetic endowments. Second, research on the development of drug dependence suggests that the causes of initiation and of dependence are not identical, and that the factors that lead adolescents to sample drugs are not necessarily the same factors that lead to drug dependence (Kendler et al. 1999). Third, research in developmental psychopathology suggests that the pattern of antisocial behavior that begins early in life, is pervasive across settings, and persists into adulthood is associated with relatively higher heritability estimates than is the pattern of late-onset, situational, transient delinquency (Taylor et al. 2000). In

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combination, these three sets of findings illustrate how quantitative genetic studies can play an important role in illuminating developmental processes.


PATTERNS OF CONTINUITY AND CHANGE IN PERSONALITY TRAITS FROM CHILDHOOD TO OLD AGE Personality traits are thought, by definition, to reflect stable individual differences. But what does the longitudinal evidence actually show? When does personality stabilize, and is there room for continued change throughout development? This section is divided into three parts. First, we review evidence about the differential or rank-order stability of personality across time. Second, we review evidence about mean-level changes in personality over time. Third, we summarize three principles that appear to govern personality development in adulthood.

Differential Continuity and Change Continuity and change are most often indexed by correlations between personality scores across two points in time (i.e., test-retest correlations). These differential or rank-order stability correlations reflect the degree to which the relative ordering of individuals on a given trait is maintained over time. Two contradictory predictions have been proposed about the rank-order stability of personality traits. The classical trait perspective argues that personality traits in adulthood are biologically based “temperaments” that are not susceptible to the influence of the environment and thus do not change over time (McCrae et al. 2000). From this “essentialist” perspective, we would expect the test-retest correlations to be high, even early in life. In contrast, the radical contextual perspective emphasizes the importance of life changes and role transitions in personality development and suggests that personality should be fluid, prone to change, and yield low test-retest correlation coefficients, especially during developmental periods characterized by rapid physical, cognitive, and social changes (Lewis 2001). Existing longitudinal studies do not support either of these positions. A metaanalysis of the rank-order stability of personality (organized according to the FiveFactor Model) revealed six major conclusions (Fraley & Roberts 2004, Roberts & DelVecchio 2000): Test-retest correlations over time (a) are moderate in magnitude, even from childhood to early adulthood. Furthermore, rank-order stability (b) increases with age. Test-retest correlations (unadjusted for measurement error) increased from 0.41 in childhood to 0.55 at age 30, and then reached a plateau around 0.70 between ages 50 and 70. Rank-order stability (c) decreases as the time interval between observations increases, and does not vary markedly (d) across the Big Five traits nor (e) according to assessment method (i.e., self-reports, observer ratings, and projective tests), or (f) by gender. Several observations can be drawn from this meta-analysis. First, the magnitude of rank-order stability, although not as high as the “essentialists” would claim, is still remarkably high. The only psychological constructs more consistent than

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personality traits are measures of cognitive ability (Conley 1984). Second, the level of continuity in childhood and adolescence is much higher than originally expected (Lewis 2001), especially after age 3. Even more impressive is the fact that the level of stability increases in a relatively linear fashion through adolescence and young adulthood. Young adulthood has been described as demographically dense, in that it involves more life-changing roles and identity decisions than any other period in the life course (Arnett 2000). Yet, despite these dramatic contextual changes, personality differences remain remarkably consistent during this period. Third, personality continuity in adulthood peaks later than expected. According to one prominent perspective, personality traits are essentially fixed and unchanging after age 30 (McCrae & Costa 1994). However, the meta-analytic findings show that rank-order stability peaks some time after age 50, but at a level well below unity. Thus, personality traits continue to change throughout adulthood, but only modestly after age 50. Finally, the levels of consistency found in this recent metaanalysis replicated smaller studies dating back to the early part of the twentieth century. There have been few if any cohort shifts in the level of rank-order stability in personality traits in recent history.

Mean-Level Continuity and Change Mean-level change refers to changes in the average trait level of a population. This type of change is thought to result from maturational or historical processes shared by a population, and is typically assessed by mean-level differences in specific traits over time, which indicate whether the sample as a whole is increasing or decreasing on a trait. Contradictory perspectives—similar to those guiding predictions about differential stability—have also guided expectations about mean-level changes in personality traits. Proponents of the Five-Factor Model of personality argue that personality traits do not demonstrate mean-level changes after adulthood is reached (Costa & McCrae 1997) and that, if they do, the change is attributable to genetic factors (McCrae et al. 2000). In contrast, proponents of a life-span developmental perspective emphasize the importance of life changes and role transitions in personality development and suggest that mean-level changes do occur and often at ages much later than young adulthood (Helson et al. 2002). A recent review synthesized and organized (according to the Five-Factor Model) data from over 80 longitudinal studies spanning the period from age 10 to 101 (Roberts et al. 2003). The pattern of change in the first domain of the Big Five, Extraversion, was complex until this superfactor was divided into constituent elements of dominance and sociability. Traits associated with dominance increased from adolescence through early middle age, whereas traits associated with sociability increased in adolescence and then showed decreases in young adulthood and old age. Consistent with evidence from cross-sectional comparisons of different age groups (McCrae et al. 2000), traits belonging to the domains of Agreeableness and Conscientiousness increased in young adulthood and middle age. Traits belonging to the domain of Neuroticism decreased mostly in young adulthood.

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Finally, traits from the Openness-to-Experience domain showed increases in adolescence and young adulthood and a tendency to decrease in old age. Three aspects of these findings deserve note. First, there are no discernible sex differences in patterns of mean-level continuity and change across the Big Five. Apparently, men and women change in the same ways over the life course, although mean-level differences between the sexes are maintained over time. This suggests that the causes of personality continuity and change across the life course are likely to be the same for the sexes. Second, the majority of personality change occurs in young adulthood, not in adolescence as one might suspect given traditional theories of psychological development. This pattern of change is not simply a recent historical phenomenon, as it was observed in different cohorts across the twentieth century. This finding suggests that the causes of normative personality change are likely to be identified by narrowing research attention to the study of young adulthood. Third, for select trait categories, change occurs well past young adulthood, demonstrating the continued plasticity of personality well beyond typical age markers of maturity. On the whole, the evidence offers support for a life-span developmental view of mean-level changes in personality traits. The evidence base about continuity and change still has several important gaps. First, the best data about personality continuity and change—and hence the most reliable conclusions—continue to be restricted to adult samples. Relatively few studies have used a comprehensive set of personality variables to characterize young children and to track continuities and changes in their personalities over time. Second, relatively little is known about ethnic differences in personality continuity and change, due to the underrepresentation of ethnic minority groups in many longitudinal studies. A third methodological weakness is that many longitudinal studies continue to estimate continuity and change over only two waves of assessment, despite the advent of new methodological approaches that are appropriate for answering more nuanced questions about both short- and long-term temporal dynamics (Biesanz et al. 2003). Finally, and most importantly, the next generation of studies should move beyond description and attempt to explain patterns of continuity and change (Mroczek & Spiro 2003).

Principles of Personality Development in Adulthood The empirical results of longitudinal studies allow us to infer a number of principles about personality development, from adolescence through adult life (Roberts & Wood 2004). We highlight three of these principles as guiding hypotheses and syntheses of extant findings. Most people become more dominant, agreeable, conscientious, and emotionally stable over the course of their lives. These changes point to increasing psychological maturity over development, from adolescence to middle age. Two distinct definitions of maturity prevail in developmental theories (Hogan & Roberts 2004). The first, humanistic definition equates maturity with

THE MATURITY PRINCIPLE

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self-actualization and personal growth, with the process of becoming less defensive and rigid and more creative and open to feelings. The data do not support this developmental progression; people do not grow increasingly open to experience toward old age; after young adulthood, they actually exhibit declines on traits related to Openness-to-Experience (Small et al. 2003). The second, functional definition equates maturity with the capacity to become a productive and involved contributor to society, with the process of becoming more planful, deliberate, and decisive, but also more considerate and charitable (traits encompassed by higher levels of Emotional Stability, Conscientiousness, and Agreeableness). According to the longitudinal data, most people do appear to become more functionally mature with age, and those who develop the cardinal traits of psychological maturity earliest are more effective in their love, work, and health (as reviewed in the final section of this chapter). As noted above, the relative consistency of personality traits continues to increase throughout the life span, peaking perhaps at age 60, and even then not being quite fixed (Roberts & DelVecchio 2000). Four factors contribute to the inevitable stabilization of personality that comes with age. First, longitudinal data on twins suggest that much of the stability in adult personality is attributable to genetic factors (e.g., McGue et al. 1993). This does not mean that genes “fix” personality, but rather that genetic factors contribute to the preservation of individual differences over long stretches of the life span, at least from adolescence onward. A possible interpretation of these findings is the set-point model (Carey 2003), which argues that environmental fluctuations may produce short-term changes in personality phenotypes, but that genetic factors contribute to individual set points to which individuals will “regress” (Lykken & Tellegen 1996). Longitudinal data with multiple, repeated measurements—over both short and long intervals—are needed to fully test the predictions derived from the model. Second, personality traits are implicated in niche-building processes that promote continuity. Niche building encompasses at least three different processes whereby people (a) create, (b) seek out, or (c) end up in environments that are correlated with their traits. Once in a trait-correlated environment, the environment may have causal effects of its own, promoting the persistence of trait-correlated behaviors and cutting off opportunities for change. More detailed data are needed about the nature of these processes across time (e.g., Roberts & Robins 2004). A third, related factor that facilitates increasing personality consistency with age is the process of developing, committing to, and maintaining an identity (Roberts & Caspi 2003). Identity development facilitates personality consistency by providing clear reference points for making life decisions. Strong identities serve as a filter for life experiences and lead individuals to interpret new events in ways that are consistent with their identities. Likewise, to the extent that a person’s identity becomes known to others in the form of a reputation, it leads others to treat the person in ways that are consistent with his or her personality (Roberts & Wood

CUMULATIVE CONTINUITY PRINCIPLE

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2004). Future research should integrate indices of identity strength and clarity into longitudinal designs to better test the long-term consequences of identity structure on personality development. Fourth, normative-developmental changes in personality may contribute to increasing personality continuity across the life course. Traits associated with the domains of Agreeableness, Conscientiousness, and Emotional Stability not only increase with age (see the Maturity Principle), they are also positively correlated with increased personality consistency; that is, people who are interpersonally effective, planful, decisive, and considerate are less likely to change (e.g., Roberts et al. 2001). Although there is no obvious explanation for this replicated finding, one possibility is that Agreeable, Conscientious, and Stable people are better equipped to deal with social-developmental challenges across the life course. They have more personal capital in the form of increased resilience that allows them to master more efficiently the life challenges that they face and to recuperate more quickly from aversive and disappointing life events that they encounter. In contrast, their more brittle counterparts may be more susceptible to the influence of their environment. The robust finding that some people are more prone to change than others calls for research that systematically tests reasons for it. Personality trait development is not a continuityversus-change proposition. Rather, continuity and change coexist due to the corresponsive principle, which summarizes the empirical observation that the most likely effect of life experience on personality development is to deepen the characteristics that lead people to those experiences in the first place (Roberts et al. 2003, Roberts & Robins 2004). For example, if people assume more leadership positions because they are more dominant, then they will become more dominant through their experience as leaders. The corresponsive principle links two mutually supportive life-course dynamics: “social selection,” wherein people select environments that are correlated with their personality traits, and “social influence,” wherein environmental experiences affect personality functioning. According to longitudinal data, the traits that “select” people into specific experiences are the traits that are most “influenced” in response to those experiences (e.g., the personality trait of dominance selects people into jobs that involve resource power, and employment in such jobs further increases dominance). That is, life experiences do not impinge themselves on people in a random fashion causing widespread personality transformations; rather, the traits that people already possess are changed (i.e., deepened and elaborated) by trait-correlated experiences that they create.

THE CORRESPONSIVE PRINCIPLE

PERSONALITY EFFECTS ON SOCIAL DEVELOPMENT Two events have served to make research on personality trait development more vibrant. First, developmental psychologists have begun to measure personality traits, rather than ignore them. Second, personality psychologists have become

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increasingly interested in relating measures of personality traits to something besides other personality measures. The result is robust evidence that early-emerging individual differences in personality shape how individuals experience, interpret, and respond to the developmental tasks they face across the life course. In this final section, we review longitudinal evidence about how personality traits shape (a) the cultivation of social relationships, (b) the mastery of educational and work tasks, and (c) the promotion and maintenance of health. For each developmental task, we identify the most relevant personality variables and outline the mechanisms by which these personality traits are hypothesized to exert their influence.

Cultivating Relationships: Friendships, Intimate Relationships, and Parenting One of the most important tasks faced by children and adolescents is the establishment of friendships and acceptance among peers (Hartup & Stevens 1999, Masten & Coatsworth 1998). Among children, all of the higher-order personality traits described previously are important predictors of social competence. Perhaps so many aspects of personality predict social competence because social functioning requires a wide array of skills, including emotional expression, emotional understanding, and emotional and behavioral regulation (Denham 1998, Rubin et al. 1998). Agreeable and Extraverted children are more socially competent concurrently and across time (Asendorpf & van Aken 2003, Gest 1997, Shiner 2000). Children high on Negative Emotionality or low on Constraint have a variety of social difficulties concurrently and across time; the interaction of these traits may be especially problematic for social functioning (Eisenberg et al. 2000). Personality continues to be an important predictor of relationships in adulthood. Neuroticism and Agreeableness are the strongest and most consistent personality predictors of relationship outcomes—including relationship dissatisfaction, conflict, abuse, and, ultimately dissolution (Karney & Bradbury 1995). These effects have been uncovered in long-term studies following samples of children into adulthood, as well as in shorter-term longitudinal studies of adults. The potential contribution of stable personality differences to shaping abusive relationships has been further underscored by longitudinal studies that find associations between early developing aggressive traits in childhood and subsequent abusive behavior in adult romantic relationships (Ehrensaft et al. 2004). One study that followed a large sample of adolescents across their multiple relationships in early adulthood discovered that the influence of Negative Emotionality on relationship quality showed crossrelationship generalization; that is, it predicted the same relationship experiences across relationships with different partners (Robins et al. 2002). Increasingly sophisticated studies that include dyads (not just individuals) and multiple methods (not just self reports) demonstrate that the link between personality traits and relationship processes is more than simply an artifact of shared method variance in the assessment of these two domains (Donnellan et al. 2004, Watson et al. 2000).

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An important research goal is to uncover the proximal relationship-specific processes that mediate these personality effects (Reiss et al. 2002). Personality traits affect relationships by influencing and altering microinteractional processes. First, individuals select their interactional contexts by choosing partners who resemble them. The tendency to form unions with similar others has implications for the course of personality development because similarities between couple members create interpersonal experiences that reinforce initial tendencies (Caspi & Herbener 1990). Second, personality differences influence people’s exposure to relationship events. For example, people high in Negative Emotionality are more likely to be exposed to daily conflicts in their relationships (Bolger & Zuckerman 1995). Third, personality differences shape people’s reactions to the behavior of their partners. For example, people who are more Neurotic are more likely to escalate negative affect during conflict (e.g., Gottman et al. 1998). Similarly, high Agreeable people are better able to regulate emotions during interpersonal conflicts (Jensen-Campbell & Graziano 2001). Cognitive processes also come on-line in creating trait-correlated experiences (Snyder & Stukas 1999). Trait-correlated anticipatory attitudes lead individuals to project particular interpretations onto new social relationships, and individuals transfer trait-correlated affective responses developed in the context of previous relationships to new relationships (Andersen & Chen 2002). Fourth, personality differences evoke behaviors from partners that contribute to relationship quality. For example, people high in Neuroticism and low in Agreeableness are prone to express four behaviors identified as detrimental to relationships: criticism, contempt, defensiveness, and stonewalling (Gottman 1994). Whereas a great deal of research has investigated the influence of personality on friendships and intimate relationships, fewer studies have considered the possibility that parents’ personalities shape their parenting styles and relationships with their children. This is a curious omission because parental personality forms a critical part of children’s developmental context (Goldsmith et al. 1994). Moreover, behavioral genetic studies show that some parenting behaviors are heritable (Spinath & O’Connor 2003). This does not mean, of course, that there is a gene for parenting styles. What it does suggest is that individual differences in parenting behaviors may be related to personality characteristics that are strongly influenced by genetic factors. The handful of studies that have examined personality/parenting associations—using self-reports as well as observations of parenting—suggest that Positive Emotionality and Agreeableness are related to sensitive and responsive parenting, whereas aspects of Neuroticism, such as anxiety and irritability, are related to less competent parenting (Belsky & Barends 2002). Much more work needs to be done. First, most of the research to date has focused on parents of very young children to the virtual exclusion of adolescents. Second, most of the research has not tested mediators (e.g., parental attributions) of observed personality/parenting associations. Third, most of the research has focused on the main effects of personality and has not addressed the conditions under which particular personality attributes are more or less important in explaining parenting behavior

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(e.g., are personality main effects moderated by qualities of the marital relationship or by the child’s temperament?). Fourth, to our knowledge, no study has examined personality effects on parenting behavior in relation to multiple children in the same family, a design that has the power to test the cross-situational generalizability of personality effects (across offspring) and to estimate the influence of parental personality on family life independently of other family-wide environmental effects. The study of personality effects on social relationships is exciting territory where hypotheses about personality dynamics can be tested using multiple and creative methodologies. These approaches need not be confined to close relationships. Bugenthal (2000) proposed a taxonomy of social relations that offers the promise of helping to coordinate personality research by focusing attention on how personality variables shape behaviors in five domains of social life: attachment relations, mating relations, hierarchical power relations between persons of unequal status, reciprocal relations among persons of equal status, and coalitional-group relations.

Striving and Achieving Across the life course, individuals assume multiple performance tasks (e.g., pursuing an education, assuming a job, managing and allocating resources). Personality traits from the domain of Conscientiousness/Constraint are the most important noncognitive predictors of educational achievement, occupational attainment, and subsequent job performance (Judge et al. 1999). Conscientiousness encompasses many traits that are necessary for completing work effectively: the capacities to sustain attention, to strive toward high standards, and to inhibit impulsive behavior. In fact, childhood Conscientiousness predicts improvements in academic achievement across time into adulthood (Shiner 2000, Shiner et al. 2003). Additional evidence suggests that childhood traits from the Positive Emotionality and Agreeableness domains predict adolescent academic performance (Shiner 2000); aspects of these same higher-order traits also predict adult job performance criteria in a more limited set of occupational groups (Mount et al. 1998). Openness-toExperience and Intellect likewise correlate with academic achievement in samples of school-age children and young adolescents (Graziano et al. 1997). Predictive associations between temperament and personality traits and achievement are apparent already early in life, at the time that children first enroll in school (Miech et al. 2001). Research with children, adolescents, and adults also demonstrates that the links between personality traits and various indices of achievement remain significant after controlling for individual differences in ability (Judge et al. 1999, Shiner 2000, Shiner et al. 2003). The finding that personality effects on achievement emerge early in life is important, because school adjustment and academic performance have cumulative effects over time. The personality processes involved may vary across different stages of development, and at least four candidate processes deserve research

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scrutiny (Schneider et al. 1998). First, the personality/achievement associations may reflect “attraction” effects, or “active niche-picking,” whereby people actively choose educational and work experiences whose qualities are concordant with their own personalities. For example, people who are more conscientious prefer conventional jobs, such as accounting and farming (Gottfredson et al. 1993). People who are more extraverted prefer jobs that are described as social or enterprising, such as teaching or business management (Ackerman & Heggestad 1997). Moreover, extraverted individuals are more likely to assume leadership roles in multiple settings (Anderson et al. 2001). Second, personality/achievement associations reflect “recruitment” effects, whereby people are selected into achievement situations based on their personality characteristics. These recruitment effects begin to appear surprisingly early in development. For example, children’s personalities influence their emerging relationships with teachers already at a young age (Birch & Ladd 1998). In adulthood, job applicants who are more extraverted, conscientious, and less neurotic are liked better by interviewers and are more often recommended for the job (Cook et al. 2000). Third, some personality/achievement associations emerge as consequences of “attrition” or “deselection pressures,” whereby people leave achievement settings (e.g., schools or jobs) that do not fit with their personality or are released from these settings because of their trait-correlated behaviors (Caspi et al. 1998). Fourth, personality/achievement associations emerge as a result of direct, proximal effects of personality on performance. To date, most research has centered on the relation between Conscientiousness and productive job performance (Barrick & Mount 1991). In addition, increasing evidence suggests that Positive Emotionality facilitates the efficient processing of complex information and is associated with creative problem solving (Ashby et al. 1999). More recent research shows that, in fact, all of the Big Five have substantial relations with better performance when the personality predictor is appropriately aligned with work criteria (Hogan & Holland 2003). This indicates that if people find jobs that fit with their dispositions they will experience greater levels of job performance, which should lead to greater success, tenure, and satisfaction across the life course (Judge et al. 1999).

Health Promotion and Maintenance Perhaps the most dramatic findings about personality development come from life-span studies that document links between personality traits and longevity: Individuals who score high on traits of Positive Emotionality and Conscientiousness/Constraint live longer (Danner et al. 2001, Friedman et al. 1995). Other research documents that individuals high in traits related to Disagreeableness (e.g., anger and hostility) are at greatest risk of disease (e.g., cardiovascular illness) (Miller et al. 1996). The evidence for the involvement of Negative Emotionality/Neuroticism in ill health is more mixed, with the some research pointing to

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links with increased risk of actual disease and other studies documenting links with illness behavior only (Smith & Spiro 2002). Personality/health associations may reflect at least three distinct processes (Contrada et al. 1999, Rozanski et al. 1999). First, personality differences may be related to pathogenesis, mechanisms that promote disease. This has been evaluated most directly in studies relating various facets of Disagreeableness/Hostility to greater reactivity in response to stressful experiences (Smith & Gallo 2001). However, part of the complexity of testing hypotheses about the role of personality in the physiological processes of a disease involves the need for greater clarity about the disease processes involved and the phases during which personality effects may be implicated. Second, personality differences may be related to physical health outcomes because they are associated with health-promoting or health-damaging behaviors. For example, individuals high in Positive Emotionality may foster social relationships, social support, and social integration, which are positively associated with health outcomes (Berkman et al. 2000). In contrast, individual low in Conscientiousness engage in a variety of health-risk behaviors such as smoking, unhealthy eating habits, lack of exercise, unprotected sexual intercourse, and dangerous driving habits (Bogg & Roberts 2004). The association between Constraint/Conscientiousness-related traits and health-risk behaviors is especially robust and appears to be stronger among adolescents than adults, which suggests that this “risky” personality trait merits greater research and public health attention. Future personality research could be usefully integrated with developmental research from a decision-theory perspective to better understand the decision-making processes that may mediate the links between traits from the Conscientiousness domain and health-risk behaviors (Hampson et al. 2000). Third, personality differences may be related to reactions to illness. This includes a wide class of behaviors, including the possibility that personality differences affect the selection and execution of coping behaviors (e.g., Scheier & Carver 1993), modulate distress reduction, and shape treatment adherence (Kenford et al. 2002). The aforementioned processes linking personality traits to physical health are not mutually exclusive. Moreover, different personality traits may affect physical health via different processes. For example, facets of Disagreeableness may be most directly linked to disease processes, facets of low Conscientiousness may be more clearly implicated in health-damaging behaviors, and facets of Neuroticism may contribute to ill health by shaping reactions to illness. The study of personality and health has historically been confined to adults. However, this may well change as health psychologists turn their attention to earlier periods in development in order to understand enduring research and public health puzzles. Consider research on social inequalities in health, which has tended to focus on low socioeconomic status in adulthood as the main causal variable and on adults’ stress experiences as the main mediating mechanism. However, mounting evidence from life-course research points to the contribution of early life experiences and to the cumulative impact of sustained social disadvantages on

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adult health, compelling health psychologists to turn their attention to examine the role that personality factors may play in mediating the associations between early social experiences and poor health across the life course (Gallo & Mathews 2003, Repetti et al. 2002).


SUMMARY We have summarized four areas of research about personality development where advances, and even some new discoveries, have been made. In some instances, new research has resolved long-standing debates and, in other instances, it has generated new ideas and hypotheses. Advances in measurement (personality structure), methodology (analysis of temporal dynamics), and technology (genetics and brain imaging) will continue to fuel exploration and understanding of the origins and developmental course of individual differences in personality. ACKNOWLEDGMENT Preparation of this review was supported by grants from the National Institute of Mental Health, the Medical Research Council, and the W.T. Grant Foundation. The Annual Review of Psychology is online at http://psych.annualreviews.org

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WORK MOTIVATION THEORY AND RESEARCH AT THE DAWN OF THE TWENTY-FIRST CENTURY Annu. Rev. Psychol. 2005.56:485-516. Downloaded from arjournals.annualreviews.org by Ball State University on 01/05/09. For personal use only.

Gary P. Latham Rotman School of Management, University of Toronto, Ontario M5S 3E6; email: [email protected]

Craig C. Pinder Faculty of Business, University of Victoria, British Columbia V8W 2Y2; email: [email protected]

Key Words needs, values, goals, affect, behavior ■ Abstract In the first Annual Review of Psychology chapter since 1977 devoted exclusively to work motivation, we examine progress made in theory and research on needs, traits, values, cognition, and affect as well as three bodies of literature dealing with the context of motivation: national culture, job design, and models of person-environment fit. We focus primarily on work reported between 1993 and 2003, concluding that goal-setting, social cognitive, and organizational justice theories are the three most important approaches to work motivation to appear in the last 30 years. We reach 10 generally positive conclusions regarding predicting, understanding, and influencing work motivation in the new millennium.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MOTIVATIONAL FRAMEWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NEEDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TRAITS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VALUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONTEXT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . National Culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Job Design Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PERSON-CONTEXT FIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . COGNITION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Goal-Setting Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contextual Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementation Intentions and Auto-Motive Goals . . . . . . . . . . . . . . . . . . . . . . . . . Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Self-Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0485$14.00

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Expectancy Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Cognitive Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . AFFECT/EMOTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Organizational Justice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION At the dawn of this new millennium,1 Miner (2003) concluded that motivation continues to hold a significant position in the eyes of scholars. “If one wishes to create a highly valid theory, which is also constructed with the purpose of enhanced usefulness in practice in mind, it would be best to look to motivation theories . . . for an appropriate model” (p. 29). Miner’s conclusion is based on a comparison with other middle range theories of organizational behavior (OB). The question remains as to whether, on an absolute standard, motivation theory and research have fared well over the last quarter of a century. In answering this question, we provide a definition of the construct and an assessment of how the field of motivation in the workplace has evolved and progressed since the year in which the last chapter (Korman et al. 1977) devoted exclusively to this topic appeared in the Annual Review of Psychology (ARP). We selectively review theory and research, emphasizing work published in the past decade, 1993–2003, with special emphasis given to research on contextual effects and mediating mechanisms. This is because scholars (e.g., Pinder 1998) have pointed to the power of context to moderate opportunities for, and constraints against, organizational behavior. In addressing this issue, the chapter concludes with an assessment of the degree to which progress has been made to predict, understand, and influence motivation in the workplace. Work motivation is a set of energetic forces that originate both within as well as beyond an individual’s being, to initiate work-related behavior and to determine its form, direction, intensity, and duration (Pinder 1998, p. 11). Thus, motivation is a psychological process resulting from the interaction between the individual and the environment. Accordingly, the importance of context is acknowledged throughout our analysis. However, because of space limitations, we focus primarily on national culture, job design characteristics, and person-environment fit, omitting reviews of other exogenous sources of motivation (e.g., organization climate and culture, leadership, and groups and teams). Job design is traditionally included in reviews of motivation. National culture and person-environment fit are relatively new to this literature, hence our choice of these three contextual variables. 1

Latham & Budworth (2004) chronicled the history of research and theories of motivation in the workplace in the twentieth century.

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MOTIVATIONAL FRAMEWORK The framework used in reviewing the literature is consistent with Locke & Henne (1986). Consequently, the flow of the chapter is as follows: A discussion of (a) needs is followed by a focus on (b) personal traits, as the latter historically have been viewed as needs or drivers. An individual-difference variable rooted in needs is (c) values. Because context affects the extent to which needs are met and values are fulfilled, emphasis is given to (d) national culture, (e) job design characteristics, and (f) person-context fit. Needs and values affect (g) cognition, particularly goals. Cognition plays an integral role in each of these concepts. Although (h) affective reactions need not depend on cognition (Bandura 1997), the two usually are reciprocally related (Lord & Kanfer 2002). Moreover, emotionfocused coping encompasses both cognitive and behavioral strategies (Kanfer & Kantrowitz 2002). Finally, affect is influenced by culture as well as by organizational norms (Lord & Harvey 2002). We close with an (i) assessment of progress in the field since 1977.

NEEDS Kanfer (1991) has stressed the importance of needs as internal tensions that influence the mediating cognitive processes that result in behavioral variability. Thus the resurgence of emphasis on needs since 1977 is not surprising. What may be surprising is the resurgence of interest in Maslow’s (1943) hierarchical need theory. Wicker et al. (1993) showed that between-goal correlations and partial correlations across four samples of college students supported Maslow’s theory when intentions to act were rated rather than measures of importance. Ronen (2001), using multidimensional scaling of employee data collected in 15 countries rather than factor analysis, found support for the taxonomic element of Maslow’s theory. Kluger & Tikochinsky (2001) advocated ongoing efforts to find ways to operationalize the theory validly. Haslam et al. (2000) presented a process-based analysis of need structure and need salience derived from the social identity approach to organizational behavior. To understand motivation, they argued, one must understand aspirations for the self that exist in a hierarchy. When personal identity is salient, needs to selfactualize and to enhance self-esteem through personal advancement and growth become dominant. When social identity is salient, the need to enhance groupbased self-esteem through a sense of relatedness, respect, peer recognition, and attainment of group goals dominate. They stated that McGregor’s (1960) Theory Y assumptions apply when the supervisor and employee share the same identity; Theory X assumptions apply when they do not do so.2 People are motivated to 2

Theory Y differs from Theory X in that the latter places exclusive reliance upon external control of behavior, whereas Theory Y emphasizes self-control and self-direction.

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attain goals that are compatible with their self-identity. Needs associated with a specific group membership are internalized; they serve as a guide for behavior in a specific working context. Their analysis of survey data of Australian employees was interpreted as supporting social identity theory. Ajila (1997) and Kamalanabhan et al. (1999) argued that the practical significance of Maslow’s theory is widely accepted. Physiological needs are considered in decisions regarding space, lighting, and overall working conditions; safety in terms of work practices; love in regard to forming cohesive work teams; esteem through responsibility and recognition; and self-actualization in terms of opportunities for creative and challenging jobs/tasks. This is particularly true in developing countries. Employees in four manufacturing companies in Nigeria rated satisfying lower needs as most important, followed by the higher order growth needs (Ajila 1997). Among bank employees in India, officers attached greater importance to growth needs than did clerks (Rao & Kulkarni 1998). Based on their socioanalytic theory, Hogan & Warremfeltz (2003) argued that people have innate biological needs for (a) acceptance and approval; (b) status, power, and control of resources; and (c) predictability and order. These needs translate into behaviors for getting along with others, getting ahead in terms of status, and making sense of the world. Need-based theories explain why a person must act; they do not explain why specific actions are chosen in specific situations to obtain specific outcomes. Moreover, they do not easily account for individual differences. Hence, along with increased attention to needs, there has also been a resurgence of interest in individual differences, particularly with regard to the effects of job characteristics on employee motivation.

TRAITS Traits have long been considered needs or drivers: their satisfaction leads to pleasure, and lack of fulfillment leads to displeasure (Allport 1951). Nevertheless, Mitchell (1979) found that individual difference variables had few or no moderating or mediating effects on motivation in the workplace. Subsequent findings from meta-analyses challenged this conclusion (e.g., Barrick et al. 2001). Failure to express one’s traits can lead to anxiety (Cote & Moskowitz 1998). In the present century, Mitchell & Daniels (2003) reported that research on personality is the fastest growing area in the motivation literature. In a review of predictor domains, Schmitt et al. (2003) concluded that personality is the primary predictor of elements of motivation. In fact, research now shows that traits predict and/or influence job search and choice of job, as well as job performance and satisfaction. These traits include extroversion, conscientiousness, self-regulatory and self-monitoring strategies, tenacity, core self-evaluations, and goal orientation. For example, a meta-analysis by Kanfer et al. (2001) examined the relation between self-regulation and personality measures with respect to job search. The results

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showed that self-regulation is more strongly related to positive than to negative affective variables. Kanfer & Heggestad (1997) proposed a developmental theory that distinguishes between distal influences on action, in the form of relatively stable motivational traits, and proximal influences that are associated with individual differences in self-regulatory or motivational skills. Heggestad & Kanfer (1999) developed a multiple-trait motivational inventory. The scale has convergent and discriminant validity with regard to measures of motivation versus intelligence (Kanfer & Ackerman 2000). A meta-analysis of a self-monitoring personality by Day et al. (2002) revealed a robust positive relationship with job performance, as well as a relatively strong positive relationship with advancement into leadership positions. This is because self-monitors are motivated to meet the expectations of others, which in turn enhances their likeability. Likeability is a key to job progression (Hogan et al. 1994). In an enumerative review, Day & Schleischer (2004) concluded that self-monitors outperform those who are low on needs in getting along and getting ahead. The evidence is relatively mixed on the need to make sense of the environment. Furnham et al. (1999) found that extroverts are attracted to enriched jobs. Those who score high on neuroticism are attracted to jobs that excel on hygiene variables. Tett & Burnett (2003) presented a person-situation interactionist model of job performance that lays the groundwork for specifying the conditions under which particular personality traits predict and explain performance in specific jobs. Their model proposes that employees seek out and are satisfied with tasks, people, and job characteristics that allow them opportunities for expressing an array of personality traits. An ideal work setting, they argued, is one that offers cues to the employee for trait expression per se as well as one where trait-expressive behavior is valued positively by others. Variance in trait-expressive behavior is maximized in “weak” situations. In “strong” situations, extrinsic rewards overpower individual differences in intrinsic rewards associated with trait expression. Mount & Barrick (1995) showed that conscientiousness is particularly important in jobs that allow autonomy. Witt & Ferris (2003) found that the relationship between conscientiousness and job performance that requires interpersonal effectiveness is moderated by social skill. Among workers low in social skill, the relationship between conscientiousness and performance was either irrelevant or negative. Hogan & Shelton (1998) argued that social skill, a learned ability, is necessary for motivation to lead to success. High needs for achievement or power are likely frustrated, they said, among people low in social skill. Gellatly (1996) too found that conscientiousness was significantly related to performance. This effect was completely moderated by self-efficacy and two measures of personal goals. Baum et al. (2001) showed that the relationship between personality and performance is mediated by situationally specific goals and self-efficacy. They concluded that traits should be examined through mediation models that test complex causal chains. Baum & Locke (2004) conducted a six-year follow-up study to understand

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the way entrepreneurs’ passion and tenacity combine to affect the success of the venture as a whole. They found that these two traits have indirect rather than direct effects through specific, nontrait mechanisms, namely goal setting and selfefficacy. Judge et al. (1997) developed a theory of traits they labeled core self-evaluations that represent one’s appraisal of people, events, and things in relation to self. Core evaluations are manifested in four traits, namely self-esteem, locus of control, neuroticism, and generalized self-efficacy. A meta-analysis showed that core self-evaluation is a strong dispositional predictor of job satisfaction (Judge & Bono 2001). Erez & Judge (2001) found that motivation mediated about half the relationship between core self-evaluations and performance. They concluded that core self-evaluation is a motivational trait, and that this explains its effect on job performance. Dweck (1999) argued that people’s conception of their ability influences the goals they pursue. Incrementalists have a learning goal orientation (LGO); they focus on the acquisition of knowledge and the perfecting of competence. Hence they choose tasks that are challenging for them. Errors are viewed as allowing opportunities to learn from mistakes. Entitists view their ability as fixed. They have a performance goal orientation (PGO), whereby they choose tasks that allow them to easily demonstrate proficiency at the expense of learning something new. A PGO disposition correlates negatively with self-efficacy (Phillips & Gully 1997). LGO is positively related to openness to new experiences and optimism (VandeWalle 1996), internal locus of control (Button et al. 1996), desire for hard work (VandeWalle 1997), and effort (VandeWalle et al. 1999). VandeWalle et al. (1999) found that an LGO correlates positively with sales performance, but it is mediated by self-regulation (goal setting, effort, planning). Nevertheless, they concluded, “There is considerable evidence of goal orientation existing as a stable individual difference” (p. 250). Brett & VandeWalle (1999) reported that goal orientation did not have a significant relationship with performance, but that it was mediated by the content of goals (performance versus learning goal) that individuals selected. Those with an LGO disposition selected a learning goal and those with a PGO selected performance goals. A longitudinal study showed that goal orientation influences initial emotional reactions and subsequent self-regulation in the face of negative feedback. In summary, the importance of personality in predicting, understanding, and influencing choice, affect, and performance has been shown, as well as the importance of job characteristics (e.g., autonomy) as a mediator/moderator. A taxonomy of motivational traits and skills, as well as three theories—socioanalytic, core self-evaluations, and goal orientation—are dominating the literature. An issue identified by Locke & Latham (2004) that has yet to be addressed is how general variables such as personality are applied to and are mediated by task and situationally specific variables in affecting performance, or how they are moderated by situations and affect situational structuring and choice. A general value or motive must be applied consciously or unconsciously to each specific task and situation.

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Situational and task-specific knowledge, assessments, and intentions are likely affected by the person’s values and assessments. Locke (2001) showed that values and personality work through goals and self-efficacy to influence performance. Yet it is likely that some trait effects are direct and thus are not mediated. Research is needed on if, when, and why this occurs.


VALUES Values are rooted in needs and provide a principal basis for goals (Locke & Henne 1986). Indeed, they can be seen as trans-situational goals, varying in importance, that serve as guiding principles in the life of a person (Prince-Gibson & Schwartz 1998). Values are similar to needs in their capacity to arouse, direct, and sustain behavior. Whereas needs are inborn, values are acquired through cognition and experience. Values are a step closer to action than needs. They influence behavior because they are normative standards used to judge and choose among alternative behaviors. Although values can be subconscious, they are usually more easily verbalized than needs. Locke & Henne (1986) argued that values are inherent in most work motivation theories. These theories focus on the influence of one or several particular values, such as perceptions of fairness on action or on the effects of values in general (expectancy theory). Goals are similar in meaning to values except that they are more specific. They hold the same means-end relationship to values as values do to needs. Goals are the mechanism by which values lead to action. Values have been examined in expectancy-valence frameworks to predict and understand work-related behavior. Foreman & Murphy (1996) applied a valenceexpectancy approach to predict job-seeking behavior. Similarly, Verplanken & Holland (2002) explored how values affect choices. Outcomes with the potential to activate a person’s central values instigate the acquisition of information and motivate choice decisions in accordance with pursuing the values in question. That is, activation and information collection mediate the relationship between values and decision behavior. In a longitudinal study, Malka & Chatman (2003) found that business school graduates who have an external work orientation reported higher job satisfaction and subjective well-being than those who reported an orientation toward the intrinsic aspects of work.

CONTEXT From 1977 to the present there has been a paucity of theory and research on workplace values alone as predictors or independent variables. As a result of globalization, however, values have been studied within the context of a person’s culture and job as well as person-environment fit.

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National Culture In an attempt to tie together needs and values, Steers & Sanchez-Runde (2002) stated that national culture determines three key sets of distal sources of motivation: (a) people’s self-concept, including personal beliefs, needs, and values; (b) norms about work ethic and the nature of “achievement,” tolerance for ambiguity, locus of control, etc.; and (c) “environmental factors” such as education and socialization experiences, economic prosperity, and political/legal systems. Based on their conceptual model, the authors concluded that these distal factors influence self-efficacy beliefs, work motivation levels, and goals, as well as the nature of incentives and disincentives to perform. Erez & Earley (1993) have developed a model of “cultural self-representation” to guide individual behavior and managerial practices in cross-cultural settings. They argued that people strive to fulfill values for self-enhancement, efficacy, and self-consistency. Their model is based on two dimensions frequently used to characterize national cultures: collectivism versus individualism, and power distance. Three principles are advanced to assist the design and interpretation of motivation and reward systems: (a) identify the cultural characteristics of a country regarding collectivism/individualism and power distance; (b) understand yourself and the cultural values you represent; and (c) understand the meaning of various managerial practices (such as differential versus flat salary reward distribution and top-down versus two-way communication styles) in each country (Erez 2000). Projecting values onto people from other cultures that differ on the above two dimensions can create dysfunctional consequences in terms of employee motivation, interpersonal communication, and overall performance (Earley 2002). A cross-cultural study by Roe et al. (2000) found differential relationships between context variables and outcome variables in a comparative test of a motivation model in Bulgaria, Hungary, and the Netherlands. Building on research findings of other scholars, Leung (2001) has offered four hypotheses for further research: (a) work teams in collectivistic cultures have higher levels of unconditional benevolence and positive social identity that, in turn, lead to higher levels of in-group involvement than is the case for groups that value individualism; (b) productivity and performance levels are more homogenous (not necessarily higher or lower) in collectivistic cultures than in individualistic cultures; (c) motivational strategies by superiors have more effect on subordinates in cultures with high levels of power distance than in cultures low in power distance; and (d) negative reactions from supervisors in high power-distance cultures generate more negative reactions among workers than is the case in low power-distance cultures. An experiment comparing Israeli and Chinese college students in Singapore supported the hypothesis that people from low power-distance cultures, the Israelis, set higher goals and reach higher performance levels than people from a high power-distance culture, the Chinese (Kurman 2001). Earley (2002) proposed a three-level construct of “cultural intelligence,” in which a person’s self-efficacy vis-à-vis social discourse in cross-cultural settings plays a key role in the effectiveness of such interactions. High self-efficacy resulted

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in the individual initiating cross-cultural interactions, persisting in the face of early failures, and engaging in problem solving as a way of mastering the necessary skills. Sue-Chan & Ong (2002) investigated the effect of goal assignment on goal commitment, self-efficacy, and performance of people from 10 different countries. Self-efficacy mediated the effect of goal assignment on performance for those low in value for power distance. Not all motivation-related values vary across cultures. A study of more than 19,000 participants from 25 countries (Scholz et al. 2002) found a high degree of consistency in the psychometric properties of a scale assessing general selfefficacy, an important concept in the mechanisms related to goal setting and selfregulation. In summary, significant progress has been made in understanding cross-cultural differences in work motivation. Mediating mechanisms explain why motivational strategies vary in effectiveness in different countries.

Job Design Characteristics The job environment affects and is affected by a person’s needs, personality, and values. Research emphasis has been on the former rather than on the latter. Motivation may be low depending on the fit between the characteristics of the job and the person’s values. Gustafson & Mumford (1995) reported that the ability of personality measures to predict performance as well as satisfaction increases when characteristics of a job are taken into account. Thus, Nord & Fox (1996) argued that contextual factors and the interplay between context and the individual should be taken into account in organizational behavior. Motivational researchers have responded to this suggestion. More than 200 studies were conducted between 1970 and 1990 on characteristics of jobs that are determinants of attitudinal and behavioral outcomes (Ambrose & Kulik 1999). Job autonomy can facilitate the time necessary for learning and development, which in turn improves job performance (Wall & Jackson 1995). Cordery (1997) argued the necessity of differentiating the importance of three dimensions of job autonomy, namely (a) method control as defined by the amount of discretion one has over the way in which work is performed, (b) timing control in terms of the influence one has over scheduling of work, and (c) discretion in setting performance goals. He found four interrelated dimensions that affect job autonomy, namely the extent to which the supervisor (a) provides clear attainable goals, (b) exerts control over work activities, (c) ensures that the requisite resources are available, and (d) gives timely accurate feedback on progress toward goal attainment. The first three influence employee perceptions of autonomy. An analysis of survey data from Australian employees led Wright & Cordery (1999) to conclude that affective well-being declines with traditional job designs, particularly where there is production uncertainty, but increases under “high control” job designs. Production uncertainty, they argued, is an important contextual variable that, similar to supervisory practice, has the potential to improve the

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prediction and explanatory capability of job design theories. Where the work is routine and predictable, attempts to increase decision control (autonomy) within an operator’s job begs the question, they said, of “control over what?” Where the opposite is true, job design can be effective in increasing motivation in the workplace. In studies of a Dutch bank and school, Houkes et al. (2001) found that there is a positive relationship between work content (skill variety) and work motivation, and between erosion of work content and emotional exhaustion. The latter was also predicted by lack of social support. Parker & Wall (1998) reported that work-related stress can result in emotional exhaustion and psychosomatic illness. In addition, Parker (2003) found that unenriched simplified jobs stemming from lean processes (LP) can affect an employee’s level of job depression. The mediator was job characteristics. Job characteristics also partially mediated organizational commitment. To the extent that LP can be introduced in such a way as to allow job autonomy, skill use, and participative decision making, the employee’s well-being and motivation increase. Failure to do so, she argued, is not likely to be conducive to an employee’s self-efficacy. These findings, from a U.K.-based company, are consistent with those of Theorell & Karasek (1996), which showed that lack of job autonomy can increase the risk of cardiovascular disease. Parker’s hypothesis regarding the possible mediating effect of self-efficacy regarding job characteristics is supported by Bandura (2001). When people believe themselves to be inefficacious, they are likely to exert little or no effort even in environments that provide opportunities for growth. Conversely, when people view their environment as controllable regarding characteristics that are important to them, they are motivated to exercise fully their perceived efficacy, which in turn enhances their likelihood of success. Edwards et al. (2000) found that mechanistically oriented job designs are associated with efficiency-related outcomes, whereas motivationally oriented job designs are associated with satisfaction-related outcomes. Moreover, these two designs typically have strong negative relationships with one another. Using the Minnesota Job Description Questionnaire within a pharmaceutical company, Morgeson & Campion (2002) outlined a process to minimize the tradeoff between employee satisfaction and efficiency: (a) define task clusters that form a natural work process, (b) quantify the task clusters in terms of their motivational (e.g., autonomy) and mechanistic (e.g., specialization) properties, and (c) combine task clusters to form a job core. In summary, the importance of characteristics of jobs, particularly job autonomy, learning, performance, OCB, and satisfaction, has been shown. Autonomy is important, however, in only those jobs where the work is not routine or predictable. Unenriched routine jobs can result in job depression. A questionnaire with excellent psychometric properties now exists to assess job characteristics. Morgeson & Campion (2002) have shown that jobs have three major components: complexity, the social environment, and physical demands. Researchers have relatively ignored the latter two.

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Locke & Latham (2004) noted that person-situation interaction studies generally focus on the effect of “strong” or constrained situations where employees feel less free to act as they want or “really are” as compared to when they are in “weak” situations. What has yet to be studied is strong versus weak personalities. As Bandura (1997) noted, people are not simply dropped into situations. Research is now needed on ways they choose, create, and change job characteristics, and the role of traits in doing so. For example, Rousseau (2004) is studying ways an employee directly shapes the terms of the employment arrangement by negotiating valued work conditions, and the effect that these idiosyncratic arrangements have on the person’s motivation.

PERSON-CONTEXT FIT By design, so-called goodness-of-fit models simultaneously consider individual and contextual variables. The basic assumption underlying these models is that the relationship between person variables (such as needs or values) and both individual and organizational outcomes is contingent upon various features of the environment (such as the job, the organization, or culture). These models originated with the seminal work of Shaffer (1953). He used Murray’s (1938) needs to develop a goodness-of-fit model that takes into account individual differences in needs as well as the characteristics of jobs. The relationship between individual differences (e.g., needs or abilities) and both individual and organizational outcomes is contingent upon characteristics of the job or the organization as a whole (Kristof 1996). Thus, goodness-of-fit models consider individual and contextual variables simultaneously. Cable & DeRue (2002), through a confirmatory factor analysis, found that employees differentiate among three varieties of fit: (a) person-environment fit (in which the focus is on organizational outcomes such as organizational identification and turnover decisions); (b) “needs-supplies” fit (in which the primary focus is on career-related outcomes such as employee satisfaction) and (c) job demands– employee abilities fit. The first two forms of fit result in benefits for both persons and organizations. Similarly, at the individual level, Holtom et al. (2002) found that matching employees’ preferences for full- or part-time work status benefited job satisfaction, commitment, and retention as well as extra-role and in-role behaviors. Kristof-Brown et al. (2002) showed that three varieties of person-context fit could have simultaneous positive effects on job satisfaction. Edwards (1996) compared two versions (“supplies/values” and “demands/abilities”) of fit models. He found that the former was more effective in predicting job dissatisfaction while the latter was better at predicting individual tension among graduate business students performing a managerial task. Hollenbeck et al. (2002) developed a model expanding notions of fit across several levels of analysis at once, and found that there is benefit in studying both the degree of fit between individuals and groups simultaneously with the degree of fit between those groups and their task environments.

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The attraction-selection-attrition (ASA) model states that people gravitate to organizations and jobs that are congruent with their values. It addresses the dysfunctions of interpersonal homogeneity (e.g., the dangers of limited perspectives for decision making, groupthink, etc.) as well as the putative benefits such as high levels of interpersonal harmony and job satisfaction (Schneider et al. 2001). ASA offers a meso-level approach that integrates individual, organizational, and industry-wide parameters, including plausible hypotheses to explain mediating mechanisms. A limitation of person-environment fit research is that interactions between the person and characteristics of the job or organization are usually treated as stable states rather than as dynamic. Moreover, there are no agreed-upon ways of assessing dynamic interactions (Borman et al. 2003). On balance, Hulin & Judge (2003) concluded that the conceptual advantages of goodness-of-fit models have yet to yield the significant gains that might be expected in understanding workplace affect and behavior. This may be the result of treating the environment as somehow independent of the employee, even though the employee affects the environment (cf. Bandura 2001). Additional research on person-environment fit is also needed on the extent to which performance, as opposed to satisfaction, is increased.

COGNITION As Locke & Henne (1986) observed, cognition is inherent in motivation. The sensations of pleasure and pain are informational. Based on needs, values, and the situational context, people set goals and strategize ways to attain them. They develop assumptions of themselves and of their identity. This too affects their choice of goals and strategies. Thus, in this section we review goal setting as a theory, research on feedback and self-regulation, as well as expectancy and social cognitive theories.

Goal-Setting Theory The content of goal-setting theory was developed inductively over a quarter of a century. Based on extensive laboratory and field experiments conducted in a wide variety of settings using many different tasks, Locke & Latham presented their first comprehensive statement of goal setting as a theory in 1990, in contrast to goal setting as a technique (Locke & Latham 1990, 1984, respectively). Research on goal-setting theory continues unabated. Mitchell & Daniels (2003, p. 231) concluded that it “is quite easily the single most dominant theory in the field, with over a thousand articles and reviews published on the topic in a little over 30 years.” Latham et al. (1994) investigated assigned versus participative goal setting in which people worked in a group (participative decision making; PDM) or alone on a complex task. No main effect was found for goal setting as the two conditions were yoked. But there was a main effect for decision making with performance significantly higher in the PDM than in the individual decision making condition.

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The main effect of PDM on performance, however, was mediated by self-efficacy and task strategy. Brown & Latham (2000) found that unionized telecommunication employees had high performance and high job satisfaction with their performance appraisal process when specific high goals were set. Moreover, self-efficacy correlated positively with subsequent performance. Lee et al. (1997) showed that if goals are perceived as impossible, offering a bonus for goal attainment can lower motivation. Klein et al. (1999) found that commitment is most important and relevant when the goal is difficult. Goal commitment measures have high reliability and validity (Klein et al. 2001, Seijts & Latham 2000a). Locke (2001) argued that the effects of incentives and personality affect performance through personal goals, goal commitment, and self-efficacy. Kirkpatrick & Locke (1996) found that goals and self-efficacy mediated the effect of visionary leadership on performance. A meta-analysis by Zetik & Stuhlmacher (2002) revealed that negotiators who have specific, challenging, and conflicting goals consistently achieve higher profits than those with no goals. Consistent with goal-setting theory, the higher the goal, the higher the outcome. No effect was found for participation in setting goals. Latham et al. (2002) updated the high performance cycle that explains how high goals lead to high performance, which in turn leads to rewards. Rewards result in high satisfaction as well as high self-efficacy regarding perceived ability to meet future challenges through the setting of even higher goals. High satisfaction is the result of high performance; it can lead to subsequent high performance only if it fosters organizational commitment, and only if the commitment is to specific challenging goals.

Contextual Conditions Seijts & Latham (2000b) examined the applicability of goal-setting principles when personal goals are potentially incompatible with those of the group. They found that social dilemmas are boundary conditions for the usual positive effects of goal setting. Self-enhancing personal goals have a detrimental effect on a group’s performance. Those in seven-person groups were more competitive than those in groups of three. Only when the individual’s goal was compatible with the group’s goal was the group’s performance enhanced. Winters & Latham (1996) replicated Kanfer & Ackerman’s (1989) finding that on a task that is complex for people, urging them to do their best results in higher performance than does setting a specific high performance goal. A high learning goal in terms of discovering a specific number of ways to solve a complex task, however, led to the highest performance. A learning goal requires people to focus on understanding the task that is required of them, and developing a plan for performing it correctly. High performance is not always the result of high effort or persistence, but rather is due to cognitive understanding of the task and strategy or plan necessary for completing it (Frese & Zapf 1994). Another impediment to the usual positive benefits of goal setting is environmental uncertainty, as the information required to set goals may become unavailable or

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obsolete because of rapid ongoing environmental changes. Latham & Seijts (1999) found support for the assertion that performance errors on a dynamic task are often due to deficient decomposition of a distal goal into proximal goals. Proximal goals increase error management (Frese & Zapf 1994). Durham et al. (1997) found that on tasks that are complex for people, there are often goal-strategy interactions, with goal effects strongest when effective strategies are used. On a complex task, Seijts & Latham (2001) found that a distal learning goal in terms of discovering appropriate strategies resulted in higher self-efficacy and goal commitment than a distal performance goal. Those with high self-efficacy discovered and implemented task-relevant strategies. Mediation analyses showed that strategies had both a direct effect on self-efficacy and an indirect effect on performance. Setting proximal goals resulted in the greatest number of strategies generated. Similarly, Knight et al. (2001) reported that difficult goals affect performance through their effect on strategies. A job analysis can obviate the necessity of a learning goal by making explicit the behaviors necessary for attaining the goal (Brown & Latham 2002). Audia et al. (2000) found that past success increased strategic decision makers’ satisfaction, and satisfaction led them to increase their past strategies. Higher satisfaction was associated with higher self-efficacy and higher performance goals that increased dysfunctional persistence subsequent to a radical change in the environment.

Implementation Intentions and Auto-Motive Goals A limitation of Locke & Latham’s (1990, 2002) theory of consciously set goals is that it does not take into account that the subconscious is a storehouse of knowledge and values beyond that which is found in awareness at any given time. Arguably the most exciting research in this domain is occurring in social psychology. Gollwitzer (1999) found that goal intentions that are accompanied by implementation intentions on tasks that are complex for people lead to a higher rate of goal attainment than do goal intentions only. An implementation intention is a mental link that is created between a specific future situation and the intended goal-directed response. Thus, it is subordinate to goal intention. Implementation intentions specify when, where, and how behavior is likely to lead to goal attainment. Thus, holding an implementation intention commits the person to goal-directed behavior once the appropriate situation is encountered. By forming implementation intentions, people strategically switch from conscious effortful control of their goal-directed behavior to being automatically controlled by situational cues. Bargh & Ferguson (2000) summarized research findings that show that automatic or nonconscious goals produce the same outcomes as conscious goal pursuit in information processing, memory storage, social behavior, and task performance, as well as in self-efficacy, self-evaluation, and mood state. The effect of priming on nonconscious goal setting appears to be so powerful that it may raise ethical issues in the workplace. For example, Bargh et al. (2001)

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found that a primed goal resulted in people continuing to work on a task after being told to stop doing so. On the positive side, primed goals were found to reduce prejudice (Bargh 1994). Consistent with goal-setting theory, feedback is a moderator to guide behavior toward the automatized goal (Bargh & Ferguson 2000). The environment can activate a person’s goal within a given situation as part of the preconscious analysis of the situation. The habitual plan for carrying out the goal is activated automatically without conscious planning. To the extent that environmental features become associated with the goal, the importance of conscious choice is removed entirely. The sine qua non of these experiments is the participant’s report of being “unaware.” Psychologists should conduct double-blind experiments when they examine the external validity of these findings in organizational settings. An organizational setting may be sufficiently structured, relative to social settings, that it masks the effect of auto-motive goals. No study yet has compared the effect on job performance of subconscious priming with explicit goal setting. The Galatea effect refers to the direct manipulation of a person’s self-expectations (Eden & Sulimani 2002). Self-expectations are a mediator of the Pygmalion effect. Recent demonstrations of the effect have essentially been exercises in ways to increase self-efficacy through persuasion by a third party (e.g., the leader).

Feedback Feedback is a moderator of goal-setting effects (Locke & Latham 2002). Active feedback seeking by new employees is related to high performance (Ashford & Black 1996). Ashford et al. (2003) stated that the processing of feedback likely involves monitoring the environment in an automatic preconscious fashion through visual, auditory, and relational cues. Significant changes in the environment or in the preconscious monitored cues themselves may cause a shift to the conscious seeking of feedback, and the conscious evaluation of the costs and benefits of doing so. Having sought feedback and resolving uncertainty associated with the interruption, the person returns to the automatic processing of information. In their enumerative review, Ashford & Black (1996) also suggested three primary motives for feedback seeking: instrumental to attain a goal and perform well, ego-based to defend or enhance one’s ego, and image-based to protect or enhance the impression others have of oneself. Unsolicited feedback may be discarded (Roberson et al. 2003) but, as the perceived value of feedback increases, people seek it actively and frequently (Tuckey et al. 2002). In a study of salespeople, Brown et al. (2001) found that self-efficacy moderates the effectiveness of information seeking from supervisors and coworkers regarding role expectations and performance. Similarly, Heslin & Latham (2004) found that managers in Australia change their behavior in a positive direction in response to feedback when they have high self-efficacy to do so. Nease et al. (1999) found that self-efficacy tends to be influenced by numerous rather than single instances of feedback. Future studies should allow for a systematic analysis of quartiles to test

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possible configured relationships. Self-efficacy may have a low positive effect, or even a negative effect at very low and very high levels of performance, and a high positive effect at moderate levels of performance (second and third quartiles). Context, personality, and self-efficacy moderate feedback seeking. Williams et al. (1999) found that a feedback source that is perceived as supportive increases feedback seeking. However, people with low self-esteem lack the resilience to seek negative feedback because it may corroborate a negative self-appraisal (Bernichon et al. 2003). Tabernero & Wood (1999) and VandeWalle et al. (2000) found that people with an LGO disposition are more likely than those with a PGO disposition to process negative feedback on ways to improve performance. Perceived value of feedback seeking fully mediated the effect of LGO on actual feedback seeking in VandeWalle’s study. LGO individuals are able to put negative feedback into perspective, and rebound from distress. Given ongoing questions on the psychometric properties and factor structure of goal orientation scales (e.g., Button et al. 1996, VandeWalle 1997), given that an LGO correlates positively with effort, self-efficacy, and goal-setting level (VandeWalle et al. 2001), and given that an LGO is easily induced (Elliott & Harackiewicz 1996), it would appear that it is time to stop examining the influence of dispositional goal orientation and continue to examine it as a state. As VandeWalle (2003) stated, when the situation provides strong cues, the dispositional goal preference is overridden. This was shown empirically by Seijts et al. (2004). Thus, to select/exclude job applicants on the basis of a disposition that is acquired easily is of dubious value. Goal orientation is by no means a stable individual difference trait across situations. Dweck (1999) now believes that goal orientation is a domain-specific personality pattern. A person could have a PGO in one area of one’s job and an LGO in another. Research emphasis should be placed on setting specific high learning goals and fostering an LGO on tasks that are complex for an individual. Brown et al. (2001) found that people with high self-efficacy use feedback to increase motivation, task focus, and effort and to decrease anxiety and selfdebilitating thoughts. Renn & Fedor (2001) reported that feedback seeking increases goal setting, which in turn increases quality and quantity of performance. Kluger & DeNisi (1996) reported that the effect of feedback is variable; 38% of feedback interventions had negative effects on performance. They proposed that task-focused individuals who receive feedback are likely to maintain cognitive resources allocated to the task whereas ego-involved people allocate their cognitive resources away from the focal task to self, which in turn decreases the potential for future task success following feedback. Heimbeck et al. (2003) found that error management instructions (e.g., “I have made an error. Great!”) help to keep attention on the task and away from the self. Ilgen & Davis (2000) provided a model to aid practitioners in providing negative feedback. A meta-analysis by Kluger & DeNisi (1996), however, shows that feedback sign per se is not a moderator of the effect of feedback on performance. Yet, control theory (Carver et al. 2000) states that failure motivates more than success, whereas goal-setting (Locke & Latham 2002) and social cognitive theories

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(Bandura 1997) state that positive feedback in relation to goal pursuit increases effort and goal difficulty levels. Drawing on Higgins’ (2000) theory, Van-Dijk & Kluger (2004) conducted a series of experiments to resolve these contradictory predictions. They showed that people who receive either positive feedback under a promotion focus or negative feedback under a prevention focus have higher motivation than do people who receive feedback that is incongruent with their regulation focus. However, as regulatory focus was easily manipulated/induced, it is questionable whether regulatory focus should be considered as an individual difference variable, or whether feedback sign should be tailored to occupations as suggested by the authors (e.g., positive feedback for people in artistic and investigative jobs).

Self-Regulation Goal setting and feedback seeking in relation to goals are the core of self-regulation (Latham & Locke 1991). Self-regulatory processes supporting goal implementation were examined by Gollwitzer & Bayer (1999). They offered a time perspective on goal striving and self-regulatory processes as mediating the effects of intentions on behavior. The latter consists of four phases: predecisional (choosing among competing wishes, based on expected value); preactional (forming implementation intentions in the service of the goal intention); actional (bringing goal direct actions to a successful end); and postactional (evaluation as to whether further action is necessary). Brandstatter et al. (2003) inferred from field interviews that the portion of variance accounted for in action initiation increases by adding expected value, goal intention step-by-step. The level of complexity in Gollwitzer’s work on self-regulation is in strong contrast with theory and research by industrial/organizational psychologists in North America. Lord & Levy (1994) suggested that self-regulation was an automatic data-driven process. De Shon et al. (1996) obtained empirical support for this assertion. They reported that self-regulation does not require a significant amount of attentional resources. Roe (1999) and Frese & Fay (2001) argued the importance of personal initiative, defined as self-starting proactive behavior that overcomes barriers to the attainment of self-set goals. Employees high on personal initiative are able to change the complexity of and control over their workplaces even when they do not change jobs (Frese et al. 2000). Personal initiative, measured within the framework of a situational interview (Latham & Sue-Chan 1999), has adequate inter-rater and scale reliabilities as well as construct validity (Fay & Frese 2001). Frayne & Geringer (2000) trained insurance salespeople in the use of selfmanagement techniques. The result was an increase in performance because of increases in self-efficacy and outcome expectancies. Bandura (2001) noted that when people are confronted by setbacks, they engage in self-enabling or selfdebilitating self-talk. Millman & Latham (2001) successfully used Meichenbaum’s (1971) methodology to change the dysfunctional self-talk of displaced managers to increase their self-efficacy and subsequent reemployment. Morin & Latham (2000) used Richardson’s (1967) methodology regarding mental practice to increase the

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self-efficacy and communication skills of supervisors in interactions with their counterparts in the union.


Expectancy Theory After reviewing the literature, Ambrose & Kulik (1999) concluded that little or no advances have been made in expectancy theory research in the past decade. Moreover, goals have been shown to mediate the effect of expectancy theory constructs on performance (Klein 1991). Thus, Ambrose & Kulik concluded that there are few theoretical or applied reasons for additional research on the application of this theory to organizational behavior. However, Lord et al. (2003) illustrated the potential value of neuropsychologically based models for explaining expectancy theory. A major criticism of the theory, they said, is that the computations it requires are unrealistically time-consuming and often exceed working memory capacity. Using simulation methodology and neural networks that operate implicitly, the authors reinterpreted the theory so that cognitive resources were not exhausted by simple computations. Pritchard & Payne (2003) updated the motivational component of Naylor, Pritchard, & Ilgen’s “NPI” theory (1980), which they stated is based on expectancy theory. Motivation is defined as the process that determines how energy is used to satisfy needs. Motivation is a resource-allocation process where time and energy are allocated to an array of tasks. Motivation includes the direction, intensity, and persistence of this allocation process. Motivation is seen as a future-oriented concept in that people anticipate the amount of need satisfaction that will occur when outcomes are received. The perceived relationship between applying energy to actions and the resulting need satisfaction influences how much of the energy pool is devoted to that action. Empirical studies are needed to test the predictive and explanatory power of this theory. An intervention known as the productivity measurement and enhancement system (ProMes) is based directly on NPI theory, with emphasis on goal setting and feedback (Pritchard et al. 2002). It is a step-by-step process that (a) identifies organizational objectives, (b) measures the extent to which the objectives are met, and (c) provides a feedback system regarding performance. Productivity is defined as how well a system uses its resources to achieve its goals. ProMes has led to organizational-level productivity improvements in European countries regardless of differences in culture.

Social Cognitive Theory With few exceptions (e.g., Komaki et al. 2000), little attention has been given since 1977 to the philosophy of behaviorism as an approach to motivation. This is due in large part to social cognitive theory (SCT) (Bandura 1977), one of the most significant theories to influence motivation research subsequent to the Korman et al. (1977) review. SCT research shows empirically that the effect of environmental antecedents and consequences are mediated by cognitive variables. SCT

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emphasizes dual control systems in the self-regulation of motivation, namely a proactive discrepancy production system that works in concert with a reactive discrepancy reduction system (Bandura 2001). Thus, people are motivated by the foresight of goals, not just the hindsight of shortfalls. A specific high goal creates negative discrepancies to be mastered. Effort and resources are mobilized based on anticipatory estimates of what is necessary for goal attainment. Therefore, at the outset a goal can enhance performance before any feedback is provided. Upon goal attainment, people with high self-efficacy set an even higher goal because this creates new motivating discrepancies to be mastered. If the goal is not attained, self-efficacy and goal commitment predict whether people redouble their effort, react apathetically, or become despondent. Meta-analyses by Sadri & Robertson (1993) as well as Stajkovic & Luthans (1998) of wide-ranging methodological and analytic work-related laboratory and field studies provide overwhelming evidence that efficacy beliefs influence the level of motivation and performance. Colquitt et al. (2000) found that self-efficacy relates to transfer of training independent of skill acquisition. Nevertheless, Vancouver and colleagues (Vancouver et al. 2001) have attacked SCT. In a laboratory experiment, they showed that as people near their goal, they slacken their effort and consequently perform poorly. High self-efficacy, they said, creates complacency that undermines performance. Based on nine meta-analyses conducted by other scholars across diverse spheres of functioning, Bandura & Locke (2003) concluded that this contradictory finding was an artifact of the particular laboratory task that was used. This is not to say that high self-efficacy is always desirable. It can be the source of inappropriate task persistence (Whyte et al. 1997). The correction for the downside of seeking success, however, is not to diminish self-efficacy. The correction likely lies in developing ways of identifying ongoing practices that have exceeded the point of utility. The necessity for doing so is evident in the airline and trucking industries. Dysfunctional persistence was shown to be the result of high goals, self-efficacy, and satisfaction with past performance. The result was less seeking of information after a radical environmental change (Audia et al. 2000). SCT rejects the trait approach to human behavior. Perceived self-efficacy and outcome expectancies are not contextless global dispositions assessed by an omnibus test (Bandura 2002). Nevertheless, Chen et al. (2004) have validated a measure of general rather than task-specific self-efficacy. They found that self-efficacy is distinct from self-esteem in predicting important outcomes in organizational settings. Eden (2001) showed that this measure, namely a person’s belief in the efficacy of the tools available to perform the requisite work, can be as motivating as self-efficacy. Bandura (personal communication, 2003) disagrees strongly with the development of this type of scale: “There is no all purpose specific self-efficacy scale. It is a contradiction in terms. Specific scales are tailored to particular domains of functioning. An already developed specific scale is usable in other studies only if the activity domain is the same as the one on which the scale was developed.”

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AFFECT/EMOTION Mowday & Sutton (1993) argued against an overemphasis on cognition in the study of motivation. This is because moods and emotions influence the attainment of complex long-term goals (Lord & Kanfer 2002) and are interrelated with the other constructs we have discussed. Brief & Weiss (2002) devoted their ARP chapter to this subject matter. Hence, we report only a few recently published studies. Erez & Isen (2002) showed that people with higher levels of positive affect exhibited higher levels of persistence, effort, self-reported motivation, and performance on two different tasks. Positive affect was associated with higher levels of valence and expectancy beliefs at these tasks as well as higher levels of instrumentality beliefs at one of them. Seo et al. (2004) offered a conceptual model to propose that affect can have direct effects on direction, intensity, and persistence, as well as indirect effects on judgments about expectancy, utility and progress, and goal characteristics. In a study of university administrative assistants, Grandey (2003) combined notions from dramaturgy with a goodness-of-fit perspective and hypothesized that different levels of acting on the job can have different effects on employees’ levels of emotional exhaustion and successful affective delivery to customers. Independent ratings of affective delivery were positively related to deep acting (which entails deliberately taking on the emotions required at the moment on a job), but negatively related to surface acting (which is seen as false and phony to customers/clients). Further, surface acting, but not deep acting, was related to self-reported stress. In a study of creative designs for helicopters, George & Zhou (2002) found that negative rather than positive mood correlated significantly with creativity. Negative moods signal that the status quo is problematic; hence employees exert effort to generate useful ideas rather than stop because of their satisfaction with the status quo. The mediator is a meta-mood process, namely clarity of one’s feelings. The moderating contextual variable is an organizational culture in which recognition and rewards are given for creativity. When clarity as to one’s feelings as well as rewards are absent, negative mood appears to have little association with creativity.

Organizational Justice A significant body of research on work motivation that has appeared since Korman et al.’s 1977 review is conceptualizations of organizational justice (Greenberg 1987). These studies, based on sociolegal research of disputants’ reactions to a conflict resolution, supplement Adam’s equity theory, the fundamental idea of which is that individuals develop beliefs about the inputs they provide in their employment relationship as well as about the outcomes (in the form of tangible and intangible compensation and benefits) they receive in return, and they form attitudes about the ratio between inputs and outcomes in relation to the corresponding ratios they perceive among comparison others. The premise of organizational

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justice is that fair procedures enhance employee acceptance of organizational outcomes. Organizational justice is as important to leadership (e.g., De Cremer & Van Knippenberg 2002, Skarlicki & Latham 1997) as it is to employee motivation in its second premise, namely that in addition to being fair, leaders must be perceived as fair with regard to outcomes and processes that serve an important psychological need (Greenberg 1990). When employees feel unfairly treated they respond both affectively (e.g., low commitment) and behaviorally (e.g., turnover). Harlos & Pinder (1999) reported the results of a qualitative study of 33 individuals who reported having been unjustly treated in the workplace. Interactional injustice was defined as “perceived interpersonal mistreatment by a hierarchical superior or authority figure,” and systemic injustice, defined as “perceptions of unfairness involving the larger organizational context within which work relationships are enacted.” Emotions were antecedents and consequences of injustice experiences. Superiors’ expressions of anger and their widespread lack of emotion were common causes of emotional responses by employees. Fear, anger, hopelessness, sadness, excitement, and decreased emotionality were common emotional consequences of perceived injustice. These same emotions, in addition to several others such as rage, irritation, shame, embarrassment, guilt, dread, and cynicism appeared in the accounts of many wronged individuals. Subsequently, based in part on incidents in the Canadian military, Pinder & Harlos (2002) developed a conceptual model of the relationship between being victimized by injustice and employee silence. Two forms of silence—quiescence and acquiescence—are proposed, along with hypotheses regarding how wronged employees move into and between these two states. Cropanzano et al. (2001) discussed how workers formulate appraisals of justice, why they do so, and what is being appraised. Lind’s (2001) fairness heuristic theory suggests that decisions can be automatic as well as deliberate. Folger’s (1998) moral virtues model adds ethical to the instrumental and relational models of justice. Justice matters to people because it facilitates maximization of personal gain, it provides information as to their value to the leader or team, and it is aligned with a basic respect for human worth (Ambrose 2002, Cropanzano et al. 2001). Van den Bos (2002) showed that the same event could be evaluated more or less severely depending on the social context. Schminke et al. (2002) found that organizational structure (decentralization, formalization, participation) affects justice perceptions of only those employees at lower rather than higher levels of the organization. Controversy continues regarding the structure of justice. Three separate metaanalyses concluded that procedural justice and interactional justice are separate constructs (Bartle & Hayes 1999, Colquitt et al. 2001, Cohen-Charash & Spector 2000). Nevertheless, Locke (2003) argued that the latter concept should be discarded because no single term can capture all the dimensions that interactional justice is said to assess. A second controversy is whether event and entity perceptions of justice have the same structure. The former refers to ways that employees react to specific

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occurrences (e.g., pay raise) within the workplace. The entity paradigm involves an appraisal of the fairness of a person, group, or organization as a whole. Event perceptions may mediate the relation between the situational elements and global justice evaluations. Entities may cause events to be perceived as unfair. As Cropanzano et al. (2001) noted, the distinction between events and social entities suggests that researchers must make explicit what is being measured. The dependent variables that are influenced by employee perceptions of fairness are not limited to traditional measures of job performance, citizenship behavior, or job attitudes. Additional variables affected by fairness perceptions include theft (Greenberg 2002), exploitation and self-sacrificing decision allocations (Turillo et al. 2002), retribution (Mclean Parks 1997), the aesthetically pleasing attributes of workplace revenge (Trip et al. 2002), sabotage (Ambrose et al. 2002), workplace retaliation (Skarlicki & Folger 1997), and reactions to being laid off (Skarlicki et al. 1998). In short, when employees feel unfairly treated, they respond both affectively (e.g., low commitment) and behaviorally (e.g., decrease in helping behavior). Rousseau (1995) has studied affect and behavior from the standpoint of idiosyncratic psychological contracts and the effect they have on trust in the workplace.

CONCLUSIONS Conclusions emanating from this review are tenfold. First, three theories dominate the motivation literature: goal-setting, social cognitive, and organizational justice.3 The latter two emerged subsequent to the Korman et al. (1977) review. In the ensuing period, behaviorism and expectancy theory have been overwhelmed by goal-setting and social cognitive theories, while equity theory has given way to conceptualizations of organizational justice. Second, whereas theory and research in the third quarter of the twentieth century focused almost exclusively on cognition (Latham & Budworth 2004), this is no longer true. Today there is recognition of the importance of affect and behavior as well as the reciprocal interactions among cognition, affect, and behavior. Research on affect is blossoming. Third, the ability to predict, understand, and influence motivation in the workplace has increased significantly as a result of the attention that has been given to all rather than only a few aspects of an employee’s motivation. There is now ongoing research on needs, values, cognition (particularly goals), affect (particularly emotions), and behavior. Fourth, whereas the dependent variables historically studied 3

Strictly speaking, there is currently no single theory of organizational justice; rather, there are multiple conceptualizations of justice in the work place. Hence, Greenberg (2004, personal communication) prefers the term organizational justice to describe the large rapidly growing body of work in this area that as yet does not reflect a unified theoretical approach.

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were limited to traditional measures of job performance and satisfaction, today’s dependent variables range from citizenship to counterproductive behavior. Fifth, Cronbach’s (1957) plea a half century ago for experimental and correlational psychology to combine forces has been heeded. Researchers have done a creditable job of explaining the mechanisms, particularly individual differences (traits), that mediate between independent and dependent variables. Sixth, the importance of context to motivation has been recognized much more in recent years than in the past; so much so that an additional chapter could be devoted to it. Significant advances have been made in understanding how national culture, characteristics of the job itself, and the fit between the person and the organization influence motivation. Seventh, these advances in the study of motivation may reflect the fact that this subject is no longer restricted to the research findings of North Americans. Today motivation is studied empirically by scholars worldwide (e.g., Africa, Asia, Australia, and Europe). Eighth, behavioral scientists in the latter half of the twentieth century responded positively to William James’ exhortation to systematically study consciousness. At the dawn of the present century they are poised to expand their domain to the study of the pre- or subconscious. Ninth, the antagonisms among theorists that existed throughout much of the twentieth century have either disappeared or have been minimized. Much of the energy expended on theory destruction has been replaced by theory construction aimed at building upon and enhancing what is already known. Relative to the 1960s to 1980s, consensus rather than controversy characterizes the field. Tenth, the nomological nets related to work motivation constructs are thicker and tighter than ever before, but the size of the aggregate net (metaphorically speaking) is not growing at a rate commensurate with the energy that scholars and practitioners have invested since 1977. Few fundamentally new models of work motivation have appeared with the groundbreaking impact that Maslow’s need theory, Vroom’s expectancy theory, or Locke & Latham’s goal-setting theory had when they were initially promulgated. Accordingly, Steers (2001) recently recognized the limitations of current theory and research in work motivation, and issued a call for groundbreaking papers for publication in a special edition of the Academy of Management Review in 2004. It is too soon to assess whether any of the papers published in response to his call will provide the new insights he sought and that we desire. ACKNOWLEDGMENTS We thank A.R. Elangovan, Gary Johns, Edwin Locke, and Benjamin Schneider for their critical comments on a preliminary draft of this chapter, and Marie-Helene Budworth and Sarah Cunningham for their research assistance. This review was supported in part by a grant from the Social Sciences and Humanities Research Council of Canada to the first author and a grant from the President’s Office of the University of Victoria to the second author.

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The Annual Review of Psychology is online at http://psych.annualreviews.org


LITERATURE CITED Ajila CO. 1997. Maslow’s hierarchy of needs theory: applicability to the Nigerian industrial setting. IFE Psychol. 5:162–74 Allport GW. 1951. Basic principles in improving human relations. In Cultural Groups and Human Relations, ed. KW Bigelow, pp. 8– 28. Oxford, UK: Bur. Publ. Ambrose ML. 2002. Contemporary justice research: a new look at familiar questions. Organ. Behav. Hum. Decis. Process. 89:803–12 Ambrose ML, Kulik CT. 1999. Old friends, new faces: motivation research in the 1990s. J. Manag. 25(3):231–92 Ambrose ML, Seabright MA, Schminke M. 2002. Sabotage in the workplace: the role of organizational justice. Organ. Behav. Hum. Decis. Process. 89:947–65 Ashford SJ, Black JS. 1996. Proactivity during organizational entry: the role of desire for control. J. Appl. Psychol. 81:199–214 Ashford SJ, Blatt R, VandeWalle D. 2003. Reflections on the looking glass: a review of research on feedback-seeking behavior in organizations. J. Manag. 29:773–79 Audia PG, Locke EA, Smith KG. 2000. The paradox of success: an archival and a laboratory study of strategic persistence following radical environmental change. Acad. Manag. J. 43:837–53 Bandura A. 1977. Social Learning Theory. Englewood Cliffs, NJ: Prentice-Hall Bandura A. 1997. Self-efficacy: The Exercise of Control. Stanford, CA: Freeman Bandura A. 2001. Social cognitive theory: an agentic perspective. Annu. Rev. Psychol. 52:1–26 Bandura A. 2002. Social cognitive theory in cultural context. Appl. Psychol.: Int. Rev. 51: 269–90 Bandura A, Locke EA. 2003. Negative self efficacy and goals revisited. J. Appl. Psychol. 88:87–99 Bargh JA. 1994. The four horseman of automaticity: awareness, efficiency, intention,

and control in social cognition. In Handbook of Social Cognition, ed. RS Wyer Jr, TK Srull, pp. 1–40. Hillsdale, NJ: Erlbaum Bargh JA, Ferguson MJ. 2000. Beyond behaviorism: on the automaticity of higher mental processes. Psychol. Bull. 126:925–45 Bargh JA, Gollwitzer PM, Lee-Chai A, Barndollar K, Tröetschel R. 2001. The automated will: nonconscious activation and pursuit of behavioral goals. J. Personal. Soc. Psychol. 81:1014–27 Barrick MR, Mount MK, Judge TA. 2001. Personality and performance at the beginning of the new millennium: What do we know and where do we go next? Int. J. Sel. Assess. 9:9– 30 Bartle SA, Hayes BC. 1999. Organizational justice and work outcomes: a meta-analysis. Presented at Annu. Meet. Soc. Indust. Organ. Psychol., Atlanta, GA Baum JR, Locke EA. 2004. The relationship of entrepreneurial traits, skill and motivation to subsequent venture. J. Appl. Psychol. 89: 587–99 Baum JR, Locke EA, Smith KG. 2001. A multidimensional model of venture growth. Acad. Manag. J. 44:292–303 Bernichon T, Cook KE, Brown JD. 2003. Seeking self-evaluative feedback: the interactive role of global self-esteem and specific selfviews. J. Personal. Soc. Psychol. 84:194– 204 Borman WC, Ilgen DR, Klimoski RJ. 2003. Stability and change in industrial and organizational psychology. In Handbook of Psychology, Vol. 12: Industrial and Organizational Psychology, ed. WC Borman, DR Ilgen, RJ Klimoski, pp. 1–17. New York: Wiley Brandstatter V, Heimbeck D, Malzacher JT, Frese M. 2003. Goals need implementation intentions: the model of action phases tested in the applied setting of continuing education. Eur. J. Work Organ. Psychol. 12:37– 59

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Brown SP. 2000. An integrated model of feedback-seeking behavior: disposition, context, and cognition. J. Appl. Psychol. 85:996– 1003 Van-Dijk D, Kluger AN. 2004. Feedback sign effect on motivation: Is it moderated by regulatory focus? Appl. Psychol.: Int. Rev. 53:113–35 Verplanken B, Holland RW. 2002. Motivated decision making: effects of activation and self-centrality of values on choices and behavior. J. Personal. Soc. Psychol. 82:434–47 Wall TD, Jackson PR. 1995. New manufacturing initiatives and shop floor job design. In The Changing Nature of Work, ed. A Howard, pp. 139–74. San Francisco, CA: Jossey-Bass Whyte G, Saks A, Hook S. 1997. When success breeds failure: the role of self-efficacy in escalating commitment to a losing course of action. J. Organ. Behav. 18:415–33 Wicker FW, Brown G, Wiehe JA, Hagen AS, Reed JL. 1993. On reconsidering Maslow: an examination of the deprivation/domination proposition. J. Res. Personal. 27:118–33 Williams JR, Miller CE, Steelman LA, Levy PE. 1999. Increasing feedback seeking in public contexts: It takes two (or more) to tango. J. Appl. Psychol. 84:969–76 Winters D, Latham GP. 1996. The effect of learning versus outcome goals on a simple versus a complex task. Group Organ. Manag. 21:236–50 Witt LA, Ferris GR. 2003. Social skill as a moderator of the conscientiousness-performance relationship: convergent results across four studies. J. Appl. Psychol. 88:809–21 Wright BM, Cordery JL. 1999. Production uncertainty as a contextual moderator of employee reactions to job design. J. Appl. Psychol. 84:456–63 Zetik DC, Stuhlmacher A. 2002. Goal setting and negotiation performance: a metaanalysis. Group Process. Intergroup. Relat. 5:35–52

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Annu. Rev. Psychol. 2005. 56:517–43 doi: 10.1146/annurev.psych.56.091103.070250 c 2005 by Annual Reviews. All rights reserved Copyright First published online as a Review in Advance on October 5, 2004

TEAMS IN ORGANIZATIONS: From


Input-Process-Output Models to IMOI Models Daniel R. Ilgen,1 John R. Hollenbeck,2 Michael Johnson,2 and Dustin Jundt1 1

Department of Psychology, 2Department of Management, Michigan State University, East Lansing, Michigan 48824; email: [email protected], [email protected], [email protected], [email protected]

Key Words teamwork, workgroup, groups, coordination, cooperation ■ Abstract This review examines research and theory relevant to work groups and teams typically embedded in organizations and existing over time, although many studies reviewed were conducted in other settings, including the laboratory. Research was organized around a two-dimensional system based on time and the nature of explanatory mechanisms that mediated between team inputs and outcomes. These mechanisms were affective, behavioral, cognitive, or some combination of the three. Recent theoretical and methodological work is discussed that has advanced our understanding of teams as complex, multilevel systems that function over time, tasks, and contexts. The state of both the empirical and theoretical work is compared as to its impact on present knowledge and future directions.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . STRUCTURING THE CURRENT REVIEW: BEYOND THE INPUT-PROCESS-OUTPUT FRAMEWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FORMING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trusting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FUNCTIONING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bonding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adapting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FINISHING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION Over a decade ago, Levine & Moreland’s (1990) Annual Review of Psychology chapter concluded that small groups/teams research was “alive and well, but living elsewhere” (p. 620)—in organizational, not social, psychology. Guzzo & Dickson (1996) made a similar observation, and Sanna & Parks (1997) documented this empirically with an analysis of the top three organizational psychology journals. Between 1996 and 2004 the trend continued. The organizational domain has shown some shift from questions of what predicts team effectiveness and viability to more complex questions regarding why some groups are more effective than others. We review what has been learned over the past seven years by categorizing findings in terms of their relevance to the formation, functioning, and final stages of teams’ existence. From the outset we note that whereas there seems to be consensus on the need to study affective, cognitive, and behavioral mediational processes, this effort has been somewhat fragmented and noncumulative due to a proliferation of constructs with indistinct boundaries at the conceptual level and item overlap between measures of constructs at the level of individual studies. As is often the case for Annual Review authors, we struggled with the boundaries of our domain. One aspect of this struggle is the recognition that there have been a number of both methodological and substantive achievements over the last seven years, but in the limited amount of space we have here, we focused primarily on substantive studies. This should not obscure the fact that during the period covered by the review, several important methodological developments took place, including major shifts toward (a) multilevel theoretic and analytic techniques (see Klein & Kozlowski 2000), (b) complex computer-generated task environments that simulate real-world phenomena while objectively capturing and time-stamping team behaviors (Schiflett et al. 2004), (c) the appearance of computational and mathematical models that provide potential for means of addressing the dynamic complexity of teams (Coovert & Thompson 2000, Losada 1999), and (d) the use of social network analysis to investigate the effects of larger social patterns on between-team and within-team behavior (e.g., Baldwin et al. 1997, Burt 2000, Hinds et al. 2000). In terms of content, two recent Annual Review of Psychology chapters (Guzzo & Dickson 1996, Kerr & Tindale 2004) were instrumental in establishing boundaries. Guzzo & Dickson’s (1996) chapter provided a clear beginning date for our review. It also provided excellent guidance for content inclusion with its focus on work teams, particularly teams embedded in ongoing organizations with pasts and futures. We share the concern for teams in similar contexts, but unlike Guzzo & Dickson, we did not limit the research setting to field research if we felt the empirical observations were relevant to work teams. Kerr & Tindale’s (2004) Annual Review of Psychology chapter reviewed the social psychological literature on small group performance and decision making, which provides an up-to-date source for that content and allows us to ignore work addressed by them.

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STRUCTURING THE CURRENT REVIEW: BEYOND THE INPUT-PROCESS-OUTPUT FRAMEWORK Conceptually, team researchers have converged on a view of teams as complex, adaptive, dynamic systems (McGrath et al. 2000). They exist in context as they perform across time. Over time and contexts, teams and their members continually cycle and recycle. They interact among themselves and with other persons in contexts. These interactions change the teams, team members, and their environments in ways more complex than is captured by simple cause and effect perspectives. A number of excellent theoretical models of teams have appeared recently. McGrath et al. (2000) describe three levels of dynamic causal interactions (local, global, and contextual). Kozlowski and colleagues’ (Kozlowski et al. 1999) theory of compilation and performance describes inputs, processes, and outcomes that develop over time as teams interact in contexts that are both external environments of the team and are shaped by actions of the teams in a reciprocal causal fashion. Knowledge, attitudes, and behaviors are both inputs and processes in a developmental sequence that impacts team performance. Team performance, while an output at time tn, is an input and a part of the process leading to performance output at time tn+1. A similar metatheoretical position, with processes unfolding over time, served as an underpinning for Marks et al.’s (2001) taxonomy of team processes and DeShon et al.’s (2004) multigoal study. Although these models contain differences in specific details regarding the nature of teams, all reflect the underlying notion that teams are complex, dynamic systems, existing in larger systemic contexts of people, tasks, technologies, and settings. The empirical research on teams in organizational contexts is also moving in the direction of increased complexity, but this work still has a way to go to match developments in the conceptual domain. However, the empirical literature in the past six years does differ from that which preceded it. Prior to 1996, much of the empirical research on teams was focused on the outcomes of team performance and viability. This research was guided by practical issues: The search was for answers to the generic question of what makes some teams more effective or more viable relative to others, and it emphasized inputs such as composition, structures, or reward allocations. Over the past six years, more attention was paid to mediating processes that explain why certain inputs affect team effectiveness and viability. In one sense, this search for mediators was well informed by previous attention to process as the link between inputs and outputs. Classic works of Steiner (1972), McGrath (1984), and Hackman (1987) expressed the nature of team performance in classic systems model ways in which inputs lead to processes that in turn lead to outcomes (the input-processes-output, or I-P-O, model). This framework has had a powerful influence on recent empirical research, much of which either explicitly or implicitly invokes the I-P-O model. In another sense, however, the convergence on consensus regarding the utility of I-P-O models as a guide to empirical research fails to capture the emerging consensus about teams as complex,

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adaptive systems. Indeed, the I-P-O framework is insufficient for characterizing teams (Moreland 1996), and the most recent team literature, in at least three specific ways. First, many of the mediational factors that intervene and transmit the influence of inputs to outcomes are not processes. Marks et al. (2001) developed a temporally based framework and taxonomy of team processes and correctly noted that many constructs presented by researchers trying to invoke the I-P-O model as process are not really process at all, but emergent cognitive or affective states. Their solution to the imprecision in the use of the term team process was to exclude from their review of team process all constructs that fit their emergent state definition rather than process definition as they developed their team process taxonomy. This strategy, while useful for their purpose of isolating a subset of conceptually pure behavioral processes, was not sufficient for our task of reviewing the broader teams literature, a domain including both behavioral processes and emergent cognitive and affective states. Second, an I-P-O framework limits research by implying a single-cycle linear path from inputs through outcomes, even though the authors of the classic works clearly stipulated the potential for feedback loops, and some (e.g., Hackman 1987, McGrath et al. 2000) explicitly recognized limits of I-P-O thinking. Yet, failure to identify the feedback loop in the I-P-O sequence is likely to have limited the development of I-P-O-focused team research more than would have resulted with the use of a different model. Indeed, research that is more recent has examined traditional “outputs” like team performance and treated them as inputs to future team process and emergent states. Finally, the I-P-O framework tends to suggest a linear progression of main effect influences proceeding from one category (I, P, or O) to the next. However, much of the recent research has moved beyond this. Interactions have been documented between various inputs and processes (I x P), between various processes (P x P), and between inputs or processes and emergent states (ES) (Colquitt et al. 2002, De Dreu & Weingart 2003, Dirks 1999, Janz et al. 1997, LePine et al. 1997, Simons et al. 1999, Simons & Peterson 2000, Stewart & Barrick 2000, Taggar 2002, Witt et al. 2001). Emergent states are constructs that develop over the life of the team and impact team outcomes. The broader focus beyond simply inputs and process places attention on boundary conditions of the traditional I-P-O framework and highlights when, where, and with whom various processes and emergent states become relevant. Thus, the I-P-O framework is deficient for summarizing the recent research and constrains thinking about teams. As an alternative model, we use the term IMOI (input-mediator-output-input). Substituting “M” for “P” reflects the broader range of variables that are important mediational influences with explanatory power for explaining variability in team performance and viability. Adding the extra “I” at the end of the model explicitly invokes the notion of cyclical causal feedback. Elimination of the hyphen between letters merely signifies that the causal linkages may not be linear or additive, but rather nonlinear or conditional.

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In keeping with the temporal features of many recent approaches, we initially organized the review around studies that focus on the early stages of team development (i.e., the IM phase), labeled the Forming Stage, followed by those examining issues that we see as the team develops more experience working together (i.e., the MO phase), labeled the Functioning Stage, and finally the Finishing Stage (i.e., the OI phase), where the team completes one episode in the developmental cycle and begins a new cycle. The paucity of literature directed at decline led us to collapse over the three in the finishing phase. Our use of the verb form throughout the review is intentional, to emphasize how these processes and states extend through time and involve change (Weick 1969). Within the three-way temporal classification, we added another three-way categorization scheme that reflects whether the primary interest of the study deals with affective, behavioral, or cognitive aspects of team development. In the formation phase, the topic of trusting focused on affective mediators, planning behavioral ones, and structuring cognitive ones. In the functioning phase, affect, behavior, and cognition were discussed under bonding, adapting, and learning, respectively. We emphasize that use of these categorical labels, while reflective of the dominating affective, behavioral, or cognitive process, was not meant to imply that other processes were excluded. Often all processes were present in any one category. For example, trusting involves not only affect but also cognitions and behavioral intentions. In sum, we present here a 3 × 3 framework in an effort to capture the domain or research on teams, not to suggest that the organizing model is a theory of team behavior.

FORMING Trusting For team members to trust in the team, they must feel that (a) the team is competent enough to accomplish their task (in the literature we reviewed, this is expressed in terms of constructs such as potency, collective efficacy, group efficacy, and team confidence), and (b) that the team will not harm the individual or his or her interests, which we refer to as safety. Potency is the team member’s collective belief that they can be effective (Guzzo et al. 1993). Campion et al. (1996) found potency was positively related to employee self-ratings of effectiveness, manager judgments of team performance, and group performance appraisals conducted by their organization. Similarly, Hyatt & Ruddy (1997) found that work group confidence was positively related to managerial ratings of group performance on a number of different objective measures. Little & Madigan (1997) found that collective efficacy was positively related to a number of different group performance behaviors as well. Finally, Seijts et al. (2000) examined how group-referenced individual ratings of group efficacy differed from individually aggregated ratings of self-efficacy for multiple trials on a mixed motive task.

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Many studies took a more complex approach to examining the relationship between potency-related constructs and team effectiveness. Hecht et al. (2002) found potency predicted performance over and above group member ability, and group goal commitment did not predict variance in performance over potency. Jung and colleagues (Jung & Sosik 1999, Jung et al. 2002) tested a reciprocal model in which group heterogeneity, preference for group work, outcome expectation, and potency were suggested to be unique predictors of group performance. Group performance at Time 1 predicted each of these constructs and predicted performance at Time 2. The major findings suggested a unique reciprocal relationship between potency and group performance. Using both a lab and a field sample, Chen et al. (2002) examined the relationships between team expertise, “team drive” (the team level analogue of achievement motivation), collective efficacy, and team performance. They found that “team drive” positively and uniquely related to collective efficacy beliefs, whereas team expertise did not. Collective efficacy predicted unique variance in team performance and team drive in the lab, but not in the field. Durham et al. (2000) found that initial task performance related to group efficacy, and indirectly to group performance through the influence on goals and information seeking. Gibson (1999) supported a contingency view in which collective efficacy exerted a positive influence on performance under conditions of low uncertainty, high task interdependence, and high collectivism. For Gonzalez et al. (2003), task cohesion mediated the relationship between collective efficacy and group effectiveness. Marks (1999) found that collective efficacy was positively related to team performance in a routine task environment, but not in a novel one. High levels of communication partially mediated the positive relationship between collective efficacy and team performance when the task environment was controlled. Sivasubramaniam et al. (2002) found a reciprocal relationship between transformational leadership and potency: Potency influenced later performance where collective efficacy was referenced to the team’s specific tasks and potency to more generalized settings past, present, and future. Lee et al. (2002) made a conscious distinction between potency and collective efficacy. Controlling for group size and initial performance, group norm strength predicted potency but not collective efficacy, and potency predicted Time 2 performance on a novel task whereas collective efficacy did not. The data supported potency and efficacy as different constructs. Finally, Gully et al. (2002) conducted a metaanalysis that examined the effects of both team efficacy and potency on performance. Their findings suggest that both team efficacy and potency are meaningful predictors of team performance, and that the relationship between team efficacy— but not potency—and performance was stronger when task interdependence was high. In addition to trusting the team’s competence, individuals must also trust the member’s intentions. Jones & George (1998) distinguished between several different kinds of trust and suggested that levels of trust (or distrust) can be

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shaped by people’s values, attitudes, and moods/emotions, as well as by previous experience. In turn, they suggested that unconditional trust, the kind most valuable to teams, should have a strong direct, positive effect on interpersonal cooperation and teamwork. Few studies have examined the impact of interpersonal trust-related constructs on team effectiveness, and none have gone into the level of detail that Jones & George supply in their theoretical piece. Edmondson (1999), however, examined both collective efficacy and a trust-related variable she called psychological safety as they related to two structural variables (team leader coaching and organizational contextual support), team learning behaviors, and team performance. She defined psychological safety as “a shared belief that the team is safe for interpersonal risk taking” (p. 354). Her model suggested a causal sequence in which the two structural variables led to higher psychological safety and team efficacy and, in turn, to greater team learning and performance. Psychological safety and team efficacy mediated the relationships between the structural variables and team learning, learning behaviors mediated the relationship between psychological safety and team performance, and team efficacy did not predict unique variance in learning behaviors. In a follow-up qualitative study, Edmondson et al. (2001) examined several hospitals implementing new cardiac surgery technology. A key characteristic of successful innovators was their ability to design preparatory practice sessions and early trials that created a sense of psychological safety. In hospitals low in psychological safety, people were less likely to engage in risk taking, and they exhibited more behaviors consistent with the status quo. Looking at both psychological and physical safety, Hofmann & Stetzer (1996) found that feelings of psychological safety led indirectly to actual physical safety through the mediating influence of communication regarding unsafe acts.

Planning Moving from the affective to the behavioral realm, at the early stages of team development one key mediating variable that explains success and viability is the degree to which the team arrives at an effective initial plan of behavioral action. Effective planning has two related, and yet distinct, components. First, the team needs to gather information that is available to the group members and/or their constituencies. The group then must evaluate and use this information to arrive at a strategy for accomplishing its mission. The studies pertaining to gathering information have focused on information sharing, information seeking, and communicating. Two cross-sectional survey studies documented the importance of effective information gathering for team performance. Barry & Stewart (1997) correlated member personality measures with open communication and team performance on student projects. Although these authors failed to find the relationship they hypothesized between group extraversion and open communication, they did report a significant

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relationship between open communication and team performance, as did Hyatt & Ruddy (1997). Drach-Zahavy & Somech (2001) examined the influence of functional diversity on information exchange and innovativeness. Functional heterogeneity predicted information exchange, and information exchange, in turn, was positively correlated with team innovation. Bunderson & Sutcliffe (2002) distinguished between withinmember and between-member diversity. Within-person diversity reflects the fact that each group member has had experience in different functional areas, and between-person diversity means that each team member has a different functional background. Information sharing was more effective in the teams that contained within-person diversity, relative to between-person diversity, and this, in turn, was related to higher team performance. Two studies examined group voice, operationalized in this research as the extent to which people speak up within their group (Erez et al. 2002, LePine & Van Dyne 1998). LePine & Van Dyne found participation rates were higher for group members who were (a) high in self-esteem, (b) male, (c) Caucasian, (d) high status, (e) highly educated, ( f ) highly satisfied with their group, and (g) in smaller, selfmanaged teams. Those with low self-esteem exhibited especially low levels of participation behavior in large groups and self-managed groups. Erez et al. (2002) examined the role of participative behavior in a quasi-experiment where leaders either rotated in or emerged and were evaluated either by peers or by external sources. Rotation of the leader’s role and the provision of peer feedback promoted higher participation levels and positively impacted performance. Durham et al. (2000) examined the effects of group goals and time pressure on information seeking and performance on a team decision-making task. These authors found that group efficacy indirectly influenced information sharing through group-set goal difficulty, which in turn had an indirect positive effect on group performance through information-seeking behaviors. Stout et al. (1999) examined the relationships between strategy development, communicating, shared mental models (a construct that we review in more detail below), and coordinated team performance on a helicopter defense/surveillance simulation. Better strategy development led to greater levels of unsolicited information sharing, more well developed team mental models, and higher performance during high workload situations. Tesluk & Mathieu (1999) investigated teams that faced roadblocks or obstacles to goal accomplishment. Teams that were most likely to overcome problems were those that anticipated problems in advance and had contingency plans in place from the very beginning. Further, crews with higher levels of coordination, potency, and familiarity (which they refer to as teamwork processes) were more likely to develop effective strategies. Effective strategy development is enhanced by unambiguous and well-prioritized goals and agreement on the best means of goal accomplishment. Pritchard (1995) and his colleagues have developed and implemented a team-based performance management system called ProMES (productivity measurement and

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enhancement system) that focuses on identifying objective team outputs, as well as the level of these outputs required to reach various levels of effectiveness for the team. Teams receive feedback referenced to these outputs, and are encouraged to develop plans that would help them achieve internally or externally set goals. ProMES has been used in a wide variety of contexts to help improve team planning and performance (Pritchard et al. 2001).


Structuring Structuring refers to the development and maintenance of norms, roles, and interaction patterns in the teams. Two cognitive structuring constructs have dominated the recent literature on teams. One is a shared mental model, which emphasizes common cognitive elements among group members. The second set of studies deals with transactive memory systems and emphasizes the unique and distinctive cognitive elements among group members. Ironically, one of these literatures suggests that high performance results when group members share cognitive elements, whereas the other suggests groups perform best when members compartmentalize and specialize in different aspects of the cognitive space that the team is required to cover. Mohammed & Dumville (2001) defined shared mental models as “organized understanding of relevant knowledge that is shared by team members” (p. 89). The focus is on collective knowledge regarding what individual team members hold in common. Whereas Mohammed & Dumville’s (2001) work was conceptual in addressing the nature of the construct, others were concerned with measuring it and treating its development as part of something that could be addressed through training (e.g., Langan-Fox et al. 2000). Much of this work grew out of the TADMUS (Tactical Decision Making Under Stress) project, which was a response to the tragic shooting down of Iran Air Flight 655 by the USS Vincennes over the Persian Gulf in 1988. The TADMUS project represented a convergence of operational, scientific, and bureaucratic efforts (Collyer & Malecki 1998) to create a partnership between behavioral scientists and operational naval personnel. The result was the development of a process that embedded team training within the dynamic task environment (Cannon-Bowers & Salas 1998). A number of principles emerged from this and related work, particularly in connection to team training (Kozlowski 1998, Kozlowski et al. 1999). The most important principle is that of treating teams, rather than individuals, as the basic unit of analysis, and viewing team members as active participants in a continuous learning process. Marks et al. (2002) examined the role of shared mental models as a factor that mediates the relationship between cross-training and team effectiveness. Crosstrained teams on a helicopter simulation were more likely to develop shared mental models, and teams with shared mental models performed better. Better performance resulted because the teams were more likely to display effective coordination and team backup behaviors. Mathieu et al. (2000) found similar results

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with dyads performing a flight combat simulation. Again, coordination and communication mediated the relationship between the team mental model and team performance. Consistent with Wegner’s (1986) work, Austin (2003) defined transactive memory as “a combination of the knowledge possessed by each individual and a collective awareness of who knows what” (p. 866). In contrast to shared mental models, transactive memory focuses on who knows what rather than on overlapping task- or team-relevant knowledge. Austin (2003) studied field groups in charge of launching different types of new products in a sporting goods/clothing company, and broke transactive memory into four elements: knowledge stock (amount of knowledge), consensus (agreement on who knows what), knowledge specialization (amount of redundancy), and accuracy (correctness of knowledge about what others know). Each facet was then examined for its ability to predict unique variance in group goal attainment and both external and internal evaluations of performance. Task transactive memory accuracy was related positively and uniquely to all three performance criteria, and task knowledge specialization was related uniquely to both external and internal evaluations of team performance. Similarly, Lewis (2003), with different subdimensions, found transactive memory positively related to performance. Two studies did not use the term transactive memory but did capture similar constructs. Druskat & Kayes (2000) assessed teams of MBA students on interpersonal understanding—accurate understanding of the spoken and unspoken preferences, concerns, and strengths of other members. Hyatt & Ruddy (1997) defined roles in terms of knowledge structures to include both (a) common expectations regarding work group behavior, and (b) knowledge about what members knew. Both studies found their constructs related to team performance. Finally, Hollenbeck et al. (2002) examined the impact of different role structures on team performance via shared cognition. In divisional structures, team members had broad roles and resources and were grouped by region, whereas team members in a functional structure each had very narrow, specific roles, and were grouped by resource or task. Results suggested that different types of role structures are better suited for different types of environments. Divisional structures were thought to promote the development of team mental models that were more complete, and these models in turn led to better performance in random environments. On the other hand, functional structures should promote the development of transactive memory, thus leading to higher performance in predictable environments.


TRANSACTIVE MEMORY

FUNCTIONING Bonding Bonding reflects affective feelings that team members hold toward each other and the team. Whereas trust represents a willingness to work together on the task,

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bonding goes beyond trust and reflects a strong sense of rapport and a desire to stay together, perhaps extending beyond the current task context. We placed studies that examined constructs such as group cohesiveness, team viability, social integration, satisfaction with the group, person-group fit, and team commitment under this heading because they share a common core that deals with the strength of the member’s emotional and affective attachment to the larger collective (Bishop & Scott 2000, Kristof-Brown et al. 2002). Because it takes time for team bonding to occur, its effects typically are observed not in the early formative phase but in the more mature functioning stage. This is an important category of studies for three reasons. First, although past research has suggested that bonding is not all that necessary for high levels of team performance, more recent meta-analytic evidence suggests otherwise, particularly when work-flow interdependence is high (Beal et al. 2003). Second, as noted in a recent edited volume by Hinds & Kiesler (2002), organizations are increasingly employing virtual teams whose members rarely meet face-to-face. Despite the rise in their prevalence, the cumulative evidence from a recent meta-analysis of 27 studies questions the degree to which members of virtual teams ever bond with one another in the traditional sense, and suggests that as a result, they are both slower and less accurate than face-to-face teams (Baltes et al. 2002). A number of elaborate interventions have been offered to help overcome this problem (Kraut et al. 2002, Nardi & Whittaker 2002, Olson et al. 2002). Finally, even in contexts that allow face-to-face interactions, attempts to implement team-based structures meet resistance due to fears among leaders or members that they will not be able to manage the conflict that arises from their differences (Kirkman & Shapiro 1997). Conflict often starts small, but then spirals out of control, and in some cases even results in violent reactions (Robinson & O’Leary-Kelly 1998) and withdrawal behaviors (Duffy et al. 2000). Although past research on composition has generally conceived of teams as existing on a single continuum ranging from demographically homogeneous to demographically heterogeneous, more recent research has focused on specific aspects of demography. Riordan & Shore (1997) showed that some demographic differences, such as race/ethnicity, were much more important relative to age or gender when it came to predicting satisfaction with the team, a finding later replicated by Pelled et al. (1999), who employed emotional conflict as a criterion. Even within the race/ethnicity categories, it was critical to distinguish among different minority groups (African American versus Hispanic); without differentiation, a great deal of predictability is lost (Riordan & Shore 1997). All of this suggests that the simple, nondelineated construct of diversity that does not reflect the specific aspect of diversity embodied in the group has little predictive or explanatory power. Others have challenged the notion that diversity is a meaningful continuum, and proposed that the opposite ends of the scale are qualitatively, not quantitatively, different. Earley & Mosakowski (2000) showed that the key to team bonding

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was developing a single culture within the team, and this was promoted by either homogeneous compositions or highly heterogeneous compositions. Worst were moderately heterogeneous compositions that created subgroups or token members. Polzer et al. (2002) also found that high levels of heterogeneity could be conducive to developing cohesive teams. Harrison and colleagues (1998, 2002) distinguished between surface-level diversity, which deals with demographic differences, and deep-level diversity, which deals with differences in attitudes and values, and showed that the importance of each varied with time. Surface-level diversity was more critical early, but its influence gave way to deep-level influence at later stages of the group’s development. Jehn et al. (1999) distinguished between social category (demographic), value, and informational diversity, and reported similar results. Over the course of team’s development, value diversity had a much more deleterious effect on commitment to the team relative to social category diversity. Other research on bonding has examined diversity operationalized by differences in personality traits among team members. Barrick et al. (1998) found that social cohesion was highest when teams were high on agreeableness, extraversion, and high emotional stability. However, variance in agreeableness harmed cohesion, variance in extraversion promoted cohesion, and variance in emotional stability was unrelated to cohesion. Clearly, one must go beyond both demographic characteristics and simple, continuum-based hypotheses regarding homogeneity when it comes to understanding the complexities of when and why teams bond. Although Barrick et al. did not explicitly show why teams high on agreeableness, emotional stability, and extraversion (and variance in extraversion) were better able to bond, Keller (2001) showed that cross-functional teams create stress, which in turn lowers cohesiveness. Teams high on emotional stability may weather this stress better than teams that are low in this trait. Simons et al. (1999) showed that another key to managing cross-functional teams is producing effective debate, which is likely to be difficult to achieve in introverted teams or teams in which all members are high in extraversion and thus fight for “airtime.” Finally, Chatman & Flynn (2001) found that the speed with which demographically heterogeneous teams developed cooperative norms was the best predictor of their eventual viability, and this probably is related closely to the level and variability of agreeableness. Several recent studies have examined interventions that might be used to minimize social conflict among team members. Druskat & Wolff (1999) showed that face-to-face developmental feedback from peers could drastically reduce conflict, especially if this feedback is delivered at the appropriate time (at the project’s midpoint). Naumann & Bennett (2000) found that leaders who promote procedural justice and apply rules consistently were able to minimize relationship conflict. De Cremer & van Knippenberg (2002) replicated and extended these findings regarding the leader’s role in minimizing relationship conflict. van der Vegt et al. (2001) showed that group satisfaction is also promoted by adopting group-level rewards that do not make fine

MANAGING CONFLICT AMONG TEAM MEMBERS

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distinctions among team members; the value of this, however, may be offset by the fact that cooperative rewards sometimes are associated with higher levels of social loafing (Beersma et al. 2003). Although consensus exists regarding the deleterious effects of relationship conflict, this is not true with respect to task conflict. Jehn (1994) showed that there was a +0.44 correlation between task conflict and team performance and a –0.45 correlation between relationship conflict and team performance. Unfortunately, subsequent research failed to replicate the Jehn (1994) results. A recent metaanalysis, based upon 26 effect sizes, found the 95% confidence interval for the relationship between task conflict and performance to be –0.13 to –0.26, making the Jehn (1994) result an extreme outlier (De Dreu & Weingart 2003). Indeed, this same meta-analysis estimated the correlation between task and relationship conflict at over 0.50. The emerging consensus is that task conflict is generally unhelpful for teams. Instead of task conflict, teams require (a) rich, unemotional debate in a context marked by trust (Simons & Peterson 2000), (b) a context where team members feel free to express their doubts and change their minds (Lovelace et al. 2001), and (c) an ability to resist pressures to compromise quickly (Montoya-Weiss et al. 2001) or to reach a premature consensus (Choi & Kim 1999).

Adapting Most of the recent literature we reviewed dealing with behavioral processes of adapting falls under two distinct subcategories, one of which deals with performance in routine versus novel contexts, and the second dealing more narrowly with one specific aspect of adaptability—workload sharing in the form of either helping behaviors or backing up behaviors. In a controlled laboratory setting, LePine (2003) extended research from the individual level to teams and found teams with higher mean levels of cognitive ability and openness to experience did better when the task environment changed. Documenting differences between variables that predict team performance under routine versus novel conditions was also the goal of a study by Marks et al. (2000), but this study examined aspects of team training rather than team composition. Using a laboratory study simulation, Marks et al. found that training aimed at increasing the team’s ability to communicate and interact, as well as expanding communication from leaders, improved team adaptability. In a study by Waller (1999), the speed with which teams recognized that the environment has changed was also shown to be critically important for improving adaptability. This study employed airline crews that were observed on a realistic flight simulator performing after a hydraulic failure caused an unexpected change in the flight plan. Although previous research had documented that adaptability was contingent on the team’s ability to reprioritize goals and redistribute tasks, Waller (1999) found that it was the speed—not necessarily the frequency—with which

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teams engaged in these behaviors that was critical for adaptability. Methodologically, observing teams over time was critical; adapting would have been missed with retrospective self-reports. It was the timing of the behaviors, not the behaviors themselves, that was critical. Subsequent research showed that the speed with which teams recognized the need for change was related to the number of “interruptions” that caused them to “stop and think” about their processes while engaged in the task (Okhuysen & Waller 2002). In addition, specific instructions to team members to raise questions helped adaptation (Okhuysen & Waller 2002), but did so less when teams members had a previous history of working together (Okhuysen 2001). In familiar teams, imposition of an external intervention disrupted established roles that already contained provisions for task interruptions. This effect is similar to that observed by Arrow (1997), who showed that feedback about deteriorating performance was not sufficient to get teams, entrained in their behavioral routines, to radically change their processes. Harrison et al. (2003) revealed entrainment on repeated trials of a task persisted even when a different type of task “interrupted” those repeated trials. Moon et al. (2004) showed that teams whose initial task experience took place in a functional structure that created simple tasks with high interdependency requirements were fully able to switch to a divisional structure characterized by increased task complexity and less interdependence when the situation demanded such a change (Hollenbeck et al. 2002). However, teams that started out in divisional structure were not able to successfully execute a change to a functional structure, even when changes in the task environment demanded such a reconfiguration. In this context, the norms of high communication and support behavior of the formerly functional teams persisted into the future and promoted their adaptation to their new divisional structure. In contrast, the norms for concentration and independence associated with the formerly divisional teams also persisted into the future, destroying their ability to adapt to the new requirements of the functional structure. This research implies that rather than conceptualizing adaptation as an all-or-nothing phenomenon (teams are either adaptable or not), a more appropriate conceptualization would propose that adaptation is a directional phenomenon that needs to consider what the team is adapting from and what it is adapting to. One specific aspect of adaptation that has received a great deal of attention recently is the degree to which team members actively share their workload, help, or back up each other when faced with high demands. The virtues of workload sharing are one of the critical reasons behind adopting team-based structures (McIntyre & Salas 1995). Recent research supports this position, but also qualifies it, suggesting that helping behavior is a doubleedged sword. On the positive side, Podsakoff et al. (1997) examined the separate facets of organizational citizenship, and found that the amount of helping behavior exhibited

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in the team was the only facet that had a positive impact on both the quality and quantity of team performance. This facet of citizenship was more important than facets such as civic virtue or sportsmanship. Barrick et al. (1998) linked helping to team composition in a study of a large number of manufacturing teams where they found teams that were high on conscientiousness, agreeableness, extraversion, and emotional stability provided more help to one another relative to teams characterized in the opposite fashion. Moreover, on all four of these attributes, the score of the member lowest on the variable provided better predictive value for helping behavior than the average- or highestscoring member for all four traits. This suggests that team members may only help each other in a reciprocal fashion, making the team as a whole look more like its worst member than its best member on this aspect of group process. Another finding that emerged from the Barrick et al. (1998) study was that both helping behavior and flexibility were negatively related to variance in the team member’s levels of general cognitive ability, suggesting that when high-ability members are teamed up with low-ability members, workload sharing is restricted and perhaps unidirectional. Other studies employing very different samples and methods have found that the frequency of helping behavior is negatively associated with team performance (Baldwin et al. 1997, Podsakoff & MacKenzie 1997). Shedding light on this, Porter et al. (2003) directly tested this speculation in a study that separated helping behaviors into two kinds—high-legitimacy helping behavior that eliminated a true workload distribution problem, and low-legitimacy helping behavior that simply reflected codependent enabling of “needy” team members. Extraversion displayed both a main and an interactive effect on backing up behavior, indicating that those who were high in extraversion sought and received much more help across all conditions, but especially when legitimacy was high. Yet, there was no main effect whatsoever for people who were high in conscientiousness, those who were the most discriminating team members when it came to helping. People who were high in conscientiousness were more likely to seek help in the high-legitimacy condition, but less likely to seek help in the low-legitimacy condition relative to those who were low in conscientiousness (thus showing no main effect). Although low legitimacy in the Porter et al. (2003) study was operationalized in terms of a factor external to the team (objective workload distribution), a help request might also be low in legitimacy if it originates from someone who is not giving his or her best effort to the team. Research on social loafing continues to demonstrate how sensitive team members are to suspected “shirking” on the part of their teammates (Plaks & Higgins 2000). Indeed, LePine et al. (2002) found that potential providers of helping behavior respond very differently to team members who seem to need help because of a lack of ability, relative to team members who seem to need help due to lack of effort. LePine & Van Dyne (1998) developed a more comprehensive model of how teams react to their weakest link, noting how characteristics of the low performer influence peers, and in turn determine the form of helping intended to benefit the group.

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Learning


Learning is often a cognitive precursor to adaptation. The studies reviewed here focus primarily on changes in the team’s knowledge base, rather than behavioral changes that may or may not flow from such learning. Within this category, most of the recent literature we reviewed falls under two distinct subcategories: (a) learning from team members who are minorities (defined in many different ways) and (b) learning who is the best team member for specific tasks and capitalizing on this knowledge. Arguments for team-based organizational structures are often predicated on the belief that different team members can broaden the team’s initial knowledge base and set the stage for expanding that base as members learn from one another. Historically, however, the scientific literature has documented repeatedly that teams often fail to benefit from minority dissent when it is offered (Esser 1998, Janis 1982, Turner & Pratkanis 1998) or fail to access unique information possessed by members (Wittenbaum et al. 1999). Thus, it is not surprising that much of the current literature has been devoted to this issue. Gibson & Vermeulen (2003), in a field study of teams working in the pharmaceutical industry, showed how learning could be accomplished by managing the team’s composition. Extending prior research by Lau & Murnighan (1998) on group fault lines, Gibson & Vermeulen argued that diversity in the team’s demographic characteristics is conceptually and empirically distinguishable from the degree to which there are identifiable subgroups in the team. A four-person team composed of two women and two men, two African Americans and two Caucasians, and two people from operations and two from marketing is diverse, but may or may not contain subgroups depending upon whether the differences are crossed. Thus, if both African Americans are also both women and also both in marketing, this creates two very strong subgroups in the team, which would not be the case if one of the African Americans was a man, and one of the men was in marketing, and one of the marketing representatives was an African American. Gibson & Vermeulen (2003) showed that unless one controls for the degree of subgroup formation, the level of the team’s diversity does not predict team learning. Teams learned best when there were a moderate number of weak subgroups. The importance of avoiding minority opinions was also documented in a study by Ellis et al. (2003) using a “connecting the dots” paradigm. In this paradigm, no one team member could learn based solely on his or her own personal experience. Unlike Gibson & Vermeulen’s (2003) compositional approach, Ellis et al. took a structural approach to this same problem. Based upon past research on collective induction and the “truth supported wins” models (Laughlin 1999), this study showed that teams learned best when their resource allocations and task structures created “role partners” who could replicate, confirm, and support each other’s personal experiences. Structures that created specialized loners failed to learn because

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of the noncommensurate nature of their experiences, and teams structured in terms of overly broad generalists failed to learn because of information overload. The presence of weak subgroups seems to afford each team member some degree of “psychological safety” (Edmondson 1999, Edmondson et al. 2001) when sharing their experiences or expressing their doubts, and this seems to be essential to promote the level (De Dreu & West 2001) and nature (Lovelace et al. 2001) of group participation that creates team-level learning. In terms of composition, Ng & Van Dyne (2001) found that value differences in terms of collectivism and individualism on the part of both dissenters and the team as a whole were critical determinants of group dynamics when there are opportunities for minority influence. Teams that were, on average, high on horizontal collectivism—a value emphasizing interdependence, sociability, and equality of in-group members—and low on horizontal individualism—a value stressing independence, self-reliance and equality—benefited more from the expression of minority dissent in their groups relative to other groups. Groups that were high on vertical collectivism—a value orientation that emphasizes interdependence but recognizes status inequalities—only obtained benefits from minority dissent when the dissenter was high in status. With respect to the dissenters themselves, the results indicated that vertical individualists were least stressed when placed in a position where they had to espouse a minority viewpoint, and this in turn led to greater social influence for these individuals. Thus, composition affected team’s ability to benefit from minority dissent, but ironically, the very people most likely to express dissent (individualists) were least likely to be influenced by it. McLeod et al. (1997) revealed a similar irony in a study that examined a more structural approach to minority dissent. Using the widely employed “hidden profile” paradigm, McLeod et al. found people were more likely to dissent when interacting in a context that was not face-to-face. Minority dissent, however, was less likely to have an impact on team members in this condition, relative to face-toface conditions. Groups that encounter a minority dissenter in face-to-face contexts seem to admire the person’s courage, and in line with norms for politeness, are more likely to work to incorporate this person’s input into the group’s discussion, whereas anonymous, electronically submitted dissent tended to be ignored. In addition to learning from minority members, teams also need to learn from their members under different circumstances, and then use this knowledge to improve performance and expand the knowledge of other team members. Indeed, although much has been written about the value of information sharing and group discussion for promoting performance, two separate recent studies showed the value of learning who is the most knowledgeable member for making decisions based on discussions (Lavery et al. 1999, Littlepage et al. 1997). The ability of the team to learn from the most knowledgeable and to perform well is greater when task difficulty is higher (Bonner et al. 2002). Research that examines how teams or team leaders develop differential weighting systems for aggregating individual member judgments into a single judgment

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for the team can be found under many different headings. The Team Lens Model (Brehmer & Hagafors 1986), Judge-Advisor Systems (Sniezek & Buckley 1995; Sniezek & Henry 1989, 1990) and the Multilevel Theory of Team Decision-making (Hollenbeck et al. 1995; Phillips 2001, 2002) all examined this issue from slightly different perspectives. A detailed description of all of the research conducted under this heading is beyond our scope (see Humphrey et al. 2002 for a recent review of this literature), but the general patterns that emerge from this literature are worth noting, especially as they relate to team-level learning. Left to their own devices, most teams fail to learn the optimal schemes for integrating diverse opinions (Humphrey et al. 2002). Finally, although this section has generally conceptualized team learning as a beneficial process that organizations might want to support, it needs to be noted that some of the factors that are known to promote learning and flexibility often do so at the expense of efficiency. Indeed, research by Bunderson & Sutcliffe (2003) found an inverted-U relationship between learning orientation and longterm performance in teams, and that the downward slope of the curve comes sooner for previously high-performing teams relative to teams that have struggled. All of this suggests the need to balance the team’s need to experiment and grow with the need to execute and survive, and nowhere is this duality more difficult to manage than in what some have referred to as “high-reliability organizations” (HROs). Weick et al. (1999) defined HROs as those that operate in an unforgiving competitive, social, and political environment that is rich for potential for error, and where the scale of consequences associated with error precludes learning through experimentation. This would include operations in nuclear power plants, air traffic control, naval aircraft carriers, and space shuttle operations. In these contexts, the team’s first error may be its last, and thus the standard approaches to learning through experimentation or trial-and-error processes cannot be employed (Weick et al. 1999). Weick et al. documented that successful HROs balance the need to learn and improve with the need for flawless execution by inducing in their members a high state of mindfulness. They identify five specific processes that organizations use to induce this state, including (a) a preoccupation with small failures or near misses that may be diagnostic for larger problems; (b) reluctance to simplify, explain away, or cover-up near misses, but a tendency instead to reward people for reporting them and studying them; (c) a high degree of sensitivity to operations at the tactical level, where team members create collective situational awareness via story-building techniques; (d) resilience, or the ability to bounce back or recover from small errors via contingency planning and containment systems; and finally (e) underspecifying structures and operations in order to prevent tight coupling of systems, thus preventing errors in one component of the system to trigger a cascading set of errors quickly down the chain. All of these processes are institutionalized by “after-action reviews,” and, although not all organizations may be classified as HROs, Weick et al. argued that many would be better off in the long term if they acted as if they were. Indeed, unlike in HROs, teams often never look

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back, thus precluding the opportunity to learn. Too little attention has been paid to processes that allow some teams to benefit more from their experiences than others.


FINISHING Groups and teams in organizational contexts disband for many reasons. The ending may be planned, as is the case for task forces or crews, or unplanned, as in the collapse due to interpersonal tensions, task failure, or many other reasons including member loss of interest in remaining together (Arrow et al. 2000). Of the three phases of teams in our framework, however, finishing processes are conspicuous in their absence from the empirical teams literature. This is somewhat surprising given the multiple theoretical statements emphasizing this phase in the life of a team. Several stage models of team development have addressed finishing processes, calling the end-stage adjourning (Tuckman & Jensen 1977), decay (Worchel 1994), or termination (van Steenberg LaFarge 1995). Although other team models have eschewed the notion of teams progressing predictably through stages, they also have dealt theoretically with finishing processes, referring to the phase as completion (Gersick 1988), transition (Marks et al. 2001), and metamorphosis (Arrow et al. 2000). Clearly, because many view the decline and eventual disbanding of members to be an important phase in the life cycle of teams, much more empirical work is needed on this final phase.

CONCLUSION We are left with two general impressions of the recent teams literature, one more positive than the other. The most striking development is a convergence on common perspective of teams along with theories and methods to address the complexities of the perspective. Teams are viewed as complex, adaptive, dynamic systems, and they are embedded in organizations and contexts and performing tasks over time (Ilgen 1999). Theories directed at teams/small groups in general (Arrow et al. 2000), adaptive teams (Kozlowski et al. 1999), team process (Marks et al. 2001), or focused on issues of training (Cannon-Bowers & Salas 1998, DeShon et al. 2004), provide excellent frameworks for addressing team behavior. Methodological and computational developments also are appearing to handle more effectively the complexities of multilevel problems (e.g., Klein & Kozlowski 2000). In addition, mathematical (Losada 1999) and computational models are being strongly advocated (Arrow et al. 2000, Hulin & Ilgen 2000) for aiding the understanding of organizational behavior in teams and other settings. A recent National Research Council study panel (Pew & Mavor 1998) shows that these models have been extremely helpful in application to military simulations. In many respects, theories and methods that have recently emerged provide a firm foundation on which to build into the future.

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The domain of empirical studies, although possessing a number of interesting and important studies (as we have pointed out in our review), is far less cohesive or coherent in its entirety than is theory and method. In part, this may be because the research is more problem-driven than theory-driven. Demands of rapidly changing markets, the need for command and control, stressful military settings, and the existence of virtual organizations spanning national borders cry for the design of organizational systems incorporating teams and research to address each specific problem. Problems and the time urgency that often accompanies them direct attention away from programmatic research directed toward the development of overarching theories. It also leads to unsystematic sampling of the theory space as is evidenced by the paucity of work on teams as they decline. It has also led to a proliferation of processes that often are not very well articulated, as Marks et al. (2001) noticed in their review of team process where the differentiation between team process and resulting states of these processes (emergent states) were often blurred. Finally, although the importance of dynamic conditions experienced over time are accepted by all, the empirical work is only beginning to consider the implications of time in research designs. Thus, the empirical research lags behind the theoretical and methodological work at this time. However, given the strength of the latter and the level of activity in all domains of the study of teams, we are optimistic that the next Annual Review of Psychology chapter on teams will see even greater progress than we witnessed. ACKNOWLEDGMENTS We thank the Office of Naval Research (N00014-00–1-0398) for support to prepare this review and for the many opportunities given to J. R. Hollenbeck, D. R. Ilgen, and their students to study and participate in many kinds of teams. While we gratefully acknowledge the support, we also acknowledge that the ideas are ours and the support does not imply endorsement by the Office of Naval Research. The Annual Review of Psychology is online at http://psych.annualreviews.org

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PRESIDENTIAL LEADERSHIP George R. Goethals


Department of Psychology, Williams College, Williamstown, Massachusetts 01267; email: [email protected]

Key Words character, charisma, motives, politics, transformational ■ Abstract This chapter reviews psychological theories of leadership and selected literature on the American presidency to highlight key psychological principles of presidential leadership. Psychological theories, framed by the principles of leadership outlined by Freud (1921), include those of Burns (1978, 2003) on transformational leadership, Bass (1997) and House & Shamir (1993) on charismatic and transformational leadership, Gardner (1995) on stories of identity, Hogg (2001, 2003) on social identity, and Tyler & Lind (1992) on procedural justice. The discussion of presidential scholarship considers work by Barber (1992) on presidential character, Simonton (1986, 1987) on presidential personality and success, Skowronek (1997) on reconstructive politics, and Winter (1987) on presidential motive profiles. These studies suggest that followers have high expectations for presidents and that successful presidential leadership depends on opportunity, high levels of activity, intelligence, optimistic resilience, and flexibility.

CONTENTS PRESIDENTIAL LEADERSHIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSYCHOLOGICAL PRINCIPLES OF LEADERSHIP . . . . . . . . . . . . . . . . . . . . . . . . Freud’s Group Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modern Psychological Theories of Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PRINCIPLES OF PRESIDENTIAL LEADERSHIP . . . . . . . . . . . . . . . . . . . . . . . . . . BEYOND PRESIDENTIAL LEADERSHIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

545 546 546 549 557 567 568

PRESIDENTIAL LEADERSHIP The presidency of the United States offers particularly compelling lessons on leadership. Political scientist and presidential scholar James David Barber (1992) writes: “The Presidency is the focus for the most intense and persistent emotions in the American polity. The President is a symbolic leader, the one figure who draws together the people’s hopes and fears for the political future” (p. 2). We can learn a great deal about leadership from the way 42 different individuals have executed this 0066-4308/05/0203-0545$14.00

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unique office. The goal of this chapter is to describe the literatures on leadership and on the presidency that are relevant to understanding presidential leadership. It is important to note at the beginning that this review differs from similar chapters in the Annual Review of Psychology in that it focuses almost entirely on theory rather than on research. There are two reasons for this. First, although a large body of empirical literature deals with the presidency and presidential leadership, very little of it addresses the theoretical formulations discussed here. A few exceptions are noted. Second, presidential scholars concerned with psychological questions tend not to contribute to that empirical literature. The work of Dean Keith Simonton and David Winter are notable exceptions, and their studies are considered here. However, the theoretical literature on the presidency discussed here has produced little research. Thus, this chapter’s focus is theory. This review of presidential leadership begins in an unusual place, with Freud’s 1921 extended essay Group Psychology and the Analysis of the Ego. Two considerations make this an appropriate beginning. First, Freud’s ideas about leadership seem unusually insightful. Modern theories of leadership represent many of his key observations and formulations, although few of them reference Freud. Although Freud’s theory is not entirely well integrated, he clearly touched on many extremely important elements of leadership. This becomes apparent in the examination of modern theories. Second, as Barber (1992) points out, the American presidency is the focus of the kind of intense symbolic and emotional follower dynamics that Freud emphasized. The outline of Freud’s key concepts is followed by a discussion of several modern psychological theories that are relevant to understanding leadership and the presidency and are related to Freud’s theory. Other leadership theories pertinent to understanding presidential leadership are also reviewed. The applicability of each of these perspectives to understanding the presidency is briefly examined. Next, the principles of presidential leadership derived from political scientists and psychologists are reviewed. Finally, the work on presidential leadership is integrated with more general considerations of leadership theory and research.

PSYCHOLOGICAL PRINCIPLES OF LEADERSHIP Freud’s Group Psychology Freud’s treatment of leadership is contained in his 1921 publication Group Psychology and the Analysis of the Ego. Heavily influenced by Gustave LeBon’s (1895) book The Crowd, Freud’s work emphasizes how highly suggestible and deeply influenced people can be in group situations. Freud initially sought to understand why crowds, as described by LeBon, are capable of such intense emotions and extreme behavior. In crowds, LeBon and Freud argue, people seem to regress to a lower intellectual level, where they are easily swayed by the words and actions of leaders toward dramatic action and rapidly changing emotions. An example from

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literature nicely captures the phenomenon that LeBon and Freud tried to understand. In Shakespeare’s Julius Caesar, a crowd of Romans hears Brutus defend his participation in the assassination of Caesar. He argues that Caesar was dangerously ambitious. The crowd is convinced, and hails Brutus. Mark Antony follows, and in his famous “Friends, Romans, countrymen. . .” speech, subtly plays on the idea that Brutus is an honorable man. Antony slowly and subtly takes issue with the charge of ambition: “The noble Brutus hath told you Caesar was ambitious. If it were so, it was a grievous fault, and grievously hath Caesar answered it.” In a few minutes, the crowd’s viewpoint has markedly changed. Brutus is no longer seen as honorable and Caesar is no longer seen as ambitious. The crowd determines to capture and kill Brutus. How does this happen? Why are crowds so suggestible? What causes their intellectual level to be reduced? Why do they feel omnipotent? Freud (1921) argues that in groups, people “place themselves instinctively under the authority of a chief. . . [A group] has such a thirst for obedience it submits itself instinctively to anyone who appoints himself its master” (p. 81). In submitting themselves, people do not think but rather do as directed by the leader. Both the leader and his ideas have what Freud describes as “a mysterious and irresistible power.” Furthermore, strong libidinal bonds exist between group members and the leader, and among group members themselves, accounting for the heightened emotionality of the group and the group’s feeling that it is capable of anything. How can we account for this kind of dominance, or perhaps more importantly, this kind of submission? Freud argues that the group’s strong libidinal ties to the leaders transform into identification with the leader. Furthermore, the identification is strengthened based on perceived similarity with the leader: “[Identification] may arise with any new perception of common quality shared with some other person. . .. The more important this common quality is, the more successful . . .” (Freud 1921, p. 122) this identification may become. What accounts for the libidinal ties? Why is there such strong love for, and subsequent identification with, the leader? Freud traces the origin of such feelings to an “archaic heritage” of feelings and perceptions that originally arose in a primitive form of social organization that he called the primal horde. “In 1912 I took up the conjecture of Darwin’s to the effect that the primitive form of human society was that of a horde ruled over despotically by a powerful male. I attempted to show that the fortunes of this horde have left indestructible traces upon the history of human descent” (Freud 1921, p. 122). In the primal horde, male members of the group loved and feared the male leader. The leader, chief, or father was entirely unfettered: “His intellectual acts were strong and independent. . . and his will needed no reinforcement from others.” This strong chief satisfied his sexual needs at will, but developed no love for others. He closed off sexual access for other males in the group. Freud argues that memory traces of this kind of leader in this kind of primitive society are recoverable in the present, and that they are reawakened when in a group a leader puts himself forward. When this archaic heritage is activated, “what is thus awakened is the idea of a paramount and dangerous personality. . . to whom one’s will had to be surrendered”

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(p. 127). Contemporary accounts of America’s first president, George Washington, and recent biographies (e.g., Brookhiser 1996) make clear that Washington had this kind of force with the officers and soldiers in the Continental army, delegates at the constitutional convention in 1787, and cabinet officers and officials in the government during his two presidential terms. He was a physically imposing man with a temper—a temper generally under tight control, but a temper nevertheless. In short, people have an archaic memory of a despotic male leader who was feared and loved, and that memory is reawakened in a group setting. Inherited tendencies to identify with and completely obey this powerful figure lead people in the present to make leaders their ego ideals and to go along with whatever they suggest. In line with this perspective, Freud (1921) offers “a definition for suggestion: a conviction which is not based upon perception and reasoning but on an erotic tie” (pp. 127–128). Freud made other observations about leadership that fill out this overall perspective. Humans are prepared by their primitive heritage to identify with and respond to leaders. However, leaders must have particular qualities. They must be imposing ideal types, and they must command language and ideas. Freud (1921) says that the group’s needs for a leader carry it “half-way” to meet the leader, “yet he too must fit in with it in his personal qualities. . . he must possess a strong and imposing will” (p. 81). Also, he must “possess the typical qualities of the [group] in a particularly clearly marked and pure form, and. . . give an impression of greater force. . .” (p. 129). In addition to being strong, imposing, and ideally representative of the group, the leader must command the power of words and ideas. Freud discusses the fact that ideas have the same power or prestige as leaders themselves. He states that the leader “must himself be held in fascination by a strong faith (in an idea) in order to awaken the group’s faith. . .. Leaders make themselves felt by means of the ideas in which they themselves are fanatical believers. . . Prestige is a sort of domination exercised over us by an individual, a work or an idea” (Freud 1920, p. 81). Freud discusses whether an idea or abstraction can take the place of the leader in unifying a group. Thus, ideas clearly have power and are one important source of a leader’s power and prestige. Freud emphasizes the importance of language in expressing those ideas: “A group . . . is subject to the truly magical power of words; they can evoke the most formidable tempests in the group mind, and are also capable of stifling them” (p. 80). The words need not appeal to rationality. Indeed, reason and argument “are incapable of combating certain words and formulas” (p. 80, quoted from LeBon 1895). “Groups have never thirsted after truth. They demand illusions” (p. 80). “Anyone who wishes to produce an effect upon [the group] needs no logical arguments; he must paint in the most forcible colours, he must exaggerate, and he must repeat the same thing again and again” (p. 78). In short, Freud’s leader must represent the group ideally and strongly, and must express the central ideas of the group in a forceful if not particularly reasoned way. He obtains the capacity to influence through an erotic tie based in turn on his prototypicality. A number of presidents have had the combination of qualities

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suggested in Freud’s analysis. As noted above, George Washington possessed great force and was highly prototypical. Andrew Jackson had similar force and could express himself extremely clearly and directly. Theodore Roosevelt had perhaps even more force and greater articulacy. John Kennedy was less overtly forceful but expressed compelling ideas with moving words. In addition, his charisma produced strong affective ties in many of those who supported him. Ronald Reagan possessed charisma based on prototypicality, and in a 1987 speech in Berlin his challenge to Mikhail Gorbachev to “tear down this wall” combined force and rhetoric in a way that well illustrates Freud’s viewpoint. Another central point in Freud’s theory of leadership is that a key element in follower perception is the “illusion that the leader loves all of the individuals equally and justly” (Freud 1921, p. 124). In the primal horde, “all of the sons knew that they were equally persecuted by the primal father, and feared him equally” (p. 125, italics in original). These perceptions are recast into the “presupposition of the father’s equal love” and provide “indestructible strength” to group formation (p. 125). The illusion of equal love is an important element in leadership in highly organized groups as well as in the family or clan. In a chapter on the church and the army, Freud argues that this illusion holds true in both. For example, in the army “the commander-in-chief is a father who loves all soldiers equally. . .. Every captain is, as it were, the commander-in-chief and the father of his company, and so is every noncommissioned officer of his section” (p. 94). George Washington had to convey equal love to the two key young men in his first cabinet, Alexander Hamilton and Thomas Jefferson. When Jefferson felt that Washington favored Hamilton, he retired to Monticello. In sum, Freud’s theory of leadership includes (a) the ideas of strong affective ties between the leader and followers, and strong follower identification with the leader; (b) the importance of leader prototypicality and the leader’s capacity to suggest a wide range of beliefs, emotions, and behaviors through ideas vividly expressed in words; and (c) the importance of just and equal treatment by a leader. A final point can be added to this list. Freud held that a leader’s “prestige. . . is also dependent on success, and is lost in the event of failure” (p. 81). Some may find it hard to remember that Jimmy Carter had prestige during the first part of his administration, particularly after the successful Camp David Accords. However, once Carter was perceived to be a failure, he was easily ridiculed and quickly rejected.

Modern Psychological Theories of Leadership There are numerous psychological theories of leadership. Although any selection is bound to exclude some theories that are important, those discussed below capture the principal psychological theories of leadership that are relevant to understanding the presidency. It is useful to tie many of these theories to Freud’s analysis, where appropriate, and to show how many of them amplify one or another of Freud’s insights into the relation between leaders and followers.

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One of the most influential leadership developments of the past few decades has been James MacGregor Burns’s (1978) concept of transformational leadership. Burns made a fundamental, and often-debated, distinction between transactional and transformational leadership. Transactional leadership “occurs when one person takes the initiative in making contact with others for the purpose of an exchange of valued things” (p. 19). The valued things may be of an economic, political, or psychological nature. The leader may secure economic benefits for followers who in turn grant authority and legitimacy to the leader. In this kind of leadership, Burns argues, the “bargainers have no enduring purpose that holds them together” (p. 20). Their exchange relationship (cf. Hollander 1993) is a business arrangement. Some leadership theorists contend that this kind of exchange describes all leadership (Hollander 1993). However, Burns argues that transactional leadership should be distinguished at a very fundamental level from transforming or transformational leadership. Transformational leadership “occurs when one or more persons engage with others in such a way that leaders and followers raise one another to higher levels of motivation and morality” (p. 20, italics in original). Burns cites Gandhi as a transformational leader who raised the aspirations of millions of Indians and in the process himself was transformed and elevated. Gandhi’s vision fundamentally reoriented the beliefs of Indians about what they could achieve politically and economically. He also completely redefined how such goals could be attained, namely, through nonviolence. Clearly, leaders have the potential to alter radically the motives and morals that govern a group. Additionally, in Burns’s view, not only are the motives and morals of the group altered, but the leader embraces the transformed motives and morals as well. The two have common cause. Burns & Sorenson (2000) criticized Bill Clinton for failing to fight for real change after the failure of Hillary Clinton’s health plan, and therefore for failing the test of transformational leadership. He was an exemplary transactional leader, in their view. James Polk is another example of an effective transactional leader— one of the most effective presidents in this regard. He was able to get support for a few ideas, e.g., annexing Texas, and he delivered on his promises. Yet, he is not remembered for any transforming principles or policy changes. Abraham Lincoln, in contrast, is remembered for eradicating America’s institution of slavery. That constituted transformational leadership. Many leaders have apparently changed not only the level but also the nature of the motivation and morality of their followers. However, have such leaders raised, or have they lowered, the jointly held motives and morals? Hitler and Osama bin Laden are examples of leaders who have transformed the thoughts, feelings, and behaviors of large groups of followers. Were—or are—their aspirations elevated or lowered? Because this is a psychological rather than philosophical review, no attempt to answer this question will be made. However, from a psychological viewpoint, it is likely that these leaders and their followers felt that they had set out on a morally superior course of action.

BURNS’S CONCEPT OF TRANSFORMATIONAL LEADERSHIP


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Somewhat surprisingly, LeBon’s and Freud’s view of leadership also considers what Burns would regard as transformational leadership, and suggests that this kind of leadership is produced by the same processes that generate less commendable group behavior. The fundamental premise of LeBon’s and Freud’s understanding of groups is simply that they can be easily led. People in groups are highly suggestible. A leader can strongly affect people’s beliefs, feelings, and behaviors. Although LeBon and Freud emphasize the negative behaviors that appear in crowds and other groups, they make clear that in groups, depending on the nature and quality of the relationship between leaders and followers, people can be led nearly anywhere, toward either good or evil. In groups, “an individual readily sacrifices his personal interest to the collective interest” (Freud 1921, p. 75, quoting LeBon). Although people in groups often express their “cruel, brutal, and destructive instincts” (p. 79), a leader can also move them to highly moral and socially responsible behavior. “Under the influence of suggestion groups are also capable of high achievement in the shape of abnegation, unselfishness, and devotion to an ideal. . .. [A group’s] ethical conduct may rise as high above [the individual’s] as it may sink below it” (p. 79). “. . .The morals of a group can be higher than those of the individuals who compose it. . . only collectivities are capable of a high degree of unselfishness and devotion” (p. 82). For a time charismatic leader John F. Kennedy, who urged the nation to “ask not what your country can do for you; ask what you can do for your country,” produced this kind of unselfish devotion to ideals, as seen, for example, in the Peace Corps. Burns’s concept of transformational leadership very usefully highlights the fact that leaders can move followers a long way. Combined with the LeBon/Freud approach, it suggests the wide range of beliefs, attitudes, and behaviors toward which followers can be moved. Burns’s theory does not pay as much attention to the characteristics of leaders that produce transformative effects. Burns & Sorenson (2000) simply argue that transforming leaders display courage, commitment, conviction, and competence, and meld these attributes toward creating change (cf. also Burns 2003). Bass & Avolio (Bass & Avolio 1993, Bass 1997) and House & Shamir (1993) develop similar themes in their work. They also distinguish behaviors from effects. As House & Shamir state, charismatic leadership produces transformational effects. Bass (1997) characterizes the behavior of transformational leaders as having four significant attributes. First, they have charisma, or idealized influence. They “display conviction,” “present. . .important values,” and “emphasize the importance of purpose, commitment, and ethical components of decisions” (Bass 1997, p. 133). Second, transformational leaders use inspirational motivation, meaning that they “articulate an appealing vision of the future, challenge followers with high standards, talk optimistically with enthusiasm, and provide encouragement and meaning for what needs to be done” (p. 133). Franklin Roosevelt excelled at providing this kind of inspirational motivation: “the only thing we have to fear

THE TRANSFORMATIONAL EFFECTS OF CHARISMATIC LEADERSHIP

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is fear itself.” Third, such leaders provide intellectual stimulation, pushing followers to consider new points of view and to question old assumptions, and to articulate their own views. Finally, they demonstrate individualized consideration, meaning that they take into account the needs, capacities, and aspirations of each individual follower in the effort to treat followers equitably. One of the empirical questions that Bass (1997) has addressed is whether these four factors are independent enough to be differentiated. The last three are correlated with charisma, which suggests that charisma is the more general factor. However, the dimensions are conceptually distinct. The most useful view may be that the latter three qualities often go along with charisma and give it a fuller definition. House & Shamir (1993) add to our understanding of the characteristics of charismatic behavior. They propose that charismatic leaders articulate an ideological goal, a vision that “describes a better future for followers” (p. 97). Often that future involves achieving fundamental values such as freedom, beauty, or dignity. They add that charismatic leaders build a positive image that appeals to followers. House & Shamir recognize the theatrical abilities that military historian John Keegan noted were typical of charismatic leaders. Keegan (1987) wrote that the exceptional leader, particularly a military leader, is “both shown to and hidden from the mass of humankind, revealed by artifice, presented by theater. . .. What [followers] know of him must be what they hope and require. What they should not know of him must be concealed at all costs. The leader of men in warfare can show himself to his followers only through a mask, a mask that he must make for himself, but a mask made in such form as will mark him to men of his time and place as the leader they want and need” (p. 11). In a historic example of positive image building by a charismatic leader, Theodore Roosevelt carefully choreographed a photograph of himself at the controls of a large Bucyrus steam shovel in 1906 to build support for the construction of the Panama Canal. Charismatic leaders must take risks and sacrifice themselves for their goals, thus demonstrating the courage and conviction noted by Burns & Sorenson. Like Bass & Avolio (1993), House & Shamir (1993) note the importance of “empowering behaviors.” These include setting high expectations for followers, and conveying the belief that the group can attain its lofty goals. Leaders who behave in these ways have the ability to influence a group toward a wide range of behaviors. In the best instances, the behaviors will promote universal values. Freud (1921) argued that the group’s “passion for authority” (p. 127) and “need for a strong chief” (p. 129) meet the leader halfway. Nevertheless, the leader must fit the followers’ expectations for a leader. The behaviors of charismatic leaders outlined above very likely fit, at least in many cultures, people’s expectations for what a leader should be like. Such behavior can reawaken the image of a “paramount and dangerous personality.” Psychologists studying “leader schemas” or “implicit leadership theories” have explored the image people have of leaders (Calder 1977, Fiske 1993, Kenney et al. 1994, Kinder

THE ROLE OF LEADER SCHEMAS

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et al. 1980, Lord et al. 1984, Philips & Lord 1981, Simonton 1987). Lord et al. (1984) found that leaders are thought of as competent, caring, honest, understanding, outgoing, verbally skilled, determined, aggressive, decisive, dedicated, educated, kind, and well dressed. In a study of conceptions of new leaders, Kenney et al. (1994) found 16 categories of expectations that formed 4 prototypes describing people’s implicit leadership theories for new leaders: learning the group’s goals, taking charge, being a nice person, and being nervous. In his book Why Presidents Succeed, Simonton (1987) proposes a leadership schema based on semantic differential research by Osgood et al. (1957). Leaders are expected to be strong, active, and good. In Freud’s terms, if a potential leader “give(s) an impression of greater force” and possesses “a strong and imposing will,” group members will “invest him with a predominance to which he would otherwise perhaps have no claim” (Freud 1920, p. 129). These different approaches, describing leadership schemas, prototypes, or implicit leadership theories, characterize people’s beliefs about leaders at different levels of specificity. What is common to all of them is the idea that people do have a schema about leadership and that this schema forms a standard for judging leaders. Hogg (2001) argues that once followers have decided that a person qualifies as a leader there is a tendency for followers to attribute the leader’s behavior to “intrinsic leadership ability, or charisma,” and to “construct a charismatic leadership personality for that person” (p. 190). Thus, leadership schemas are used to evaluate leaders or potential leaders, and they can have a profound effect on what personal qualities are attributed to someone who has been judged to meet the leader standard. A person who has “the typical qualities of the individuals concerned in a particularly clearly marked and pure form” (Freud 1920, p. 129) will be perceived as the leader, and will be granted many of those qualities that are not initially attributed. As Hollander (1993) stated, charisma is a quality “that can be considered to be invested by followers and accorded or withdrawn by them” (p. 41). One of the central concepts in Hogg’s (2001) social identity approach to leadership is prototypicality. Hogg holds that individuals who are prototypical of the group become leaders. Prototypes are defined as “context specific, fuzzy sets of attributes that define and prescribe attitudes, feelings, and behaviors that characterize one group and distinguish it from other groups” (Hogg 2001, p. 187). The idea that the prototype can define and prescribe the attributes of the group suggests that the leader must not only be representative of the group but also must represent it in an ideal way. Prototypical individuals are perceived as having more influence than other members of the group, whether they actually do or not, because as group members conform to a group prototype, the most prototypical individuals have to change less to conform than those who are less prototypical. Thus, those individuals are perceived to be causing others to change, and they are regarded as highly influential and therefore as leaders. Freud emphasizes that individuals who possess typical

LEADER PROTOTYPICALITY

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qualities of the group are granted authority. Hogg’s perspective is slightly different. It suggests that such individuals give the “appearance” of having exercised authority. Then over time, and whether they have actually been influential or not, the perception that they are influential becomes “stronger and more entrenched” (Hogg 2001, p. 189). This process lends prototypical individuals real influence over group members. Hogg also suggests that the processes above are strongest when the salience of group identity is greatest. Much of the research addressing the social identity theory of leadership manipulates identity salience (Hogg 2003). Hogg also argues that leaders sometimes attempt to maintain their position by “redefining, usually through rhetoric and polemic. . .the prototype in a self-serving manner to prototypically marginalize contenders and prototypically centralize self. This can be done by accentuating the ingroup prototype, by pillorying ingroup deviants, or by demonizing an appropriate outgroup” (Hogg 2001, p. 191). Hogg notes that former British Prime Minister Margaret Thatcher acted in this way during the Falklands war in 1983. Members of the George W. Bush administration did likewise during the 2003 Iraq war. Both Freud and Hogg discuss the use of rhetoric and polemic to manipulate emotions and retain influence. Howard Gardner’s (1995) theory of leadership particularly emphasizes this aspect of leadership. Gardner holds that the central element in leading is relating and/or embodying stories, typically about identity. Leaders of nations or other large groups or institutions often lead “through the stories and acts they address to an audience” (p. 13). The most powerful stories are about identity. Effective leaders tell stories “about themselves and their groups, about where they were coming from and where they were headed, about what was to be feared, struggled against, and dreamed about.” Such stories are dynamic. They unfold over time, and the leader and followers are the “principal characters or heroes” (Gardner 1995, p. 14). Lincoln’s second inaugural address is a particularly good example of a powerful identity story. Somewhat rewriting history, Lincoln persuasively proclaimed slavery as the cause of the civil war, declared slavery finished, and then explained how the nation was to proceed in the war’s aftermath: “With malice toward none; with charity for all; with firmness in the right as God gives to see the right, let us strive on to finish the work we are in. . . to do all which may achieve a just and lasting peace. . ..” Freud argued that leaders influence through words and ideas. Gardner’s (1995) emphasis on stories is entirely congruent. He sharpens the focus of Freud’s ideas by showing that the leader’s words and ideas generally focus on identity, inspiring and leading followers by providing a vision or story about where a group is going, and what needs to be done to get there. Gardner also adds the important concept, perhaps implicit in Freud’s work, that the leader’s actions, or embodiment of the story, are as influential as the words of the story. One of Gardner’s most interesting examples is Pope John XXIII. Pope John expressed in words a story of humility, openness, and inclusion. This story ran into the “counterstories” of the entrenched

TELLING IDENTITY STORIES

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Catholic leadership, stories that emphasized hierarchy, obedience, and formality. Pope John’s behavior also represented humility, openness, and inclusion. “I always try to show people that I am a regular person. I have two eyes, a nose—a very big one—a mouth, two ears and so on” (Gardner 1995, p. 178). His story was effective, and appealed broadly to non-Catholics as well as to Catholics, because it was firmly grounded in the words and deeds of Jesus. A story that resonated so closely to that of Jesus himself was difficult for the church elders to counter effectively. Gardner (1995) introduced other important concepts. First, he distinguished between direct and indirect leaders. Direct leaders tell or embody their stories to followers through some kind of contact or interaction. Indirect leaders may have no contact with followers. In this case, it is simply their ideas or products, often artistic products, that have impact. In this way, Einstein can be considered a leader—specifically, an indirect leader. His written theories carried the weight of influence. There was little about him as a man that added to his impact. Freud also emphasized the paramount importance of ideas. Second, Gardner notes that the stories of most successful political leaders are innovative. They are not entirely new. Rather, they touch on themes that have been important in the group in the past, but have been temporarily lost or overshadowed by other stories. Ronald Reagan, for example, revitalized a story of American goodness and greatness that had come unraveled during the administrations of his four immediate predecessors, Johnson, Nixon, Ford, and Carter. Third, Gardner distinguishes between stories that are inclusive, and tell about the common identities of many people, like that of Pope John XXIII, and exclusive stories, like those of Hitler and Margaret Thatcher, which emphasize the differences between ingroups and outgroups, and trumpet the virtues of the ingroup. In his social identity theory, Hogg (2001, 2003) describes leaders who seek to retain leadership by redefining the ingroup prototype in a self-serving manner and thus behave similarly to leaders who tell exclusionary stories. Work by Tyler & Lind (1992) focuses on the various ways persons in authority make decisions and the effects different kinds of decisions have on compliance. They discuss a large body of literature that shows that procedural justice is more important than distributive justice. People’s willingness to comply with the decisions of authorities is more influenced by whether those decisions are based on fair procedures than by whether those decisions give them favorable outcomes. Tyler & Lind (1992) suggest that it is easier to judge the procedural fairness of a decision than whether its resulting distributions are fair, and that people believe that they will manage well in the long term if procedures are fair. Authorities who show concern for one’s needs, who consider one’s views, who are polite and treat one with dignity, and who are honest and unbiased are perceived as procedurally fair and just. When an authority treats an individual fairly, that fair treatment signals that the individual has value in the group. The authority powerfully represents the group such that good treatment by the authority predicts positive treatment by the group as a whole.

PROCEDURAL JUSTICE AND COMPLIANCE WITH AUTHORITY

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Under these circumstances, the individual accords the authority legitimacy and voluntarily complies with the authority’s decisions. This perspective clearly aligns with Freud’s theory that followers need to have the illusion of equal love from the leader. This perception is central to their feeling of having a common attachment and to their willingness to obey the leader. Interestingly, Tyler & Lind (1992) describe a similar perception, specifically an “illusion of personal justice” (p. 155). People have the general expectation that legal and political authorities have treated them fairly and will continue to do so. Finally, a number of other perspectives on leadership are highly relevant to understanding the presidents. These approaches are mentioned last in this section to emphasize that they deal with issues not addressed by Freud. By no means does that suggest their lesser importance. Hogan et al. (1994) review some of the basic issues in leadership and identify the Big Five model of personality (cf. Norman 1963, McRae & Costa 1987) as having particular relevance for understanding leadership. Surgency, emotional stability, agreeableness, conscientiousness, and intellectance are all related to perceived, emergent, and effective leadership. Bill Clinton’s charisma reflects surgency, agreeableness, and intellectance. Surgency and stability are particularly ubiquitous in leadership effectiveness. The Big Five are “bright side” characteristics that all contribute to good leadership. Hogan et al. (1994) also very usefully identify “dark side” characteristics that derail leadership or undermine its effectiveness. These include being arrogant, hostile, passive aggressive, selfish, compulsive, abrasive, and aloof. Richard Nixon possessed several of these traits. One of the earliest studies of emergent leadership suggests that individuals with different personalities may adopt one of two different leadership roles (Bales 1958). One is the role of task leader. This involves actively suggesting ideas to solve group problems, and thus draws on surgency, conscientiousness, and intellectance. The other role is that of socio-emotional leader, a role that involves keeping other group members happy. This role draws mainly on agreeableness, but also on stability and sometimes on surgency. Although on occasion a leader has enough personal resources to play both roles well, a complementary leadership structure often exists in a group where there are separate task and socio-emotional leaders. The distinction between task and socio-emotional roles is central in Fiedler’s (1993) well-known contingency theory of leadership. Fiedler outlines the conditions under which leaders oriented toward task performance rather than interpersonal relations will be more effective. For our purposes, the most general implication of Fiedler’s theory is important: Different individuals are better leaders in different circumstances.

PERSONALITY, LEADERSHIP STYLE, AND CONTINGENCY

The foregoing treatment of leadership theories is selective. A number of useful theories, including those of Heifetz (1994) and Hollander (1993, Hollander & Julian 1969), have not been reviewed because

SUMMARY OF LEADERSHIP THEORIES

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their relevance to presidential leadership is less direct than that of the theories discussed above. The theories described above suggest a picture of the followers of presidential leadership, and of ourselves more generally, in fact. Followers thirst for leadership, they are highly suggestible by the ideas forcefully expressed and vividly embodied by a leader, they have strong emotional attachments to leaders, and they expect and demand fair treatment. Followers have leader schemas such that they expect leaders, particularly presidents, to be strong, active, and good. The reader may have noticed that these theories of leadership treat the characteristics of followers and followership in much more detail than the attributes of leaders. In the discussion below of the specific consideration of presidential leadership, the qualities of presidents themselves come more clearly into focus. Still, however, the nature of followers and following occupies an important place in those theories.

PRINCIPLES OF PRESIDENTIAL LEADERSHIP The nation’s need to perceive effective presidential leadership was clearly demonstrated during and immediately after the terrorist attacks on New York and Washington, DC, in September 2001. During much of the day on September 11, the public did not know where President George W. Bush was. When he emerged in the next few days, commentators spoke repeatedly about whether Bush appeared confident and whether he was effectively exercising leadership. Slowly he seemed to find his voice. Citizens gave him high approval ratings. If ever there was a thirst for authority and leadership, it was in those traumatic days. Once President Bush’s words and actions appeared to meet and surpass the minimal expectations for leadership, he was perceived as highly effective. What do presidents bring to the complex, highly emotional relationship between leaders and followers? How does what they bring interact with the shifting and complex needs of the polity? Five useful approaches to presidential leadership are considered in this section. As in the review of theories of leadership, this review is selective. The approaches that are considered address the principle psychological questions of presidential leadership. James David Barber’s (1992) book, Presidential Character: Predicting Performance in the White House, was first published in 1972. It gained considerable currency after its portrayal of incumbent president Richard Nixon as an “active-negative” was seen to have effectively predicted the kind of self-defeating behavior that was Nixon’s undoing during the Watergate scandal. Since then presidential candidates have been anxious to be categorized by Barber and others as having the highly praised “active-positive” character. Barber is a psychologically minded political scientist. One of his key concepts is self-esteem. Barber asserts that “every story of Presidential decision-making is really two stories: an outer one in which a rational man calculates and an inner one in which an emotional man feels” (p. 4). How much the inner man feels, and how—and how

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much—emotions rooted in low self-esteem disturb his rationality, is the central story in presidential performance. Barber identifies three essential components of presidential personality: character, worldview, and style. These are formed in childhood, adolescence, and early adulthood, respectively. Character is fundamental in Barber’s theory. He classifies presidents as having one of four types of character, determined largely by self-esteem developed in early childhood. The four types of character are determined by two dimensions, not surprisingly similar to two of the basic dimensions of meaning identified by Osgood et al.’s (1957) semantic differential studies. Barber’s (1992) dimensions are active versus passive, and positive versus negative. He contrasts the active Lyndon Johnson, who “went at his day like a human cyclone, coming to rest long after the sun went down,” with the passive Calvin Coolidge who “often slept eleven hours a night and still needed a nap in the middle of the day” (p. 8). Similarly, he contrasts Theodore and Franklin Roosevelt, who were positive and both understood and enjoyed the use of power, with negatives such as Wilson and Nixon, who felt that power was a burden, or was overwhelming, or who experienced high doses of hostility in their work. In reviewing twentieth-century presidents, Barber (1992) includes among the active-positives Franklin Roosevelt, Harry Truman, and John Kennedy. He argues that for all the active-positives there is a congruence between working hard and being active, and enjoying that activity. The active-positives are the high selfesteem presidents who have enough confidence to be flexible and adaptive in their approach to the nation’s problems. They are open to experience and learn from it. Barber calls them the adapted type. The active-negatives include Woodrow Wilson, Herbert Hoover, Lyndon Johnson, and Richard Nixon. These presidents seldom enjoyed their hard work. They were compulsive and often petty and aggressive. Their inflexibility contrasts with the active-positives. Their “self-image is vague and discontinuous. Life is a hard struggle to achieve and hold power, hampered by the condemnations of a perfectionistic conscience” (Barber 1992, p. 9). They are Barber’s compulsive types. The passive-positives include William Howard Taft and Ronald Reagan, who had low self-esteem based on perceptions of being unlovable and unattractive, but they possessed a superficial optimism. They were fragile and dependent, and were easily pushed around by powerful advisers. They were likely to be pushed into scandal or other self-defeating circumstances. Barber describes them as compliant. The passive-negatives include Calvin Coolidge and Dwight Eisenhower. These presidents did not much engage with the activities of governing and did not enjoy the little they did. They are called withdrawn. Barber notes that passive types have become increasingly rare in American politics, and are likely to become even more rare given the growing demands of the presidency. Barber (1992) also discusses each president’s basic philosophy or worldview and the way in which that credo develops in adolescence, and how in early adulthood men who become president develop a style, learning how successfully to use some combination of homework, interpersonal relations, and rhetoric to lead

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others and to achieve their political goals. Barber does not see presidential effectiveness as solely dependent on character, worldview, and style. One important external variable is the power situation. This includes the popular mandate with which the president is elected, and the degree of support he has for his program in the congress. A more psychological external variable is what Barber calls the climate of expectations. Barber suggests there is a cyclical series of expectations based in part on present circumstances, but in large measure on past expectations and actions. Initially, the public may want action. They want things to be accomplished; they want goals to be achieved. However, the pushy activist president often bends the rules in an effort to get his program through. Franklin Roosevelt’s attempt to pack the Supreme Court is a memorable example. The public grows skeptical of the hard-driving, rule-bending, action-oriented president and seeks legitimacy. For example, after the Watergate scandal of the second Nixon term, people were seeking legitimacy in their leaders. Jimmy Carter’s born-again piety and personal virtue met the legitimacy expectation well. However, things continue to change, and people grow weary of the moralistic, critical, near preaching of the legitimacy-oriented president. For example, many voters said they found Carter’s 1979 “malaise speech” disturbing and offensive: Carter seemed to blame the public for the country’s difficulties. The legitimacy approach then gives way to a craving for reassurance. In 1980, Ronald Reagan offered the nation just that. The United States, Reagan said, had the capacity to meet the world’s challenges and solve the nation’s problems. “Why shouldn’t we believe that?” Reagan concluded his inaugural address, “After all, we are Americans.” Barber’s (1992) arguments have been challenged on many grounds for many years. For example, a recent article (Ellis 2003) suggested that the enjoyment of the presidency is a twentieth-century construal of the office, influenced greatly by Theodore and Franklin Roosevelt. Many eighteenth- and nineteenth-century presidents regarded the office as a burden (Ellis 2003). This is the not the place to review criticism of Barber’s theory. For our purposes, it convincingly suggests a useful picture of confident, engaged, essentially positive leaders who wisely, adaptively, and flexibly cope with the burdens of the office, and learn from their experience. It also convincingly shows us how less self-assured presidents can become embroiled in their compulsions, their aggressions, or their weakness and passivity to the detriment of their presidencies and, of course, the nation. Stephen Skowronek’s (1997) book, The Politics Presidents Make, has contributed a great deal to our understanding of presidential leadership, especially the interaction of personal and situational factors. Like Barber’s work, it proposes a two-dimensional, four-category classification of presidential politics and leadership. Like Barber, Skowronek attempts to make predictions about the fate of different presidencies based on his classification scheme. As much as Barber is credited for predicting an eruption like the Watergate scandal in his 1972 publication, Skowronek is noted for predicting in early 1997 something very much like impeachment proceedings for Clinton (Skowronek

RECONSTRUCTIVE PRESIDENTIAL LEADERSHIP

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1999). Beyond that, there is little similarity in their approaches, although the possible overlap is tantalizing. Skowronek classifies presidents along two dimensions. The first is whether they are affiliated with or opposed to the “established regime.” The second is whether that established regime is resilient or vulnerable. The idea of the established regime is critical. Skowronek also employs the following essentially synonymous terms: the pre-established regime, its basic commitments, received arrangements, the received agenda, and previously established commitments. These terms all refer to the dominant political regime that is holding power in the government as a whole. For example, there was a long-standing Jacksonian regime, beginning with the first inauguration of Andrew Jackson and going through the administration of James Buchanan, a period from 1829 to 1861. That period was dominated by Jackson, Jackson’s protégés, and then, after Jackson’s death, his basic philosophy of an activist, expansionist, Western-oriented government that generally protected Southern slave interests. Similarly, there was a Democratic regime, born in the New Deal, whose policies and philosophy largely dominated the federal government from Franklin Roosevelt through Jimmy Carter. Thus, in the twentieth century Harry Truman, John Kennedy, Lyndon Johnson, and Jimmy Carter were affiliated to varying degrees with that established regime, whereas Dwight Eisenhower, Richard Nixon, and Gerald Ford opposed it. The other major dimension in Skowronek’s 1997 analysis is whether the established regime, to which a president may be affiliated or opposed, is resilient or vulnerable. For example, by the 1970s the New Deal Democratic regime begun by FDR was quite vulnerable. In Truman’s and Kennedy’s time, it was still resilient. Skowronek (1997) describes four kinds of presidential politics. A president affiliated with a resilient regime practices a politics of articulation, where he continues in his own way the politics, policies, and practices of the established regime with a good deal of support. The first President Bush essentially continued the policies of Ronald Reagan. A president affiliated with a vulnerable regime practices a politics of disjunction. Jimmy Carter is a good example. He shared central values with the Democratic politics of the preceding 40 years, but that “big government” approach to governing was perceived as stale and tired, and thus became vulnerable. There are two kinds of politics of opposition. First, there are presidents who oppose resilient regimes. They are said to practice the politics of preemption. Bill Clinton is a good example, as is Richard Nixon. Clinton opposed the resilient Reagan/Gingrich regime that has been in power more or less continuously since 1981. Nixon opposed the still-resilient FDR/Democratic regime. Second, there are presidents who oppose a vulnerable regime. They include Thomas Jefferson, Andrew Jackson, Abraham Lincoln, Franklin Roosevelt, and Ronald Reagan. These presidents were the lucky ones. They had the good fortune to oppose and repudiate a vulnerable regime and to practice a politics of reconstruction. A president who successfully opposes a vulnerable regime has the warrants to institute a new way of doing politics and of governing. He is free to create a new regime that may dominate government for decades to come.

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In comparison to the reconstructive presidents who oppose a vulnerable regime, presidents practicing the other three kinds of politics are much more constrained, some dangerously so. The presidents who practice the politics of articulation— those who are affiliated with a resilient regime, such as the first President Bush—are constrained by their received commitments. They attempt to be orthodox innovators, trying hard to be independent without betraying the basic philosophy with which they are affiliated. Thus, the first President Bush vowed to continue the basic Reagan program, but in a “kinder and gentler” manner. The Republican right wing vilified him when he violated his own orthodox “no new taxes” pledge in 1990. Those who practice the politics of preemption by opposing a resilient regime receive even rougher treatment. Woodrow Wilson, Richard Nixon, and Bill Clinton all fell into this category. They oppose the regime, but because it is resilient, they make certain compromises with it. They are trusted by no one, and are seen as having large character flaws. They are actually or nearly impeached. Thus, Skowronek (1997) writing about Bill Clinton at the start of his second term, noted that “The wellsprings of potential constitutional crisis appear particularly full at this writing, and the prospects of Clinton completing the profile of the typical preemptive leader are all too real. Another collapse of Clinton’s political authority and the election of a Republican committed to moving the nation down a more orthodox road. . .” would confirm the predictions of Skowronek’s book in an emphatic way. Skowronek in effect anticipated something like impeachment a year before the public was introduced to Monica Lewinsky, and a year before most citizens became familiar with Kenneth Starr. Finally, the presidents affiliated with a vulnerable regime, like Herbert Hoover and Jimmy Carter, have a difficult time leading. Their program has little credibility, their warrants for leadership are weak, and they often try to salvage something in their administrations by devising policies and programs that have technical merit and propriety even if they do not really address the major problems of the day. The four kinds of politics tend to follow each other in a largely predictable manner. Presidents like Jimmy Carter, affiliated with a vulnerable regime, are often voted out of office. The politics these presidents represent loses popularity, and a challenger opposed to the vulnerable regime—in Carter’s case, Ronald Reagan— soon defeats the president (or his politics). Reagan then was free to practice the politics of reconstruction. He was free, especially with a Republican senate, and a generally cooperative house of representatives, to institute new approaches and policies, to support new values and perspectives, and, in general, to make a new brand of politics that was still dominant at the time this review was written. Reagan was followed by someone affiliated with the regime Reagan established. As noted above, George H. W. Bush practiced the politics of articulation and tried hard to be the orthodox innovator. At some point, if history repeats itself, the politics of Reagan/Bush/younger Bush will become discredited, and a new regime will come to power in a dramatic way. Skowronek (1997) shows that the following pattern happens regularly: The reconstruction president makes the breakthrough, the

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articulation president oversees some breakup of the new politics, and the disjunction president helplessly watches a breakdown of the regime. Along the way, for a variety of reasons—often the personal appeal of a particular individual—a preemptive president who is opposed to the dominant regime is elected. As noted above, Bill Clinton was such a president, but was succeeded by someone who clearly represented the philosophy of the breakthrough reconstructive leader, Ronald Reagan. Dwight Eisenhower was also a preemptive president, elected because of his personal popularity, and because he never fully disavowed the policies of FDR. He could easily have run as a Democrat: Truman and the Democrats courted him before he identified himself as a Republican. Many leadership theories consider the interaction of personal and situational variables (e.g., Fiedler 1993). Barber’s (1992) approach emphasizes the role of personal variables in presidential effectiveness while giving distinctly less weight to situational factors such as electoral mandate and congressional support. In contrast, Skowronek’s approach seems to attribute the effectiveness of presidential leadership almost entirely to situational variables, that is, whether the regime that one opposes or supports is resilient or vulnerable. However, in discussing particular cases Skowronek (1997) makes clear that a president’s personal qualities, particularly his energy and competence, make all the difference. In the case of presidents opposed to the established regime, he notes that there is a thin “line separating towering achievement from de facto impeachment” (p. 45). That is, some presidents take on the establishment and succeed—through tenacity, cunning, and strong and active leadership—in defeating the regime. Thus, the regime turns out to be vulnerable, although that was not clear at the outset. The president then practices his own independent politics of reconstruction. Andrew Jackson provides a compelling example. Skowronek (1997) begins his chapter on Jackson’s politics by quoting a statement Jackson made to his vice president, Martin Van Buren: “[T]he Bank, Mr. Van Buren, is trying to kill me, but I will kill it!” Skowronek then shows how Jackson defeated an extremely powerful, unrelenting, and unforgiving opposition in the congress and the banking community by actively and forcefully telling a story about what he was trying to do for the American people. In Gardner’s terms, it was a compelling narrative about what Jackson was elected to achieve. Jackson “retained all along a coherent and compelling narrative about his place in history. He was still fighting to rid the government of the corruptions of the recent past. . .. As the establishment threw up its best defenses, Jackson’s repudiative authority became a battering ram for radical change” (Skowronek 1997, p. 143). In contrast to Jackson, who became a reconstructive leader by defeating an established regime that he made vulnerable, Grover Cleveland in 1897 was perfectly positioned to lead a politics of reconstruction. He had been elected as a Democrat and the congressional elections turned out the Republicans, yielding large Democrat majorities in both houses. However, Skowronek points out, Cleveland was simply disinclined to seize the opportunity to push through an aggressive program for his own party. In a short time, the opportunity had passed. Skowronek acknowledges that Cleveland does not fit any of his four categories.

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In sum, Skowronek (1997) paints a very clear picture of the various situational variables that constrain a president’s opportunities for independent and creative leadership. However, he also emphasizes that the president’s own personality will interact with the situation to determine how he manages. In particular, his analysis demonstrates that strong, active, flexible, and adaptive presidents, whose characters are essentially, in Barber’s terms, active-positive, can take advantage of the political opportunity to make their marks on history. One lively tradition in the psychological study of presidential leadership is that of Winter and others exploring motive profiles (Spangler & House 1991, Winter 1987, Winter & Stewart 1977). Although their methodology has generated some controversy, their results have been quite compelling. Using scoring methods developed by David McClelland (1961, 1975), Winter (1987) scored first presidential inaugural addresses (for all presidents through Reagan) for three motives originally studied by Henry Murray (1938): achievement, affiliation, and power. Winter argues that the presidents’ underlying motive profile can be accurately assessed using this method, even though many inaugural addresses are composed in part by speechwriters: “[I]n the end, an inaugural address says almost exactly what the president wants it to say” (Winter 1987, p. 198). This method has tested several interesting hypotheses, and it seems remarkable that the method works as well as it does. Winter (1987) explored which motive profiles predicted presidential appeal and which predicted presidential performance. Interestingly, quite different motive patterns predicted appeal and performance. Presidential appeal was predicted by a high correlation between the president’s needs and the motives that were dominant in the culture according to an analysis of needs expressed in popular novels, children’s readers, and hymns. When there was good fit between what the president wanted and what the public as a whole wanted, the presidents had great appeal, according to measures of electoral success. On the other hand, such congruence did not predict successful presidential performance, according to various measures of presidential greatness, including historians’ judgments of great decisions (cf. Morris 1967). Rather, president/public need congruence was negatively associated with greatness, whereas high power motivation was positively associated with greatness. Among other things, power motivation was associated with “dramatic, crisis-oriented, perhaps confrontational foreign policy, which may end peacefully but which can easily end in war” (Winter 1987, p. 201). Subsequent studies have supported the positive correlation between power motivation and presidential success, especially when the power-motivated president is also “impatient, forceful, radical, demanding, and active” (Spangler & House 1991, p. 451). In contrast, a high affiliation motive is negatively associated with presidential success. This is reminiscent of Freud’s primal horde leader, who was despotic and unconcerned with affection from others. Achievement motivation does predict the successful negotiation of peace treaties, but high achievement-oriented presidents are not happy in their jobs (Simonton 1987). They derive satisfaction from personal achievement and attempt to do too much by


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themselves. They do not enjoy mobilizing and having an impact on others, as do the power-motivated presidents. Clearly, then, individual motives are an important predictor of both effective presidential leadership and a president’s personal appeal in his time. Although there is some controversy about the use of motive scores based on inaugural addresses, their effectiveness in prediction supports their validity. Simonton (1986) has also explored the personal characteristics of individual presidents, using scores from a shortened version of Gough’s Adjective Check List. The scores were based on information about each president, from George Washington through Ronald Reagan, gleaned from biographical reference works. The identities of the presidents to whom the information applied were concealed from raters. Factor analyses of the ratings revealed 14 different personality dimensions, called Moderation, Friendliness, Intellectual Brilliance, Machiavellianism, Poise and Polish, Achievement Drive, Forcefulness, Wit, Physical Attractiveness, Pettiness, Tidiness, Conservatism, Inflexibility, and Pacifism. Two particularly interesting findings derive from this research. First, a hierarchical cluster analysis allowed a grouping of presidents according to the similarity of each one’s personality profile to other profiles. Second, there were insights into the personal characteristics of presidents with high historical greatness ratings. Simonton (1986) notes a number of groupings of interest. John Adams and John Quincy Adams were very similar and in turn were quite comparable to Tyler, Cleveland, and Wilson. “These five presidents were highly idealistic, even selfrighteous, and eminently stubborn” (Simonton 1986, p. 153). Close to them is another group, consisting of Jackson, Theodore Roosevelt, Andrew Johnson, and Truman, and these two groups are similar to another consisting of Polk, Nixon, and Lyndon Johnson. All were somewhat compulsive and hard working, and roughly fit James David Barber’s (1992) active-negative category. Another cluster fits Barber’s passive-positive category, linking Fillmore, Pierce, Arthur, and Harding. They were popular but did not attempt to achieve much. Finally, Barber points out the similarity of Franklin Roosevelt, Kennedy, and to a lesser extent, Reagan. These seem to fit Barber’s active-positive category, though Barber classified Reagan as passive-positive. They were all “attractive, optimistic and persuasive” as well as “energetic and decisive” (p. 153). Interestingly, one president, Ulysses S. Grant, is a complete outlier, with a personality profile unlike any other. How does one explain Grant? His rise to the presidency through military success was by no means unique. Perhaps he had a truly unique set of personal characteristics, or perhaps historians badly misjudged him (Smith 2001). Presidents who were most successful in regard to legislation were “Machiavellian, forceful, moderate, poised and polished, and flexible” (Barber 1992, p. 153). In terms of greatness, there is very little correlation between the 14 dimensions and overall greatness, except for intellectual brilliance, which correlates 0.51 to 0.70, depending on the greatness rating employed. Simonton finds that the four


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most brilliant presidents were Jefferson, Kennedy, Wilson, and J.Q. Adams, and the four least brilliant were Harding, Coolidge, Monroe, and Grant. The correlation is apparent, but not overwhelming. Beyond his study of presidential personality profiles, Simonton (1987) has written a comprehensive account of presidential success that makes important contributions. First, it distinguishes various measures of presidential success, including electoral success, popular approval during presidential terms, actual performance as measured by specific domestic and foreign policy achievements, and, finally, the judgments of historical greatness. Second, it thoroughly examines empirical data assessing the presidents’ successes with respect to these standards. Third, it provides an overall interpretive account of the wealth of data brought to bear in assessing the presidents. Consistent with the leadership literature in general, Simonton points to personal factors, contextual factors, and interactions that account for presidential success. Simonton bases the personal characteristics he cites in large measure on his own research. The characteristics include a high need for power, Machiavellianism and forcefulness, a low need for affiliation or intimacy, and intellectual brilliance. The association of these variables with success depends on the measure of success, but these attributes are clearly important qualities. Context matters greatly—more, Simonton argues, than individual traits. The state of the economy and whether there is a war are both important—peace and prosperity during a president’s time account for a great deal in his success. As Barber points out, the president’s support in congress is important, certainly in legislative success. An interaction of his personal qualities with situational factors over which he may have little control likewise affects a president’s standing. For example, the data Simonton (1987) reviews suggest that firstborns may perform better during international crises, whereas laterborns may do better in placid times. Good timing and good luck account for much of the success. Of particular interest, Simonton also points out that “greatness can be predicted by how long the president served in office, the number of years he served as wartime commander-in-chief, whether he was assassinated, whether his administration was plagued by a scandal, and whether the president had been a national hero previous to running for office” (Simonton 1986, p. 156). All of these situational factors, including assassination (presidents high in power motivation are more often the target of assassins), are related to personal variables, but most of them are influenced by external variables beyond a president’s control. Finally, Simonton explains that a president’s standing on all the measures of success can be best understood in terms of an attributional model that includes leadership schemas, especially schemas that apply particularly to presidents. As briefly discussed previously in the chapter, one schema specifies that presidents “display drive, forcefulness, firmness, determination, courage, and decisiveness (strength); initiative, persuasiveness, enthusiasm, extroversion, and mental and physical alertness (activity); and a sincere interest in people, diplomacy and


WHY PRESIDENTS SUCCEED

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consideration, and good moral judgment (goodness) (Merenda 1964)” (Simonton 1987, p. 238). However, the schema is abstract and represents “an ideal or archetype having transhistorical, even cross cultural, relevance” (p. 239). (Certainly, such a schema resembles Freud’s archetype of the leader in the primal horde.) Whether a president is viewed as fitting the presidential schema by the public or by historians is influenced by the range of variables that psychologists working in the social cognition tradition have long since identified as biasing person perception: information availability, the salience and hedonic relevance of that information, and attribution biases. Much of the work reviewed in this chapter suggests that situational variables and variables that are not fully understood account for effective presidential leadership. But can anything be suggested about the personal qualities of successful presidents? The motive and personality studies of Winter (1987) and Simonton (1987) add intellectual brilliance and power motivation to a cluster of personal qualities associated with both Barber’s (1992) “active-positive character” category and Skowronek’s “reconstructive politics” category to suggest a set of personal characteristics important to successful presidents. Such a set of personal characteristics can be compared to the personal characteristics of those who have been considered successful, and such a comparison, in turn, helps in the assessment of whether the literature as a whole usefully points toward personal qualities associated with presidential success. First, it must be acknowledged that judging the most effective or successful presidents is a daunting task. Some of the measures considered by Simonton, e.g., electoral success and popular support, are highly reliable, but do they really reveal presidential success? As Simonton points out, presidential success, like that of all leaders, is in the eye of beholders. The public frequently looks to historians as the most credible beholders, although historians’ ratings sometimes differ substantially from those of the public as a whole, as in the case of John Kennedy. He is consistently rated more highly by the public than by historians. Moreover, the judgments of history are fickle. New books on John Adams (McCullough 2001), Theodore Roosevelt (Morris 2001), Kennedy (Dallek 2003), and Lyndon Johnson (Caro 2002) differ from earlier treatments and are likely to produce changes in historians’ ratings. Nevertheless, using historians’ ratings, which reflect their judgments and their assessments of the public’s judgments, seems like the most promising assessment approach. In 1996, Arthur M. Schlesinger Jr. conducted a poll of 32 presidential historians. Each historian rated all but two presidents who had served very short terms. The poll identified three “great” presidents—Washington, Lincoln, and FDR, and six “near-greats”—Jefferson, Jackson, Polk, Theodore Roosevelt, Wilson, and Truman. What connections can be drawn between these top nine presidents and the personal factors identified by scholars of presidential leadership? In general, the top nine do support the value of scholarship on presidential leadership, and of the larger body of more general leadership scholarship. In light

CONCLUSION

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of what has been written about leadership and the presidency, the list of greats and near-greats is no surprise. Situational variables, or context, loom large. Four of the nine—Jefferson, Jackson, Lincoln, and FDR—practiced what Skowronek called reconstructive politics. They were fortunate enough to oppose a vulnerable regime. However, it should not be forgotten that Jackson, and to some extent Jefferson, had a large hand in making the regimes they opposed vulnerable. Their activity level was highly important. Also, five of the nine—Polk, Lincoln, Wilson, FDR, and Truman—were wartime presidents. Times of crisis, particularly war, offer presidents the opportunity to lead forcefully. The public expects and accepts strong leadership in such times. The importance of power motivation seems clear when one considers that six of the nine—Jackson, Polk, both Roosevelts, Wilson, and Truman—had high need for power, according to Winter (1987), or were high on the dimension of forcefulness, according to Simonton (1987). Also, according to Barber (1992), three were activepositive—Jefferson, FDR, and Truman. Barber doesn’t classify all the presidents, but it would be hard to deny Theodore Roosevelt the active-positive label: “No President has ever enjoyed himself as much as I have enjoyed myself, and for the matter of that I do not know any man of my age who has had as good a time” (cf. Simonton 1987, p. 231). Finally, several of these presidents were extremely intellectual. Jefferson, Wilson, and both Roosevelts are among the highest on Simonton’s Intellectual Brilliance factor. Lincoln’s law career and presidential speeches certainly reveal an unusually keen intellect.

BEYOND PRESIDENTIAL LEADERSHIP Conceptualizations from presidential scholarship can be used to broaden our general understanding of leadership. Barber’s (1992) theory contains a number of helpful ideas. First, the personality and behavior of a leader always intersect with the requirements of a given situation. This is not a new idea, but Barber’s concept of shifting climates of expectation—from action to legitimacy to reassurance and back to action again—has general applicability to understanding what followers want and expect from leaders. Any of these three concerns may be relevant in education, business, the military, sports, or the arts. Different leaders can address these needs or expectations to varying degrees, depending on their style. Second, the behavior of all leaders needs to be understood in terms of both the situation they face and their unique life history. Basic character and temperament develop in childhood and combine with worldviews in adolescence. Then character and worldviews shape a specific behavioral style that combines different elements of working alone, face-to-face interaction with other people in the group, and communication to broader audiences of followers or potential followers. Underlying character, worldview, and style is a basic sense of self, secure and positive, or uncertain and troubled. This basic aspect of self determines how adaptively a leader can deal with followers and the challenges the group faces. Barber’s emphasis

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on the developmental continuity of personality and its interaction with external challenges is relevant to understanding leadership in many situations. Stephen Skowronek’s (1997, 1999) writings on presidential politics offer an unusually rich perspective on general issues of leadership. Some leaders are fortunate enough to successfully challenge a regime that is inherently vulnerable or that is made vulnerable by the new leader’s vigorous opposition. When the old ways of doing things have passed by, the new leader has the opportunity, for a time, to reorient the group and lead it in a very different direction. Other leaders (the preemptives) may be frustrated in trying to change ways of thinking and acting that are too deeply ingrained to be changed or defeated. Still others are limited by the conservatism of the ways of doing things that both the group and they themselves are identified with. The opposition leaders are the most interesting. The reconstructive ones, such as Martin Luther King, successfully initiate entirely new modes of action by which the group conducts itself. They have the skill and force to overcome older models of belief and action. Others, such as Pope John XXIII, are partially and temporarily successful because of their uniquely compelling vision and action, but are not able to produce lasting change in their preemptive opposition to a resilient regime. Winter’s exploration of motive profiles has direct applicability to a wide range of leadership situations. One of the most fundamental contributions is the distinction between the leadership implications of the need for achievement and the need for power. The need for achievement drives an individual to attempt to perform exceptionally well against a relevant standard of excellence or to solve a particular problem. Wilbur Wright and (to a lesser extent) his brother Orville serve as models of achievement motivation (cf. Tobin 2003). For leaders in nontechnical leadership positions, this motive may be irrelevant or even counterproductive. In contrast, a high need for power, a need to have an emotional impact on others through vigorous action, and to gain reputation and status, can energize the behavior that leads groups effectively (Spangler & House 1991). The need to be personally productive and excellent serves leaders less well than the need for action and impact. In sum, literature directed specifically to understanding presidential leadership has implications for leadership in many other domains. And clearly, the scholarship on presidential leadership, particularly in the case of Simonton (1986, 1987) and Winter (1987), has been usefully informed by the general psychological leadership literature.

CONCLUSION Taken together the general psychological leadership literature and the scholarship on presidential leadership have lent a wealth of insights into the personal qualities that contribute to an effective presidency, and the attributes that can undermine successful presidential leadership. It is also apparent that success depends on personal qualities interacting with contextual variables in a favorable way, and that much that affects presidential success is simply a matter of good fortune.

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Despite the indubitable roles of chance and context, it does seem clear that effective presidential leadership in the past has come from men with energy, intelligence, vision, articulacy, optimism, and commitment. Our research suggests that men and women with these qualities can also become good presidents in the future. The Annual Review of Psychology is online at http://psych.annualreviews.org


LITERATURE CITED Bales RF. 1958. Task roles and social roles in problem solving groups. In Readings in Social Psychology, ed. EF Maccoby, TM Newcomb, EL Hartley pp. 437–47. New York: Holt, Rinehart & Winston Barber JD. 1992. The Presidential Character: Predicting Performance in the White House. Englewood Cliffs, NJ: Prentice-Hall Bass BM. 1997. Does the transactionaltransformational leadership paradigm transcend organizational and national boundaries? Am. Psychol. 52:130–39 Bass BM, Avolio BJ. 1993. Transformational leadership: a response to critiques. See Chemers & Ayman 1993, 3:49–80 Brookhiser R. 1996. A man on horseback. Atl. Mon. 227:50–64 Burns JM. 1978. Leadership. New York: Harper & Row Burns JM, Sorenson GJ. 2000. Dead Center: Clinton-Gore Leadership and the Perils of Moderation. New York: Scribner Burns JM. 2003. Transformational Leadership. New York: Atlantic Monthly Press Calder BJ. 1977. An attribution theory of leadership. In New Directions in Organizational Behavior, ed. B Staw, G Salancik, pp. 179– 204. Chicago: St. Clair Press Caro RA. 2002. The Years of Lyndon Johnson: Master of the Senate. New York: Knopf Chemers MM, Ayman R, eds. 1993. Leadership Theory and Research. San Diego, CA: Academic Dallek R. 2003. An Unfinished Life: John F. Kennedy, 1917–1963. Boston: Little, Brown Ellis RJ. 2003. The joy of power: changing conceptions of the presidential office. Presid. Stud. Q. 33:269–90

Fiedler FE. 1993. The leadership situation and the black box in contingency theories. See Chemers & Ayman 1993, 1:1–28 Fiske ST. 1993. Controlling other people: the impact of power on stereotyping. Am. Psychol. 48:621–28 Freud S. 1920. Group psychology and the analysis of the ego. In The Standard Edition of the Complete Works of Sigmund Freud, Vol. 28: Beyond the Pleasure Principle, Group Psychology and Other Works, ed. J Strachey, pp. 65–143. London: Hogarth Gardner H. 1995. Leading Minds: An Anatomy of Leadership. New York: Basic Books Heifetz RA. 1994. Leadership Without Easy Answers. Cambridge, MA: Harvard Univ. Press Hogan R, Curphy GJ, Hogan J. 1994. What we know about leadership: effectiveness and personality. Am. Psychol. 49:493– 504 Hogg MA. 2001. A social identity theory of leadership. Personal. Soc. Psychol. Rev. 5:184–200 Hogg MA. 2003. Social identity and leadership processes in groups. In Advances in Experimental Social Psychology, ed. MP Zanna, 35:1–52. San Diego, CA: Academic Hollander EP. 1993. Legitimacy, power, and influence: a perspective on relational features of leadership. See Chemers & Ayman 1993, 2:29–48 Hollander EP, Julian JW. 1969. Contemporary trends in the analysis of leadership processes. Psychol. Bull. 71:387–91 House RJ, Shamir B. 1993. Toward the integration of transformational, charismatic, and visionary theories. See Chemers & Ayman 1993, 4:81–107

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PERSONNEL PSYCHOLOGY: Performance Evaluation and Pay for Performance


Sara L. Rynes Department of Management & Organizations, Tippie College of Business, University of Iowa, Iowa City, Iowa 52242; email: [email protected]

Barry Gerhart Graduate School of Business, University of Wisconsin, Madison, Wisconsin 53706; email: [email protected]

Laura Parks Tippie College of Business, University of Iowa, Iowa City, Iowa 52242; email: [email protected]

Key Words appraisal, pay, motivation, incentives, feedback ■ Abstract Although there is a voluminous psychological literature on performance evaluation (PE), surprisingly little of this research examines the consequences of linking pay to evaluated performance in work settings. Rather, PE research has been dominated by cognitive processing, measurement, and construct validity issues. At the same time, a large literature on pay-for-performance (PFP) linkages does exist, but most of it has been conducted in disciplines other than psychology. We think this pattern should change. To this end, we briefly trace the origins of the general separation of PE research from PFP research in psychology. From there, we review recent research on the relationship between PE and performance improvement, particularly with respect to multisource or 360-degree evaluation. We then turn to research on various PFP systems, such as merit pay and individual and group incentives. We conclude with suggestions as to how psychological research can make useful contributions to knowledge of PE, PFP, and performance improvement.

CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ORIGINS OF THE SEPARATION OF PERFORMANCE EVALUATION AND PAY IN PSYCHOLOGICAL RESEARCH . . . . . . . . . . . . . . . Motivation Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Empirical Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meyer, Kay, and French’s “Split Roles” in Performance Evaluation . . . . . . . . . . . . Kluger & DeNisi’s Feedback Intervention Theory . . . . . . . . . . . . . . . . . . . . . . . . . . Multisource (360-Degree) Performance Evaluation Research . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0571$14.00

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SUMMARY OF MOTIVATION THEORY AND PERFORMANCE EVALUATION RESEARCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . PAY FOR PERFORMANCE: EVIDENCE AND PROCESSES . . . . . . . . . . . . . . . . . CHOOSING A PERFORMANCE MEASURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Behavior-Based (Subjective) versus Results-Based (Objective) Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Incentive Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual versus Group (or Collective) Performance . . . . . . . . . . . . . . . . . . . . . . . . EMPIRICAL EVIDENCE ON VARIOUS PAY-FOR-PERFORMANCE PROGRAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual Incentives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Merit Pay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Profit Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stock Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gain Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moderating Variables in Group Pay-For-Performance Plans . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FUTURE RESEARCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moving to the Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intervening Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sorting and Individual Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360-Degree or Multisource Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . Merit Pay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONCLUDING REMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INTRODUCTION “The one issue that should be considered by all organization theories is the relationship between pay and performance” (Lawler 1971, p. 273). The vast majority of organizations (at least in the United States) claim to use pay-for-performance (PFP) systems, and most U.S. workers say they want to be paid on the basis of performance (LeBlanc & Mulvey 1998, U.S. Bureau of National Affairs 1988). In addition, meta-analytic results show that increasing the connection between performance and pay can be very effective for improving performance. After conducting the first meta-analysis comparing alternative motivational interventions, Locke et al. (1980) concluded, “Money is the crucial incentive . . . no other incentive or motivational technique comes even close to money with respect to its instrumental value” (p. 379). Subsequent meta-analyses have tended to support this conclusion (e.g., Jenkins et al. 1998). Given the importance of pay and performance to employers and employees as well as the potential for well-designed PFP systems to improve performance, one would think that research examining PFP would be plentiful in psychology. However, this has not been the case, particularly in recent years. Although there is a voluminous psychological literature on performance evaluation (PE), surprisingly little of this research examines the consequences of linking pay to performance in

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work settings (Arvey & Murphy 1998, Smither et al. 2004). Conversely, although there is a large literature on the effects of PFP, most of it has been produced in disciplines other than psychology. We would like to see this pattern change. We believe that psychology has much to contribute to some of the major practical issues concerning pay and performance in work settings. For example, academics in other disciplines often assume a great deal about the psychological mechanisms (perception, evaluation, goal choice) that explain employee reactions to pay plans. Greater attention to psychological theories and development of new research streams would put these assumptions to the test and also help identify reasons why plans do not always work as intended. However, in order for psychology to contribute, it is important to understand the current state of findings from both of the two distinct literatures mentioned above: research on PE (particularly with respect to PE’s role in improving performance) and research on the performance effects of PFP. To this end, we briefly trace the origins of the separation of PE research from pay research in psychology.

ORIGINS OF THE SEPARATION OF PERFORMANCE EVALUATION AND PAY IN PSYCHOLOGICAL RESEARCH Although organizations conduct performance evaluations for many reasons, the most basic one is to improve performance (Murphy & Cleveland 1995). Performance, in turn, is believed to be a joint function of both motivation and ability (Campbell & Pritchard 1976, Vroom 1964). Consistent with this notion, PE is believed to be capable of improving performance in two ways: through developmental feedback (directed primarily at improving ability to perform), and through administrative decisions that link evaluated performance to organizational rewards and punishments such as pay, promotion, or discharge (aimed primarily at enhancing motivation). In practice, however, psychological research on PE has focused far more heavily on the first route to performance improvement (i.e., feedback) than the latter (rewards). Indeed, the relative neglect of the administrative function of PE by research psychologists has become so notable in recent years that the most recent Annual Review of Psychology chapter on PE (Arvey & Murphy 1998) did not even mention research linking PE to pay or other rewards. This relative neglect of the administrative functions of PE is unfortunate because performance is likely to improve most when employees both (a) receive information that will enable them to perform better and (b) have an incentive to act on that information. As such, we begin by examining the roots of the split between PE and PFP in psychological research, which appears to have begun with certain motivational theories that originated around the middle of the last century.

Motivation Theories Typically, Annual Review chapters do not review much old research. However, it is important for a new generation of readers to understand the origins of the curious

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split between PE and PFP in psychological research, a split that occurred despite the fact that the two practices are integrally related in most work organizations. At least three historical theories of motivation have dampened psychologists’ interest in investigating the links between performance, PE, and pay: Maslow’s hierarchy of needs theory, Herzberg’s motivator-hygiene theory, and Deci & Ryan’s cognitive evaluation theory. A major implication of all three theories is that monetary rewards are not a major determinant of work motivation, except perhaps for employees at low income levels (Maslow 1943). Maslow’s need hierarchy theory (1943) posited that human needs are arranged in a hierarchy of prepotency that is biological or instinctive in nature (Miner 1980). According to the theory, a need that is deprived acts as a primary motivator (i.e., “monopolizes consciousness”), while a need that is satisfied has less motivational impact. At the bottom of Maslow’s hierarchy are physiological (food, sleep) and safety (e.g., housing) needs—precisely those needs posited to be most effectively satisfied by money. However, once these basic needs are satisfied, individuals are hypothesized to focus on “higher” needs, such as love, esteem (including achievement, independence, confidence), and self-actualization (“to become everything that one is capable of becoming”) (Maslow 1943, p. 382). Maslow hypothesized that these higher-order needs are more likely to be met through engagement in meaningful work than through monetary rewards. Although Maslow did not view need satisfaction as an all-or-nothing process (i.e., a person could simultaneously be motivated to varying degrees by needs at different levels), the expectation was that on average, all people progress through the hierarchy in roughly the same manner, and that as people move up the hierarchy, pay becomes less important. The second theory, Herzberg’s motivation-hygiene theory, focused on identifying factors that contribute to either satisfaction, or dissatisfaction, at work (Herzberg et al. 1957). Herzberg viewed satisfaction and dissatisfaction not as opposite ends of the same continuum, but rather as two distinct constructs: “The factors involved in producing job satisfaction (and motivation) are separate and distinct from the factors that lead to job dissatisfaction” (p. 9). Like Maslow, Herzberg saw hygienic needs as being driven by people’s “animal nature” (p. 9). In contrast, motivational factors or factors associated with the work itself were posited to “relate to that unique human characteristic, the ability to achieve and, through achievement, to experience psychological growth” (p. 9). Most importantly for present purposes, Herzberg posited that money was more likely to be a “hygienic” factor—i.e., one capable of causing or reducing dissatisfaction—than a satisfying or motivating one. Thus, Herzberg believed that money played a role in creating or reducing dissatisfaction, but not in contributing to satisfaction or motivation. The third theory to challenge the role of money in motivation is Deci & Ryan’s (1985) cognitive evaluation theory (CET). Although the theory is somewhat more complicated than described here, in general, CET argues that placing strong

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emphasis on monetary rewards is likely to decrease people’s intrinsic interest (i.e., interest in the work itself), thus dampening a potentially powerful alternative source of motivation. Deci & Ryan (1985) describe intrinsic motivation as “based in the innate, organismic needs for competence and self-determination” (p. 33), and argue that it occurs in its purest form when “a person does an activity in the absence of a reward contingency or control” (p. 35). Deci and Ryan argue that when effort is exerted in exchange for pay, pay takes on a controlling aspect that threatens the individual’s need for self-determination. According to Ryan et al. (1983), if one must perform a task “in some particular way, at some particular time, or in some particular place. . .to receive the reward, the reward tends to be experienced as controlling” (p. 738). This perception of being “controlled” is assumed to be demotivational and to work against the potential incentive effects of extrinsic rewards (i.e., rewards not associated with the work itself, such as pay, candy, trinkets, or praise). For the most part, Deci and his colleagues have tended to argue that the net effect of monetary rewards on intrinsic interest is generally negative. However, Ryan et al. (1983) propose that the overall effects of pay on intrinsic interest depend on the information embedded in pay outcomes. Specifically, they argue that intrinsic interest is likely to be impaired to the extent that information about rewards is seen to be controlling. However, to the extent that pay provides meaningful information regarding self-competence in contexts where people have discretion in choosing how to perform tasks, then monetary rewards can actually increase intrinsic interest. Thus, in statements of CET theory that are more complex, the net effect of rewards on intrinsic interest depends on the relative impact of monetary rewards on perceived control versus perceived self-competence.

Empirical Evidence As we show below, extensive prior reviews of PFP research suggest that pay is far more motivational than assumed by Maslow, Herzberg, or Deci & Ryan (Gerhart & Rynes 2003). Thus, it is important to evaluate the cumulative evidence with respect to each of these theories. With respect to Maslow’s hierarchy, reviews of the evidence suggest that pay is important for fulfilling higher-order needs, as well as lower-order ones. Lawler’s (1971) review of then-existing evidence concluded, “Money can buy food, security, social relations, and esteem, and to some extent, it can satisfy self-actualization needs” (p. 26), and subsequent research has continued to support this conclusion (e.g., Frank 1985). Tests of Herzberg’s research also challenge the idea that pay cannot satisfy higher-order needs. Indeed, Herzberg’s 1987 summary of his own research (12 studies, 1685 subjects) showed that pay was mentioned as a motivator nearly as often as it was a dissatisfier. Pay was particularly likely to be mentioned as a motivator when it was seen as a form of recognition. According to Lawler, “the tendency for pay to be mentioned as a contributor to satisfaction as often

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as it is mentioned as being unfairly low or dissatisfying has appeared in most of the studies that have attempted to replicate or test Herzberg’s theory” (1971, pp. 32–33). Moreover, Herzberg’s conclusions are even less well supported when methodologies other than Herzberg’s own (i.e., storytelling critical incidents) are used to assess the proposed dichotomy (Campbell & Pritchard 1976). Evidence regarding CET’s presumption of a negative relationship between rewards and intrinsic interest is still a matter of debate, with support for the theory being mixed at best. For example, a meta-analysis by Eisenberger & Cameron (1996) examined 83 studies that compared a rewarded group (verbal or tangible rewards) with a no-reward control group on two traditional measures of intrinsic interest: amount of free time devoted to a task when the tangible or verbal reward is subsequently withdrawn (44 effect sizes) or self-reported attitude toward the task (e.g., interest, enjoyment, or satisfaction; 39 effect sizes). Across studies, the mean correlation between reward condition and free time was (only) −0.04, while the correlation between reward condition and task-related attitudes was actually positive (ρ = 0.14) rather than negative (as would be predicted by CET). Therefore, the detrimental effects of rewards were generally not supported in their meta-analysis, even using the free time measure. Subsequently, Deci et al. (1999) conducted a meta-analysis on the same issues addressed by Eisenberger & Cameron (1996). Although Deci et al. found more overall support for the detrimental effects of extrinsic rewards than did Eisenberger & Cameron, even they found “no effect for performance-contingent rewards” on the attitude measure of intrinsic interest (p. 644). In addition, Deci et al. found that extrinsic rewards were “more detrimental for children than for college students” (p. 656). This is a very important point because in the workplace, it is the motivation of adults (rather than of elementary school-aged children) that is at issue. This last point raises a more general concern about the generalizability of CETinspired research, which is that the vast majority of this research has been conducted in the laboratory under conditions that differ substantially from real work settings. For example, the monetary rewards in CET lab experiments generally fall in the sub-$10 range. In contrast, compensation for full-time work in real organizations ranges from the tens to hundreds of thousands of dollars (or more). Similarly, lab studies on motivation typically observe performance over trials of a few hours or less. In contrast, most people spend approximately two thousand hours at work each year. Finally, most subjects in CET research have been school-aged children rather than working adults. Given these substantial differences in background conditions, the effects of monetary rewards might play out considerably differently in the workplace than they do in the educational laboratory. In a field examination of CET theory, Fang & Gerhart (2000) investigated the idea that Deci & Ryan (1985) may have focused too much on the potentially controlling aspects of rewards and not enough on the informational aspect. Consistent with this hypothesis, they found that employees covered by PFP plans reported higher intrinsic interest than employees not covered by such plans. Although causality cannot be demonstrated, this result is consistent with the fact that

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people frequently report pay as a motivator when it is perceived as recognition for good performance (Lawler 1971). It is also consistent with the positive correlation between intrinsic and extrinsic motivation reported by Amabile and her colleagues (1996), as well as with meta-analytic evidence that is even more recent (Cameron et al. 2001). In summary, although the ideas developed by Maslow and Herzberg have had considerable appeal to many people, the prevailing view in the academic literature is that the specific predictions of these theories are not supported by empirical evidence (Kanfer 1990). Although Deci and Ryan’s ideas have received stronger (albeit mixed) support, they remain largely untested in ongoing work settings, which differ in important ways from the laboratory settings in which they have been investigated. Nevertheless, it would be a mistake to underestimate the influence these theories have had on both research and practice. For example, in research, we probably owe the dearth of psychological research on PFP primarily to these theories. This outcome remains a serious concern, as the influence of CET theory seems to be spreading toward human resources and industrial/organizational psychology research, rather than receding (e.g., Sheldon et al. 2003). In addition, these theories’ influence on practice also remains substantial. For example, in the widely read Harvard Business Review, Stanford professor Jeff Pfeffer (1998a, p. 112) called the idea that people work for money a “myth.” In addition, he asserted, “a substantial body of research has demonstrated, both in experimental and field settings, that large external rewards can actually undermine intrinsic motivation” (1998b, p. 216). Kohn (1993) has made similar claims.

Meyer, Kay, and French’s “Split Roles” in Performance Evaluation In addition to the three motivation theories reviewed above, a final development that contributed to the separation of PE and pay research in psychology was Meyer et al.’s (1965) notion of “split roles” for PE. As mentioned earlier, PE has two distinct functions: to develop employees through such mechanisms as feedback and goal setting, and to evaluate employees for purposes of making administrative decisions (e.g., pay increases or promotions; Murphy & Cleveland 1995). In a seminal article that had widespread impact on both researchers and practitioners, Meyer and colleagues (1965) argued that these two functions of performance appraisal should be kept completely separate from one another—that is, evaluations of performance and discussions of pay should be separated in time from discussions of how to improve (or “develop”) performance. This recommendation was based on the supposedly distracting and demoralizing aspects of criticism, as well as the authoritarian nature of evaluation: “Interviews designed primarily to improve a person’s performance should not at the same time weigh his or her salary or promotion in the balance. . .. It seems foolish to have a manager serving the self-conflicting role as counselor (helping someone improve performance)

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when, at the same time, he or she is presiding as a judge over the same employee’s salary action” (Meyer et al. 1989, p. 26). We are aware of three field studies that attempted to test this assertion, and none obtained supportive results. Dorfman et al. (1986) found that discussion of pay and advancement during the PE session led to higher employee satisfaction with the PE process. Prince & Lawler (1986) found that discussion of salary during PE had either no impact or a slightly positive impact on PE processes (e.g., employee participation) and outcomes (e.g., satisfaction with the PE). Boswell & Boudreau (2002) found no difference in employee satisfaction with PE, satisfaction with supervisor, or awareness of developmental opportunities between development-only PEs and those where development was combined with evaluation for administrative and reward purposes. Moreover, they found that employees reported higher intentions to use developmental feedback when developmental feedback was combined with evaluation. Another of Meyer et al.’s assertions was that criticism has a negative effect on goal achievement, while praise has little effect in either direction. Two bodies of empirical research bear on these assertions: a meta-analysis of (mostly laboratory) studies of feedback interventions (Kluger & DeNisi 1996), and recent field research pertaining to multisource feedback. We begin with the meta-analysis.

Kluger & DeNisi’s Feedback Intervention Theory Kluger & DeNisi (1996) conducted an extensive historical review and metaanalysis (607 effect sizes, reflecting 23,663 observations) that reached some interesting conclusions about the effects of feedback interventions (FIs) on performance. Their findings suggested that, on average, FIs (i.e., providing information about performance) improved subsequent performance (d = 0.41). One of the effects they proposed is that following an FI, “effort is increased if the feedback sign is negative, and decreased or maintained if the sign is positive” (p. 263). In other words, people work harder when they find out that they are not performing up to expectation and relax when feedback suggests that performance is adequate, consistent with cybernetic or self-regulating theories of motivation (Kanfer 1990). On the other hand, Kluger & DeNisi also found that despite the generally positive effect of feedback, in more than one third of the cases, performance decreased following feedback. In light of the nonuniform effects of feedback on performance, Kluger & DeNisi proposed and tested a feedback intervention theory (FIT) suggesting that different types of performance feedback differentially affect people’s locus of attention among three hierarchically organized levels of control: task learning, task motivation, and meta-task processes (including focus on the self). They hypothesized, and found, that the effectiveness of feedback decreases as it causes attention to move up the hierarchy—i.e., closer to the self and away from the task. For example, feedback specifically designed to be demoralizing was more likely than other kinds of feedback to have detrimental effects (p. 273). Thus, Meyer et al.’s contention that negative feedback can impair performance

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appears to be true, at least when the feedback draws attention away from the task and toward the person him- or herself. On the other hand, negative feedback does not always, or perhaps even “usually,” have that effect. For example, FIs that focused on the task or on task learning did not have such effects. Moreover, the overall test of differences for feedback direction (i.e., positive versus negative) was not significant. Thus, the common advice given to practicing managers to “criticize the work, but not the person” appears to be a sound one. However, Meyer et al.’s assertion that negative feedback usually causes a decline in performance does not appear to be supported. This is also the case in field studies of PE, as described below.

Multisource (360-Degree) Performance Evaluation Research The vast majority of studies examined by Kluger & DeNisi were conducted in the laboratory and used student subjects. More recently, there have been a number of studies in which multisource or 360-degree feedback (from peers, subordinates, and customers as well as supervisors) has been given in ongoing work environments. Multisource feedback is seen as potentially more useful than supervisoronly evaluations in modern-day work environments, which increasingly incorporate team-based production, organizational structures that are more flat, and work organized around horizontal rather than vertical flows. In addition, because 360degree feedback is gathered from multiple individuals, feedback reliability and validity may be substantially improved over the typical supervisor-only evaluation (Mount et al. 1998). Results across these studies show that individuals generally improve their performance following 360-degree feedback, at least in terms of subsequent ratings by the same observers (e.g., Hazucha et al. 1993, Reilly et al. 1996, Smither et al. 1995; for an exception see Atwater et al. 2000). However, average effect sizes generally appear to be modest. For example, a recent unpublished meta-analysis by Smither and colleagues (2004) estimated that unweighted ds for feedback from direct reports, peers, and supervisors were 0.24, 0.12, and 0.14, respectively, with weighted ds being even smaller. However, most studies also show wide variations in improvement across individuals. As such, it is important to examine personal or organizational factors that may moderate responses to multisource feedback. In light of Meyer et al.’s hypotheses, of particular interest here is the variable most likely to convey either positive or negative information to the recipient—i.e., the degree of congruence between self- and observer ratings. In general, studies have found that individuals who overrate their own performance (as compared to observer ratings) tend to improve most following feedback (Atwater 1995, Johnson & Ferstl 1999, Smither et al. 1995, Smither et al. 2004), even after taking into account initial performance and the possibility of regression to the mean. These findings are consistent with Kluger & DeNisi’s (1996) speculation that, in general, effort increases after receipt of negative feedback.

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Thus, there is evidence that the degree of incongruence between self- and observer ratings impacts subsequent changes in performance. The more positive improvement for overraters (i.e., those who receive negative feedback) is once again consistent with cybernetic theories of motivation (Kanfer 1990) and Kluger & DeNisi’s (1996) FIT theory, which posits that only “feedback-standard gaps receive attention and foster change.” In summary, laboratory research on feedback interventions and field research on split-role and multisource evaluations suggests that (a) there is no general decrement to satisfaction, motivation, or performance when evaluative aspects of PE are combined with developmental ones; (b) on average, FIs produce positive performance improvements, although these appear to be smaller in field than in laboratory settings; (c) there are substantial situational moderators of the feedbacksubsequent performance relationship, with stronger performance gains tending to follow the receipt of negative feedback; but (d) this last finding may not hold in cases where the feedback causes the individual to focus on the self or other meta-processes, rather than on the task or task learning. Unfortunately, 360-degree field research has not yet looked directly at this last question (i.e., focus of attention). However, field research has produced evidence that managers’ ratings improve more when they seek input from others (e.g., Hazucha et al. 1993), meet with subordinates to discuss feedback results (Walker & Smither 1999), or set improvement goals following feedback (Atwater et al. 2000). We also hypothesize that the consequences of substandard performance will act as a moderator. For example, in organizations where substandard performers are terminated or encouraged to leave, negative feedback would seem likely to have a stronger effect. We would expect this effect to show up either through improved performance (an incentive effect) or through turnover (e.g., quits or firing), either of which might be functional from the organization’s point of view in improving average employee performance.

SUMMARY OF MOTIVATION THEORY AND PERFORMANCE EVALUATION RESEARCH As we have shown, a good deal of work in psychology has focused on testing hypotheses that negative evaluative feedback and extrinsic rewards (including money) are largely detrimental to motivation and performance. For the most part, empirical evidence on both points fails to support these hypotheses, particularly in work settings. Before moving to research on PFP, it should be noted that some psychologists have taken strong positions supporting the importance of pay as a motivator. For example, Lawler (1971) reviewed empirical evidence on pay, finding that it was rated more highly than Herzberg et al. (1957) had reported years earlier. In addition, Lawler developed a process model of motivation suggesting that pay should in fact be quite an important motivator because of its instrumentality for obtaining so

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many other outcomes. Similarly, Locke has produced both empirical (Locke et al. 1980) and conceptual work (e.g., Bartol & Locke 2000) suggesting that pay has considerable effectiveness as a motivator. Still, the “taint” around evaluative appraisals and using money as a reward lingers, and continues to discourage the study of relationships between PE, PFP, and performance within psychology. As such, we turn now to other literatures— particularly management, economics, and finance—that have not shied away from examining relationships between performance-based rewards and subsequent performance. While the PE literature tends to focus on the developmental and ability side of the performance equation, the PFP literature tends to focus on the evaluative and motivational side.

PAY FOR PERFORMANCE: EVIDENCE AND PROCESSES Despite concerns and arguments against the use of PFP, most organizations use it in some form. Moreover, this use appears to be growing, rather than declining (R. Heneman et al. 2000). In addition, our reading of the evidence on PFP is generally positive. To be sure, there are some very important caveats: pay is not the only important motivator in organizations, and PFP programs can yield serious, unintended negative results. Nevertheless, it can also deliver powerful improvements in performance. This combination of upside potential and downside risk is part of what makes PFP so interesting. The clearest evidence of the impact of PFP comes from meta-analytic reviews of research conducted in actual work settings. In the first such study, Locke et al. (1980) found that the introduction of individual pay incentives increased productivity by an average of 30%. In contrast, Locke and colleagues found that job enrichment produced increases of 9%–17%, whereas enhanced employee participation yielded less than 1%. This meta-analysis is particularly compelling because it included only studies that used either control groups or before-and-after designs in real work settings, and measured performance via “hard” criteria (e.g., physical output) rather than supervisory ratings. Subsequent meta-analyses have yielded similar results regarding the substantial impact of individual pay incentive systems (e.g., Guzzo et al. 1985, Jenkins et al. 1998, Judiesch 1994). What processes are responsible for these effects? In addition to the theories discussed earlier (e.g., Deci & Ryan 1985, Herzberg et al. 1957, Maslow 1943), a variety of psychological theories have been used to explain money’s role in motivation. These include equity and justice theories, drive theory, goal theory, self-efficacy theory, prospect theory, and expectancy theory (space limitations preclude reviews of these theories; see Bartol & Locke 2000 and Gerhart & Rynes 2003 for more information). In addition, economists and management scholars have turned to agency theory (Jensen & Meckling 1976) and tournament theories (Lazear & Rosen 1981). Although the preceding theories propose a variety of ideas about pay and motivation, essentially PFP operates on motivation and performance through two

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general processes (Gerhart & Rynes 2003). First, there is the potential for an “incentive effect”: the impact of PFP on current employees’ performance, holding the attributes of the workforce constant. Incentive effects have been the primary focus of most PFP theory and research. Second, there is the potential for a “sorting effect,” which we define as the impact of PFP on the attributes of the workforce through differential attraction and retention processes (Gerhart & Milkovich 1992, Lazear 1986). For example, different types of PFP systems may cause different types of people to apply to and stay with an organization (i.e., to self-select), and these people may have different levels of ability or other attributes (e.g., need for achievement) that enhance effectiveness more in some organizations than in others. Organizations, too, may differentially select and retain employees depending on the nature of their culture or PFP strategies. To the extent that studies reviewed in the preceding meta-analyses tracked the same individuals before and after the PFP intervention, the observed effects are pure incentive effects. However, to the degree that individuals making up the workforce changed in response to changes in pay systems, then some of the improvements in performance might be due to sorting effects. Lazear (1986), for example, found a 44% increase in productivity when a glass installation company switched from salaries to individual incentives. Of this increase, roughly 50% was due to existing workers increasing their productivity, while the other 50% was attributable to less productive workers quitting and being replaced by more productive workers over time. In this vein, evidence suggests that PFP is more attractive to those higher in academic achievement (Trank et al. 2002), need for achievement (Bretz et al. 1989), and self-efficacy (Cable & Judge 1994). Research also shows that high performers are most likely to seek other employment if performance is not sufficiently recognized with financial rewards (Trevor et al. 1997). Conversely, low performers are more likely to stay with an employer when PFP relationships are weaker (e.g., Harrison et al. 1996).

CHOOSING A PERFORMANCE MEASURE Although PFP can significantly improve performance, most of the above evidence has been obtained in contexts where individual contributions are separable and where performance can be measured objectively. However, in most jobs, individual contributions are difficult to identify and/or performance is more difficult to measure objectively. Consequently, subjective performance measures, particularly supervisory ratings, are more often used. In addition, organizations increasingly supplement individual-level measures with group, unit, or organization-level performance measures, especially to the degree that work is interdependent. Moreover, organizations are increasingly building risk into both individual (e.g., bonuses) and unit-based pay programs (e.g., gain sharing), despite the fact that employees are typically risk averse with respect to pay.

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In deciding on performance measures that will be used to determine pay, there are at least three key choices: (a) How much emphasis can or should be placed on results-oriented performance measures (e.g., number of units produced) relative to behavior-based ones (e.g., supervisory evaluations of effort or quality)? (b) How strong (or intensive) should incentives be and how will risk aversion influence their effectiveness? (c) How much emphasis should be placed on individual contributions relative to collective contributions? Although we discuss each choice individually, in practice, many organizations use multiple performance measures to balance multiple (and sometimes conflicting) objectives.

Behavior-Based (Subjective) versus Results-Based (Objective) Measures Turning to the first question, subjective behavior-oriented measures (such as traditional PE ratings) offer a number of potential advantages relative to results-based measures (Gerhart 2000). First, they can be used for any type of job. Second, they permit the rater to factor in variables that are not under the employee’s control but nevertheless influence performance. Third, they permit a focus on whether results are achieved using acceptable means and behaviors. Fourth, they generally carry less risk of measurement deficiency, or the possibility that employees will focus only on explicitly measured tasks or results at the expense of broader prosocial behaviors, organizational citizenship behaviors, or contextual performance (see, e.g., Arvey & Murphy 1998, Wright et al. 1993). Despite these potential advantages, the subjectivity of behavior-oriented measures limits their ability to differentiate employees (Murphy & Cleveland 1995). In addition, meta-analytic evidence finds a mean interrater reliability of only 0.52 for performance ratings (Viswesvaran et al. 1996), making it difficult for organizations to justify differentiating employees based on such error-laden performance measures (360-degree appraisals may be helpful in this regard). The subjectivity in these PE measures has led the PE literature to focus on identifying cognitive biases in evaluation (in hopes of reducing them) and examining how various features of PE measures (and PE-related processes) influence employees’ perceptions of fairness (in hopes of improving PE’s legitimacy and effectiveness; e.g., Folger & Konovsky 1989). Even if subjectivity in PE could be sufficiently controlled and performance reliably and credibly differentiated, managers may be reluctant to do so because of concerns about adverse consequences for workgroup cohesion, prosocial behaviors, and management-employee relations (H. Heneman & Judge 2000, Longenecker et al. 1987). Indeed, a long line of research suggests that managers tend to become considerably more lenient in PE when they know that ratings will be used for administrative rather than purely developmental purposes (e.g., Jawahar & Williams 1997). Perhaps for these reasons, research suggests that pay is rarely seen by employees as strongly differentiated across employees when it is tied to subjective measures of performance (HayGroup 1994, 2003).

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Results-oriented measures of performance, such as productivity, sales volume, shareholder return, and profitability, would seem to provide an antidote to the subjectivity and unreliability of performance ratings. Lawler (1971), for example, concluded, “[I]n general, objective measures enjoy higher credibility; that is, employees will often grant the validity of an objective measure . . . when they will not accept a superior’s rating” (p. 166). Nevertheless, such measures are not available for most jobs, at least at the individual level. Moreover, agency theory emphasizes that results-based plans (such as gain- or profit-sharing) increase risk bearing among employees (Gibbons 1998). Because most employees derive the bulk of their income from employment, they cannot diversify their employment-related earnings risk, making them more risk-averse than, say, are investors. The consequences of risk bearing may go unnoticed when performance (e.g., profitability) is good and a PFP plan is paying out, but can quickly become a major employee relations issue when results decline and payoffs go down. If poor organizational results are perceived as being strongly influenced by factors that are beyond employees’ control (e.g., poor decisions by top executives), feelings of injustice can be strong. Indeed, many such plans are abandoned due to employee pressure (e.g., Petty et al. 1992). Finally, even though results-based measures are objective and possibly more reliable, they may also be more deficient as indicators of the full domain of expected performance. Lawler warned nearly 35 years ago, “[I]t is quite difficult to establish criteria that are both measurable quantitatively and inclusive of all the important job behaviors,” and “if an employee is not evaluated in terms of an activity, he will not be motivated to perform it” (Lawler 1971, p. 171). Unfortunately, efforts to make measures more inclusive (e.g., unit profitability rather than individual piece rates) generally reduce employees’ expectation that they can influence the performance measure, thus reducing effort.

Incentive Intensity Theories and empirical research generally concur that stronger PFP links increase motivation and performance. However, there are at least two offsetting disadvantages to strong PFP relationships: They may (a) exacerbate problems having to do with risk aversion and (b) be more prone to deficiency in performance measures (e.g., paying for quantity of production without adequate attention to quality). Turning to the first issue, agency theory (Jensen & Meckling 1976) hypothesizes that employees are risk-averse and will prefer “insurance” to avoid downside earnings risk. In addition, employees or executives may be tempted to manipulate results-based systems by artificially inflating results measures (e.g., revenues or profits), resulting in short-term incentive payouts but long-term harm to organizations. Gaming may also arise if management believes the standards for payout are too easy, or if employees believe the standards are too difficult (Whyte 1955). Conflict over standards is a leading cause of plan failure. Thus, using strong incentives might be described as a high-risk, high-return strategy (Gerhart et al. 1996).

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Milgrom & Roberts (1992) suggest that the optimal intensity of incentives depends on four factors: the extent to which better results can be created by additional effort, the precision with which the desired activities can be assessed, employees’ risk tolerance, and their responsiveness to incentives. The trick, of course, is getting the right balance.


Individual versus Group (or Collective) Performance Criticisms have been leveled at organizations for focusing too much on individual performance and rewards. For example, Pfeffer (1998b) critiqued individual merit and incentive plans as being ineffective, inciting grievances, and reducing product quality. Similarly, Deming (1986) argued that management’s “excessive” focus on individual performance often obscures apparent differences in individual performance that “arise almost entirely from the system that (people) work in, rather than the people themselves” (p. 110). Deming and Pfeffer also argue that focusing on individual performance discourages teamwork: “Everyone propels himself forward, or tries to, for his own good. . . . The organization is the loser” (Deming 1986, p. 110). While the potential pitfalls of individually based PFP are important, the literature is quite clear that group-based plans also have their own drawbacks. One is that most employees (at least in the United States) prefer that their pay be based on individual rather than group performance (Cable & Judge 1994, LeBlanc & Mulvey 1998). Another is that this preference is strongest among the most productive and achievement-oriented employees (see, e.g., Bretz et al. 1989, Lazear 1986, Trank et al. 2002, Trevor et al. 1997). This means that group-based pay may also have unfavorable sorting effects, causing the highest performers to choose alternative opportunities where individual results will be rewarded more heavily. Yet another drawback has to do with weakened incentive effects: “Unless the number of individuals in a group is quite small, or unless there is coercion or some other special device to make individuals act in their common interest, rational self-interested individuals will not act to achieve their common or group interests” (Olson 1965, pp. 1–2, emphasis in original). This last phenomenon has been widely studied (Kidwell & Bennett 1993), and goes by many names (e.g., the common-resource problem, public-goods problem, free-rider problem, or social-loafing problem). The general idea is that when people share the obligation to provide a resource (e.g., effort), it will be undersupplied because the residual returns (e.g., profit-sharing payouts) to the effort often are shared relatively equally, rather than distributed in proportion to contributions. Empirical reviews suggest that the free-rider problem is sufficiently important (e.g., Albanese & Van Fleet 1985, Cooper et al. 1992, Shepperd 1993) that researchers have devoted considerable attention to how to mitigate free-rider effects. One potential solution is to give differentiated rewards to group members based on their individual contributions (this solution has been used successfully for many years by Lincoln Electric). As mentioned above, differentiating rewards based on performance can yield benefits via both incentive and sorting effects (Bishop 1984).

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In the same vein, differentiating pay on the basis of individual performance, even within group systems, may reduce the tendency of high-performing employees to leave organizations that switch to group-based pay systems (e.g., Weiss 1987). On the other hand, to the extent that pay dispersion is not accepted as being based on true performance differences or other acceptable equity considerations, differentials may have negative effects on productivity. In a pair of studies that examined the effects of within-group pay dispersion on group productivity, both Pfeffer & Langton (1993) and Bloom (1999) concluded that higher pay dispersion harms group productivity. In Pfeffer & Langton’s study of faculty departmental research productivity, the authors obtained a negative regression coefficient for the independent variable representing salary dispersion. Similarly, in a study of the relationship between pay dispersion and team performance in major league baseball, Bloom (1999) reported that teams with lower pay dispersion had better performance (operationalized in a number of different ways). However, we believe that both studies probably drew overly pessimistic conclusions about pay dispersion because of certain features of their analytic models. For example, in Pfeffer & Langton’s study, the departmental productivity equation controlled for the correlation between pay and productivity in each department. As such, the negative coefficient for pay dispersion essentially reflected the effect of pay dispersion that was unexplained by differences in performance (see Gerhart & Rynes 2003, pp. 180–182, for a more thorough explanation). Similarly, in specifying his model of baseball team performance, Bloom (1999) controlled for both team talent and team pay in estimating the coefficient for pay dispersion. However, team talent and team pay arguably should not be treated as control variables in this case because to do so parcels out the positive effects of pay dispersion, i.e., the attraction and retention of star players who are paid a great deal, thus resulting in better team performance, higher team pay, and greater dispersion. By controlling for both team pay and team ability, these advantages of performance-based pay dispersion are omitted from Bloom’s observed dispersion coefficient. In short, we suspect that both studies underestimated the positive effects of pay dispersion, at least in the contexts they studied. In summary, both individual- and group-based pay plans have potential limitations. Individual-based plans may generate too little cooperation when work is highly interdependent and may be seen as unfair when system factors rather than individual effort and ability determine performance. In contrast, group-based plans can weaken incentive effects via free-rider problems, which generally increase with group size. Group-based plans can also result in detrimental sorting effects if high achievers go elsewhere to have their individual contributions recognized and rewarded. In our discussion below, plans that use aggregate performance measures (gain sharing, profit sharing, stock plans) would seem most likely to suffer from weakened incentive effects and unfavorable sorting with respect to individual performance. On the other hand, aggregate plans may have positive incentive effects if they promote cooperation, attract people with cooperative values, and avoid overly narrow individual goals.

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PERSONNEL PSYCHOLOGY TABLE 1

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Properties of select pay-for-performance plansa Type of performance measure

Level of aggregation Individual Collective


a

Results

Behaviors

Individual incentives Gain sharing Profit sharing Stock plans

Merit pay

Adapted from Milkovich & Wigdor 1991.

EMPIRICAL EVIDENCE ON VARIOUS PAY-FOR-PERFORMANCE PROGRAMS Table 1 classifies various PFP programs using two of the dimensions identified above: (a) results-based versus behavior-based performance and (b) group versus individual performance. Although not explicitly included in Table 1, the third factor (incentive intensity) also is captured to an important degree in that it tends to be stronger for results-based plans. Although we describe each program separately, it should be kept in mind that people are often paid using a combination of programs.

Individual Incentives The meta-analytic evidence reviewed earlier demonstrates a strong, positive average effect of incentives on employee productivity (e.g., Jenkins et al. 1998, Judiesch 1994, Locke et al. 1980).

Merit Pay Although merit pay continues to be the most widely used PFP program (especially among salaried employees), there is surprisingly little evidence about the performance implications of adopting, or not adopting, merit pay programs. R. Heneman (1992, pp. 247–252) listed 10 studies that purported to address the consequences of merit-based PFP. Five of the reported relationships were positive (using statistical significance as the criterion), whereas the rest of the relationships were nonsignificant. No statistically significant negative relationships between merit pay and performance were reported. These studies, however, are limited by the scarcity of control groups, longitudinal assessment, objective measures of performance, and unit measures of performance. Perhaps the most well-known and best effort to incorporate these design features is a longitudinal four-year study of a governmental agency by Pearce et al. (1985), which is widely cited as suggesting that merit pay programs are not effective. Indeed, the authors themselves conclude, “the positive effects of the implementation of merit pay. . .were not supported by the data” (p. 271). Although Pearce

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et al. represents a laudable attempt to tackle a difficult issue, we believe that it is inappropriate to draw such a negative conclusion about merit pay from their study. First, unit performance climbed during the entire 48-month period of their study. As such, the failure to obtain statistically significant (before-and-after) effects may be due in part to the small sample size (n = 20). In addition, the study ended before two-thirds of the units had actually distributed any merit increases based on performance (the program had been implemented and initial training begun, but no raises administered in the majority of their cases). In their words, “Eight of our 12 tests assess the effect of training and the start of the program on organizational performance, an emphasis somewhat different from testing changes in organizational performance after merit pay rewards were distributed” (p. 271). Another concern is that incentive intensity was weak, with high-performing units receiving merit increases that were not much different from those received by lowerperforming units (Gerhart & Milkovich 1992). Other difficulties with the study, including the likelihood of political interference with program implementation in some units, are cited in the original study and reviewed in Gerhart & Rynes 2003 (pp. 187–188). Although further research is greatly needed, the evidence that does exist on merit pay is more positive than negative (R. Heneman 1992). Given the typically weak link between performance and pay as well as problems with getting supervisors to provide credible measures of performance for administrative purposes (see, e.g., Jawahar & Williams 1997), perhaps any positive results at all should be regarded as impressive.

Profit Sharing Profit-sharing plans pay out based on meeting a profitability target (e.g., return on assets or net income). As such, they are “risky” compensation schemes. Incentive effects of profit sharing, although hypothesized to be positive, may nevertheless be limited by the large number of employees involved (thus lowering expectancies of being able to influence results through individual effort), the riskiness of returns, and the fact that many profit-sharing plans defer the income generated until retirement. Weitzman & Kruse (1990) reported generally positive employee attitudes toward profit sharing, although this was “tempered . . . by the risk of fluctuating income” (p. 123). They estimated that the mean effect of profit sharing on productivity was 7.4% (median = 4.4%). Kruse (1993) reported similar, but somewhat smaller effects, as did a meta-analysis by Doucouliagos (1995).

Stock Plans Employee stock ownership and options have increased dramatically since 1990. (Note: A stock option is the right to purchase a fixed number of shares of stock during a fixed time period at a fixed price, regardless of the actual stock price.) According to the National Council on Employee Ownership website, in 2001

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employees owned or had options to own stock worth “about $800 billion, or about 8% of all the stock in the U.S.” This figure compares with only 1%–2% a decade earlier. Early research provided positive evidence on the effectiveness of stock plans, at least among the top five executives (for which public data are readily available; see Masson 1971 and Brickley et al. 1985). Gerhart & Milkovich (1990) moved beyond studying only the top five managers and examined the relationship between the percentage of top- and mid-level managers eligible for stock options in a firm and the firm’s return on assets. Their results suggested that companies having 20% of managers eligible for stock options had predicted returns on assets of 5.5%, as compared with predicted returns of 6.8% (or roughly 25% higher) for companies having 80% of managers eligible. Despite these generally positive results, the potential drawbacks of stock-based pay have received increasing attention in recent years. Investors have questioned the number of options issued to employees (including executives), as well as the historically favorable accounting treatment of options. Indeed, the Financial Accounting Standards Board (2004) recently proposed a landmark change in this area (requiring companies to expense options, which typically reduces net income) that is causing companies like Microsoft to eliminate or drastically curtail their use of stock options (Krim & Spinner 2003). In addition, companies that historically relied heavily on stock options to attract and retain talent have had trouble competing when stock prices declined. Finally, a number of prominent examples of executives’ manipulation of stock prices for personal gain have further thrown the desirability of stock options as performance motivators into question.

Gain Sharing Gain sharing links pay to results-based performance at a collective (usually facility) level. Although incentive effects should be lower relative to individual-level plans (due to lowered expectancy perceptions; Schwab 1973), they should be stronger than for corporate-level programs such as stock- and profit-based plans. Milkovich & Wigdor (1991, p. 86) suggested that group incentive plans such as gain sharing “may provide a way to accommodate the complexity and interdependence of jobs, the need for work group cooperation, and the existence of work group performance norms, but still offer the motivational potential of clear goals, clear PFP links, and relatively large pay increases.” Indeed, the empirical evidence on gain sharing appears to be quite favorable (e.g., Welbourne & Gomez-Mejia 1995). In one interesting study, Petty et al. (1992) compared one division of an electric utility company that implemented a gainsharing plan with another division that did not. The gain-sharing division performed better on 11 of 12 objective performance measures, providing an estimated savings between $875,000 and $2 million. In addition, employee perceptions of teamwork and other factors designed to be influenced by the plan were also positively affected.

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In another well-designed multiyear longitudinal study, Wagner et al. (1988) found that even without a strong worker participation element, a foundry improved its productivity by more than 100% after implementing gain sharing, and experienced statistically significant decreases in labor costs and grievances. Moreover, the authors also reported greater employee concern for cooperative behaviors as well as coworker “policing” of quantity and quality to assure equitable contributions.


Moderating Variables in Group Pay-For-Performance Plans One widely replicated finding is that group size is a moderator of group plan effectiveness, consistent with the idea that expectancy perceptions weaken as each individual sees less effect of his or her own efforts on total group output. For example, Kruse (1993) reported that the impact of profit sharing on annual productivity growth was roughly twice as great in companies having fewer than 775 employees than in larger companies. Similarly, Kaufman (1992) reported that doubling the number of employees covered by a gain-sharing plan from around 200 to 400 was associated with a reduction in the average productivity gain of nearly 50%. Another variable that has long been hypothesized to moderate the effectiveness of group plans, as well as their specific design components, is the extent to which tasks are truly interdependent. For example, Shaw et al. (2002) found that in an industry where tasks are mostly independent (long-distance trucking), firm accident rates and time spent out of service were lowest (i.e., performance was highest) when there were high individual pay incentives and high pay dispersion across drivers. In contrast, in a second study in the concrete pipe industry, they found that safety performance was lowest when high pay differentiation was coupled with high task interdependence (measured via the existence of self-managed teams).

Summary Every pay program has its advantages and disadvantages. Programs differ in their sorting and incentive effects, their incentive intensity and risk, their use of behaviors versus results, and their emphasis on individual versus group measures of performance. Because of the limitations of any single pay program, organizations often elect to use a portfolio of programs, which may provide a means of reducing the risks of particular pay strategies while garnering most of their benefits (Gerhart et al. 1996). For example, using only an individual incentive program could result in unacceptably high levels of competitive behavior and a focus on overly narrow objectives. On the other hand, relying exclusively on gain sharing could result in the under-rewarding of high individual performers, thus risking their attraction, motivation, and retention. However, offering a mix of these different programs offers the possibility that the advantages of each can be captured, while minimizing the disadvantages.

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FUTURE RESEARCH


Moving to the Field At several places in this review, we have discussed the issue of questionable generalizability from laboratory research on PE and pay (particularly when performed on children) to the reactions and behaviors of employees in the workplace. Among the issues we noted are the smaller amounts of pay available in the lab, the shorter time frames involved, and the fact that adults generally expect (and often need) to be paid for working, but not necessarily for solving puzzles in a laboratory. We are aware that in many areas of personnel psychology, laboratory results generalize well to the field (Locke 1986). However, there is already some evidence that this may not be the case with respect to the relationship between intrinsic and extrinsic motivation (e.g., Fang & Gerhart 2000), and we suspect that this may be the case for certain aspects of performance feedback as well (see below). As such, although there is still a role for laboratory research in PE and compensation, at present we see a far greater need for research that is conducted under conditions that are more realistic (see also Arvey & Murphy 1998, Rousseau & Fried 2001 with respect to the growing recognition of the importance of context in industrial/organizational research). For example, in addition to the issues discussed earlier with respect to intrinsic and extrinsic motivation, it is possible that some aspects of Kluger & DeNisi’s FIT theory will play out differently in real work settings. For example, we suspect that in many cases, employees who receive negative 360-degree feedback have their attention diverted—at least initially—away from managerial “tasks” to their own sense of self-worth, just as laboratory subjects do. According to FIT theory, such employees would be expected to be at risk of lower subsequent performance. However, on average, field research suggests that most individuals who receive negative 360-degree feedback subsequently improve as a result. It may be that in real work settings, both the longer time frame and the crucial importance of succeeding at the task are likely to increase both effort and task focus, at least once the initial reactions of distress, anger, or self-denigration have worn off (see, e.g., Smither et al. 2003). In contrast, both the size of the incentives and the time available for emotional adjustment and learning usually are dramatically curtailed in the lab. This is just one more example of how findings from laboratory and field research might diverge due to substantially different contextual conditions in PE and pay research. As such, we applaud the field-oriented direction taken by 360-degree evaluation research and encourage more such research in future.

Intervening Processes One of the most frequent calls in the burgeoning “strategic human resource management” literature has been for research that will help illuminate the “black

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box” between various HR practices (e.g., profit sharing or 360-degree evaluation) and organizational outcomes (e.g., profits or growth; Becker & Gerhart 1996). Although economists and strategic management researchers have produced dozens of studies correlating various HR practices with organization-level outcomes, most such studies leave the reader guessing as to the causal processes involved and, consequently, how to enhance practical effectiveness. To tease out causal processes as well as to provide guidance about how best to implement various PE and PFP programs, researchers need to begin measuring mediating psychological variables such as employee attitudes, beliefs, and behaviors. To date, few studies (in any discipline) have conducted cross-organizational research that simultaneously measures pay or evaluation policies, employee reactions or behaviors, and unit/organization performance. We believe psychologists could make particularly valuable contributions in this area. One notable exception that addresses these issues is Klein’s (1987) study of 2804 employees covered by 37 employee stock ownership plans (ESOPs). Klein hypothesized that ESOP performance would be mediated by three factors: (a) mere “pride of ownership” from belonging to an ESOP, (b) the amount of the employer’s financial contribution to the ESOP, and (c) the extent to which the ESOP was a core part of the management philosophy and communications program. She found support for the last two factors, suggesting that managerial commitment to group plans is indeed associated with their degree of success. Studies of intervening processes, particularly longitudinal ones, might also be used to reveal much more about the difficulties of successfully implementing and maintaining new PE or PFP practices. We know, for example, that many gain-sharing and individual incentive programs have been discontinued due to implementation difficulties. For example, in the Petty et al. (1992) study reported above, despite better performance on 11 objective performance measures and a variety of perceptual measures, the gain-sharing plan was discontinued because of disagreements between management and the union about how to divide the gains among employees if the plan were to spread to other units. In another study, Pritchard et al. (1988) reported that a combined goal-setting, feedback, and incentive intervention was dismantled—despite productivity improvements of up to 75%—after the arrival of a new manager who was philosophically opposed to the use of incentives. There was also resistance from people who believed that employees “should not get something for doing what they are already supposed to do” (p. 354), as well as from some supervisors who felt that the incentive would undermine “their power and prerogatives to reward individuals and units informally” (p. 354). One thing both these studies illustrate is the serious difficulty of aligning perceived conflicts of interest between individuals’ (both employees’ and managers’) goals and objectives and those of the broader organization. In any event, these two studies are exemplary in terms of presenting important information on both causal processes and practical implementation challenges.

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Sorting and Individual Differences Another area in which psychologists could make valuable contributions would be to build on the progress that has been made in integrating individual difference variables into pay research (e.g., Cable & Judge 1994). In particular, we encourage additional work on compensation systems that investigates the distinction between incentive and sorting effects, the latter of which appear to be very important based on the limited work available (e.g., Lazear 1999, Trevor et al. 1997). In particular, the sorting effects of group incentive plans need further attention, particularly since some studies suggest that there may be higher turnover of high performers under such plans (e.g., Park et al. 1994, Weiss 1987). However, it is also possible that contextual variables such as strong communications, inspirational leadership, a highly participative culture, or limited alternative employment opportunities might ameliorate or even reverse these potentially negative sorting effects (e.g., Hamilton et al. 2003, Reichheld 1996). In addition, we encourage increased attention to individual differences in how people perceive risk (Gomez-Mejia et al. 2000). Despite the fact that risk is a central factor in many current forms of pay (e.g., incentives, gain sharing, profit sharing, and stock options), little attention has been given to its measurement. Recent work by Hall & Murphy (2000) and Wiseman et al. (2000) provides a glimpse of just how important risk can be in affecting employee perceptions of value. The rapid growth of “risky” pay, in combination with the dramatic fluctuations in stock value and profits in recent years, suggests that this may be an individual difference variable of considerable importance. More generally, the increased risk of job loss at all employment levels (Reingold 2004) may also make risk an important topic in future investigations of PE, although it has not been so to date. Psychologists would seem well suited to take on this challenge.

360-Degree or Multisource Performance Evaluation Some of the potential advantages of using multiple raters in 360-degree appraisal include higher reliability, credibility, and perhaps lower deficiency (e.g., traditional appraisals capture supervisors’ perspectives but not those of peers, customers, or subordinates). With better PE measures, improved perceptions of fairness may make it possible to tie pay more closely to performance, which in turn should translate into employees seeing a stronger link between behaviors and rewards. At present, however, too few companies use multisource feedback for administrative purposes (Smither et al. 2004) to provide reliable tests of these hypotheses. Given the generally modest average effect sizes but high variability within and across studies (Smither et al. 2004), future 360-degree research would do well to focus on determining which individual or program characteristics are most likely to improve the returns from multisource PE. Research to this point suggests that a variety of actions (e.g., discussing results with subordinates or coaches or setting goals) may be associated with improvements in performance following

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multisource feedback. However, the fact that some employees take such actions while others do not suggests that individual differences in underlying traits such as conscientiousness, need for achievement, or mastery orientation may be important precursors of observed responses to multisource feedback. With respect to situational differences, it seems that most implementations of 360-degree are quite weak in terms of providing motivation for improvement (Smither et al. 2004). By deliberately separating multisource feedback from organizational rewards, 360-degree implementation has nearly always taken the same route suggested by Meyer et al. (1965, 1989) in their “split roles” perspective on traditional appraisal. (For the logic behind the development-only approach to 360degree appraisal, see London & Smither 1995 or Waldman et al. 1998.) Although most 360-degree researchers have speculated that linking multisource evaluations to money or other administrative consequences is unwise, we believe this is an important matter for future research. In saying this, we are taking into account our findings that the use of PFP can be considerably more effective than it is portrayed in much of the psychological literature, and that research has failed to validate Meyer et al.’s claims about the dangers of integrating feedback and reward in more traditional appraisal settings (e.g., Boswell & Boudreau 2002, Dorfman et al. 1986, Prince & Lawler 1986).

Merit Pay We mentioned earlier how little well-designed research exists on the most common PE–pay combination of all: merit pay systems combined with subjective supervisory appraisals. Additional research is sorely needed of the quality exemplified by Klein (1987), Petty et al. (1992), or Pritchard et al. (1988) with respect to ESOPs and incentive systems—i.e., studies that include control groups, intervening process variables, and/or careful longitudinal analyses. One important priority for future research is to conduct more studies that examine the link between the operationalizations of merit pay programs and unitor organization-level performance. The optimal sites for such research will probably be in multiple units of the same organization (the strategy used by Pearce et al. 1985). Evidence should be gathered concerning the relationships between variability in both PE ratings and merit increases, the size of performance rating– merit increase correlations, and organizational performance. Ideally, these would be gathered in combination with measures of employee satisfaction, perceived fairness, and employee turnover that might help to tease apart the motivational and sorting processes involved in producing the observed outcomes. In addition, future research should also incorporate the indirect (but potentially very important) effects of merit ratings on pay via their nontrivial influence on promotion, cumulative (year-after-year) earnings, and quality of employees attracted and retained (Gerhart & Milkovich 1992). Such modifications are almost certain to demonstrate stronger PFP linkages (and, consequently, larger impacts on performance) than those reported to date in the merit pay literature.

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CONCLUDING REMARKS In most organizations (at least in the United States), PE is used both to provide developmental feedback and to motivate employees via linkages between PE and rewards. However, psychological research has focused primarily on the former role to the exclusion of the latter, whereas other disciplines (e.g., economics and finance) have tended to do the opposite. This is unfortunate, because in the real world of organizations, PE and PFP are two of the most powerful tools in an organization’s motivational arsenal (Welch 2001). Yet, in some branches of psychological research, PFP is still assumed a relatively ineffective motivator or even an impediment to motivation (Deci et al. 1999, Kohn 1993). However, as our chapter indicates, money is a very powerful motivator indeed. In fact, it is so powerful that one of the main challenges for managers is to make sure that their compensation systems are not motivating the wrong kinds of behaviors. Psychologists potentially have much to contribute to the practical problems faced by managers who want to know the most effective and motivational ways of evaluating, developing, and rewarding people. However, psychology’s contribution will remain less than it otherwise might be if some of the evidence revealed in this chapter—e.g., that monetary rewards and intrinsic motivation generally do not work in opposition to one another among working adults and that, on average, performance improves after negative feedback—remains unknown by many psychologists who are interested in industrial and organizational issues. As such, we appreciate the wisdom of the editors in seeing the need for a chapter that addresses both PE and PFP simultaneously. We hope that it results in new and vibrant psychological research. ACKNOWLEDGMENTS We wish to thank Angelo DeNisi, Susan Fiske, Robert Heneman, and James Smither for helpful comments on an earlier draft of this manuscript. The Annual Review of Psychology is online at http://psych.annualreviews.org

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Annu. Rev. Psychol. 2005. 56:601–30 doi: 10.1146/annurev.psych.56.091103.070302 c 2005 by Annual Reviews. All rights reserved Copyright First published online as a Review in Advance on July 13, 2004


PSYCHOLOGICAL APPROACHES TO UNDERSTANDING AND TREATING DISEASE-RELATED PAIN Francis J. Keefe, Amy P. Abernethy, and Lisa C. Campbell Duke University Medical Center, Department of Psychiatry, Durham, North Carolina 27705; email: [email protected], [email protected], [email protected]

Key Words coping skills, arthritis, cancer, psychosocial treatment, cognitive-behavioral therapy ■ Abstract Psychologists are increasingly involved in the assessment and treatment of disease-related pain such as pain secondary to arthritis or cancer. This review is divided into four sections. In the first section, we provide a conceptual background on this area that discusses the limitations of the biomedical model of disease-related pain and traces the evolution of psychosocial theories of pain. In the second section, we discuss special issues and challenges involved in working with persons having diseaserelated pain, including the reluctance of some persons to report pain and to become involved in psychological treatments for pain. Section three provides an overview of psychosocial research conducted on arthritis pain and cancer pain that addresses both psychosocial factors related to pain and psychosocial interventions for pain management. In the final section, we describe important future directions, including strategies for disseminating psychosocial treatments and disparities in pain management. CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rationale for Psychological Approaches to Management of Disease-Related Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SPECIAL ISSUES IN MANAGEMENT OF DISEASE-RELATED PAIN . . . . . . . . . Barriers to Medical Pain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Barriers to Psychological Pain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSYCHOLOGICAL APPROACHES TO ARTHRITIS PAIN . . . . . . . . . . . . . . . . . . . Rheumatoid Arthritis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Osteoarthritis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychological Factors Related to Arthritis Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSYCHOSOCIAL INTERVENTIONS FOR ARTHRITIS PAIN . . . . . . . . . . . . . . . . Coping Skills Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emotional Disclosure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSYCHOLOGICAL APPROACHES TO CANCER PAIN . . . . . . . . . . . . . . . . . . . . . 0066-4308/05/0203-0601$14.00

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Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychological Factors Related to Cancer Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSYCHOSOCIAL INTERVENTIONS FOR CANCER PAIN . . . . . . . . . . . . . . . . . . Cancer Pain Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hypnosis and Imagery-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coping Skills Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FUTURE DIRECTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

615 617 618 618 619 620 621


INTRODUCTION For more than 30 years, psychologists have developed and refined psychological approaches to assessing and treating persistent pain. Much of this work has focused on patients suffering from chronic pain syndromes of nonmalignant etiology, e.g., chronic low back pain, migraine headache, and tension headache. Psychologists only relatively recently have begun to explore the utility of psychological approaches to persistent pain conditions that are related to ongoing diseases such as arthritis, cancer, and sickle cell disease. The purpose of this review is to provide an overview of the emerging literature on psychological approaches to persistent, disease-related pain. The review is divided into four sections. In the first section, we review the rationale and conceptual background for psychological approaches. In the second section, we highlight some of the special clinical issues involved in applying psychological methods to disease-related pain. In the third section, we provide an overview of research conducted on two of the most common disease-related pain conditions: arthritis and cancer. In the final section of this chapter, we highlight important future directions for clinical research in this area.

Rationale for Psychological Approaches to Management of Disease-Related Pain Persistent, disease-related pain conditions are particularly appropriate disorders in which to apply psychological approaches. First, epidemiological studies indicate that pain is quite common in chronic diseases such as arthritis (Lozada & Altman 2001, Pincus 1996) and cancer (Caraceni & Portenoy 1999, Portenoy 1989). Second, pain is often one of the major concerns of individuals suffering from these diseases. Third, although medical or surgical approaches can be helpful in managing pain, they have several limitations. For example, surgery or radiation designed to eliminate a cancerous tumor may produce tissue damage that results in persistent pain. Medications designed to manage persistent pain also may have significant effects such as constipation or fatigue that limit their use over long periods. These side effects may be particularly problematic in the many older adults who suffer from chronically painful diseases. Finally, psychological approaches can build upon and enhance a person’s own self-help efforts. Recent clinical guidelines for chronic diseases emphasize the important

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role that self-help efforts play in the overall management of these conditions (American Geriatric Society Clinical Practice Committee 1997, American Pain Society Quality of Care Committee 1995, Grossman et al. 1999, Schug et al. 1990, Simon et al. 2002). A traditional biomedical model has long dominated the understanding of diseaserelated pain. This model is not only prevalent among health professionals, but also among patients who seek their help. It views pain as a symptom of underlying disease activity or tissue damage. Inherent in the model are several basic assumptions. First, it assumes that the amount of pain experienced is proportional to the degree of disease activity or tissue damage. Thus, a patient who experiences severe pain in an arthritic knee assumes that he or she must have a high level of disease activity. Second, it assumes that interventions to eliminate or dramatically reduce disease activity or tissue damage will abolish or significantly reduce pain. Thus, a patient who has pain related to a malignant tumor assumes that removal of this tumor will eliminate the pain. Finally, the traditional model ignores or minimizes the role psychological factors play in disease-related pain. Psychological responses such as anxiety or depression are viewed as secondary to pain and are not considered to play a direct role in the pain experience. The traditional biomedical model has a number of important limitations. First, research has shown that the amount of pain reported is very often not proportionate to underlying evidence of disease activity. In a study conducted in our laboratory, we found that X-ray evidence of disease activity explained only a small proportion of variance in the reports of knee pain provided by osteoarthritis (OA) patients (Keefe et al. 1987). Coping variables were much more important predictors of pain and adjustment to pain (Keefe et al. 1987). Second, there is evidence that medical or surgical interventions designed to eliminate underlying disease activity or tissue damage do not always abolish pain. Research, for example, has shown that up to 30% of women who have undergone removal of a breast because of breast cancer (i.e., mastectomy) report phantom pain in the breast that is no longer there and that women who had pain prior to mastectomy are more likely to experience phantom breast pain (Kroner et al. 1989). Finally, as reported below, there is growing evidence that psychological factors such as self-efficacy or helplessness are important in explaining pain and adjustment to pain in persons suffering from persistent, disease-related pain. Dissatisfaction with the traditional biomedical model of pain has led to advances in pain theories. Ron Melzack, a psychologist, and Patrick Wall, a neurophysiologist, were among the first to propose a pain theory, the gate control theory (Melzack & Wall 1965), which incorporated both biological and psychological perspectives on pain. This theory maintains that pain is more than a simple sensory event that signals tissue damage, but in fact is a complex, multidimensional experience that has sensory, affective, and evaluative components. Furthermore, the theory maintains that pain processing is dynamic and that higher centers of the brain responsible for cognition and emotion influence the transmission of nociceptive signals from the site of injury to the brain. Melzack & Wall (1965) proposed that these brain

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centers activate descending systems that block pain through a gating mechanism located in the spinal cord. The gate control theory has had an enormous impact on the psychology of pain. By proposing a biological basis for the influence of psychological factors, it legitimized psychological approaches for many in the biological sciences. It also stimulated laboratory-based pain research designed to examine the influence of cognitive variables (e.g., expectations and memories) and emotional variables (e.g., anxiety and depression) on the perception of experimental pain stimuli. Finally, it heightened awareness of the potential role that psychological interventions could play in clinical pain management. In the 1970s and 1980s, psychologists began to develop and refine pain theories based on behavioral and cognitive-behavioral principles. Fordyce (1976), for example, pioneered the extension of operant conditioning principles to the understanding of chronic pain. The behavioral model he developed focused on a careful analysis of pain behavior patterns. Fordyce maintained that pain behavior patterns learned in response to an acute pain episode could come, over time, under the control of powerful environmental contingencies. Maladaptive pain behaviors such as excessive guarding of a painful area or an overly sedentary lifestyle could be maintained in some patients by reinforcing consequences such as attention from a solicitous spouse or avoidance of unwanted home or work responsibilities. Fordyce (1976) developed structured behavioral treatment programs that used contingency management methods to extinguish maladaptive pain behaviors while increasing the frequency of more adaptive “well behaviors” such as exercise. Fordyce’s behavioral model and treatment methods primarily have been used to understand how persons adjust to nonmalignant pain conditions such as low back pain. The appropriateness of these methods for persons having disease-related pain has been questioned, primarily because it has been assumed that disease-related pain is primarily under the control of disease activity, rather than environmental contingencies. Psychologists only recently have recognized the potential role that operant behavioral principles might be able to play in understanding and treating disease-related pain (Keefe & Lefebvre 1999). In the mid 1980s, Turk, Turner, and others proposed cognitive-behavioral models of pain that could be easily applied to persons having disease-related pain (Turk et al. 1983, Turner & Clancy 1988). According to the cognitive-behavioral model, cognitive responses such as thoughts, beliefs, and expectations play a key role in the perception of pain and how people adjust to pain. The cognitive-behavioral model has had a major impact on the field for several reasons. First, it is flexible and can be easily applied to persons with disease-related pain and nonmalignant chronic pain conditions. Second, it is comprehensive and enables one to integrate information on biological, psychological, and social influences on pain. Third, it has led to the development of standardized treatment protocols that teach patients specific cognitive and behavioral pain coping skills. Recently, Melzack (1999) proposed an update of the gate control theory, the neuromatrix theory. This theory fits quite well with the cognitive-behavioral model

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of pain. According to the neuromatrix theory, the brain has a neural network that integrates information from multiple sources to produce the sensation of pain. Key inputs into this network not only include sensory information, but also phasic and tonic inputs from brain areas responsible for cognition and emotion, as well as inputs from the body’s stress-regulation systems. The neuromatrix theory clearly underscores the role that psychological factors play in pain and has stimulated psychosocial laboratory research (e.g., brain imaging studies examining the psychosocial correlates of pain; see Villemure & Bushnell 2002) as well as clinical research (e.g., studies of the effects of psychological interventions on brain activation; see Rainville et al. 2002).

SPECIAL ISSUES IN MANAGEMENT OF DISEASE-RELATED PAIN Although there have been many advances in pain theories over the past decade, most lay persons and health professionals continue to view pain from the traditional, biomedical perspective that views pain as a symptom of disease activity that can be treated only by identifying and correcting underlying tissue pathology. Because this perspective has such a pervasive impact, we consider its implications for both medical and psychological approaches to assessing and treating pain.

Barriers to Medical Pain Management A number of patient, family, and health system barriers interfere with medical management of disease-related pain. Sandra Ward and her colleagues have conducted an interesting series of studies in which they have identified and documented such barriers to medical pain management in cancer patients. Ward et al. (1993), for example, developed and validated the Barriers Questionnaire, a 27-item measure that assesses eight different patient barriers that interfere with the management of disease-related pain. These barriers include fear of addiction, concerns about tolerance, concerns about side effects, concern that pain means disease progression, fear of distracting one’s physician from treating the disease, fear of injections, fatalism, and the belief that “good” patients do not complain about pain. The number of barriers reported was significantly higher in cancer patients who were older, less educated, and had lower incomes. The level of patient-reported barriers was also linked to pain and pain management in that patients who reported a higher level of these barriers had significantly higher pain and tended to be undermedicated. Patient-related barriers have been found to be a problem in studies conducted in cancer patients not only in the United States but also in other countries. A study conducted in Taiwan by Lin & Ward (1995), for example, reported results that were quite similar to those reported by Ward et al. (1993). Barriers to medical pain management also have been studied in family caregivers. Ward et al. (1998), for example, modified the Barriers Questionnaire to

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study the concerns about pain management reported by 37 primary caregivers of cancer patients who were at the end of life and were being treated in a hospice setting. Surprisingly, given the fact that these patients were at the end of life, caregivers reported high levels of barriers to pain management including concerns about side effects of medications, addiction, fear of injections, and that increasing pain signified disease progression. Older caregivers were particularly likely to report that they were concerned that patients’ reports of pain might distract the physician from treating or curing the cancer, even though curative treatment was not an option for this group of patients. Taken together, these findings suggest that even in an end-of-life situation, there may be significant caregiver barriers to reporting pain and using pain medications. This is particularly disconcerting given that, at the end of life, primary caregivers are often important decision makers influencing when and how often patients take pain medication. In addition to patient and caregiver barriers to adequate pain management, health system barriers also limit care. Doctor-related barriers to adequate cancer pain management have been studied systematically and include inadequate pain assessment, inadequate knowledge, fear of tolerance, and fear of addiction (American Cancer Society 2003, Mokdad et al. 2004, Vuorinen 1993). Research has shown that health care providers often lack basic education about the roles of pharmacologic and nonpharmacologic therapy and the impact of pain on quality of life (Mokdad et al. 2004, Portenoy et al. 1992, Vuorinen 1993). Additional health system barriers include inadequate reimbursement, rising costs of medications, restrictive regulation, multiple venues of care, and an overall health system that focuses on cure rather than on quality of life (Portenoy 1994). The addition of the Joint Commission on Accreditation of Healthcare Organizations’ Pain Standards (1999) has forced many health professionals and health care organizations to start attending to these barriers by implementing educational programming, insisting on adequate pain assessments, and expanding access to pain interventions (Caraceni & Portenoy 1999). With growing evidence that there are important barriers to cancer pain management has come increased interest in educational programs to address these barriers. For example, over the past 10 years, a number of educational interventions have been developed to teach cancer patients about the need to report pain and to take pain medications (Devine & Westlake 1995, Gordon & Ward 1995, Wilkie et al. 1995). These programs address misconceptions about addiction and side effects, and encourage open communication about pain between patients and health professionals. Some of these programs emphasize the need to include family members and caregivers. Educational programs about pain management also have been directed at health professionals who treat cancer patients. One result has been an increased prescription of opioid medications in patients having pain related to advanced cancer (Vielhaber & Portenoy 2002). Most psychological pain management programs for patients having diseaserelated pain include an educational component that is designed to address issues related to patients’ willingness to report pain and to use pain medications. These

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educational efforts enhance the effectiveness of medical regimens being used to manage pain and provide an important foundation upon which to build a rationale for psychological approaches.


Barriers to Psychological Pain Management Individuals vary in their openness to psychological evaluation and treatment of disease-related pain. Although the barriers to psychological pain management have not been studied systematically, they are evident in clinical encounters with patients, family members, and health care providers. Individuals experiencing pain, for example, may be reluctant to admit that psychological factors such as stress or mood influence their pain because of fear that their pain may be dismissed as psychological and not treated as real, physical pain. Alternatively, a person may refuse a psychological intervention for pain (e.g., hypnosis) for fear that, if it works, then the pain will be viewed as psychological in nature. Balancing medication management and psychological management of pain is a challenge for many persons. Skeptical patients may refuse to use psychological methods, believing that their disease is physical and that nothing can be done to manage it outside of medications and surgery. Patients with a keen interest in alternative medicine may decide to rely solely on a psychological approach to their pain and to discontinue disease-modifying medications that are important in keeping their pain and disease under control. Even patients who are very sophisticated may have trouble deciding when to employ medical versus psychological approaches to pain control. An important part of any treatment protocol is teaching patients how to judiciously employ the full array of medical and psychological coping methods at their disposal. Negative attitudes and stereotypes about psychological approaches to pain may be heavily influenced by a person’s spouse or significant others. It is likely that many of these individuals have never interacted with a psychologist. They may be wary and suspicious of psychological assessment or treatment approaches. In our own experience, we have encountered many patients having arthritis pain or cancer pain who express initial willingness to participate in a cognitive-behavioral pain management intervention, but who later decline to do so because of concerns expressed by a spouse, family member, or coworker. In recognition of the influence that significant others have on pain management, there is growing interest in educating them about the important role that medical and psychological management can play in pain control (Keefe et al. 1996, 1999; F.J. Keefe, T.A. Ahles, L. Sutton, J. Dalton, D. Baucom, M.S. Pope, V. Knowles, E. McKinstry, C. Furstenberg, K. Syrjala, S.J. Waters, D.C. McKee, C. McBride, M. Rumble, & C. Scipio, unpublished manuscript; Radojevic et al. 1992). Finally, health care professionals who are treating the patient may be unsupportive or actively critical of the person’s involvement in psychological pain management interventions. Health care professionals are viewed as highly credible by their patients and their perspectives on psychological approaches to pain may play a key

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role in a patient’s willingness to engage in assessment or treatment efforts. Educational efforts directed at health care professionals, for example the 1999 Joint Commission on Accreditation of Healthcare Organizations’ Pain Standards, are playing an important role in helping these professionals appreciate the impact that psychological factors and psychosocial interventions can have on disease-related pain.


PSYCHOLOGICAL APPROACHES TO ARTHRITIS PAIN Most of the psychological research on disease-related pain has been conducted in two disease-related pain conditions: arthritis and cancer. In the next section we illustrate the research for each of these conditions by providing a brief description of each disease followed by a discussion of key psychological factors related to pain in that disease and psychological interventions used for pain management.

Rheumatoid Arthritis The earliest psychological research on pain in arthritic disorders is on rheumatoid arthritis (RA), a systemic inflammatory disease characterized by inflammation of the synovial membrane that surrounds and lubricates the joints (McCracken 1991). RA patients often experience painful and disabling joint symptoms. The disease can also affect multiple organ systems and is associated with premature mortality (Simon et al. 2002). RA affects more women than men, with a peak incidence at ages 20–50 and a prevalence of 1%–2% among adults (Harris & Zorab 1997). As a particularly painful form of arthritis, RA is associated with 42% greater daily pain when compared to OA (Affleck et al. 1999). RA pain tends to be more severe in the early and active phases of the disease process (Williams & Wood 1988). The etiology of RA is unknown but it is believed that RA might represent a maladaptive immune response to viral particles or bacterial proteins, which initiates joint destruction. Current clinical guidelines emphasize the need for aggressive early intervention for RA to prevent joint destruction (Simon et al. 2002). As a result, RA patients are treated with powerful disease-modifying antirheumatic drugs (DMARDs), such as methotrexate, sulfasalazine, and infliximab, which can bring the disease under control but which also have serious side effects. Even when receiving DMARDs, RA patients can experience unpredictable flares in pain and disease activity that challenge coping efforts. Pain is typically managed using analgesics, nonsteroidal anti-inflammatory drugs (NSAIDs) and physical modalities (e.g., heat and cold). Surgical procedures such as joint replacement can be effective in pain relief but are often used as a last resort (Nassar & Cracchiolo 2001).

Osteoarthritis OA is the most common type of arthritis (National Institute of Health 2001). OA is a progressive disease that affects at least 80% of adults over the age of 50 years

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(Sharma 2001). OA results from a breakdown of joint cartilage, typically in weightbearing joints such as the knees, hips, and spine. Risks for OA include factors that contribute to excessive joint loading or stress (e.g., obesity) or traumatic injuries that damage cartilage or bone. The pain associated with OA is predictable in that it is typically related to weight-bearing activities, it often increases over the course of the day as joints continue to bear weight, and it becomes more evident when individuals remain in a stationary position (e.g., prolonged standing). At present, there is no known cure for OA. Treatment focuses primarily on management of symptoms such as pain and joint swelling. Analgesics and NSAIDs are the primary forms of pain treatment for persons with OA. Medical practitioners often encourage OA patients to manage pain through exercise, weight loss, physical modalities, and orthotic devices (e.g., specially designed shoes, wrist guards, gloves). Pain relief and improvement of function may be attained through surgical interventions such as knee or hip replacement, but may be contraindicated in some cases, such as when age and obesity increase surgical risk.

Psychological Factors Related to Arthritis Pain In a recent review (Keefe et al. 2002), a number of psychological factors that influence pain in arthritis were identified. These factors can be grouped into two broad classes: (a) coping variables and (b) pain control and appraisal variables. Table 1 (based on Keefe et al. 2002) lists pain coping variables that have been studied in OA and RA patients along with key findings and references. Although the results of this research vary, three general conclusions can be drawn. First, there is strong evidence that pain coping strategies can be assessed in a reliable fashion in both OA and RA patients. Second, studies suggest that forms of pain coping that are more active are related to improved adjustment to arthritis, whereas coping strategies that are more passive (e.g., catastrophizing) are related to poorer adjustment. Finally, there is evidence that pain coping variables are important in explaining adjustment even after one controls for pain level. Table 2 (based on Keefe et al. 2002) lists pain control and appraisal variables that have been studied in OA and RA patients along with key findings and references. Three variables, in particular, have received a great deal of attention: (a) pain control and rational thinking, (b) self-efficacy, and (c) helplessness. The variables have shown consistent, strong relationships to pain. Patients scoring high on measures of pain control and rational thinking or self-efficacy have consistently reported lower levels of pain and psychological distress, whereas patients scoring high on measures of helplessness have consistently reported higher levels of pain and psychological distress. The effects of pain control and appraisal variables are apparent even after controlling for pain level and controlling for demographic and medical status variables such as disease duration or severity. These findings have important clinical implications. First, they underscore the importance of attending to pain control and appraisal when assessing the clinical status

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Pain coping variables in osteoarthritis and rheumatoid arthritis

Pain coping strategies Active coping


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Definition

Key findings

References

Purposely engaging in adaptive cognitive and behavioral strategies to manage pain

Frequent use of active coping linked to lower levels of pain, functional disability in RA patients Increases in active coping over the course of coping skills training related to improved depression, fatigue, and medication adherence in RA patients

Brown & Nicassio 1987

Evers et al. 2002

Passive coping

Passive or maladaptive strategies involving withdrawal or giving up instrumental control over pain

Frequent use of passive coping linked to higher levels of pain, functional disability, or depression in RA patients

Brown & Nicassio 1987, Watkins et al. 1999

Problem-focused coping

Efforts to directly alter pain

Linked to improved next-day pain and mood in RA patients

Affleck et al. 1992, Keefe et al. 1997

Emotion-focused coping

Efforts to regulate emotional responses to a stressor

Linked to increased pain in RA patients

Affleck et al. 1999

Positive religious and spiritual pain coping

Looking to God or a higher power for assistance in pain coping

Linked to positive mood and social support in RA patients

Keefe et al. 2001

Negative religious and spiritual pain coping

Sensing that God or a higher power is not providing assistance in pain coping

Linked to less positive mood in RA patients

Keefe et al. 2001

Catastrophizing

The tendency to focus on and exaggerate the threat value of painful stimuli and negatively evaluate one’s ability to deal with pain

Linked to higher arthritis pain and disability in OA patients Associated with heightened pain and disability in RA patients

Keefe et al. 2000 Keefe et al. 1989

OA, osteoarthritis; RA, rheumatoid arthritis.

of arthritis patients. Second, they raise the possibility that psychosocial interventions designed to enhance pain control and appraisal may benefit OA and RA patients.

PSYCHOSOCIAL INTERVENTIONS FOR ARTHRITIS PAIN Interventions designed to improve how arthritis patients adjust to pain can be grouped into two broad categories: coping skills training and emotional disclosure.

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TABLE 2

Pain control and appraisal variables in osteoarthritis and rheumatoid arthritis

Pain appraisal variables


Self-efficacy

Helplessness

Pain control and rational thinking

611

Definition

Key findings

References

Confidence that one has the capability to cope with pain and other arthritis symptoms

Self-efficacy for pain linked to lower ratings of thermal pain in lab setting in OA patients Linked to lower pain and disability in RA patients High self-efficacy linked to lower use of passive coping strategies Mediated relationship between perceived control and disability in RA patients Improvements in self-efficacy following a self-help intervention were correlated to improvements in pain and psychological functioning in RA patients Increased self-efficacy following pain coping skills training linked to decreased pain, psychological disability, and physical disability in OA patients Improved self-efficacy following stress management training was linked to improvements in pain, depression, health status, and disease activity in RA patients

Keefe et al. 1997

Linked to higher anxiety, depression, or impairment in activities of daily living in RA patients Mediated relationship between disease severity and depression in RA patients Related to greater psychological and physical disability in RA patients Related to early mortality in RA patients Linked to increased pain and disability in RA patients

Nicassio et al. 1985 Smith et al. 1994

Following coping skills training, improvements correlated with reduced pain, physical disability, and pain behavior in OA patients

Keefe et al. 1990a,b

Expectation that negative outcomes will occur or positive outcomes will not occur; perceived lack of control over outcomes

Perceived control over pain

OA, osteoarthritis; RA, rheumatoid arthritis.

Lefebvre et al. 1999 Keefe et al. 1997 Schiaffino & Revenson 1995 Lorig et al. 1993

Keefe et al. 1996, 1999

Smarr et al. 1997

Smith et al. 1990

Schoenfeld-Smith et al. 1996 Callahan et al. 1996 Nicassio et al. 1993

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Coping Skills Training Coping skills training interventions are based on the cognitive behavioral model of pain and teach patients cognitive and behavioral skills for managing pain to enhance their perceptions of control over pain. Table 3 provides an overview of a 10-session protocol used in a randomized clinical trial of coping skills training for patients having pain due to OA of the knee(s) (Keefe et al. 1990a,b). Training begins with an educational rationale, the gate control theory of pain (Melzack & Wall 1965), that describes how the brain influences the transmission of pain signals from the periphery to the brain by blocking pain signals at the level of the spinal cord. This rationale helps patients understand how the mind and body work together to influence the pain experience. It also enables them to see how training in coping skills that alter thoughts, behaviors, and feelings can improve pain. Following the rationale, patients are taught three sets of coping skills. The format for each skills training session is similar and starts with a review of home practice, instruction in a specific skill, guided practice with that skill, and a home practice assignment. The first set of coping skills, introduced in sessions 2 to 4, teach patients to control pain through attention diversion strategies including progressive relaxation training, guided imagery, and visual distraction. The second set of skills, introduced in sessions 5 to 7, teach patients to control pain by altering their activity patterns. Patients are taught how to use activity-rest cycling to pace their activities and gradually build up their activity level. They are also taught goal-setting strategies in order to build up the level and range of pleasant activities. The third set of skills, introduced in sessions 8 and 9, teach patients to control pain by altering negative pain-related feelings. Using methods drawn from cognitive therapy, patients are taught how to identify and challenge negative thoughts that occur during episodes

TABLE 3

Overview of coping skills training protocol

Session

Topic

Session 1

Educational rationale—gate control theory

Session 2 Session 3

Progressive relaxation training Guided imagery

Session 4

Distraction techniques

Session 5

Activity pacing

Session 6

Goal setting

Session 7

Pleasant activity scheduling

Session 8 Session 9

Identifying negative thoughts Challenging negative thoughts to reduce psychological distress

Session 10

Maintenance plan

Attention diversion techniques Altering activity patterns

Altering negative pain-related emotions

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of increased pain and that contribute to psychological distress. During training, patients learn to replace overly negative and distorted thoughts with more adaptive coping thoughts that reduce emotional distress and enhance other coping efforts. In the final session of training, patients develop a maintenance plan in which they list goals for continued practice of coping skills and strategies for coping with setbacks in coping efforts. In a randomized trial of this 10-session protocol (Keefe et al. 1990a,b), 99 older adults (mean age = 64 years) with persistent OA knee pain were randomly assigned to the 10-session pain coping skills training protocol, a 10-session arthritis information/education protocol, or standard care. Patients who received pain coping skills training showed significant improvements in pain and psychological disability compared to those receiving the arthritis information/education protocol or standard care. Furthermore, correlational analyses showed that patients who reported increases in perceived control over pain (pain control and rational thinking) over the course of pain coping skills training were significantly more likely to show improvements in pain, physical disability, and pain behavior at a six-month follow-up. Taken together, these findings suggest that coping skills training can benefit patients having OA and that changes in a pain control and appraisal variable are related to long-term treatment outcomes. The efficacy of pain coping skills training in RA also has been demonstrated in a number of randomized clinical trials (e.g., Kraaimatt et al. 1995, Leibing et al. 1999, Parker et al. 1995, Sinclair et al. 1998). Interestingly, the success of coping skills training interventions has had an important impact on arthritis educators and led them to more fully integrate training in behavioral self-management skills into their educational and informational protocols (Lorig & Holman 2003). A recent meta-analysis of patient education interventions, many of which included training in coping skills, showed that these interventions “provide additional benefits that are 20–30% as great as the effects of NSAID treatment for pain relief in OA and RA, 40% as great as NSAID treatment for improvement in functional ability in RA, and 60–80% as great as NSAID treatment in reduction in tender joint counts in RA” (Superio-Caulsby et al. 1996). Based on the notion that pain can influence and be influenced by significant others, several investigators have tested the effects of involving spouses or family members in pain coping skills training. Radojevic et al. (1992) reported that a behavioral coping skills protocol that involved family support was more effective than an education protocol that involved family support in improving pain and joint swelling in persons having RA. In a study of OA patients, a consistent pattern was found in which patients who received spouse-assisted pain coping skills training showed the best outcomes, those receiving traditional pain coping skills training (i.e., training alone with no spouse involvement) the next best outcomes, and arthritis education with spousal support the worst outcomes (Keefe et al. 1996). Young and others (1995) have reported that pain coping skills training leads to significant reductions in health-care costs (i.e., costs of clinic visits,

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hospitalizations, and overall medical services) in patients with RA pain when compared to control conditions. Taken together, these studies support the notion that arthritis patients may benefit from coping skills training protocols that involve their spouse or family members. Evidence is also accumulating to suggest that protocols that include skills training and social support may reduce medical costs related to arthritis. In a randomized clinical trial of 363 patients having pain due to OA, Cronan et al. (1998) reported that patients who received education, social support, or a combination of the two interventions that emphasized self-management showed significantly lower health-care costs at a three-year follow-up than did patients in a routine-care control group. The added savings amounted to more than $1100 per year for each participant.

Emotional Disclosure A growing body of research suggests that reluctance or inability to process events that are emotionally stressful may lead to increases in symptom complaints and poor immune functioning (Pennebaker & Seagal 1999). Both of these outcomes could adversely impact arthritis sufferers, in particular patients with RA (Zautra et al. 1999, 2001). Emotional disclosure is an intervention designed to facilitate psychological adjustment to stressful experiences that remain incoherent and emotionally laden because they have not been fully processed (Pennebaker & Seagal 1999). During emotional disclosure interventions, individuals attend three to four consecutive private, daily sessions in which they write or talk into a tape recorder about thoughts and feelings associated with a stressful life experience. Emotional disclosure interventions have only recently been applied in patients with arthritis pain. In two randomized studies with RA patients, emotional disclosure interventions led to significantly fewer long-term problems with activities of daily living and lower levels of negative mood and tension (Kelley et al. 1997) as well as improvements in disease activity (Smyth et al. 1999) as compared to attention/neutral control conditions. Interestingly, studies using emotional disclosure paradigms have documented initial increases in negative mood immediately following the emotional disclosure task. The benefits of disclosure on adjustment thus appear to be delayed. To date, emotional disclosure interventions have not been shown to improve pain in arthritis patients. However, Kelley et al. (1997) found that patients who experienced the largest increases in negative mood following emotional disclosure showed the greatest long-term improvements in the condition of their painful joints. This suggests that the depth of feelings experienced during disclosure may be important. We are currently conducting a randomized clinical trial to test the effects of an emotional disclosure protocol in which a nurse helps facilitate RA patients’ abilities to articulate and focus on the negative feelings associated with disclosure. We believe that this facilitated disclosure protocol may enhance emotional processing and lead to significant improvements in pain outcomes.

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PSYCHOLOGICAL APPROACHES TO CANCER PAIN


Description Approximately one quarter of newly diagnosed cancer patients will complain of pain, with prevalence increasing to 50%–70% among those undergoing active therapies (Portenoy et al. 1992, 1994; Vuorinen 1993). As disease advances, pain becomes more common; 60%–90% of people with advanced cancer will complain of pain and over 65% will complain of two or more sites of pain (Caraceni & Portenoy 1999, Cleeland et al. 1994, Grond et al. 1996, Portenoy 1989, Vainio & Auvinen 1996). Cancer pain can be attributed to (a) tumor progression and invasion (e.g., malignant brachial plexopathy); (b) cancer-related operations and other diagnostic or therapeutic procedures (e.g., resection of stomach cancer or bone marrow biopsy); (c) antineoplastic chemotherapy, hormone therapy, or radiotherapy (e.g., mucositis); (d) cancer-related infections (e.g., herpetic neuralgia); and (e) musculoskeletal complaints related to inactivity and generalized fatigue (Jacox et al. 1994). The patients can be cured of their cancer but the cancer-related pain can persist. Further, multiple etiologies are commonly identified in the same patient and even in the same location (Portenoy 1989). A lung cancer patient may undergo curative resection and then suffer from debilitating chronic post-thoracotomy pain and postherpetic neuralgia in the same distribution. What makes cancer pain unique? First and most importantly, there is the meaning of “cancer” to the patient, family, and health practitioner. The cancer diagnosis connotes death, uncertainty, and loss of control (Turk 2002). Second, although 62% of adults with cancer are disease-free five years after diagnosis, the anticipation of tumor progression by patients and health practitioners persists and pain is one of the cardinal symptoms of tumor progression (American Cancer Society 2003). The primary rule of cancer pain management is to remove the tumor or offensive agent through surgery, radiation, chemotherapy, or other means. When it is not possible to remove the underlying pathology, the focus is on management of pain itself. Although numerous cancer pain management guidelines exist (American Geriatric Society Clinical Practice Committee 1997, American Pain Society Quality of Care Committee 1995, Grossman et al. 1999, Jacox et al. 1994, Schug et al. 1990), most current cancer pain therapy is based on the analgesic ladder of the World Health Organization (WHO) (Jadad & Browman 1995), which is depicted in Figure 1. According to the ladder, pain is divided into mild, moderate, and severe categories based on initial assessment, and is treated with a combination of opioids, nonopioids, adjuvant medications, and other adjuvant therapies (Jacox et al. 1994). Pain is treated continually, around the clock, using a long-acting analgesic with additional rescue prescriptions for medications to manage breakthrough pain. Mild pain is treated with a nonopioid analgesic with or without an adjuvant analgesic. Moderate pain is treated with a weak opioid plus a nonopioid analgesic with or without an adjuvant analgesic. Severe pain is treated with a strong opioid plus a nonopioid analgesic with or without an adjuvant analgesic. Adjuvant

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Figure 1 Overview of the cancer pain management steps, based on the World Health Organization’s analgesic ladder. As pain increases, stronger opioids are substituted for weaker opioids and additional adjuvant medications and nonpharmacological interventions are added.

medications vary and include neuropathic pain medications such as anticonvulsants and tricyclic antidepressants as well as corticosteroids, benzodiazepines, and antibiotics. Side effects, especially constipation, are anticipated and treated expectantly. According to the WHO ladder, surgery, procedural interventions, radiotherapy, chemotherapy, acupuncture, and psychological interventions are all adjuvant

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therapies that supplement the general plan of achieving pain relief around the clock (Jacox et al. 1994).


Psychological Factors Related to Cancer Pain Research on psychological factors influencing cancer pain has focused on two main areas: psychological distress and pain coping. Numerous studies have examined the relationship between cancer pain and various forms of psychological distress, including mood disturbance, anxiety, depression, emotional distress, fear, and worry. Zaza & Baine (2002) systematically reviewed this relationship. Fourteen of 19 studies supported an association between pain and psychological distress; the positive studies were larger and of higher quality, and the authors concluded that the evidence was strong. Glover and colleagues reported their findings from 369 outpatients with cancer of whom 200 had pain (Glover et al. 1995). They demonstrated that increasing pain intensity correlated with worsening mood disturbance and that the worst pain intensity correlated most with the psychological impacts. Kane et al. (1985) studied 247 terminally ill cancer patients in 1985 and demonstrated a significant association between pain with anxiety or depression using the Center for Epidemiological Studies Depression Scale and the anxiety subscale from the Ware General Well Being Scale. Kelsen et al. (1995) recapitulated these findings in 130 pancreatic cancer patients in 1995, using the Beck Depression Inventory and the State-Trait Anxiety Inventory. Further, the study by Kelsen et al. demonstrated that increasing pain relief correlated with decreasing depression on the Beck Depression Inventory. In summary, these studies strongly support an association between pain and psychological distress, predominantly manifested as mood disturbance, anxiety, and depression. Worsening pain leads to worsening psychological distress, and improvement in pain mitigates the effect. The effect is evident across the disease spectrum, from relatively well outpatients to terminally ill people with limited life expectancies. Various pain coping strategies have been studied in cancer patients, including catastrophizing, diverting attention, reinterpretation, active coping, and passive coping (Bishop & Warr 2003, Zaza & Baine 2002). Despite the common references to coping in the cancer pain literature, the relationship between coping and pain is not entirely clear; some components of coping are studied more than other components. The relationship between catastrophizing and pain is the most certain. In the vast majority of studies of cancer pain, catastrophizing is associated with increased pain intensity, pain interference, and anxiety (Bishop & Warr 2003, Jacobson & Butler 1996, Lin 1998, Wilkie & Keefe 1991). These findings make sense in the setting of the apocalyptic meaning of the diagnosis of cancer for many people. Studies of pain control and appraisal have shown that high selfefficacy and perceptions of control over pain are associated with reduced pain in cancer patients (Bishop & Warr 2003, Wilkie & Keefe 1991). Overall, the studies on pain coping and appraisal suggest that cognitive behavioral techniques that

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address catastrophizing and promote self-efficacy would lead to improved pain management, especially in the chronic cancer pain setting.


PSYCHOSOCIAL INTERVENTIONS FOR CANCER PAIN Interventions designed to improve how cancer patients adjust to pain can be grouped into three broad categories: (a) cancer education, (b) hypnosis and imagery-based methods, and (c) coping skills training. An advantage of all of these approaches is that they can be combined with other cancer pain therapies, taking an adjuvant role on the WHO analgesic ladder. Although the use of these interventions is often encouraged through cancer pain practitioners and cancer pain management guidelines, the scientific evidence supporting their effectiveness is variable and they are all in need of further study.

Cancer Pain Education Educational programs are one of the most common ways of addressing the known barriers to cancer pain management and increasing knowledge (Allard et al. 2001, Hanson et al. 1997). Interventions are directed at one or two learners alone or in combination (e.g., patients and informal caregivers). The fundamental goal of interventions is to increase knowledge with an expectation that improved knowledge will lead to improved pain relief. Various formats have been studied, including didactic sessions, role modeling, videos, and tutorials. Recently, randomized controlled trials have demonstrated the clinical effectiveness of educational interventions. De Wit and colleagues (1997) published a randomized trial of a cancer patient education intervention that included intensive pain nurse specialist input, a brochure, an audiocassette, and a pain diary. Patient-based information was tailored for interest and patients were encouraged to increase help-seeking behavior. Mean pain intensity was statistically improved for the intervention group and this difference persisted over the eight-week study period. Oliver et al. (2001) published a randomized trial of individual patient coaching designed to address patient-based pain management barriers and to integrate goal-directed pain interventions. The control group was offered standard pain information. Intervention patients reported significantly less pain by the end of the study period (p = 0.014). Some high-quality studies have been negative; however, the trend is supportive of the role of patient-directed educational interventions (Dalton 1987, Rimer et al. 1987, Ward et al. 2000). Other potential learners include informal caregivers. To date, no randomized clinical trials have tested the efficacy of caregiver education specifically. However, Ferrell and colleagues (1995) evaluated the impact of an educational intervention on caregivers of elderly cancer pain patients within a nonrandomized study. Teaching led to improved caregiver psychological functioning and social well-being; patients had similar degrees of improvement in reported pain intensity.

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Overall, the current studies provide high-quality evidence that cancer pain education is practical and can improve patient outcomes.


Hypnosis and Imagery-Based Methods Hypnosis and imagery appear to have a role in the management of acute procedural pain, especially with children. The most compelling study is a randomized trial of 80 children undergoing lumbar punctures for hematological malignancies; the groups who were given a hypnosis or guided imagery intervention peri-procedure (N = 40) experienced significantly less pain, anxiety, and behavioral distress than those who were randomized to distraction (N = 20) or control (N = 20) (Liossi & Hatira 2003). Several years earlier, the same group demonstrated that children undergoing bone marrow biopsy had significantly less pain when trained in hypnosis or cognitive behavioral coping skills than in the usual treatment situation (N = 30) (Liossi & Hatira 1999). There was also a trend for hypnosis to be superior to cognitive behavioral skills training in this study (p = 0.20). These results were reinforced by several older studies in the same area (Wall & Womack 1989, Zeltzer & Lebaron 1982). The experiences for adults with acute cancer-related pain are similar to those of children. Women who were given a hypnosis pain management intervention prior to breast biopsy reported significantly less postsurgical pain and distress than those randomized to the standard care arm (Montgomery et al. 2002). Syrjala and colleagues (1992) found that the acute mucositis pain associated with bone marrow transplant could be mitigated with hypnosis. A follow-up study in 1995 by the same team involving 94 participants found that relaxation and imagery training with or without additional cognitive behavioral skills training led to reduced pain in comparison to controls despite higher mucositis severity for the intervention groups (Syrjala et al. 1995). The investigators concluded that additional cognitive behavioral skills training did not provide additional benefit over relaxation and imagery training alone. In contrast to the above, one randomized study of breast cancer patients undergoing autologous bone marrow stem cell transplant found that a comprehensive coping skills training protocol that included guided imagery did not alter pain intensity or character when compared to usual care (GatsonJohansson et al. 2000). The role of hypnosis and imagery in chronic malignant pain is less clear, but still promising. Spiegel & Bloom (1983) randomized 58 women with metastatic breast cancer to weekly group therapy with or without hypnosis. The hypnosis component lasted 5 to 10 minutes and focused on teaching patients not to fight the pain and to filter the hurt out by imagining competing sensations in the affected area. While hypnosis plus group therapy led to significantly less pain sensation (p < 0.02) and pain suffering (p < 0.03), the groups did not differ in terms of pain frequency or duration. To our knowledge, this study has not been repeated. Overall, hypnosis and imagery are effective at reducing acute pain in pediatric and adult cancer patients, especially children undergoing short-lived procedures

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and adults undergoing bone marrow transplant. Chronic cancer pain is likely responsive to hypnosis and imagery although repeat studies are warranted. Cancer pain is likely a fertile area for hypnosis and imagery interventions, and the value of this technique has yet to be realized by the cancer pain community as a whole.


Coping Skills Training Relatively few studies have assessed the efficacy of coping skills training in patients with persistent cancer pain. As reviewed in the previous section, the work of the Liossi and Syrjala groups demonstrates that coping skills training is no more effective than hypnosis/imagery alone in the management of acute pain in children and adults (Liossi & Hatira 2003, Syrjala et al. 1992). Berglund et al. (1994) studied a program of physical training plus coping skills training called “Starting Again” versus control in 199 patients who recently underwent curative resection of a primary tumor. Starting Again included exercise, progressive muscle relaxation training, information provision, and coping skills training. Intervention patients had better pain control over time, especially 3 to 6 months from initiation of the program (Berglund et al. 1994). Most recently, Dalton et al. (2004) studied a personalized program that tailors the coping skills intervention to the needs of the cancer pain patient. The team randomized 131 participants to standard coping skills training (CST; information provision, coping skills, problem-solving, relaxation, cognitive restructuring, and promotion of personal control), profile-tailored CST (matching of CST modules to the patient’s behavioral pain profile), or control. Profile-tailored CST provided better pain control in the month immediately after the intervention, standard CST patients had better pain control at six months, and both groups fared better than controls (Dalton et al. 2004). Though potentially helpful, psychosocial interventions for pain management are rarely used at end of life. We recently completed a preliminary study of a partner-guided cancer pain management protocol for 78 cancer patients who were having pain at end of life (F.J. Keefe, T.A. Ahles, L. Sutton, J. Dalton, D. Baucom, M.S. Pope, V. Knowles, E. McKinstry, C. Furstenberg, K. Syrjala, S.J. Waters, D.C. McKee, C. McBride, M. Rumble, & C. Scipio, unpublished manuscript). The three-session protocol, conducted in patients’ homes, combined educational information about cancer pain with the training of patients and partners in cognitive and behavioral pain coping skills. Partners receiving this protocol reported significant increases in ratings of their self-efficacy for helping the patient control pain and self-efficacy for controlling other symptoms. They also showed a tendency to report improvements in caregiver strain. The protocol had no significant effects on patients’ pain, likely because the patients in this study were very ill and in the terminal phase of their illness. To our knowledge, this preliminary study is the first test of a partner-assisted psychosocial intervention at end of life. Its results suggest that partner-guided pain management training may have a role in the context of end-of-life care. In summary, there is little evidence of an additive advantage of additional coping skills training interventions over hypnosis and guided imagery alone for patients

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with acute malignant pain. Studies suggest that coping skills training delivered to patients alone or to patients and their partners may be beneficial for chronic cancer pain, though the ideal components and tailoring of the coping skills training regimen is still to be defined.


FUTURE DIRECTIONS Evidence indicates that psychological approaches can make important contributions to our understanding and treatment of disease-related pain. Although this area is a relatively new one, psychological approaches do appear to be promising. Before the promise of these approaches can be fully realized, however, a number of important challenges will need to be met. One important challenge is how to best enhance the integration of psychological approaches to pain into the ongoing medical care of patients with chronically painful diseases. At present, it is relatively rare that a person having arthritis or cancer pain routinely receives any type of psychological pain management. Referral to a psychologist likewise is rare unless the individual is experiencing exceptionally high levels of depression or psychological distress. Reimbursement for psychological pain management services provided to patients with disease-related pain is also difficult to obtain. It is probably fair to conclude that psychological factors are not usually the focus of medical pain management and typically are not addressed by primary care physicians in a systematic or consistent fashion. In a busy medical practice, nursing staff are more likely to spend time talking with patients about their experience of and adjustment to pain. For this reason, there is growing interest in training nurses in cognitive-behavioral and other psychological pain management methods (Dalton et al. 2004). A major challenge for the future is developing practical strategies for integrating psychological pain management into medical practice. It appears that many patients with disease-related pain who could benefit from psychological interventions for pain management currently are not receiving them. Obstacles to reaching these patients not only include the difficulty of integrating psychological treatments into busy medical settings, but also the travel and costs involved in attending traditional face-to-face treatment sessions at tertiary care medical settings. Over the past 15 years, researchers have begun to explore innovative approaches to delivering pain management interventions. Lorig and her colleagues, for example, have conducted a series of studies examining the effects of a community-based, lay-led arthritis self-management program (see Lorig & Holman 2003). This program is delivered in 6 two-hour sessions in community settings such as local churches or schools. Although early versions of the self-management program were more educational and informational in nature, later versions included training in behavioral and psychological interventions such as skills mastery, modeling, and goal setting. Research studies have shown that individuals who participate in this program show significant improvements in pain and psychological distress and these improvements can be maintained up to five

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years after completing treatment (Holman et al. 1989). This low-cost approach is now available in many communities through the efforts of the Arthritis Foundation, which provides training for lay leaders in the self-help protocol and educational materials. A Spanish language version of the program has been developed and, in a randomized clinical trial, was found to produce significant improvements in pain, disability, self-efficacy, depression, and general health (Lorig et al. 1999a). Lorig’s self-help program serves as an excellent example of how a self-management intervention can be implemented in a cost-effective fashion that can reach a very wide range of persons in need. Interestingly, Lorig has recently modified the content of her program to make it more relevant to patients with a variety of chronic diseases (Lorig et al. 1999b). The Chronic Disease Self-Management protocol has obtained results similar to those of Lorig’s earlier arthritis self-management studies (Lorig et al. 1999b). Weinberger and his colleagues have conducted a series of studies exploring the utility of a telephone-based educational counseling protocol for patients having arthritis pain. The telephone is an ideal way to reach patients because it is accessible and easy to use. In Weinberger’s first study, a trained nurse phoned osteoarthritis patients on a biweekly basis to provide educational information and suggestions on their medical treatment (Weinberger et al. 1986). In this uncontrolled study, patients reported significant improvements in their pain and functional status and these positive effects persisted up to six months after the calls had ceased. A subsequent randomized study by this research team (Rene et al. 1992, Weinberger et al. 1989) tested the efficacy of a telephone counseling protocol delivered by nonmedical personnel. During the call, the counselor conducted an interview to review patients’ medications, joint pain, side effects of medications, early warning signs of other chronic disease, outpatient visits, and ways to overcome barriers to office visits. Data analyses revealed that patients receiving the counseling reported significant improvements in pain and physical disability when compared to a symptom-monitoring control and usual-care control condition. The benefits achieved were similar to those reported in drug trials for OA. A cost-effectiveness analysis also showed this treatment was relatively inexpensive (Weinberger et al. 1993). Weinberger and his colleagues have extended this approach to management of symptoms of other chronic diseases including diabetes and lupus (Weinberger 1998). Key issues to address in the use of telephone-based counseling include the viability of automatic voice messaging to contact patients who are hard to reach (Piette & Mah 1997) and payment for telephone-based counseling (Weinberger 1998). Although the approach used by Weinberger and his colleagues is mainly educational, rather than psychological, his research does demonstrate the potential of telephone-based intervention. The Internet provides another way to reach patients who otherwise might not receive intervention. Lorig et al. (2002) conducted a randomized study to test the efficacy of an email discussion group in the management of chronic back pain, a persistent non-disease-related pain condition. In this study, 580 people who reported persistent back pain were randomly assigned to a closed, moderated email

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discussion group or to a no-treatment control condition. All patients in the email discussion condition were on a listserv discussion group in which they received email messages from all other members of their group. The discussion group topics were open but the discussion was guided by two moderators and three content experts (a physician expert in back pain, a physical therapist, and a psychologist). Participants remained on the listserv for the one-year period of the study. All participants in the email discussion condition also received a book and videotape based on self-help pain management principles. Data analyses revealed that at the end of the one-year treatment period, participants in the email discussion group showed significant improvements in pain, disability, role function, health distress, self-efficacy, and self-care orientation when compared to the control participants. The participants in the email discussion group also reduced their physician office visits by 46% in the last six months of the study, a pattern that supports the costeffectiveness of the intervention. These findings suggest that an email discussion intervention may have benefits for persons suffering from persistent pain. This study had several limitations. First, all of the patients suffered from chronic back pain and it is unclear whether these results are generalizable to individuals suffering from disease-related pain. Second, attrition was relatively high in this study, with only 64% of the email discussion group participants and 81% of the control group participants providing data at the end of the one-year study. Finally, the level of involvement in the email discussion varied, with some individuals posting messages regularly and others rarely. Most of the clinical trials reviewed in this paper can be characterized as efficacy studies. Efficacy studies test an intervention under conditions that maximize the likelihood the treatment will be a success. In efficacy studies, patients are carefully screened to ensure they meet diagnostic criteria, therapists are experienced in the intervention and highly trained, and the ongoing quality of treatment is carefully monitored. Although efficacy studies represent an important first step in testing an intervention, their findings may not generalize to typical clinical settings where patients may have multiple comorbid conditions, the therapists delivering treatment may be relatively inexperienced, and the quality of treatment is not carefully monitored. At this point, more effectiveness trials are needed in which the setting and treatment delivery are more likely to mimic clinical practice. Given the emerging number of efficacy studies supporting cognitive-behavioral pain management interventions for rheumatoid arthritis, an effectiveness study in this population would be especially appropriate. Such a study would provide a unique opportunity to test the effects of cognitive-behavioral therapy in a painful and very disabling disorder. By collecting data on the potential savings related to treatment, such an effectiveness trial could provide important new findings that could have a vital impact on public policy. More research also needs to be done to address the role that psychological and social factors play in health disparities in disease-related pain. There is growing evidence that members of minority groups are more prone to adverse health outcomes from painful diseases such as lupus (Jordan et al. 2002) or cancer

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(Cleeland et al. 1997). Although reduced access to care and less aggressive medical management among minority patients have been documented as possible contributors to health disparities in both cancer and arthritis (Cleeland et al. 1997, Hoaglund et al. 1995), much less is known about the psychological and social mechanisms that may underpin these health disparities. Converging lines of evidence also show disparities in disease-related pain outcomes in persons of lower socioeconomic status (Callahan et al. 1996, Pincus & Callahan 1985). In particular, having a lower level of formal education, which is one correlate of having a lower income level, has been linked to both disease characteristics and psychological variables that influence pain and other disease outcomes. Among persons who have rheumatoid arthritis, there is evidence that those with a lower level of formal education not only report higher pain and disability, but are also more likely to show more rapid disease progression and early mortality (Callahan & Pincus 1995). In addition, a recent study by Callahan et al. (1996) found that helplessness mediated the relationship between lower formal education and mortality in rheumatoid arthritis. These results suggest an intervention designed to reduce helplessness may be effective in reducing the disparities in health outcomes associated with lower formal education. Future studies need to explore other potential psychological mechanisms (e.g., low self-efficacy, depressive symptoms) and social mediators (e.g., low social support) that may contribute to the disparities in pain-related treatment outcomes. ACKNOWLEDGMENTS Preparation of this review was supported by several NIH Grants (AR50245, NS46422-01, CA91947, AR047218, MH63429, AR46305) and, in part, by funds provided by the Arthritis Foundation and Fetzer Institute. Preparation of this chapter was also supported by a Clinical Scientist Career Development award from the Doris Duke Charitable Foundation. The Annual Review of Psychology is online at http://psych.annualreviews.org

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PSYCHOLOGICAL EVIDENCE AT THE DAWN OF THE LAW’S SCIENTIFIC AGE Annu. Rev. Psychol. 2005.56:631-659. Downloaded from arjournals.annualreviews.org by Ball State University on 01/05/09. For personal use only.

David L. Faigman Hastings College of the Law, University of California, San Francisco, California 94102; email: [email protected]

John Monahan School of Law, University of Virginia, Charlottesville, Virginia 22903; email: [email protected]

Key Words evidence, testimony, law, expert, Daubert ■ Abstract The Supreme Court’s 1993 decision in Daubert v. Merrell Dow Pharmaceuticals, Inc., holding that the admissibility of scientific evidence depends on its scientific merit, has made American law receptive to valid science to an unprecedented degree. We review the implications for psychology of the law’s taking science seriously. We consider the law before Daubert, and the ways that Daubert as well as modifications to the Federal Rules of Evidence have affected the admissibility of expert testimony. We describe the ramifications of these changes for psychology used as authority to create a general legal rule, as evidence to determine a specific fact, and as framework to provide context. Finally, future prospects for improving psychological testimony are offered: Court-appointed experts will increase the psychological sophistication of judges and juries, and evidence-based practices on the part of psychologists will increase the sophistication of the expert testimony that they proffer in court. CONTENTS INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THE TIME BEFORE DAUBERT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THE DAUBERT TRILOGY AND THE FEDERAL RULES OF EVIDENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Operating Premises of Daubert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daubert in Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THE STRUCTURE OF PSYCHOLOGICAL SCIENCE AND THE RULES OF ADMISSIBILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Authority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FUTURE PROSPECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Greater Legal Sophistication Through Court-Appointed Experts . . . . . . . . . . . . . . . Greater Psychological Sophistication Through Evidence-Based Practice . . . . . . . . 0066-4308/05/0203-0631$14.00

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INTRODUCTION In Daubert v. Merrell Dow Pharmaceuticals, Inc. (1993), the U. S. Supreme Court held that the admissibility of scientific evidence depends on its scientific validity. The Daubert holding was deepened and broadened by subsequent developments, including other Supreme Court decisions and extensive amendments to the Federal Rules of Evidence governing expert testimony. Additionally, the federal courts decide hundreds of cases per year involving Daubert-related issues, and the law reviews have produced literally thousands of articles on the subject. The states too are actively involved in this area, with a majority now following a Daubert approach and many more deeply influenced by that decision. Psychology is fully caught in the Daubert maelstrom. In fact, psychological science offers a particularly revealing window on the entire subject of expert evidence. Because of the many different types of expertise captured within the umbrella of psychology, psychological evidence presents the full range of issues that have bedeviled the courts since Daubert decided to take science seriously. The Daubert test applies to all types of psychological knowledge, whether based on clinical experience or on extensive empirical testing in the laboratory or in the field. A basic lesson of Kumho Tire Ltd. v. Carmichael (1999), one of the cases that followed Daubert, is that all expert evidence—whether it is described as “scientific” or otherwise—must pass a basic threshold test of validity. This is not to say that all expertise is, or is expected to be, comparable. But under Daubert, the courts need not make bright-line distinctions among kinds of knowledge. Federal Rule of Evidence 702 speaks in terms of “scientific, technical or specialized knowledge,” with all three being subject to the Daubert requirement that the judge act as a “gatekeeper.” However, as the Kumho Tire Court recognized, no bright lines divide these three kinds of expert opinions. In fact, all expert opinion is actually based on “specialized knowledge” (Faigman 2002). The bases for expert opinions range from years of experience to rich literatures of empirical research. As a practical matter, science is a method of inquiry, not a category of knowledge. The challenge lies in determining what methods are adequate to support a particular expert opinion. The approach introduced in Daubert changes the focus of the judicial inquiry from the ostensible category the opinion falls into (scientific/nonscientific) to the methods that were employed to evaluate the empirical basis for the opinion. In terms of applied science, no one method is fully privileged, though certain methods may be disfavored because they provide little dependable data. The ultimate legal issue presented is whether the methods are adequate and sufficient to support the expert opinion proffered in court. To this end, the Federal Rules of Evidence and a majority of state evidence codes now require judges, as gatekeepers, to determine whether the basis for expert testimony is more likely than not reliable and valid. Daubert has largely changed the way the field of psychology and law will henceforth conceive of itself. Daubert’s emphasis on the scientific method is likely to create a situation of the haves and the have-nots in psychology and the law. Many

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areas of psychology—too many to give them their due in this review—readily pass muster under Daubert. The most prominent example of this Daubert success, though hardly without exception, is the large percentage of the voluminous literature on the reliability of eyewitness testimony (Wells & Olson 2003). But many areas of psychology that are relevant to legal decision making lack robust research traditions and are likely to suffer under the Daubert lens. The years ahead will be difficult ones for experts whose opinions rest on shaky empirical foundations. Indeed, Daubert is likely to throw fuel on the already raging debate among psychologists regarding what methods are adequate to study human behavior. With the courts now participating, this debate assumes even greater practical significance. In time, as lawyers become more sophisticated in their understanding of science, many areas of psychology, previously allowed easy entry to the courtroom by virtue of general consensus in the profession, will now be stopped and turned away. The field of psychology and law is inextricably bound to developments in the area of expert evidence (Borgida & Hunt 2003). This review endeavors to describe and analyze these developments. Because the extensive empirical literature on jurors’ understanding of expert evidence has been reviewed recently and comprehensively by Vidmar & Diamond (2001; see also Diamond et al. 2003, Ivkovic & Hans 2003), we do not address this topic. Rather, we first explore the time before Daubert, when the “general acceptance” test of Frye v. United States (1923) provided the lodestar that guided the introduction of scientific evidence in court. The Frye test remains highly relevant today, though it has changed fundamentally due to the new understanding brought about by Daubert. We then turn more fully to the Daubert case and subsequent developments in case law and in amendments to the Federal Rules of Evidence. With these legal developments as background, we examine the basic lessons Daubert provides for psychology. At least in theory, if not always in practice, strategies for dealing with one type of expertise ought to apply to analogous kinds of expertise. Lessons for psychology can thus be drawn from the myriad other forms of expertise vying for court’s attention, including chemistry, biology, sociology, and medicine. Finally, we consider the future prospects for psychology and psychologists under Daubert. We address procedural matters, such as court-appointed experts and technical advisers, as well as the ways that Daubert bears on substantive debates within the field of psychology.

THE TIME BEFORE DAUBERT All rules of admissibility are constructed on the premise that, although U. S. Courts employ the adversarial process, the parties do not fully control what evidence reaches the trier of fact. In the realm of expert evidence, this means that some principle must control what sorts of evidence are admitted and what sorts are excluded. Prior to Daubert’s validity test, and still the alternative to that test that is most often posited, the general acceptance standard of Frye v. United States

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(1923) provided the basic procedure for handling scientific and technical evidence. Under this standard, novel expert testimony is admissible only once it has reached general acceptance in the particular field from which it comes. Although few courts cited Frye immediately after it was decided, general acceptance eventually became the standard to which all subsequent tests of admissibility have been compared (Faigman et al. 1994). Courts’ and scholars’ views of Frye were decidedly mixed. Frye plainly possesses an assortment of positive attributes. Foremost, perhaps, the general acceptance test does not require judges and lawyers to have any substantive knowledge of the science underlying proffered expertise. Lawyers are not generally known for their proficiency with science (Gatowski et al. 2001). The Frye test puts little pressure on them to learn any science. General acceptance employs the logical proposition that the best groups to ask about a technology or set of research findings are the very scientists who specialize in the subject. Waiting for a consensus to develop also operates to insulate courts from changing fads and fashions. The law is principally a conservative discipline and, on the whole, judges would prefer not to amend or overrule settled law with the publication of each new study. Frye seemed to ensure an orderly and accurate, albeit gradual, development of the jurisprudence of scientific evidence. Frye also had more than its share of detractors. Some critics charged that the test was too conservative, leading courts to exclude great advances until the respective fields embraced the new work, a process that could take years in some cases. In many applications, therefore, Frye was a very conservative influence. At the same time, Frye was also criticized for being too liberal, in that it might permit experts to testify to opinions based on little or no research. Because Frye only consults the pertinent field, findings from scientific disciplines that bring little rigor to their claims might pass legal muster quite readily despite the absence of adequate data or the use of poor research methods. In this way, Frye effectively constitutes deference to established professional fields and admissibility becomes a guild issue (Faigman 1989). Moreover, the Frye inquiry contains an inherent ambiguity. Judges are prompted to ask whether particular findings or techniques are generally accepted among researchers and practitioners in the field. However, the relevance of the science may be very different for the law than it is for the researchers and practitioners. Consider, for example, polygraphs. Polygraph utility for the courts depends wholly on the test’s power to distinguish between truthful answers and those that are deceptive for particular questions asked. Use in the field includes this utility, but may also include many others, including as a prop to interrogations, deterrence in national security screening, and detection of general nervousness to a line of questioning. Inquiring about the general acceptance of a technique, without being specific regarding what it is generally accepted to do, is a source of great confusion under Frye. There is little question that polygraphs are generally accepted among police, government security agencies, and professional polygraphers. However, the acceptance of the polygraph as a tool of interrogation says little about its scientific

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validity. A National Research Council report, for instance, concluded that polygraphs had doubtful validity, especially for national security screening purposes, but did not reach any conclusions about their utility as props for interrogations or their deterrent value (National Research Council 2003). Psychology, perhaps more than most sciences, is rife with examples of this phenomenon. A diagnosis may be “generally accepted” for therapeutic purposes, for example, but not for forensic purposes. Judges must ask the right questions under Frye in order to get useful answers. In 1993, the Supreme Court considered the question of whether the Federal Rules of Evidence had incorporated the Frye test when they were enacted in the 1970s. The Court held that they had not and went on to define the test that would rock the legal world. In Daubert v. Merrell Dow Pharmaceuticals, Inc. (1993), the Court held that judges could not simply defer to respective fields when assessing the validity of proffered expert testimony. Trial judges are gatekeepers and they have the responsibility to assess for themselves the reliability and validity of the methods and principles underlying expert evidence (Note 2003). A judge’s responsibility thus changed from merely assessing agreement in a field, the test under Frye, to evaluating the research methods and statistics that lay behind expert opinion. Therein lay the revolution of Daubert.

THE DAUBERT TRILOGY AND THE FEDERAL RULES OF EVIDENCE The Operating Premises of Daubert Among both lawyers and scientists, Daubert may be the best-known Supreme Court decision outside of constitutional law. It is also one of the least understood. Blame for the confusion surrounding the decision lies in part in the opinion itself. Justice Harry Blackmun, Daubert’s author, has generally been considered one of the more scientifically sophisticated justices and, at first glance, Daubert would seem to support this view of him. Yet, his opinion contains a hodgepodge of conflicting statements, enough so that almost everyone can find something to love and something to hate in the decision. In addition, the philosophy of science he employed was deemed reasonable by some, anachronistic by others, and incomplete by almost everyone (Haack 2003, Sanders et al. 2002). Yet, like many Supreme Court decisions, it established a foundation upon which a new edifice could be built. Daubert, in time, is likely to become more important for the principle it stands for than for what it actually says. That principle, in short, is that the law must join the scientific age. The very first question raised by scholars following the Daubert decision was whether the opinion would lead to more expert testimony or less (Chesebro 1994). This debate has not fully disappeared (Owen 2002), though most observers now believe that the answer is generally less. Blackmun’s opinion at first extolled the

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liberal admissibility standards of the Federal Rules of Evidence. It then adopted the most conservative standard the Rules offered for assessing expert opinion. Blackmun also described the trial court’s role as gatekeeper and explicitly recognized that the rule would lead to the exclusion of certain kinds of evidence. “[I]n practice,” Blackmun wrote, “a gatekeeping role for the judge, no matter how flexible, inevitably on occasion will prevent the jury from learning of authentic insights and innovations.” But this was the balance struck by Rules of Evidence, since they were “designed not for the exhaustive search for cosmic understanding but for the particularized resolution of legal disputes” (Daubert 1993, p. 597). Both civil plaintiffs and civil defendants proclaimed victory following the decision (Greenhouse 1993). Because Daubert changed the focus of the admissibility inquiry, it is not surprising that it should have a different impact than was felt under Frye (Groscup et al. 2002). Although research is ongoing regarding Daubert’s actual impact, it appears that the new rule will be both more permissive and more strict than the old rule, depending on the culture of the field from which the expertise comes (Faigman et al. 1994). Under Frye, the test was whether the field accepted the proffered knowledge, whereas under Daubert the test is whether the proffered knowledge is valid. Daubert should be expected to be more liberal for knowledge coming from rigorous fields in which adequate testing is part of the research tradition. In those fields, such as DNA profiling, robust discussion and debate might indicate lack of agreement despite the existence of sufficient data on which courts might permit expert opinion. In contrast, Daubert should be expected to be more conservative for knowledge coming from fields in which there is little or no research tradition of rigorous testing, e.g., psychoanalysis. In such fields, consensus replaces critical inquiry. Because Daubert asks what data support the proffered opinion, fields that operate by consensus rather than through data collection should be expected to have difficulty passing muster. Overall, however, it has become clear that civil defendants have more to celebrate than their counterparts. Many lower courts immediately understood Daubert to be a stand against so-called junk science. On remand to the Ninth Circuit, Judge Alex Kozinski (Daubert v. Merrell Dow Pharmaceuticals, Inc. 1995) interpreted Daubert as creating a brave new world in which trial court judges donned white coats and were forced to play scientist. Indeed, in Daubert itself, Chief Justice Rehnquist had concurred in part to register his lack of confidence in federal judges becoming successful “amateur scientists” (pp. 600–601). In their new capacity as gatekeepers, judges have brought greater scrutiny to bear on proffered expertise. The impact that this increased scrutiny has had in criminal cases remains a controversial and still unresolved matter. Many commentators speculated early on that the forensic sciences relied upon by prosecutors would fare poorly under Daubert (Risinger & Saks 1996, Saks 1998). Little of it is supported by quality research (Risinger et al. 2002). Moreover, prosecutors, even more so than plaintiffs, bear the burden of proof at trial. The courts, however, with minor exceptions, have so far resisted applying Daubert vigorously to prosecutorial expert testimony.

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This reluctance has resulted in opinions, especially for example on the subject of fingerprinting, that stretch credulity beyond the breaking point (e.g., United States v. Havvard 2000). Many legal scholars have commented on the seeming hypocrisy of applying Daubert strictly to civil plaintiffs and leniently to criminal prosecutors (Seton Hall Symposium 2003). This is also the subject of social science research, which appears to validate the observed trend (Groscup et al. 2002, Groscup & Penrod 2003). If there remained any doubt about the philosophical tenor of Daubert, three subsequent Supreme Court decisions dispelled it. The first two join Daubert to make up the often-cited “trilogy” of scientific evidence cases. The third is a sometimesoverlooked decision interpreting the scope and import of Daubert. In 1997, the Court ruled in General Electric Co. v. Joiner that the Eleventh Circuit Court of Appeals had erred in reversing a trial court’s exclusion of expert testimony. Chief Justice Rehnquist wrote for a unanimous Court, holding that appellate courts owe deference to a trial court’s admissibility rulings. The Eleventh Circuit had thus erred in second-guessing the trial court’s determination that the proffered expert testimony was unreliable. Of great significance, Rehnquist, who had worried over judges playing amateur scientists in Daubert, carefully scrutinized the proffered expertise and, in some detail, pointed out its many deficiencies. Moreover, the Court revised Blackmun’s statement that the trial court’s obligations were restricted to methods and principles, and made clear that conclusions too were part of the court’s gatekeeping duties. As Rehnquist put it, “nothing in either Daubert or the Federal Rules of Evidence requires a district court to admit opinion evidence that is connected to existing data only by the ipse dixit of the expert” (General Electric Co. v. Joiner, p. 146). Two years later, in Kumho Tire Ltd. v. Carmichael (1999), the Court closed the biggest loophole left from Daubert. Justice Stephen Breyer, the heir apparent to Blackmun’s label as resident scientist, held that a court’s gatekeeping responsibilities extended to all expert opinion, not just that of the scientific variety. The Kumho Tire opinion is replete with statements regarding trial courts’ abilities to become sophisticated consumers of science and the resources available, such as court-appointed experts who could assist them in their assigned task. Finally, in Weisgram v. Marley (2000), the Court affirmed an Eighth Circuit opinion that reversed a district court’s admission of expert testimony and then held that the expert testimony was not admissible as a matter of law and dismissed the suit. The district court had admitted the testimony of a fire captain, a fire investigator, and a metallurgist in a civil action involving whether a home heater had been defective and had caused the fire. The Court of Appeals reversed. Instead of remanding for a new trial, however, the Eighth Circuit held, as a matter of law, that the expert opinion was inadmissible and then remanded and ordered the district court to enter judgment for the defendant. The Supreme Court affirmed the dismissal of the suit. The Court thus sustained the remarkable power of appellate courts to find expert testimony inadmissible as a matter of law and to direct a verdict accordingly. In agreeing that the plaintiffs should not be given a second

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chance to find an admissible expert, Justice Ruth Bader Ginsburg stated (Weisgram v. Marley 2000, pp. 456–457): Since Daubert. . . parties relying on expert evidence have had notice of the exacting standards of reliability such evidence must meet. It is implausible to suggest, post-Daubert, that parties will initially present less than their best expert evidence in the expectation of a second chance should their first try fail. We therefore find unconvincing [the plaintiffs’] fears that allowing courts of appeals to direct the entry of judgment for defendants will punish plaintiffs who could have shored up their cases by other means had they known their expert testimony would be found inadmissible.

Daubert in Practice In light of the multitude of contexts in which Daubert is employed, from accident reconstruction to nuclear physics, it is not surprising that commentators’ views about how the rule applies should vary widely. Nonetheless, there are certain principles upon which virtually all Daubert-watchers, whether its supporters or detractors, would agree. At its most basic, Daubert requires the proponent of expert testimony to bear the burden of proof to show that his or her expert is qualified, that the proffered testimony is relevant to a fact in dispute, and that the basis for the expert’s opinion is reliable and valid. In this section, we consider these three criteria in regard to psychological expertise. Rule 702 of the Federal Rules of Evidence speaks of expertise in broad terms. The rule contemplates a wide assortment of potential experts. The rule does not contain degree requirements. An expert must only be qualified “by knowledge, skill, experience, training or education.” The case law, therefore, is replete with statements of deference to proffered experts’ qualifications, recognizing the value of experience and practical training. At the same time, the case law is also rich in demands that experts be better qualified than those that have been offered. Cases that exclude experts based on qualifications fall generally into two overlapping camps. In one, the expert lacks the basic credentials or experience to testify on the subject for which he or she is offered. Examples of this might be a nonlicensed therapist seeking to testify regarding the competency of a defendant to stand trial or a research psychologist without an adequate background in statistics and research methods. In the second category, the expert may be well qualified in one area or as a generalist, but has insufficient expertise to testify on the specific subject in dispute. This latter category is by far the more prevalent as well as the more controversial. Some courts adopt a permissive approach to qualifying experts and hold that once the proponent demonstrates the expert’s general competency to testify, questions regarding expertise in a specific subdiscipline are a matter of weight and not admissibility. In Zemaitatis v. Innovasive Devices, Inc. (2000, p. 633), for example,

QUALIFICATIONS

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the court permitted the testimony of a physician who was a “jack-of-all-trades.” However, a large number of courts agree, “[a] witness may be qualified as an expert on certain matters and not others” (United States v. Roldan-Zapata 1990, p. 805). These courts require that the expert display the knowledge or skill necessary to address the specific subarea that is the subject of the litigation (Faigman et al. 2002). Although examples can be found in many areas, courts have particularly frowned upon medical doctors who are offered to testify on every conceivable medical question (e.g., Alexander v. Smith & Nephew 2000). Psychology presents a situation similar to that of medical science, in that many practitioners are trained as generalists although there are a number of subdisciplines in which psychologists can specialize. On the whole, however, this issue appears to arise more frequently with medical doctors then it does with psychologists. One reason for this may be that in medicine, doctors typically specialize as a matter of residency training and they rarely jump specialties. An oncologist is likely to have little claim to be an expert on podiatry. If one does make such a claim, courts notice. In psychology, in contrast, specialty areas tend to be less rigidly defined and generalists often engage in a variety of specialties. In particular, many clinical psychologists define themselves by either theoretical orientation or the developmental stage of the subjects of study. These categories tend to cut across legal issues. Thus, it would not be unusual to see the same behavioral clinician testify in both a competency hearing and a parole determination. The more likely qualifications debate to be heard in courtrooms involves what credentials should qualify someone to testify as a “psychologist.” A wide assortment of psychology-related degrees are available, including but not limited to the PhD, EdD, PsyD, MS, and MA. Complicating matters further, state certification standards vary widely, and often depend on supervised training more than on academic education. In general, courts have been permissive in allowing specialists in human behavior to testify. Experience alone often appears to be sufficient to qualify an expert. This has been true, for example, for therapists seeking to testify on the rape trauma syndrome and the battered woman syndrome (Faigman et al. 2002). It is important to note, however, that qualifying an expert is merely the first step in determining admissibility. Whereas experience alone may be enough to meet the qualifications requirement, it may not be sufficient to validate the substantive aspects of the proposed testimony. Increasingly, courts tend to measure qualifications against the content of the proffered testimony. Consequently, an expert must demonstrate facility with the actual subject matter of the facts in dispute. Accordingly, a DNA technician cannot testify about the underlying science and a police officer is not permitted to testify on the intoxicating effects of blood alcohol level. However impressive may be their technical expertise, if expert witnesses do not have sufficient grounding in the underlying science, they should be precluded from testifying regarding the validity of the techniques they employ. The same holds true for psychological experts. Clinicians may have little familiarity with the extant research literature on the multitude of subjects that they put into practice. A clinician trained to apply

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the DSM-IV, for instance, may have little idea what sort of research is available to validate the diagnoses he or she employs daily. Similarly, a research psychologist might be unqualified to conduct a diagnostic interview. A prerequisite for all admitted evidence is that it must be relevant to some fact in dispute. Although no single piece of evidence must create the chain of inferences necessary to construct a case, every piece of evidence must at least be a link in the chain. The same is true for expert testimony. The Daubert Court described this requirement as an issue of “fit.” The methodological basis underlying an expert’s testimony must allow him or her to offer an opinion on a subject in dispute at trial. The most basic query concerning relevance asks whether the evidence permits an inference to be drawn regarding some fact in dispute. For instance, proof that a defendant was in San Francisco the day a robbery occurred is relevant to the government’s case if the crime took place in San Francisco, but irrelevant if the crime were wire fraud that could have occurred during a phone call from anywhere. Similarly, expert testimony that a defendant lacks volitional control over his or her behavior is relevant in a state employing the American Law Institute test for insanity, but it is irrelevant in states using the M’Naghten test under which the defendant must prove that he or she cannot distinguish right from wrong. Relevance ordinarily is a basic logical proposition that asks whether knowing X makes Y more or less likely, where Y is a fact that is material to the law that applies to the case. Under the American Law Institute test for insanity, lack of volitional control is an element of the defense, but under M’Naghten it is not. The notion of fit as a component of the relevancy inquiry involves a somewhat different level of analysis. As a general matter, it involves whether the methods used in the research underlying the disputed expert evidence are sufficient to support the opinion offered in a particular case. This is primarily an issue of external validity. A classic instance of this concerns whether research on the toxicity of certain substances generalizes from animal research to humans. Psychological research is replete with such issues. Does jury research on undergraduates generalize to real jurors? Would a finding of discrimination in one state’s capital sentencing system permit inferences about another state’s process? Does research on children’s competency in one legal context generalize to other legal contexts? Because so much psychological research uses subjects or situations that depart widely from the legal realities, this aspect of relevancy can present substantial obstacles to the admission of psychological opinion (Monahan & Loftus 1982).


RELEVANCE OR “FIT”

The core holding of Daubert was the Court’s interpretation of Federal Rule of Evidence 702 that the proponent of expert testimony has the burden to demonstrate by a preponderance of the evidence that the basis for the proffered opinion is valid. Although the Daubert Court stated that it was merely interpreting the plain meaning of the Federal Rules, the Rules themselves were amended in 2000 to reflect the meaning the Court had found in them

RELIABILITY AND VALIDITY

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seven years before. The three principal rules, Rules 701, 702, and 703, underwent revision. Rules 701 and 703 were both revised with the intent of shoring up identified weaknesses and ensuring the smooth operation of Rule 702. Rule 701 was strengthened to make sure that opinion evidence that should be evaluated under Rule 702 did not slip in through the backdoor of the lay opinion rule. Rule 701 only applies to lay opinion testimony that was within the personal knowledge of the witness. The rule provides as follows: If the witness is not testifying as an expert, the witness’ testimony in the form of opinions or inferences is limited to those opinions or inferences which are (a) rationally based on the perception of the witness, (b) helpful to a clear understanding of the witness’ testimony or the determination of a fact in issue, and (c) not based on scientific, technical, or other specialized knowledge within the scope of Rule 702. Rule 703 was also strengthened and, in particular, the rule was changed to end the frequent practice of parties using experts to introduce otherwise inadmissible hearsay statements. The new rule 703 provides as follows: The facts or data in the particular case upon which an expert bases an opinion or inference may be those perceived by or made known to the expert at or before the hearing. If of a type reasonably relied upon by experts in the particular field in forming opinions or inferences upon the subject, the facts or data need not be admissible in evidence in order for the opinion or inference to be admitted. Facts or data that are otherwise inadmissible shall not be disclosed to the jury by the proponent of the opinion or inference unless the court determines that their probative value in assisting the jury to evaluate the expert’s opinion substantially outweighs their prejudicial effect. The original Rule 703 suffered from an Achilles’ heel that rendered it of great service to practitioners with hearsay evidence they were otherwise having difficulty getting admitted. Rule 703 appeared to allow experts to testify to hearsay statements that would otherwise be excluded, so long as they were “facts or data . . . of a type reasonably relied upon by experts in the particular field in forming opinions or inferences upon the subject” (Carlson 1992). It thus operated as a conduit for otherwise inadmissible evidence. Rule 703 was amended to close this loophole. The Ninth Circuit observed in Turner v. Burlington Northern Santa Fe Railroad Co. (2003) that the new Rule 703 requires a court to ask two questions when evaluating otherwise inadmissible evidence: The first question is “whether the facts are of a type reasonably relied on by experts in the particular field.” The second question is whether the probative value of the underlying data substantially outweighs its prejudicial effect. Initially, as a simple factual matter, the court must determine whether the facts or data truly are of the type that an expert in the field would reasonably rely upon. Ordinarily, this will depend on the credibility of the expert’s warranting that this

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is so, as well as what evidence is adduced by the opponent calling this matter into question. Underlying this determination, however, is the Daubert demand that judges not simply rely on professional fields to determine what sorts of facts or data might be admitted. Typically, courts consider the initial factual question of whether the facts or data are the sort that experts from that particular field would rely upon separately from the reliability of the field more generally (Montgomery Cty. v. Microvote 2003). The second question requires a balancing of probative value and prejudicial effect. In Turner (2003), the Ninth Circuit emphasized that evidence that is otherwise “inadmissible will be kept out unless the court determines that any potential prejudice is substantially outweighed by the probative value” (p. 1062). This assessment lies within the discretion of the trial court and will not be disturbed on appeal unless it constitutes an abuse of discretion. The revision to Rule 703 is likely to have a disproportionate impact on psychologists and medical doctors. These professionals are the most likely, given the nature of their respective disciplines, to relate statements made by clients and patients as a basis for the opinions they offer in court. These experts will no longer be permitted to testify to hearsay statements unless the statements would be admissible under a hearsay exception or exclusion or the statements’ probative value substantially outweighs their prejudicial effect. Rule 702 essentially codified Daubert: If scientific, technical, or other specialized knowledge will assist the trier of fact to understand the evidence or to determine a fact in issue, a witness qualified as an expert by knowledge, skill, experience, training, or education, may testify thereto in the form of an opinion or otherwise, if (1) the testimony is based upon sufficient facts or data, (2) the testimony is the product of reliable principles and methods, and (3) the witness has applied the principles and methods reliably to the facts of the case. The Daubert Court suggested four factors that courts might use to assess the scientific validity underlying expert testimony. These were (a) testing, (b) error rate, (c) peer review and publication, and (d) general acceptance. Many commentators expressed fear that Daubert might be used woodenly by unsophisticated courts. Indeed, some courts initially believed that the Daubert factors constituted a “checklist” and sought to apply them mechanically. From the start, however, the Court made clear that the Daubert factors were not exclusive, and this fact was later emphasized in Kumho Tire. Moreover, courts and commentators have usefully suggested additional factors that might be employed to complete the assigned task (Faigman et al. 2002). More importantly still, courts have increasingly recognized that the myriad kinds of expertise they confront require a flexible and nuanced approach. Hence, peer review and publication may be an appropriate criterion for both histologists and historians, but error rate is not. Daubert mandates that trial courts determine whether the basis for proffered expert opinion is reliable and valid. The process trial courts use

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to reach this determination can be adjusted for each category of expertise and is largely within each court’s discretion. At the same time, it is clear that some process and some factors must be employed to make this judgment. Justice Antonin Scalia emphasized this point in his Kumho Tire (1999) concurrence. He stated, “[t]hough, as the Court makes clear today, the Daubert factors are not holy writ, in a particular case the failure to apply one or another of them may be unreasonable and hence an abuse of discretion” (p. 159). Because psychological testimony is ostensibly based on scientific knowledge, the Daubert factors are a reasonable starting point in most cases. As the Court in Certain Underwriters at Lloyd’s v. McDermott International, Inc. (2002, p. 4) put it, “in the vast majority of cases, the district court should first consider the Daubert factors before addressing whether other factors are relevant to the particular case.” In Daubert (1993, p. 590), the Court emphasized that “[i]n a case involving scientific evidence, evidentiary reliability will be based on scientific validity.” We, therefore, use the Daubert factors below as a launching point to begin our discussion of psychological expertise. Testing Although the Daubert Court listed testability as one of its four basic factors, it is almost certainly a prerequisite to all that follows. In psychology, as in all science, without testing, error rates could not be ascertained, peer-reviewed journals ordinarily would not publish the work, and general acceptance would not be forthcoming. It is important to remember that Daubert requires not merely that the basis for the opinion be testable, but that it have been adequately tested (Black et al. 1994). Ultimately, the challenge of Daubert lies in measuring this adequacy. Early on, it was generally thought that the form in which testimony was offered, scientific or nonscientific, mattered. Several courts proceeded on the theory that scientific evidence was usefully distinguished from nonscientific evidence. This was based foremost on the belief that some sort of bright line could be drawn between the two. Second, many courts also believed that scientific evidence cast a wondrous aura that might overwhelm triers of fact. As a consequence, these courts maintained, scientific experts should have to overcome many more hurdles than “specialists” or “technicians” if they were to be permitted to speak to jurors. The Kumho Tire (1999) Court rejected the distinction between science and nonscience and refocused the inquiry. Justice Breyer, writing for the Court, said that “it would prove difficult, if not impossible, for judges to administer evidentiary rules under which a gatekeeping obligation depended upon a distinction between ‘scientific’ knowledge and ‘technical’ or ‘other specialized’ knowledge. There is no clear line that divides the one from the others” (p. 148). The Court also rejected the proposition that jurors are likely to be more impressed with the science label than with technical or specialized expertise. Breyer observed that all experts are given considerable leeway to offer opinion evidence. Any concern with triers of fact yielding to the authority of scientific opinion was similarly present with all expert testimony.

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The more difficult issue presented under Daubert involved the manner in which trial courts should carry out their gatekeeping duties given the multitude of disciplines that enter courtrooms every day. The Daubert factors could not be applied to every sort of expert, and the rigor represented by those factors might not be appropriate in all cases. The Court had outlined an approach by which real estate appraisals, accounting, DNA technology, social psychology, and clinical medicine would all have to be judged. Kumho Tire (1999) extended Daubert beyond just “scientific” expertise but elided over the question of how trial courts should measure the validity of the great variety of expertises that courts confront daily. The Court stated: The conclusion, in our view, is that we can neither rule out, nor rule in, for all cases and for all time the applicability of the factors mentioned in Daubert, nor can we do so for subsets of cases categorized by category of expert or by kind of evidence. Too much depends upon the particular circumstances of the particular case at issue. . .. [A] trial court should consider the specific factors identified in Daubert where they are reasonable measures of the reliability of the expert testimony (Kumho Tire 1999, pp. 150–152). Properly understood, the Daubert factors represent a set of criteria by which fairly traditional scientific evidence might be judged. Although the actual inquiry might be significantly more robust, scientists themselves would consider whether and how a hypothesis had been tested, the degree and type of error associated with the science or technology, whether the report of the research was published in a high-quality peer-reviewed journal, and what scientists in the respective or associated fields thought about the work. When an expert proposes to testify regarding knowledge that is amenable to evaluation by those criteria, courts should use the criteria to assess the success of that knowledge. As regards the myriad of “subsets of cases categorized by category of expert or by kind of evidence,” other factors in addition to, or replacing, the Daubert set of four might be useful. This ought to be true whether the proffered expert is an art historian or a structural engineer. Professionals in different fields inevitably have criteria by which they measure the merit of the expert information that is produced by members of the field. If courts find a discipline’s evaluative criteria to be adequate, they can employ them just as the Daubert Court did in the category of expertise (i.e., epidemiology) involved in that case (Denbeaux & Risinger 2003). Increasingly, in order to help manage the many forms expert opinions take, scholars have sought to posit taxonomies of expertise to aid this effort (Gross & Mnookin 2003, Risinger 2002). The basic objective of this work is to devise factors and strategies by which a vast assortment of expertise might be evaluated. Courts have yet to embrace this fledgling movement, but they appear amenable to the effort. Some courts have even joined in. For example, the court in Ebenhoech v. Koppers Industries, Inc. (2002) suggested nine factors to be considered for engineering experts. Psychologists have yet to embark on a similar taxonomic endeavor within their field, with most commentators seemingly willing to accept Daubert’s factors at

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face value. Indeed, so far there has been a surprising quiet among psychologists who might have been expected to be critics of Daubert. Psychology presents a microcosm of modern expertise as it is found in courts today. But those psychologists with little data who nonetheless seek to testify have yet to complain very loudly about Daubert’s rigorous standards. Instead, these experts argue that the anecdotal method is a form of testing that meets Daubert. They also tend to rely on the criteria of peer review and publication and general acceptance to buttress their arguments for admissibility. We consider these issues below. Error Rate The Daubert Court’s use of error rates is at once completely understandable and quite perplexing. Error, of course, is a core component of all science and it comes in an assortment of varieties. Moreover, from the judicial perspective, the amount of potential error expert evidence poses ought to affect judgments about its admissibility. The cost of making a mistake, whether of the false positive or false negative variety, is an integral component of the policy implications of any admissibility determination. Yet, Justice Blackmun’s mention of error rate was perfunctory and not well designed to give most scientists substantial guidance. He merely stated, “[I]n the case of a particular scientific technique, the court ordinarily should consider the known or potential rate of error” (Daubert v. Merrell Dow Pharm., Inc. 1993, p. 594). He thus appeared to limit his concern with error to techniques like DNA profiling or polygraph tests that might have an identifiable error rate with repeated applications. Blackmun’s simplistic treatment of error is more perplexing still because the principal scientific subject of the Daubert case was epidemiology, a research area that employs the concept of error rate in complex and robust terms. Lower courts since Daubert have done little to remedy the deficiencies in Blackmun’s analysis of error rates. Indeed, their tendency is to gloss over or ignore this factor altogether. As any bright undergraduate psychology major knows, error in science is a fairly complex multidimensional concept. Yet courts hardly consider even the most rudimentary and unidimensional aspects of it. For instance, courts rarely distinguish between Type I and Type II error when they discuss this factor. Courts also have provided precious little discussion of random or systematic error, either as a general matter or in relation to specific proffered expert evidence. When courts discuss error, they typically limit their analyses to methodological problems that might bias the results. These concerns might generally be better considered under the first Daubert factor concerning the adequacy of the testing. Courts usually limit their discussions to such things as debates about the generalizability of animal testing, especially in toxic tort cases, and the use of undergraduates in psychology experiments. This sort of error is mainly nonquantifiable, which may be one of its principal virtues as far as courts are concerned. Peer Review and Publication In Daubert, the Court cited peer review and publication as a helpful proxy of good quality research, but emphasized that it should not be considered the sine qua non of validity. Although publication in peer-reviewed journals is the hallmark of the successful completion of research, the process does

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not guarantee quality data. Much that is published in the very best journals can be criticized, is impervious to replication, or turns out to be invalid (Jasanoff 1996). In addition, some work that appears outside the strict scientific peer review process is well done and well worth relying upon. Nonetheless, the ordinary culmination of most scientific research is publication in a peer-reviewed journal and, as implied in Daubert, expert opinion that comes without this imprimatur is suspect. A somewhat less noticed aspect of the peer review and publication factor is the more general dynamic that is present in healthy scientific communities. In describing the peer review criterion, Blackmun observed, “submission to the scrutiny of the scientific community is a component of ‘good science’. . . because it increases the likelihood that substantive flaws in methodology will be detected” (Daubert 1993, p. 593). Peer review and publication is just one instance of a much more general phenomenon of critical self-assessment that mature sciences engage in as a matter of course. Psychology, like many applied scientific fields, does not always carry out this checking function successfully. The single most effective checking tool in science is replication. If one laboratory announces the discovery of cold fusion, for instance, others inevitably follow fast on its heels to assess the validity of the claim. This is true even with many complex applied phenomena, such as the epidemiology of the effects of some alleged toxic substance or product. One or two studies of a phenomenon from one or two laboratories are ordinarily not enough to recommend policy. In psychology, in contrast, it is not unusual for one or two studies to serve as the substantive foundation for policy recommendations (Faigman 1989). There is an assortment of reasons for this phenomenon. Good applied psychological research tends to be expensive. Given limited resources, it is not surprising that many researchers would prefer to extend knowledge beyond the last study, rather than “waste” time, money, and energy replicating another’s research. In addition, prestige and professional recognition are associated with investigating new hypotheses rather than with confirming old ones. In the better research programs, researchers strive to replicate previous work in the process of extending those discoveries or moving the enterprise in new directions. The step-by-step construction of empirical knowledge, through the efforts of independent research groups, is a sign of a healthy field. An even greater threat to the checking function inherent in good science is the concern that significant areas of psychology serve policy agendas, whether of the researchers or of certain political communities more generally (Redding 2001). Research that appears to substantiate a good policy outcome may be resistant to subsequent work. Later studies that cast doubt on politically fashionable research findings may not be a career-enhancing move. Although this concern might be leveled at most of the social sciences—and many of the applied physical sciences as well—psychologists with an interest in affecting legal discourse should be greatly concerned if their findings are too easily dismissed as the product of politics. Scientists with strong policy agendas are readily discounted by courts, however strong their research program might be.

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General Acceptance Like peer review and publication, the use of general acceptance as a criterion of validity depends on the quality of the field from which the findings come. Unlike the testing and error rate factors, general acceptance is merely a proxy for validity and is only as good as those doing the accepting or rejecting. The biggest danger associated with this factor from the law’s standpoint is that consensus might replace critical assessment. This has largely occurred, for example, in the forensic specialties, such as bite mark and handwriting identification analysis, where the main community involved is law enforcement and dissent is strongly frowned upon. Although psychology, as a field, demonstrates much greater critical judgment than many forensic areas, it shares some of the difficulties that are endemic in the forensic specialties. Especially deleterious to self-criticism is that some psychologists measure the success of their work by whether courts accept or reject it.

THE STRUCTURE OF PSYCHOLOGICAL SCIENCE AND THE RULES OF ADMISSIBILITY Law and science approach the empirical world in fundamentally different ways. Whereas science attempts to discover the universals hiding among the particulars, trial courts attempt to discover the particulars hiding among the universals (Faigman 1999, p. 69). Research on eyewitness identification, for example, examines those factors that might interfere with memory and perception and thus could render eyewitness accounts less reliable. The result has been a robust list of general factors that, if present, may be relevant in particular cases (Wells & Olson 2003). The trier of fact, of course, must decide whether a particular witness’s testimony is reliable. In most cases, this means that, if allowed, experts provide triers of fact with general information and leave it to them to apply these insights to the particular case. Another well-regarded area of psychological study, work on the suggestibility of children’s memory, operates very similarly (Bruck & Ceci 1999). Research has produced substantial insights regarding children’s memory, but it does not speak directly to the question of whether a particular child-witness is accurate. In many contexts, however, experts seek to supply both the general knowledge and the specific application of that knowledge to the particular case. For instance, recitation of the general research basis for posttraumatic stress disorder (PTSD) is ordinarily followed by clinical testimony informing the trier of fact that the subject is or is not an example of the general phenomenon. This combination of general and specific is endemic to the law and science intersection and presents a substantial challenge to both lawyers and scientists. Although not easily resolved, the phenomenon of the general nature of science and the particularized interest of the law is generally understood by lawyers and scientists. Courts so far have dealt with this issue primarily in the medical context in which it is starkly presented. There, courts routinely distinguish between “general causation” and “specific causation.” The former refers to whether, for example,

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research demonstrates that a substance can cause cancer; the latter refers to whether the substance caused a cancer in a particular patient. Courts agree that general causation must be shown before evidence of specific causation is allowed. Although somewhat less developed in the case law, psychology presents an essentially identical situation to that of medicine. The basic schema now being developed under Daubert parallels the schema suggested in a series of articles published in the 1980s (Monahan & Walker 1986; Walker & Monahan 1987, 1988) that identified three basic ways in which the law employs social science research. These are as social authority, social facts, and social frameworks. Social authority refers to social science research relevant to the determination of facts that are used in the formulation of legal rules or policy. According to this proposal, social authority is analogous to legal authority and should be consulted similarly. Hence, judges would consider social science “precedent” (i.e., past research) as presented through briefs, oral arguments, and sua sponte (of one’s own accord). Relevant research would then be incorporated into the judge’s conclusions of law. Alternatively, social science might be relevant to facts that are specific to the litigation. Most survey evidence commissioned for a given case fits into this category. Social fact evidence would be presented to the trier of fact through expert testimony. The final category is social framework evidence, in which social science research has relevance as a combination of social authority and social fact. In social frameworks, some issue in the particular dispute is claimed to be an instance of a social scientific finding or theory of general import. All three categories have been affected by Daubert and its progeny, though social framework evidence supplies the lion’s share of the case law.

Social Authority Although the Daubert decision concerns the meaning of rules of evidence, and most of the subsequent case law involves the practical application of the Daubert test to expert evidence at trial, its significance extends well beyond the four corners of the courtroom. Empirical judgments enter legal analyses in ways well outside of the ordinary evidentiary process. In particular, empirical judgments are often an essential component of the policy-making function of courts, especially in the context of constitutional law. In abortion cases, for example, the empirical judgment of when a fetus becomes medically viable sets the time at which states can prohibit abortions (Planned Parenthood v. Casey 1992). Other empirical judgments of import to abortion litigation are of a more psychological nature, including whether previability abortion regulations, such as spousal notification or 24-hour waiting provisions, constitute a “substantial obstacle” to the exercise of a fundamental right (Casey 1992). In capital sentencing cases, empirical judgments concerning the psychological deterrent effect of execution, its possible discriminatory application, and whether juries from which opponents of capital punishment have been removed are biased against the defendant have been the subjects of the Supreme Court’s attention (Gregg v. Georgia 1976, McCleskey v. Kemp 1987, and

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Lockhart v. McCree 1986, respectively). Additional examples of the Supreme Court’s confronting empirical judgments upon which psychological research was brought to bear include school segregation (Brown v. Board of Education 1954), the testimony of child witnesses via closed-circuit television in sexual abuse cases (Maryland v. Craig 1990), the consequences of permitting physician-assisted suicide (Washington v. Glucksberg 1997), the effects of jury size (Ballew v. Georgia 1978), and the learning styles of men and women (United States v. Virginia 1999), among many others (Faigman 2004). The relationship between Daubert and empirical judgments relevant to the making of constitutional law is complex and courts and scholars have yet to fully work through the subject’s many complexities. Consider, for example, the empirical judgments called for in the litigation involving the Violence Against Women Act (42 U.S.C. §13,981), which was struck down by the Supreme Court in United States v. Morrison (2000) on the basis that Congress did not have the authority to pass the Act under the Commerce Clause. Congress had held extensive hearings on the question of whether domestic violence substantially affected interstate commerce, including considering testimony on the psychological consequences of such violence. Congress explicitly found that violence against women was a national problem that substantially affected commerce and thus the Constitution authorized federal regulation. Opponents challenged this factual finding, arguing that regulation of domestic violence was within the purview of the states. The Supreme Court agreed with this argument and invalidated the statute. Initially, however, this factual question had to be litigated before a trial court. For a trial court hearing this matter, at least three sources of evidence might be available to consider. The first is expert testimony proffered at trial by the parties, the second is the congressional record of the hearings upon which the law was based, and the third is briefs filed by the parties and amici that substantively discuss the science. The lessons of Daubert apply differently to these three sources of evidence. Expert testimony regarding constitutionally relevant empirical judgments introduced at trial are subject to the procedural strictures of Daubert and its progeny (National Abortion Federation v. Ashcroft 2004). Accordingly, expert opinion is not admissible unless the proponent of the evidence demonstrates that it is based on reliable and valid methods and principles and that the opinion reasonably follows from the underlying research. In contrast, because congressional hearings and briefs do not come in through ordinary evidentiary procedures, Daubert and the Rules of Evidence do not formally apply to this kind of empirical information. This is not to say, however, that Daubert or the principles it represents should not be applied to this kind of empirical information. Although legislative hearing testimony and briefs are not subject to admissibility determinations, courts must nonetheless weigh the information presented at the hearings or contained in the briefs in making the ultimate constitutional determination. Daubert, and in particular its principle of scientific sophistication, can be employed usefully to evaluate the value of extraevidentiary expert opinion. Therefore, courts should consider, for instance, whether the research presented in the congressional testimony or that is

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set forth in a brief, was adequately tested, had reasonable error rates, was subject to peer review and publication, and is generally accepted among knowledgeable members of the field. To the extent that psychological or other research found in legislative hearings or briefs fails a Daubert-styled review, courts should give it less weight or no weight at all.


Social Facts Psychological researchers sometimes conduct empirical studies to determine a specific “social fact” at issue at trial. This most often occurs in trademark litigation. The Trademark Act of 1988 states that the federal Patent and Trademark Office will refuse to register a new trademark if it so resembles a trademark already registered to another person “as to be likely . . . to cause confusion.” Psychological surveys—or, sometimes, true experiments—to establish whether consumers or potential consumers are likely to be confused, or are actually confused, between products with similar trademarks have been admissible in court at least since Zippo Manufacturing Company v. Rogers Imports, Inc. in 1963. Increasingly, the admissibility of expert psychological testimony on consumer confusion is being held to Daubert standards. For example, in Bicardi v. New York Lighter (2000), the Court applied Daubert to a survey proffered by the plaintiff, the manufacturer of Bicardi rum, addressing whether consumers would be confused by the defendant’s naming its cigarette lighter “Bicarbi.” Excellent guides now exist (Diamond 2002) that walk psychologists through the steps necessary to conduct and analyze survey research in trademark and related cases that will survive Daubert review. Obscenity is a second area that in the past has often seen psychologists testify as “social fact” witnesses. The Supreme Court, in Miller v. California (1973), held that one of the prongs of the test to determine whether a publication or film was obscene was “whether the average person, applying contemporary community standards, would find that the work, taken as a whole, appeals to the prurient interest.” Surprisingly, however, no published case has yet subjected a psychological survey of community standards regarding obscenity to a Daubert analysis. The likely reason for this nonapplication of Daubert is that obscenity prosecutions declined to almost zero during the Clinton administration (Blum 2004), and so few surveys of community standards were proffered in court. The Bush administration, however, has vowed to begin the vigorous prosecution of obscenity cases, so the first applications of Daubert to the issue of community standards can be expected soon. When those applications take place, courts will have to resolve an issue of operational definition on which existing case law is conflicted: Are the “community standards” at issue in obscenity prosecutions to be determined by assessing and aggregating the personal standards of a representative sample of the relevant community, or by asking respondents about their perceptions of the standards of others in the community? Linz et al. (1991), in the context of an actual obscenity case, demonstrated how assessing and aggregating the personal standards of community

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members leads to much more acceptance of pornography than asking community members their perceptions of their neighbors’ standards. As in Lake Wobegon, everyone appears to be more tolerant than average. Given these results, they recommend that courts determine “community standards” by expert testimony that uses the former rather than the latter methodology. The great bulk of what is usually seen as psychological “fact” testimony is presented by clinical psychologists rather than by researchers who conduct original studies that might be introduced in court (Slobogin 2002). Judging from the content of forensic handbooks such as Melton et al. (1997), Heilbrun et al. (2002), Goldstein (2003), and Grisso (2003), the factual issues that clinical psychologists most often address in court include a variety of legal competencies (e.g., to stand trial, to plead guilty, to testify, to waive rights, to be executed, to be a parent, to consent to or to refuse treatment, to make a will), the existence of mental disorder for various legal purposes (e.g., civil commitment, the insanity defense and diminished capacity, workers compensation, tort damages, invocation of the Americans With Disabilities Act), and predictions of specified kinds of future behavior—often, violence—in many legal contexts (e.g., civil commitment, sex predator commitment, criminal sentencing, child custody). Daubert places the onus squarely on the clinician-witness to demonstrate the empirical validity of the factual conclusions to be offered in court. There is, of course, a voluminous research literature underlying many (though not all) of the mental disorders listed in the DSM-IV. Recent years also have seen the development of a wide variety of what Grisso (2003, p. 42) has called “forensic assessment instruments.” Some (though, again, by no means all) of these forensic assessment instruments are supported by an extensive body of tested, published, and peer-reviewed validation research and are, or rapidly are becoming, generally accepted in the field of forensic evaluation. For example, Grisso & Appelbaum (1998) have developed and validated an instrument to assess competence to consent to treatment, and Poythress et al. (2002) have developed and validated an instrument to assess competence in the criminal adjudication process, derived both from an analysis of the underlying legal concerns, as revealed in the case law, and from basic psychological research in decision making. Such instruments should have little difficulty in surviving Daubert review (e.g., Commonwealth v. Morasse 2001). Expert opinion on social facts bears some resemblance to the social framework evidence discussed in the next section. Opinions about competence, mental disorder, or future violence, contain both a general component and a specific component. For instance, “dangerousness” determinations should depend on wellvalidated measures that are appropriate to use in the particular forensic context in which they are employed. Although clinical assessments of future violence may be valid and reliable when based upon “general” actuarial measures, those assessments may be invalid if based solely on unstructured clinical judgment limited to a “specific” patient. Similarly, a test that is employed for therapeutic purposes may not be valid for forensic purposes. For example, most courts agree that while “penile plethysmographs can help in the treatment and monitoring of sex offenders,

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these tests have no indicia of reliability as evidence at trial” (Doe v. Glanzer 2000, p. 1266).


Social Frameworks If courts treat social frameworks such as the “battered woman syndrome” or the “rape trauma syndrome” the way they treat what might be called medical frameworks, they should require that both general causation and specific causation be demonstrated independently under Daubert. Moreover, without adequate proof of general causation, no testimony at all on specific causation will be permitted. Even with proof of general causation, proof must still be forthcoming that specific causation is within the capabilities of the science and the proffered expert. Much of psychology is likely to struggle to meet these standards, as medicine has struggled as well (Kassirer & Cecil 2002). As it turns out, courts themselves have struggled to devise a consistent response to social framework evidence and there is much variability in the case law (Monahan & Walker 2002). Indeed, U.S. Courts of Appeal disagree on the amount of rigorous proof that is necessary to permit expert opinion on questions of medical causation. The Third Circuit, for instance, has adopted a relatively lenient approach under Daubert and has allowed a good deal of clinical judgment to support the opinion (Heller v. Shaw 1999). The Fifth Circuit, in contrast, has taken a decidedly more rigorous approach and only allows opinion on medical causation that is supported by substantial research (Black v. Food Lion, Inc. 1999). The Supreme Court will likely have to settle this issue before long. However the courts eventually resolve these differences will have a substantial impact on psychology and the experts who testify on the subject (Fiske & Borgida 1999). Clinical assessments in psychology are closely analogous to clinical assessments in medicine. As noted above, the initial question presented in medical cases is whether substantial research exists to support an inference of general causation. In Daubert, for example, the issue was whether Bendectin, a morning sickness drug, causes birth defects. Research on general causation took a variety of forms, including epidemiology. The key issue was whether the exposed population had a higher incidence of birth defects than the nonexposed population. In the end, the best research indicated no statistically significant increased risk of birth defects in the population exposed to Bendectin (Sanders 1993). Consider PTSD as it is used in “rape trauma syndrome” sexual assault cases. It is quite analogous to the Bendectin case. One should expect good quality research demonstrating a higher incidence of PTSD in the affected population than in the general population. Research that fails to use a comparison group, therefore, has little probative force. In addition, courts should expect that good research methods would be used to assess the relative risk, just as would be true in epidemiological work. To date, however, studies on rape and the short-term development of PTSD have largely failed to use comparison groups or have suffered from other methodological flaws (Frazier 2002).

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With psychology, as with medicine, courts sometimes muddle the issues of general and specific causation. Consider, for example, the subject of repressed memories. In Logerquist v. McVey (2000), the Arizona Supreme Court believed that proffered expert testimony regarding the plaintiff’s claim of repressed memories of abuse committed by the defendant was not “scientific” and thus did not have to pass muster under the Frye test. In effect, the Logerquist Court found that there was no general causation issue by which a specific causation deduction had to be ascertained. The only issue was specific causation, which the Court believed could be the product of inductive reasoning. The Court explained that Frye “is applicable by deduction from the application of novel scientific principles, formulae, or procedures developed by others” (p. 133). However, the Court argued, Frye “is inapplicable when a witness reaches a conclusion by inductive reasoning based on his or her own experience, observation, or research.” This bypass of Frye (or Daubert) is called the opinion rule, because it allows an expert to offer his or her opinion on technical subjects without clearing admissibility hurdles so long as it is based on personal experience. Several states employ the opinion rule, including California and Florida. The opinion rule, to put it mildly, represents approximately a sixteenth-century understanding of the scientific method. An expert’s statement that a person had a repressed memory that has now been recalled is a product (must be a product) of both general causation and specific causation inferences. However implicit the expert wishes to leave it, the statement that person X had repressed memories is actually saying that person X is an instance of a more general set of similarly situated people who have, or have had, repressed memories. The Court sought to permit testimony on the particulars without any proof of the universals. According to the Logerquist view of the scientific method, an expert should be allowed to categorize an observed phenomenon, such as repressed memories, without first having to show that such a category exists. What the Court called inductive reasoning is simply hypothesis formation through anecdotal experience, but without hypothesis testing through formal scientific inquiry. The plaintiff’s expert said that he had treated “hundreds of survivors of childhood sexual abuse,” and that he was prepared to testify that some of these “victims do have delayed memories, that their memories are as reasonably accurate as normal memories, if not better” (p. 117). How would his “experience” give him this information? Contrary to Logerquist, the key to success as an expert should turn out to be the same as the key to success in science—substantial amounts of high-quality research on general causation. Such work not only permits experts to rule in certain factors as possible causative agents, but also to rule out seemingly plausible alternative factors. Much contemporary psychology, as much contemporary medicine, will fail to meet this standard. This is so for many clinical tests relied upon regularly, such as the Rorschach or other projective techniques (Garb et al. 2003). It is also true for many of the myriad syndromes that seem to replicate at extraordinary rates [e.g., “shopping addiction” (United States v. Roach 2002)].

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As to specific causation, even some of the best work in psychology provides little basis for expert opinion on this topic. Consider the premier example of good psychology and law over the past several decades, research on the reliability of eyewitness identifications (e.g., Wells & Loftus 2003). This research has done an excellent job of identifying a wide variety of factors that might interfere with eyewitness perception, memory, and recall. Researchers have demonstrated rather less interest in factors that might aid eyewitness accuracy. Yet, there is no research whatsoever on whether experts are able to distinguish reliable from unreliable eyewitnesses. Instead, researchers have studied the phenomenon at a general level and, with only an occasional exception, researchers do not attempt to offer opinions on the reliability of specific eyewitnesses.

FUTURE PROSPECTS Greater Legal Sophistication Through Court-Appointed Experts As we noted at the start of this chapter, the revolution sparked by Daubert primarily involved a changed perspective. Under Frye’s general acceptance test, admissibility determinations consisted of little more than counting noses of professionals in a particular field. Daubert demands that judges evaluate the research methods supporting expert evidence and the principles used to extrapolate from that research to the task at hand (Risinger 2003). This is a daunting task, especially for judges who, on average, have little formal training in statistics and research methods. How, it may reasonably be asked, are judges to become sophisticated consumers of psychological and other expertise, as Daubert requires? One proposed solution to this question is greater use of court-appointed, rather than party-retained, experts. Scholars have actively advocated this device (Berger 1994, Gross 1991). Although research indicates that courts are disinclined to appoint experts for themselves, there are signs that this reluctance is slowly changing (Faigman et al. 2002). Cecil & Willging (1994) found that judges relied little on this appointment power; moreover, most (62%) of those surveyed thought that the “appointment of an expert [is] an extraordinary action” (pp. 1015–1018). The principal reason for this view was judges’ belief that court-appointed experts undermine the adversarial process. Yet, as experience with the challenges of implementing Daubert has been gained, courts appear increasingly sympathetic to the possibility of appointing experts (Hall v. Baxter Healthcare Corp. 1996). Indeed, Justice Breyer, concurring in Joiner (1997), quoted approvingly the New England Journal of Medicine’s amicus brief calling for greater use of court-appointed experts: [A] judge could better fulfill this gatekeeper function if he or she had help from scientists. Judges should be strongly encouraged to make greater use of their inherent authority . . . to appoint experts. . .. Reputable experts could be recommended to courts by established scientific organizations, such as the

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National Academy of Sciences or the American Association for the Advancement of Science. (p. 149) In federal cases, court experts assume primarily one of two forms. The first, properly termed court-appointed experts, are selected mainly to assist jurors in determining what weight to give the expert evidence that is presented by the parties. These experts give testimony in court, are subject to the ordinary procedures of discovery, and may be cross-examined by one or both parties. The second type of court expert, increasingly prevalent in the case law, is the technical adviser. These experts’ primary function is to assist judges rather than juries. They typically are not subject to adversarial processes such as discovery or cross-examination. In effect, technical advisers sit at judges’ sides, like law clerks, assisting them to understand the expert evidence that the parties seek to have admitted at trial. In the future, greater use of psychologists as court-appointed experts or as technical advisors could go far in educating juries and judges to be sophisticated consumers of psychological evidence.

Greater Psychological Sophistication Through Evidence-Based Practice It is sometimes suggested, especially by legal scholars and judges, that the Daubert standard should be relaxed in light of the enormous difficulty in studying human behavior (Kinports 2004). Raeder (1996), for example, has questioned the application of strict standards to a difficult-to-test subject like domestic violence. Obviously, such topics cannot be studied experimentally. While domestic violence or the effects of severe trauma are surely challenging subjects for psychologists to research, in principle they present no greater obstacles than many other subjects of interest to the law, such as DNA typing, engineering, and epidemiology. In this context, we note that our review appears at a time when a debate rages in clinical psychology around what is being termed “empirically validated treatment” or, more broadly, “evidence-based practice.” On one side of this debate are Scott Lilienfeld (2002), David Barlow et al. (1999), and Richard McFall (1991), clinicians themselves, who take the view that clinical psychologists should restrict their professional activities to those that have ample support in the scientific literature. As Lilienfeld (2002) has stated, Once we abdicate our responsibility to uphold scientific standards in administering treatments, our scientific credibility and influence are badly damaged. . .. Our students will most likely follow in our footsteps and continue to turn a blind eye to the widening gap between scientist and practitioner, and between research evidence and clinical work. (p. 9) On the other side of this debate are Ronald Fox (2000) and Ronald Levant (2003), both of whom have been recent presidents of the American Psychological Association. Fox (2000, pp. 1–2) has written, “Psychologists do not have to apologize for their treatments. Nor is there any actual need to prove their effectiveness.”

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Levant (2003) has taken the position that in clinical psychology “many treatments have not been empirically studied, and that there is a big difference between a treatment that has not been tested empirically, and one that has not been supported by the empirical evidence.” He believes that Lilienfeld and others have “gone overboard in their enthusiasm for scientific vetting of therapeutic techniques” (Goode 2004). Such vetting, Levant believes, has already hurt practitioners by restricting the interventions that are reimbursed by insurance companies, and “could create additional hazards for practitioners in the courtroom if empirically validated treatments are held up as the standard of care in our field” (Levant 2003). Rarely has the law come down so forcefully on what might be seen as an intraprofessional dispute (Appelbaum 2002). Daubert unequivocally endorses “empirically validated treatments” and “evidence-based practices.” Under Daubert, there is in fact no difference “between a treatment that has not been tested empirically, and one that has not been supported by the empirical evidence.” Neither is admitted. Whatever clinical value the unvalidated psychological assessment or treatment techniques may or may not have for patients, Daubert makes plain that testimony employing such techniques has no place on the witness stand. For psychologists, Daubert stands as a guardian at the courtroom door, warmly welcoming those with relevant and valid scientific evidence, and brusquely sending others on their way. ACKNOWLEDGMENT The authors would like to thank Shari Diamond, Kirk Heilbrun, Michael Saks, and Larry Walker for their comments and suggestions on a draft of this chapter. The Annual Review of Psychology is online at http://psych.annualreviews.org

LITERATURE CITED Alexander v. Smith & Nephew, 90 F.Supp.2d 1225 (N.D. Okla. 2000) Appelbaum PS. 2002. Policing expert testimony: the role of professional organizations. Psychiatr. Serv. 53:389–90 Ballew v. Georgia, 435 U.S. 223 (1978) Barlow DH, Levitt JT, Bufka LF. 1999. The dissemination of empirically supported treatments: a view to the future. Res. Ther. 37: S147–62 Berger M. 1994. Novel forensic evidence: the need for court-appointed experts after Daubert. Shepard’s Expert Sci. Evid. 1:487– 512 Bicardi v. New York Lighter, 54 U.S.P.Q.2d 1335 (2000)

Black B, Ayala J, Saffran C. 1994. Science and the law in the wake of Daubert: a new search for scientific knowledge. Texas Law Rev. 72:715–802 Black v. Food Lion, 171 F.3d 308 (5th Cir. 1999) Blum V. 2004. D.O.J’s new porn police. Legal Times, Mar. 8, p. 1 Borgida E, Hunt JS. 2003. Psychology and law. In Social Psychology, ed. SE Taylor, LA Peplau, DO Sears, pp. 496–514. Upper Saddle River, NJ: Prentice Hall. 11th ed. Brown v. Board of Education, 347 U.S. 483 (1954) Bruck M, Ceci SJ. 1999. The suggestibility of children’s memory. Annu. Rev. Psychol. 50:419–39

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PSYCHOLOGICAL EVIDENCE Carlson R. 1992. Experts as hearsay conduits: confrontation abuses in opinion testimony. Minn. Law Rev. 76:859–75 Cecil J, Willging T. 1994. Accepting Daubert’s invitation: defining a role for court-appointed experts in assessing scientific validity. Emory Law J. 43:995–1070 Certain Underwriters at Lloyd’s v. McDermott International, Inc., 2002 WL 14379 ∗ 4 (E.D.La. 2002) Chesebro KJ. 1994. Taking Daubert’s “focus” seriously: the methodology/conclusion distinction. Cardozo Law Rev. 15:1745–53 Commonwealth v. Morasse, 15 Mass. L. Rptr. 115 (2001) Daubert v. Merrell Dow Pharm., Inc., 509 U.S. 579 (1993) Daubert v. Merrell Dow Pharm., Inc., 43 F.3d 1311 (9th Cir. 1995) Denbeaux MP, Risinger DM. 2003. Kumho Tire and expert reliability: how the question you ask gives the answer you get. Seton Hall Law Rev. 34:15–75 Diamond SS. 2002. Survey research. In Modern Scientific Evidence: The Law and Science of Expert Testimony, ed. DL Faigman, DH Kaye, MJ Saks, J Sanders, 1:286–330. St. Paul, MN: West/Thompson. 2nd ed. Diamond SS, Vidmar N, Rose M, Ellis L, Murphy B. 2003. Jury discussions during civil trials: studying an Arizona innovation. Univ. Ariz. Law Rev. 45:1–81 Doe v. Glanzer, 232 F.3d 1258 (9th Cir. 2000) Ebenhoech v. Koppers Ind., Inc., 239 F.Supp.2d 455 (D.N.J. 2002) Faigman DL. 1989. To have and have not: assessing the value of social science to the law as science and policy. Emory Law J. 38:1005–95 Faigman DL. 1999. Legal Alchemy: The Use and Misuse of Science in the Law. New York: Freeman Faigman DL. 2002. Is science different for lawyers? Science 197:339–40 Faigman DL. 2004. Laboratory of Justice: The Supreme Court’s 200-Year Struggle to Integrate Science and the Law. New York: Holt

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