Behavioral and Brain Sciences, Volume 32, Issue 2, April 2009

An International journal of current research and theory with open peer commentary Volume 32 | Issue 2 | April 2009 | ISSN: 0140-525X

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Editorial Board Atypical Neurodevelopment Simon Baron-Cohen/Cambridge U. Behavioral Neurogenetics Wim E. Crusio/CNRS UMR Cognition and Artificial Intelligence Zenon Pylyshyn/Rutgers U. Cognitive and Decision Sciences Nick Chater/University College London Cognitive Development Annette Karmiloff-Smith/Birbeck College Cognitive Neuroscience Moshe Bar/Harvard Medical School Computational Neuroscience Nestor A. Schmajuk/Duke U. Evolution of Brain and Cognition Dean Falk/Florida State U. Evolution of Cognition Celia Heyes/Oxford U. Experimental Analysis of Behavior A. Charles Catania/U. Maryland, Baltimore County Language and Language Disorders Max Coltheart/Macquarie U. Linguistics Robert A. Freidin/Princeton U. Perception Bruce Bridgeman/U. of California, Santa Cruz Philosophy of Science Massimo Piattelli-Palmarini/U. of Arizona Primate Cognition Laurie R. Santos/Yale U. Social Cognition Mahzarin R. Banaji/Harvard U. Social Cognitive Neuroscience Rebecca Saxe/MIT Vision, Language and Computation Shimon Edelman/Cornell U.

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Contents

Volume 32:2

April 2009

Carruthers, P. How we know our own minds: The relationship between mindreading and metacognition Open Peer Commentary Anderson, M. L. & Perlis, D. What puts the “meta” in metacognition? Baars, B. J. Is feeling pain just mindreading? Our mind-brain constructs realistic knowledge of ourselves Buckner, C., Shriver, A., Crowley, S. & Allen, C. How “weak” mindreaders inherited the earth Catania, A. C. Cognitive science at fifty Couchman, J. J., Coutinho, M. V. C., Beran, M. J. & Smith, J. D. Metacognition is prior Evans, J. St. B. T. Introspection, confabulation, and dual-process theory Fernyhough, C. What can we say about the inner experience of the young child? Fiala, B. & Nichols, S. Confabulation, confidence, and introspection Frankish, K. How we know our conscious minds: Introspective access to conscious thoughts Friedman, O. & Petrashek, A. R. Non-interpretative metacognition for true beliefs Hernik, M., Fearon, P. & Fonagy, P. There must be more to development of mindreading and metacognition than passing false belief tasks Huebner, B. & Dennett, D. C. Banishing “I” and “we” from accounts of metacognition Hurlburt, R. T. Unsymbolized thinking, sensory awareness, and mindreading Kornell, N., Schwartz, B. L. & Son, L. K. What monkeys can tell us about metacognition and mindreading

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Langland-Hassan, P. Metacognition without introspection Lewis, C. & Carpendale, J. I. M. Carruthers’ marvelous magical mindreading machine Lombardo, M. V., Chakrabarti, B. & Baron-Cohen, S. What neuroimaging and perceptions of self-other similarity can tell us about the mechanism underlying mentalizing Lurz, R. W. Feigning introspective blindness for thought Mills, C. M. & Danovitch, J. H. Getting to know yourself . . . and others Murphy, D. Varieties of self-explanation Pereplyotchik, D. Global broadcasting and self-interpretation Petty, R. E. & Brin˜ol, P. Introspection and interpretation: Dichotomy or continuum? Proust, J. Overlooking metacognitive experience Robbins, P. Guilt by dissociation: Why mindreading may not be prior to metacognition after all Rochat, P. Social-affective origins of mindreading and metacognition Wiffen, B. & David, A. Metacognition, mindreading, and insight in schizophrenia Williams, D. M., Lind, S. E. & Happe´, F. Metacognition may be more impaired than mindreading in autism Zinck, A., Lodahl, S. & Frith, C. D. Making a case for introspection

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Author’s Response Carruthers, P. Mindreading underlies metacognition

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Mitchell, C. J., De Houwer, J. & Lovibond, P. F. The propositional nature of human associative learning Open Peer Commentary Baeyens, F., Vansteenwegen, D. & Hermans, D. Associative learning requires associations, not propositions Baker, A. G., Baetu, I. & Murphy, R. A. Propositional learning is a useful research heuristic but it is not a theoretical algorithm Beckers, T. & Vervliet, B. The truth and value of theories of associative learning Bliss-Moreau, E. & Barrett, L. F. What’s reason got to do with it? Affect as the foundation of learning Boakes, R. A. Learning without thinking Castro, L. & Wasserman, E. A. Rats and infants as propositional reasoners: A plausible possibility? Chater, N. Rational models of conditioning Dawson, M. E. & Schell, A. M. Is propositional learning necessary for human autonomic classical conditioning? Dwyer, D. M., Le Pelley, M. E., George, D. N., Haselgrove, M. & Honey, R. C. Straw-men and selective citation are needed to argue that associative-link formation makes no contribution to human learning Gawronski, B. & Bodenhausen, G. V. Operating principles versus operating conditions in the distinction between associative and propositional processes Gopnik, A. Rational constructivism: A new way to bridge rationalism and empiricism Greenwood, J. D. Cognition, consciousness, and the cognitive revolution Hall, G. Learning in simple systems Lagnado, D. A. A causal framework for integrating learning and reasoning Li, N. Trace conditioning, awareness, and the propositional nature of associative learning Livesey, E. J. & Harris, J. A. Is there room for simple links in a propositional mind? Lyn, H. & Rumbaugh, D. M. Saliences, propositions, and amalgams: Emergent learning in nonhumans

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Mandler, G. Propositional encodings are a subset of organization theory Matute, H. & Vadillo, M. A. The Proust effect and the evolution of a dual learning system McLaren, I. P. L. Both rules and associations are required to predict human behaviour Miles, J. D., Proctor, R. W. & Capaldi, E. J. Associative learning without reason or belief Morsella, E., Riddle, T. A. & Bargh, J. A. Undermining the foundations: Questioning the basic notions of associationism and mental representation Newell, B. R. What is the link between propositions and memories? Nolan, R. The new enlightenment hypothesis: All learners are rational ¨ hman, A. Is cultivating Olsson, A. & O “biological blindness” a viable route to understanding behavioral phenomena? Penn, D. C., Cheng, P. W., Holyoak, K. J., Hummel, J. E. & Povinelli, D. J. There is more to thinking than propositions Schmajuk, N. A. & Kutlu, G. M. The computational nature of associative learning Schultheis, H. & Lachnit, H. Of mice and men: Revisiting the relation of nonhuman and human learning Shanks, D. R. The associative nature of human associative learning Sternberg, D. A. & McClelland, J. L. How do we get from propositions to behavior? Uleman, J. S. Automatic (spontaneous) propositional and associative learning of first impressions Witnauer, J. E., Urcelay, G. P. & Miller, R. R. A one-system theory that is not propositional

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Authors’ Response Mitchell, C. J., De Houwer, J. & Lovibond, P. F. Link-based learning theory creates more problems than it solves 230

Erratum/Corrigendum Bakermans-Kranenburg, M. J. & van IJzendoorn, M. H. No reliable gender differences in attachment across the lifespan

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BEHAVIORAL AND BRAIN SCIENCES (2009) 32, 121 –182 Printed in the United States of America

doi:10.1017/S0140525X09000545

How we know our own minds: The relationship between mindreading and metacognition Peter Carruthers Department of Philosophy, University of Maryland, College Park, MD 20742 [email protected] http://www.philosophy.umd.edu/Faculty/pcarruthers/

Abstract: Four different accounts of the relationship between third-person mindreading and first-person metacognition are compared and evaluated. While three of them endorse the existence of introspection for propositional attitudes, the fourth (defended here) claims that our knowledge of our own attitudes results from turning our mindreading capacities upon ourselves. Section 1 of this target article introduces the four accounts. Section 2 develops the “mindreading is prior” model in more detail, showing how it predicts introspection for perceptual and quasi-perceptual (e.g., imagistic) mental events while claiming that metacognitive access to our own attitudes always results from swift unconscious self-interpretation. This section also considers the model’s relationship to the expression of attitudes in speech. Section 3 argues that the commonsense belief in the existence of introspection should be given no weight. Section 4 argues briefly that data from childhood development are of no help in resolving this debate. Section 5 considers the evolutionary claims to which the different accounts are committed, and argues that the three introspective views make predictions that are not borne out by the data. Section 6 examines the extensive evidence that people often confabulate when self-attributing attitudes. Section 7 considers “two systems” accounts of human thinking and reasoning, arguing that although there are introspectable events within System 2, there are no introspectable attitudes. Section 8 examines alleged evidence of “unsymbolized thinking”. Section 9 considers the claim that schizophrenia exhibits a dissociation between mindreading and metacognition. Finally, section 10 evaluates the claim that autism presents a dissociation in the opposite direction, of metacognition without mindreading. Keywords: Autism; confabulation; conscious thought; introspection; metacognition; mindreading; schizophrenia; self-interpretation; self-monitoring; self-knowledge

1. Introduction Human beings are inveterate mindreaders. We routinely (and for the most part unconsciously) represent the mental states to the people around us (thus employing metarepresentations – representations of representational states). We attribute to them perceptions, feelings, goals, intentions, knowledge, and beliefs, and we form our expectations accordingly. While it isn’t the case that all forms of social interaction require mindreading (many, for example, follow well-rehearsed “scripts” such as the procedures to be adopted when boarding a bus or entering a restaurant), it is quite certain that without it, human social life would be very different indeed. But human mental life, too, is richly metarepresentational, containing frequent attributions of mental states to ourselves. This sort of first-person metarepresentation is generally referred to as “metacognition.” The present target article is about the cognitive basis (or bases) of our dual capacities for mindreading and for metacognition, and the relationships between them. For reasons that emerge in section 2, however, our main focus will be on propositional attitude mindreading and metacognition (involving attributions of beliefs, judgments, intentions, decisions, and the like) rather than on our capacities for attributing mental states more generally. At least four different accounts of the relationships that obtain between mindreading and metacognition can be # 2009 Cambridge University Press

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distinguished. Three of them maintain that our access to our own minds is quite different in kind from our access to the minds of other people (because they involve a form of introspection), whereas the fourth (which will be defended here) denies this. The present section provides a brief explanation of each, before making some further introductory comments. 1.1. Model 1: Two mechanisms

One possibility is that mindreading and metacognition are two independent capacities, realized in distinct cognitive mechanisms. Nichols and Stich (2003) have elaborated

PETER CARRUTHERS is Professor of Philosophy at the University of Maryland, College Park. He is the author or co-author of eleven books and co-editor of seven, and has published around a hundred articles and reviews. Most of his recent work has concerned reductive explanations of phenomenal consciousness, the involvement of language in thought, mental modularity, and the character of self-knowledge. His most recent book is The Architecture of the Mind: Massive Modularity and the Flexibility of Thought, published in 2006 by Oxford University Press.

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Carruthers: How we know our own minds and defended this view. Their model of the mindreading system is an eclectic one, involving both simulation-like aspects and information-rich components (both theorylike and modular). There are postulated mechanisms for detecting the perceptual states of other people, for detecting the desires of other people, and for detecting the beliefs of other people where they differ from our own. A “Possible Worlds Box,” or hypothetical reasoning system, is used to construct a representation of the world as seen by the other person (containing as suppositions the beliefs and goals attributed to the other), and then the subject’s own inferential and planning mechanisms are used to figure out what else the target might believe, or to work out what the target might do. (Crucially, and in distinction from most other forms of simulation theory, this stage isn’t supposed to involve introspection of one’s own states.) While most of the basic components are held to be innate, there is said to be much work left for learning to do in the course of childhood development. When Nichols and Stich (2003) then turn to provide an account of self-awareness, they defend the view that there are two (or more) distinct self-monitoring mechanisms. There is at least one such mechanism for monitoring and providing self-knowledge of our own experiential states, and one (at least) for monitoring and providing self-knowledge of our own propositional attitudes. These mechanisms are held to be distinct from one another, and also from the mindreading system that deals with the mental states of other people. They are also held to be innate and to emerge under maturational constraints early in infancy. An account of this sort predicts a double dissociation between mindreading and metacognitive capacities. Since these are held to be realized in two (or more) independent mechanisms, there should exist cases where each is damaged or interfered with in the absence of damage or interference occurring to the other (Sternberg 2001). So there should be cases of people who can attribute mental states to others successfully but who have difficulty in attributing mental states to themselves, as well as instances of people who maintain reliable access to their own mental states while losing their capacity to attribute such states to other people. Nichols and Stich (2003) argue that people with passivitysymptom schizophrenia fit the first profile, whereas people with autism fit the second, thus confirming their account. These arguments are discussed and evaluated further on. 1.2. Model 2: One mechanism, two modes of access

A second account maintains that there is just a single metarepresentational faculty, but one that has two distinct kinds of access to the mental states with which it deals, using distinct informational channels. This single faculty has both a perception-based mode, used when interpreting other people, and an introspective mode, used when accessing and representing one’s own mental states. Although it is unclear whether such a view has ever been defended explicitly in print, it is implicit in Carruthers (1996a), and it is often suggested in conversation, especially among those who endorse a “modular” account of the mindreading faculty. Moreover, both Frith and Happe´ (1999) and Happe´ (2003) are quite 122

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naturally interpreted in this way (although they might also be read as endorsing model 4). This account has one significant advantage over the “two independent mechanisms” proposal just considered: It provides a smooth and natural explanation of the fact that self-knowledge and other-knowledge utilize the same conceptual resources. This will be because the very same concepts and/or the very same body of “core knowledge” of mental states are housed in one and the same metarepresentational faculty, albeit a faculty that has input connections deriving not only from the mental states of other people (indirectly, via perception) but also from oneself (more directly, via introspection). This sort of single-mechanism account makes slightly different predictions regarding the expected dissociations. Like model 1, it entails that there should be cases in which self-knowledge is compromised (because the introspective inputs to the metarepresentational faculty have been disrupted), whereas other-knowledge is intact (because the faculty itself remains undamaged and still has access to perceptual input). And it predicts that there should be cases where both self-knowledge and other-knowledge are compromised, by virtue of damage to the metarepresentational faculty itself. (Frith and Happe´ [1999] can quite naturally be interpreted as arguing that people with autism fit this profile.) But there should be no cases where other-knowledge is damaged while self-knowledge is left intact, except by virtue of massive multi-modal perceptual failure.1 These predictions, too, are examined in due course. 1.3. Model 3: Metacognition is prior

A third view maintains that metacognition is prior to mindreading, in such a way that the attribution of mental states to others depends upon our introspective access to our own mental states, together with processes of inference and simulation of various sorts. Goldman (1993; 2006), among others, have proposed and defended accounts of this kind. They also lie behind much of the excitement surrounding the discovery of so-called mirror neurons (Gallese & Goldman 1998; Gallese et al. 1996; Rizzolatti et al. 1996). For it is by virtue of awareness of our own action-tendencies, caused by observing the actions of others, that we are supposed to gain our initial social understanding. Goldman’s account of our introspective abilities has evolved over the years. In his 1993 target article, he thought that our access to our own propositional attitudes was mediated via awareness of the phenomenal feelings that are distinctive of them. This view came in for heavy criticism, however (Carruthers 1996c; Nichols & Stich 2003), and he now maintains that introspection uses an innate code in the language of thought, whose basic elements are caused by the various mental state types, responding to features of their neural realization (Goldman 2006). But the account of mindreading remains essentially the same: One adopts, in imagination, the perspective of a target subject, reasons on one’s own behalf within the scope of that imagination (hence simulating the reasoning processes of the other), and then introspects the resulting mental state of belief or decision, before attributing such a state to the agent in question.

Carruthers: How we know our own minds Model 3 makes predictions similar to those of model 2, but with an opposite valence. Both accounts agree that there should be cases in which both mindreading and metacognition are damaged. (In the case of Goldman’s model, this will occur whenever the introspective capacity is disrupted, since mindreading is held to be grounded in introspective access to one’s own mind.) But instead of predicting that there should be cases where metacognition is poor while mindreading is normal, as did model 2, the present account predicts the opposite: that there should be cases where metacognition is normal while mindreading is damaged. This would happen whenever the simulative abilities utilized in mindreading are disrupted. Following Nichols and Stich (2003), Goldman (2006) argues that people with autism fit this profile. 1.4. Model 4: Mindreading is prior

A fourth view, in contrast, claims the reverse of the third: Instead of mindreading being grounded in metacognition, it maintains that metacognition is merely the result of us turning our mindreading capacities upon ourselves. A variety of different versions of such an account have been proposed (Carruthers 2006; Gazzaniga 1995; 2000; Gopnik 1993; Wegner 2002; Wilson 2002; some differences among these authors will emerge as we proceed).2 The purpose of the present target article is to explain, elaborate, and defend the most plausible variant of this final sort of view. Section 2 will embark on that task. This fourth account entails that there should be no dissociations between mindreading and metacognition. This is because there is just a single faculty involved in both forms of activity, using essentially the same inputs, which are all perceptual or quasi-perceptual in character (including visual imagery and “inner speech”; see sect. 2). However, the account also predicts that it should be possible to induce subjects to confabulate attributions of mental states to themselves by manipulating perceptual and behavioral cues in such a way as to provide misleading input to the self-interpretation process (just as subjects can be misled in their interpretation of others). Likewise, the account predicts that there should be no such thing as awareness of one’s own propositional attitudes independently of any perceptually accessible cues that could provide a basis for self-interpretation. The accuracy of these predictions will be discussed and evaluated in due course. Note that the “mindreading is prior” account is the only one of the four to make such predictions. Notice that each of the first three accounts just described endorses the existence of some variety or other of introspection, understood broadly to encompass any reliable method for forming beliefs about one’s own mental states that is not self-interpretative and that differs in kind from the ways in which we form beliefs about the mental states of other people. (It should be emphasized that the term “introspection” is used in this broad, negatively defined, sense throughout this target article. Many different specific views are thereby included.) Notice that to say that an introspective process is not self-interpretative doesn’t mean that it isn’t inferential. On the contrary, those who take seriously the analogy between introspection and external perception, and who think that the former is realized in a self-monitoring mechanism of some sort, are apt to think

that it achieves its output by effecting computations on the data that it receives as input (just as does vision, for example). But these inferences will presumably rely on general principles, such as (in the case of vision) that light shines from above, or that moving objects are locally rigid. For present purposes, an interpretative process, in contrast, is one that accesses information about the subject’s current circumstances, or the subject’s current or recent behavior, as well as any other information about the subject’s current or recent mental life. For this is the sort of information that we must rely on when attributing mental states to other people. In contrast with the first three accounts, proponents of view 4, who maintain that metacognition results from us turning our mindreading abilities upon ourselves, must deny the existence of introspection (at least for a significant class of mental states; see sect. 2). So also at stake in this target article is the commonsense view that we have introspective access to our own minds (or at least to certain aspects of them). 2. Elaborating the “mindreading is prior” model As we noted earlier, a number of different versions of the “mindreading is prior” view have been proposed. These come in different strengths. At one extreme is Gopnik (1993). In her target article on this topic, she urged that the attribution of all mental states to oneself (with the exception, perhaps, of what she described as some sort of “Cartesian buzz”) is equally theory-based, and equally interpretational. But this strong view has come in for heavy criticism. For as Nichols and Stich (2003) and Goldman (2006) both point out, I seem to be able to know what I am currently thinking and planning even though I am sitting quiet and motionless (in which case there will be no behavior available for the mindreading system to interpret). How is this possible, the critics ask, unless we have access to our own mental states that isn’t interpretative, but is rather introspective? At the other extreme lie Wegner (2002) and Wilson (2002), who are often interpreted as proponents of a “mindreading is prior” account. Each makes a powerful case that we often attribute propositional attitudes to ourselves via self-interpretation (and often false and confabulated interpretation, at that). But both seem to allow that we also have access to some of our attitudes that is introspective in character. For each allows that we undergo conscious as well as unconscious thoughts, and that the former can provide part of the evidence base for selfattributing the latter. I argue in section 7 that they have been misled, however, and that they have run together the sensory accompaniments of attitudes – such as inner speech and visual imagery (to which we do have introspective access, I allow) – with the attitudes themselves. In contrast with the preceding accounts, the position to be defended in the present target article is as follows. There is just a single metarepresentational faculty, which probably evolved in the first instance for purposes of mindreading (or so I shall argue in sect. 5). In order to do its work, it needs to have access to perceptions of the environment. For if it is to interpret the actions of others, it plainly requires access to perceptual representations of those actions.3 Indeed, I suggest that, like BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Carruthers: How we know our own minds most other conceptual systems, the mindreading system can receive as input any sensory or quasi-sensory (e.g., imagistic or somatosensory) state that gets “globally broadcast” to all judgment-forming, memory-forming, desireforming, and decision-making systems. (For evidence supporting a global broadcasting cognitive architecture, see Baars 1988; 1997; 2002; 2003; Baars et al. 2003; Dehaene & Naccache 2001; Dehaene et al. 2001; 2003; Kreiman et al. 2003.) By virtue of receiving globally broadcast perceptual states as input, the mindreading system should be capable of self-attributing those percepts in an “encapsulated” way, without requiring any other input. Receiving as input a visual representation of a man bending over, for example, it should be capable of forming the judgment, “I am seeing a man bending over.” (At least, this should be possible provided the visual state in question has been partially conceptualized by other mental faculties, coming to the mindreading system with the concepts man and bending over already attached. I return to discuss the significance of this point shortly.) This is the way in which introspection of perceptual, imagistic, and somatosensory mental events is achieved, I suggest. Given that the mindreading faculty possesses the concepts sight, hearing, and so forth (together with a concept of self), it should be able to activate and deploy those concepts in the presence of the appropriate sort of perceptual input on a recognitional or quasi-recognitional basis (Carruthers 2000). Because no appeals to the subject’s own behavior or circumstances need to be made in the course of making these judgments, the upshot will qualify as a form of introspection, in the broad sense being used here. Let me stress, however, that what is being offered here is an account of introspection for perceptual states, not an account of experiential, or “phenomenal,” consciousness. (And although I sometimes use the language of “consciousness” in this target article, this should always be understood to mean access consciousness rather than phenomenal consciousness; see Block [1995] for the distinction.) Although global broadcasting is often put forward as a theory of phenomenal consciousness (Baars 1988; 1997), that isn’t how it is being used in the present context. Rather, it forms part of an account of how we come to have knowledge of our own perceptual and quasi-perceptual states. Whether global broadcasting provides a sufficient explanation of the “feely” qualities of phenomenal consciousness is another matter entirely. And although I myself have defended a higher-order account of phenomenal consciousness, according to which it is the availability of globally broadcast states to the mindreading faculty that is responsible for their phenomenally conscious status (Carruthers 2000), I don’t mean to rely on that here, either. Indeed, I intend the discussion in this target article to be neutral among proposed explanations of phenomenal consciousness. Although the mindreading system has access to perceptual states, the proposal is that it lacks any access to the outputs of the belief-forming and decision-making mechanisms that feed off those states. Hence, self-attributions of propositional attitude events like judging and deciding are always the result of a swift (and unconscious) process of self-interpretation. However, it isn’t just the subject’s overt behavior and physical circumstances that provide the basis for the interpretation. Data about perceptions, 124

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visual and auditory imagery (including sentences rehearsed in “inner speech”), patterns of attention, and emotional feelings can all be grist for the self-interpretative mill. Such an account can plainly avoid the difficulties that beset Gopnik (1993). For consider someone sitting quietly in his living room, who has just become aware of deciding to walk to his study to get a particular book from the shelf (Goldman 2006, p. 230). His mindreading system has access to a variety of forms of evidence in addition to overt behavior (which in this case is lacking). The agent might, for example, have verbalized or partially verbalized his intention, in “inner speech.” And then, since inner speech utilizes the same perceptual systems that are involved in the hearing of speech (Paulescu et al. 1993; Shergill et al. 2002), this will be available as input to the mindreading system. Or he might have formed a visual or proprioceptive image of himself selecting that particular book, which will be similarly available (Kosslyn 1994). Or the context provided by his prior verbalized thoughts and visual images, together with a shift in his attention towards the door, might make it natural to interpret himself as having decided to walk to his study to collect that particular book. Notice that allowing the mindreading system to have access to visual imagery, proprioceptive data, and emotional feelings is pretty much mandatory once we buy into a global broadcasting architecture, even though such events will presumably play little or no role in third-person mental-state attribution. For perceptual and quasi-perceptual states of all kinds are capable of being globally broadcast when attended to, and will thus become available to any conceptual system that looks to such broadcasts for its input. But the upshot is to blur the boundary somewhat between the “mindreading is prior” account and model 2 (“one mechanism, two modes of access”). For we now have to concede that the mindreading system does have available to it information when attributing mental states to the self that it never has access to when attributing mental states to others. For unless subjects choose to tell me, I never have access to what they are imagining or feeling; and certainly I never have the sort of direct access that my mindreading system has to my own visual images and bodily feelings. Despite this “blurring of boundaries,” there remains good reason to insist on the distinctness of our account from model 2. This is because the latter is committed to the claim that the metarepresentational faculty has introspective, non-interpretative access to mental states of all types, including propositional attitudes as well as sensory experiences. The account being proposed here, in contrast, maintains that our access to our own propositional attitudes is always interpretative, while conceding that the evidence base for self-interpretation is somewhat wider than we normally have available when interpreting other people. One final point needs to be emphasized: As the example of seeing a man bending over should make clear, the thesis that judgments aren’t introspectable requires important qualification. In particular, it should be restricted to judgments that aren’t perceptual judgments. According to Kosslyn (1994) and others, the initial outputs of the visual system interact with a variety of conceptual systems that deploy and manipulate perceptual templates, attempting to achieve a “best match” with the incoming

Carruthers: How we know our own minds data. When this is accomplished, the result is globally broadcast as part of the perceptual state itself. Hence, we see an object as a man or as bending over. Because this event is apt to give rise immediately to a stored belief, it qualifies as a (perceptual) judgment. But because it will also be received as input by the mindreading system (by virtue of being globally broadcast), it will also be introspectable. In the discussion that follows, therefore, whenever I speak of “judgments,” I should be understood to mean “non-perceptual judgments,” such as the judgment that 17 is a prime number or that polar bears are endangered.4 2.1. Mindreading and speech

If we lack introspective access to our own propositional attitudes, then how is it that we can report on those attitudes, swiftly and unhesitatingly, in the absence of anything that could plausibly be seen as input to a process of self-interpretation? If someone asks me for the date on which I think the Battle of Hastings took place, for example, I can reply immediately, “1066, I believe.” But on what basis could I interpret myself as possessing such a belief? I can recall no Battle-of-Hastings-related behavior; and there need have been nothing relevant of an imagistic sort passing through my mind at the time, either. There is surely no reason to think, however, that the verbal expression of a belief requires prior metacognitive access to it. Rather, one’s executive systems will conduct a search of memory, retrieving an appropriate first-order content which can then, in collaboration with the language faculty, be formulated into speech. And then attaching the phrase, “I think that . . .” or “I believe that . . .” to the first-order sentence in question is a trivial matter (Evans 1982), and is often a mere manner of speech or a matter of politeness (so as not to appear too confident or too definite). It certainly needn’t require that subjects should first formulate a metacognitive judgment to the effect that they believe the content in question. Hence, it may be that the first metacognitive access subjects have to the fact that they have a particular belief is via its verbal expression (whether overtly or in inner speech). And such speech, like all speech, will need to be interpreted to extract its significance. General considerations of cognitive engineering support such a view. For we already know that executive systems would need to have access to stored information, and that they would have been honed by evolution to conduct efficient searches for the information required to solve each type of practical task in hand. Moreover, this capacity would surely have been of ancient evolutionary provenance, long pre-dating the emergence of language and mindreading. Nor does it qualify as a form of introspection, since it isn’t metarepresentational in character. When the mindreading system was added in the course of human evolution, therefore, there would have been no need for it to be built with its own capacities to conduct searches of all memory; and on the contrary, since all data-mining is computationally expensive, this would have come at significant additional cost. And while there is every reason to think that capacities for language and for mindreading would have coevolved (Gomez 1998; Origgi & Sperber 2000), there isn’t any reason to think that the language faculty can only produce an output when provided with a metacognitive

content as input, either issued by the mindreading faculty or by a separate faculty of introspection. Many cognitive scientists think that the speech-production process begins with a thought-to-be-expressed (Levelt 1989). I myself believe that this is an exaggeration (Carruthers 2006). Speech is an action, and like other actions can be undertaken for a variety of purposes (the expression of belief being only one of them). Hence, any utterance in the indicative mood needs to be interpreted to determine whether it is made ironically, or in jest, or as a mere supposition; or whether it is, indeed, expressive of belief. However, I know of no theorist who thinks that speech needs to begin from a metacognitive representation of the thought to be expressed. So even utterances that do express a corresponding belief don’t qualify as a form of introspection, since no metarepresentational thought occurs until one’s own words are heard and interpreted. Similar points hold in respect to the verbal expression of desire. No doubt we often give voice to our desires, having first envisaged the thing or circumstance in question and monitored and interpreted our affective responses, in the manner proposed by Damasio (1994; 2003). (This is, of course, fully consistent with a “mindreading is prior” account.) But often our current desires can recruit appropriate speech actions in their own service, with use of the terminology of “want” or “desire” being just one possible means among many. Thus, the two-year-old child who says, “I want juice,” is unlikely to have first formulated a metacognitive thought. Rather, desiring juice, the child is seeking ways to achieve that goal. And for these purposes a number of different speech actions might be equally effective, including, “Give me juice,” “Juice, please,” and so on. If she chooses to say, “I want juice,” then she does make an assertion with a metacognitive content, and hence (if she understands the concept of wanting) she will subsequently come to entertain a metacognitive thought. But there is no reason to think that her utterance must begin with such a thought, any more than does the utterance of someone who answers the question, “Is it the case that P?” by saying, “Yes, I believe that P.” It might be objected that even if we sometimes learn of our own beliefs and desires by first becoming aware of their formulation into speech (whether inner or outer), this still gives us reliable, non-interpretative access to them. Hence this can still count as a form of introspection. But this appearance of immediacy is illusory. All speech – whether the speech of oneself or someone else – needs to be interpreted before it can be understood. Unless we beg the point at issue and assume that subjects have direct introspective access to their own articulatory intentions, the language-comprehension system will need to get to work on the utterance in the normal way, figuring out its meaning in light of the utterance’s linguistic properties (lexical meanings, syntax, etc.) together with knowledge of context. And even if, as is likely, the result of this process (the content of the utterance) is attached to the existing representation of the sound of the utterance and globally broadcast to all conceptual systems, including the mindreading faculty, the latter will still only have interpretative access to the underlying beliefs or goals that initiated the utterance. But how is it, then, that our own utterances are not ambiguous to us, in the way that the utterances of other BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Carruthers: How we know our own minds people often are? If I find myself thinking, “I shall walk to the bank,” then I don’t need to wonder which sort of bank is in question (a river bank, or a place where one gets money). And this fact might be taken to indicate that I must have introspective access to my intentions. However, there will generally be cues available to disambiguate our own utterances, which wouldn’t be available to help interpret the similar utterances of another. For example, just prior to the utterance I might have formed a visual image of my local bank, or I might have activated a memory image of an empty wallet. But even when no such cues are available, there remains a further factor that will serve to disambiguate my own utterances, but which won’t always help with the utterances of others. This is the relative accessibility of the concepts involved, which is a pervasive feature of speech comprehension generally (Sperber & Wilson 1995). Because the goals that initiated the utterance, “I shall walk to the bank,” would almost certainly have included an activation of one or other specific concept bank, this will ensure the increased accessibility of that concept to the comprehension system when the utterance is processed and interpreted. I conclude, therefore, that while subjects can often express their beliefs in speech, and can hence acquire more-or-less reliable information about what they believe, this gives us no reason to think that introspection for propositional attitudes exists. 3. The introspective intuition There is no doubt that the denial of introspection for propositional attitudes, entailed by the “mindreading is prior” view, is hugely counterintuitive to most people. Almost every philosopher who has ever written on the subject, for example – from Descartes (1637), Locke (1690), and Kant (1781), through to Searle (1992), Shoemaker (1996), and Goldman (2006) – has believed that many (at least) of our own judgments and decisions are immediately available to us, known in a way that is quite different from our knowledge of the judgments and decisions of other people. We are (pre-theoretically) strongly inclined to think that we don’t need to interpret ourselves in order to know what we are judging or deciding (or that we don’t need to do so all of the time, at least; many of us now have enough knowledge of cognitive science to concede that such events can also occur unconsciously). Rather, such events are often (somehow) directly available to consciousness. Since it is generally thought to be a good thing to preserve intuitions ceteris paribus, this might be taken to create a presumption in favor of one of the three alternative accounts that we considered at the outset. The strategy of this section is to draw the teeth from this argument by showing, first, that the intuition underlying it is unwarranted, and then by using reverse engineering to explain why (from the perspective of a “mindreading is prior” account) it nevertheless makes good sense that such a folk-intuition should exist. 3.1. The subjective experience of introspective access isn’t evidence of introspection

The thesis expressed in this subsection’s title is clearly demonstrated by research with commissurotomy (“split-brain”) 126

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subjects, conducted over many years by Gazzaniga (1995; 2000) and colleagues. In one famous case (representative of many others of similar import), Gazzaniga (1995) describes how different stimuli were presented to the two half-brains of a split-brain patient simultaneously. The patient fixated his eyes on a point straight ahead, while two cards were flashed up, one positioned to the left of fixation (which would be available only to the right brain) and one to the right of fixation (which would be available only to the left brain). When the instruction, “Walk!” was flashed to the right brain, the subject got up and began to walk out of the testing van. (The right brain of this subject was capable of some limited understanding of language, but had no production abilities.) When asked why, he (the left brain, which controlled speech-production as well as housing a mindreading system) replied, “I’m going to get a Coke from the house.” This attribution of a current intention to himself is plainly confabulated, but delivered with all of the confidence and seeming introspective obviousness as normal. It is important to note that although commissurotomy patients can often have a good understanding of their surgery and its effects, they never say things like, “I’m probably choosing this because I have a split brain and the information went to the right, non-verbal, hemisphere” (Gazzaniga 1995). On the contrary, they make their confabulated reports smoothly and unhesitatingly, and their (i.e., their left brain’s) sense of self seems quite unchanged following the operation. Even reminders of their surgery during testing have no effect. On a number of occasions testing was paused and the experimenter said something like, “Joe, as you know, you have had this operation that sometimes will make it difficult for you to say what we show you over here to the left of fixation. You may find that your left hand points to things for that reason, OK?” Joe assents, but then on the very next series he is back to showing the interpreter effect once again (Gazzaniga, personal communication). If patients were aware of interpreting rather than introspecting, then one would expect that a reminder of the effects of commissurotomy would enrich the hypothesis pool, and would sometimes lead them to attribute some of their own behavior to that. But it doesn’t do so. Of course it doesn’t follow from the extensive commissurotomy data that normal human subjects never have privileged, non-interpretative, access to their own judgments and decisions, as Goldman (2006) points out. (And for this reason the defense of a “mindreading is prior” account that is mounted by Gazzaniga [1998] strikes many people as massively under-supported. One way of viewing the present target article is that it is an attempt to rectify that deficiency.) Gazzaniga’s data were collected from patients who had undergone serious brain damage (a severed corpus collosum). Hence, it may be that in normal brains the mindreading system does have immediate access to the agent’s judgments and intentions. The split-brain data force us to recognize that sometimes people’s access to their own judgments and intentions can be interpretative (much like their access to the judgments and intentions of other people), requiring us at least to accept what Goldman (2006) calls a “dual method” theory of our access to our own thoughts. But one could believe (as Goldman does) that introspection is the normal, default, method for acquiring knowledge

Carruthers: How we know our own minds of our own propositional attitudes, and that we only revert to self-interpretation as a back-up, when introspection isn’t available. The split-brain data show decisively that we don’t have any introspective, subjectively accessible, warrant for believing that we ever have introspective access to our own judgments and decisions, however. This is because patients report plainly confabulated explanations with all of the same sense of obviousness and immediacy as normal people. And if normal people were to rely upon subjectively accessible cues to identify cases of introspection, then commissurotomy patients should be able to use the absence of such cues to alert them to the interpretative status of their reports. The best explanation is therefore that subjects themselves can’t tell when they are introspecting and when they are interpreting or confabulating. So for all we know, it may be that our access to our own judgments and decisions is always interpretative, and that we never have introspective access to them. Now philosophers will note, of course, that given so-called reliabilist conceptions of knowledge and justification, we might count as knowing, and as justified in believing in, the existence of introspection, despite our inability to discriminate cases of introspection from cases of confabulation. This will be so provided that introspection really does exist and is common, and provided that our belief in it is reliably caused by the fact that we do often introspect, and is caused in the right sort of way. My point here, however, is that our inability to discriminate shows that we have no subjectively accessible reason to believe in the existence of introspection. So anyone who is wondering whether or not introspection is real should realize that they have no reason they can offer for thinking that it is, in advance of examining the evidence. 3.2. The mindreading system’s model of its own access to the mind

The intuition that there is introspection for propositional attitudes is therefore unwarranted. But in addition, we can explain why we should have such an intuition in the first place, even if (as I am suggesting) it turns out to be false. This is because the mindreading system’s operations will be greatly simplified, but without any significant loss of reliability (and perhaps with some gain), if its model of its own access to the mind is an introspective (non-interpretative) one. We should then predict that just such a model would be arrived at, whether by natural selection or through individual learning. This argument is laid out in some detail in Carruthers (2008a). In consequence, this section is brief.5 In order to be effective, the mindreading system needs to contain some sort of model of the way that minds, in general, work. Such a model should include an account of the access that agents have to their own mental states. And here there are essentially two choices. The mindreading system can either represent agents as interpreters of themselves, or it can picture them as having direct introspective access to their own mental states. The former would complicate the mindreading system’s computations, and would mandate consideration of a wider range of evidence, taking into account the possibility of misinterpretation. But there is unlikely to be any compensating gain in reliability. One reason for this is that people are, probably,

excellent interpreters of themselves. (We know that they are remarkably good interpreters of others.) Hence, in normal circumstances instances of confabulation will be rare, and thus any errors introduced by a belief in introspection will be few. A second reason is that selfattributions of mental states, even if initially confabulated, are likely to be self-fulfilling. This is because agents will feel obliged to act in ways that are consistent with the mental states that they have attributed to themselves. And a third reason is that any expansion in the computational complexity of a system will introduce additional sources of error (as well as imposing a cost in terms of speed of processing, of course), as will any increase in the types of evidence that need to be sought. It is now a familiar point in cognitive science, not only that simple (but invalid) heuristics can prove remarkably reliable in practice, but that they can often out-compete fancier computational processes once the costs imposed by computational errors, as well as missing or misleading information, are factored in (Gigerenzer et al. 1999).6 What we should predict, therefore, is that the mindreading system should model the mind as having introspective access to itself. And then that very same model will render agents blind to the fact (if it is a fact) that their mode of access to their own mental states is actually an interpretative one. I conclude that the playing field is now leveled between the competing theories, in the sense that there is no initial presumption against model 4. And given a level playing field, we should prefer the simplest theory ceteris paribus. This means that the “mindreading is prior” account should now be our default option, because it postulates just a single mechanism with a single mode of access to its domain, whereas the other accounts postulate greater complexity. 4. The data from development Gopnik (1993) bases much of her case for a “mindreading is prior” account on developmental evidence, claiming that there is a parallelism between children’s performance in mindreading tasks and matched metacognitive tasks (see also, Gopnik & Meltzoff 1994). This claim has held up well over the years. In an extensive meta-analysis of hundreds of experiments, Wellman et al. (2001) are able to find no evidence of any self/other asymmetry in development. Taken at face value, these data count strongly against both a “two independent mechanisms” account and a “metacognition is prior” view, each of which predicts that metacognitive competence should emerge in development in advance of mindreading. What most parties in these debates have overlooked, however, is the existence of the remaining alternative to a “mindreading is prior” account, namely the “one mechanism, two modes of access” view. For this, too, predicts that development in the domains of both self- and otherunderstanding should proceed in parallel. Like the “mindreading is prior” view, this account claims that there is just a single mechanism or body of core knowledge underlying both mindreading and metacognitive competence. Hence, one would expect children’s capacities in both domains to emerge at about the same time. What this means is that developmental evidence is inherently incapable of BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Carruthers: How we know our own minds discriminating between views that endorse, and those that deny, the existence of introspective access to propositional attitudes. There is another, equally important, reason why developmental evidence is of no use to us in this inquiry, however. This is that all parties in the debate over the existence of introspection for attitudes have shared a traditional and widely accepted understanding of the developmental timetable for mindreading competence (Goldman 2006; Gopnik 1993; Nichols & Stich 2003). This was thought to proceed through well-defined stages over the first four or five years of life, with competence in false-belief reasoning not emerging until after the age of four (Wellman 1990). Yet there have always been those who have maintained that an underlying competence with false-belief might be present much earlier, but masked by young children’s difficulties in executive functioning (Fodor 1992; Leslie & Polizzi 1998). Indeed, Birch and Bloom (2004; 2007) refer to the latter as “the curse of knowledge,” pointing out that adults, too, can often have difficulty in allowing for the false beliefs of another. And this general perspective has now received dramatic confirmation through the use of nonverbal looking-time and expectation measures. These show competence with false-belief understanding and other allegedly late-emerging aspects of mindreading capacity at around 15 or 24 months, long before this had traditionally been thought possible (Bosco et al. 2006; Onishi & Baillargeon 2005; Onishi et al. 2007; Song & Baillargeon, forthcoming; Song et al., forthcoming; Southgate et al. 2007; Surian et al. 2007). But no one has, as yet, been able to develop nonverbal measures of metacognitive understanding in infants for purposes of comparison. Of course there is much here that needs to be explained. In particular, if metarepresentational competence is present in the second year of life, we want to know why it takes two or more additional years for that competence to manifest itself in verbally based tasks. But this isn’t a question for us. Our focus is on adjudicating between accounts that endorse the existence of introspection and those that deny it. And for these purposes it is plain that we need to seek evidence of other sorts. 5. The evolution of mindreading and metacognition The differing accounts outlined in section 1 lead to different commitments concerning the likely course of human evolution, and these in turn lead to different predictions about what we should expect to find in contemporary human cognition, and also in other species of animal. The present section shows that the “mindreading is prior” account comes out significantly ahead of its rivals in the former respect, before arguing that the animal data lend no support to either side. All four of the accounts of the relationship between mindreading and metacognition can, and probably should, converge on essentially the same explanation of the evolutionary origins of human mindreading capacities. (Even those who think that mindreading capacities emerge in the course of childhood development through processes of learning that are akin to scientific theorizing insist that such theorizing has to begin with a specific 128

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innate basis; see Gopnik & Meltzoff 1997.) This will be some or other variant of the “Machiavellian intelligence” hypothesis (Byrne & Whiten 1988; 1997; Dunbar 2000), which points to the immense fitness advantages that can accrue to effective mindreaders among highly social creatures such as ourselves. And all should predict that one might expect to find simpler versions of mindreading capacity among other animals (perhaps confined to recognition of perceptual access and ignorance together with intention), especially among mammals who live in complex social groups. These predictions appear to be borne out (Call & Tomasello 2008; Cheney & Seyfarth 2007; Hare 2007; Hare et al. 2000; 2001; Tomasello et al. 2003a; 2003b). Where the various accounts diverge is over the evolution of metacognition. From the perspective of a “mindreading is prior” account, no separate story needs to be told. Since metacognition, on this view, results from turning one’s mindreading capacities upon oneself, its emergence will be a by-product of the evolution of mindreading. (This isn’t to say that metacognition might not have come under secondary selection thereafter, perhaps by virtue of helping to build and maintain a positive selfimage, as Wilson [2002] suggests.) All three competitor accounts, in contrast, have some explaining to do. This is most obvious in connection with a “two independent mechanisms” account of the sort championed by Nichols and Stich (2003). For if mindreading and metacognition are subserved by two (or more) cognitive mechanisms, then plainly there should be a distinct evolutionary story to be told about the emergence of each. But the same also holds in respect of a “one mechanism, two modes of access” account. Because neural connections are costly to build and maintain (Aiello & Wheeler 1995), some distinct evolutionary pressure will be needed to explain why the metarepresentational faculty (which might well have evolved initially for purposes of mindreading) should have acquired the input channels necessary to monitor the subject’s own propositional attitudes. The most natural way of explaining the structures postulated by the “metacognition is prior” account (championed by Goldman 2006) would likewise involve a distinct evolutionary pressure of some sort for the emergence of metacognition. The latter would happen first, followed subsequently by the integration of introspection with processes of imagination and simulative reasoning, presumably driven by the pressure to develop forms of “Machiavellian intelligence.” Would it be possible to argue, however, that metacognitive capacities evolved to subserve mindreading from the start? It might be suggested that each incremental increase in metacognitive capacity was selected for because of its role in mindreading. For this account to work, however, it would have to be supposed that capacities to identify with others in imagination, together with dispositions to think and reason in simulation of the other within the scope of such a pretence, were already in place in advance of the appearance of both metacognition and mindreading. And one then wonders what such capacities would have been for. In the absence of any plausible suggestions, therefore, I shall assume that the “metacognition is prior” account, like the other two introspection-involving views, needs to postulate some evolutionary pressure in addition to those that issued in mindreading.

Carruthers: How we know our own minds Thus, all three of the competitor accounts need to tell some story about the evolution of introspection. What I argue in section 5.1 is that the most popular such story – that metacognition evolved for purposes of self-monitoring and executive control of our own cognitive processes – makes predictions that are not borne out by the data. To the extent that this is true, then each one of those accounts is simultaneously disconfirmed. And this will therefore provide us with a further reason to accept the “mindreading is prior” account (in addition to the fact that it is the simplest, and should in consequence be accepted by default). Although all three competitor accounts are committed to the existence of a distinct evolutionary pressure to explain the emergence of metacognition, only the “metacognition is prior” model makes a specific prediction about the order of emergence of the two capacities in phylogeny. It predicts, in particular, that we should be able to find metacognitive capacities in creatures that lack any capacity for mindreading (presumably because they lack the requisite imaginative abilities). Just this idea appears to motivate the recent flurry of interest in the metacognitive capacities of nonhuman animals (Terrace & Metcalfe 2005). This topic is examined in section 5.2. 5.1. The evolution of metacognition What evolutionary pressures might have shaped the emergence of a distinct metacognitive capacity? One natural and very popular suggestion is that it was designed to have a supervisory role with respect to regular, firstorder, cognitive processes – troubleshooting and intervening in those processes in cases of difficulty, initiating new strategies, checking that tasks are proceeding as expected, and so on (Shallice 1988). What I argue, however, is that although there is indeed a supervisory role for metacognition, it is one that does not require an introspective capacity distinct from the third-person mindreading system. I also argue that our metacognitive interventions are not capable of the sort of direct impact on cognitive processing that would be predicted if metacognition had, indeed, evolved for the purpose. But we first need to notice an important distinction. Unfortunately, cognitive scientists use the term “metacognition” in two quite distinct ways, often without noticing the difference. (See Anderson & Perlis [2005a] for what seems to be a clear example. For distinctions related to the one drawn here, see Dennett 2000.) Generally the term is used, as it has been throughout this target article, to mean cognition about one’s own cognition. Metacognition, in this sense, is inherently higher-order, involving metarepresentations of one’s own first-order cognitive processes as such. But the word “meta” literally just means “above.” And consequently many people understand metacognition to be any process that goes on above regular cognitive processes, performing a number of kinds of executive-function roles, such as monitoring the progress of a task and initiating new strategies when progress is blocked. On this view, any cognitive architecture that is organized into layers – containing not only a set of automatic information-generating and decisionmaking systems, but also a supervisory layer of some sort that can intervene in or alter the processes taking place

in the first layer – will count as “metacognitive.” But it is important to see that these supervisory processes needn’t involve anything metacognitive in our first sense. For example, monitoring the progress of a task may just require a (first-order) representation of the goal-state, together with some way of comparing the current output of the system with the represented goal-state and making adjustments accordingly. Indeed, all of the supervisory processes that Anderson and Perlis (2005a) describe as requiring both “self-awareness” and a “metacognitive loop” are actually just first-order processes organized into layers in this sort of way. For example, they describe a robot that is capable of noticing that it is no longer making forward progress (because it keeps bumping into a fence that it cannot see), and initiating an alternative strategy (e.g., traveling in an alternative direction for a while). There is plainly nothing metacognitive (in the sense of “metarepresentational”) required here. The robot just needs to be on the lookout for failures to move forwards, and it needs to have been programmed with some alternative strategies to try when it doesn’t. Even a mechanism that is capable of recognizing and responding to contradictions need only be sensitive to the formal properties of the representations involved, without representing them as representations. Thus, if representations of the form “P” and “P” are detected within active memory, the system might be programmed to place no further reliance on either of these premises, just as Anderson and Perlis suggest. A significant portion of what gets described within cognitive science as “metacognition,” then, should be set aside as irrelevant to the issues we are discussing. But of course a very large body of genuinely metacognitive data remains, especially in the domain of metamemory (e.g., Metcalfe & Shimamura 1994; Nelson 1992). But even where cognitive processes are genuinely metacognitive in the sense of being metarepresentational, deploying concepts of mental state types, they often operate without the capacity to intervene directly in the states and processes represented. For example, most metamemory capacities only require an ability to initiate or to intervene in behavior. Thus, a child might select one memorization task rather than another on the grounds that it contains fewer items (thus implicating knowledge about memory, but not intervening in the process of memory itself). Likewise, someone might mentally rehearse items in inner speech as an aid to memorization, which is an indirect behavioral influence on memory, not a direct intervention. And in the same spirit, it should be noted that while the intention to learn has an effect on study patterns, it has no effect on learning and recall once study patterns are controlled for (Anderson 1995). This is not what one would predict if metamemory were some sort of introspective capacity that had evolved for purposes of executive control, enabling subjects to intervene directly in the processes of memorization or memory retrieval. (Guiding behaviors that tend to issue in memorization or retrieval, in contrast, can be done equally well by a mindreading system.) Koriat et al. (2006) review much of the extensive literature on metamemory, and experimentally contrast two competing models. One is that metacognitive monitoring serves the function of controlling and directing the underlying cognitive processes. (Plainly this would be consistent with the evolutionary explanation of introspection BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Carruthers: How we know our own minds sketched earlier.) The other is that metacognitive judgments are evidence-based, cued by experiences that are caused by the cognitive processes in question. (This would be consistent with the self-interpretative position being developed here.) Although they do find metacognitive phenomena that fit the former profile, none of these suggests any real role for introspection of attitudes. Rather, they include such phenomena as allocating greater study time to items that attract a larger reward. In contrast, there is extensive evidence of cue-based metacognitive judgments. Thus, feelings of knowing are often based on the ease with which one can access fragments of the target knowledge (Koriat 1993) or items related to the target (Schwartz & Smith 1997). And judgments of learning made during or after study are based on the “fluency” with which items are processed during study itself (Begg et al. 1989; Benjamin & Bjork 1996; Koriat 1997). Again, this isn’t at all what one would predict if one thought that a capacity for introspection of attitudes had evolved for purposes of metacognitive control. For why, in that case, would one need to rely on indirect cues of learning? While the influence of metacognitive judgments on cognitive processes is often indirect, it should be stressed that such judgments are actually intrinsic to the sorts of processes that would be characterized as belonging to “System 2,” as we will see in section 7. Human beings sometimes engage in forms of conscious thinking and reasoning that are thoroughly imbued with metacognitive beliefs and judgments. But what appears to make such forms of thinking consciously accessible is that they are conducted in inner speech and other kinds of imagery. In which case the type of metacognitive access that we have, here, will turn out to be fully consistent with a “mindreading is prior” account. The preliminary upshot of this discussion, then, is that the predictions generated by the most common evolutionary explanation of an introspective capacity (namely, that its purpose is executive monitoring and control) are not borne out by the data. This provides us with good reason to embrace the alternative “mindreading is prior” account instead. 5.2. Metacognitive processes in nonhuman animals

The last few years have seen a flurry of experiments purporting to demonstrate the presence of metacognitive processes in nonhuman animals (Beran et al. 2006; Call & Carpenter 2001; Hampton 2001; 2005; Hampton et al. 2004; Kornell et al. 2007; Shields et al. 1997; Smith 2005; Smith et al. 1995; 1997; 2003; Son & Kornell 2005; Washburn et al. 2006). If these experiments were to prove successful, and if the animals in question were to lack any capacity for mindreading of attitudes (as most researchers assume), then this would provide dramatic support for the view that metacognition is prior to and underpins mindreading. (By the same token, it would provide powerful evidence against the “mindreading is prior” account being defended here.) These studies are reviewed and critiqued in detail in Carruthers (2008b), where I demonstrate that all of the phenomena in question are readily explicable in first-order terms. Here I shall confine myself to outlining my treatment of just one of the simpler alleged instances of animal metacognition. 130

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Smith et al. (2003) argue that the adaptive behavioral choices made by monkeys and dolphins in conditions of uncertainty demonstrate that the animals are aware of their own state of uncertainty and are choosing accordingly. Thus, monkeys who have been trained to discriminate between dense and sparse visual patterns, and to respond differentially as a result, will increasingly make use of a third “don’t know” option (which advances them to a new trial without the penalty of a delay) when the patterns are made harder and harder to distinguish. But all that is really needed to explain the animals’ behavior here is an appeal to degrees of belief and desire. For an animal that has a weak degree of belief that the pattern is dense and an equally weak degree of belief that the pattern is sparse, will have correspondingly weak and balancing desires to make the “dense” response as well as to make the “sparse” response. In contrast, the animal will have a high degree of belief that the “don’t know” response will advance to a new trial without a timeout, and a timeout is something that the animal wants to avoid. Hence, pressing the “don’t know” key will be the strongest-motivated action in the circumstances. No metacognitive forms of awareness of the animal’s own mental states are required. Of course humans, when they have performed tasks of this sort, will report that they were aware of a feeling of uncertainty, and will say that they chose as they did because they were uncertain. There is no problem here. Although these reports are metacognitive, and reflect metacognitive awareness, the processes reported on can be first-order ones, just as they are for the monkeys. In both species uncertainty will be accompanied by feelings of anxiety, which will motivate various forms of information-seeking behavior (such as moving one’s head from side to side for a better view), as well as a search for alternatives. But humans, with their highly developed mindreading capacities, will interpret these feelings and resulting behaviors for what they are – manifestations of uncertainty. It is only if a human reports that she acted as she did, not just because she was uncertain, but because she was aware of being uncertain, that there will be any conflict. Such reports are likely to be false, in my view. For the most part the “executive function” behaviors that we share with other animals are best explained in terms of the first-order processes that we also share (Carruthers 2008b). It is only when we consider forms of behavior that are unique to humans that we need to appeal to metacognitive processes.7 But these can all be processes that I shall describe in section 7 as belonging to “System 2”, which don’t require any faculty of introspection distinct from mindreading. 6. The confabulation data There is extensive and long-standing evidence from cognitive and social psychology that people will (falsely) confabulate attributions of judgments and decisions to themselves in a wide range of circumstances, while being under the impression that they are introspecting (Bem 1967; 1972; Eagly & Chaiken 1993; Festinger 1957; Nisbett & Wilson 1977; Wegner 2002; Wicklund & Brehm 1976; Wilson 2002). These data are consistent with a “dual method” account of metacognition (Goldman 2006), according to which metacognition is

Carruthers: How we know our own minds sometimes self-interpretative and sometimes introspective. But given that we have been offered, as yet, no positive reasons to believe in the reality of introspection for attitudes, the best explanation at this stage is that metacognition always results from people turning their mindreading abilities upon themselves. Literally hundreds of different studies have been conducted charting confabulation effects and the circumstances under which they occur; and a number of different explanatory frameworks have been proposed (“cognitive dissonance,” “self-perception,” and others). I have space only to describe a few salient examples and to discuss some of the ways in which an introspection theorist might attempt to respond. First, however, let me mention some types of confabulation data that aren’t relevant for our purposes. One emerges from studies that find people to be inaccurate in reporting the causes of their judgments or behavior. For example, people are notoriously bad at identifying the factors that persuade them of the truth of a message or the quality of a job interviewee. Such cases raise no difficulty for a believer in introspection. The reason is simple: no one thinks that causation can be introspected. It is supposed to be the occurrence of our attitudes that is accessible to introspection, not the causal role (if any) that those attitudes have in any given situation. This could only be known by theorizing. Likewise, we should set to one side studies in which subjects are required to report on their attitudes some significant time afterwards. Thus, the fact that subjects will, at the end of the experiment, confabulate lesser enjoyment in playing with a game when they had been paid to play with it (belied by the amount of time that they had freely devoted to the game in their spare time; Kruglanski et al. 1972) raises no difficulty for an introspection theorist. For, given the proposed on-line monitoring function for introspection, it makes sense that no medium- or long-term record of introspected mental events should normally be kept. And in the absence of any such record, subjects have no option but to self-interpret. (The cognitive monitoring account must require that brief records of introspected events should be kept in some sort of working memory system, however. So we should expect subjects to be capable of giving introspective reports for a few moments after the events have occurred. This point is relevant to a number of the experiments described below.) Now consider one of the classic studies conducted by Nisbett and Wilson (1977). Subjects chose between four items of panty-hose (which were actually identical), thinking that they were taking part in a market survey. They displayed a strong right-hand bias in their choices, but all offered judgments of quality (“I thought that pair was the softest,” etc.) immediately afterwards in explanation of their choice. Nisbett and Wilson themselves cast this result in terms of confabulation about the causes of action, and those who believe in the introspectability of judgments will often dismiss it on that ground (Rey 2008). But this is to miss the point that subjects are also confabulating and attributing to themselves a judgment (albeit one they believe to have caused their action, and at least on the assumption that they didn’t actually judge the right-hand item to be softest – otherwise the firstorder mechanisms discussed in sect. 2.1 could underlie their reports). How could one claim otherwise? Well, it is

likely that the root cause of the right-hand choice bias is a right-hand attention bias, and someone might claim that attending more to the right-hand items causes subjects to judge that those items are softer (or are of better quality, or a nicer color, etc.). These judgments can then be introspected and veridically reported. But the causal pathways postulated here are pretty mysterious. And the most likely candidates for fleshing them out are ones that already involve confabulation. (For example, noticing that I am attending more to the right-hand item, and noticing that it is soft, my mindreading faculty might hypothesize that I am paying more attention to it because it is the softest, leading me to ascribe to myself just such a judgment.) There is also ample evidence of confabulation for decisions. For example, Brasil-Neto et al. (1992) caused subjects to move one index finger or another via focal magnetic stimulation of areas of motor cortex in the relevant brain hemisphere. (Subjects had been instructed to freely decide which finger to move when they heard a click, which was actually the sound of the magnet being turned on.) Yet the subjects themselves reported deciding to move that finger. Now, it is very unlikely that stimulation of motor cortex should itself cause a decision (as well as causing movement), hence giving rise to a propositional attitude event that can be introspected. For if the back-projecting pathways between motor cortex and frontal cortex were used for this purpose, then one would predict that stimulation of premotor cortex would also have such an effect; but it does not (Brasil-Neto et al. 1992). Further evidence of confabulation for decisions is provided by Wegner and Wheatley (1999), who induced in subjects the belief that they had just previously taken a decision to stop a moving cursor on a screen (which was controlled via a computer mouse operated jointly with a confederate of the experimenter), by the simple expedient of evoking a semantically relevant idea in the subject just prior to the time when the confederate actually caused the cursor to stop. (Subjects heard a word through headphones, ostensibly as a distracter, shortly before the confederate was able to bring the cursor to a stop beside a picture of the named object.) It seems that the subject’s mindreading faculty, presented with the evidence that the subject had been thinking of the relevant object shortly before the cursor came to a stop beside it, reasoned to the most likely explanation, and concluded that the subject had taken a decision to stop beside that very object. (A control condition ruled out the possibility that hearing the semantically relevant word caused an actual decision to stop the cursor next to the named object.) It might be objected that all of the examples considered so far are ones where (plausibly) actually no judgment was made, nor any decision taken, although behavior occurred that led subjects to think that it had. Hence, someone might propose that it is only in such cases that confabulation occurs. Whenever there is a propositional attitude event, it might be said, it can be introspected; and only when there isn’t, will subjects self-interpret. However, if there really were two distinct ways of attributing judgments and decisions to oneself (an introspective mode as well as an interpretative one), then it would be odd that the latter should always win out in cases where no judgment or decision has actually been made. For presumably BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Carruthers: How we know our own minds an introspective mechanism can detect an absence. And if the introspective mechanism is delivering the judgment, “No judgment,” or, “No decision” at the same time as the mindreading system is attributing one to oneself, then why is it that the latter should always dominate, leading to confabulated answers to the experimenters’ questions? On the contrary, since the introspective mechanism is supposed to have evolved to be especially direct and reliable, one would expect it to be routinely given precedence in cases of conflict. Consider some further data: Subjects who emerge from a hypnotic trance, and then later carry out an instruction given to them while hypnotized, will often confabulate an explanation for their action (Sheehan & Orne 1968; Wegner 2002). Presumably what happens is that they decide, while hypnotized, to comply with the request of the hypnotist. And the effect of this decision is to set up a conditional intention – for example, “When I see the book on the table I shall place it on the shelf” – which remains in existence once the hypnotic episode and original decision are forgotten. This intention is then activated thereafter when the antecedent of the intention is fulfilled (e.g., the book is seen). In which case, there is a decision here to report. And if the subject were to confine herself to reporting just that decision (e.g., to put the book on the shelf), then she would report veridically. But in fact she confabulates a further judgment and/or goal – for example, that the book is out of place and makes the room look untidy. It might be said in reply that placing a book on a shelf isn’t something that people normally do for its own sake. Hence, there are powerful pragmatic reasons for the agent to confabulate a further attitude when pressed by the experimenter to explain her action, even given that the introspective mechanism is detecting the absence of any such state (Rey 2008). But this explanation is problematic. For there are all sorts of circumstances in which people are perfectly content to say, “I don’t know why; I just did it” when asked to explain why they acted in a particular way. Why should the same not be true here? Indeed, it isn’t uncommon to catch oneself performing actions of precisely this sort – absent-mindedly moving a household item from one place to another – in circumstances where one is prompted to ask oneself, “Why did I just do that?”, or where one replies if challenged for an explanation, “I don’t know; just a nervous tic I suppose.” But in any case, Rey’s suggestion should be testable: The hypnotist could instruct a subject to perform a movement that is ambiguous between two distinct actions (e.g., greeting someone with a wave versus waving away a bug), one of which is very much more likely in the circumstances (e.g., indoors, occurring just as someone known to the subject enters the room). The hypnotist’s instruction would be formulated in terms of the less likely action: “When John enters the room you will raise your arm and move it back and forth with the palm facing forwards to shoo away any bugs.” On Rey’s introspective account, subjects should offer the latter in explanation of their arm movement. A “mindreading is prior” theorist will predict, in contrast, that subjects should offer the more likely explanation: “I was waving to John.” There is also an extensive and long-standing set of data that subjects’ behavior, when caused in ways that they are unaware of or inattentive to, will lead them to confabulate 132

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when describing their own degree of belief in some proposition. (See Bem 1967; 1972; Cooper & Duncan 1971; Festinger 1957; Greenbaum & Zemach 1972; Wicklund & Brehm 1976; see also, Eagly & Chaiken 1993 for a more recent review.) Thus, subjects who are manipulated into writing a counter-attitudinal essay for meager pay, but who do so believing that they have made a free decision, will say that they have a greater degree of belief in the proposition that their essay was defending than will subjects in the same circumstances who are paid a decent sum of money. It seems that subjects reason: “I wrote the essay freely, but I can’t have done it for the money, so I must believe it.” And indeed, subjects who don’t participate, but have the circumstances of the various essaywriters described to them, make just such an inference. Likewise, it has long been known that subjects who are induced to nod their heads while listening to a tape via headphones (ostensibly to test the headphones themselves) will say that they have a greater degree of belief in the propositions being defended on the tape than will subjects who are induced to shake their heads (Wells & Petty 1980). It seems that subjects reason: “Since I am nodding/shaking my head, this is evidence that I believe/disbelieve the propositions asserted.” Admittedly, this is not the only explanation possible. It might be that head-nodding primes for positive thoughts about the message, which in turn cause greater agreement, which is then veridically reported. Brin˜ol and Petty (2003) set out to test this alternative by varying the persuasiveness of the messages themselves. When the message is persuasive, nodding increases belief and head-shaking decreases it, which is consistent with either one of the two explanations. But when the message is unpersuasive, the opposite occurs: nodding decreases belief and head-shaking increases it. The authors present evidence that what is actually happening is that subjects interpret their own nodding behavior as confirming their own initial negative reactions to the message, whereas head-shaking is interpreted as disagreement with those reactions. Now, it does not follow, logically, from all this (and much more) data that there is no such thing as introspection for propositional attitudes. For there might be one set of such events to which we have introspective access while there is another set that we can’t introspect; and hence, whenever our behavior is caused by attitudes drawn from the latter set, we are forced to self-interpret (and often to confabulate). What might be proposed, in effect, is that there is both a conscious and an unconscious mind. Judgments and decisions within the conscious mind are introspectable, whereas judgments and decisions within the unconscious mind can only be known (if at all) by turning our mindreading capacities upon ourselves. And just such a view seems to be endorsed by some of those who have been most prolific in demonstrating the reality of metacognitive attitude attribution via processes of interpretation and confabulation. Thus, both Wegner (2002) and Wilson (2002) allow that we do sometimes have introspective access to our (conscious) thoughts, even if much of the time our access to our own propositional attitudes is interpretative, and often confabulatory. In order for this proposal to count as a realistic competitor to the interpretation-only alternative, however, we need some principled account of the two forms of mentality and their relationships to each other. This isn’t by any

Carruthers: How we know our own minds means an easy thing to provide. For we need to know what it is about some judgments and decisions that makes them available for introspection, while others are cut off from such availability. What kind of cognitive architecture can underlie and explain these patterns of availability and unavailability in anything more than an ad hoc way? I take up this challenge in the next section, where the only such account that I know of is outlined and discussed. It will turn out on closer investigation, however, that the account actually lends no support to the introspectionist position. 7. Is there a conscious mind? One possible response to our challenge is to distinguish between two different levels of mental process (conscious and unconscious). And the only worked-out account of these two levels that I know of is as follows. It would be allowed that the access we have to our unconscious attitudes (whether or not they get expressed in speech or other imagery) is always interpretative, as argued earlier. But it might be claimed that the stream of inner speech and other forms of imagery is constitutive of a distinct kind of (conscious) mentality (Frankish 2004). Certainly such events are not epiphenomenal, but they often make an important causal contribution to subsequent thought and behavior (Carruthers 2002; 2006; Clark 1998). And it might be said that such events are routinely available to introspection. This suggestion comports very naturally with an idea that has been gaining increasing ground among those who work on the psychology of reasoning (Evans & Over 1996; Kahneman 2002; Sloman 1996; 2002; Stanovich 1999). This is that human reasoning processes may be divided into two very different types, often now referred to as “System 1” and “System 2. System 1 (which is really a set of systems, arranged in parallel) is fast, unconscious, hard to alter, universal to all thinkers, and evolutionarily ancient. System 2, in contrast, is slow and serial, characteristically conscious, malleable in its principles of operation, admits of significant variations between individuals, and is evolutionarily novel. And a number of authors have emphasized the important constitutive role played by imagery (especially inner speech) in the operations of System 2 (Carruthers 2009; Evans & Over 1996; Frankish 2004). Likewise, others have demonstrated the crucial role played by inner speech in the performance of tests of executive functioning (which are likely to implicate System 2), such as the Wisconsin Card Sorting Task (Baddeley et al. 2001). For when inner speech is suppressed by the need to shadow an irrelevant speech stream while performing the task, performance collapses. In order for this account to be successful, however, it is obviously crucial that the conscious imagistic events in question should play the right sorts of causal role, constitutive of the roles of the various attitude types. Not any old causal role will do. Thus, it is a conceptual constraint on an event being an instance of deciding, for example, that it should fit one of two causal profiles (Bratman 1987; 1999). In the case of a decision to act here-andnow, the decision should issue in motor instructions without the intervention of any further practical reasoning. A decision is supposed to end the process of practical

reasoning and to settle what I do (unless something goes awry with my motor system, of course). Something similar is true of a decision to act in the future: this should settle that I act (unless something significant changes in the interim) and what act I shall perform. Any further reasoning in the future should be confined to the question of how to act. Intentions for the future place constraints on our practical reasoning. They have the form of partial plans, in which details may be left blank to be filled in later, but in which the overall structure is fixed. A similar point can be made about judgments. Just as a decision is an event that ends a process of practical (actionoriented) reasoning, so a (non-perceptual) judgment is an event that concludes a piece of theoretical (belieforiented) reasoning. A judgment, then, is an event that will normally (a) immediately (without further inference) give rise to a stored standing-state belief with the same content, and (b) will immediately be available to inform practical reasoning, interacting with the subject’s goals (where appropriate) in the construction of plans. If an event is genuinely a judgment, then there should be no further cognitive activity standing between it and the normal roles of judgment (the formation of belief and the guidance of action). We need to ask, therefore, in what way is it that the events that constitute System 2 achieve their characteristic effects. For only if these events have the right sorts of causal roles, can they be said to be propositional attitude events of judging, deciding, and the like. And so, only if they have the right sorts of roles, can our introspective, non-interpretative, awareness of them (which I grant) constitute introspective, non-interpretative, awareness of a set of propositional attitudes. The processes that take place in System 2 don’t simply mirror those that take place in System 1, of course, tracking them one-for-one. Rather, sequences of imagery can occur in accordance with well-practiced rules or habits, or they can be guided by subjects’ beliefs about how they should reason, often issuing in an assertoric statement, for example, that isn’t simply the expression of a pre-existing (System 1) judgment.8 So let us consider such a case. As a result of an episode of System 2 conscious activity, I might formulate and rehearse the assertoric utterance, “Polar bears are endangered.” Under interpretation, this event will likely be heard as an assertion that polar bears are endangered. And as a result, I will think and act in the future much as if I had formed just such a judgment. I shall, for example, reply positively if asked whether or not polar bears are endangered. And if one of my goals is to try to protect endangered species, then I might, in consequence of this event, begin writing a suitable letter to my congressional representative. How does the rehearsed assertion achieve these effects? There are a number of possibilities. (These aren’t mutually exclusive, I should stress. On the contrary, a pluralist position concerning the realization of System 2 processes is probably correct; see Carruthers 2009.) One is that the event causes me to believe of myself (unconsciously, at the System 1 level) that I believe polar bears to be endangered. Then this, together with a standing desire to think and act consistently, will lead me to answer positively when asked whether or not I believe that polar bears are endangered. And it might also issue in letter-writing behavior. For if I believe myself to believe that polar bears are BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Carruthers: How we know our own minds endangered, and want to do something to help endangered species, then consistency requires that I should act. Another possibility is that my mentally rehearsed assertion causes me to believe I have committed myself to the truth of the proposition that polar bears are endangered. And then a standing (System 1) desire to execute my commitments will lead me to act in ways I consider to be appropriate to that commitment. And yet another possibility is that the rehearsed sentence is treated by my cognitive systems much as if it were an item of testimony from a putatively reliable informant, and after checking for coherence with existing belief, it is then stored as a first-order (System 1) belief, which then issues in appropriate behavior in the normal way. The important point to notice is that on each of these three accounts, the rehearsal of the assertion “Polar bears are endangered” does not give rise to a standingstate belief immediately, without the mediation of any further cognitive processing. Nor is it immediately available to guide planning with respect to endangered species. For in each case further, down-stream, cognitive activity must occur first. Either I must form the belief that I believe polar bears to be endangered, which then interacts with a higher-order desire to guide activity consistent with my possessing such a belief. Or I must form the belief that I have made an appropriate commitment, which again has to interact with a higher-order desire to execute my commitments in order to guide behavior. Or the assertion must be evaluated in something like the way that the testimony of other people is (checking for coherence with existing belief, and so on; see Harris [2002a; 2002b] who shows that even young children don’t automatically accept the testimony of others, but evaluate it in light of a variety of “gate-keeping” criteria first). In each of these cases the relevant assertion does not have the right sort of causal role to be a judgment. For it does not by itself settle what I believe. An exactly parallel argument can be constructed for System 2 episodes that might be candidate decisions, such as saying to myself (in inner speech) at the conclusion of a period of System 2 activity, “So, I shall write to my congressional representative.” This utterance does not, by itself, settle anything. For it first has to give rise to the belief that I have decided to write, or to the belief that I have committed myself to write, and then the causal pathways operate as described. So in each case, then, although there is a conscious System 2 event to which I have introspective access, it is not an event of deciding on an action, or of forming a new judgment. And this argument generalizes to other candidate types of propositional attitude, such as supposing something to be the case, or fearing that something is the case, and so forth. (Interestingly, however, System 2 conscious activity is constitutive of thinking. For there are few significant conceptual constraints on what sorts of processes can count as thinking. Roughly speaking, any sequence of contentbearing events that makes some difference to subsequent attitude-formation or to behavior can count as thinking. So we do have introspective access to some forms of thinking – specifically to imagistically expressed System 2 thinking – even if, as I have argued, we don’t have such access to any propositional attitudes.) I conclude there is, indeed, such a thing as conscious mentality. In addition to globally broadcast experiences 134

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of various sorts, there are also sequences of visual and auditory imagery that make an important difference to our cognitive and practical lives. But our introspective access to these events doesn’t thereby give us introspective access to any propositional attitudes. On the contrary, our only form of access to propositional attitudes of judging, deciding, and so forth, is interpretative. 8. The evidence of unsymbolized thinking Recall from section 1 that a “mindreading is prior” account makes two distinctive predictions. The first is that it should be possible for subjects to be misled, in attributing propositional attitudes to themselves, by being presented with manipulated behavioral or sensory data. As we have seen in sections 6 and 7, this prediction is amply confirmed, in ways that the opposed accounts cannot easily accommodate. But the second prediction is that subjects should be incapable of attributing propositional attitudes to themselves in the absence of behavioral or sensory data. All three of the opposing positions, in contrast, make the opposite prediction. Because they maintain that introspection for propositional attitudes exists, subjects should generally have no need of evidence of any kind when making self-attributions. The presence of behavioral and sensory cues should be entirely accidental. However, we have already seen in section 5.1 that many kinds of metacognitive judgment – such as judgments of learning – are actually dependent upon sensory cues. Hence, in these cases, at least, the sensory cues are not accidental. The present section evaluates some additional evidence that bears on this matter. The data in question derive from “introspection sampling” studies conducted with normal subjects, using the methodology devised by Hurlburt (1990; 1993). Subjects wear a paging device throughout the day, via which they hear a “beep” at randomly generated intervals. Subjects are instructed to “freeze” the contents of their consciousness at the very moment of the beep, and to make notes of it, to be discussed and elaborated in a later meeting with the experimenter. All normal subjects report, in varying proportions, the occurrence of inner speech, visual imagery, and emotional feelings. But many subjects also report the presence of “purely propositional,” unsymbolized thoughts at the moment of the beep. In these cases subjects report thinking something highly determinate – such as wondering whether or not to buy a given box of breakfast cereal – in the absence of any visual imagery, inner speech, or other sensory accompaniments. So far there isn’t any difficulty here for a “mindreading is prior” account. For such an account doesn’t have to claim that all thinking should be imagistically expressed. Indeed, quite the contrary: the thoughts generated by the mindreading system itself will characteristically remain unexpressed. What the account does claim is that self-attributions of thought should be dependent on the presence of either sensory/imagistic or behavioral/circumstantial data. And what is striking about a good many of the instances of self-attributed unsymbolized thought is that they occur in circumstances in which a third-party observer might have made precisely the same attribution. If you saw someone standing motionless, looking reflectively at a box of breakfast cereal on a supermarket shelf, for

Carruthers: How we know our own minds example, you might well predict that she is wondering whether or not to buy it. Our suggestion can therefore be that when prompted by the beep, subjects turn their mindreading systems on their own behavior and circumstances (together with any sensory or imagistic cues that might be present), often enough interpreting themselves as entertaining a specific thought. Provided that the process happens swiftly, this will then be self-attributed with all of the phenomenological immediacy and introspective obviousness as normal. Although a great many of the examples in the literature can be handled in this way, not quite all of them can. For instance, at the time of the beep, one subject reported that she was wondering whether her friend who would be picking her up later that day would be driving his car or his truck. This thought seemed to occur in the absence of any inner speech or visual imagery. Yet there was nothing in the subject’s immediate circumstances or behavior from which it could be derived, either. What cannot be ruled out, however, is that the thought in question was self-attributed because it made the best sense of sensory activity that had been taking place just prior to the beep – for example, two memory images deriving from previous experience, in one of which the friend arrives in his car and in the other of which he arrives in his pickup truck. Since Hurlburt’s methodology makes no provision for collecting data on experiences occurring shortly before the beep, we simply don’t know. An extension of the methodology might provide us with a valuable test, however. Another possible test would be to look for correlations between the extent to which different subjects report purely propositional thoughts (with quantities of inner speech and visual imagery controlled for) and the speed of their mindreading abilities in third-person tasks. Because subjects will only have the illusion of introspecting if they can reach a self-interpretation smoothly and swiftly, I predict that there should be a positive correlation. Hurlburt and Akhter (2008) concede that it is possible that attributions of unsymbolized thought to oneself might result from swift and unconscious self-interpretation. But they present the following consideration against such a possibility. Subjects are initially quite reluctant and hesitant in describing instances of unsymbolized thought, presumably because they share the commonly held folk theory that all conscious thinking is accompanied by images of one sort or another. But explicitly held folk theories are one thing; assumptions built into the operations of the mindreading faculty are quite another. And there is no reason to think that the latter will share all of the culturally developed assumptions made by the folk. Hence, the mindreading system might have no hesitation in attributing a thought to the self in the absence of any sensory cues, even though the person in whom that system resides does so hesitate. I conclude this section, therefore, with the claim that although there is no support to be derived for a “mindreading is prior” account from the introspection-sampling data, neither is there, as yet, any evidence to count against it. 9. The evidence from schizophrenia Recall from section 1 that two of the three competitor models (namely, models 1 and 2) predict that there

should exist cases in which mindreading is intact while metacognition is damaged. The “mindreading is prior” account, in contrast, must deny this. Nichols and Stich (2003) cite certain forms of schizophrenia as confirming the former prediction. More specifically, patients with “passivity” symptoms, who claim that their own actions are not under their control and that their own episodes of inner speech are somehow inserted into their minds by other people, are supposed to demonstrate such a dissociation (presumably on the grounds that such patients no longer have normal introspective access to their own behavioral intentions).9,10 This is because such patients perform normally when tested on batteries of mindreading tasks. There is no reason to think that the symptoms of passivity forms of schizophrenia are best explained by a failure of metacognitive competence, however. Rather, the damage lies elsewhere, resulting in faulty data being presented to the mindreading system. Frith et al. (2000a; 2000b) provide a detailed account designed to explain a range of disorders of action and awareness of action (including passivity-symptom schizophrenia). The account builds on well-established models of normal action control, according to which an “efference copy” of each set of motor instructions is transformed via one or more body emulator systems and used to construct a “forward model” of the expected sensory consequences of the movement. This can then be compared, both with the motor intention itself and with the incoming perceptual data, allowing for swift correction of the action as it unfolds (Grush 2004; Wolpert & Ghahramani 2000; Wolpert & Kawato 1998). Frith et al. think that the symptoms of passivity and “alien control” in schizophrenia can be explained as issuing from damage to this actionmonitoring system, which results in no forward model ever being created for comparison. Now the important point to note for our purposes is that the kind of action-monitoring just described is entirely first-order in character, and qualifies as “metacognitive” only in the weak and irrelevant sense distinguished in section 5.1. There is no reason to think that it should involve metarepresentions of our own motor intentions, let alone introspective access to them. And indeed, the speed with which the monitoring process operates suggests very strongly that introspection is not involved (Jeannerod 2006). But why should the absence of a forward model lead subjects to feel that their actions are not their own? Frith et al. (2000a) point out that the forward model is normally used to “damp down” experiences resulting from movement that are of the sort predicted in the forward model. This is why it is normally impossible to tickle yourself, whereas if you wear special gloves that introduce a slight delay in your movements, then self-tickling suddenly becomes possible (Blakemore et al. 1998; Weiskrantz et al. 1971). And it is also why when you unwrap a candy at the opera you barely hear it while those around you are disturbed. If no forward model is created, however, then perceptions resulting from your actions will be experienced with full vividness, just as if the movements had been caused by another person. The suggestion is that passivity-symptom schizophrenics have the sense that their actions are caused by others because those actions literally feel that way to them. BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Carruthers: How we know our own minds In addition, one might expect the comparator process to give rise to heightened attention and feelings of anxiety in cases where there is too great a mismatch between the forward model and the perceptual data received. These feelings would be especially enhanced in cases where there is no forward model, as a result of some pathology. For the comparator system would be receiving perceptual input of an action being performed, but without receiving the normally attendant input deriving from an efference copy of a motor intention. So this would, as it were, be a case of maximum mismatch. An additional suggestion, then, is that these feelings of anxiety might signal to the mindreading system that something is amiss, perhaps reinforcing the impression that the actions are not one’s own. Put differently: Only when everything is going smoothly, with no feelings of anxiety or surprise specifically attending one’s action, does the mindreading system attribute agency to the self by default. I conclude that passivity-symptom forms of schizophrenia are not best interpreted as instances of a dissociation between mindreading and metacognitive capacities. Rather than being cases in which mindreading is intact while introspection is damaged, the damage is to lower-level forward modeling and/or comparator systems. This results in experiences that are naturally interpreted as indicating that one’s actions (including one’s mental actions, such as inner speech) are not one’s own. 10. The evidence from autism The final major area in which the relationship between mindreading and metacognition can be assessed concerns autism. Almost everyone agrees that third-person mindreading is significantly impaired in autism. (There is, however, disagreement over whether this impairment lies at the heart of the syndrome.) In which case the prediction of a “mindreading is prior” account will be that autistic people’s access to their own propositional attitude states must be impaired as well. Nichols and Stich (2003) and Goldman (2006) each maintain, in contrast, that introspection is intact in autism, with difficulties in otherunderstanding arising from difficulties in supposing or empathizing. One set of data concerns an introspection sampling study conducted with three adult autistic men (Frith & Happe´ 1999; Hurlburt et al. 1994). All three were able to report on what was passing through their minds at the time of a randomly generated “beep,” although one of them experienced significant difficulties with the task. This is interpreted as demonstrating that introspection is intact in autism. There are two points to make. First, none of these three subjects was entirely deficient at mindreading. On the contrary, two of them could pass secondlevel false-belief tasks, and the third could pass simple first-level false-belief tasks. So no one should predict that any of them would be entirely deficient at selfattribution, either. (It is worth noting, moreover, that the experimenters found a strong correlation between the subjects’ abilities with third-person tasks and the sophistication and ease of their introspective reports. This finding is problematic for the view that introspection is undamaged in autism.) Second, the form of “mindreading is prior” account being defended here predicts that people 136

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with autism should have no difficulty in reporting the occurrence of perceptions, images, or emotional feelings, provided that they possess the requisite concepts. For these events will be globally broadcast and made directly accessible to their (damaged but partially functioning) mindreading faculties. And indeed, much of the content of the introspective reports of the three autistic subjects concerned visual imagery and emotional feelings. Reports of their own occurrent attitudes tended to be generic (“I was thinking . . .”), and one of the three men (the one who could only pass first-level false-belief tasks) had significant difficulties in reporting his own attitudes at all. Another set of data of the same general sort concerns the autobiographical reports of adults with autism, who are often able to describe with some vividness what their mental lives were like at ages when they almost certainly wouldn’t have been capable of attributing mental states to other people. Nichols and Stich (2003) comment that (provided we accept the memory reports as accurate), the individuals in question must have had reliable introspective access to their own mental states prior to having any capacity for mindreading. But actually we have no reason at all to believe that memory is itself a secondorder (metarepresentational) process. When I observe an event, a first-order representation of that event may be stored in memory. When that memory is later activated, I shall describe it by saying that I remember seeing the event in question (say). But it doesn’t at all follow that the original event involved any metarepresentation of myself as seeing something. Likewise for other sorts of memories and other sorts of mental events. The fact that autistic adults give metarepresentational reports of their mental lives as children does not show that autistic children are capable of metarepresenting their own mental states. It just shows that they are capable of memory formation. Nichols and Stich (2003) also place considerable reliance on a study by Farrant et al. (1999), who tested autistic children, as well as learning-disabled and normal children matched for verbal mental age, on a range of metamemory tasks. Since they were able to find no significant differences between the groups, the authors conclude that metacognition is unimpaired in autism. Two preliminary points should be emphasized about this study, however. One is that almost all of the autistic children tested were sufficiently well advanced to be able to pass first-order false-belief tasks. So we should predict that they would have some understanding of their own minds, and that they would be capable of completing simple metacognitive tasks. Another point is methodological: The small group sizes meant that statistically significant differences were not detected even when a trend (namely weaker performance by the autistic children) was plainly visible in the raw data. We simply don’t know whether those trends would have been significant had larger groups of children been used. A deeper problem with the Farrant et al. data is that none of the experimental tasks was metacognitive in the right sort of way, requiring access to the subject’s current propositional attitudes. On the contrary, they could be solved by anyone who possessed the requisite mental concepts who was also a smart behaviorist. For example, one experiment tested whether children with

Carruthers: How we know our own minds autism were aware that it is easier to learn a small number of items than a larger number. Not surprisingly, the children did well on this test. But they would have had ample opportunity over a number of years of schooling to have established a reliable correlation between the number of items studied in a task and the number of responses later given that are evaluated as correct. (Note that the average age of the autistic children in this experiment was 11 years.) It is true that many of the autistic children in question could give simple verbal descriptions of some memorization strategies. But many of these involved such tasks as looking in likely places (for an object that had been mislaid) or listening carefully to the instructions (from someone reciting a list of things to remember). This is metacognitive only in the minimal sense of mentioning looking and listening. Moreover, in order to develop a cognitive strategy like mental rehearsal (which a number of the autistic as well as normal subjects suggested), it is doubtful that much mindreading ability is required. Rather, children just need to notice a positive correlation between a behavior (rehearsal) and an outcome (getting the correct answer), which should be well within the reach of even a clever behaviorist (provided that the latter had access also to inner behavior, such as inner speech). Thus, the data from autistic people considered by Nichols and Stich (2003) and by Goldman (2006) do not support their introspectionist positions against an interpretative, “mindreading is prior” account. But there are other data that these authors don’t discuss, which suggest that people with autism are decidedly poor at attributing propositional attitudes to themselves. Let me describe just a couple of strands of evidence here. Phillips et al. (1998) tested children with autism against learning-impaired controls (matched for verbal mental age) on an intention reporting task. The children had to shoot a “ray gun” at some canisters in the hopes of obtaining the prizes contained within some of them. But the actual outcome (i.e., which canister fell down) was surreptitiously manipulated by the experimenters (in a way that even adults playing the game couldn’t detect). They were asked to select and announce which canister they were aiming at in advance (e.g., “The red one”), and the experimenter then placed a token of the same color next to the gun to help them remember. After learning whether they had obtained a prize, the children were asked, “Did you mean to hit that [for example] green one, or did you mean to hit the other [for example] red one?” The autistic children were much poorer than the controls at correctly identifying what they had intended to do in conditions where there was a discrepancy between intention and goal satisfaction. For example, if they didn’t “hit” the one they aimed at, but still got a prize, they were much more likely to say that the canister that fell was the one they had meant to hit.11 Likewise Kazak et al. (1997) presented autistic children with trials on which either they, or a third party, were allowed to look inside a box, or were not allowed to look inside a box. They were then asked whether they or the third party knew what was in the box, or were just guessing. The autistic children got many more of these questions wrong than did control groups. And importantly for our purposes, there was no advantage for answers to

questions about the child’s own knowledge over answers to questions about the knowledge of the third party. This result is especially striking because the children could have answered the self-knowledge version of the question merely by asking themselves the first-order question, “What is in the box?”, without needing to engage in metacognitive processes at all (except when transforming the result into a metacognitive answer to the experimenter’s question). I conclude that data from people with autism provide no support for the view that metacognition can remain intact in the absence of mindreading. On the contrary, the evidence suggests that if mindreading is damaged, then so too will be metacognition. Now admittedly, this by itself is just as consistent with model 2 (“one mechanism, two modes of access”) as with model 4 (“mindreading is prior”). But our discussion in section 9 failed to find the alleged evidence that might speak in favor of the former (i.e., individuals in whom mindreading is intact but metacognitive access is blocked). And we have discussed a variety of other forms of evidence that support the latter. 11. Conclusion This target article has evaluated four different accounts of the relationship between mindreading and metacognition, three of which endorse the existence of introspection for attitudes, whereas the fourth denies it. Since we know that people have the illusion of introspecting even when they demonstrably aren’t doing so, and since design considerations suggest that the mindreading faculty would picture the mind as having introspective access to itself, I have argued that no weight should be placed on the introspective intuition. In which case the “mindreading is prior” account should be accepted by default, as the simplest of the four possibilities. In addition, I have argued that various predictions made by the three accounts that endorse introspection for attitudes are not borne out by the data. In contrast, the central prediction of the “mindreading is prior” account is confirmed: This is that subjects should be caused to misattribute attitudes to themselves by misleading sensory or behavioral data. Although an introspection theorist can attempt to save this data post hoc, such attempts are less than convincing. Hence, the “mindreading is prior” account is, overall, the best supported of the four alternatives. ACKNOWLEDGMENT I am grateful to the following for their helpful comments on a previous draft of this article: Jose´ Bermu´dez, Paul Bloom, Daniel Dennett, Shaun Nichols, Rebecca Saxe, and an anonymous reviewer. In addition, I am grateful to the students in my graduate seminar on this topic, who critiqued my work and helped me to think through the issues: Mark Engleson, Marianna Ganapini, Yu Izumi, David McElhoes, Christine Ng, Elizabeth Picciuto, Vincent Picciuto, Yashar Saghai, Elizabeth Schechter, and Sungwon Woo, with special thanks to Mark Engelbert and Brendan Ritchie.

NOTES 1. One might wonder why the dedicated input channels between the various perceptual systems and the metarepresentational faculty couldn’t be damaged while leaving those systems BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds themselves intact. The answer is that there are no such channels. Rather, the attended outputs of perception are globally broadcast to all conceptual systems, including the metarepresentational faculty inter alia. See section 2 for some discussion and references. 2. All of these authors endorse broadly “theory-theory” accounts of mindreading. A very different kind of “mindreading is prior” account is defended by Gordon (1986; 1996), who develops a form of simulation theory that denies any need for introspection. But this account makes both mindreading and metacognition dependent upon the acquisition of natural language. Likewise, Dennett (1991) is a sort of theory-theorist who denies introspection for attitudes, but he, too, appears to make our knowledge of our own mental states dependent upon their expression in language. Discussion of these issues would take us too far afield. For present purposes I assume, as seems plausible, that basic capacities for both mindreading and metacognition are independent of our capacity for natural language. 3. Note that for this reason Nichols and Stich’s (2003) introduction of a separate perception-monitoring mechanism is wholly unnecessary. Since the mindreading system would need to have access to the agent’s own perceptual states in order to do its work, there is simply no need for a distinct system to monitor and self-attribute those states. 4. In allowing that perceptual judgments are introspectable, I don’t mean to imply that perceptually based beliefs are likewise introspectable. On the contrary, once formed and stored, the only way that those beliefs can be consciously accessed is via their expression in visual imagery (in the form of an episodic memory, perhaps) or in inner speech. But such events, although introspectable, will need to be interpreted to extract the information that they are, indeed, expressive of belief (as opposed, for example, to supposition or mere idle fantasy). See section 2.1 for further discussion. 5. An alternative account to the one sketched here is outlined by Wilson (2002), who suggests that the introspective assumption may make it easier for subjects to engage in various kinds of adaptive self-deception, helping them build and maintain a positive self-image. In fact, both accounts might be true. 6. We also know that in other domains – such as physics – the unconscious theories that guide behavior often make false, but simplifying, assumptions. See, for example, McCloskey (1983). 7. This isn’t quite accurate. For, to the extent that apes, for example, do have limited mindreading abilities (e.g., in respect of perception and goal-directed action), to that extent one might expect to find metacognitive processes also. At any rate, this is what a “mindreading is prior” account would predict. 8. Sometimes a System 2 utterance does express an underlying System 1 judgment with the same content, no doubt. But in such a case it is all the clearer that the utterance in question isn’t itself a judgment. Nor does the expressibility of judgments in speech provide any reason for believing in introspection, as we saw in section 2.1. 9. Similar claims are made by Bayne and Pacherie (2007). They argue against an interpretative account of self-awareness of the sort defended here, preferring what they call a “comparator-based” account. But I think they mis-characterize the models of normal action-monitoring that they discuss. Properly understood, those models lend no support for the claim that metacognition is damaged in schizophrenia. See the paragraphs that follow. 10. The claim that we have introspective access to our own motor intentions seems also to underlie the idea that “mirror neurons” might play an important role in the development of mindreading (Gallese & Goldman 1998). For what would be the use, for purposes of social understanding, of an activation of one’s own motor system in response to an observation of the action of another, unless one could acquire metacognitive

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access to the motor plan in question? (For a variety of criticisms of this account of the mirror neuron system, see Csibra [2007] and Southgate et al. [2008].) 11. Russell and Hill (2001), however, were unable to replicate these results. This is probably because their population of autistic children, although of lower average age, had higher average verbal IQs, suggesting that their autism was much less severe. Since most researchers think that intention-reading is among the easiest of mindreading tasks, one might predict that only very young or more severely disabled individuals with autism would be likely to fail at it.

Open Peer Commentary What puts the “meta” in metacognition? doi:10.1017/S0140525X09000557 Michael L. Andersona,b and Don Perlisb,c a Department of Psychology, Franklin & Marshall College, Lancaster, PA 17604; bInstitute for Advanced Computer Studies, University of Maryland, College Park, MD 20742; cDepartment of Computer Science, University of Maryland, College Park, MD 20742. [email protected] http://www.agcognition.org [email protected] http://www.activelogic.org

Abstract: This commentary suggests an alternate definition for metacognition, as well as an alternate basis for the “aboutness” relation in representation. These together open the way for an understanding of mindreading that is significantly different from the one advocated by Carruthers.

Carruthers suggests that cognitive scientists are confused about the meaning of “metacognition,” citing our work as an illustrative example. In fact, we follow a standard definition of the term, adopted from Nelson and Narens (1990). (This particular formulation appears in Anderson & Oates [2007], but the definition is in widespread use. See, e.g., Dunlosky 2004; Dunlosky & Bjork 2008; Dunlosky & Metcalfe 2009; Metcalfe 1993; Metcalfe & Shimamura 1994.) The definition runs as follows: Imagine two components X and Y (where X and Y could be the same), related in such a way that state information flows from Y to X, and control information flows from X to Y. Component X is in a monitoring and control relationship with Y, and when Y is a cognitive component, we call this relationship metacognitive monitoring and control.

This offers an information-theoretic characterization of metacognition that is neutral regarding the form that information takes, or the processing it undergoes. Thus, it is quite incorrect to say that cognitive scientists use the term “in two quite distinct ways, often without noticing the difference” (target article, sect. 5.1, para. 2). We use the term consistently in a way that leaves open the various ways in which such a relationship could be implemented. We are not confused about the difference between systems that involve “metarepresentations of [its] own first-order cognitive processes as such” (sect. 5.1, para. 2) and those that don’t; rather, this distinction is not relevant to the definition of metacognition. In fact, some of the processes in the systems we implement are indeed metacognitive in Carruthers’ more restricted sense. To take just one example, mentioned by Carruthers: If an active logic system notices the presence of both P and : P in its knowledge base (KB), it will assert Contra(P, : P, t).

Commentary/Carruthers: How we know our own minds That is a statement about – a metarepresentation of – the state of the KB at time t (i.e., that it contained that contradiction). Our systems can reason about this fact with that metarepresentation, and consequently take various control steps, the simplest of which is to refrain from using these premises in further deduction (Anderson & Perlis 2005a). But other processes in active logic systems, and other of our metacognitive systems, effect such monitoring and control without explicit metarepresentations of this sort (see, e.g., Anderson et al. 2006). Of course, Carruthers is free to define his terms and circumscribe his interests as best serves his argument, and if this were merely a terminological dispute, we would not be submitting a commentary. But there is a more substantive point in the background, which potentially affects Carruthers’ overall proposal. Carruthers writes: “Generally the term is used, as it has been throughout this article, to mean cognition about one’s own cognition. Metacognition, in this sense, is inherently higher-order, involving metarepresentations of one’s own first-order cognitive processes as such” (sect. 5.1, para. 2, emphasis in original). The implication seems to be that for something to be about another requires a higher-order metarepresentation. But we would like to suggest that this associates higher-order-ness with meta-ness and aboutness (if we can be forgiven the neologisms) in a way that is not necessary. First, it is not clear that aboutness requires higher-order-ness. Surely a representation or a process can be about another without being at a different level, or in a different representational language. Indeed, can’t a process (or representation) be about itself? (See, e.g., Perlis 1985; 1988; 1997; 2000; Perlis & Subrahmanian 1994.) It is a common bias, perhaps stemming from Tarski, that there must be a hierarchy of meta-languages, each standing back from the one it refers to. But Tarski adopted that approach to avoid technical difficulties in formal logic; it is not necessary a priori. Second, it is not clear that meta-ness requires higher-orderness. In related writings, we have suggested that representation requires only the following: tokens, whatever their form/ content, that can be used to guide actions with respect to certain targets (Anderson & Perlis 2005b; Anderson & Rosenberg 2008). On these accounts, the information being used and manipulated during cognition is representational just in case it is used to guide behavior with respect to targets in various circumstances. Likewise, a metacognitive monitoring and control process represents a cognitive process, just in case it allows the metacognitive component to guide actions with respect to the cognitive process. Such monitoring and control is indeed (we maintain) cognition about cognition – is thus metacognition – without having to be/utilize higher-order representations of cognition as such. As should be clear from the preceding, we have a somewhat different understanding of what the representational aboutness relation requires. This most definitely applies to self-representation as well (Anderson & Perlis 2005b), although it is perhaps worth noting that the account of self-awareness we develop in the cited paper is – despite differences in the fundamental criteria for aboutness – nevertheless compatible with the “mindreading is prior” framework that Carruthers advocates. So why might all of this matter to Carruthers? Because of Carruthers’ understanding of what aboutness requires, he is driven to adopt a higher-order, meta-representational account of what having certain thoughts about another’s thoughts (“mindreading”) requires. In contrast, the less restrictive option offered by us opens the door for a broader range of theories of what our responsiveness to the mental states of others requires. This would include, for instance, Shaun Gallagher’s interesting, and interestingly different, interaction-based account of understanding self and others (Gallagher 2004; 2005). It would have been useful and instructive to see how this rather broader portrayal

of the competing possibilities might have affected Carruthers’ argument, discussion, and conclusions.

Is feeling pain just mindreading? Our mind-brain constructs realistic knowledge of ourselves doi:10.1017/S0140525X09000569 Bernard J. Baars The Neurosciences Institute, San Diego, CA 92121. [email protected] http://bernardbaars.pbwiki.com

Abstract: Carruthers claims that “our knowledge of our own attitudes results from turning our mindreading capacities upon ourselves” (target article, Abstract). This may be true in many cases. But like other constructivist claims, it fails to explain occasions when constructed knowledge is accurate, like a well-supported scientific theory. People can know their surrounding world and to some extent themselves. Accurate self-knowledge is firmly established for both somatosensory and social pain.

Brain imaging studies show that social pain (like social rejection, embarrassment, and guilt) activates brain regions characteristic of painful bodily experiences. The brain regions that are activated by both evoked social and physical pain include the anterior cingulate cortex, the right prefrontal lobe, the insula, amygdala, and somatosensory cortex. Even deep brain structures, such as the brainstem periaqueductal gray (PAG), are known to be evoked by mother – infant separation, marked by intense and repeated distress cries. These functions are highly conserved among mammals and, perhaps, birds (Eisenberger & Lieberman 2004; Nelson & Panksepp 1998). This evidence contradicts Carruthers’ hypothesis that we learn about ourselves by turning our social mindreading capacities upon ourselves. No doubt we do learn about ourselves based upon what we have learned about others. After all, we constantly transfer knowledge between different domains of reference. However, it is simply not the case that all of our introspective self-knowledge is of this kind. Children acquire “theory of mind” abilities in about the fourth year of life. But long before that time we can observe, pain and pleasure perception, the distress of abandonment, anticipatory fear and joy, and a wide spectrum of social and imaginary emotional experiences. Carruthers could maintain that such emotional experiences are not true cases of “metacognition” and “introspection.” It is possible to define such terms in very limited ways, but there is no doubt that emotional feelings express propositional attitudes: They are about something, namely the well-being of the self. Thus, hunger, thirst, air-hunger, social distress, fear of rejection by the mother, peer envy, and numerous other infant emotions are by no means simple “reflexes.” They are socially contingent, though not explicitly deliberated, reactions to real-world events that are critical to the infant’s survival. This crucial self-related information has extraordinary breadth of conservation among mammals, suggesting an evolutionary history of some 200 million years (Baars 2005). Pain is not the only kind of introspective experience humans have with minimal social input, but it is perhaps the most compelling. Metacognitive self-report (“introspection”) has been used for two centuries in psychophysics. It is a well-established methodology that converges extremely well with other empirical evidence, such as brain recording methods (Baars & Gage 2007). Science is a constructive enterprise, but it is tightly constrained by evidence. That is why, like other human activities such as farming and tax accounting, it is not merely constructed, but also bound by considerations of accuracy and predictability. BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds That is true for humans, but it is equally true for animals, who must survive real-world challenges in environments in which errors lead to extinction. Brain evolution is not separate from the ability to observe and know the real world. On the contrary, when we are given truthful feedback about the world, humans and other animals become quite reality-based. There is no contradiction between constructivism and realism.

How “weak” mindreaders inherited the earth doi:10.1017/S0140525X09000570 Cameron Buckner,a Adam Shriver,b Stephen Crowley,c and Colin Allend a

Department of Philosophy, Indiana University, Bloomington, IN 47405-7005; Philosophy-Neuroscience-Psychology Department, Washington University in St. Louis, St. Louis, MO 63130; cDepartment of Philosophy, Boise State University, Boise, ID 83725-1550; dDepartment of History and Philosophy of Science, Indiana University, Bloomington, IN 47405. [email protected] http://www.indiana.edu/~phil/GraduateBrochure/IndividualPages/ cameronbuckner.htm [email protected] http://artsci.wustl.edu/~philos/people/ index.php?position_id¼3&person_id¼60&status¼1 [email protected] http://philosophy.boisestate.edu/Faculty/faculty.htm [email protected] http://mypage.iu.edu/~colallen/ b

Abstract: Carruthers argues that an integrated faculty of metarepresentation evolved for mindreading and was later exapted for metacognition. A more consistent application of his approach would regard metarepresentation in mindreading with the same skeptical rigor, concluding that the “faculty” may have been entirely exapted. Given this result, the usefulness of Carruthers’ line-drawing exercise is called into question.

Carruthers’ recent work on metacognition in the target article (and in Carruthers 2008b) can be seen as an extended exercise in “debunking” metarepresentational interpretations of the results of experiments performed on nonhuman animals. The debunking approach operates by distinguishing “weak” metacognition, which depends only on first-order mechanisms, from “genuine” metacognition, which deploys metarepresentations. Shaun Gallagher (2001; 2004; with similar proposals explored by Hutto 2004; 2008) has been on a similar debunking mission with respect to metarepresentation in human mindreading abilities. Gallagher’s position stands in an area of conceptual space unmapped by Carruthers’ four models, which all presuppose that an integrated, metarepresentational faculty is the key to mindreading. Gallagher argues that most of our mindreading abilities can be reduced to a weakly integrated swarm of firstorder mechanisms, including face recognition and an ability to quickly map a facial expression to the appropriate emotional response, a perceptual bias towards organic versus inorganic movement, an automated capacity for imitation and proprioceptive sense of others’ movements (through the mirror neuron system), an ability to track the gaze of others, and a bias towards triadic gaze (I-you-target). Notably, autistic individuals have deficiencies throughout the swarm. Someone pushing a “metarepresentation was wholly exapted” proposal might argue as follows: Interpretative propositional attitude ascription is a very recent development, likely an exaptation derived from linguistic abilities and general-purpose conceptlearning resources. Primate ancestors in social competition almost never needed to think about others not within perceptual range; in the absence of language which could be used to raise questions and consider plans concerning spatially or temporally absent individuals, there would have been little opportunity to

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demonstrate third-person mindreading prowess. After developing languages with metarepresentational resources, our ancestors’ endowment with the swarm would have left them well placed to acquire metarepresentational mindreading and metacognition through general learning. While such abilities were likely favored by cultural evolution in comparatively recent history, it is not clear that any further orders to genetic evolution needed to be placed or filled. Evolutionary “just so” stories come cheap; if Carruthers wants to make a strong case that the faculty evolved in response to social pressures (instead of just excellence with the swarm and/or other general aspects of cognition thought to be required for Machiavellian Intelligence, such as attention, executive control, and working memory), he needs further argument. Two issues must be overcome for the swarm proposal to be considered a serious alternative. First, the concurrent appearance of success on verbal first- and third-person false-belief tasks must be explained. Here, we point the reader to Chapter 9 of Stenning and Van Lambalgen (2008), which makes a strong case that the logic of both tasks requires a kind of conditional reasoning which does not develop until around age 4 and is also affected by autism (and see also Perner et al. [2007] for a related account). Second, there is the work on implicit false-belief tasks with prelinguistic infants (Onishi & Baillargeon 2005). These findings are both intriguing and perplexing (consider, for example, that the infants’ “implicit mastery” at 15 months is undetectable at 2.5 years), and the empirical jury is still out as to whether the evidence of preferential looking towards the correct location can support the weight of the metarepresentational conclusions which have been placed on it (see Perner & Ruffman 2005; Ruffman & Perner 2005). The infants’ preferential looking can be explained if they quickly learn an actor-object-location binding and register novelty when the agent looks elsewhere. More recent studies (e.g., Surian et al. 2007) claiming to rule out alternatives to the metarepresentational explanation have produced findings that are ambiguous at best (Perner et al. 2007). One might concede that the mechanism generating the gaze bias in infants is not itself metarepresentational, but nevertheless hold that it evolved because it enabled its possessors to develop metarepresentation – likely wielding a poverty of the stimulus (PoS) argument to the effect that even with language, metarepresentational mindreading does not come for free. We suggest that such reasoning no longer carries the weight it once did. Recent work on neural network modeling of the hippocampus, which highlights its ability to quickly discover abstract, informationally efficient bindings of stimulus patterns (especially when fed neutral cues like words – e.g., see Gluck & Myers 2001; Gluck et al. 2008) dulls the PoS sword. Finally, even if the PoS argument is accepted, there remains a huge leap to the conclusion that the bias evolved because of its ability to bootstrap metarepresentation – and not for something simpler. In light of the swarm alternative, the usefulness of Carruthers’ distinction between “weak” and “genuine” forms of mindreading and metacognition becomes questionable. Our overarching worry is that Carruthers’ emphasis on a single faculty of metarepresentation, combined with his acknowledgment of the rich heritage of cognitive abilities shared between humans and animals, leaves the faculty almost epiphenomenal in human cognition (except, perhaps, for Machiavelli himself) – a position that Carruthers has previously been driven to adopt with respect to his account of phenomenal consciousness (Carruthers 2005; see also Shriver & Allen 2005). An alternative approach might be to tone down the deflationary invocation of first-order mechanisms, and focus instead on what creatures endowed with a swarm of weakly integrated mechanisms can do and learn. Once we abandon the assumption that mindreading is centralized in a single metarepresentational faculty, we can investigate whether something like Gallagher’s swarm could implement various degrees of competence in reacting adaptively to the mental states of others. This perspective focuses us on the flexibility and adaptive significance of the evolved mechanisms which

Commentary/Carruthers: How we know our own minds constitute the swarms, for a wide range of organisms in a variety of social environments (including humans in theirs). These suggestions are in the spirit of Dennett (1983), who advocated the usefulness of metarepresentational hypotheses in devising new experiments, accepting from the beginning that animals and humans will “pass some higher-order tests and fail others” (p. 349). Ultimately, we think that the questions Carruthers raises about the relationship between self-regarding and otherregarding capacities are interesting and should be pursued; and they can be pursued without engaging in the line-drawing exercise which de-emphasizes the significance of good comparative work for understanding human cognition. ACKNOWLEDGMENT We thank Jonathan Weinberg for his extensive comments on earlier versions of this commentary.

Cognitive science at fifty doi:10.1017/S0140525X09000582 A. Charles Catania Department of Psychology, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250. [email protected] http://www.umbc.edu/psyc/personal/catania/catanias.html

Abstract: Fifty years or so after the cognitive revolution, some cognitive accounts seem to be converging on treatments of how we come to know about ourselves and others that have much in common with behavior analytic accounts. Among the factors that keep the accounts separate is that behavioral accounts take a much broader view of what counts as behavior.

Roughly half a century has passed since the cognitive revolution declared behaviorism dead and promised solutions to long-standing problems of philosophy and psychology. Carruthers provides an opportunity to assess the progress that has taken place. Mind remains central in his account, and its hierarchical structure is illustrated in the pivotal roles of metarepresentations and metacognitions. In place of behavior and events in the world, the action takes place in the dynamics of their surrogates, such as perceptions and intentions and beliefs and concepts and attitudes, none of which lend themselves to measurement in the units of the physical or biological sciences. Most of the entities in Carruthers’ account existed in the vocabularies of the mid1950s, though typically more closely anchored to their origins in colloquial talk, which since then has sometimes been called folk psychology. What has most obviously changed are the linkages among the mentalistic terms. Carruthers deals with the particular priorities of mindreading and metacognition. Are they independent mechanisms or a single mechanism with two modes of access? Is one a prerequisite for the other? Carruthers concludes that metacognition is grounded in mindreading. If one argues that judgments about oneself must be distinguished from judgments about others, his conclusion is sound. But this conclusion is one that a variety of behaviorism reached long before the advent of the cognitive revolution. In his “Behaviorism at 50,” Skinner (1963) recounted the history of Watsonian methodological behaviorism in the early decades of the twentieth century and its rejection of introspection (see also Catania 1993), but he also noted the unnecessary constraints that Watson’s account had imposed on theory. Skinner’s later radical behaviorism rejected the Watsonian constraints and extended his approach to the origins of the language of private events. As a contribution to a symposium organized by his advisor, E. G. Boring, Skinner (1945) made explicit his interest in “Boring from Within.” The 1945 paper,

“The Operational Analysis of Psychological Terms,” was a renunciation of operationism, but, more important, it provided an account of how a vocabulary of private events (feelings, emotions, etc.) could be created even though those who taught the words and maintained consistencies of usage had access only to shared public accompaniments of those private events.1 Given these origins of the private or introspective language, Skinner’s resolution of the issue in terms of the public practices of the verbal community is the only feasible way of dealing with the problem that Carruthers has so aptly described in terms of his mindreading system, which never has access to what others are imagining or feeling. To the extent that it does have access to what one feels or imagines oneself, one can speak of those events only in a vocabulary that is anchored in public correlates. Carruthers’ point that instances of self-attributed unsymbolized thought occur in circumstances in which a third party might have made the same attribution is perfectly consistent with this argument. The irony, then, is that with respect to introspection, judgments about the behavior of others (mindreading) and judgments about one’s own behavior (metacognition), Carruthers has reached conclusions that are consistent with Skinner’s. One can guess that he took so long only because of the complexity of the terms that entered into his account. Skinner’s account is far more parsimonious. Skinner does not begin with something called discriminating and follow it with differential responding; the differential responding is itself the discriminating. He does not say that perceiving and sensing and thinking are something different from behaving; they are kinds of behavior, defined not by whether they involve movement but rather by whether they are involved in contingent relations with environmental events (for this reason, Carruthers notwithstanding, a lot of behavior goes on even when one is sitting quiet and motionless, and one has just as much access to this behavior as to that of standing or walking). There is no more need to appeal to seeing and hearing as prerequisite concepts than there is to say that we cannot sit or stand or walk without concepts of sitting or standing or walking; these are all names for things we do. To invoke them as explanations does not serve our theories well. Carruthers’ account also converges on other concepts that have been elaborated by Skinner. For example, his System 1 and System 2 have features that are closely paralleled by what Skinner (1969) respectively called rule-governed and contingency-shaped behavior, and Carruthers is surely on the right track in saying that speech is an action that does not begin with metacognitive representations of thought (a more detailed account is beyond the scope of this commentary, but see Catania 2006, Chs. 14 and 15). Furthermore, in considering the different environmental contingencies that operate on verbal and nonverbal classes of behavior, the behavioral account has no trouble dealing with the various confabulations that Carruthers has surveyed. Just as speech errors can tell us a lot about language structure, so confabulations may tell us a lot about the nature of our judgments about ourselves and others. It is good to see cognitive science at last converging on conclusions that had once been reached in behavioral accounts. If that were the only point, this commentary would serve little but a historical purpose. But there is extensive behavior analytic research relevant to these issues (in particular, see Wixted & Gaitan 2002), and some of it may prove useful to those of any theoretical orientation. Of course, it would be not at all surprising if the suggestions here are not well received. That likelihood is enhanced by the fact that this has been a necessarily brief and superficial presentation of the behavioral case. But the literature is there, so perhaps a few will check it out. NOTE 1. Two articles by B. F. Skinner cited in this commentary (Skinner 1945; 1969) were reprinted in Behavioral and Brain Sciences (Vol. 7, December 1984). BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Metacognition is prior doi:10.1017/S0140525X09000594 Justin J. Couchman,a Mariana V. C. Coutinho,a Michael J. Beran,b and J. David Smitha a Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY 14260; bLanguage Research Center, Georgia State University, Atlanta, GA 30303. [email protected] [email protected] [email protected] [email protected]

Abstract: We agree with Carruthers that evidence for metacognition in species lacking mindreading provides dramatic evidence in favor of the metacognition-is-prior account and against the mindreading-is-prior account. We discuss this existing evidence and explain why an evolutionary perspective favors the former account and poses serious problems for the latter account.

Carruthers acknowledges that evidence for metacognition in species lacking mindreading would provide dramatic evidence for the metacognition-is-prior view and against the mindreading-isprior view, and he asserts that the existing evidence can be explained using a first-order system of belief and desire strengths (target article, sect. 5.2; see also Carruthers 2008b). We evaluated similar response strategies using formal modeling (Smith et al. 2008) and found indeed that some animal metacognition findings could be explained using first-order strategies. Yet Carruthers’ use here of the field’s earliest paradigms and oldest data to make his argument is unfortunately selective. More recent paradigms often do not support his first-order argument and description. Smith et al. (2006) dissociated monkeys’ uncertainty responding from any reinforcement and stimulus cues that could have organized Carruthers’ gradients of first-order beliefs and response tendencies. It was clear in that study that monkeys’ uncertainty-response strategies were adjudicated cognitively and decisionally, not using first-order cues. They followed the animal’s subjective decisional construal of the task. Couchman et al. (submitted) extended this dissociation to situations of broader task transfer in which animals had to establish functional regions of judged difficulty and uncertainty even when forced to self-organize their task performance. Recent cross-species research on uncertainty monitoring also speaks against first-order interpretations of uncertainty-monitoring performances. Beran et al. (in press) gave capuchin monkeys a Sparse-Uncertainty-Dense task that was matched to a SparseMiddle-Dense task. Capuchins used the middle (first-order) response easily and naturally. They almost never used the uncertainty response, despite having the reinforcement history needed to do so. Likewise, elegant research by Shettleworth and her colleagues (Inman & Shettleworth 1999) has shown that pigeons also do not express an uncertainty-responding capability, even when there are strong first-order reasons for them to do so. It is an important implication from these cross-species results that the organizing psychology underlying uncertainty responding is not first-order, because adept first-order animals such as capuchins and pigeons cannot find and use that psychology. In other writings, Carruthers (2008b) also acknowledges that firstorder beliefs and desires will not explain the wide-ranging empirical findings of uncertainty monitoring and information seeking by animals. He devises a secondary mental construct to explain why an animal uses the uncertainty response in too-close-to-call situations. He suggests that some species have a gate-keeping “mechanism . . . which when confronted with conflicting plans that are too close to one another in strength will refrain from acting on the one that happens to be strongest at that moment, and will initiate alternative information-gathering behavior instead” (p. 66 ). The gatekeeper mechanism operates on first-order cognition’s outputs to assess their ability to produce a correct response. It meets the definition of a second-order controlled cognitive process. It produces a qualitative change in behavior and cognitive strategy (information seeking, uncertainty responses, etc.). It typifies the metacognitive utility that all theorists have

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envisioned. Even in Carruthers’ own description of animals’ cognitive self-regulation, it seems, metacognition is prior. Another analytic problem in the target article concerns the different standard of evidence that is applied to studies of animal metacognition and studies of animal mindreading. It seems highly unlikely, and it goes completely undefended in the target article (sect. 5, para. 2) that all the metacognition paradigms fall prey to behaviorist explanations, but that all the mindreading paradigms are veridical. They clearly are not (Heyes 1998). Carruthers makes a valid suggestion that, if metacognition is prior, one should be able to explore the evolutionary pressures that produced a free-standing metacognitive utility. Fortunately, James (1890/1952), Dewey (1934/1980), Tolman (1938), and many others have provided this evolutionary narrative (see also Smith et al. 2003). Animals often encounter doubtful and uncertain situations in which their habitual stimulus-response associations do not clearly indicate a safe and adaptive response. They would benefit enormously in those situations from having an online cognitive utility that will let them assemble the relevant facts and recollections and choose an adaptive course of action. Metacognition provides exactly this utility. It is also a remarkable phylogenetic fact that there appear to be no species that show mindreading ability but fail to show metacognitive ability. This could be used to support more than one of the possibilities discussed in the target article. However, it clearly supports least of all the mindreading-is-prior account. Finally, we believe that an evolutionary perspective on this issue raises a serious problem for the mindreading-is-prior account. The author’s account may, in principle, explain the development of metacognition ontogenetically, especially if one assumes a parent is constantly informing you of the intentions of others. Your mother may tell you, “Johnny wants a cookie” while you see Johnny reaching for the cookie jar, and the next time you find yourself reaching for the cookie jar, you may well apply “wants a cookie” to yourself. This works only because humans communicate their knowledge of concepts and intentions from one generation to the next. The first mindreading animal would have no basis for which to make an attribution of a mental state. How would it be possible or beneficial to attribute “wants a cookie” to Johnny, if the attributer has no known experience with “wanting,” no understanding of “what it is like to want” and no idea that it has ever “wanted”? The mindreading-is-prior account must explain how, from nothing but observed physical behavior, and with no reason to ever attribute anything but cause-and-effect mechanical processes, animals came to attribute subjective belief and desire states to others. This would be equivalent to knowing there is something “that it is like” to be a bat (Nagel 1974) prior to knowing that there is anything “that it is like” to be you! Indeed, exactly the opposite seems to be true. We have great access to and a rich understanding of our own mental states and only a very limited understanding of the mental states of others. We first knew what it was like to know, and then assumed that others might be having an analogous experience. This process of extending mental concepts outward is surely a more plausible and tractable evolutionary narrative. Within that narrative, metacognition is prior.

Introspection, confabulation, and dual-process theory doi:10.1017/S0140525X09000600 Jonathan St. B. T. Evans Centre for Thinking and Language, School of Psychology, University of Plymouth, Plymouth PL4 8AA, United Kingdom. [email protected]

Abstract: This excellent target article helps to resolve a problem for dualprocess theories of higher cognition. Theorists posit two systems, one of

Commentary/Carruthers: How we know our own minds which appears to be conscious and volitional. It seems to control some behaviours but to confabulate explanations for others. I argue that this system is only conscious in an illusory sense and that all selfexplanations are confabulatory, as Carruthers suggests.

I have long held (Evans 1980) that while we can introspect on our mental experiences, we have no access to the processes which underlie our behaviour, and I have equally long held the view (Evans 1989; Wason & Evans 1975) that strategy reports frequently reflect confabulations. Crossing the disciplinary divide with philosophy, this accords well with Carruthers’ claims that (a) we have no introspective access to our propositional attitudes and (b) that we may have an illusion of conscious control resulting from applying our “mindreading” abilities to ourselves as well as others. Although Carruthers seeks to reconcile his massively modular view of the mind with dual-process theories of reasoning (see also Carruthers 2006), it is not clear how many tenets of the standard theory he would accept. I therefore examine his “mindreading is prior” argument with respect to this standard dualprocess approach. Dual-process theorists propose that humans have two distinct forms of cognitive processing: one fast, automatic, and high capacity (Type 1), and another slow, controlled, and low capacity (Type 2) (Evans 2008). It is commonly assumed that these two modes of thought reflect two distinct forms of knowledge: implicit and explicit (Carruthers does not appear to endorse this distinction). Implicit knowledge may be encapsulated in cognitive modules or acquired from associative or procedural learning. Explicit knowledge has some propositional format and can be “called to mind.” Neuroscientific evidence strongly supports the existence of dissociable implicit and explicit memory systems (Eichenbaum & Cohen 2001). Intuitive and reflective judgments are assumed to reflect access to these two forms of knowledge and to comprise two distinct cognitive systems. This can explain, for example, why people seem to possess implicit attitudes and stereotypes, which may conflict and compete with their explicit social attitudes (Smith & DeCoster 2000). There is, however, a point of discomfort within dual-process theory to which the current target article is highly relevant. Since theorists claim that there are two systems with functional control of behaviour, System 2 – the “conscious” one – cannot be epiphenomenal. On the other hand, as Carruthers correctly states, the evidence that people frequently confabulate explanations for their behaviour is overwhelming. Evans and Over (1996) were clearly struggling with this issue, when they stated (p. 160) that “we do not regard explicit thinking as simply serving to rationalise behaviour, and believe that decisions and actions can result from explicit processes.” Does this mean that Type 2 processes sometimes really control a response and at other times confabulates an explanation for a Type 1 response? Could we not instead argue that all strategy reports are selfinterpretations in just the same way as Carruthers argues for propositional attitudes? The problem as I see it (Evans 2008; 2009) is that it is a mistake to use consciousness in the definition of the distinction between Systems 1 and 2. A more satisfactory definition is that the latter requires access to central working memory, whereas the former does not. For sure, this implies that something about a Type 2 process is conscious, as the contents of working memory tend to reflect in conscious experiences (Andrade 2001). However, it can still be the case that (a) most of the workings of System 2 are unconscious – for example, the processes that direct our current locus of attention, and those that retrieve memories relevant to the current context, and (b) that we lack introspective access to the nature of System 2 processing. (Note that [b] may be at odds with Carruthers’ claim that we do have a conscious mind.) Introspection provides no access to Type 1 processes, that either (preconsciously) pass information into working memory for further processing, or by-pass it altogether (Evans 2009). Because System 2 uses working memory, something about it can be introspected – the locus of our attention, for

example, or relevant perceptual and emotional experiences. These will be used as inputs to the self-interpretation process that Carruthers discusses, together with contextual knowledge and any current goals we are pursuing. Because we generally have good theories of our own behaviour, we can often produce veridical reports. On other occasions, we confabulate, but I suggest that mental processes in either case are exactly the same. I suggest that it is unhelpful to describe dual-process theories as contrasting conscious and nonconscious processing, and that the theory in no ways rests upon either introspective access or any notion of conscious control. Dual-process research methods depend instead on the assumptions that (a) Type 1 and Type 2 processing are qualitatively and measurably distinct in their characteristics and outputs, and (b) that the balance between the two forms of processing is related to a number of well-defined variables. These include cognitive ability, motivation, time available for reasoning, and the presence of competing or distracting tasks. However, we still need some account of why confabulation is common and why we do feel as though we have conscious control of our behaviour. This is where I find Carruthers’ “mindreading is prior” argument helpful. If our brains have an in-built folk psychology for understanding the minds of others, why would the same system not interpret our own behaviour? In summary, Carruthers’ account helps to resolve a problem for standard dual-process accounts of higher cognition. People, he argues, are conscious of perceptual and quasi-perceptual experiences, the latter being formed by mental rehearsal that involves inner speech and/or imagery. It is precisely such processes – that involve working memory – that cause people to confabulate. People do not confabulate accounts of Type 1 processes such as those underlying recognising a face, or understanding a sentence; they do confabulate explanations for Type 2 processes such as decision making. As Carruthers says, people will also confabulate on the basis of behavioural data, but they omit reference to relevant Type 1 processes when they do so. For example, people never refer to a number of well-documented perceptual, cognitive, and social biases in their verbal reports, for folk psychology knows nothing of such matters. This also explains our chronic tendency to overly attribute conscious reasons for actions in ourselves and in others.

What can we say about the inner experience of the young child? doi:10.1017/S0140525X09000612 Charles Fernyhough Institute of Advanced Study, Durham University, Durham DH1 3RL, United Kingdom. [email protected] http://www.dur.ac.uk/c.p.fernyhough

Abstract: Inner experience is proposed as a basis for self-interpretation in both children and adults, but young children’s inner experience may not be comparable to our own. I consider evidence on children’s attribution of inner experience, experience sampling, and the development of inner speech, concluding that Carruthers’ theory should predict a developmental lag between mindreading and metacognition.

Carruthers’ “mindreading is prior” model holds that we gain knowledge of our own propositional attitudes through applying our mentalizing capacities to our own behavior and inner experience. In evaluating this claim, Carruthers considers the question of developmental asymmetry between self- and otherknowledge. Both the “two independent mechanisms” and the “metacognition is prior” views would predict that metacognition BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds should appear before mindreading. These two models do not, however, exhaust the possibilities for non-interpretative accounts of metacognition (specifically, they leave “one mechanism, two modes of access” as a remaining competitor account). This fact, together with recent evidence for very early mentalizing competence which, for methodological reasons, cannot be matched by data on early metacognition, means that such evidence cannot discriminate between Carruthers’ interpretative account and a non-interpretative alternative. There is one respect, however, in which a developmental asymmetry between mindreading and metacognition could have a bearing on Carruthers’ model. Carruthers proposes that self-interpretation can proceed on the basis of information about overt behavior and physical circumstances, along with elements of inner experience such as inner speech, visual imagery, and feelings. He notes that there will be some instances where, behavioral data being lacking (as in the example of someone sitting quietly in their living room), self-interpretation will be based exclusively on information about inner experience. The question, then, is what sort of information that could support self-interpretation is available to young children. Presumably, young children have the same possibilities for interpreting external information as adults do. But is their inner experience comparable? At least three sources of evidence lead us to scepticism on this point. First, there are the findings of experimental research on children’s understanding of inner experience. For example, consider the findings of Flavell and colleagues (e.g., Flavell et al. 1993; 2000) that preschool children frequently deny the presence of inner experience in individuals (including themselves) when it would be appropriate to attribute such experience. These results are usually interpreted as evidence that young children have only weak powers of introspection. But an alternative interpretation is that young children do not experience a stream of consciousness in the way that older children and adults do, and that this accounts for their weak understanding of the inner experience of others (Hurlburt & Schwitzgebel 2007, sect. 11.1.7.8; Meins et al. 2003). A second line of evidence comes from the limited data from experience sampling in children. Descriptive Experience Sampling (DES; Hurlburt & Heavey 2006) involves careful interviewing around records made of inner experience shortly preceding a random electronic beep. Hurlburt describes an episode of DES with a nine-year-old boy who reported an image of a hole in his backyard containing some toys (Hurlburt & Schwitzgebel 2007, Box 5.8). When asked whether this image was an accurate description of his backyard, the participant replied that he had not yet had time to put all of the toys into the image. Hurlburt’s conclusion from this and other instances of childhood experience sampling is that constructing a visual image is a skill that takes time to develop. Although much remains to be done in adapting experience sampling techniques for use with young children, the evidence currently available invites caution in making assumptions about young children’s inner experience. Third, there are theoretical reasons for not assuming that certain aspects of children’s inner experience, particularly inner speech, are comparable to those of adults. The most fully developed theory of the development of inner speech is that of Vygotsky (1934/1987). In his theory, inner speech is the developmental outcome of the internalization of social speech via the transitional stage of private speech. Findings from the study of private speech suggest that its transformation into inner speech is unlikely to be complete until middle childhood (Winsler & Naglieri 2003). The view that there is a general shift towards verbal mediation of cognition in the early school-age years is supported by findings that phonological recoding of visually presented material in short-term memory tasks is linked to private speech use at this age (Al-Namlah et al. 2006). Speech that is still in the process of being internalized is likely to appear to the child’s consciousness as something other than adult-like

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inner speech (Fernyhough et al. 2007). Further experimental research on the transition to verbal mediation, complemented by more developmentally sensitive experience sampling studies, should provide a clearer indication of when inner speech can be assumed to be present in young children. There are reasons, then, for doubting that young children have access to the full range of inner experiences proposed by Carruthers to form the basis of self-interpretation. Because inner speech is one of the main sources of evidence supposed to feed into individuals’ interpretations of their own propositional attitudes, the emergence of metacognition should be developmentally constrained by the emergence of inner speech. Other aspects of inner experience, such as visual imagery, are also likely to take time to develop. Given what we know about the timetable for the emergence of mindreading capacities (particularly the evidence for some mentalizing competence in the second year of life), Carruthers’ theory should predict a developmental lag between mindreading and metacognition. An alternative for Carruthers would be to argue that behavioral and contextual evidence was sufficient for self-interpretation in young children, but then his account would be indistinguishable from that of Gopnik (1993).

Confabulation, confidence, and introspection doi:10.1017/S0140525X09000624 Brian Fiala and Shaun Nichols Department of Philosophy, University of Arizona, Tucson, AZ 85721. [email protected] [email protected] http://dingo.sbs.arizona.edu/~snichols/

Abstract: Carruthers’ arguments depend on a tenuous interpretation of cases from the confabulation literature. Specifically, Carruthers maintains that cases of confabulation are “subjectively indistinguishable” from cases of alleged introspection. However, in typical cases of confabulation, the self-attributions are characterized by low confidence, in contrast to cases of alleged introspection.

What is confabulation? Carruthers’ central argument hinges on this notion, so we need to get clear on what he has in mind. Carruthers doesn’t present an explicit characterization, but the overall discussion suggests that the relevant confabulations are a class of first-person mental state attributions that are generated by an “interpretative” process, as opposed to an “introspective” process. By “interpretative,” Carruthers means any process “that accesses information about the subject’s current circumstances, or the subject’s current or recent behavior, as well as any other information about the subject’s current or recent mental life” (sect. 1.4, para. 3). This characterization seems too broad because introspection itself is supposed to be a process that accesses information about the subject’s current mental life. But Carruthers means to count as interpretative only those processes that do not employ any “direct” access or any mechanism specifically dedicated to detecting one’s current mental states. On Carruthers’ view, all attributions of propositional attitude events are, in fact, interpretative. So what is the relation between “confabulation” and “interpretation”? Here are several different possibilities: 1. Confabulations include all self-attributions that result from interpretation. 2. Confabulations include all false self-attributions that result from interpretation, and accurate interpretative self-ascriptions do not count as confabulatory. 3. Confabulations include only a proper subset of false selfattributions resulting from interpretation.

Commentary/Carruthers: How we know our own minds We doubt that Carruthers has possibility 1 in mind, as this would mean that one is confabulating even when one quite consciously uses interpretative processes to discern one’s past mental states. If Carruthers has option 3 in mind, then we need to know much more about what distinguishes the proper subset. As a result, we proceed on the assumption that possibility 2 captures what Carruthers has in mind. Our experience with identifying our own current mental states is characteristically quick, accurate, and confident. By contrast, when it comes to attributing mental states to others, our attributions seem much slower, more accident prone, and unsure. This subjective difference is thought to provide prima facie evidence that we have (non-interpretative) introspective access to our own mental states. Carruthers attempts to defeat this prima facie consideration by proclaiming that confabulated reports are subjectively indistinguishable from cases of alleged introspection. People confabulate attributions of their own propositional attitude events “while being under the impression that they are introspecting” (sect. 6, para. 1). Thus, we have no reason to think that canonical cases of “introspection” differ from confabulation in this respect (i.e., that we are interpreting in the latter case but not the former). Carruthers goes on to argue that since there is no other positive reason to believe in the reality of introspection for the attitudes, the best explanation is that all self-attribution (confabulation and alleged introspection) is subserved by the same kinds of processes: that is, interpretative ones. Carruthers’ argument depends on the claim that people confabulate attributions of propositional attitudes while being under the impression that they are introspecting. But we are given no evidence that this has been systematically investigated. Certainly no one has ever asked participants in these cases whether they think they are introspecting or interpreting. Without some more direct evidence, Carruthers is not warranted in claiming that when people confabulate they are often “under the impression that they are introspecting.” A closer look at the confabulation cases gives further reason to doubt the argument. The evidence on confabulation cited by Carruthers is all anecdotal, but even the anecdotes are illuminating if one looks at the behavior a bit more closely. For we find that across many different paradigms in which people confabulate, the confabulations are not reported with a sense of “obviousness and immediacy.” Consider the following examples: a. In a classic misattribution study, subjects took more shock because they thought a pill caused their symptoms. In a debriefing procedure subjects were asked, “I noticed you took more shock than average. Why do you suppose you did?” Nisbett and Wilson (1977) present one instance of confabulation and claim it as typical. The confabulation begins as follows: “Gee, I don’t really know . . .” (p. 237). b. In a dissonance reduction experiment involving shocks, Zimbardo reports that a typical confabulation would have been, “I guess maybe you turned the shock down” (Nisbett & Wilson 1977, p. 238). c. Thalia Wheatley, one of the most inventive researchers using hypnotic suggestion (e.g., Wheatley & Haidt 2005), reports that when she has participants perform actions under hypnotic suggestion, she often asks them why they performed the action. Although they do often confabulate, their initial response to the question is typically “I don’t know” (T. Wheatley, personal communication). In each of these research paradigms, we find typical confabulations delivered with manifestly low confidence, rather than the sense of obviousness and immediacy that is supposed to be characteristic of introspective report. Carruthers also draws on widely cited cases of confabulation involving split-brain patients. And, although Carruthers claims that split-brain patients confabulate with a sense of obviousness and immediacy, the situation is not so clear. In footage of splitbrain patients, we find them showing little confidence when asked to explain behavior issuing from the right hemisphere.

For instance, in a typical study with split-brain patient Joe, Joe is shown a saw to his right hemisphere and a hammer to his left. He is then told to draw what he saw with his left hand. Predictably, Joe draws a saw. Gazzaniga points to the drawing and says, “That’s nice, what’s that?” Saw. “What’d you see?” I saw a hammer. “What’d you draw that for?” I dunno (Hutton & Sameth 1988). Carefully controlled studies are clearly needed. However, these anecdotes provide prima facie reason to think there are systematic differences in confidence levels between confabulation and apparent introspection, which in turn suggests a difference in underlying mechanism. The fact that confabulations are accompanied by low confidence does not, of course, provide conclusive evidence in favor of introspection. But it does suggest that given the present state of the evidence, the confabulation argument is toothless.

How we know our conscious minds: Introspective access to conscious thoughts doi:10.1017/S0140525X09000636 Keith Frankish Department of Philosophy, The Open University, Milton Keynes, Buckinghamshire MK7 6AA, United Kingdom. [email protected] http://www.open.ac.uk/Arts/philos/frankish.htm

Abstract: Carruthers considers and rejects a mixed position according to which we have interpretative access to unconscious thoughts, but introspective access to conscious ones. I argue that this is too hasty. Given a two-level view of the mind, we can, and should, accept the mixed position, and we can do so without positing additional introspective mechanisms beyond those Carruthers already recognizes.

In section 7 of the target article, Carruthers considers the proposal that we have two levels of mentality, conscious and unconscious, corresponding to the two reasoning systems posited by many psychologists, and that we have different forms of access to the attitudes at the two levels – merely interpretative access to those at the unconscious level, but introspective access to those at the conscious level. Prima facie, this mixed position is an attractive one, which does justice both to the evidence for psychological self-interpretation cited by Carruthers and to the everyday intuition that we can introspect our conscious thoughts. Carruthers rejects the option, however. Although conceding that we have introspective access to conscious thinking, he denies that we have such access to conscious judgments and decisions. I argue here that this conclusion is too hasty. Carruthers’ argument turns on the claim that judgments and decisions terminate reasoning processes and produce their characteristic effects directly, without further processing. Conscious thinking, on the other hand, involves rehearsing mental imagery, especially inner speech, and this has only an indirect influence on thought and action. The route may be metacognitive: A rehearsed assertion with content p may give rise to an (unconscious) metacognitive belief, to the effect that one believes that p or that one is committed to the truth of p, which, together with suitable desires, will lead one to think and act as if one believes that p. Or the rehearsed assertion may be processed as testimony, leading one to form the first-order belief that p, which will then guide behaviour in the normal way. On either route, Carruthers argues, the conscious event gives rise to the effects of a judgment only through the mediation of further cognitive processing, and so does not count as a judgment itself. Similar considerations apply to decisions, although here Carruthers mentions only the metacognitive route. BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds I am sympathetic to Carruthers’ account of conscious thinking, and I agree that imagistic rehearsals influence thought and action through the mediation of unconscious cognitive processes. But this is not incompatible with the commonsense view that some conscious events are judgments and decisions. To see this, we need to take seriously the suggestion that the conscious mind is a distinct level of mentality. Carruthers has himself developed a version of this view, arguing that the conscious mind (the psychologists’ System 2) is not a separate neural structure, but rather, a higher-level “virtual” one, realized in cycles of operation of a more basic unconscious system (System 1), which, among many other tasks, generates and processes the imagery involved in conscious thinking (Carruthers 2006; 2009; for a related version, see Frankish 1998; 2004; 2009). And from this perspective it is natural to regard appropriate utterances in inner speech as genuine judgments and decisions – at least when they achieve their effects via the metacognitive route. For these events will terminate reasoning processes at the higher level and on the relevant topic. The further processing occurs at the lower level and is devoted to a different topic. When I rehearse the sentence, “Polar bears are endangered” in assertoric mode, this terminates my reasoning about polar bears. The subsequent unconscious reasoning is about how to interpret and respond to this assertion, not about whether the conclusion it expresses is correct. These processes can be thought of as implementing the higher-level attitude, and their existence does not compromise the status of the conscious event as a judgment. It is true that the lower-level processes may sometimes fail to generate the appropriate effects (for example, if the desire to execute one’s commitments is overridden by a stronger desire), but this is irrelevant. On every view there are some implementing processes, at least at a neurological level, and these processes may go awry. And if we have a settled habit of interpreting appropriate utterance rehearsals as expressions of belief or commitment, and a settled desire to act consistently or to discharge our commitments, then the right effects will follow most of the time. Similar considerations apply to decisions. The only peculiarity of the two-level view is that the processes that implement conscious judgments and decisions are cognitive ones. But why should that matter? Compare the way the judgments and decisions of a company are implemented. The edicts emerging from the boardroom require further processing in order to affect the activities of the organization, and this processing involves reasoning on the part of the staff involved. (Again, this will have a metarepresentational character, involving beliefs about what the directors have concluded.) But we still want to say that the judgments and decisions were made in the boardroom, rather than in the cubicles of the junior staff. What about cases in which a rehearsed utterance generates its effects via the second route, being processed as testimony and generating a first-order belief? Here I think Carruthers is right. If further processing serves to evaluate the conclusion reached rather than simply to implement it, then this does disqualify the conscious event from judgment status. But note that in such cases, the agents themselves will not think of the conscious events as judgments. For if they did, they would naturally come to believe that they believed, or were committed to, the conclusions expressed, and the subsequent processing would follow the metacognitive route. Thus, there is no reason to regard such events as candidates for judgments in the first place. (We might think of them as hypotheses or self-suggestions.) Again, the same goes for decisions. I conclude that Carruthers’ case against a mixed position is not compelling. It is important to stress that the proposed mixed position does not involve positing additional introspective mechanisms. Carruthers allows that we have introspective access to conscious (System 2) thinking; I am simply claiming that some of the introspectable events can be legitimately classified as judgments and decisions. The proposal is merely a reconceptualization of the processes Carruthers describes. But it is a natural

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one, given a two-level view of the sort Carruthers endorses, and one that accords with intuition. For these reasons it should be preferred. Of course, it would be ad hoc if a two-level view were not independently motivated, but it is (see aforementioned citations).

Non-interpretative metacognition for true beliefs doi:10.1017/S0140525X09000648 Ori Friedman and Adam R. Petrashek Department of Psychology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada. [email protected] http://www.psychology.uwaterloo.ca/people/faculty/friedman/ [email protected]

Abstract: Mindreading often requires access to beliefs, so the mindreading system should be able to self-attribute beliefs, even without self-interpretation. This proposal is consistent with Carruthers’ claim that mindreading and metacognition depend on the same cognitive system and the same information as one another; and it may be more consistent with this claim than is Carruthers’ account of metacognition.

Mindreading often requires access to one’s own beliefs.1 Consider the following mental state attributions: Bill believes a first-aid kit contains bandages, though the kit actually contains feathers; Louise is an expert in British history, so she knows that the Battle of Hastings occurred in 1066; and Sally, age 2, desires candy when offered a choice between this and sushi as a snack. These mental state attributions do not depend on the interpretation of others’ speech or behavior. Instead, they primarily depend on your beliefs (i.e., first-aid kits normally contain bandages; the Battle of Hastings occurred in 1066; children typically prefer candy over unfamiliar foods) in combination with other principles (e.g., experts in British history know a lot about British history). The need to access beliefs is not restricted to just a few cases of mindreading. Instead, such access may be the rule in belief attribution: Most beliefs are true, and so one’s own beliefs are indicative of what others believe. Because of this, people may have a default tendency to attribute their “true” beliefs to others (Fodor 1992; Leslie & Thaiss 1992; see Leslie et al. [2004] for a review of much evidence favoring an account making this claim). To operate according to this default tendency, the mindreading system requires access to beliefs. The mindreading system’s access to beliefs is problematic for Carruthers’ account of metacognition, which denies such access (target article, sect. 2, para. 6).2 For if the system accesses beliefs when attributing mental states to others, then it should also access them when attributing mental states to the self. For instance, if the mindreading system accesses the belief “the Battle of Hastings occurred in 1066” when attributing it to Louise the historian, then the system should also be able to attribute this belief to the self. The mindreading system’s access to beliefs allows people to engage in non-interpretative metacognition. This proposal does not necessarily imply non-interpretative access to other mental states, such as intentions, desires, and past (currently false) beliefs. Unlike currently held beliefs, these other mental states are typically uninformative about the world and about others’ mental states. One’s intention to drink coffee says little about the world except perhaps that people sometimes drink coffee; and it says little about other people because relatively few share this intention at any time, meaning that it will seldom be useful to quickly extend this intention to

Commentary/Carruthers: How we know our own minds others. So mindreading may not require access to such mental states. If the mindreading system lacks this access, it will also be lacking for metacognition. Against our proposal, it might be claimed that the mindreading system does not access beliefs, but only inner speech and mental imagery that express beliefs. But this claim requires people to know which fragments of inner speech to use when attributing mental states to others. This claim also contradicts the view that people have a default tendency to attribute true beliefs. And given that inner speech and mental imagery are not required when answering questions about when the Battle of Hastings occurred (sect 2.1, para. 1), it seems doubtful that either is needed when answering when Louise thinks it occurred. Put more baldly, it is difficult to believe that attributing a desire for candy to Sally requires one to express in inner speech the belief “young children typically like candy.” Our proposal is not strongly challenged by evidence that people sometimes confabulate when reporting beliefs. Confabulation is only problematic to the extent that it involves metacognitive errors in which people misreport beliefs. But such errors are difficult to distinguish from accurate reporting of irrational beliefs. When subjects reported that the rightmost of four identical pantyhose was softest (Nisbett & Wilson 1977), they might have been misreporting a belief (i.e., reporting a belief they did not have), but they also might have been faithfully reporting a false belief formed while deciding which item was softest. Also, that people sometimes err in reporting beliefs does not imply that they never have non-interpretative access to their beliefs. Self-interpretation and metacognitive errors may be particularly common for certain sorts of beliefs, and perhaps they are particularly common when people are motivated to report beliefs they do not actually have. In the pantyhose experiment, subjects might have had no belief about which item was softest, but still might have felt compelled to answer. Coming to this answer might open the way for metacognitive errors. But this does not imply that self-interpretation would be needed if subjects were instead asked about something they already believed, such as whether they thought the pantyhose samples were soft at all. One might also challenge our proposal by conceding that the mindreading system accesses beliefs when making attributions about others, but then denying that it has this access for self-attributions. This defense makes little sense in light of the most detailed account of how beliefs are actually attributed (Leslie et al. 2004). According to this account, the mindreading system operates according to the default assumption that beliefs are true, but sometimes overrides this assumption, as when reasoning about beliefs that are false. This account makes little distinction about whether beliefs are attributed to others or to oneself. Carruthers’ “mindreading is prior” model claims that mindreading and metacognition depend on the same cognitive system and on the same information. Our proposal is consistent with this claim and seems more consistent with it than is Carruthers’ account of metacognition. Mindreading requires access to beliefs. Carruthers denies that such access is available in metacognition, which implies that the two processes draw on different information. The account we propose claims that access to beliefs occurs in both mindreading and metacognition, and this implies non-interpretative self-attribution of true belief.

NOTES 1. By access we always mean non-interpretative access. This access might involve a direct link between beliefs and the mindreading system, or it might be indirect and mediated by some other system. We are unsure whether this access conforms to what is normally meant by introspection. 2. Carruthers (2006, especially pp. 181–86) discusses a different version of this problem.

There must be more to development of mindreading and metacognition than passing false belief tasks doi:10.1017/S0140525X0900065X Mikolaj Hernik,a Pasco Fearon,b and Peter Fonagyc a

Baby Lab, Anna Freud Centre, London, NW3 5SD, United Kingdom; School of Psychology and Clinical Language Sciences, University of Reading, Reading, RG6 6AL, United Kingdom; cResearch Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, United Kingdom. [email protected] http://www.annafreudcentre.org/infantlab/mhernik [email protected] http://www.reading.ac.uk/psychology/about/staff/r-m-p-fearon.asp [email protected] http://www.ucl.ac.uk/psychoanalysis/unit-staff/peter.htm b

Abstract: We argue that while it is a valuable contribution, Carruthers’ model may be too restrictive to elaborate our understanding of the development of mindreading and metacognition, or to enrich our knowledge of individual differences and psychopathology. To illustrate, we describe pertinent examples where there may be a critical interplay between primitive social-cognitive processes and emerging self-attributions.

Carruthers makes a good case that self-awareness of propositional attitudes is an interpretational process, and does not involve direct introspective access. He also argues that mindreading and metacognition rely on one cognitive mechanism; however, in this case we are less persuaded by the evidence which hinges on Carruthers’ reading of well-rehearsed data from autism and schizophrenia. We think that these two predictions have distinct bases and it is at least conceivable that there are two dissociable interpretative meta-representational systems capable of confabulation: one self-directed, one other-directed. Thus, the argument in favour of model 4, over, say, a version of model 1 without a strong commitment to non-interpretative access to self-states, is based purely on parsimony. Our intention is not to defend such a two-system model, but rather to point out that even if one accepts that metacognition involves interpretation, mindreading and metacognition may still be dissociable. Furthermore, Carruthers pays little attention to the differences between input channels associated with first- and third-person mindreading and the surely distinct mechanisms (arguably within the mindreading system) that translate them into attitude-interpretations. As a result, we worry that Carruthers may end up with a rather impoverished model that struggles to do justice to the broader phenotype of first- and third-person mindreading, its development, and the ways in which it may go awry in psychopathology. Carruthers’ reading of developmental evidence is restricted to the standard strategy of comparing children’s performance across false-belief tasks. These are inherently conservative tests of mindreading ability, as false-belief-attribution is neither a common nor a particularly reliable function of the mindreading system (Birch & Bloom 2007; Keysar et al. 2003). Clearly, there are earlier and more common abilities central to development of third-person propositional-attitude mindreading – for example, referential understanding of gazes (Brooks & Meltzoff 2002; Senju et al. 2008) or pretense. However Carruthers does not discuss development of the mechanism that is central to his model. He also overlooks evidence that the tendency to engage in pretence has no primacy over the ability to understand pretence in others (Leslie 1987; Onishi et al. 2007). There are other developmental areas potentially useful to Carruthers’ argument. Several socio-constructivist accounts (e.g., Fonagy et al. 2002; 2007) attempt to describe the developmental mechanisms by which early social-cognitive competences, expressed especially in early interactions with the attachment figure (Sharp & Fonagy 2008), give rise to metacognitive awareness. Arguably, the most advanced of these theories is the BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds social-biofeedback model proposed by Gergely and Watson (1996; 1999; Fonagy et al. 2002; Gergely & Unoka 2008). Currently, this model assumes that in repetitive episodes of (mostly) nonverbal communication (Csibra & Gergely 2006) mothers provide marked emotional “mirroring” displays which are highly (but inevitably imperfectly) contingent on the emotional displays of the infant. By doing so, mothers provide specific forms of biofeedback, allowing infants to parse their affective experience, form separate categories of their affective states, and form associations between these categories and their developing knowledge of the causal roles of emotions in other people’s behaviour. It is important to note that socio-constructivist theory is an essential complement to Carruthers’ model 4, bridging a potentially fatal gap in his argument. People do attribute propositional emotional states to the self, and it seems reasonable to assume that their actual emotional states (propositional or not) play a role in generating such attributions. Carruthers’ current proposal under-specifies how the mindreading system, which evolved for the purpose of interpreting others’ behaviour, comes to be capable of interpreting primary somatic data specific to categories of affective states and of attributing them to the self. Furthermore, according to Carruthers, when the mindreading system does its standard job of third-person mental-state attribution, this sort of data “play little or no role” (target article, sect. 2, para. 8). Presumably, they can contribute, for example, by biasing the outcome of the mindreading processes (like when negative affect leads one to attribute malicious rather than friendly intentions). However, in first-person attributions, their function is quite different. They are the main source of input, providing the mindreading system with cues on the basis of which it can recognize current emotional attitude-states. The socialbiofeedback model assumes that the mindreading system is not readily capable of doing this job and spells out the mechanism facilitating development of this ability. Putting it in terms of Carruthers’ model 4: it explains how primary intra- and proprioceptive stimulation gains attentional focus to become globally accessible and how the mindreading system becomes able to win competition for these data. Research on borderline personality disorder further illuminates the value of the socio-constructivist model (Fonagy & Bateman 2008). The primary deficit in borderline personality disorder (BPD) is often assumed to be a deficit in affect selfregulation (e.g., Linehan 1993; Schmideberg 1947; Siever et al. 2002). We have evidence of structural and functional deficits in brain areas of patients with BPD normally considered central in affect regulation (Putnam & Silk 2005). Accumulating empirical evidence suggests that patients with BPD have characteristic limitations in their self-reflective (metacognitive) capacities (Diamond et al. 2003; Fonagy et al. 1996; Levy et al. 2006) that compromise their ability to represent their own subjective experience (Fonagy & Bateman 2007). There is less evidence for a primary deficit of mindreading (Choi-Kain & Gunderson 2008). Evidence from longitudinal investigations suggests that neglect of a child’s emotional responses (the absence of mirroring interactions) may be critical in the aetiology of BPD (Lyons-Ruth et al. 2005), more so even than frank maltreatment (Johnson et al. 2006). We think that the BPD model may become an important source of new data that could illuminate relationships between mindreading and self-awareness and their developmental antecedents. We suggest that children who experience adverse rearing conditions may be at risk of developing compromised secondorder representations of self-states because they are not afforded the opportunity to create the necessary mappings between the emerging causal representations of emotional states in others and emerging distinct emotional self-states. ACKNOWLEDGMENTS The work of the authors was supported by a Marie Curie Research Training Network grant 35975 (DISCOS). We are grateful for the help and suggestions made by Liz Allison and Tarik Bel-Bahar.

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Banishing “I” and “we” from accounts of metacognition doi:10.1017/S0140525X09000661 Bryce Huebnera,b and Daniel C. Dennetta a

Center for Cognitive Studies, Tufts University, Medford, MA 02155; and Cognitive Evolution Laboratory, Harvard University, Cambridge, MA 02138. [email protected] http://www.wjh.harvard.edu/~huebner [email protected] http://ase.tufts.edu/cogstud/incbios/dennettd/dennettd.htm b

Abstract: Carruthers offers a promising model for how “we” know the propositional contents of “our” own minds. Unfortunately, in retaining talk of first-person access to mental states, his suggestions assume that a higher-order self is already “in the loop.” We invite Carruthers to eliminate the first-person from his model and to develop a more thoroughly third-person model of metacognition.

Human beings habitually, effortlessly, and for the most part unconsciously represent one another as persons. Adopting this personal stance facilitates representing others as unified entities with (relatively) stable psychological dispositions and (relatively) coherent strategies for practical deliberation. While the personal stance is not necessary for every social interaction, it plays an important role in intuitive judgments about which entities count as objects of moral concern (Dennett 1978; Robbins & Jack 2006); indeed, recent data suggest that when psychological unity and practical coherence are called into question, this often leads to the removal of an entity from our moral community (Bloom 2005; Haslam 2006). Human beings also reflexively represent themselves as persons through a process of self-narration operating over System 1 processes. However, in this context the personal stance has deleterious consequences for the scientific study of the mind. Specifically, the personal stance invites the assumption that every (properly functioning) human being is a person who has access to her own mental states. Admirably, Carruthers goes further than many philosophers in recognizing that the mind is a distributed computational structure; however, things become murky when he turns to the sort of access that we find in the case of metacognition. At points, Carruthers notes that the “mindreading system has access to perceptual states” (sect. 2, para. 6), and with this in mind he claims that in “virtue of receiving globally broadcast perceptual states as input, the mindreading system should be capable of self-attributing those percepts in an ‘encapsulated’ way, without requiring any other input” (sect. 2, para. 4). Here, Carruthers offers a model of metacognition that relies exclusively on computations carried out by subpersonal mechanisms. However, Carruthers makes it equally clear that “I never have the sort of direct access that my mindreading system has to my own visual images and bodily feelings” (sect. 2, para. 8; emphasis added). Moreover, although “we do have introspective access to some forms of thinking . . . we don’t have such access to any propositional attitudes” (sect. 7, para. 11; emphasis over “we” added). Finally, his discussion of split-brain patients makes it clear that Carruthers thinks that these data “force us to recognize that sometimes people’s access to their own judgments and intentions can be interpretative” (sect. 3.1, para. 3, emphasis in original). Carruthers, thus, relies on two conceptually distinct accounts of cognitive access to metarepresentations. First, he relies on an account of subpersonal access, according to which metacognitive representations are accessed by systems dedicated to belief fixation. Beliefs, in turn, are accessed by systems dedicated to the production of linguistic representations; which are accessed by systems dedicated to syntax, vocalization, sub-vocalization, and so on. Second, he relies on an account of personal access, according to which I have access to the metacognitive representations that allow me to interpret myself and form person-level beliefs about my own mental states. The former view that treats the mind as a distributed computational system with no central controller seems to be integral to

Commentary/Carruthers: How we know our own minds Carruthers’ (2009) current thinking about cognitive architecture. However, this insight seems not to have permeated Carruthers’ thinking about metacognition. Unless the “I” can be laundered from this otherwise promising account of “self-knowledge,” the assumption of personal access threatens to require an irreducible Cartesian res cogitans with access to computations carried out at the subpersonal level. With these considerations in mind, we offer what we see as a friendly suggestion: translate all the talk of personal access into subpersonal terms. Of course, the failure to translate personal access into the idiom of subpersonal computations may be the result of the relatively rough sketch of the subpersonal mechanisms that are responsible for metarepresentation. No doubt, a complete account of metarepresentation would require an appeal to a more intricate set of mechanisms to explain how subpersonal mechanisms can construct “the self” that is represented by the personal stance (Metzinger 2004). As Carruthers notes, the mindreading system must contain a model of what minds are and of “the access that agents have to their own mental states” (sect. 3.2, para. 2). He also notes that the mindreading system is likely to treat minds as having direct introspective access to themselves, despite the fact that the mode of access is inherently interpretative (sect. 3.2). However, merely adding these details to the model is insufficient for avoiding the presumption that there must (“also”) be first-person access to the outputs of metacognition. After all, even with a complete account of the subpersonal systems responsible for the production and comprehension of linguistic utterances, the fixation and updating of beliefs, and the construction and consumption of metarepresentations, it may still seem perfectly natural to ask, “But how do I know my own mental states?” The banality that I have access to my own thoughts is a consequence of adopting the personal stance. However, at the subpersonal level it is possible to explain how various subsystems access representations without requiring an appeal to a centralized res cogitans. The key insight is that a module “dumbly, obsessively converts thoughts into linguistic form and vice versa” (Jackendoff 1996). Schematically, a conceptualized thought triggers the production of a linguistic representation that approximates the content of that thought, yielding a reflexive blurt. Such linguistic blurts are protospeech acts, issuing subpersonally, not yet from or by the person, and they are either sent to exogenous broadcast systems (where they become the raw material for personal speech acts), or are endogenously broadcast to language comprehension systems which feed directly to the mindreading system. Here, blurts are tested to see whether they should be uttered overtly, as the mindreading system accesses the content of the blurt and reflexively generates a belief that approximates the content of that blurt. Systems dedicated to belief fixation are then recruited, beliefs are updated, the blurt is accepted or rejected, and the process repeats. Proto-linguistic blurts, thus, dress System 1 outputs in mentalistic clothes, facilitating system-level metacognition. Carruthers (2009) acknowledges that System 2 thinking is realized in the cyclical activity of reflexive System 1 subroutines. This allows for a model of metacognition that makes no appeal to a pre-existing I, a far more plausible account of self-knowledge in the absence of a res cogitans.

Unsymbolized thinking, sensory awareness, and mindreading doi:10.1017/S0140525X09000673 Russell T. Hurlburt Department of Psychology, University of Nevada, Las Vegas, Las Vegas, NV 89154-5030. [email protected] http://www.nevada.edu/~russ

Abstract: Carruthers views unsymbolized thinking as “purely propositional” and, therefore, as a potential threat to his mindreading-is-prior position.

I argue that unsymbolized thinking may involve (non-symbolic) sensory aspects; it is therefore not purely propositional, and therefore poses no threat to mindreading-is-prior. Furthermore, Descriptive Experience Sampling lends empirical support to the view that access to our own propositional attitudes is interpretative, not introspective.

Section 8 of Carruthers’ target article considers my Descriptive Experience Sampling (DES) work, particularly its finding of unsymbolized thinking (Hurlburt 1990; 1993; 1997; Hurlburt & Akhter 2008; Hurlburt & Heavey 2006). Carruthers implies that I characterize unsymbolized thinking as being purely propositional: “many subjects also report the presence of ‘purely propositional,’ unsymbolized thoughts at the moment of the beep” (sect. 8, para. 2). As a result, he supposes that my claim that unsymbolized thoughts are introspected (Hurlburt 1990; 1993) might present a difficulty for his mindreading-isprior view, which holds that purely propositional events are not introspected but are, instead, interpreted. Against this supposition, Carruthers argues that the introspection of unsymbolized thinking is an illusion; what is mistaken for introspection is a swift but unconscious interpretation of external events (Carruthers 1996b) and/or internal events such as images (present target article). As a result, he concludes in the target article that DES is neutral regarding Carruthers’ mindreading view: “although there is no support to be derived for a ‘mindreading is prior’ account from the introspection-sampling data, neither is there, as yet, any evidence to count against it” (sect. 8, para. 5, emphasis in original). I think Hurlburt and Akhter (2008) successfully rebutted Carruthers (1996b), and the target article does not change my mind. But I agree that unsymbolized thinking does not threaten Carruthers’ mindreading-is-prior position, not because unsymbolized thinking is an unconscious interpretation but because it is not “purely propositional.” Unsymbolized thinking is a directly apprehendable experience that may well have some kind of (probably subtle) sensory presentation, is therefore not purely propositional, and for that reason is not at odds with the mindreading-is-prior view. In seeking to discover why Carruthers might hold, mistakenly, that I believe that unsymbolized thinking is “purely propositional,” I reviewed what I have written on unsymbolized thinking and discovered this sentence: Unsymbolized Thinking is the experience of an inner process which is clearly a thought and which has a clear meaning, but which seems to take place without symbols of any kind, that is, without words, images, bodily sensations, etc. (Hurlburt 1993, p. 5; emphasis added)

“Without . . . bodily sensations” might be understood to mean “purely propositional,” but that is not at all what I intended. I should have written “without . . . bodily sensory awareness” instead of “without . . . bodily sensations.” “Sensory awareness” is a term of art in DES: “A sensory awareness is a sensory experience (itch, visual taking-in, hotness, pressure, hearing) that is in itself a primary theme or focus for the subject” (Hurlburt & Heavey 2006, p. 223). That is, sensory awareness is not merely a bodily or external sensation, but is a sensation that is itself a main thematic focus of experience. Thus, for example, Jack picks up a can of Coke, and, while preparing to drink, particularly notices the cold, slippery moistness against his fingertips. Jill picks up a can of Coke, and, while preparing to drink, says to herself in inner speech, “Carruthers is right!” Both Jack and Jill are having bodily sensations of the coldness, the moistness, and the slipperiness of the can (neither drops it). Jack’s central focus is on the cold, slippery moistness; therefore, he is experiencing a sensory awareness as DES defines it. Jill’s central focus is on her inner speech, not on the can; therefore she is not experiencing a sensory awareness as defined by DES (see Hurlburt & Heavey, in preparation). Thus, unsymbolized thinking, as I and my DES colleagues describe the phenomenon, is an experience that is directly apprehended at the moment of the DES beep but which does not BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds involve the direct apprehension of verbal, imaginal, or other symbols and does not involve sensory awareness as DES defines that term. The apprehension of an unsymbolized thought may involve the apprehension of some sensory bits, so long as those sensory bits are not organized into a coherent, central, thematized sensory awareness. Thus, I believe that unsymbolized thinking is a perceptual event, just as are inner speech, visual imagery, and feelings; it is therefore not purely propositional and is therefore not a threat to the mindreadingis-prior view. Access to propositional attitudes is interpretative. Far from being neutral, DES lends empirical support to the main thrust of Carruthers’ analysis that propositional attitudes are interpreted, not observed. The DES procedure trains subjects carefully, repeatedly, and iteratively (Hurlburt & Akhter 2006; Hurlburt & Heavey 2006; Hurlburt & Schwitzgebel 2007) to distinguish between directly observed (Carruthers’ “perceptual”) events and all else; that training typically requires several days. DES tries, moment by moment, to cleave to the directly observed and to bracket all that is inferred, supposed, presupposed. There is no a priori assumption about what is or is not directly observable. Attitudes are not singled out; if an attitude is directly observed at the moment of some beep, then that attitude is the proper target of DES. If not, then it isn’t. As a result of 30 years of carefully questioning subjects about their momentary experiences, my sense is that trained DES subjects who wear a beeper and inspect what is directly before the footlights of consciousness at the moment of the beeps almost never directly apprehend an attitude. Inadequately trained subjects, particularly on their first sampling day, occasionally report that they are experiencing some attitude. But when those reports are scrutinized in the usual DES way, querying carefully about any perceptual aspects, those subjects retreat from the attitude-was-directly-observed position, apparently coming to recognize that their attitude had been merely “background” or “context.” That seems entirely consonant with the view that these subjects had initially inferred their own attitudes in the same way they infer the attitudes of others. (I note that subjects do not similarly retreat from their initial reports about unsymbolized thinking; they continue to maintain that the unsymbolized thought had been directly observed.)

What monkeys can tell us about metacognition and mindreading doi:10.1017/S0140525X09000685 Nate Kornell,a Bennett L. Schwartz,b and Lisa K. Sonc a Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095-1563; bDepartment of Psychology, Florida International University, Miami, FL 33199; cDepartment of Psychology, Barnard College, New York, NY 10027. [email protected] http://nkornell.bol.ucla.edu/ [email protected] www.fiu.edu/~schwartb [email protected] http://lisason.synthasite.com/index.php

Abstract: Thinkers in related fields such as philosophy, psychology, and education define metacognition in a variety of different ways. Based on an emerging standard definition in psychology, we present evidence for metacognition in animals, and argue that mindreading and metacognition are largely orthogonal.

The target article proposes that “mindreading is prior to metacognition,” meaning that just as we know the minds of others by observing what they do, we know our own minds by observing what we do. According to this view, metacognition – that is, cognition about one’s own cognition – requires mindreading abilities. Rhesus monkeys (Macaca mulatta) do not appear to

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possess mindreading abilities (Anderson et al. 1996; but see Santos et al. 2006). Here we present evidence, however, that rhesus monkeys are metacognitive. We offer a different definition of mindreading than that used by Carruthers, and we contend that the mechanisms of mindreading and metacognition are largely orthogonal. The target article reports in detail on only a few seminal studies of metacognition in animals (see Smith et al. 2003; Smith & Washburn 2005). We begin by elaborating on subsequent studies that provide evidence of animal metacognition (reviewed by Kornell, in press). For example, Hampton (2001) tested monkeys in a modified delayed match-to-sample task: On each trial, a sample picture was presented on a touch-sensitive computer monitor, and then, after a delay, the same sample picture was presented among three distractors, and the subject had to touch the sample. On some trials, after viewing the sample, the monkey could choose to skip the test and receive a small reward. If the monkey instead chose to take the test, he could earn a large reward, or, if his response was incorrect, forfeit reward completely. Memory accuracy was better on self-selected test trials than on mandatory test trials. It appears that the monkeys chose to take the test when they knew that they knew the answer, in the same way that a student raises her hand in class when she knows that she knows the answer (see Suda-King 2008, for similar results in orangutans). In another study, two male rhesus monkeys were asked, essentially, to bet on their memories (Kornell et al. 2007). A given monkey was shown six pictures sequentially for “study,” followed by a display of nine pictures presented simultaneously, one of which had been “studied.” The monkey’s task was to select the studied picture. After he responded, two “risk” icons were presented, which allowed the monkey to bet his tokens (which could be exchanged for food). A high-risk bet resulted in the gain of three tokens if the monkey had responded correctly, but a loss of three tokens otherwise. Choosing low-risk resulted in a sure gain of one token. The monkeys made accurate confidence judgments: They bet more after correct responses than after incorrect responses. This finding was especially impressive because the monkeys were originally trained on tasks that involved neither pictures nor remembering (e.g., select the longest line); following that training, they were able to respond metacognitively beginning on the first day of the picturememory task. The monkeys appear to have learned a general metacognitive response, not one that was task-specific. In addition to being able to make judgments about their memories, monkeys have demonstrated that they can choose behaviors, based on metacognition, that advance their own knowledge – that is, they have demonstrated metacognitive control (see Nelson & Narens 1990). To investigate this ability, we allowed two monkeys to request information when they were uncertain, just as a person might ask for a hint when answering a difficult question (Kornell et al. 2007). The monkeys could request a “hint,” that is, a blinking border that surrounded the correct response, on some trials in a listlearning experiment. As the monkeys’ response accuracy on no-hint trials improved steadily, their rate of hint requests showed a corresponding decline. By requesting hints when they were unsure, the monkeys went beyond making an uncertain response; they took steps to rectify their ignorance. Based on the studies described above, we conclude that monkeys have metacognitive abilities – that is, they can monitor the strength of their own internal memory representations. According to the target article, these findings fall short of metacognition, however. Carruthers writes, “It is only if a human reports that she acted as she did, not just because she was uncertain, but because she was aware of being uncertain, that there will be any conflict [with the metacognition is prior account]” (sect. 5.2, para. 3). We do not agree that metacognition requires awareness; we have previously argued that the metacognitive abilities that animals possess are not necessarily conscious (Kornell, in press; Son & Kornell 2005; also see Reder 1996).

Commentary/Carruthers: How we know our own minds For example, a monkey might make a high-risk bet without being aware that it is monitoring its internal memory trace. We are not arguing that mindreading cannot subsume metacognitive functions. Indeed, we can learn much about ourselves by observing our own behavior: for example, after playing a round of golf, we decide we are not quite ready for the pro tour. Moreover, numerous experiments have shown that metacognition is largely based on unconscious inferential processes, not direct examination of memories; for example, we infer that we know something well based on the fluency (i.e., ease and speed) with which it comes to mind (Schwartz et al. 1997). Given the way we, and many other cognitive psychologists, define metacognition, we assert that it is likely that metacognition and mindreading are separate processes. The argument that one should only see metacognition in species that can mindread is, to the best available evidence, false. For example, some have suggested that dogs, which have shown no metacognitive abilities but show high levels of social cognition, may have rudimentary mindreading abilities (Horowitz, in press; Tomasello et al. 1999). Conversely, we offer rhesus monkeys as a case study in a metacognitively competent animal that fares poorly at mindreading. In the tasks we describe, metacognitive processing can lead to positive outcomes that are evolutionarily adaptive. Indeed, metacognitive monitoring seems to have its own rewards.

Metacognition without introspection doi:10.1017/S0140525X09000697 Peter Langland-Hassan Department of Philosophy, The Graduate Center of the City University of New York, New York, NY 10016. [email protected] https://wfs.gc.cuny.edu/PLangland-Hassan

Abstract: While Carruthers denies that humans have introspective access to cognitive attitudes such as belief, he allows introspective access to perceptual and quasi-perceptual mental states. Yet, despite his own reservations, the basic architecture he describes for thirdperson mindreading can accommodate first-person mindreading without need to posit a distinct “introspective” mode of access to any of one’s own mental states.

Carruthers argues that passivity symptoms (e.g., thought insertion) in schizophrenia result not from a special metacognitive deficit, but from “faulty data being presented to the mindreading system” (sect. 9, para. 2). Although I endorse Carruthers’ Frith-inspired (Frith et al. 2000a; 2000b) appeal to efference-copy deficits in the explanation of passivity symptoms, his claim that the mindreading faculty itself is undamaged raises questions. First, any attribution of one’s own thoughts to another is equally a mistake in first- and third-person mindreading (false positives count as errors just as much as false negatives do). Carruthers should therefore hold that mindreading – first- and third-person – is deficient in these forms of schizophrenia; this still allows him to deny any dissociation between mindreading and metacognitive abilities, in line with what his theory predicts. It also avoids his having to make the hard-to-test claim that it is intermittently faulty data and not an intermittently faulty mechanism that is to blame for passivity symptoms. Second, Carruthers holds that humans have introspective access to some mental states (e.g., perceptual states, imagery, and inner speech), but not to cognitive attitudes such as belief. But if information is extracted from globally broadcast perceptual states in third-person mindreading without introspection occurring, why think that the extraction of information from inner speech and visual imagery during first-person mindreading involves an introspective process different “in kind” from the way we form beliefs about the mental states of others? If, as

Carruthers argues, passivity symptoms result from faulty data being input to the mindreading system (data that should have been interpreted as internally generated is interpreted as externally generated), then it seems the very determination of whether an input is self or other-generated – and thus whether one is seeing or visualizing, hearing or sub-vocalizing – requires an inferential or interpretative step (Langland-Hassan 2008). Carruthers would likely respond that this inner-or-outer inferential step involves nothing more than the “trivial” form of inference that occurs in any layered representational scheme, where representations at one level can, in a “supervisory” role, intervene on those at another. However, many instances of third-person mindreading are equally fast and automatic, and they are implicit in the very cases of metacognition that, on Carruthers’ theory, would be achieved through the “encapsulated” process of introspection. Consider a visual representation had by someone who looks up and sees another person staring at him. Suppose this visual perceptual state is accessed by the mindreading system, which issues in the introspective judgment: “I see a man seeing me.” This judgment contains within it a judgment that another person is having a visual experience of a certain kind (cf. Jeannerod & Pacherie’s [2004] “naked intentions”). So, unless the mindreading faculty in its introspective mode lacks the concepts needed for this judgment (unlikely, since it must have the concepts of self and of sight in order to issue any introspective judgments about visual experience), third-person mindreading can occur through the encapsulated “introspective” process that Carruthers describes. Yes, some cases of third-person mindreading require much more sophisticated feats of interpretation, but so too do many cases of first-person mindreading, as revealed by the confabulation data Carruthers discusses (Gazzaniga 1995). Thus, even if it is possible to draw a line between mindreading that is informationally encapsulated and that which is not, it will not cut cleanly across cases of first- and third-person mindreading. Nor is the existence of such domain-specific mechanisms supported by recent neuroimaging studies (Decety & Lamm 2007). What we have instead are inferences, concerning both first- and third-person mental states, that require greater or lesser degrees of supporting information; none of this implies a special mode of access to facts about one’s own mental states. This is obscured by the tendency of researchers to compare easy cases of metacognition (e.g., inferring one’s intentions from one’s own inner speech) with difficult cases of third-person mindreading (e.g., inferring what someone thinks based solely on their posture and facial expression) – for it creates the impression that first-person mindreading occurs through some more “direct” process. But if we instead compare the third-person mindreading that occurs when we judge that a person believes what we hear her saying, to the first-person mindreading that draws on “listening” to one’s own inner speech, there is less intuitive pressure to posit a difference in the kind of inference. Of course, if there were genuine dissociations revealed between third- and first-person mindreading abilities, as Nichols and Stich (2003) and Goldman (2006) claim, then we would have reason to posit differences in the kinds of mechanisms and inferences involved in each; but Carruthers is at pains to deny any such dissociations, and his alternative explanations are plausible enough. The issue can be reframed in terms of the larger evidence base we have for first-person rather than third-person mindreading. Carruthers notes that the resources available to first-person mindreading are different because, “unless subjects choose to tell me, I never have access to what they are imagining or feeling” (sect. 2, para. 8). This is potentially misleading; the situation is rather that the single mindreading system, as he describes it, only ever has access to globally broadcast perceptual and quasi-perceptual representations (and memory), and, with this single source of information, must accomplish both its first- and third-person mindreading tasks – one of which is to determine whether the signal counts as a case of imagining or perceiving in the first place. The fact that we have so much more “evidence” for first-person mindreading than third-person may still tempt some to posit BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds a special form of access. Yet, if humans always audibly narrated their inner speech and expressed the contents of their visual imagery, the evidence bases for first- and third-person mindreading would be comparable. So it may be a contingent fact about how humans behave that accounts for the difference in evidence bases, not a special mode of access. I therefore urge Carruthers to adopt a more thoroughgoing anti-introspectionism. Not only can first-person mindreading be explained without appeal to the introspection of propositional attitudes, it can be explained without granting a distinct introspective form of access to any of one’s own mental states.

Carruthers’ marvelous magical mindreading machine doi:10.1017/S0140525X09000703 Charlie Lewisa and Jeremy I. M. Carpendaleb a Department of Psychology, Fylde College, Lancaster University, Bailrigg, Lancaster, LA1 4YF, United Kingdom; bDepartment of Psychology, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada. [email protected] http://www.psych.lancs.ac.uk/people/CharlieLewis.html [email protected] http://www.psyc.sfu.ca/people/faculty.php?topic¼finf&id¼67

Abstract: Carruthers presents an interesting analysis of confabulation and a clear attack on introspection. Yet his theory-based alternative is a mechanistic view of “mindreading” which neglects the fact that social understanding occurs within a network of social relationships. In particular, the role of language in his model is too simple.

In his analysis of four identifiable accounts of the relationship between “mindreading” and “metacognition,” Carruthers makes a bold attempt to explain a major inconsistency in three of these and assert the primacy of “mindreading” as a precursor to self-understanding. He does this mainly by highlighting a common key feature in the literature in neuropsychology, reasoning, and psychopathology: why adults with sophisticated skills in accounting for their own and others’ thoughts still seem to show flaws in their abilities to demonstrate and reconcile these skills. However, we make three connected points to argue that his account falls short of a clear answer to his main concerns about the nature of the two processes and how they relate. First, Carruthers treats the terms “metacognition” and “mindreading” as unproblematic, distinct, and clearly grounded within an overarching, internalist cognitive architecture. Yet, the article is unclear about how this architecture fits together into a working psychological system. There is more than a hint of a homunculus in Carruthers’ descriptions of how we access and use these skills (Kenny 1971/1991). Let’s look at the role of the role of language in mindreading as an example. Early in the target article Carruthers dismisses the importance of language in a note because it seems “plausible” to him “that basic capacities for both mindreading and metacognition are independent of our capacity for natural language” (target article, Note 2). Yet in his analysis of reasoning (sect. 8), he acknowledges its importance in certain circumstances, but does not convincingly show how this contradiction can be reconciled. Then, in the bulk of the article, speech only serves as input for Carruthers’ “magical mindreading machine,” revealing a view that thinking is computation and meaning is mechanistic, entailing a code model of language. All of these assumptions have been repeatedly critiqued (e.g., Bickhard 2001; Goldberg 1991; Heil 1981; Proudfoot 1997; Tomasello 2003; Turnbull 2003; Wittgenstein 1968). For example, problems have been pointed out with how the symbols involved in computation could acquire meaning, and whether understanding can adequately be conceived of as a process of computation (Proudfoot 1997).

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These criticisms have been applied to the theoretical approach that Carruthers advances. Carruthers claims to have considered four different theories. However, since he starts with such a restricted set of theories to consider, knowing that he declares his version of theory-theory as the winner in this contest tells us nothing about how the many other approaches would fare. The one place where we do agree with Carruthers is that assuming introspection as a source of social understanding is problematic. We find it odd, however, that he ignores Wittgenstein’s far stronger arguments against introspection. Alternative accounts based upon a more sophisticated grasp of language deal more easily with the confabulation literature that is so well described in the target article (sect. 6), but not fully explained. Indeed, second, the theory-theory’s response to these criticisms is simply to ignore it (Carpendale & Lewis 2004; Leudar & Costall, in press). Carruthers’ perspective is symptomatic of an insularity that has been commented upon repeatedly in the wider literature, but which barely gets a mention in the “mainstream.” The idea of the 1980s that we, or something inside ourselves, can manipulate our own social understanding was criticised by Bruner (1986; 1990), who rejected a cognitive system that is bereft of serious contact with the outside world. Many commentators have repeated this claim. Carruthers buys into forms of attributing mental states to ourselves and others which assume the theory metaphor so loosely as to be bereft of meaning (see Campbell & Bickhard 1993). The fact that this movement has been as immune to debate in the outside world as the cognitive system that they depict has led its critics (e.g., Leudar & Costall, in press, Ch. 1) to use the term ToMism to refer to the assumption that the individual is trapped behind a mindreading mechanism that filters their interpretation and observations of social interactions. Third, in his neglect of the growing literature which attempts to explore the interactional basis of social understanding, Carruthers’ account is both parasitic upon the evidence from developmental psychology and simultaneously dismissive of its analysis of the nature of the two processes. We suspect that without a clear analysis of development of “mindreading” and/or “metacognition,” Carruthers’ definition of these two terms remains grossly underspecified. There is a large body of recent work that attempts to explore the dynamics of interaction in early (Reddy 2008) and later infancy (Moll & Tomasello 2007) and in childhood (Carpendale & Lewis 2004), as well as with reference to special populations (Hobson 2002), which describes how social understanding, as opposed to some miraculous yet unspecified mindreading system, gradually emerges in social interaction. In these approaches there is no Cartesian split between the individual in the spectator’s role observing others’ physical movements and having to attribute mental states. Without a reference to this expanding and exciting literature, and its data and critique of the solipsism of theory-theory, Carruthers’ argument is a better description of atypical “mindreading” like autism, not an account of how we understand ourselves and others.

What neuroimaging and perceptions of self-other similarity can tell us about the mechanism underlying mentalizing doi:10.1017/S0140525X09000715 Michael V. Lombardo, Bhismadev Chakrabarti, and Simon Baron-Cohen Autism Research Centre, University of Cambridge, Cambridge CB2 8AH, United Kingdom. [email protected] [email protected] [email protected] http://www.autismresearchcentre.com

Abstract: Carruthers’ “mindreading is prior” model postulates one unitary mindreading mechanism working identically for self and other.

Commentary/Carruthers: How we know our own minds While we agree about shared mindreading mechanisms, there is also evidence from neuroimaging and mentalizing about dissimilar others that suggest factors that differentially affect self-versus-other mentalizing. Such dissociations suggest greater complexity than the mindreading is prior model allows.

The “mindreading is prior” model proposed by Carruthers postulates that one mechanism (mindreading) is all that is needed to understand the mental states of oneself and others. Although we agree that shared mechanisms can implement the computations underlying both self- and other-mentalizing, we question whether all types of mentalizing use this one mechanism in an identical fashion. We present evidence from functional neuroimaging and research suggesting that mentalizing differs when the target individuals are of differing similarity to oneself. Because the “mindreading is prior” model asserts that one mechanism is used indifferently for self- and other-mentalizing, this generates three predictions that all need to be satisfied in the brain. First, there should be an identical neural implementation of mentalizing for self and other. Thus, the same brain regions that code for self-mentalizing computations should also be recruited for other-mentalizing. Second, such identical neural implementation should occur to a similar degree for both self and other. Thus, activation in shared regions should be equal in self- and other-mentalizing. Finally, there should be no other areas in the brain that are recruited specifically for just self- or just other-mentalizing. In our own recent work (Lombardo et al., submitted) we find evidence that while predictions 1 and 3 are satisfied, prediction 2 is not. We find that although the ventromedial prefrontal cortex (vMPFC), posterior cingulate/precuneus (PCC), and right temporo-parietal junction (RTPJ) are all recruited for mentalizing about both self and other, they do so in a target-biased fashion. The vMPFC is recruited more for self-mentalizing than other-mentalizing, whereas PCC and RTPJ are recruited more for other- than self-mentalizing. Thus, while it is the case the identical neural mechanisms are implementing self- and other-mentalizing computations, they do not do so in an equal fashion. Some of these shared neural representations are tailored more for handling selfmentalizing, whereas other shared representations are tuned in to handle other-mentalizing. The “mindreading is prior” model is silent about how one unitary mindreading mechanism can be tuned preferentially for mentalizing about self more than other, or vice versa. In addition to these neuroimaging results, there is behavioral evidence in individuals with autism spectrum conditions (ASC) that the magnitude of impairment in self- and other-mentalizing is unequal (Lombardo et al. 2007). In the domain of emotion understanding, on measures of other-mentalizing involving reading complex emotions (Reading the Mind in the Eyes Test; Baron-Cohen et al. 2001), adults with ASC show less impairment (Cohen’s d ¼ 0.61) than on measures assessing understanding one’s own emotions (Toronto Alexithymia Scale; Cohen’s d ¼ 1.40). Thus, although impairment may exist in both selfand other-mentalizing in ASC, the impairments are unequal in magnitude. This evidence presents a paradox: How can one unitary mechanism working identically for self and other affect self and other in a differential manner? The “mindreading is prior” model is also silent about what happens when an individual mentalizes about an individual of varying similarity to oneself. Carruthers treats the “other” as completely distinct from oneself, and he bases his theory on such a “monolithic other.” However, social psychological research suggests that the mechanisms for mentalizing about others functions differently depending the degree to which an individual perceives the other person to be similar or dissimilar to oneself. When another is perceived to be dissimilar to oneself, mentalizing responses follow a pattern similar to stereotyping; we expect dissimilar individuals to share mental states of

individuals from the salient group that they belong to. However, when another individual is similar to oneself, social inference proceeds in a congruent manner to what we ourselves think or feel (Ames 2004). A similar distinction can be seen within neuroimaging research on this topic. Jason Mitchell and colleagues (Jenkins et al. 2008; Mitchell et al. 2006) present elegant work showing that similar neural responses occur in vMPFC during self-mentalizing and mentalizing about similar others, but not during mentalizing about dissimilar others. Mentalizing about dissimilar others involves processing in the dorsomedial prefrontal cortex (dMPFC) rather than the vMPFC. Furthermore, when stereotyping is applied in the context of mentalizing about others, areas involved in semantic retrieval and selection such as the ventrolateral prefrontal cortex (VLPFC) are recruited, but not the vMPFC or dMPFC (Mitchell et al., in press). Thus, behavioral and neural distinctions can be made about the mechanism underlying mentalizing about others of differing similarity to oneself. In the case of similar others, the regions involved in both self- and other-mentalizing may overlap considerably. However, different regions appear to be at work when making inferences about the mental states of dissimilar others. How does the “mindreading is prior” model account for such differences in the mindreading mechanism, solely based on the perceived similarity of others to oneself? Finally, in our own work, we find that even in ASC the mechanism underlying mentalizing about self and similar others may be different to mentalizing about dissimilar others (Lombardo et al. 2007). We asked participants to make judgments about personality traits (e.g., honest, caring, anxious) in relation to themselves, a similar close other, and a dissimilar non-close other. In a control task, they were asked to simply count the syllables in personality trait words. In a surprise recognition memory test, we found that individuals with ASC had no impairment in memory for words encoded by syllable counting or in relation to a dissimilar other. However, when looking at memory for self-encoded or similar other-encoded traits, we found substantial impairment. Individuals with ASC had an impairment in processing linked to thinking about themselves and similar others, but no deficit in regards to a dissimilar other. Such dissociations for similar and dissimilar others imply the mindreading mechanism may conceal a greater complexity than is suggested by Carruthers’ model.

Feigning introspective blindness for thought doi:10.1017/S0140525X09000727 Robert W. Lurz Department of Philosophy, Brooklyn College, City University of New York, Brooklyn, NY 11218. [email protected] http://depthome.brooklyn.cuny.edu/philo/Lurz.htm

Abstract: I argue that the very reasons Carruthers gives for why the “mindreading is prior” account should allow introspective access to perceptual/quasi-perceptual states, can be given for thought, as well. I also argue that we have good subjectively accessible grounds for the intuition in introspective thoughts, notwithstanding Carruthers’ argument to the contrary and his attempt to explain the intuition away. I. Carruthers argues that a consequence of the “mindreading is prior” account is that the mindreading faculty should have introspective access to perceptual and quasi-perceptual states. Two reasons are given. First, because the mindreading faculty must have access to perceptual inputs about the actions of others (targets), it “should be capable of self-attributing those percepts in an ‘encapsulated’ way, without requiring any other input” BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds (target article, sect. 2, para. 4). But arguably the mindreading faculty would not have evolved unless it were able to predict the behaviors of others; and for this, the faculty must have access to non-perceptual beliefs about past and general facts about particular targets. If a mindreading animal, for example, observes a target orient its eyes toward food and thereby attributes sees-food, it is unlikely that it will be able to predict what the target will do unless it has access to relevant past and general facts about the target that are not perceptually available in the scene or hardwired into the faculty itself (e.g., that the target is a member of its own group/family, has recently eaten, has shared food in the past, etc.). By parity of reasoning, since the mindreading faculty must have access to non-perceptual beliefs, the faculty should be capable of self-attributing such thoughts in an “encapsulated” way without requiring any further input. Carruthers argues that introspection of quasi-perceptual states is also likely on the “mindreading is prior” account because, being perceptual, they are (when attended to) “globally broadcast” to all concept-forming systems, including the mindreading faculty. But arguably attended-to thoughts are “globally broadcast” as well. In fact, Baars (1997) argues that attended-to thoughts, just as much as attended-to percepts, “create a vast access to perhaps all parts of the nervous system” (p. 59). For example, if one were to observe a subject looking at a red apple, one’s mindreading faculty would likely infer that the subject sees the color of the apple; however, this default inference would have been prevented if, prior to observing the subject, the thought should have occurred to one that this subject had recently informed one that she was color-blind. It is quite plausible, therefore, that attended-to thoughts are also broadcasted to the mindreading factually, and by parity of reasoning, the faculty should be capable of self-attributing such thoughts in an “encapsulated” way without requiring any further input. II. The standard argument in support of introspective access to thoughts runs as follows (see Goldman 2006, p. 230, for an example): 1. Sometimes we know what we think, and yet what we think is quite unrelated (for interpretative purposes) to any of the contents to which we have subjective access (such as the contents of perception, proprioception, episodic memory, or the contents of introspection regarding perceptual and quasi-perceptual states). 2. Therefore, it is unlikely in such cases that we know what we think as a result of an interpretation from the subjectively accessible contents rather than as a result of an introspective access to the thought itself. This is an inductive argument to the best explanation. Hence, the more interpretatively irrelevant the subjectively accessible contents are to the self-ascribed thought, the more likely the process is introspection. Furthermore, because the processes involved in interpretation and introspection are unconscious, it is to be expected that the greater the degree of interpretative relevance the subjectively accessible contents bear to the selfascribed thought, the greater the chances are that it will appear to the subject as if he knows what he thinks on the basis of introspection when it is actually the result of interpretation. I suspect that this is what is happening in the split-brain case that Carruthers describes. The subject has access to the contents of his perceptual and proprioceptive states that represent him as walking out of the testing van, and he probably has access to the contents of his memory which represent the location of the van to the house, and that the house has a Coke in it, and so on. All of this would likely allow him to interpret (at an unconscious level) himself as going to get a Coke from the house. Is it possible for it to always appear as if our knowledge of our own thoughts is introspective when it is interpretative? Yes, but it is unlikely the more interpretatively unrelated these contents are to the self-ascribed thought.

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The preceding argument assumes that the belief in introspective thoughts is the result of an inductive argument and is not simply built into our pre-theoretic concept of the mind’s epistemic access to itself. Carruthers disagrees. He argues that on the “mindreading is prior” account, it is to be expected that the mindreading system should represent this epistemic access as introspection and not interpretation, since doing so would “greatly simplify” the system’s computational operations without any significant loss in reliability. However, if the mindreading system’s concept of introspection is Carruthers’ broad and negative definition (roughly, a reliable method for forming metacognitive judgments that is not interpretative and is different in kind from our access to other minds), then in order for the mindreading system to apply its concept of introspection to a subject, it will need to assume or have reason to believe that the subject’s access to its mind is not interpretative. This would seem to undermine Carruthers’ claim of there being a greater degree of simplification in the operations of a mindreading system that represented the mind’s access to itself as introspection compared to one that represented it as interpretation only, since the former would require the possession and application of the concepts of introspection and interpretation, whereas the latter would require the possession and application of the concept of interpretation only. It is more plausible to suppose that the mindreading system’s model of the mind’s access to itself would simply be that of a reliable judgmentforming process that is different in kind from that used to form judgments about other minds. But such a model of the mind’s access to itself would be neutral regarding whether it is introspection (as Carruthers defines it) or interpretation – at least, with respect to the kind of interpretivism that Carruthers defends in the “mindreading is prior” account, which also holds that the mind’s interpretative access to its own thoughts is reliable and different in kind from its access to the thoughts of others.

Getting to know yourself . . . and others doi:10.1017/S0140525X09000739 Candice M. Millsa and Judith H. Danovitchb a School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX 75083-0688; bDepartment of Psychology, Michigan State University, East Lansing, MI 48824-1116. [email protected] http://www.utdallas.edu/research/thinklab [email protected] http://psychology.msu.edu/people/faculty/danovitch.htm

Abstract: Carruthers rejects developmental evidence based primarily on an argument regarding one skill in particular: understanding false beliefs. We propose that this rejection is premature; and that identifying and examining the development of other subcomponent skills important for metacognition and mindreading, such as the ability to assess levels of knowledge, will in fact be useful in resolving this debate.

Although we find his proposal thought-provoking, we disagree with Carruthers’ conclusion that “developmental evidence is of no use” (target article, sect. 4, para. 3) in determining the relationship between mindreading and metacognition, because it is based primarily on an incorrect assumption that false belief performance is the only relevant measure of mindreading and metacognition in children. False beliefs tasks are just one way of measuring mindreading, and not necessarily a good way (Bloom & German 2000). Indeed, most developmental psychologists believe that mindreading and metacognition involve a number of subcomponent skills. Some of these skills, such as the ability to understand that others have intentions, may be mastered by infants and toddlers (e.g., Meltzoff 1995), whereas

Commentary/Carruthers: How we know our own minds others, such as the ability to appreciate false beliefs, are mastered in the early preschool years (Wellman et al. 2001). Other skills, such as the ability to understand that a person can have conflicting desires (e.g., Choe et al. 2005) or the ability to accept that two people can interpret the same information in different ways (e.g., Carpendale & Chandler 1996), are not grasped until middle childhood. Indeed, many of the component skills that enable mindreading and metacognition are unaccounted for in Carruthers’ account of why developmental evidence is not useful. Thus, by examining self/other comparisons in the development of these skills, developmental data can help us better understand potential asymmetries in the emergence of mindreading and metacognition. Carruthers identifies one possible area for further examination. In his introduction, he points out that mindreading involves not only intentions and beliefs (which are measured in the false belief task), but also components such as knowledge. However, his review of the developmental literature does not take into account the growing body of research examining young children’s assessments of what other people know versus what they themselves know. Determining what another person is likely to know can be considered mindreading because it requires an understanding of the contents and limitations of that person’s thoughts. In order to make a judgment about what another person knows, children cannot always rely on associations or broad generalizations. Rather, the evidence suggests that young children have a sophisticated understanding of what other people know or do not know. For instance, they understand that one person can be knowledgeable about some things but not knowledgeable about other things (e.g., Harris 2007; VanderBorght & Jaswal, in press). Even 3year-olds, who might be considered poor at mindreading based on the false belief task, are often adept at identifying knowledgeable individuals when contrasted with ignorant or inaccurate sources (e.g., Birch et al. 2008; Jaswal & Neely 2006; Koenig & Harris 2005; Sabbagh & Baldwin 2001). By age 4, children are also capable of drawing inferences about another person’s knowledge that go beyond familiar associations to reflect an understanding of the underlying principles that make up a person’s expertise (e.g., Lutz & Keil 2002). This suggests that young children understand both the contents and limits of another person’s knowledge. Conversely, young children’s ability to assess their own knowledge accurately – a form of metacognition – is quite weak (Flavell et al. 1970; Mills & Keil 2004). For instance, 4- to 5year-old children are notoriously poor at realizing how much they have learned of a new piece of information (Esbensen et al. 1997; Taylor et al. 1994). The younger they are, the more children overestimate their understanding (and underestimate their ignorance) of familiar objects and procedures, often not appropriately assessing their own level of knowledge until age 9 or later (Mills & Keil 2004). Thus, despite the evidence that children accurately judge what other people know, there seems to be a real developmental gap in applying these principles to their own knowledge. One obstacle in applying this research to determining whether metacognition or mindreading is mastered first is that the kinds of questions used to ask children to reflect on their own knowledge are very different from the questions used to measure children’s understanding of others’ knowledge. Research examining how children think about their own knowledge often requires them to evaluate their own knowledge level using a scale or to estimate the number of problems they can answer correctly. In contrast, studies examining how children think about the knowledge of others typically require them to choose the source that would be able to give the most helpful or accurate information. Given that little research has been conducted directly comparing children’s accuracy estimating the knowledge of others to their own knowledge, many open questions remain.

Therefore, in order to provide direct evidence in favor of or against Carruthers’ hypothesis, we propose that additional research is necessary. Comparing the development of an understanding of one’s own knowledge with an understanding of others’ knowledge, or making self/other comparisons based on the other subcomponent skills required for mindreading and metacognition besides false belief, can provide information regarding the developmental trajectory for these skills. In designing this research, it is essential to identify analogous subcomponent skills important for mindreading and metacognition and test them using parallel measures. For instance, one could compare children’s ability to identify what they know about a novel object versus another person’s knowledge, given the same exact experience with the object. We believe such developmental evidence could go a long way in resolving this debate, and that, based on the research so far, it is likely to support Carruthers’ hypothesis. To conclude, there may be times when developmental evidence is of little use in resolving philosophical debates, such as when the debate is over the existence of free will or whether God exists. We do not believe this is one of those times; rather, Carruthers’ proposal addresses a debate that is perhaps best understood in terms of child development. Certainly his hypothesis that mindreading is essential for metacognition poses specific empirical questions that can be tested using developmental methods. We feel strongly that careful research identifying analogous subcomponent skills necessary for mindreading and metacognition and examining their developmental trajectory will provide valuable evidence of whether mindreading really, truly is prior.

Varieties of self-explanation doi:10.1017/S0140525X09000740 Dominic Murphy Unit for History and Philosophy of Science, University of Sydney, Carslaw FO7, Camperdown, NSW 2006, Australia [email protected] http://www.usyd.edu.au/hps/staff/academic/Dominic_Murphy.shtml

Abstract: Carruthers is right to reject the idea of a dedicated piece of cognitive architecture with the exclusive job of reading our own minds. But his mistake is in trying to explain introspection in terms of any one mindreading system. We understand ourselves in many different ways via many systems.

I agree with Carruthers that there is no piece of human cognitive architecture dedicated to introspection. But the right response is to abandon the search for one introspective metacognitive system, whether dedicated or coopted. We become aware of our states of mind by a variety of methods, which depend on a variety of systems. Consider Hurlburt’s experiments. Carruthers suggests that subjects employ their mindreading systems on their own behavior and circumstances when beeped. But he worries that not all cases can be handled like this, since there is nothing going on at the time of the beep that looks like evidence for one interpretation or another. To save the self-mindreading view, Carruthers is forced to conclude that subjects are basing their interpretation on their immediately prior behavior. This is a rather desperate expedient. People do not seem to report any awareness in these cases of their own behaviour or even, very often, their own states of mind. This supports the view that we put ourselves into a position to assert a state of mind by doing whatever we do to get into that state in the first place (Evans 1982). BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds When Hurlburt (1997) discusses the case of Donald, for instance, he does not show that Donald was interpreting himself at all when he was beeped. Donald noted, for instance, that his son had left the record player on again (Hurlburt 1997, p. 944). He reported attending to a fact about his environment. He was later brought to see the facts he noticed as evidence that he harbored unacknowledged anger toward his son. Donald went over his own transcript after beeping and interpreted his behaviour as he might anyone else’s. This case helps Carruthers in one way, but it also shows something he misses about the beep cases. Donald did not report anything about his own mind or engage in any self-interpretation when he was beeped. Many of Hurlburt’s subjects do not, when beeped, report anything that looks like introspection or any other self-examination. They report thinking about the world, not about themselves. Carruthers mentions a subject who is wondering what her friend will be driving later; this is not a thought about one’s own state of mind at all, and it is not clear why we need to call this interpretation. The subject is not self-interpreting. She is in a first-order state of wondering about the world, and being in that state is what lets her express it; she knows what she thinks by looking at the world, not by treating her deliberations as evidence for self-mindreading. The assumption that ordinary deliberation must be accompanied by mindreading for one to report it is unnecessary and it gets Carruthers into trouble with those cases where subjects lack interpretative evidence from their own behaviour. Subjects report what they are thinking about, and often it is not themselves. If they were really engaged in self-mindreading, you would expect them to talk explicitly about their own beliefs and desires. But they often don’t, which suggests that we should understand them as doing something other than either self-monitoring or self-interpretation. The absence of evidence for interpretation that Carruthers frets about is real, but it doesn’t support the view he opposes. Rather, it supports the view that that we often know what we think by thinking about the world and not about ourselves. This supports the picture of self-attribution of belief that we find in Evans (1982, pp. 223– 26), in which it is often just a matter of making up one’s mind. Carruthers acknowledges that in cases of settled belief we can access our beliefs through memory. He reads Evans as showing that metacognitive access can arise through turning our mindreading capacities on our memory reports. But that is needlessly baroque. The simplest theory is that belief self-attributions are often just episodes of remembering. I can assert a belief that p via the same procedure that would I go through in order to assert that p. This might be the result of working out what I believe via self-mindreading. But in other cases, when I state my belief that p, I am just remembering that p is true. Interpretative evidence is not needed. We do not have to assume that any interpretation is going on at all. In other cases, I put myself in a position to assert a belief by wondering if it is true. This is the way to handle the case of the woman who wonders what car she will go home in; we do not need to think of her as interpreting herself at all. Rather, the beeper leads her to say where her attention is focused, and it is focused on the world, so that is what she talks about. And attending to the world is not introspection or self-interpretation, even if it lets you say what you are thinking about. Carruthers remains in needless thrall to the idea that metacognition needs a device that is directed at the mind. But when you self-attribute a propositional attitude, you are often not using an inward glance but an outward one: you are thinking about the world. Evans is concerned with this wider capacity to figure things out. When he talks of putting oneself in a position to report a belief, he is thinking of our abilities to deliberate about the world. Carruthers has isolated one way in which we may think about objects in the world – that is, we may treat them as things with minds, and we may look at our own behavior

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in that light too. But there is no reason to suppose that all our selfattributions come from self-mindreading. Introspection does not rely on any one system, neither an inner eye nor a mindreading device; it depends on all the ways one might think of states of affairs as believable or desirable. We can know our thoughts by looking at the world.

Global broadcasting and self-interpretation doi:10.1017/S0140525X09000752 David Pereplyotchik Department of Philosophy, Baruch College, City University of New York, New York, NY 10010. [email protected]

Abstract: Carruthers claims that global workspace theory implies that sensory states, unlike propositional attitudes, are introspectible in a non-interpretative fashion. I argue that this claim is false, and defend a strong version of the “mindreading is prior” model of first-person access, according to which the self-ascription of all mental states, both propositional and sensory, is interpretative.

According to the strong version of the “mindreading is prior” model (MPM), all metacognition is interpretative. On the weak version, we have non-interpretative access to both sensory states and propositional attitudes. Carruthers’ version of MPM is a middle-ground position. In accord with the strong version, he insists that first-person awareness of propositional attitudes is always self-interpretative and, hence, never “introspective” (in his quasi-pejorative sense). However, he denies that selfattribution of “sensory-imagistic” states is interpretative, claiming that such states are introspectively available as data for the mindreading system. On his view, “the mindreading system can receive as input any sensory or quasi-sensory (e.g., imagistic or somatosensory) state that gets ‘globally broadcast’ to all [cognitive] systems” (sect. 2, para. 3). The set of such states includes “perceptions, visual and auditory imagery (including sentences rehearsed in ‘inner speech’), patterns of attention, and emotional feelings” (sect. 2, para. 6). I argue that Carruthers’ appeal to the distinction between sensory states and propositional attitudes involves an error, and that avoiding this error leads to the collapse of his view into one of the competing versions. The preferable collapse is, I argue, toward the stronger view. The states that Carruthers takes to be introspectively available are supposed to be “sensory-imagistic,” not conceptualized or propositional. But it is not obvious that perceptual judgments satisfy this description. Perceptual judgments are plainly a species of propositional attitude. True, such states may also have qualitative properties, but they are nevertheless constituted by concepts, and have “sentence-sized” intentional contents. Carruthers acknowledges this in discussing how the mindreading faculty would make use of perceptual judgments: Receiving as input a visual representation of a man bending over, for example, [the mind-reading system] should be capable of forming the judgment, “I am seeing a man bending over.” (At least, this should be possible provided the visual state in question has been partially conceptualized by other mental faculties, coming to the mindreading system with the concepts man and bending over already attached). (target article, sect. 2, para. 4, my emphasis)

In explaining why perceptual judgments appear on his list of introspectible states, Carruthers’s appeals to global workspace theory. That perceptual judgments are introspectible is, in his view, “pretty much mandatory once we buy into a global broadcasting architecture” (sect. 2, para. 8). That’s because, in perception, “the initial outputs of the visual system interact with a variety of conceptual systems that deploy and manipulate

Commentary/Carruthers: How we know our own minds perceptual templates, attempting to achieve a ‘best match’ with the incoming data. . . . [T]he result is globally broadcast as part of the perceptual state itself” (sect. 2, para. 10). Although this is certainly plausible, Carruthers neglects the fact that similar grounds are available for the claim that other propositional attitudes are broadcast as well. Indeed, given his concession that global workspace theories allow for the broadcasting of at least one propositional attitude, one wonders why he assumes that such theories would not allow for the broadcasting of all the rest. On the face of it, the claim that all propositional attitudes can be globally broadcast has much going for it. Intentions, for instance, routinely recruit a wide array of cognitive resources, as do the conceptual-intentional aspects of emotions like fear and anger (e.g., that one is being attacked). Why not count these as instances of global broadcasting? Carruthers does not say. Pending further argument, we should assume, pace Carruthers, that global workspace theory does allow for the broadcasting of all propositional attitudes. If so, then whatever we say about first-person access to sensory states, we should say the same about first-person access to propositional attitudes. Do these considerations support the view that the mindreading system has direct, non-interpretative access to all mental states, both propositional and sensory? Not if one also rejects Carruthers’ assumption that globally broadcast states are ipso facto available to the mindreading system in a non-interpretative fashion. Below, I explore grounds for adopting the strong version of MPM, according to which self-attribution is interpretative in the case of all mental states. Interpretation takes place by deploying a propositional attitude that emerges from a background of theoretical commitments. Consequently, the cost of embarking on an interpretative venture is the possibility of partial misconstrual or wholesale error. These characteristics of interpretative activity fit well with Carruthers’ usage of the term “interpretative,” as applied to mechanisms of self-attribution. As Rosenthal (2005) has argued, self-attribution is a matter of tokening potentially erroneous, theoretically loaded propositional attitudes – occurrent higher-order thoughts (HOTs). On this view, confabulation and error occur even with regard to sensory states. Dental fear, for instance, is a phenomenon in which dental patients under the drill mistake fear, anxiety, and a sensation of vibration for pain in a fully anaesthetized or nerveless tooth – a compelling demonstration that HOTs need not be veridical. Nevertheless, judged on independent grounds, self-attributions of sensory states are often relatively accurate. Doubtless, this consideration compels theorists to posit a reliable monitoring mechanism, such as Carruthers’ mindreading system. But, as Rosenthal points out, simply positing such a mechanism amounts to no more than stipulating a solution to the problem of explaining the frequent accuracy of HOTs about sensory states. An explanatory account of the mechanism’s accuracy is not provided. Extending Sellars’s (1956/1997) treatment, Rosenthal argues that HOTs concerning sensory states arise as a result of a creature’s reflection on cases in which its perceptual judgments are mistaken. The creature formulates a rudimentary theory, in effect positing qualitative sensory states as the causes of nonveridical perceptual judgments. Against the background of such a theory, the creature is disposed, for instance, to construe itself as having a sensation of red when perceiving a red object. Carruthers gives no grounds for rejecting this alternative and appealing picture. Global broadcast theory does not, by itself, settle the issue, for it is consistent with the claim that the mindreading system relies on a tacit theory in interpretatively self-ascribing sensory states. Nor does the data from autistic children disconfirm Rosenthal’s view, which allows that even nonlinguistic, cognitively unsophisticated creatures may come to have

HOTs concerning their sensory states. All that is required is that such creatures take note of their perceptual errors and account for them. ACKNOWLEDGMENT I am grateful to David Rosenthal for helpful comments on an earlier draft.

Introspection and interpretation: Dichotomy or continuum? doi:10.1017/S0140525X09000764 Richard E. Pettya and Pablo Brin˜olb a

Department of Psychology, Ohio State University, Columbus, OH 43210; Departamento de Psicologia Social, Universidad Autonoma de Madrid, 28049 Madrid, Spain. [email protected] www.psy.ohio-state.edu/petty [email protected] www.psy.ohio-state.edu/gap/Pablo/pablo.htm

b

Abstract: Judgments vary in the extent to which they are based on interpretation versus relatively direct access to mental contents. That is, a judgment might require a trivial amount of interpretation (e.g., translating one’s immediately accessible “inner speech”) or a rather substantial amount of confabulation. Recognizing this continuum of interpretation underlying judgment could be more fruitful than debating a categorical introspection versus interpretation distinction.

Some prior authors have noted that people have no unique access to why they believe what they believe (e.g., Nisbett & Wilson 1977). Others have gone a step further and postulated that people do not know their own attitudes (e.g., I like ice-cream) but must construct them when needed from other available information that they either retrieve from memory (e.g., ice cream tastes good) or extract from the immediate context (e.g., Schwarz & Bohner 2000). Carruthers takes this “constructivist” position to the ultimate extreme by arguing that people have no direct access to any attitudes or relevant beliefs. According to this view, introspection does not exist, and is merely an illusion. Furthermore, he provides many examples where people either clearly or plausibly are confabulating when they express what they believe. In his view, at best individuals only know what they feel and perceive, not what they think. In our view, it is not clear why an intelligent organism would have evolved to have direct access to its feelings and perceptions but not its cognitions. Nevertheless, Carruthers has an important point. Whenever someone expresses a belief or has a thought, some degree of interpretation likely is involved, if only to understand the meaning of the “inner speech” in which the thought is expressed. Thus, if a person has a positive reaction to some stimulus (ice-cream), this can be translated into “it’s good” (Fazio 1985). Or even if the word “good” immediately springs to mind, the meaning of the word must be understood by the self if an internal thought, or by others if expressed. However, this very minimal form of “interpretation” is very different from the kind of interpretation involved in most of the examples of confabulation provided by Carruthers. Indeed, we argue that it may not be wise to think of introspection and interpretation as dichotomous categories in which to place any given judgment. Rather, there are various degrees of interpretation. At the low end of the interpretation continuum, judgments are introspection-like in that they involve at most some trivial interpretation. At the other end of the continuum, the judgment is totally confabulated from external sources also available to outside observers. Although dichotomous categories can be useful in understanding some phenomena, as illustrated by the distinction between primary and secondary (meta-) cognition, we believe that it is not conducive to understanding human information processing BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds to simply lump all judgments into the same overarching “interpretation” category and stop there. This is because putting all judgments into the same category might suggest that there are no meaningful differences within the catetory. In contrast to lumping all judgments together into one interpretation category, we espouse a continuum view in which people express beliefs based on very little interpretation in some cases but based on extensive confabulation in others. We further argue that differences in the degree of interpretation are meaningful. Previous research on psychological elaboration provides one instance of the usefulness of the continuum view. The term “elaboration” is used in social psychology to describe that people add something of their own to the specific information provided, for example, in a persuasive communication. In the domain of social judgment, variations in elaboration are consequential. For example, when people are relatively unmotivated or unable to think, they are more likely to rely on immediately accessible information that originates either internally (one’s attitude) or externally (e.g., the attractiveness of the message source). When people are more motivated and able to think, then these initial reactions and the judgments that follow from them can be overridden by more complete interpretative analyses. Furthermore, judgments based on high levels of elaboration are more consequential than those based on low levels (Petty & Cacioppo 1986). Viewing interpretation as a continuum has a number of implications. Most obviously, it means that interpretation can go from zero (i.e., introspection) to extensive. More interestingly, the continuum view suggests that the point on the continuum that corresponds to minimal or trivial interpretation has more in common with zero interpretation than it does with extensive interpretation. One can draw an analogy to a distinction that attitude theorists used to favor between attitude formation versus attitude change. Different mechanisms of influence were thought to be operative depending on whether a person had an existing attitude or did not (a categorical view). Today, it is more common to think of attitudes as falling along a continuum such that they can vary in how accessible they are or upon how much knowledge they are based. An attitude formation situation would be present when a person has no prior attitude. But, a continuum approach to attitudes suggests that a person who has an attitude that is difficult to bring to mind and based on little information (Person B) has more in common with an individual who has no attitude (Person A) than a person who has an attitude that comes to mind spontaneously and is based on much knowledge (Person C). That is, the first two individuals – A and B – are more similar to each other in psychologically relevant ways than they are to C, despite the fact that a dichotomous approach places Person B in a different category from A and in the same category as C. So too is it the case that a judgment based on minimal interpretation (B) is closer to a judgment based on no interpretation (A) than it is to a judgment based on extensive interpretation (C; see Fig. 1). In sum, we conclude that an all-or-none frame regarding the existence of introspection may not be the best way to make the most progress in understanding social judgment. Instead, drawing from the literature on elaboration and attitude strength, we suggest that it might be more fruitful to approach

Figure 1 (Petty & Brin˜ol). Continuum of extent of interpretation underlying judgment

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interpretation as a continuum where the low end is anchored at introspection. That is, sometimes interpretation can be quite minimal as when people recall their birth-date or liking of a favorite book. At other times, interpretation can be quite extensive, such as when there is either nothing relevant to recall or one’s interpretation totally overwhelms any mental content introspected.

Overlooking metacognitive experience doi:10.1017/S0140525X09000776 Joe¨lle Proust Department of Cognitive Studies, Ecole Normale Supe´rieure, and Institut JeanNicod, EHESS and ENS, 75005 Paris, France. [email protected] http://joelleproust.hautetfort.com

Abstract: Peter Carruthers correctly claims that metacognition in humans may involve self-directed interpretations (i.e., may use the conceptual interpretative resources of mindreading). He fails to show, however, that metacognition cannot rely exclusively on subjective experience. Focusing on self-directed mindreading can only bypass evolutionary considerations and obscure important functional differences.

Carruthers’ main goal is to show that metacognition is a form of self-directed interpretation, akin to other-directed mindreading. Introspection, he claims, defined as “any reliable method for forming beliefs about one’s own mental states that is not selfinterpretative and that differs in kind from the ways in which we form beliefs about the mental states of other people” (sect. 1.4, para. 3, emphasis in the original), is not needed to have access to one’s own mental attitudes. One can agree with the author that metacognition in humans may involve self-directed interpretations (i.e., may use the conceptual interpretative resources of mindreading), without accepting the stronger claim that metacognition can never be based on “introspection.” In cognitive science, “metacognition” refers to the capacity through which a subject can evaluate the feasibility or completion of a given mental goal (such as learning a maze, or discriminating a signal) in a given episode (Koriat et al. 2006). In Carruthers’ use, however, metacognition refers to first-person metarepresentation of one’s own mental states; as a result, the theoretical possibility that metacognition might operate in a different representational format cannot be raised (Proust, in press b). Revising the meaning of a functional term such as “metacognition” is a bold strategy. It generally seems more adequate to leave it an open empirical matter whether a capacity of type A (reading one’s own mind) or type B (evaluating one’s cognitive dispositions) is engaged in a particular task. A revision is deemed necessary, according to Carruthers, because “B” capacities in fact always involve self-directed mindreading; therefore apparent contrary cases (self-evaluation in non-mindreading animals) either (1) are simply instances of first-order types of learning, and/or (2) are capacities “too weak to be of any interest” (Carruthers 2008b, p. 62; cf. target article, sects. 5.1 and 9). Two methodological problems, however, hamper the discussion so conceived. First, it is quite plausible that, in human forms of metacognition (as instantiated in speech production, metamemory, etc.), judgments of self-attribution redescribe elements of metacognitive experience. Metacognitive feelings might, on this view, represent subjective uncertainty and guide noetic decision-making, without needing to involve a conceptual interpretative process. What needs to be discussed, in order to establish the superiority of model 4, is whether or not subjects can rely on dedicated feelings alone to monitor their ongoing cognitive activity. A second, related problem is that Carruthers’ discussion conflates two domains of self-control, namely, the control of one’s

Commentary/Carruthers: How we know our own minds physical actions through perceptual feedback and the control of one’s mental actions through metacognitive feedback (see sects. 5.1 and 9). Meta-action, however, is only functionally similar to metacognition when a metarepresentational reading is imposed on both, in spite of their different evolutionary profiles (Metcalfe & Greene 2007; Proust, in press a). If extracting, from a given task context, an evaluation of the mental resources available to complete the task were just another case of first-order action control, then one might agree that B-metacognition is nothing other than executive function. But metacognitive and executive abilities can be dissociated in schizophrenia (Koren et al. 2006). Mental action control is thus distinct both from executive memory as usually understood and from physical action control. These methodological problems strongly bias the discussion against models 1 and 3. Here are three examples. 1. Our metacognitive interventions don’t require introspection; they have no direct impact on cognitive processing (sect. 5.1). From a B-sense viewpoint, prediction and evaluation of one’s mental states and events presuppose appreciating one’s subjective uncertainty regarding correction, adequacy, and so on, of first-order decisions or judgments; this evaluation does not require that the target states are represented qua mental. For example, a child chooses to perform one memorization task rather than another by relying not on what she knows about memory, as the author claims, but on the feeling she has that one task is easier than another (Koriat 2000; Proust 2007). The impact on decision is quite direct, and it is independent of mindreading. 2. A combination of first-order attitudes is sufficient to explain how animals select the uncertainty key in Smith et al.’s metaperceptual paradigm (sect. 5.2). If this is correct, how can monkeys rationally decide to opt out when no reinforcement of the uncertainty key is offered, and when, in addition, novel test stimuli are used? Why should there be transfer of the degree of belief associated with firstorder items to novel tasks where these items are no longer included? A second rule must apply, as Carruthers (2008b) himself admits: having conflicting impulsions to act or not to act on a given stimulus, the subject becomes uncertain of its ability, say, to categorize. So the decision to act depends, after all, on subjective – not objective – features. Can these subjective features influence behavior only through their being metarepresented ? This is the crucial question that fails to be raised. 3. “Evidence suggests that if mindreading is damaged, then so too will be metacognition” (sect. 10, para. 10). Clinical research on autism and on schizophrenia suggests rather a dissociation of metacognitive and mindreading skills as predicted by model 1 (cf. Bacon et al. 2001; Farrant et al. 1999; Koren et al. 2006). However, its relevance for the present discussion is downplayed as a smart behaviorist effect; introspection in patients with autism is rejected because it is not “metacognitive in the right sort of way” (sect. 10, para. 5). Negative results in meta-action from patients with autism are presented as evidence for impaired metacognition. Such appraisals implicitly appeal to the preferred metarepresentational interpretation of metacognition under discussion. Similarly, rejecting the relevance of metacognitive capacities which are “too weak to be of any interest” presupposes recognizing the superiority of model 4 over models 1 and 3. A fair examination of the contribution of “introspection” in metacognition in models 1 and 3 would require studying the respective roles of control and monitoring in nonhuman and human epistemic decisions, in experimental tasks engaging metaperception, metamemory, and planning (Koriat et al. 2006; Proust, in press a). Focusing on self-directed mindreading can only bypass evolutionary considerations and obscure important functional differences.

ACKNOWLEDGMENTS I am grateful to Dick Carter, Chris Viger, and Anna Loussouarn for their remarks and linguistic help. The preparation of this commentary was supported by the European Science Foundation EUROCORES Programme CNCC, with funds from CNRS and the EC Sixth Framework Programme under Contract no. ERAS-CT-2003–980409.

Guilt by dissociation: Why mindreading may not be prior to metacognition after all doi:10.1017/S0140525X09000788 Philip Robbins Department of Philosophy, University of Missouri – Columbia, Columbia, MO 65211. [email protected] http://philosophy.missouri.edu/people/robbins.html

Abstract: Carruthers argues that there is no developmental or clinical evidence that metacognition is dissociable from mindreading, and hence there is no reason to think that metacognition is prior to mindreading. A closer look at the evidence, however, reveals that these conclusions are premature at best.

In psychology, evidence of dissociation comes in one of two forms: synchronic or diachronic. Two capacities are synchronically dissociable if there are adults in whom the first capacity is defective and the second capacity is intact, or vice versa. Evidence for dissociation of this sort comes from studies of clinical mental disorders, such as autism and schizophrenia. Two capacities are diachronically dissociable if the first capacity emerges before the second capacity does, or vice versa. Evidence for dissociation of this sort comes from studies of normally developing children. A central issue in the target article is the dissociability – or lack thereof – of metacognition (first-person metarepresentation) from mindreading (third-person metarepresentation). After waving the issue of diachronic dissociation mostly to one side (sect. 4), Carruthers argues that there is no good evidence of synchronic dissociation (sects. 9 and 10). With respect to the first point, his dismissal of the developmental evidence is premature; on the second point, he makes a slightly better case, but the evidence isn’t all in – so the jury is still out. Or so I argue. Let’s start with the case of synchronic dissociation and the view from developmental psychology. Carruthers opens his discussion here by citing a large-scale meta-analysis that found no evidence of developmental self-other asymmetries on metarepresentation tasks (Wellman et al. 2001). This is grist for the “mindreading is prior” mill but only up to a point. That’s because the meta-analysis by Wellman et al. was exclusively concerned with studies using standard false-belief tasks and no other measures of metarepresentational capacity. And once we take into account the results of studies using such nonstandard measures, the idea that metacognition is developmentally prior to mindreading becomes more plausible (Nichols & Stich 2003; see also Robbins 2006). A key piece of evidence here comes from a study by Wimmer et al. (1988) using a version of the “seeing leads to knowing” paradigm. (Curiously, Carruthers never cites this study, though he does cite a later study of children with autism that employed much the same paradigm, albeit with different – and for his purposes, more favorable – results; see Kazak et al. 1997.) In this study, done with normally developing 3-year-olds, subjects were divided into two groups. In the first group, the children were instructed to look inside a box; in the second group, visual access to the interior of the box was denied. Subjects in both groups were then asked whether or not they knew what was in the box, and most of the children in each group gave the correct answer: those in the first group said yes, those in BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds the second group said no. Subsequently, children in both groups observed another person either looking inside of the box or not looking into it. They were then asked whether or not the person they had observed knew what was inside the box. The answers they returned were surprising (especially for anyone familiar with the literature on egocentric biases). For example, 14 children represented their own epistemic state correctly while misrepresenting the epistemic state of the other person, whereas only 2 children displayed the opposite pattern of responses (i.e., correct for other, incorrect for self) – a highly significant contrast (p , .01). In short, results from the Wimmer et al. study suggest that young children are better at reporting their own knowledge state than the knowledge state of others with whom they knowingly share access to the relevant information. Because knowledge is a propositional attitude par excellence, this looks like good support for the synchronic dissociability of metacognition from mindreading in precisely the domain of interest, namely, the metarepresentation of propositional attitudes. Let’s turn now to the issue of synchronic dissociation. Carruthers points out that passivity symptoms in schizophrenia, such as thought insertion and delusions of control, need not be (and probably are not) due to a breakdown in metacognition, and hence that studies of passivity-symptomatic schizophrenia – a condition in which mindreading appears to be relatively intact – do not support the idea that metacognition is defective in this condition. But this is a double-edged sword. For if the “mindreading is prior” view is correct, then metacognition should be impaired in those subtypes of schizophrenia in which mindreading is defective. Passivity-symptomatic schizophrenia is unrepresentative in this respect, in that patients with this subtype of the disorder tend to perform normally on standard first- and second-order false-belief tasks (Corcoran 2000). By contrast, individuals in the paranoid subtype of schizophrenia perform poorly on a wide range of mindreading tasks, including tasks involving the attribution of intentions and the understanding of jokes, hints, and conversational implicatures (Bru¨ne 2005). Indeed, the defining symptoms of paranoid schizophrenia include persecutory delusions and delusions of self-reference, both of which involve misattributing to other people intentions toward oneself. Exactly what explains this tendency is a matter of controversy, but its characterization in terms of a deficit in the mindreading system is not (Blackwood et al. 2001). Hence, if the “mindreading is prior” view is right, then we should expect to find impaired metacognition in these patients, especially in the domain of intention attribution; otherwise, we should conclude that metacognition and mindreading are dissociable after all. As things stand, however, there appears to be no credible evidence to this effect. There are at least two possible explanations for this fact, only one of which is immediately damaging to Carruthers’ position. The first possibility is that evidence of metacognitive impairments in paranoid schizophrenia is lacking because metacognition is spared in this disorder. That would be bad news for the “mindreading is prior” view. A second possibility, however, is that evidence of this sort is lacking simply because the requisite empirical studies have not been done yet. This second possibility seems at least as likely as the first, especially as far as the metacognition of propositional attitudes like intention is concerned. Schizophrenia researchers have paid more attention to the metacognition of emotions, that is, nonattitudinal mental states. But here the news isn’t good for Carruthers either, for it appears that third-person deficits, such as difficulties with face-based emotion recognition, need not be accompanied by first-person deficits, such as difficulties with recognizing one’s own emotions (Brunet-Gouet & Decety 2006). It is entirely possible, then, that future studies of the metacognition of intention in schizophrenia will point toward a similar dissociation. And in that case, proponents of the “mindreading is prior” view will have some explaining to do.

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Social-affective origins of mindreading and metacognition doi:10.1017/S0140525X0900079X Philippe Rochat Department of Psychology, Emory University, Atlanta, GA 30322. [email protected]

Abstract: The engineer’s look at how the mind works omits a central piece of the puzzle. It ignores the dynamic of motivations and the social context in which mindreading and metacognition evolved and developed in the first place. Mindreading and metacognition derive from a primacy of affective mindreading and meta-affectivity (e.g., secondary emotions such as shame or pride), both co-emerging in early development.

William James in his 1872 publication entitled “Are We Automata?” makes the point that “a succession of feelings is not one and the same thing with a feeling of succession, but a wholly different thing” (James 1872, p. 5 ). James insists that: “The latter feeling requires a self-transcendency of each item, so that each not only is in relation, but knows its relation, to the other.” James concludes that this “self-transcendency of data” constitutes what consciousness is, in his own words “what constitutes the conscious form” (p. 6 ). It is also what makes us nonreducible to automata. If we agree with James that we are more than machines, an engineer look at self-consciousness (e.g., metacognition and introspection) and the consciousness of others (mindreading and theories of mind) is insufficient and does not capture the essence of the phenomena. At the core of these issues, there is more than the rational attribution of mental states to the self or to others (theories of mind). There are also feelings and emotions, what make us sentient and incommensurate to machines. In the social domain, “selftranscendency” corresponds first and foremost to the feeling of our relation to others, not just to a propositional attitude turned toward others or onto the self (metacognition). Children in their social-affective development tell us that upstream to mindreading and metacognizing, there is the affective urge to see the self as seen and evaluated by others – the deepseated affective propensity to have “others in mind” (Rochat 2009). The origins of a tendency to attribute attitudes (i.e., adopt a propositional attitude stance) toward others as well as toward the self are rooted in the basic propensity to know where we stand emotionally in relation to others: whether we feel affective distance or closeness, a sense of affiliation and approval, or on the contrary a sense of disapproval and rejection from others. There is now much evidence showing that long before children display signs of explicit “theorizing” about the mental states of others, as well as their own, whether for example they have an appreciation of their own knowledge or ignorance, they have already developed astute implicit intuitions of whether they can more or less “trust” someone, or “trust” themselves in what they believe and know. By 2 months, infants expect someone to behave in a certain way in certain social contexts. They show distress and emotional withdrawal toward an adult who abruptly and for no apparent reasons adopts a frozen facial expression. They also show resistance in recovering positive affects following a still-face episode (Tronick 2005; Tronick et al. 1978). Infants are indeed highly sensitive to the attention received from others, constantly gauging how much others are engaged toward them (Reddy 2003). They are also quick to develop a preference for the interactive contingency style of their mother, generalizing such preference when encountering strangers (Bigelow & Rochat 2006). These facts demonstrate that at the roots of mindreading and metacognition, there is a complex pragmatics of intentional communicative exchanges that is not just rational, but ontologically affective and emotional. The first nonverbal language of children is well documented, expressed from at least 2 months of age in

Commentary/Carruthers: How we know our own minds the context of face-to-face interactions, made of mutual gazes, precise contingency of exchanges and turn taking, with compulsive “motherese” and other affective markers from adults. The syntax of this early nonverbal grammar (e.g., when I smile, the other should not scream or cry) and the semantics that children derive from it (e.g., if I smile and the other looks away, something is wrong) is primarily emotional and affective. These rich and reciprocal communicative exchanges quickly determine a proto-rationality that is expressed in the epistemic as well as affective trust that children from the earliest age place in others, as well as eventually into themselves. Topal et al. (2008) provide further evidence of the deep affective and emotional roots of mindreading and metacognition. These authors show that the famous Piagetian stage 4A-not-B error of object permanence found in 10-month-olds and younger infants, depends in part on the presence or absence of subtle communicative and emotional cues (i.e., eye contact, motherese, affective attunement, or social contingency) from the experimenter hiding the object. Infants are inclined to interpret the adult as wanting to teach them something (a hiding game), while having a good, intimate social time of sustained mutual attention. Topal et al. show that 10-month-olds’ perseverative search errors can be induced by pragmatic misinterpretation of an adult’s intentions. By the time children objectify themselves in mirrors passing the famous mark test (Amsterdam 1972), they also begin to express secondary emotions such as embarrassment, shame, or pride (Lewis 1992). Early on, infants demonstrate a complex appreciation about their public appearance, long before they express rational theories of mind and metacognition. Affectively, children first feel how others like or dislike them (affective reading), gauging also how they think they might be liked or disliked by others (meta-affectivity). Both seem to co-emerge in development, necessarily codependent. From a developmental vantage point, affective reading and meta-affectivity are ontologically linked, representing two sides of the same coin. Findings on early social and emotional development demonstrate that there is not much empirical grounding for conceptualizing them as separate processes. In short, let us not forget that we are born in need of social attention; and it is, I would suggest, in this primary motivational context that early social expectations develop. These expectations, co-constructed in the history of interactions with others, become by 4 to 5 years of age explicit “theories” about self and others’ propositional attitudes. This primary motivational context represents an invaluable source of information that cannot be dismissed or overlooked, particularly if one attempts at capturing “how we know our own mind.” Dealing with the question outside of any motivational context, as done by Carruthers in the target article, is a disembodied, abstract exercise. By analogy, it is like playing chess or moving armies on a map instead of being physically and emotionally engaged on the battlefield. The battlefield is riskier, but closer to what it is all about.

Metacognition, mindreading, and insight in schizophrenia doi:10.1017/S0140525X09000806 Ben Wiffen and Anthony David Department of Psychological Medicine and Psychiatry, Institute of Psychiatry, King’s College London, London SE5 8AF, United Kingdom. [email protected] [email protected] www.iop.kcl.ac.uk/staff/?go¼10055

Abstract: Mindreading in schizophrenia has been shown to be impaired in a multitude of studies. Furthermore, there is increasing evidence to

suggest that metacognition is damaged as well. Lack of insight, or the inability to recognise one’s own disorder, is an example of such a failure. We suggest that mindreading and metacognition are linked, but separable.

Here we review the evidence for deficits in mindreading (or Theory of Mind [ToM]) tasks in schizophrenia and look at some work on true metacognitive tasks in schizophrenia, in which schizophrenia patients also display deficits. We argue that what psychiatrists refer to as a “lack of insight” is an example of a failure to make judgements of themselves that they do make of others. Since the publication of Chris Frith’s seminal Cognitive Neuropsychology of Schizophrenia (Frith 1992), research investigating ToM in schizophrenia has been widespread. A deficit in mindreading is clearly demonstrable in schizophrenia, but whether this is specific and causally related to certain symptoms or merely further evidence of generalised cognitive impairment found in virtually all patients with the disorder remains unclear (Harrington et al. 2005). Sprong et al.’s (2007) meta-analysis showed a large and statistically significant impairment on ToM tasks (Cohen’s d ¼ 1.26) across all schizophrenia sub-types and tasks, larger than studies restricted to “paranoid” patients, and claimed that there was support for mindreading impairment as a trait marker of schizophrenia. Studies apparently supporting Carruthers’ claim that mindreading is intact in “passivity” schizophrenia have had small sample sizes in their group (e.g., 7 in Corcoran et al. 1995; 1 in Pickup & Frith 2001). Furthermore, these studies may be confounded by other common symptoms; that is, someone with passivity symptoms may well exhibit paranoid or negative symptoms. So claiming that passivity symptom patients “perform normally when tested on batteries of mindreading tasks” (target article, sect. 9, para. 1) is overly bold. Indeed, recent work comparing controls with “passivity” patients showed different neural activity in ToM tasks, even though responses were broadly similar (Bru¨ne et al. 2008). This suggests that a different cognitive strategy is used by such patients in mindreading tasks. We question whether passivity “experiences” are ever pure metacognitive failures. Although classical phenomenologists like Kurt Schneider attempted to separate what in today’s parlance we would call the experience from the attribution, the tiny corpus of examples in the literature invariably link the experience with a “psychotic” explanation – that a person or organisation is doing the movement or speaking through me using some dastardly device or technology. When the experience alone is reported, we can’t be sure that there is no abnormal attribution. Our clinical impression is that the two are inextricably linked – at least in people presenting to clinical services. We believe this is the essence of Frith’s model: that delusions, such as those of alien control, may build on passivity experiences but are the result of ToM reasoning which has gone awry. One might even say that psychosis is the result of excessive attribution – usually of malign intent – to people or things. People with psychosis have a theory of mind – it is just the wrong theory. We would also argue that there is evidence for impairment in metacognition in schizophrenia in a truer sense than the “weak” variety described. The Beck Cognitive Insight Scale (Beck et al. 2004) shows differences between schizophrenia patients and controls in their agreement with statements on subscales assessing self-reflection and self-certainty (Warman et al. 2007). Furthermore, some studies show considerable deficits in psychotic patients’ assessment of their own poor neurocognitive function (Medalia & Thysen 2008), although accurate assessment of such deficits may coexist with lack of awareness of the implausibility of beliefs suggesting fractionation of metacognitive awareness (Gilleen et al., in press). However, metacognitive failure is not for a want of trying. Bedford and David (2008) showed that patients actually place time and value in self-reflection, but just struggle to do it accurately. Additionally, patients’ metacognitive performance on the Wisconsin Card Sort Task (i.e., the BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Carruthers: How we know our own minds confidence they place in the decisions they make) predicts disrupted insight in schizophrenia, suggesting a link between a failure of metacognition in a narrow sense, to a broader one (Koren et al. 2004). What psychiatrists refer to as a lack of insight (the ability to judge oneself to have a mental disorder and to “relabel” abnormal experiences as related to such a disorder) is very common in schizophrenia and has even been conceptualised as a core aspect of the condition (David 2004). It seems to be an example of a failure of metacognition. Patients fail to make accurate judgements about themselves – in relation to their thoughts and experiences – and instead choose often bizarre and impossible explanations. However, it does not seem to be the case that patients who lack insight into their own illness are completely unable to make accurate judgements about others. Their mindreading abilities remain intact to the extent that they are relatively unimpaired in their attributions of “madness” to others. Rockeach’s Three Christs of Ypsilanti (Rockeach 1964) reports the reactions of three patients presenting with broadly the same delusion. They all continued to believe in their identity whilst rejecting the others as mad: “Truth is truth, no matter if only one person speaks it” (p. 150), claims one, steadfastly defending his identity. Indeed, empirical studies using vignettes have shown no differences between patients and controls in attribution of illness. Startup (1997) showed that patients could distinguish between normal and psychotic thoughts, feelings, and behaviours in the vignettes but this showed no correlation with the patients’ insight into their own condition. McEvoy et al. (1993) found that patients correctly labelled characters with psychotic symptoms in vignettes as suffering from mental illness, but failed to note their own similarity to the character – although it was obvious to the patients’ doctor. Further studies assessing the relationship of mindreading and metacognition as related to their applications in clinical insight are required: a simple correlation between the two functions seems unlikely (Pousa et al. 2008). We note that metacognition can be enhanced by presenting the self as if another person – for example, by replaying a video of himself when ill to the patient (Davidoff et al. 1998). A conceptually similar approach taken to neurological patients with anosognosia for hemiplegia seems to yield increased awareness (Marcel et al. 2004). These observations are not decisive in dissociating mind reading from metacognition but suggest the following hypotheses: That self-awareness may at least make use of cognitive mechanisms which afford awareness of others’ intentions and beliefs; that this is not necessarily automatic and may be prevented, presumably by attentional mechanisms or strategic failures (not looking), but can be overcome by adopting a third-person perspective to the self. Thus, some practical benefit may accrue from considering metacognition and ToM as linked but separable.

Metacognition may be more impaired than mindreading in autism doi:10.1017/S0140525X09000818 David M. Williams,a Sophie E. Lind,b and Francesca Happe´c a Institute of Child Health, University College London, London, WC1 N 1EH, United Kingdom; bDepartment of Psychology, City University, London, EC1V 0HB, United Kingdom; cSocial, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, London, SE5 8AF, United Kingdom. [email protected] [email protected] [email protected]

Abstract: This commentary focuses on evidence from autism concerning the relation between metacognition and mindreading. We support

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Carruthers’ rejection of models 1 (independent systems) and 3 (metacognition before mindreading), and provide evidence to strengthen his critique. However, we also present evidence from autism that we believe supports model 2 (one mechanism, two modes of access) over model 4 (mindreading is prior). Impaired metacognition in autism. We agree with Carruthers’ claim that both mindreading and metacognition are impaired in autism, and that this speaks against models 1 and 3. However, we wish to provide more decisive evidence for impaired metacognition in autism, given that the evidence cited by Carruthers is problematic. For example, contrary to Carruthers’ suggestion (sect. 10, para. 10), Kazak et al. (1997) did not find statistically significant differences between participants with and without autism in either mindreading or metacognition (see Kazak et al., p.1005). Clearer evidence for metacognitive deficits among children with autism emerges from Williams and Happe´ (in press a), who assessed awareness of own intentions. Compared to ageand ability-matched comparison children, children with autism were significantly less likely to (a) correctly report their reflex movements as unintentional, and (b) correctly recognise their own mistaken actions (drawing a picture different to that intended, through experimenter manipulation) as unintended. The performance of children with autism on these measures was significantly associated with performance on traditional false belief tasks, independent of verbal ability. These findings suggest that children with autism have a limited ability to represent their own intentions and that these difficulties are fundamentally associated with established difficulties in representing others’ mental states (in this case, false beliefs). These findings provide robust evidence against both models 1 and 3, but do not differentiate models 2 and 4. Impaired episodic memory in autism implies impaired metacognition. Several researchers (e.g., McGeer 2004;

Nichols & Stich 2003; Raffman 1999) have claimed that the autobiographical reports of individuals with autism show that metacognition is intact in autism (supporting models 1 and 3). However, none of these authors, nor Carruthers in his target article, distinguishes between semantic and episodic memory. Episodic memory is associated with consciously remembering personally experienced events, whereas semantic memory is concerned with factual information. Caution must be exercised when attributing to people with autism memory processes of the episodic kind. I may know that a particular event has happened to me in the past, and hence report details of the event quite accurately, without actually remembering the event. Only this latter kind of “remembering” is thought to rely on metacognition (e.g., Perner 2000). Contrary to models 1 and 3, it may be that people with autism do not engage the same metacognitive processes as typical individuals do when reporting events from their past (Lind & Bowler 2008). Lind and Bowler (under revised review; see also Lind 2008) found that children with autism (n ¼ 53) were as able as age- and ability-matched comparison participants to distinguish events that had occurred from events that had not (whether or not a picture had been picked up and named earlier in the test session). However, participants with autism were significantly impaired at remembering the source of such events (i.e., who – themselves or the experimenter – had picked up the picture and named it). That is, the participants knew that X event had occurred, but they had difficulty in remembering the spatio-temporal context in which X occurred. Therefore, the metacognitive status of the personal memories reported by individuals with autism might justifiably be questioned, and cannot be taken as support for models 1 and 3. A case of impaired metacognition but intact mindreading? Distinguishing model 2 from model 4. The autism research

cited earlier supports equally models 2 and 4. As far as we can tell, once Carruthers concedes (sect. 3) that the mindreading system has different information available to it for the cases of self and others, respectively, the only way in which model 2 differs theoretically from model 4 is with respect to the role of

Commentary/Carruthers: How we know our own minds introspection of own propositional attitudes. Other commentators will no doubt debate whether it is possible to introspect our propositional attitudes directly. Here we concentrate on Carruthers’ suggestion that different predictions emerge from models 2 and 4. Williams and Happe´ (in press b; see also Williams 2008) addressed a potential confound within the classic “Smarties” unexpected contents task (Hogrefe et al. 1986), the task used most widely to assess awareness of false beliefs in self and others. In the Smarties task, participants state what they (falsely) believe is inside a Smarties box before they are asked the critical false-belief test questions. As such, it may be possible to answer the Self test question (‘What did you think was inside the box, before you looked?’) simply by remembering what one said and not necessarily what one believed. Although parallel performance across the Self and Other test questions of the task is usually observed among children with autism (e.g., Fisher et al. 2005), this potential confound may have led to an over-estimation of the ability of children with autism to reflect on their own false beliefs. To test this possibility, we rigged a situation in which participants were asked by the experimenter (who feigned mild injury) to “get me a plaster,” and had ready access to three different boxes: a plasters box, a crisps tube, and a sweets box. By opening the plasters box (which actually contained candles), participants demonstrated their (false) belief that the box contained plasters. However, having never verbalised their belief, success of the Self test question of this task (“What did you think was in the box, before you looked?”) must reflect participants’ recall of their false belief rather than of any prior statement. We found that participants with autism were unique in finding the Self test question significantly harder than the Other-person question (“What will x think is inside the box, before s/he looks inside?”). Almost a quarter (21%) of our sample of 52 children with autism failed the Self question but passed the Other-person question. In contrast, less than 4% of participants with autism showed the opposite pattern of performance. Parallel performance across the test questions was observed in age- and ability-matched comparison participants, and in typically developing 3- to 5-yearolds. These results seem to show the kind of dissociation between mindreading and metacognition that Carruthers suggests would follow from model 2, but not from model 4. In conclusion, we believe the data so far from autism support model 2 over model 4. But we are grateful for Carruthers’ bold and exciting analysis, which helps to shape a new research agenda to answer the fascinating question: How well can people with autism “read their own minds”?

Making a case for introspection doi:10.1017/S0140525X0900082X Alexandra Zinck,a Sanne Lodahl,b and Chris D. Frithb,c a LWL-Universita¨tsklinik Bochum der Ruhr-Universita¨t Bochum, Psychiatrie – Psychotherapie –Psychosomatik –Pra¨ventivmedizin, Institut fu¨r Philosophie, Ruhr-Universita¨t Bochum, 44791 Bochum, Germany; bCentre of Functionally Integrative Neuroscience (CFIN), Danish National Research Foundation, and Institute of Philosophy and History of Ideas (IFI), and Faculty of Humanities, Aarhus University, Aarhus University Hospital, 8000 Aarhus, Denmark; c Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, United Kingdom. [email protected] http://www.ruhr-uni-bochum.de/philosophy/staff/zinck/index.html [email protected] www.cfin.au.dk/menu478-en [email protected] http://www.fil.ion.ucl.ac.uk/Frith/

Abstract: Defending first-person introspective access to own mental states, we argue against Carruthers’ claim of mindreading being prior to meta-cognition and for a fundamental difference between how we

understand our own and others’ mental states. We conclude that a model based on one mechanism but involving two different kinds of access for self and other is sufficient and more consistent with the evidence. Making a case for introspection. Comparing four different accounts of the relationship between third-person mindreading (meta-representing mental states of others) and first-person metacognition (meta-representing one’s own mental states), Carruthers concludes that the capacity to mindread is prior to metacognition. According to him, basic mindreading is either turned upon others or turned upon ourselves, the latter constituting metacognition. Mindreading is thus the capacity to interpret the other or the self and therefore does not require introspection. This brings us to the core problem of our critique: Assuming that there is one basic meta-representational mechanism that underlies both understanding the self and other, how can this mechanism be characterized? In what follows, our analysis hinges on the way Carruthers uses the term introspection in relation to basic mindreading. Most accounts of mindreading use introspection to describe a special kind of access that we have to ourselves that is not available for third-person mindreading. Carruthers’ account dispenses with this difference of access and the function of introspection. He gives a negative definition of introspection as “any reliable method for forming beliefs about one’s own mental states that is not self-interpretative and that differs in kind from the ways in which we form beliefs about the mental states of other people” (sect. 1.4, para. 3, emphasis in original). Yet, in his architecture of the mind, there is no place for an introspective capacity constituting an immediate and direct inner perception of a belief. This conclusion results from Carruthers’ extreme caution about the phenomenology that characterizes introspection and his dismissal of it as misleading. This thesis of the unreliability of introspection and the necessity to dismiss it as a mode of access to beliefs is supported by data from confabulation and commissurotomy. However, this does not show that one cannot know one’s beliefs to be true. We don’t necessarily have to concede that beliefs are – or can become – consciously uninterpreted; instead we can assume that there are unconscious belief attitudes that can give rise to a conscious event whose content is a belief. In cases of confabulation, this doesn’t mean, however, that we are not introspecting this event; it simply means that there is a discrepancy with the underlying belief attitudes. So, the data suggests that there are mental processes that we are not conscious of. This is not a principled argument against introspective access to our own mental states that is independent of lengthy interpretation. The scope and quality of selfknowledge is limited, whether it is gained by introspection or by self-interpretation. Commissurotomy patients mistake their beliefs for certain actions, yet they do so also under an account of self-interpretation. Self-knowledge and its acquisition by introspection has certainly been overrated in philosophy, but the limitations are equal for self-interpretation. Aside from this – although he claims that according to his account of mindreading applied to the self, there is “no . . . awareness of one’s own propositional attitudes independently of any perceptually accessible cues that provide a basis for self-interpretation” (sect. 1.4, para. 2) – Carruthers does not completely differentiate introspection and self-interpretation according to his mindreading account and does concede there sometimes seem to be introspective qualities during self-interpretation, such as immediacy and effortlessness (sect. 8, para. 3). So the question still remains of how best to characterize the access we have to ourselves. Contrary to Carruthers, we would like to argue that the immediacy and directness that characterizes BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Carruthers: How we know our own minds introspection is also present when mindreading others and that this is not a conscious interpretational endeavour. Just as with the perception of the outside world, our brain makes “unconscious inferences” (von Helmholtz 1866) when perceiving ourselves. This is the basis for the experience that introspection is direct and immediate. The same immediacy also occurs when perceiving others (Frith 2007). Nevertheless, there is a difference between the way we meta-represent our own and the mental states of others. When thinking of ourselves, there are more data available, that is, visceral and somaesthetic sensations in addition to a richer knowledge of our own past history. Thus, we are dealing with the same mechanism but with two different modes, one for the self and one for the other. This corresponds to Carruthers’ model 2 account. It accordingly also does not matter whether the mechanism evolved first for understanding others or for understanding oneself. We assume both involve the same underlying mechanism of meta-representation that, endowed with additional sources of information, makes up the different modes of access. Another point of criticism against a mindreading is prior account is that the mechanism of mindreading is third-person directed. Thus, when I direct my mindreading capacity upon myself, I should use a third-person stance. Apart from being an interpretative process, this is also an unnecessarily complex and computationally expensive way of accessing the self. It can be argued that the best explanation is to simply accept the immediate first-person data instead of adopting the complex third-person setup. A further argument for introspection as a specific mode of access for the self comes from considering why it might be valuable for survival: (1) we can inform others about our reasons for acting in a certain way; (2) we can gain high-level control of our emotion and our behaviour (e.g., Zelazo 2004). Take, for example, a simple learning process. We can learn associations between stimuli even when the stimuli are presented subliminally (i.e., not available to introspection). However, this learning is slow and gradual. If the stimuli are supraliminal, then insightful learning becomes possible through introspection. At some point subjects notice the contingency and will immediately jump to 100% performance (Pessiglione et al., in press). Contrary to Carruthers, we prefer his model 2 that makes use of one mechanism but involves two different kinds of access: one which is perception-based for interpreting others, and additional introspective access which is available when assessing one’s own mental states. Altogether, model 2 is more consistent and parsimonious. It also makes better predictions for pathologies such as autism and schizophrenia in which both kinds of access are impaired. In sum, this discussion exemplifies that the understanding of how self and other are related is an important topic for research that is generating exciting new empirical and theoretical investigations.

Author’s Response

Relevant evidence is considered, including that deriving from studies of childhood development and other animal species. Also discussed are data suggesting dissociations between metacognitive and mindreading capacities, especially in autism and schizophrenia.

R1. Introduction The target article set out to consider four different accounts of the relationship between our mindreading and metacognitive abilities (“two independent mechanisms,” “one mechanism, two modes of access,” “metacognition is prior,” and “mindreading is prior”). It argued in support of the fourth (“mindreading is prior”) account, according to which metacognitive competence results from us turning our mindreading abilities upon ourselves. The target article considered a wide array of evidence bearing on the choice between the four accounts. This included evidence from childhood development, evidence from the role that metacognitive beliefs play in guiding human cognitive processes and behavior, evidence of confabulation in reports of one’s own attitudes, alleged evidence of direct introspection of attitudes, comparative evidence of metacognitive competence in other species, evidence from autism, and evidence from schizophrenia. The commentaries take up an equally disparate set of topics. Some raise fundamental challenges that need to be confronted at some length, whereas others (as might be expected) are based upon misunderstandings. Table R1 Table R1. No.

Section

Commentaries

R2.

The nature of the mindreading faculty

R3.

The development of mindreading

R4.

The question of developmental priority What is introspection?

Buckner et al.; Friedman & Petrashek; Lurz; Pereplyotchik Anderson & Perlis; Buckner et al.; Hernik et al.; Lewis & Carpendale; Rochat Fernyhough; Mills & Danovitch; Robbins Baars; Murphy; Pereplyotchik; Petty & Brin˜ol; Zinck et al. Fiala & Nichols; Hurlburt; Zinck et al. Anderson & Perlis; Couchman et al.; Kornell et al.; Proust Couchman et al.; Kornell et al.; Proust Buckner et al.; Frankish

R5.

R6. R7.

Evidence for and against introspection What is metacognition?

R8.

Metacognition in animals?

R9.

Mindreading underlies metacognition doi:10.1017/S0140525X09000831 Peter Carruthers Department of Philosophy, University of Maryland, College Park, MD 20742. [email protected] www.philosophy.umd.edu/Faculty/pcarruthers/

Abstract: This response defends the view that human metacognition results from us turning our mindreading capacities upon ourselves, and that our access to our own propositional attitudes is through interpretation rather than introspection.

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Dual processes and judgment R10. The evidence from autism R11. Neuroimaging evidence R12. The evidence from schizophrenia R13. Some friendly suggestions R14. Behaviorism bites back R15. Conclusion

Lombardo et al.; Williams et al. Lombardo et al. Robbins; Wiffen & David Evans; Huebner & Dennett; Langland-Hassan Catania; Lewis & Carpendale

Response/Carruthers: How we know our own minds provides a guide to the structure of the present reply, together with an indication of which commentaries are responded to (in whole or in part) in each section (including the notes attached to that section). R2. The nature of the mindreading faculty In the target article I had hoped that my argument in support of a “mindreading is prior” account of selfknowledge was independent of any specific commitments concerning the character of the mindreading faculty itself, beyond rejection of a simulation-based “metacognition is prior” alternative. I still think that is partly correct. Certainly I can accept that the mindreading faculty is not monolithic, but is actually a cluster of more specialized mechanisms working together in concert, somewhat as Buckner, Shriver, Crowley, & Allen (Buckner et al.) suggest. Indeed, this is what I actually believe, following Baron-Cohen (1995) and Nichols and Stich (2003). But one powerful objection to the proposal that there is no such thing as introspection for attitudes, raised independently by Friedman & Petrashek and by Lurz, has made me realize that the argument cannot be free of all such commitments. I shall first outline the objection, before showing how an independently motivated account of the architecture and mode of operation of the mindreading faculty can accommodate it. The objection is that the mindreading system needs to have access to the agent’s own beliefs in order to do its interpretative work; therefore self-attributing beliefs should be just as trivially easy as self-attributing experiences. Friedman & Petrashek claim, for example, that in order to form the metarepresentational belief that Bill believes that the first-aid box contains bandages, the mindreading system must access the attributor’s own belief that first-aid boxes normally contain bandages. And they go on to stress that the mindreading system’s default is to attribute the subject’s own beliefs to other people, saying that this requires it to have access to those beliefs. Likewise, Lurz imagines a mindreader who observes a conspecific seeing some food. In order to draw any inferences from that fact, Lurz tells us, the mindreading system would have to access such beliefs as that the conspecific has recently eaten, or has shared food with others in the past. And again the moral is that the mindreading system needs to have access to the agent’s own beliefs in order to do its work. In light of these plausible claims, what might motivate one to deny that the mindreading system can access all of the agent’s own beliefs? The answer is that the objectors forget about the frame problem. The idea that any single mental faculty might be conducting searches among all of a subject’s beliefs is extremely problematic. Rather, there are likely to be a whole swarm of different decision-making systems that can conduct local searches of aspects of memory (Carruthers 2006). And a large part of the point of organizing cognition around a global workspace is that queries posted in that space can co-opt the resources of all the different consumer systems in parallel (Shanahan & Baars 2005). If the mindreading system is one of the consumer systems for global broadcast, as the target article assumes, then what we should predict is that it only has access to a limited database of domain-specific

beliefs necessary to perform its computations.1 But if this is so, then the challenge is to explain the datum that any one of one’s beliefs can seemingly get appealed to in the course of mindreading. To meet this challenge, I need to make two closely related distinctions. One is between System 1 mindreading (which is comparatively fast and done “online”) and System 2 mindreading (which is slower, more reflective, and often involves supposition and simulation). This first distinction should need no defense. For reasoning about the minds of other people, like every other domain of reasoning that we know of, should admit of both System 1 and System 2 varieties. The other distinction is between verbally mediated forms of mindreading (such as answering a question about what someone believes) and kinds of mindreading that don’t involve access to linguistic representations. (We can be quite sure that the latter exist, since even severely agrammatic aphasic people can retain their competence in nonverbal mindreading tasks. See Siegal & Varley 2002; Varley 1998.) Consider, first, the fact that people will by default attribute their own beliefs to other people if asked. There is no reason to think that this requires the mindreading faculty to access those beliefs, any more than answering a question about one’s own beliefs requires such access, as I argued in the target article (sect. 2.1). Rather, the executive and language-production systems cooperate (and partly compete) with one another, searching the attributor’s own memory and issuing the result in the form of a metarepresentational verbal report – “I think/he thinks that P” – where the form of the report can be copied from the form of the initial question. The mindreading system has the power to intervene in this process when it possesses a representation of the target’s belief that differs from the subject’s own, but it plays no part in the default attribution process itself. Consistent with this suggestion, Apperly et al. (2007) show that people are significantly slower when responding to a probe about a target’s false belief than they are when responding to a realityprobe. Now consider a reflective, System 2, instance of mindreading (whether verbal or nonverbal). A query about the target’s thoughts, goals, or likely behavior is posted in the global workspace (either in the form of a verbal question, or in an image of oneself in the situation of the target, say). The entire suite of consumer systems gets to work, drawing inferences and reasoning in their normal way, accessing whichever of the subject’s beliefs they normally would, and the results are posted back into the global workspace once more, where they are accessible to the mindreading faculty as input, perhaps issuing in a conclusion or a further query. Here the entire process, collectively, has access to all of the agent’s beliefs; but the mindreading system has access only to whatever gets posted in the global workspace (in addition to its own domain-specific database, of course, which is accessible to it when processing). Finally, consider a case of “on-line” unreflective System 1 mindreading, of the sort that might be engaged in by the infants in the false-belief studies conducted by Onishi and Baillargeon (2005), Southgate et al. (2007), or Surian et al. (2007). Perceptions of the main aspects of the unfolding events are attended to and globally broadcast, thereby being made available to the full range of conceptual BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Carruthers: How we know our own minds systems including mindreading. These systems conceptualize and draw inferences from the input, with the former being fed back and broadcast as part of the perceptual state itself, and with the results of the latter being held briefly in the relevant domain-specific working memory system. (All System 1 reasoning systems will need to possess their own form of working memory, of course, to hold the results of previous computations while the next steps are undertaken. See Carruthers 2006.) Included in these broadcasts, then, will be the information that the target subject sees an object in one box rather than another, for example. And the working memory system that is internal to the mindreading faculty will contain such information as that the target expects the object to be where it was last seen and is ignorant of the fact that it has been moved. When combined with a novel perceptual input (e.g., the target subject returns on the scene after a brief absence), these beliefs enable an expectation to be generated concerning the target’s likely behavior. Notice that on this account no beliefs need to be accessible to the mindreading system beyond those residing in its domain-specific database, with the exception of those that are made perceptually available to it, on the one hand, and those that are immediately past products of its own operations, on the other. This is consistent with the fact that adults as well as children fail to take account of the mental states of other people in their online reasoning once the relevant facts are no longer perceptually salient and sufficient time has elapsed for any record to have been expunged from the mindreading system’s working memory. See Keysar et al. (2003) for a dramatic demonstration of this point. R3. The development of mindreading Just as I had hoped to make the argument of the target article largely independent of assumptions about the nature of the mindreading faculty, so I had hoped to minimize assumptions about the latter’s development. (Of course I do need to assume that development does not begin with firstperson awareness of our own attitudes, in the way that Goldman [2006] suggests.) In particular, I tried to steer clear of the dispute between nativist or “core knowledge” approaches, on the one hand (e.g., Fodor 1992; Leslie et al. 2004) and constructivist or theorizing-theory accounts, on the other (e.g., Gopnik & Melzoff 1997; Wellman 1990). Here, too, my efforts were partly, but by no means entirely, successful, as I now explain. Both Hernik, Fearon, & Fonagy (Hernik et al.) and Rochat emphasize the crucial roles played by emotion and emotional engagement with others in the development of mindreading; and each appears to think that this claim conflicts with some aspect of the target article. But I see no problem with accepting these data. Both nativists and theorizing-theorists can believe in the developmental importance of emotional engagement, but will interpret the sources of that importance differently. Moreover, no evidence is provided that an understanding of one’s own emotions precedes an understanding of the emotions of others in development (which would be problematic for a “mindreading is prior” account). On the contrary, Rochat writes: “From a developmental vantage point, affective reading and meta-affectivity are ontologically 166

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linked, representing two sides of the same coin.” This is, of course, further grist for my mill. Construed as a thesis about the architecture of the mature mind, the “mindreading is prior” account is independent of the debate between nativists and theorizingtheorists about the development of the mindreading system. But Buckner et al. are correct in pointing out that one (though only one) of the arguments that I use in support of the “mindreading is prior” architecture depends upon some or other variety of nativist position (whether this be an innately given body of knowledge, or an innate module, or an innate domain-specific learning mechanism). For I claim that there is a good evolutionary explanation of the emergence of mindreading in highly social creatures such as ourselves, whereas there are no good evolutionary reasons for the emergence of introspection for attitudes (or else those reasons makes predictions that are not borne out by the metacognitive data, either human or comparative). This is supposed to count in favor of a “mindreading is prior” account. And it plainly commits me to some or other version of nativism about the course of mindreading development. Buckner et al. argue, in contrast, that metarepresentational mindreading may be a late exaptation of more primitive capacities, grounded in these together with our linguistic abilities and general-purpose concept-learning and theorizing skills. They think that the only true adaptations in the social-cognitive domain are a swarm of first-order, non-metarepresentational, mechanisms for face recognition, eye-tracking, automated imitation via the mirror neuron system, and so forth. But there are two main problems with this view. One is the rapidly expanding body of evidence of very early metarepresentational competence in infants, embracing false-belief understanding inter alia (Bosco et al. 2006; Onishi & Baillargeon 2005; Onishi et al. 2007; Song & Baillargeon, forthcoming; Song et al., forthcoming; Southgate et al. 2007; Surian et al. 2007). And not all of these studies, it should be stressed, use looking time as a measure of expectation violation. On the contrary, Southgate et al. (2007) use anticipatory looking as their dependent measure, which is much less ambiguous. The other major problem with Buckner et al.’s suggestion is that mindreading is required in order to learn a language in the first place. I don’t deny that syntax may be innate, or acquired through the offices of a dedicated domain-specific learning mechanism. But learning the lexicon requires children to figure out the referential intentions of the speakers around them (Bloom 2002). And this plainly requires metarepresentation. Moreover (and just as this account predicts), we have ample evidence that infants can attribute goals and intentions to others in the first year of life, significantly before they can attribute beliefs and misleading appearances (Csibra et al. 2003; Johnson 2000; Luo & Baillargeon 2005; Woodward 1998). Buckner et al. write admiringly of the work of Gallagher (2001; 2004) in this connection, as do Anderson & Perlis and Lewis & Carpendale. But Gallagher’s work is subject to both of the objections just outlined. Moreover, he goes awry in his critique of the opposing approach, to which he refers with the generic “theory-theory” (intended to cover both nativist and theorizing-theory varieties). In particular, it is simply false that theory-theorists must (or do) assume that mentalizing usually involves the

Response/Carruthers: How we know our own minds adoption of a third-person, detached and observational, perspective on other people. On the contrary, theorytheorists have always emphasized that the primary use of mindreading is in interaction with others (which Gallagher calls “second-personal”). That, after all, is what “Machiavellian intelligence” is all about. And the fact that our apprehension of the meaning of other people’s behavior is often phenomenologically immediate does not, of course, show that it isn’t underpinned by theory-driven computations of underlying mental states. Indeed, there is simply no other way of explaining our competence in this domain. Appealing just to sensorymotor skills (as Gallagher does) is plainly inadequate to account for the flexibility of the ways in which adults and infants can interact with others. Indeed, in order to interact flexibly with any complex system (be it physical or human), you need a good enough understanding of how it works. R4. The question of developmental priority The target article expressed skepticism about the capacity of developmental data to discriminate between a “mindreading is prior” account and its three main competitors (sect. 4). Mills & Danovitch disagree. They cite a number of forms of evidence suggesting that mindreading skills of various sorts emerge in development prior to metacognition, which they say supports a “mindreading is prior” account. Since one of my main grounds for skepticism concerned arguments for the priority of mindreading that are premised on the parallel emergence of mindreading and metacognition in development (which fails to discriminate between the “mindreading is prior” view and the “one mechanism, two modes of access” account), I am happy to agree. But let me sound the following cautionary note. Until we have a good understanding of the reasons for the two-year developmental lag between children’s capacities to pass nonverbal and verbal versions of mindreading tasks, arguments that rely upon the latter need to be treated with some caution. For it may be that the “self” and “other” versions of a verbal task differ along whatever turns out to be the relevant parameter. Put differently: you can’t control for confounding factors that you don’t yet know about. In response to Mills & Danovitch, I should also stress that although a finding that mindreading competence is developmentally prior to metacognition would support a “mindreading is prior” account (because it would be inconsistent with the other three alternatives), this is not actually a prediction of the account. For the latter claims only that it is the same system that underlies our mindreading capacity that gets turned upon ourselves to issue in metacognition. It does not claim that the first occurs in development before the latter. (In this respect, the label “mindreading is prior” may be somewhat misleading. I intend it only to refer to a functional and/or evolutionary priority.) Fernyhough would plainly disagree with the point made in the previous paragraph. He gives reasons for thinking that it may take time for aspects of children’s inner lives to develop. In particular, the transformation of private speech (“talking to oneself”) into inner (“silent”) speech may not be complete until middle

childhood; and capacities to build and sustain visual images may likewise be slow to develop. Because the target article claims that these are among the data that the mindreading system uses when attributing propositional attitudes to oneself, Fernyhough says that the “mindreading is prior” account must therefore predict that metacognition should lag significantly behind mindreading in development. But there is no such implication. All that follows is that there will be many more moments in the daily lives of children at which they will be unwilling to attribute occurrent thoughts to themselves than is true of the daily lives of adults, because the conscious mental events that might underlie such self-attributions simply are not present. Nothing follows about children’s competence to self-attribute attitudes. Nor does it follow that children will be weaker at attributing attitudes to themselves than they are at attributing attitudes to others, provided that the tasks are suitably matched. Robbins claims that I have overlooked crucial conflicting evidence, which demonstrates that metacognition is prior to mindreading in development. He cites a study by Wimmer et al. (1998) which seems to show that young children have awareness of their own knowledge before they have awareness of the knowledge of other people. But the study in question admits of an alternative explanation. In the “self” condition, the children are allowed to look, or not look, into a box, and are then asked whether they know what is in the box; whereas in the “other” condition they observe a subject either looking, or not looking, into the box before being asked whether the subject knows what is in the box. Answering the question in the “other” condition requires the children to reason appropriately from the generalization that seeing leads to knowing (or some such). But answering the question in the “self” condition requires no such thing. The children can answer simply by accessing, or by failing to access, their knowledge of what is in the box. They can substitute a first-order question in place of the second-order question asked – namely, “What is in the box?” – and answer “Yes,” that they do know what is in the box, if an answer comes to mind, otherwise answering “No.” R5. What is introspection? Baars thinks that the target article is committed to denying that nonhuman animals and young infants feel pain. This is because these subjects are incapable of mindreading, and because the target article denies the existence of introspection. But there are two distinct misunderstandings at work here. One is based upon an unfortunate ambiguity in the use of the term “introspection.” In one sense, introspection is any form of looking within the body. In this sense, perceptions of pain or of one’s own beating heart count as introspections. In another sense, introspection is a form of looking within the mind. In this sense, the outputs of an introspective process are always metarepresentational, involving representations of one’s mental states as such. And in this sense, perceptions of pain or of heartbeat are definitely not introspections, since they issue in firstorder representations of properties of the body. It should be stressed that it is only this latter, metarepresentational, sense of “introspection” that is at stake in the BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Carruthers: How we know our own minds target article. Hence, even if I denied the existence of introspection in this sense altogether, there is no reason why this should commit me to denying that animals feel pain, or fear, or hunger, or thirst. For what is in question in these cases is only introspection in the first “within the body” sense. Baars’s second misunderstanding lies in believing that the target article denies the existence of introspection (in the metacognitive sense) for all categories of mental state. For he thinks that the view will have difficulty in accounting for the reliability of metacognitive self-report in psychophysics. But I specifically allow (indeed, I insist) that globally broadcast perceptual and quasiperceptual states can be introspected, because they are available as input to the mindreading faculty. Self-attribution of such states should therefore be trivial for anyone who possesses the requisite concepts, which can then be applied to the input-states on a recognitional (noninterpretative) basis. Pereplyotchik, too, misunderstands the sense of “introspection” that is at issue. For he thinks that it will be sufficient to demonstrate that there is no such thing as introspection for perceptual states if it can be shown that the mindreading system relies upon a tacit theory in self-ascribing such states. This is a mistake. That a process is introspective is not supposed to be inconsistent with it involving computations or inferences of various sorts (provided they are unconscious ones), so long as the inferences rely only on information of a general kind, and do not access information about the agent’s circumstances, behavior, or earlier mental states. For remember, what is at stake is whether our access to our own minds is different in kind from our access to the minds of other people. And the latter always involves just such inferences. This was also the reason why I defined introspection negatively for the purposes of the target article. For I wanted to leave open “inner sense” accounts as well as “application of a tacit theory” views of introspection, according to each of which the attribution of mental states to oneself is inferential (but still quite different from the attribution of mental states to other people). Zinck, Lodahl, & Frith (Zinck et al.) mistake the nature of the intended contrast between a “one system, two modes of access” account and a “mindreading is prior” view. They insist that when metarepresenting our own mental states, the mindreading system has access to a richer array of data, such as visceral and somaesthetic sensations, and that this therefore supports a “one system, two modes of access” account. But I, too, claim that the mindreading system can utilize data when attributing states to the self that are not available when attributing the same states to others, and I maintain that this is consistent with a “mindreading is prior” view. As I intend the distinction, the difference between the two forms of account is not whether there are different data available to the mindreading system when attributing mental states to oneself or to another. Rather, the difference concerns whether the mindreading system employs two different informational channels in the two cases. The distinction is intended to be an architectural one. Because the mindreading system utilizes the very same mechanism of “global broadcast” of attended outputs of perceptual systems, whether attributing mental states to oneself or to another, this means that there are not two 168

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different modes of access to mental states, even though the perceptual and quasi-perceptual states that are utilized in the two cases are often different. To provide evidence supporting a “one system, two modes of access” account, Zinck et al. would need to show that we can self-attribute propositional attitude states independently of any sensory or imagistic information accessible via global broadcast. But they themselves seem to doubt whether any such evidence exists. Murphy denies that the mindreading system is always implicated in our knowledge of our own attitudes, while agreeing with me that there are no special mechanisms that enable us to detect and describe those attitudes. Rather, he thinks that we can do whatever we would normally do to determine a truth about the world and can then use the result of that same process to self-ascribe the resulting belief. This might well work as an account of how we express our beliefs in speech. Indeed, so construed, it is an account that I endorsed in the target article (sect. 2.1). The language production system can take as input the result of a belief-forming process, or the result of a search of memory, and can formulate that input-state into a belief report. We can imagine this happening via a two-step process: the language system accesses a belief with the content P and draws on lexical and syntactic resources to express this in a sentence, “P,” before elaborating it and articulating the result in the form, “I believe that P.” But I deny that such an account can succeed as an account of metacognition, or as an account of how we form beliefs about our own beliefs. Murphy is confronted with the following dilemma. Suppose, first, that the assertion, “I believe that P” is an encoding into language of a previously existing metacognitive belief (the belief, namely, that I believe that P). Then the challenge is to explain how this belief is arrived at without either implicating the mindreading faculty or appealing to any special introspective channel. But there would seem to be just two possible ways for whatever process that issues in such a belief to do its work (in a reliable enough way). One would be for it to have access to the output of the process that issues in the belief or memory that P (which would then surely involve some sort of introspective channel of access to the latter). The other would be for the metacognitive belief-forming process to involve interpretation and inference from other events, such as a prior tokening of the assertion, “P,” or the occurrence of a memory-image caused by the belief in question (which would surely then implicate the mindreading faculty, or else some system with many of the same powers as the mindreading faculty). So Murphy must intend (much more plausibly) that the assertion, “I believe that P” can be generated directly from the belief that P, without subjects first needing to form the metacognitive belief that they believe that P. As described above, the language system (working in concert with executive systems, no doubt) can access the belief that P but then formulate this into the sentence, “I believe that P,” rather than the first-order sentence, “P.” But this assertion is not itself a metacognitive belief (nor, by hypothesis, does it involve one). Rather, it is a linguistic action (albeit one with a metarepresentational content). The system that issues in the metacognitive belief that I believe that P must take this assertion as input and deliver the metacognitive belief as output. But in order to do this, it would have to

Response/Carruthers: How we know our own minds engage in interpretation, just as when hearing a similar assertion made by another person. Because the assertion could be a lie, or be meant ironically, or meant as a joke, it is hard to see how the necessary interpreting could be done except by the mindreading faculty (or else some system with many of the same powers as the mindreading faculty). But this is now the view endorsed by the target article: In such cases I come to know what I believe by hearing and interpreting what I say (whether overtly or in inner speech). Murphy has failed to present us with a genuine alternative. Petty & Brin˜ol agree with the target article that selfattributions of attitudes always involve interpretation. But they insist that interpretation is a matter of degree, and that sometimes interpretation can be so minimal as to be almost indistinguishable from introspection. I agree with the former point but not with the latter. Of course it is true, as Petty & Brin˜ol point out, that there is a big difference between interpretations of oneself that rely only on publicly available information (such as one’s own behavior and circumstances) and interpretations that rely only on subjectively accessible mental events (such as one’s own somatic feelings and/or one’s own “inner speech”). But the main point at issue in the target article is a dichotomous, architectural one. It concerns the existence (or not) of a distinct informational channel to our own attitudes, different from the sensory channels that are available to the mindreading system for use in interpreting other people. There either is such a channel or there is not. (The target article claims the latter, and Petty & Brin˜ol appear to agree.) Moreover, even minimal-interpretation cases are much less similar to introspection than Petty & Brin˜ol seem to think. Consider their example of someone who says to himself, “It is good,” when tasting some ice-cream, and thereby interprets himself as liking ice-cream. The mindreading faculty, functioning together with the language comprehension system, has to fix on the object of evaluation (“What is good?”), interpret the evaluative predicate (“In what sense is it good?”), and determine what sort of speech act is being expressed (whether literal, suppositional, ironic, or whatever). No doubt the answers can, in context, be settled quite easily. But they are exactly the same answers that would need to be provided when interpreting the speech of another person. And no one should think that the latter is at all similar in its nature to introspection. R6. Evidence for and against introspection Fiala & Nichols challenge the claim made in the target article that confabulators often have the impression that they are introspecting rather than self-interpreting (sect. 3.1), which is a crucial component of the argument against introspection for attitudes. They first point out that no one has ever asked a split-brain subject whether or not he thinks he is introspecting. But this would be a bad question to ask, for a number of reasons. One is that “introspection” is a term of art, and requiring people to make judgments involving an unfamiliar term is unlikely to be a reliable way of finding out what they believe. Another is that the direct-question method is a poor way of accessing people’s tacit beliefs in general (Scholl

2007). I doubt that many people have explicit, verbalizable, beliefs about the nature of their access to their own mental states – with the possible exception of those who have taken an introductory course in philosophy. Rather, the way in which people think and reason about their own mental states just assumes that the latter are transparently accessible to them. But if asked about that access, who knows what they might say? For they will almost certainly find the question to be confusing, and they might revert to bits and pieces of knowledge acquired about Freud, or about cognitive science, or whatever, when trying to say something sensible by way of answer. So what is really in question is whether it seems to splitbrain subjects that they are formulating beliefs about their own mental states and processes in whatever way they normally would – in a way that doesn’t seem to them to involve self-interpretation – not whether they have explicit beliefs about the process in question. This is hard to assess directly. But those who work with such people say that their own sense of themselves following the splitbrain operation seems to be unchanged (Gazzaniga 1995). And even reminders of their split-brain status that are made immediately prior to testing – and that are given, moreover, to those who have a good theoretical understanding of the effects of the operation – have no effect (Gazzaniga, e-mail communication, November 8, 2006). The subject goes right on confabulating. This isn’t what one would predict if subjects were, at any level, aware of interpreting themselves, since one would expect that a reminder of their split-brain status should enrich their hypothesis pool. But it does not. Fiala & Nichols also point out that there are many examples from the confabulation literature where subjects express their metacognitive thoughts with low confidence, suggesting that they are not only interpreting themselves but are at some level aware that they are doing so. The point is entirely correct. But it doesn’t have the consequences destructive of my argument that Fiala & Nichols allege. This is because there are also a great many instances in which subjects express their metacognitive beliefs unhesitatingly and with high confidence. And these are all that I require to make my case. Indeed, the self-interpretative model of attitude self-awareness predicts that there should be cases of both sorts. For only if an interpretation can be arrived at smoothly and unhesitatingly will subjects have the impression that they are introspecting. In more problematic cases such as those that Fiala and Nichols cite, or such as especially bizarre actions performed following hypnosis, it will be more difficult for the mindreading system to generate an interpretation (just as it would be difficult to interpret such behavior observed in another). And as soon as subjects become aware of themselves as interpreting, they are likely to express any belief that they formulate with some caution. Note that exactly the same distinction can be made with respect to other-person mindreading. In many cases the interpretation process is swift and unconscious, and the resulting phenomenology is that we just seem to see someone’s behavior as informed by certain beliefs and goals. (Here I am in full agreement with Zinck et al.) But in other cases an interpretation is harder to come by, and we become aware that we are trying to interpret. (See also the discussion of System 1 versus System 2 mindreading in sect. R2.) BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Carruthers: How we know our own minds In the target article I assumed that one of the biggest challenges to a “mindreading is prior” account derives from the “descriptive experience sampling” studies conducted over the years by Hurlburt and colleagues (Hurlburt 1990; 1993; Hurlburt & Akhter 2008; Hurlburt & Heavey 2006), specifically the finding that subjects will sometimes report engaging in “unsymbolized thinking” at the time of the beep. I took this to be evidence that subjects are capable of introspecting their propositional attitudes, and tried to respond. However, Hurlburt now replies that I have misinterpreted his position. Unsymbolized thoughts are merely thoughts that don’t have any semantically relevant images, words, or other sensations as the “primary theme or focus” of the subject’s attention at the time of the beep. Hurlburt concedes that such experiences are generally present in the periphery of attention, providing a basis for self-interpretation. Moreover, he argues that the ways in which subjects respond when probed about these episodes actually speaks in favor of a “mindreading is prior” position on our awareness of our own attitudes. This additional support from an unexpected quarter is, of course, most welcome. R7. What is metacognition? A number of commentators accuse me of using the term “metacognition” in a non-standard sense (Anderson & Perlis; Couchman, Coutinho, Beran, & Smith [Couchman et al.]; Proust).2 These commentators allege that the normal usage in cognitive science is that metacognition is involved in any process that has a controlling influence on the way that another cognitive process unfolds. On this account, it is left open whether or not metacognition need involve metarepresentations of the events within the cognitive process that gets controlled. I am happy to allow that some authors might use the term in this (hereafter “control”) sense. But I deny that it is a common – let alone a standard – usage. In general in the metacognition literature in psychology, metacognition is defined in terms of thought about our own thoughts. Indeed, Proust herself provides the standard definition (Proust 2007, p. 271): “This is the domain of metacognition: thinking about one’s own thinking.” (See also Dunlosky & Metcalfe 2009; Flavell 1979; Koriat 2007.) And it is then a matter of substantive investigation whether or not, and to what extent, metacognition has a controlling function. (See especially Koriat et al. 2006.) This wouldn’t even make sense if metacognition were defined in terms of control. It is important to emphasize that the control and metarepresentational senses of “metacognition” are two-way independent of one another. There are certainly many instances in which one cognitive process exercises a causal influence on another without the former involving any metarepresentations of any aspect of the latter. (See sect. 5.1 of the target article for some examples.) And in connection with any metarepresentational form of metacognition, it will always be an open question whether or not it has a causal influence upon the cognitive state or process represented. Although these points are very well understood by most researchers, some are apt to think that they can move freely from talk of metacognition in the control sense to metacognition in the metarepresentational sense. This is 170

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especially true of some of those who work in the field of animal metacognition. Some, I think, are quite clearheaded that they are seeking forms of metacognitive control for which the best available explanation will be the occurrence of a metarepresentational process. (See especially Metcalfe 2008; Son & Kornell 2005.) But some seem unaware that any additional argumentation is needed to get from metacognition in the control sense to metacognition in the metarepresentational sense. This is especially true of the commentary by Couchman et al., as well as the articles by members of their team cited therein, which I discuss in section R8. Proust raises a more substantive challenge to the assumptions of the target article. She suggests that the latter overlooks the possibility of nonconceptual forms of metacognition (in the metarepresentational sense of the latter term). Specifically, she suggests that epistemic feelings like surprise and confidence should be seen as nonconceptual representations of the underlying mental states (such as violated expectations or high degrees of belief). Hence, any person or animal that can use such feelings as a cue to guide further behavior (such as looking more closely at the target event) can be said to be acting as a result of a metacognitive process. This is an interesting idea, which deserves examination. It will require us to delve a bit into competing theories of the nature of intentional, or representational, content. Let us assume (with Proust) that epistemic feelings like surprise and confidence are distinctive forms of somatosensory experience that are caused by an underlying cognitive state or process, but without involving any conceptualization of that state or process as such. So an animal that feels surprise has an expectation (a belief) that is violated by what it is currently perceiving, which in turn causes a suite of bodily reactions of which the animal is aware (heightened alertness, widening of the eyes, automatic orienting towards the stimulus, and so on), but without the animal necessarily knowing that it has an expectation that has been violated. Because the epistemic feeling is reliably caused by a cognitive state or event, it thereby carries information about it. And then on any purely informational account of representational content (e.g., Fodor 1990), the feeling can count as a nonconceptual representation of the representational state or event in question (that is, it counts as a metarepresentation). One problem with this proposal, however, is that it makes metarepresentations come too cheap. For almost all mental states, processes, and behaviors will carry information about the existence of some other mental state or process, thereby becoming nonconceptual metarepresentations of the latter, on the proposed account. Thus inferential processes will characteristically carry information about (and hence metarepresent) the presence of beliefs, decision-making processes will carry information about the presence of beliefs and desires, and so forth. Moreover, few researchers in cognitive science actually rely upon an informational account of representation in their own work. Most adopt some or other variety of inferential or conceptual role semantics (e.g., Block 1986), according to which what a symbol represents depends (at least partly) upon the use that the rest of the cognitive system is apt to make of that symbol. This is probably wise, because purely informational accounts of intentional

Response/Carruthers: How we know our own minds content face notorious difficulties (one of which will be mentioned further on; see Botterill & Carruthers 1999 for discussion). And then the question for us becomes: Does the animal make use of the epistemic feeling in question in such a way that the feeling is thereby constituted as a nonconceptual representation of a cognitive state? Consider, first, paradigmatic cases of nonconceptual representation, such as a perceptual representation of a colored surface or of the detailed shape of an object. In virtue of what does the perceptual state represent the colored surface rather than, for instance, a particular pattern of activity on the retina or in the optic nerve (since it equally carries information about both)? A natural answer is that the animal itself treats that representation as a representation of color – it thinks and acts in the sort of way that would be appropriate if it were a representation of the color of a surface. For example, perceiving the red surface of a fruit, and believing that red fruits of that type are ripe, the animal might grasp and eat it. Likewise, a perceptual representation of the detailed shape of an object will be used to guide the animal’s choice of grip size and hand orientation when it reaches out for it. It seems that a nonconceptual representation of some property of the world represents what it does partly in virtue of its role in guiding thought and action that is focused on that aspect of the world. Consider, now, epistemic feelings, such as the feeling of low confidence that an animal might experience when faced with an especially difficult judgment or discrimination. This is a feeling that involves an aversive state of anxiety, caused by the animal’s low degree of belief. Should it be considered a nonconceptual representation of a cognitive state (one of low degrees of belief or of conflicts of belief), as Proust suggests? To answer, we need to look at how it is used by the animal. One thing that the animal might do in consequence is opt for a high-confidence, low-anxiety, option instead. But this is an action that is targeted on the world rather than on the animal’s own beliefs. It should lead us to say that the feeling of uncertainty is a representation of the riskiness of certain worldly options or events, rather than a representation of the animal’s own low degree of belief. For the animal doesn’t act in a way that is directed at its own beliefs; rather it acts on the world. Likewise for an animal that is led by its feeling of uncertainty to engage in information-seeking behavior such as examining the object more closely, walking around it to look at it from the other side, sniffing it, pressing a “hint” key of the sort employed by Kornell et al. (2007), and so on: These are behaviors that are aimed at answering a first-order question about the object – “Is it edible?”, “Is it safe?”, “What comes next?”, and so on – rather than being aimed at changing the animal’s own degrees of belief. It seems reasonable to conclude, therefore, that epistemic feelings should not be regarded as possessing metarepresentational nonconceptual content.3 Moreover, there is no reason to think that epistemic feelings are a first evolutionary step on the road to metarepresentation. This is because metarepresentation requires the development of concept-wielding consumer systems for the bodily cues in question, which contain implicit or explicit theories of the nature and causal roles of the underlying mental states. (Note that even a simulation theorist like Goldman [2006] needs to postulate an

innately structured set of representations in a language of thought linked up to the different mental state kinds.) It should be stressed that the bodily feelings in question – that are distinctive of surprise, or the anxiety that attends uncertainty, for examples – are just that: bodily feelings. By themselves they give no clues as to the nature of the mental states that cause them (a violated expectation, in the case of surprise, and low or conflicting degrees of belief, in the case of uncertainty). How would an animal that as yet had no conception of those types of mental state be expected to acquire one? Certainly not via individual learning. And if via evolution, then it is far from clear where the pressure to develop such theories is to come from. Not from the benefits of metacognition in the control sense, presumably, since by hypothesis the animals in question already have that (see sect. R8). Hence, the pressure is presumably social, in which case what develops will be a mindreading system (albeit one that is capable of taking bodily cues as input). R8. Animal metacognition? There are two distinct ways in which an animal might behave in metacognitive fashion (in the control sense) without engaging in metacognition (in the metarepresentational sense). First, it might utilize degrees of belief and desire (without metarepresenting them as such, of course), combined with one or two simple first-order (non-metarepresentational) mechanisms and/or acquired first-order beliefs. This is the explanatory strategy followed in Carruthers (2008b) and described briefly in the target article. But second, as Proust explains, an animal might utilize its own bodily changes and reactions (including feelings that are distinctive of surprise, uncertainty, and familiarity, for examples) as cues. Thus, an animal might be innately predisposed, or might have learned, that the bodily feeling distinctive of uncertainty is a cue to engage in actions that obtain improved information (e.g., by moving its head from side to side for a better view), or to engage in actions that have the effect of “opting out” of the current situation and entering a new one. (Actually, this might be one way of implementing the “gate-keeping” mechanism postulated in Carruthers 2008b, which takes roughly equivalently motivated but incompatible plans of action or inaction as input, and which motivates a search for additional information or alternative behavior.) Note that on this second alternative, the animal does not have to recognize that it is surprised or uncertain; indeed it need not even possess the concepts of surprise or uncertainty. Rather, it just has to be capable of recognizing a distinctive bodily gestalt or feeling, and initiating an innately prepared or learned response. (Nor, as sect. R7 argues in reply to Proust, does the feeling itself possess a nonconceptual metacognitive content.) Such an animal might display any of the metacognitive controlbehaviors currently investigated by comparative psychologists, as I shall show briefly below. But it might be wholly incapable of metacognition in the metarepresentational sense. Carruthers (2008b) describes exhaustively and in detail how all of the data cited in the commentary by Kornell, Schwartz, & Son (Kornell et al.) are explicable in nonmetarepresentational terms, utilizing degrees of attitude BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Carruthers: How we know our own minds strength. Kornell et al. make no attempt in their commentary to respond to those arguments, or to demonstrate why their own metarepresentational interpretation of their data is superior. It wouldn’t be fruitful just to repeat the arguments of my 2008b paper here. Rather, I shall show briefly how an animal that can treat its own epistemic feelings as a cue might behave in the ways that Kornell et al. describe without being capable of metarepresentation. Thereafter I shall show how Couchman et al. chronically conflate the control and metarepresentational senses of metacognition in their commentary and other work. Consider what is perhaps the most challenging set of data presented by Kornell et al., citing Kornell et al. (2007). Rhesus monkeys were initially trained in a gambling task that required them to first make a difficult perceptual discrimination before choosing between a “high-risk” and a “low-risk” symbol. Pressing the former would issue in a large reward if the initial discrimination was correct but a large loss if the discrimination was incorrect; pressing the latter would issue in a guaranteed small reward. The monkeys were then trained on a quite different – memory – task (initially without the gambling option). Following training, when the “high-risk” and “low-risk” symbols were re-introduced, the animals immediately made adaptive use of them. Kornell et al. take this to show that the monkeys had learned a general metacognitive strategy for solving the tasks. Carruthers (2008b) shows how these data can be better explained in terms of degrees of belief combined with a capacity for abstract rule-formation. Here let me sketch a further alternative: that the animals might have learned to use their own feelings of uncertainty as a cue. We can presume that monkeys are capable of both being, and feeling, uncertain, even if they are incapable of metarepresentation of any sort. The monkeys in the first phase of the experiment just described could then have learned to treat their own feeling of uncertainty when making an initial discrimination as a cue to press the “low-risk” symbol thereafter. They would therefore have acquired, and learned to act upon, a rule of the form, “When that bodily feeling/gestalt is present, press the ‘low-risk’ symbol when it arrives.” (Note that there is nothing metarepresentational contained here. The feeling in question is a state of the body, not of the mind. See sects. R5 and R7.) When the monkeys then entered the second phase of the experiment they would, of course, sometimes feel uncertain, but this time whenever they were presented with a difficult memory task. The introduction of the gambling option might then have activated, and led them to act upon, the exact same rule. I now turn to consider Couchman et al. It is plain that at the outset of their commentary they actually use “firstorder” to mean “behaviorist,” and that by “metacognitive” they mean any process that is genuinely cognitive, with the animal taking decisions in light of its beliefs. For they describe Smith et al. (2006) as supporting a “metacognitive” account. In those experiments both feedback and rewards were deferred until the animal had completed a block of trials, thus preventing the creation of stimulusresponse pairings that might otherwise explain the animals’ adaptive use of the uncertainty response. Couchman et al. write, “It was clear in that study that monkeys’ uncertainty-response strategies were adjudicated cognitively and decisionally, not using first-order cues.” 172

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I agree (at least, if by “first-order cues” one means “stimulus-response pairings”). But the training would have given the animals ample opportunity to acquire a set of nonmetarepresentational beliefs about the contingencies of the experiment. By the time that they entered the test phase, they would know that pressing the “dense” key if the stimulus was dense would thereafter issue in a reward, whereas pressing the “dense” key if the stimulus was sparse would thereafter issue in a penalty, and that pressing the “uncertain” key would issue in neither a reward nor a penalty. These beliefs, combined with degrees of belief that a given stimulus is dense, or sparse, can then explain the data in an entirely nonmetarepresentational way, as Carruthers (2008b) demonstrates. Couchman et al. point out, quite correctly, that the non-metarepresentational explanation adverted to in the foregoing requires the postulation of what Carruthers (2008b) calls a “gate-keeping mechanism” (which might be absent in capuchins and pigeons, note, thus accommodating the findings of Beran et al. [in press] and Inman & Shettleworth [1999] that neither species makes adaptive use of an uncertainty response). This is a mechanism that is sensitive to the presence of beliefs or motivations for action of roughly equal strength, issuing in a search for additional information or alternative strategies when receiving such states as input. Couchman et al. object that this commits me to a metacognitive explanation of the data, and they write, “It [the gatekeeper mechanism] meets the definition of a second-order, controlled cognitive process.” Since it is plain that the mechanism in question need not involve any metarepresentations for it to operate as envisaged, Couchman et al. must here be using “metacognitive” in the control rather than the metarepresentational sense. So far there isn’t any substantive disagreement between Couchman et al. and myself, just “crossed wires” resulting from differences in the use of the term, “metacognitive.” But they go on to conclude their commentary by claiming victory for a “metacognition is prior” account over my own “mindreading is prior” model, despite the fact that the two are perfectly consistent with one another if the former is taken in their control sense and the latter is understood in my metarepresentational sense. They also offer an account of the origins of mindreading that is blatantly and explicitly Cartesian, presupposing that we have prior awareness and understanding of our own mental states as such (i.e., presupposing the prior existence of metacognition in the metarepresentational sense). I fear that Couchman et al. have engaged in a fine body of experimental work that is framed and guided by theoretical confusion. R9. Dual processes and judgment Frankish takes issue with the argument of section 7 of the target article, which claims that the conscious events that take place at the System 2 level (e.g., verbalizing to myself, “P,” or, “I shall do Q”) don’t have the right kind of causal role to constitute a judgment or a decision. For they only achieve their effects via further (unconscious) processes of reasoning. So although these events are introspectable, this doesn’t mean that any judgments or

Response/Carruthers: How we know our own minds decisions are introspectable. Frankish replies that these events have a System 2 role appropriate for a judgment or decision. For they are the last System 2 events that occur prior to the characteristic effects of judgments and decisions. While he acknowledges that further reasoning processes of a System 1 sort occur subsequent to those events, mediating their causal effects on behavior, he says that these should be thought of as belonging to the realizing base of a System 2 judgment or decision. However, our commonsense notions of judgment and decision don’t make any allowance for the System 1/ System 2 distinction. A judgment is a content-bearing event that gives rise to a stored belief with the same content immediately, and which is likewise immediately available to inform practical decision-making, without the intervention of any further reasoning. Similarly, a decision is a content-bearing event that causes intention or action without the mediation of any further reasoning about whether or not to act. By these lights, neither the judgment-like event of saying to myself, “P,” nor the decision-like event of saying to myself, “I shall do Q,” can qualify. Moreover, while it may be true enough that System 2 processes in general are realized in those of System 1 (Carruthers 2009), the realizing conditions for a particular event surely cannot occur subsequent to that event itself. And yet it is only once the conscious events of saying to myself, “P,” or, “I shall do Q,” are completed that the System 1 reasoning leading to belief or action kicks in. In addition, if we opt to say that the judgment or decision isn’t either one of those events, but rather the more extended event that also includes the subsequent System 1 practical reasoning, then that event isn’t an introspectable one. So either way, there is no one event, here, that is both introspectable and is a judgment/decision. However, let me emphasize that the introspectable events that are involved in System 2 processes are by no means epiphenomenal. On the contrary. Nor, I should stress, is metacognition itself epiphenomenal either, contrary to a claim Buckner et al. make about the commitments of the target article. Quite the reverse. System 2 reasoning processes are shot through with – and are largely dependent upon – metacognitive thoughts and beliefs. And on any account, System 2 plays an important part in human cognition and behavior (albeit one that is subject to significant individual differences; see Stanovich 1999). R10. The evidence from autism The target article maintains that there is no convincing evidence that in autistic subjects metacognition is preserved while mindreading is damaged (sect. 10). This is contrary to the claims of Goldman (2006) and Nichols and Stich (2003), who cite such evidence in support of a “metacognition is prior” account, or a “two independent systems” view, respectively. Williams, Lind, & Happe´ (Williams et al.) agree with the target article in this respect, and cite Williams and Happe´ (in press a) as demonstrating that autistic children have equivalent difficulty attributing intentions to themselves and to other people, with their performance on these tasks being significantly correlated with their performance on traditional false-belief tasks. These new results are very welcome.

However, Williams et al. also cite evidence provided by Williams and Happe´ (in press b), which is said to favor a “one system, two modes of access” account over my preferred “mindreading is prior” thesis. In a modified version of the Smarties task, autistic children have significantly greater difficulty with the “self” version of the task than they do with the “other” version.4 Williams et al. are mistaken in their interpretation of the significance of their own data, however. This is surprising, since all of the materials for a correct analysis are contained in the very article that they cite (Williams & Happe´, in press b), as I shall now explain. The upshot is that these new data are fully consistent with a “mindreading is prior” account. Suppose, first, that autistic children lack the normal mentalizing system altogether. (This seems to be the preferred view of Williams & Happe´, in press b.) Such children would therefore lack whatever basic “core knowledge,” or innate module, or innate domain-specific learning mechanism underlies the development of mentalizing abilities in normal children. Autistic children may nevertheless achieve some limited success in performance by other routes – by means of explicit domain-general theorizing, by memorizing rules and explicit strategies, and so forth. If an account of this sort is correct, then data from autistic subjects are inherently incapable of discriminating between the “mindreading is prior” and the “one mechanism, two modes of access” views of the relationship between mindreading and metacognition. For each of the latter applies only to those people who possess a normal (or near-normal) mentalizing system, or faculty. The “mindreading is prior” account claims that there is just a single mentalizing system, designed initially for mindreading, which is turned upon the self to issue in metacognition. In contrast, the “one mechanism, two modes of access” account, although agreeing that there is just a single mentalizing system, claims that the system in question has both perception-based and introspective channels of access to the mental items in its domain. The former predicts that no one with a normal mentalizing system should possess mindreading competence but lack metacognitive competence; whereas the latter predicts that there might be individuals with a normal mentalizing system who can mindread successfully but who lack a capacity for metacognition, because the introspective channel has been broken or disrupted. Importantly, neither model makes any predictions about what might happen in individuals who lack the normal mentalizing system altogether, but who rather “hack” their way to success by other methods. There might be all kinds of reasons why it could be easier to develop rules and strategies that apply to other people than it is to acquire such rules to apply to oneself, as Williams and Happe´ (in press b) themselves argue. Now suppose, in contrast, that autistic children (or at least those who are comparatively high functioning) do possess a mentalizing system, only one that is significantly delayed in its normal development (and is perhaps slower and less reliable in its operations thereafter). And suppose that the “mindreading is prior” account of that system is correct. Still there might be reasons why individuals with a partly formed mentalizing faculty should find some mindreading tasks easier than parallel metacognitive ones. For example, as Williams and Happe´ (in press b) BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Carruthers: How we know our own minds themselves suggest, it may be that the perceptions of action that provide the main input for mentalizing are much more salient and easily accessible in the case of others’ actions than in the case of one’s own actions.5 However, wouldn’t such an account predict (contrary to fact) that normally developing children should likewise pass mindreading tasks before they pass the equivalent metacognitive ones? Not necessarily. For the recent data mentioned in R3 suggest that a basic mentalizing competence is in place well before children start to be able to pass verbal versions of mentalizing tasks, and that there is some extraneous factor or factors that inhibit verbal performance. And the latter might contain no bias in favor of “other” versus “self” versions of the tasks. In the case of autistic children, in contrast, it is the delayed development of the mentalizing system itself that delays successful performance, enabling a bias in favor of mindreading over metacognition to display itself. R11. Neuroimaging evidence Lombardo, Chakrabarti, & Baron-Cohen (Lombardo et al.) cite neuroimaging evidence showing that identical neural regions are implicated in mentalizing about self and other, and that there are no other areas of the brain that are recruited specifically for mentalizing about self, or about other. These data are very welcome, and provide strong support for the “mindreading is prior” model. This is because all three of the competing accounts predict that there should be some brain regions used specifically for mentalizing about oneself and/or brain regions used specifically for mentalizing about others. Lombardo et al. claim, however, that it is an implication of the “mindreading is prior” account that the various brain regions implicated in mentalizing should be activated to the same degree when mentalizing about the self or about another. Because their data conflict with this prediction, they take this to raise a puzzle for the view. However, the “mindreading is prior” account makes no such prediction. For it allows that different kinds of data are implicated in the two forms of mentalizing. Specifically, mentalizing about the self can utilize visual and auditory imagery, somatosensory experiences, and so forth, in a way that mentalizing about others normally cannot. I suggest that these differences are sufficient to explain the different degrees of neural activation in question. Nor, it should be stressed, does the “mindreading is prior” account predict that mindreading tasks are always performed in the same way (on the contrary; see sect. R2). So the findings reported by Lombardo et al. – that people tend to rely more on stereotypes when reasoning about the mental states of dissimilar others, while using simulation strategies when reasoning about the mental states of people who are perceived to be similar to themselves – raise no particular challenge for the account. R12. The evidence from schizophrenia The target article discusses the significance of the finding that schizophrenic patients with “passivity” symptoms have difficulties in attributing intentions to themselves while being normal at reading the minds of others. 174

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Nichols and Stich (2003) argue that this reveals a dissociation between metacognitive and mindreading abilities, whereas the target article suggests that the data are better explained in terms of faulty or unusual experiences being presented as input to an intact mindreading system. In contrast, Wiffen & David cast doubt upon the reliability of the data in question. If they are right, then that makes a “mindreading is prior” account even easier to defend. Robbins, on the other hand, argues that schizophrenic patients with paranoid symptoms seem to display the contrary dissociation. For such patients perform poorly in mindreading tasks of various sorts, whereas there is no evidence (he tells us) that they show equivalent metacognitive deficits. Wiffen & David present two such strands of evidence, however. One is that schizophrenic patients characteristically lack insight into their own condition, which is (Wiffen & David claim) a failure of metacognition. But here they weave a tangled story. For although most if not all schizophrenic patients do perform poorly on tests of mindreading and do lack insight into their own illness, they appear to have no difficulties in distinguishing between normal and psychotic thoughts, feelings, and behavior in another person (Startup 1997). This raises a puzzle. If the lack of insight that these patients have into their own condition results from poor mindreading abilities, then how is it that they can nevertheless possess insight into the disordered minds of others? We can begin to unravel this puzzle by noting that even if paranoid beliefs result partly from faulty mindreading, they cannot result from faulty mindreading alone. There must also exist a willingness to believe propositions whose prior probability is very low, in some circumstances. (Most of us may have entertained a paranoid thought or hypothesis at one time or another, but have immediately dismissed the idea as absurd.) And indeed, there is an extensive body of literature demonstrating that people with schizophrenia display a marked “jumping to conclusions” bias, forming beliefs from new data much more swiftly and with higher degrees of confidence than do controls. (See Blackwood et al. 2001, for a review.) Moreover, the bias in question seems to be one of data-gathering rather than a failure of probabilistic reasoning as such, since patients with schizophrenia reason normally about the plausibility of hypotheses that are presented to them, or when presented with the same range of data that lead normal individuals to formulate a new belief. This could explain why patients with schizophrenia lack insight into their own condition while showing insight into the conditions of others. For in the first case they are forming a paranoid belief from limited data, whereas in the latter case they are assessing the prior probability of someone else’s belief. Wiffen & David’s other strand of evidence suggesting that schizophrenic patients have parallel difficulties in mindreading and metacognition is much more direct. They cite the demonstration by Koren et al. (2004) that schizophrenic patients do poorly on tests of metacognitive ability (see also Koren et al. 2006). Specifically, Koren et al. administered the Wisconsin Card Sorting Test to patients with schizophrenia, while also asking them to provide confidence ratings of their recent choices and while allowing them to decide whether or not each

Response/Carruthers: How we know our own minds sorting of the cards should count toward their final score (and potential monetary gain). The patients performed normally on the test itself, but displayed a marked deficit on the metacognitive measures. Although this is not yet an extensive body of data, it does suggest that deficits in mindreading and metacognition are paired together in schizophrenia, just as the “mindreading is prior” account would predict.

and so forth. All of this is entirely correct. What I should have said is that the contents of globally broadcast states can be self-attributed without interpretation, but interpretation is required for one to know to what kind those states belong. This leaves untouched the claim that the mindreading system has accessible to it data which it can use when self-ascribing propositional attitude states that are of no help in ascribing such states to other people.

R13. Some friendly suggestions

R14. Behaviorism bites back

A number of commentaries are entirely friendly to the approach taken in the target article, and hence need only a brief mention. First, Evans uses the position defended in the target article to resolve a tension in many theorists’ thinking about dual systems of reasoning. For System 2 is often characterized as a conscious system, whereas we know that people’s reports of System 2 processes are often confabulated. The solution is to note that only the globally broadcast contents of working memory are ever accessible to the mindreading system that is responsible for self-report, whereas many other aspects of System 2 processing will remain inaccessible to it. The contents of working memory represent but small islands of consciousness within the overall operations of System 2, leaving plenty of scope for confabulation about the remainder. Second, Huebner & Dennett emphasize the dangers inherent in the use that is made of first-person pronouns throughout the target article, as in, “I have access to my own visual images,” or, “We do have introspective access to inner speech.” For these seem to imply a place for the self in the account, in addition to the various subpersonal systems described (for language, for mindreading, and so forth). Of course I intend no such thing. The outputs of the mindreading system are passed along as input to a variety of other systems, included in which is a language production mechanism that might issue in a (covert or overt) expression of the metarepresentational content in question; that is all. While use of personal pronouns in cognitive science is a handy fac¸on de parler, we need to take care that their use is eliminable from the theories in question. I have no doubt, however, that they can be eliminated from all aspects of the “mindreading is prior” account. Third, Langland-Hassan offers a welcome corrective to what I actually wrote in the target article, though not to anything that I believe or really intended to say. I had claimed that perceptual and quasi-perceptual states can be self-ascribed without interpretation by virtue of being globally broadcast. But Langland-Hassan points out that the question whether the speech that I seem to hear running through my head is my own or is really the voice of another person, cannot be answered without interpretation. For by hypothesis the mindreading system has no access to my own articulatory intentions. All it has access to is the resulting experience. Likewise for the question whether a visual image that I am currently entertaining is a memory-image or a fantasy-image. No experience can wear its own provenance on its face. Hence, describing myself as remembering the event depicted will have to be based on an inference grounded in aspects of the immediate context, feelings of familiarity,

Catania offers a behaviorist alternative to my account, citing the work of Skinner (1945; 1963). Likewise, Lewis & Carpendale challenge the computationalist assumptions made by the target article, while criticizing me for not taking account of the work of the later Wittgenstein. I don’t believe that I should need to argue in support of either cognitivism or computationalism, since both are foundational assumptions of most of cognitive science. In any case I don’t have the space to defend them here. (See Gallistel & King [2009] for the definitive argument.) In addition, I don’t believe that Wittgenstein’s work contains any challenges that cognitive science cannot easily answer. There is some irony, moreover, in the charge that I should have paid more attention to Wittgenstein. For I spent the first fifteen years of my academic career focused on his philosophy, and much of that time was devoted to the so-called private language argument that Lewis & Carpendale refer to admiringly. This formed the topic of my doctoral dissertation. I ultimately came to believe that no version of the argument can be successful that doesn’t already rely on anti-realist (e.g., behaviorist) or verificationist premises. R15. Conclusion I am grateful to my commentators for the care and attention they devoted to the target article. As a result, the theoretical options have been further clarified, and the “mindreading is prior” model of self-awareness has been additionally elaborated and strengthened. At the very least, that model will now need to be taken seriously by anyone considering the nature of self-awareness and its relationship to our mindreading abilities. And now that the strengths and weaknesses of the four main theoretical options have been clearly laid out, there is an urgent need for additional experimental data that will enable us to discriminate between them. As things stand, my own verdict is that the “mindreading is prior” account is the one that is best supported by the existing evidence (in part because it is the most parsimonious). But future findings could change all that. NOTES 1. And then to respond to Lurz’s question why we should not believe that thoughts as well as perceptual states can be globally broadcast – raised also by Pereplyotchik – note that all of the evidence we have of global broadcasting concerns perceptual or quasi-perceptual events. And note, too, that the best established models of general-purpose working memory require the operation of one or another perceptual “slave system” – either the phonological loop or the visuospatial sketch pad; see Baddeley (1990). BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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References/Carruthers: How we know our own minds 2. Although Kornell, Schwarz, & Son (Kornell et al.) make a similar claim, in their case it is based on a misreading of my own view. So far as I can tell, they mean by “metacognition” precisely what I do. 3. Note that the behavior-guidance account of representational content proposed by Anderson & Perlis will also have exactly this consequence, because epistemic feelings guide action targeted on the world rather than on the animal’s own mental states. 4. Note that this result is actually the reverse of the claims made by Goldman (2006) and Nichols and Stich (2003). For the data seem to show mindreading relatively intact while metacognition is damaged. Lombardo et al. mention similar data in respect of emotion understanding, showing that autistic people do significantly worse on measures of understanding their own emotions than they do on measures of understanding the emotions of others. 5. Note, too, that explanations similar to those provided here can accommodate the data cited by Lombardo et al. on autistic people’s differential understanding of emotion in self and other.

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BEHAVIORAL AND BRAIN SCIENCES (2009) 32, 183 –246 Printed in the United States of America

doi:10.1017/S0140525X09000855

The propositional nature of human associative learning Chris J. Mitchell School of Psychology, University of New South Wales, Kensington 2052, Australia [email protected] http://www.psy.unsw.edu.au/profiles/cmitchell.html

Jan De Houwer Department of Psychology, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium [email protected] http://users.ugent.be/~jdhouwer/

Peter F. Lovibond School of Psychology, University of New South Wales, Kensington 2052, Australia [email protected] http://www.psy.unsw.edu.au/profiles/plovibond.html

Abstract: The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research. Keywords: association; associative link; automatic; awareness; conditioning; controlled; dual-system; human associative learning; propositional

1. Introduction The idea that behavior is determined by two independent and potentially competing systems has been used repeatedly in psychology (see Evans [2008] for a recent review of some of these ideas). The diversity of research areas in which this idea has been reproduced is striking. It ¨ hman & includes, for example, fear learning (e.g., O Mineka 2001), memory (e.g., Schacter 1987), reasoning (e.g., Evans 2003), decision making (e.g., Kahneman & Frederick 2002), and the activation of attitudes (e.g., Wilson et al. 2000). In each case, one system is generally characterized as conscious, cold, and calculating; the other, as unconscious, affective, and intuitive. In this target article, we reconsider (and reject) one of the oldest and most deeply entrenched dual-system theories in the behavioral sciences, namely the traditional view of associative learning as an unconscious, automatic process that is divorced from higher-order cognition. The classic empirical demonstration of associative learning comes from Pavlov (1927). He presented his dogs with a ringing bell followed by food delivery. As a consequence, the dogs would salivate on hearing the sound of the bell, even in the absence of food. This shows that Pavlov’s dogs learned to associate the bell with the presentation # 2009 Cambridge University Press

0140-525X/09 $40.00

of food. The biologically neutral bell is usually referred to as a conditioned stimulus (CS), and the biologically relevant food (to a hungry dog) is referred to as an unconditioned stimulus (US). Most contemporary animal learning theorists now consider that the dogs salivated on hearing the bell because a link formed between the mental representations of the bell (CS) and food (US). This link allowed the presentation of the bell to activate the mental representation of food (see Fig. 1) and, therefore, produce salivation in much the same way as would actual presentation of the US itself. It is clear from this description of Pavlov’s (1927) hugely influential work, that the term associative learning has two meanings. These meanings are often confused. The first refers to a phenomenon – the capacity possessed by a broad range of organisms to learn that two or more events in the world are related to one another. That is, one event may refer to, signal, or cause the other. This meaning of associative learning is silent as to the psychological mechanism responsible for learning. The second meaning of associative learning does specify a psychological mechanism. This mechanism is the formation of links between mental representations of physical stimuli as illustrated in Figure 1. The links are said to be formed passively and automatically as a direct consequence of 183

Mitchell et al.: The propositional nature of human associative learning

Figure 1. Elipses indicate mental representations (of the bell and the food). The arrow between the two elipses indicates the mental link formed as a consequence of bell-food pairings. The bell ringing produces salivation because it activates the mental representation of food, which, in turn, produces salivation.

contiguous (with some restrictions) pairings of those physical stimuli. These mental links then allow the presentation of one stimulus to activate the representation of – that is, bring to mind – the other stimulus. Many researchers assume that learning about the relationships between events in the environment (the phenomenon) takes place via the formation of links between mental representations of those events (the mechanism). Our target article argues against this position and aims to show that associative learning results, not from the automatic formation of links, but from the operation of controlled reasoning processes. These processes result in beliefs about the world in the form of propositions, rather than simply links that allow one representation to activate another. Hence, in the context of the present argument, the term “associative learning” refers to the ability to learn about relationships between events, not to a mechanism by which mental CHRIS MITCHELL is senior lecturer at the University of New South Wales. From 1991– 1997 he investigated associative learning in rats at University College London. He then worked at Unilever Research, Port Sunlight, as a consumer psychologist, before returning to academia in 2000 to pursue his interests in attention, memory, and associative learning. He has published in the Journal of Experimental Psychology: Animal Behavior Processes, the Journal of Experimental Psychology: Learning, Memory, and Cognition, and the Journal of Experimental Psychology: General. He is an Associate Editor of the Quarterly Journal of Experimental Psychology. JAN DE HOUWER is Professor of Psychology at Ghent University. He is the author of over 100 publications in the field of experimental psychology, including publications in Psychological Bulletin and the Journal of Experimental Psychology: General. His main research interest is the manner in which spontaneous (automatic) preferences are learned and can be measured. He is Editor of Cognition and Emotion and was Laureate of the Royal Flemish Academy of Belgium for Science and the Arts. PETER LOVIBOND is Professor of Psychology at the University of New South Wales. He has qualifications in experimental and clinical psychology, and has carried out research on animal learning and motivation, human learning, cognition, psychophysiology, and anxiety and depression. He is a Consulting Editor for Learning and Behavior, the Journal of Experimental Psychology: Animal Behavior Processes, and the Journal of Abnormal Psychology. He is a Fellow of the Australian Psychological Society and the Association for Psychological Science.

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links are formed. In order to distinguish the two main approaches to theorizing about mechanisms of associative learning, we refer descriptively to the automatic link-formation mechanism and its alternative, the propositional approach. A core difference between the two approaches (propositional and link-based) is related to the way in which knowledge is assumed to be represented. As Shanks (2007, p. 294) points out, propositional representations: have internal semantic or propositional structure in the same way that language does. The English sentences “John chased Mary” and “Mary chased John” have the same elements but do not mean the same thing as they are internally structured in different ways. The alternative to such propositional or cognitive representations is an association that simply connects the mental images of a pair of events in such a way that activation of one image causes activation (or inhibition) of the other.

Dickinson (1980, p. 85) similarly describes “an excitatory link which has no other property than that of transmitting excitation from one event representation to another.” These quotes reveal that a proposition differs from a link in that it specifies the way in which events are related. For instance, a proposition can specify that the bell signals food. In contrast, a link between representations only allows activation to pass between those representations. The link itself has no representational content; there is nothing stored to indicate the nature of the relationship between the stimuli (Fodor 2003). This means that a proposition has a truth value (see Strack & Deutsch 2004), but a link does not. That is, a proposition can be shown to be true or false. In the case above, it can be demonstrated that the bell does or does not signal food. A link cannot be shown to be true or false because it does not represent any particular relationship between the bell and food. Proponents of the automatic link mechanism do not deny that propositional reasoning processes can generate knowledge of relationships between events in the world. However, they argue that the link-formation mechanism is able to produce learning independently and in an automatic manner. This point has already been made by Shanks (2007). As he says, It is important to realise that when arguing for a contribution of associative processes, supporters of this approach have never denied that rational causal thinking takes place . . . Rather, the question is whether all causal thought is of this form, or whether instead there might be a separate type of thinking (associative) when people make intuitive judgments under conditions of less reflection. (Shanks 2007, p. 297)

Likewise, McLaren et al. (1994) “agree there exist two qualitatively different types of learning,” (p. 315) “an associative system which cumulates information about contingencies between events and a cognitive system with beliefs and reasons for those beliefs” (p. 327). “By associative learning, we mean learning that can be characterised in terms of the establishment of links between representations” (p. 316). They assume that the formation of links occurs “automatically, regardless of the subject’s plans or intentions” (p. 321). Thus, the alternative to the propositional approach is a dual-system approach; behavior is determined by both the propositional reasoning system and the automatic link-formation mechanism.

Mitchell et al.: The propositional nature of human associative learning A critical issue then is whether there is evidence for the second component of the dual-system approach, the automatic link-formation mechanism. It is important to be clear that our aim is not to evaluate individual models of learning or propositional reasoning, of which there are many. Our aim is simply to compare the broad class of dual-system models with the broad class of propositional models. It is for this reason that we use the terms propositional approach and dual-system approach. These two approaches differ in fundamental and testable ways. To summarize, the propositional approach suggests that controlled reasoning processes are necessary for learning to take place, and learning results in beliefs about the relationship between events. This can be contrasted with the idea that learning is sometimes the consequence of the automatic formation of excitatory and inhibitory links between stimulus nodes or representations. In this target article, we present a brief and selective survey of the literature on associative learning (for more complete reviews of some specific aspects of the literature, see De Houwer 2009; De Houwer et al. 2005; Lovibond & Shanks 2002). In this survey, we find clear support for the role of propositional processes in learning. In stark contrast, little unambiguous support is found for an automatic link-formation mechanism. We conclude that there is very little to be lost, and much to be gained, by the rejection of the dual-system approach that incorporates an automatic link-formation mechanism. This is true for our understanding of the basic processes of associative learning (at both the psychological and physiological level) and in the application of learning theory to pathological behaviors in the clinic. 2. The dual-system approach to learning The dual-system approach incorporates all of the reasoning processes of the propositional approach plus an additional automatic link-formation mechanism. Therefore, it is this link formation mechanism that is the focus of section 2. 2.1. Learning

As outlined in section 1, the usual view is that links between representations can be formed automatically in the sense that they are independent of the goals, processing resources, and causal beliefs of the individual (see Moors & De Houwer [2006] for an analysis of the concept “automatic”). Thus, as Le Pelley et al. (2005a, p. 65) have argued, imposing a cognitive load will “hamper participants’ use of cognitive strategies in contingency learning, instead forcing them to rely on ‘automatic’ associative processes.” This implies that these (link-based) associative processes are automatic in the sense that they are efficient (see also, Dickinson 2001, p. 23). Although the link mechanism is often thought to be efficient and to operate independently of the subject’s goals, link formation is not assumed to be completely unconditional. A number of different learning rules have been proposed that can be seen as setting restrictions on the conditions under which the pairing of events leads to the formation of a link between the representations of those

events (e.g., Mackintosh 1975; Pearce 1987; Pearce & Hall 1980; Rescorla & Wagner 1972; Wagner 1981). For example, it is generally accepted that links will be formed only if the CS is attended (e.g., Mackintosh 1975; Pearce & Hall 1980). Similarly, Rescorla and Wagner (1972) proposed that contiguous pairings of a CS and US will not produce an associative link between the two if the representation of the US is already activated (or is unsurprising), for instance because a second pretrained CS is present on that trial. This is the phenomenon of blocking (Kamin 1969) – the pre-trained CS will block the formation of a link between the target CS and the US – which is an example of competition between cues to gain “associative strength.” Blocking is a very important phenomenon in the study of learning, precisely because it shows that contiguous stimulus pairings do not always produce associative learning. The link-formation mechanism is thought to be responsible not only for blocking, but also for many other conditioning phenomena (e.g., conditioned inhibition, overexpectation effects, etc.) and is thought to apply equally to all stimuli across different modalities and in a wide range of species. The generality of the phenomena (perhaps most importantly, blocking) across these different situations and species is often argued to demonstrate that all species possess a common learning mechanism (e.g., Dickinson et al. 1984). The mechanism must, it is sometimes further argued, be very simple and automatic because surely species such as the humble rat could not possess the complex hypothesis testing abilities of humans. 2.2. Performance

The link model provides a ready explanation for conditioned responses (CRs) such as salivation to a CS that has been paired with food. Once the link is formed, activation can be transmitted from one representation to another just as a piece of copper wire conducts electricity. Thus, when a CS such as a bell is presented on test, it activates the mental representation of that bell. This activation is then transmitted along the link, and so the US representation also becomes activated (see Fig. 1). Salivation (the CR) is observed because activation of the US representation is functionally equivalent to actual physical presentation of food. Thus, the link mechanism provides a very simple and intuitive account of why, when a CS is presented in the absence of the US on test, behaviors consistent with actual US presentation, such as salivation, are often observed. Of course, this characterization of operation of the link model is overly simplistic and easily discredited (see Wagner & Brandon 1989). Within this model, activation of the US representation by the CS (via the link) is equivalent to activation of the US representation by presentation of the US itself. Associative learning theorists are well aware that presentation of the CS and US do not have exactly the same consequences; the CS is not a substitute for the US. Wagner’s (1981) influential Sometimes Opponent Processes (SOP) model of associative learning addresses this issue. Wagner distinguishes between a primary and a secondary state of activation, termed A1 and A2, respectively. It is only when a US is physically present that its representation (or some part thereof) will be activated into the (primary) A1 state. Following BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Mitchell et al.: The propositional nature of human associative learning earlier CS-US pairings (conditioning), presentation of the CS will associatively activate the US representation into the (secondary) A2 state. Thus, Wagner’s model postulates two different states of activation to distinguish between perception of the US when it is physically present (the A1 state) and anticipation of that US (the A2 state). There are also other ways in which a US representation can be activated into the A2 state. When a US is presented (and its representation is activated into A1), removal of that US will allow the representation to decay into A2. In this case, A2 activation of the US representation would seem to equate to memory of the US. One thing that is striking about this model is that it does not distinguish between memory for a US in the recent past and anticipation of a US in the future (which have very different behavioral consequences; see Bolles & Fanselow 1980). That is, both US memory and US anticipation are represented by A2 activation of the US representation. Further refinement would be needed to accommodate this important distinction. However, what is important is that if one postulates different states of activation, then the idea of simple activation can come to mean different things, and the link model becomes much more flexible. Anticipatory CRs such as salivation or fear are not the only responses said to be produced by the link mechanism. Learning theorists have also applied this same approach to the analysis of human contingency learning. An example of a contingency learning task is the allergist task (e.g., Larkin et al. 1998). Participants play the role of an allergist who is asked to determine which food cues produce an allergic reaction outcome in a fictitious Mr. X. In the case of simple conditioning, Mr. X eats a food such as carrots on each trial and always suffers an allergic reaction. Participants learn that carrots are associated with the allergic reaction. The automatic link-formation mechanism is thought to operate in this scenario just as it does in Pavlovian conditioning; a carrot-allergic reaction (cue-outcome) link is formed, such that presentation of the cue is able to activate the representation of the outcome. When a food that has been followed by the allergic reaction during training is judged to be allergenic on test, it is argued that this judgment is the consequence of the cueoutcome link that has formed. In fact, Pearce and Bouton (2001) suggest that the link between cue and outcome can serve to represent a whole range of different associative relationships. This further implies that a causal relationship between the cue and outcome (e.g., drinking alcohol causes a headache) is represented in exactly the same way as a predictive relationship (e.g., hearing the platform announcement predicts, but does not cause, the arrival of a train). It also implies that causal and predictive relationships are represented in the same way as purely referential relationships, in which the cue merely refers to the outcome without an expectation that the outcome will actually occur (e.g., the word “sun” uttered at night refers to the sun but does not produce an expectation that the sun will appear in the immediate future), or to the relationship between a category (e.g., animals) and an exemplar of that category (e.g., a cat). However, these relationships are not equal. It is known, for example, that whether the cues and outcomes in an associative learning experiment are presented in a causal or a predictive scenario has a profound effect on the 186

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pattern of responding seen on test (Pinen˜o et al. 2005; Vadillo & Matute 2007; see also Waldmann 2000, for a similar argument in the context of causal and diagnostic learning). The simple link mechanism, because it cannot capture the precise nature of the associative relationship between cue and outcome, cannot explain these effects and so cannot explain many aspects of human associative learning. Of course, as was pointed out in section 1, the automatic link-formation mechanism has been argued to be only one system in a dual-system approach to learning. It is open to proponents of this approach to argue that the differences observed between causal and predictive cues are a consequence of the second, propositional, process, not the automatic links (e.g., Vadillo & Matute 2007). We shall return to this issue further on. In summary, the dual-system approach suggests that, in addition to the reasoning processes that produce conscious propositional knowledge, there exists an automatic, hardwired mechanism that produces links between CSs and USs (or cues and outcomes). In Pavlovian conditioning, these links allow the presentation of the CS to activate the US representation, and this produces a CR. The link-formation mechanism is also thought (under certain circumstances) to be responsible for the learning of other types of relations, including predictive, causal, and referential relations, and is assumed to operate in all species, including humans. 3. The propositional approach to learning According to the propositional approach, associative learning depends on effortful, attention-demanding reasoning processes. The process of reasoning about the relationship between events produces conscious, declarative, propositional knowledge about those events. 3.1. Learning

When we learn that Mr. X has an allergy to carrots, or that a bell will be followed by food, we use the same processes of memory and reasoning that we use to plan our grocery shopping, to play chess, or to behave appropriately at a black-tie function. When presented with a bell, we may recall that the last time the same bell rang, we received food. Given a number of assumptions (e.g., that relations are stable over time and that the bell is a potential signal for food), this might lead us to hypothesize that when we hear that bell, we are about to receive food again. We may also recall having previously hypothesized that the bell signals food. When we do indeed receive food, that experience constitutes a test (a confirmation) of our hypothesis. Thus the strength of our belief in the bellfood relationship will increase. The encoding of this hypothesis in memory, and the degree to which we have confidence in it, constitutes learning. There is no mental link between the bell and food, but a proposition of the form, “When I hear a bell, I will receive food.” Propositions can be regarded as qualified mental links, that is, links that specify how two events are related. This approach is also adopted in the Bayesian network approach to the analysis of belief acquisition and revision (see Lagnado et al. 2007, for a very useful overview). In Bayes nets, events are joined by, for example, a causal

Mitchell et al.: The propositional nature of human associative learning link – an arrow that has a particular strength and direction. Thus, an arrow that points from “bacterial infection” to “peptic ulcer” might indicate that bacterial infection causes peptic ulcers. Because the links in Bayes nets represent propositions about relationships, they, like all propositions, have truth value (e.g., it is either true or not true that bacterial infection causes peptic ulcers). Therefore, the arrows do not simply indicate that activation can spread from one mental representation to another in that direction. Despite these similarities, the Bayes net framework and the propositional approach are not identical. Most importantly, the Bayesian approach is silent as to whether belief acquisition involves controlled or automatic processes. The propositional approach presented here makes the strong claim that associative learning is never automatic and always requires controlled processes. Associative learning theorists are often concerned not simply with whether or not a CR is produced, but with the strength of the CR, which is thought to be a measure of “associative strength.” Within the propositional approach, associative strength relates to two things. The first is the belief about the strength of the CS-US relationship. Thus, a belief may be held that a CS is followed by a US on 50% of occasions. This will, of course produce a weaker CR than a belief that the CS is followed by the US on 100% of occasions. The second is the strength of the belief, which will typically be low at the start of training and high after many CS presentations. Therefore, associative strength will be jointly determined by how strong the CS-US relationship is believed to be (the content of the belief) and the strength of that belief (the degree of confidence with which it is held). The description of learning presented above leaves some important issues unspecified. First, we do not specify the nature of the controlled processes, beyond characterizing them as propositional reasoning. That is, we do not propose a new model of propositional reasoning. There are many ways to model reasoning processes (e.g., Braine 1978; Chater et al. 2006; Evans & Over 1996; Johnson-Laird 1983), some of which are specifically designed to account for the learning of causal relationships between events (e.g., Cheng 1997; Kruschke 2006). We would not argue for the virtues of any particular model of reasoning, only that associative learning requires reasoning, in whichever way this is achieved. Second, even though we postulate that associative learning is influenced by memory for prior events, we do not propose a new model of memory. Probably the simplest memory model that would be consistent with our view is an instance model of memory (e.g., Hintzman 1986). According to this model, separate experiences are stored as separate memory traces that can be retrieved on the basis of similarity with the current stimulus input. Thus, a bell can retrieve memories of past occasions on which the bell was presented, and therefore, of past bell-food pairings. Third, we do not rule out a role for automatic processes in learning. Memory retrieval has many features of automaticity, and so some of the processes that result in learning must also be automatic. However, this does not imply that learning itself is automatic. According to the propositional approach, recollections of past bell-food pairings alone cannot produce learning. These recollections only serve as one kind of input into the propositional reasoning

processes that are responsible for learning. Other kinds of input will include, for example, the knowledge that there was no other signal for food present when bell-food pairings were experienced, and the belief that bells are, in general, potential signals for food delivery. It is important to make clear that allowing automatic processes of memory (and, indeed, perception) to play a role in learning, does not imply that the propositional approach is simply another dual-system approach. The way in which automatic and controlled processes interact to produce learning in the propositional approach is quite unlike that of the dual-system approach. In the dual-system approach, two incompatible CS-US (e.g., bell-food) relationships might simultaneously be learned by the two systems (although, it should be noted, it is seldom explained how these systems might interact under such circumstances). For example, a strong bellfood link may form in the absence of any belief that presentation of the bell signals food delivery. In contrast, in the propositional approach this is not possible because the automatic processes of perception and memory serve only as an input to the non-automatic processes of propositional reasoning. These two types of process are simply different parts of the same learning system. Lastly, it is important to be clear on the way in which the propositional approach deals with the role of consciousness in learning. We do not claim that people are necessarily aware of all of the processes that lead to the formation of propositions about relationships between events, including the reasoning processes. What we do claim is that the propositional beliefs themselves are available to consciousness. Thus, it is not possible to have learned about a relationship between two events in the environment without being, or having been, aware of that relationship (see De Houwer 2009). 3.2. Performance

The consequence of entertaining a belief that the bell CS signals the food US (or, in other cases, that the CS causes the US) is that, “When I next hear the bell, I shall (all things being equal) anticipate, or expect, the food to be presented.” Early cognitive psychologists also viewed conditioned responses to be the consequence of US expectancy. They assumed that the strength of the CR (e.g., skin conductance elevation) in conditioning with a shock US would be a product of the strength of the expectancy of shock and the value (intensity or aversiveness) of that shock (e.g., MacCorquodale & Meehl 1954). However, expectancy was thought of in terms of a link that allowed the CS to activate the US representation. The propositional approach departs from these early theories in that the knowledge of the associative relationship between CS and US is a belief represented in propositional form. Thus, the expectancy of the US when the CS is presented is a consequence of the belief that the CS causes or signals the US. One problem that is often raised in the context of expectancy-based models of emotional and physiological conditioned responses is how an expectancy can give rise to such responses. We do not have a solution to this long-standing problem. However, we already know that instructions can produce physiological and emotional responses in the absence of any CS-US link. For instance, BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Mitchell et al.: The propositional nature of human associative learning the mere instruction that an electric shock is forthcoming leads to an increase in fear and skin conductance (Cook & Harris 1937). Hence, if it is assumed that instructions produce CRs by generating an expectancy of the US, then there must be a process by which US expectancy can generate physiological CRs, even though this process is not yet well understood. A related issue is that skin conductance and heart rate CRs seem uncontrollable and, in this sense, therefore, automatic. This seems to imply that an automatic learning system is in operation. However, the idea that conditioned responses can arise automatically can be accounted for within the propositional approach in two ways. First, we do not argue that subjects have control over their responses to expected USs, but rather that learning to expect those USs is a non-automatic process. Once there is an expectancy that the US will occur (the subject has learned that the CS that has been presented predicts the US), this can automatically lead to emotional and physiological responses; if one believes that a painful shock is imminent, it is difficult not to experience fear. Second, once a proposition has been formed that the CS causes or signals the US, it will be stored in memory and may be activated automatically. Hence, the presentation of a CS can automatically lead to the expectation of the US (and thus to conditioned responding) if the previously formed CS-US proposition is retrieved automatically from memory. Whether the CS-US proposition is retrieved automatically from memory will depend on a number of factors, including the number of times that the CS-US proposition has been consciously entertained. In summary, although learning results from controlled processes, performance may be automatic. With regard to performance in causal or contingency learning, the propositional approach applies in a very straightforward way. Take the example of the foodallergy paradigm. Participants are assumed to form propositions about the relation between foods and allergies (e.g., carrots cause an allergic reaction). When asked to rate the contingencies between different foods and allergies, participants simply need to express their propositional knowledge. That is, the report of contingency knowledge is merely the verbal expression of a belief. 3.3. Predictions of the propositional and dual-system approaches

The propositional and dual-system approaches make a number of different predictions about the conditions under which learning will occur, and about the pattern of responding that might be observed when different contingencies are in place. First, whether learning can take place in the absence of awareness of the CS-US (or cueoutcome) contingencies is relevant to this debate. The propositional approach assumes that learning involves testing hypotheses and that it results in conscious propositional beliefs. One would, therefore, expect participants who successfully learn the CS-US contingencies to be aware of, and be able to report, those contingencies. By contrast, if learning is automatic, it may take place in the absence of such awareness. Second, the propositional approach suggests that all learning is effortful and so should depend on the availability of sufficient cognitive resources. The link-formation mechanism, because it is 188

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automatic (in the sense that it is efficient) should be less dependent on cognitive resources. Third, hypotheses about how events are related to each other can be acquired by verbal instruction and will be influenced by abstract rules and deductive reasoning processes. Therefore, the propositional approach predicts that learning will similarly be affected by these factors. The automatic link-formation mechanism is non-propositional. It cannot, therefore, be affected directly by verbal instruction, rules, or deduction. In section 4, we present the findings that lend support to the propositional approach. In section 5, we outline the evidence that has been argued to provide strong support for the dual-system approach. It will be suggested at the end of section 5 that the balance of evidence strongly favors the propositional approach. 4. Evidence for the propositional approach 4.1. The role of awareness in associative learning

Because learning is assumed to involve the strategic testing of hypotheses and to result in conscious propositional knowledge about relations between events in the world, a propositional approach predicts that learning should be found only when participants have conscious awareness of the relevant relations. If evidence for unaware conditioning were uncovered, this would, therefore, strongly support the existence of multiple learning mechanisms (Lovibond & Shanks 2002; see also Boakes 1989; Brewer 1974; Dawson & Schell 1985; Shanks & St. John 1994). In Pavlovian conditioning of human autonomic responses, for example, a CS (e.g., a light) is paired with an aversive US such as an electric shock. On test, learning is evidenced by the ability of the CS to increase the participant’s skin conductance, a measure of fear. The results consistently show evidence for skin conductance CRs only in participants who are aware of the CS-US contingency (for reviews, see Dawson & Schell 1985; Lovibond & Shanks 2002). Moreover, CRs occur only after the participants become aware of the CS-US contingency. Such results have led to the conclusion that awareness of the CS-US contingency is a necessary condition for Pavlovian conditioning to occur (Dawson & Shell 1985). Other studies of conditioning with shock USs suggest that the close link between learning and awareness is due to the fact that consciously available hypotheses determine how the participant will respond. For instance, interindividual differences in human autonomic conditioning are closely related to interindividual differences in the extent to which the US is expected at a particular moment in time (e.g., Epstein & Roupenian 1970). When participants have incorrect beliefs about the association between events or between a response and an event, their conditioned behavior is most often in line with the incorrect beliefs rather than with the objective contingencies (e.g., Parton & DeNike 1966). Lovibond and Shanks (2002) concluded that the available evidence, from a whole range of conditioning procedures, is consistent with the idea that conditioning is accompanied by awareness. Although there are many papers arguing for unaware conditioning, close inspection reveals, in almost all cases, that the measure of conditioning was most likely more sensitive than that of awareness.

Mitchell et al.: The propositional nature of human associative learning This may have been because, for example, a recall rather than a recognition test of contingency awareness was used, or because contingency awareness was only tested after an extinction phase (see Dawson & Schell [1985; 1987] for excellent reviews of these issues). These flaws have the potential to lead to an apparent dissociation between conditioning and awareness when, in fact, none exists. Only two possible exceptions were identified by Lovibond and Shanks, evaluative conditioning (e.g., Baeyens et al. 1990a) and the Perruchet effect (e.g., Perruchet 1985). We shall return to these in section 5. Before we accept that the absence of evidence for unaware conditioning constitutes evidence against the automatic link mechanism, we should consider the alternatives. For example, perhaps the observed concordance between awareness and CRs does not result from the US expectancy causing the CR (as we have suggested), but rather from the CR causing the US expectancy. Thus, following CS-shock training, presentation of the CS will elicit CRs such as increased anxiety, heart rate, and arousal. When participants experience these physiological CRs, they may then draw the conclusion that the shock is about to be presented, and so they become aware of the ¨ hman & Soares CS-US contingency (Katkin et al. 2001; O 1993; 1994; 1998). Alternatively, it may be argued that, although the link-formation mechanism is automatic in some respects (e.g., it is efficient and independent of the learner’s goals), it is not automatic in the sense that it is unconscious. This would be a second way in which the absence of unaware conditioning might be argued not to be inconsistent with the dual-system approach. To summarize, a demonstration of unaware conditioning would be highly damaging to the propositional approach, and would provide strong evidence for a second (automatic) learning mechanism. However, a large body of literature shows a clear concordance between conditioning and awareness, and provides, therefore, no unique support for an automatic learning mechanism. So what can be concluded from these data? The observed concordance between conditioning and awareness is strongly predicted by the propositional approach. And, although the absence of unaware conditioning cannot be taken as decisive evidence in the present debate (an absence of evidence rarely is decisive), it is only consistent with the existence of the link-formation mechanism if certain additional assumptions are made. Thus, if anything, the data support the propositional approach. Finally, it should be noted that if we acknowledge that learning depends on awareness, then we remove one of the reasons for postulating a dualsystem approach in the first place. If all learning is aware, there is less to be gained from postulating an automatic link-formation mechanism in addition to a propositional reasoning mechanism. 4.2. Cognitive load and secondary tasks

According to the propositional approach, learning depends on the involvement of propositional reasoning processes that require attentional/cognitive resources. Therefore, secondary tasks that consume cognitive resources, or instructions that divert attention away from the target association, are predicted to impair learning. A small decrease in attention may not be sufficient to reduce learning, but any manipulation that is sufficient

to interfere with the formation or deployment of propositional knowledge about the CS-US relation should also reduce CRs to that CS. One way in which processes can be automatic is that they require only limited cognitive resources. Hence, if reduced attention to the target relationship leads to a reduction in learning of that relationship, this would seem to suggest that learning is cognitively demanding and, in this sense, not automatic. The most thorough investigation of the effect of attentional manipulations on conditioning was conducted by Dawson and colleagues in the 1970s (e.g., Dawson 1970; Dawson & Biferno 1973). They embedded a differential autonomic conditioning design within an “auditory perception” masking task that required participants to answer several questions at the end of each trial concerning the pitch of a series of six tones. In fact, one tone was paired with shock (CSþ) and another tone was never paired with shock (CS2). Propositional knowledge of the differential contingency was assessed by online expectancy ratings and by a post-experimental interview. The results were clear-cut. The addition of the masking task substantially reduced both contingency knowledge and differential electrodermal CRs. Participants who were classified as unaware of the differential contingency failed to show any differential CRs. Furthermore, the expectancy ratings and electrodermal CRs were closely related. When the data for “aware” participants were aligned around the trial on which they first showed expectancy discrimination, the electrodermal measure similarly showed differentiation after, but not before, that point. Dawson’s results are not unusual; the same pattern has been observed repeatedly across different conditioning preparations, and there is no convincing example of a differential impact of reduced attention on verbalizable knowledge and CRs (see Lovibond & Shanks 2002). The finding that learning processes are disrupted by the addition of a masking task suggests that learning requires cognitive resources and is, in this sense, not automatic. It is, therefore, evidence against an automatic linkformation mechanism. However, it might be argued that no psychological mechanism or process places zero requirements on cognitive resources; there are no automatic processes in this very strict sense. There are degrees of automaticity (Moors & De Houwer 2006). Thus, the link-formation mechanism, although cognitively demanding, may be less demanding than other tasks such as reasoning and problem solving. Alternatively, perhaps cognitive load does not prevent the automatic link-formation mechanism itself from operating, but rather, it reduces the degree to which the stimulus input (the CS and US) is processed. If the participant fails to notice the stimuli, there will be no input to the automatic learning system, and nothing will be learned. Either of these interpretations of the effect of cognitive load would, of course, constitute quite a large concession. If all learning depends on cognitive resources, then one of the reasons for postulating the existence of an automatic link-formation mechanism has been removed (as was the case for the role of awareness in conditioning; see section 4.1 above). Moreover, such a concession weakens the testability of Dickinson (2001) and Le Pelley et al.’s (2005a) claim that when the cognitive system is overloaded, the operation of the link mechanism will be revealed. If the link-formation mechanism depends on cognitive resources, then imposing a BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Mitchell et al.: The propositional nature of human associative learning mental load during a learning task cannot, as has been claimed, reveal the operation of that mechanism in the absence of propositional reasoning. Furthermore, one recent study seems to suggest that the introduction of a secondary task does not simply reduce stimulus processing. This time the evidence comes from studies of blocking in human contingency learning. In blocking, as described previously, pairing of one cue, A, with the outcome (Aþ) in a first phase prevents learning about the target cue T on subsequent ATþ trials. De Houwer and Beckers (2003) found that blocking in human contingency learning was less pronounced when participants performed a demanding secondary task during the learning and test phases, than when they performed an easy secondary task. In other words, increasing the demands of the secondary task increased the degree to which participants learned a T2outcome relationship. Waldmann and Walker (2005) obtained a similar result, attesting to the reliability of this finding. This is the precise opposite of the outcome predicted by the account outlined above, according to which cognitive load has an effect on learning by reducing the degree of stimulus processing. By that account, the secondary task should have reduced learning about T on ATþ trials. The result is, however, in line with the hypothesis that blocking depends on effortful controlled processes, as predicted by the propositional approach; participants were prevented from reasoning that, because A is a cause of the outcome, T is, therefore, redundant. 4.3. Verbal instructions

Many studies have shown that informing participants verbally about a relationship between stimuli is sufficient to produce evidence of learning. In an example presented earlier (see sect. 3.2), if one informs a participant that a tone will always be followed by a shock, the tone will produce an increase in skin conductance, even though the tone and shock have never actually been presented together (Cook & Harris 1937). Likewise, if one first presents tone-shock trials and then verbally instructs the participants that the tone will no longer be followed by the shock (instructed extinction), the skin conductance CR will be dramatically reduced (e.g., Colgan 1970). Thus, verbal instructions can lead to the same effects as the actual experience of a contingency, and can interact with knowledge derived from actual experience. Recent studies have shown that these conclusions also hold for more complex learning phenomena. Lovibond (2003), using an autonomic conditioning procedure, trained a compound of A and T with shock (ATþ) and then presented CS (A) without the US (A2). The A2 training in the second phase increased the CR observed to T on test, a phenomenon known as release from overshadowing. Release from overshadowing could result from reasoning that (a) at least one of the cues A or T must signal the shock on ATþ trials and (b) because A was subsequently found to be safe, T must be the signal. Importantly, Lovibond (2003) also found release from overshadowing when the ATþ and A2 trials were described verbally (Experiment 2) and when the ATþ trials were actually presented, but the subsequent A2 contingency was described verbally (Experiment 3). This shows that the knowledge acquired verbally and that 190

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acquired by direct experience are represented in a similar way. Thus, the implication is that the knowledge acquired by experience is propositional in nature. It is very difficult to explain effects such as instructed conditioning in terms of an automatic link mechanism. Perhaps the mention of the bell activates the representation of the bell, and the mention of the shock activates a representation of shock. This contiguous activation might foster the formation of a link between these two representations (mediated learning; Holland 1990). Of course, this theory is easily refuted; verbal instructions that “on none of the following trials will the bell be followed by shock” activate the bell and shock representations in the same way, but these instructions will not produce an anticipatory response. Perhaps knowledge in propositional form creates CSUS links in some way that we have not yet considered. However, even if this translation process were possible, there is a deeper problem with this general idea. Proponents of the dual-system approach would like to argue for a distinction between the acquisition of conscious propositional knowledge, on the one hand, and automatic learning, on the other. Allowing that a single verbal instruction might produce a link between two representations of the same kind as does the experience of multiple training trials, seems to blur this distinction. Remember that, in their analysis of causal learning, the dual-system theorists also argue that the links formed by the automatic system can generate propositional knowledge. Taken together, these two ideas suggest that all propositional knowledge is immediately translated into links, and all knowledge in the form of links can be translated into propositional form. One of the two systems is, therefore, redundant. The only coherent solution to this problem is to assume that there is a single system, and the evidence presented here suggests that this system is propositional in nature. The experiments presented in the following section, concerning the effects of abstract rules and deductive reasoning in conditioning, lend further support to this conclusion. 4.4. Abstract rules and deductive reasoning

Shanks and Darby (1998) reported a striking demonstration of the use of rules in associative learning. They presented Aþ, Bþ, AB2, C2, D2, and CDþ trials together with Iþ, Jþ, M2, and N2 trials. During a test phase, participants judged that the outcome was more likely to occur after the (previously unseen) compound MN than after the (also previously unseen) IJ compound. In terms of links between representations, this is the reverse of the prediction based on the elements that made up the compounds. Participants appeared to have learned a rule from observing trials on which cues A –D were presented, that the outcome of compounds of two stimuli (i.e., AB2, CDþ) is the reverse of the outcome of the individual elements that make up that compound (i.e., Aþ, Bþ, C2, D2). They then applied this reversal rule to cues I –N. Other evidence for the role of propositional reasoning in human associative learning comes mainly from studies on cue competition, in particular, blocking (see De Houwer et al. 2005, for review). For example, De Houwer et al. (2002) observed blocking only when it was possible to

Mitchell et al.: The propositional nature of human associative learning infer deductively that cue T in the Aþ/ATþ design was not associated with the outcome. Because T does not add anything to the effect of A alone (i.e., the outcome was as probable and as intense on Aþ trials as on ATþ trials), it can be inferred that T is not a cause of the outcome. However, De Houwer et al. (2002) argued that this inference is valid only if it is assumed that the effect of two causes is additive (that when two causes are presented in compound, a larger than normal effect will be produced). De Houwer et al. (2002) provided one group of participants with an alternative explanation for why T did not add anything to the effect of A. They told these participants that A alone already caused the outcome to a maximal extent. That is, the outcome was at ceiling on Aþ trials. In this case, participants can reason that no increase in the effect was seen on ATþ trials, not because T was non-causal, but because an increase in the size of the effect was impossible. In line with the idea that blocking is based on propositional reasoning, no blocking effect was found in this condition (causal ratings of T were not reduced as a consequence of prior Aþ trials). Many other studies have confirmed this result. Beckers et al. (2005; see also Lovibond et al. 2003) raised doubts in their participants’ minds about the inference underlying blocking by giving pretraining in which the effect of two cues was shown to be subadditive (i.e., Gþ, Hþ, GHþ, and Iþþ, where þ stands for a US of low intensity and þþ for a US of high intensity). Blocking was significantly smaller after this type of pretraining than after pretraining that confirmed the additivity of causes (i.e., Gþ, Hþ, GHþþ, Iþ). Mitchell and Lovibond (2002), using a similar approach, showed blocking of skin conductance CRs only when blocking was a valid inference. Finally, Vandorpe et al. (2007a) obtained the same result in a causal judgment study that involved a very complex design. This is important because dual-system theorists often argue that the link-formation mechanism will be revealed in very complex tasks such as that used by Vandorpe et al. (see the discussion above in section 4.2 concerning cognitive load), and so the propositional system is unable to operate or is off-line (e.g., Dickinson 2001; Le Pelley et al. 2005a). Vandorpe et al.’s (2007a) results showed, however, that propositional reasoning processes can operate even in these complex tasks. 4.5. Conclusions

Many experiments, using a wide range of procedures, have shown a concordance between associative learning and contingency awareness. Furthermore, results of experiments in which a secondary task was imposed are consistent with the operation of a cognitively demanding reasoning process, especially in the case of blocking. Thus, manipulations that prevent reasoning also prevent the learning mechanism from operating. Many more experiments have demonstrated the impact of verbal instructions, rules, and deductive reasoning processes on the acquisition of associative knowledge. These data make a very strong case for the idea that associative learning is based on reasoning processes that yield conscious propositional knowledge. Of course, the dual-system approach cannot be said to be inconsistent with these findings, because it incorporates

both the link-formation and propositional reasoning systems. However, what is important is that, within the dual-system account of the data outlined above, the link mechanism itself is redundant. We now turn to the evidence that has been argued to provide unique support for the link-formation mechanism. 5. Evidence for the automatic formation of links Dual-system theorists point to a number of sources of evidence that they believe provide unique support for linkformation models. First, although associative learning is generally accompanied by awareness of the CS-US contingency, there are two learning procedures that do seem to provide some evidence of unaware conditioning (see Lovibond & Shanks 2002). These are evaluative conditioning and Perruchet’s (e.g., 1985) findings relating to the effects of trial sequence in partial reinforcement schedules. Second, some experiments have demonstrated learning that is not always rational (or normative). The absence of rationality has been argued to support the idea that learning can result from an automatic link mechanism. Lastly, it has been suggested that some neuroscientific data indicate the existence of a multiple learning system. We address these lines of evidence in turn. 5.1. Unaware associative learning

In evaluative conditioning research (see De Houwer et al. 2001; De Houwer 2007, for reviews), neutral stimuli (across a range of modalities) have been shown to increase or decrease in rated pleasantness as a consequence of pairings with strongly liked or disliked stimuli. Some researchers have provided evidence for evaluative conditioning in the absence of awareness (Baeyens et al. 1990a; Dickinson & Brown 2007; Fulcher & Hammerl 2001; Walther & Nagengast 2006; and see Stevenson et al. 1998, for a related finding). However, insensitivity of testing procedures and aggregating awareness scores across both participants and items may have hidden some contingency awareness in these studies (see Lovibond & Shanks [2002] for a review). An example of this second issue can be seen in Dickinson and Brown (2007). They found that their participants, when analyzed as a single group, did not demonstrate reliable contingency awareness but did show evaluative conditioning. However, Wardle et al (2007) reanalyzed these data and found that when participants were divided into two groups, aware and unaware, it was only the aware group that produced a reliable conditioning effect. Other researchers have suggested an even more fine-grained analysis. They have argued that, although participants might show very little contingency awareness when the cues are aggregated, they are, nevertheless, aware of the outcomes with which a subset of cues were paired. It is possible that it is this subset of cues that are responsible for the evaluative conditioning observed in earlier studies (Pleyers et al. 2007). It is very difficult to provide a satisfactory demonstration of unaware conditioning simply by showing conditioning in the absence of awareness. This is because it is very difficult to be sure that the awareness measure and the conditioning measure are equally sensitive. Lovibond and Shanks (2002) identified Baeyens et al.’s (1990a) BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Mitchell et al.: The propositional nature of human associative learning finding as being the most convincing evidence of unaware evaluative conditioning, because flavor-flavor conditioning was seen in the absence of any contingency awareness, but color-flavor conditioning was not seen despite awareness of the color-flavor contingency. The latter finding appears to confirm that the awareness measure used was sensitive (albeit to contingencies involving different stimuli). Thus, participants in the flavor-flavor condition appear to have been unaware of the contingencies they were exposed to. Given the uniqueness of this finding, it is important that Baeyens et al’s design is replicated, perhaps with the awareness measure used by Dickinson and Brown (2007), and that the awareness-learning relationship is analyzed at the item level. Even more convincing than Baeyens et al’s (1990a) dissociation would be a demonstration of conditioning in participants unaware of the flavor-flavor contingencies, but not in participants aware of those same contingencies (rather than color-flavor contingencies). This is exactly the reverse association (see Dunn & Kirsner 1988) sought by Pierre Perruchet in his analysis of eyeblink conditioning and cued reaction time learning. It is to this work that we now turn. Perruchet (1985) exposed participants to a pseudorandom series of tone-air puff and tone-alone trials and measured both eyeblink CRs and expectancy that an air puff would be delivered on the following trial (tones appeared on every trial). Participants’ self-reported expectancy of an air puff followed the gambler’s fallacy. Hence, after a run of three tone-air puff trials, participants tended to predict that the tone would not be followed by an air puff on the next trial. Conversely, after a run of three tone-alone trials, an air puff was strongly predicted to follow the tone on the next trial. The eyeblink CR, however, followed the opposite pattern; eyeblinks to the CS were most likely to be observed on trials following a run of tone-air puff trials and least likely following a run of tone-alone trials. Thus, recent CS-US pairings appeared to strengthen the CS-US link and increase the probability of the CR, despite a reduction in US expectancy. Perruchet has more recently observed the same dissociation using a simple cued reaction time task (Perruchet et al. 2006). Perruchet’s dissociations between US expectancy and the occurrence of the CR in eyeblink conditioning (and the equivalent effect in the cued reaction time task) are certainly intriguing. However, the findings are somewhat peculiar and are open to alternative interpretation. They are peculiar in the sense that the dissociation is not really between contingency awareness and the observation of the response (CR or reaction time). Participants know the contingency from the start of the experiment and the training trials confirm this; the tone will be followed by the US on 50% of trials. The effect observed seems to be much more a performance effect. Furthermore, the recency of CS-US pairings is perfectly confounded with recency of US presentations in this experiment. The observed fluctuation in the CR may, therefore, be due to sensitization produced by US recency alone, and not an associative phenomenon at all. Perruchet’s own experiments (see also Weidemann et al., in press) go some way to ruling out this alternative explanation, but further work remains to be done. Despite these issues, Perruchet’s gambler’s fallacy effect remains the strongest available 192

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evidence for dissociation between a CR and the conscious expectancy of a US. 5.2. Rationality

It is often assumed that rationality is a hallmark of the propositional system. If behavior is rational, then a propositional mechanism was in operation; if it is not rational, an automatic mechanism was in operation (Shanks 2007; Shanks & Dickinson 1990). Therefore, if it can be shown that associative learning is non-rational, it must be based on the automatic formation of links. The example of irrational behavior that most readily comes to mind is phobia. For example, arachnophobes can be fearful of spiders despite claiming to know that spiders are not harmful. This would appear to undermine the idea that learning is a propositional process – how could such a system produce behavior that contradicts the verbally reported belief? There are three ways that the irrational behavior of arachnophobes can be explained which are consistent with the propositional approach to learning: (1) The verbally reported belief that spiders are not harmful may simply be a consequence of social demands; the patient may believe the spider to be harmful but not wish to contradict the clinician’s view that the spider is harmless. (2) This phenomenon may relate to performance, not to learning. The patient may have a long-standing and strong belief that spiders will do him or her harm. He or she may also have acquired more recently a perhaps more fragile appreciation that certain spiders are not harmful. On presentation of a harmless spider, the old belief that spiders are harmful may be retrieved automatically from memory and thus lead to fear (see sect. 3.2). Because the retrieval of the old belief occurs automatically, the resulting fear might seem irrational and difficult to control. According to the propositional model, both beliefs (that the spider is harmful and that it is not harmful) will have been acquired through a process of propositional reasoning. (3) There is, in fact, little evidence that specific phobias of this kind result from learning at all, and therefore they may have a genetic etiology (see Menzies & Clarke 1995, for review). If fear of spiders has a large genetic component that affects behavior independently of learning, the fact that fear remains even when it is known that spiders are not harmful does not represent a challenge to the propositional approach to associative learning. Nevertheless, there are examples of what appears to be irrational associative learning. Karazinov and Boakes (2007) trained participants on a causal learning task with a conditioned inhibition design (Xþ/XT2). Thus, X was followed by the outcome when presented alone (Xþ) but not when it was presented in compound with the target cue (XT2). This training can give rise to inhibition; presentation of T has the ability to reduce the causal attribution to another exciter, Y, on test. This seems to be a rational inference because T prevented the outcome produced by X in training, and so might prevent the outcome that would otherwise have been produced by Y on test. Karazinov and Boakes (2007) found the reverse effect, however, when participants were given little time to think during training. Thus, participants did not learn that T prevented the outcome, but they appeared to

Mitchell et al.: The propositional nature of human associative learning learn that it caused the outcome. Karazinov and Boakes concluded that participants did not have time to reason about the relationship between T and the outcome, and so their behavior was the result of the automatic formation of a (second-order) link between T and the outcome (or between T and the response of giving a high causal rating). There are other related findings in the literature. For example, Le Pelley et al. (2005a) paired cue A with two outcomes (A-O1O2) in a first phase of training and found blocking following a second phase in which cue T was added (AT-O1O2); pretraining with A reduced the degree to which an association between T and the two outcomes was learned. This blocking was disrupted, however, when one of the outcomes changed in the second phase (AT-O1O3). Not only did participants learn to associate T and O3 (they failed to show blocking with respect to the outcome not predicted by A), but also T and the unchanged outcome, O1. Le Pelley et al. (2005a) argued that, because learning an association between T and O1 is not rational (O1 is predicted by A), and was not observed in a much simpler version of the task, the learning of T-O1 association must be a result of a non-rational, automatic mechanism. Shanks (2007) presented the following phenomenon as the most compelling evidence of an irrational link-formation mechanism in the context of contingency learning. In one condition, the probability of the outcome in the presence of the cue (P(O/C)) was 0.75, and the outcome did not occur in the absence of the cue (P(O/C) ¼ 0). In the other condition, the probability of the outcome both in the presence and in the absence of the cue was 0.75. Thus, although the probability of the outcome following the cue was equivalent in both cases (0.75), the outcome was contingent on the cue in the first condition, but not in the second. It has been found that judgments of the probability that the outcome will follow the cue are greater in the former case than in the latter. Thus, the cue-outcome contingency appears to have an impact on the judgment of outcome probability, despite the fact that this probability is identical in both cases (see De Houwer et al. 2007; Lagnado & Shanks 2002; Lo´pez et al. 1998a; Price & Yates 1993). It is irrational to give a higher rating of probability when the contingency is increased but the probability of the outcome stays the same. Shanks (2007) attributed these higher probability ratings to the formation of links between cues and outcomes that have a contingent relationship. We agree that these are very interesting findings, and each suggests that our reasoning abilities are sometimes not optimal. However, we do not think that these findings provide evidence for an automatic link-formation mechanism. The irrational behavior observed can equally be attributed to sub-optimal operation of the reasoning system.1 In each case, an explanation for the behavior can be given that is consistent with the propositional approach. For example, when given little time to ponder over the implications of seeing Xþ and XT2 trials, perhaps Karazinov and Boakes’ (2007) participants mistakenly thought that T might somehow signal the presence of X, which itself caused the outcome. Such an inference would lead to the conclusion that T itself might be associated with the outcome to a greater extent than the control cue. Perhaps Le Pelley et al.’s (2005a) participants knew that something about the outcomes had changed

between A-O1O2 trials and AT-O1O3 trials, but they could not remember exactly what had changed. As a consequence, they may have concluded that it was safest to assume that T caused O1 and O3 equally. Finally, in the studies Shanks (2007) refers to, participants may merely have been confused about the meaning of the term “probability” in the test instructions. It is not at all obvious that participants would readily distinguish between probability and contingency in the way that the experimenters did. Alternatively, participants in the non-contingent condition probably assumed that there existed some other cause of the outcome. Then, on test, they may have thought that the experimenter was asking about the probability of the outcome following the cue, but in the absence of any other potential causes. That is, an assumption may have been made that the cue was presented in a different context on test. These alternative explanations might be argued to be somewhat far-fetched. However, they are presented only to demonstrate that irrational behavior is not inconsistent with the operation of an imperfect propositional reasoning system cooperating with an imperfect memory system. It might also be argued that this position leaves the propositional approach untestable. This is not so. First, one can test propositional explanations of irrational behavior empirically. For instance, if Le Pelley et al.’s (2005a) finding is due to confusion as to which outcome changed between the two phases of training, increasing the distinctiveness of the two outcomes should reduce the unblocking effect with respect to O1. If the impact of contingency on probability judgments featured by Shanks (2007) depends on confusion about the instructions given on test, then the effect should be reduced in magnitude if these instructions leave less room for misunderstanding. Also, presenting the test question in terms of frequency (“You will see ten further trials on which the cue will be present, on how many will the outcome occur?”), rather than probability, should reduce the size of the effect (see Gigerenzer & Hoffrage [1995] for an example of frequency formats reducing base rate neglect). If, on the other hand, the participants assumed that the test context was different from the training context, then making it explicit that the cue was presented in the same context on test should eliminate the effect. Second, and more importantly, evidence that participants are not always rational when they learn does not undermine the main predictions of the propositional approach; that learning will occur only when participants are aware of the cue-outcome (or CS-US) contingencies, will be disrupted by secondary tasks, and will be affected by verbal instructions, rules, and deductive reasoning processes. 5.3. Dissociable systems within the brain

One could argue that a dual-system approach is supported by neurological data showing that different brain regions are involved in different types of learning. These different brain regions could be seen as the neurological basis of different learning systems. For example, there is now abundant evidence that the amygdala plays an important role in, for instance, fear learning (e.g., Le Doux 2000; ¨ hman & Mineka 2001). A quite different area of the O brain, the cerebellum, has been shown to be important in conditioning of the nictitating membrane (Thompson BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Mitchell et al.: The propositional nature of human associative learning 2005). Therefore, based on such neuroscientific dissociation data, it might be argued that the amygdala is part of a fear learning system that is quite separate from the system responsible for nictitating membrane conditioning. This conclusion, however, is not necessarily correct (see Henson [2006] for a detailed discussion of the validity of theoretical inferences based on neuroscientific dissociation data). One alternative interpretation is that neither the amygdala nor the cerebellum is able to produce learned behavior alone, but that they operate as individual components in a coordinated learning system. For instance, these brain regions might be important in processing specific kinds of stimuli or generating specific kinds of responses rather than being responsible for the learning process as such. Thus, the learning may take place neither in the amygdala nor cerebellum but in another part of the brain entirely, or, indeed, in many parts of the brain simultaneously. A related argument can also be applied to the idea that the striatum and its dopaminergic afferents are responsible for habitual behavior (Jog et al. 1999), but prefrontal areas are responsible for higher-level cognition. Again, these dissociations seem to imply separate learning systems. However, they may simply reflect a single learning system solving problems of differing complexity or concreteness (see Chater, in press). Although there can be no doubt that recent advances in the neurosciences have provided a wealth of knowledge about the brain mechanisms necessary for learning, these findings are not inconsistent with the single-system view of learning. Furthermore, the available behavioral evidence concerning human associative learning does not support the view that there are multiple learning systems. The behavioral evidence, therefore, presents a challenge to neuroscientists to discover how a single, integrated, propositional learning system with multiple subcomponents might be implemented in the brain. 5.4. Conclusions

To summarize the data presented in the present section, it would appear that two or three studies provide support for the link-formation mechanism. These are demonstrations of the Perruchet effect (Perruchet 1985; Perruchet et al. 2006) and perhaps one example of flavor-flavor evaluative conditioning (Baeyens et al. 1990a). It is important, therefore, that these findings are subject to the closest empirical and conceptual scrutiny in the future. Findings that provide evidence for irrational learning should also be studied further, but they do not provide direct evidence against the propositional approach. Lastly, it is not at all clear that evidence from studies of the brain can inform us as to the existence of distinct learning systems. Overall, therefore, we see no reason to postulate the existence of a link-formation system in addition to a propositional reasoning system. 6. Conceptual arguments There are a variety of reasons why the link mechanism has been so popular as an explanation for associative learning, even in the absence of strong supporting data. In the present section, we discuss three of these reasons: 194

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(1) the learning models developed within this traditional approach (e.g., Rescorla & Wagner 1972) seem parsimonious; (2) mental links, and the way they increase and decrease in strength, provide a very intuitive analogy for neural plasticity; and (3) researchers are resistant to the idea that nonhuman animals engage in propositional reasoning. We will evaluate the relative strengths and weaknesses of the propositional and link-based approaches with regard to these conceptual issues. 6.1. Simple models of learning

The first and perhaps strongest reason for learning theorists’ adherence to the idea of a link-formation mechanism is that a range of very tightly specified theories have been developed within this approach. Theories such as those proposed by Mackintosh (1975), Pearce and Hall (1980), Rescorla and Wagner (1972), and Wagner (1981) are formalized, can be simulated on a computer, and can, therefore, make precise and testable predictions. The power of these models comes from the fact that they often make few assumptions but apply to a wide range of phenomena. For this reason, it could be argued that these models are preferable to the propositional approach to learning. The first thing that needs to be pointed out is that the precision of the predictions of associative models from the link-formation tradition is somewhat overstated. A lot depends on the particular parameter values and the particular model variant from which the predictions are derived. In fact, from experience we have learned that it is difficult to produce a pattern of data that cannot be explained by one or the other variant of these associative models. For example, one can explain blocking (Kamin 1969) and the opposite phenomenon, augmentation (Batsell et al. 2001). One can also explain overshadowing (Pavlov 1927) and the opposite phenomenon, potentiation (Garcia et al. 1989). For each case of competition between cues, the opposite pattern of results can be explained by postulating links (“within-compound associations”) between the stimuli that might otherwise be in competition (e.g., Durlach & Rescorla 1980). The notion of within-compound associations is only one way in which freedom is gained to explain results that are not predicted by the formal versions of the models. Another way is to postulate different levels of generalization between cues. Schmajuk and Larrauri (2008), for instance, added such assumptions to a variant of the Rescorla-Wagner model in order to explain the finding that additivity pretraining can influence blocking (Beckers et al. 2005; see section 4.4). To recap, blocking is the finding that little is learned about T in a design in which Aþ trials precede ATþ trials. According to the Rescorla-Wagner model, blocking occurs because, on ATþ trials, the outcome is already predicted by A. Schmajuk and Larrauri (2008) argued that more blocking is seen following additivity pretraining (Gþ, Hþ, GHþþ, Iþ) than subadditivity pretraining (Gþ, Hþ, GHþ, Iþþ) because learning about GH during pretraining generalizes to later ATþ trials. In Beckers et al.’s (2005) experiment, the AT compound can be expected to acquire more generalized associative strength from GH following GHþþ pretraining (the additive group) than following GHþ pretraining (the subadditive group). This is because the associative strength of GH is higher in the additive

Mitchell et al.: The propositional nature of human associative learning group. In other words, participants expect the outcome to a larger extent at the start of ATþ trials in the additive than in the subadditive group. It follows from the RescorlaWagner model, therefore, that less can be learned about the T2outcome relation (more blocking will be observed) in the additive group. There are two problems with this alternative explanation. Firstly, Schmajuk and Larrauri (2008) focus on generalization between compounds (e.g., GH and AT). However, generalization between elements is ignored, as is generalization from compounds (e.g., GH) to elements of those compounds (e.g., G). Hence, Schmajuk and Larrauri (2008) can explain the results of Beckers et al. (2005) only by choosing very specific and selective parameters of generalization. It is not clear whether the model would still be able to explain the findings of Beckers et al. when more realistic assumptions are made about generalization between different kinds of cue. Secondly, as Schmajuk and Larrauri (2008) admit, the explanatory power of this model is limited. There are, for example, other experiments presented by Beckers et al. (2005) that the model is unable to account for, such as the effects of additivity on backward blocking, in which ABþ training is given before Aþ training. To explain these data, further assumptions would be required. Elsewhere in the literature there are other similar effects that this model cannot explain. For example, in a similar experiment to that of Beckers et al. (2005), Mitchell et al. (2005) showed that Gþ, Hþ, and GH2pretraining (subtractivity) can also produce a strong blocking effect. In this case, the compound of two causal cues in pretraining (Gþ and Hþ) was non-causal (GH2). The variant of the Rescorla-Wagner model proposed by Schmajuk and Larrauri (2008) cannot account for blocking in this case; it predicts very little blocking here, because the GH compound acquires no associative strength in pretraining. In contrast, the propositional approach provides a straightforward explanation for the strong blocking seen in both Mitchell et al.’s (2005) subtractivity condition and Beckers et al.’s (2005) additivity condition. Participants in both of these conditions can reason that T was noncausal because the AT compound did not produce a different outcome (either smaller or larger) from that observed when the A cue was presented alone. The conclusion from the examples above seems clear. While individual models such as the Rescorla-Wagner model are quite parsimonious, the entire class of theories that are assumed to describe the way in which links are formed is not. Although extending models in a post hoc manner is not, in principle, problematic, the evaluation of the extended model against only a single data set (for which that extension was specifically designed) is dangerous. The generalizability of the new model to other data sets must be demonstrated; otherwise there is a risk that a different link-based model will be generated post hoc to account for each observed experimental result. There is also another issue related to parsimony. In order to account for our manifest ability to, for example, solve problems and play chess, traditional learning theorists must supplement the link-formation system with a system that forms propositions on the basis of reasoning. As we argued above, these theorists are calling for a dual-system approach. No approach that needs two systems can be more parsimonious than an approach

that proposes only one of those systems, no matter how parsimonious the second system might be. Nevertheless, the apparent precision and parsimony of traditional learning models might be an important reason why many researchers are not ready to give up these models. It is important to realize, therefore, that adopting a propositional approach does not imply that one must give up traditional models of learning. The propositional approach is not an alternative to specific learning models such as the Rescorla-Wagner model (or any of its relatives); but it is an alternative to the dual-system approach that postulates an automatic link-formation mechanism. We can clarify this argument using Marr’s (1982) distinction between functional and algorithmic levels of explanation. Both functional and algorithmic models make predictions about which pattern of input (e.g., learning trials) leads to which pattern of output (e.g., CRs or causal ratings). Only algorithmic models, however, incorporate assumptions about the processes and representations that translate the input into the output. That is, models at the algorithmic level make assumptions about how the stimulus input is processed to produce the output. The propositional approach and the automatic link-formation mechanism are thus clearly explanations at the algorithmic level, because they do incorporate (different) assumptions about how the input is processed to produce the output (i.e., controlled reasoning vs. automatic link-formation and activation) and about the nature of the representations over which these processes operate (i.e., propositions vs. links between stimulus representations). Many individual models of associative learning, however, can be regarded as functional models. Take the example of the Rescorla-Wagner model. In essence, this is a mathematical formula that allows one to predict whether a CR will be observed given information as to the nature of the learning trials experienced. Hence, it is a functional model. It is not an algorithmic model because Rescorla and Wagner (1972) do not commit to a particular type of underlying process. Their model was developed to account for what is learned under certain conditions. This can be contrasted with models at the algorithmic level that give an account of how this learning takes place. In fact, Rescorla and Wagner (1972) are explicitly agnostic about algorithmic level explanations (that is, how organisms learn and therefore why they behave according to the Rescorla-Wagner model). They offer two quite different algorithmic level explanations, one in the language of links and another in terms of the constructs of expectancy and surprise. Hence, when the Rescorla-Wagner model is tested against other models such as the Pearce-Hall model, it is the fit of the mathematical formulae to the behavior that is being tested (i.e., predictions at the functional level), not the nature of the underlying processes or representations (e.g., automatic formation of links or propositional reasoning). From this perspective, a functional model such as the RescorlaWagner model is not incompatible with the propositional approach because the two can be seen as focusing different levels of explanation. In fact, from this point of view, the Rescorla-Wagner model can even be thought of as a simple mathematical model of propositional reasoning, not, as is usually assumed, a model of link formation. At the functional BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Mitchell et al.: The propositional nature of human associative learning level, it captures many of the operating principles of propositional reasoning. To take one simple example, a belief is most likely to change when it is demonstrated to be wrong – that is, when the belief leads to an expectancy that is violated. The Rescorla-Wagner model captures the essence of this idea; according to this model, learning only takes place (or beliefs only change) when the outcome on a learning trial is not predicted (i.e., that outcome is surprising). Lastly, it is interesting that so many learning models developed since the 1960s include constructs such as limited capacity working memory, selective attention, and interference in memory (Bouton 1993; Mackintosh 1975; Pearce & Hall 1980; Wagner 1981). We would argue that these constructs describe much more naturally the operation of controlled cognitive processes of propositional reasoning operating in cooperation with the memory system, than they do the automatic formation of links. 6.2. There are links in the brain

A second reason for the continuing success of the link-formation mechanism is that the idea of a link between mental representations that can increase or decrease in strength is a very powerful analogy for links between neurons in the brain. When associative learning theorists think in terms of the mental link between representations, there seems no doubt that this mechanism feels more real by virtue of its similarity to the hardware in which it must be implemented. However, there are two problems with this claim. First, this implicit reductionism loses all of its force when it is considered that the dual-system approach also postulates complex propositional reasoning capacities that cannot be explained (at least at the present time) in terms of links between representations. These more complex capacities must also be implemented in the brain. Within the dual-system approach, therefore, both systems must have strong (and equal) neural plausibility. Second, although a link between a CS and US representation might resemble two connected neurons in the brain, mental representations are not identical to neurons, and links are not identical to dendrites. Representations and links between representations are unobservable theoretical constructs. They are invented by psychologists in order to help understand behavior at an algorithmic level. In that sense, they are no more neurologically plausible than other theoretical constructs such as propositional representations. A very similar argument applies to the success of parallel distributed processing (PDP) models as support for the link-formation approach. In PDP models, structures with properties very similar to a collection of interconnected neurons are simulated within a computer. The strengthening of links within such PDP models is very similar to the strengthening of dendrites between neurons. Thus, both PDP models and neurological structures are structures (simulated in the computer or present in the brain) in which algorithmic processes can be implemented (see Marr 1982). The link model described in Figure 1 is quite different from these PDP models, just as it is different from structures in the brain. This is because, in Figure 1, links are formed between nodes that each 196

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represent a stimulus in a symbolic manner (i.e., the CS and US). In contrast, a single node in a PDP model does not represent anything, just as a neuron in the brain does not represent anything. In PDP models, representations are an emergent property of the network and correspond to particular patterns of activation across a number of nodes. PDP models thus offer a way to implement representations of stimuli and relations in a nonsymbolic, distributed manner. It is certainly true that the link model in Figure 1 is one possible algorithmic-level model that can be implemented in a PDP network. But models of highly complex cognitive abilities, such as propositional reasoning, can, in principle, also be implemented within PDP models, just as they are in the brain. In summary, the idea of a link can be used in many different ways, and it is important that these different uses are not confused. In this section, we have distinguished between links at the implementational level (neurons and PDP networks) and the idea that links form between representations, which is a model at the algorithmic level (see Fig. 1). This target article does not focus on the implementational level. Rather, we aim to distinguish between two algorithmic models of associative learning, one in which links are automatically formed that transmit excitation between representations, the other in which beliefs are formed, as a consequence of controlled processes, about the relationship between the events. We would argue that both the dual-system approach (incorporating the automatic link-formation mechanism) and propositional approaches are equally consistent with a link-based implementation such as a PDP model or, indeed, the brain. 6.3. Propositional reasoning in nonhuman animals

Although our subject matter here is human learning, we would not want to argue that humans possess a unique cognitive learning system. This stance implies that nonhuman animal learning is also a process of belief acquisition. Therefore, the complex representational system we possess evolved from similar, but simpler, cognitive systems in our ancestors; and many differences observed between human and nonhuman learning are quantitative, not qualitative. We have argued that learning is the consequence of an interaction between propositional reasoning and memory for past events. There is also evidence for primitive versions of these abilities in nonhuman animals. For example, Clayton and Dickinson (1998) have demonstrated episodic-like memory in scrub jays. There is also some evidence to support the idea that rats are able to reason about cause and effect (Beckers et al. 2006; Blaisdell et al. 2006). For example, Beckers et al. (2006) followed De Houwer et al. (2002) and Mitchell and Lovibond’s (2002) approach to the demonstration of propositional reasoning in blocking, but they used rats as subjects. Beckers et al.’s (2006) data closely paralleled those found with human participants. This supports the idea that rats engage in propositional reasoning. If propositional reasoning abilities underlie associative learning in humans, and these abilities are shared (perhaps in a primitive form) by other species, then it is not unreasonable to suggest that propositional reasoning may also

Mitchell et al.: The propositional nature of human associative learning be responsible for associative learning in nonhuman animals. Whatever the merits of this view, one should at least be open to the possibility that learning in animals is not always based on an automatic link-formation mechanism but could also result from other, reasoning-like processes. Of course, there must be limits to this line of argument. In the extreme case, surely invertebrates such as Aplysia do not have conscious beliefs. Indeed, we would agree that it would not be useful to apply the propositional approach to Aplysia. Rather than representing the two events and the relationship between them, such that one event leads to anticipation of the second event, Aplysia simply learn to respond to a particular stimulus. That is, a stimulus-response (S-R) relationship is learned by which a certain input leads to a certain response in a reflexive manner and thus without the involvement of mental representations (see Moors 2007). However, humans, and many other animals, have in the course of evolution been endowed with a more flexible system that allows responding to be more contextually appropriate. For example, the more sophisticated system is, unlike an S-R mechanism, sensitive to changes in the reinforcement value of the outcome (e.g., Adams & Dickinson 1981). This is because the mental representations of the events and their relationship intervene between the stimulus and the response. In other words, we suggest that humans have cognition and Aplysia do not. Between these two extremes lies a continuum of cognitive complexity. Animals with more sophisticated cognitive abilities use these abilities to learn about their environment, so that they can, to the greatest extent possible, adapt their behavior when that environment changes. It would now appear that at least certain nonhuman animals have cognitive capabilities that go beyond the simple automatic formation of links. It would be surprising if those capabilities were not utilized in the process of learning to adapt to and control the environment. One last important point is that all human and nonhuman animals (including Aplysia) also display plasticity at the neural level. Within all species, reflexive (and therefore, cognitively unmediated) learning can be observed at the neural level. This reflexive type of learning, however, falls beyond the scope of both the propositional and the link-formation approach. As indicated earlier, these approaches operate at the algorithmic level, that is, at the level of psychological processes and representations. Neither approach operates at the implementational (in this case neural) level. 7. Implications for the lab and clinic In this section, we see how the present proposal fits with the way that psychology has changed over the past half century, both from a theoretical and an applied (clinical) perspective. 7.1. The cognitive revolution

The received view is that behaviorism (more particularly S-R theory) gave way to the cognitive revolution in the mid-1950s at the time when the computer was invented and when a number of findings were published for

which no parsimonious S-R account could be provided (see Gardner 1985, for a review). Within learning research, it became clear that many phenomena, such as sensory preconditioning, blocking, and reinforcer devaluation, could not be explained in S-R terms and were better explained by a model in which an internal representation of the CS was connected to an internal representation of the US (a stimulus-stimulus [S-S] link; see Dickinson 1980; Mackintosh 1974). An S-S model is a giant leap towards a fully-fledged symbolic system, because an S-S model postulates that associations between stimuli in the environment are represented by links between internal representations of those stimuli. In 1973, Seligman and Johnston published a cognitive or expectancy-based theory of instrumental learning. On this theory, if a rat presses a lever to obtain food (or avoid shock), it does so because it desires food (or wishes to avoid the shock) and believes that the lever press will produce that outcome (see Dickinson 1989). However, Seligman and Johnston maintained the view that Pavlovian conditioning results from an automatic mechanism, in which links form between the CS and US representations. Resistance to the idea that Pavlovian conditioning is the result of the same processes as instrumental conditioning (that is, processes of belief acquisition) continues to the present day. In this context, the view presented in the target article should not be seen, as it no doubt is by the majority of psychologists, as an example of extremism. Rather, the beliefbased approach to S-S learning is merely a small step in the same direction that we have been heading for the past 50 years: away from S-R learning theory and towards a propositional approach to all learning. Furthermore, as argued above, adoption of the propositional approach does not imply that the important insights gained from research conducted within a behaviorist approach, or within the more recent S-S approach, are to be discarded. It is merely that learning theorists have been mapping out the properties, not of the mechanisms that form S-R or S-S links, but of the propositional reasoning processes that result in learning. 7.2. The clinic

The propositional approach is consistent with developments in clinical psychology over the past 20 years. It is now commonly proposed that patients display false or exaggerated beliefs and distortions in reasoning that contribute to their symptoms and maladaptive behavior. For example, anxious patients overestimate the probability and cost of future harm, and patients with anorexia perceive their bodies to be overweight (e.g., Clark 2004). Early “cognitive-behavioral” interventions were based on a dual-process model of learning (Zinbarg 1990). They assumed that “behavioral” techniques like reinforcement and extinction worked on unconscious automatic responses, whereas “cognitive” (verbal) techniques worked on consciously available beliefs. However, more recent (and more effective) cognitive-behavioral interventions feature a closer integration of experience and language, and hence are more consistent with the propositional approach to learning. For example, exposure to interoceptive sensations (e.g., breathlessness, pounding heart) in panic disorder is used explicitly as a way of BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning testing the patient’s catastrophic interpretations (e.g., heart attack) and is linked to verbal information concerning the true causes of those sensations (e.g., hyperventilation, anxiety). Thus, direct experience and language can be seen as two different and potentially synergistic ways of targeting patients’ distorted beliefs and thereby normalizing their behavior (Lovibond 1993). Further exploration of the ways in which learning experiences impact on propositional knowledge may well facilitate progress in developing effective clinical interventions.

NOTE 1. The recent reasoning literature often attributes non-normative performance on reasoning tasks to an automatic process, which is part of a dual-process or dual-system view of reasoning (e.g., Sloman 1996; Stanovich 1999). Quite confusingly, this automatic process is sometimes labeled “associative.” However, no link-formation mechanism is imputed here. “Associative” in this context refers to a heuristic whereby responding is determined by the overall similarity of the test stimulus to stored prototypes. Therefore, the automatic component of this particular dual-system model operates at the level of performance, not learning – it is quite different from the link mechanism that is the focus of the target article.

8. Conclusion Within the propositional approach presented here, learning is not separate from other cognitive processes of attention, memory, and reasoning, but is the consequence of the operation of these processes working in concert. There is, therefore, no automatic mechanism that forms links between mental representations. Humans learn the causal structure of their environment as a consequence of reasoning about the events they observe. For example, when a bell is followed by food on a number of occasions, it is inferred that the bell stands in some predictive or causal relationship to the food. Therefore, food will be expected the next time the bell rings. Later ringing of the bell will then generate the belief that food presentation is imminent and so will produce salivation. The available evidence largely supports the propositional approach to learning. Thus, learning does not take place outside of awareness; it requires cognitive resources, and it is affected by verbal instructions, rules, and deductive reasoning processes. There are some fragmentary pieces of evidence that seem to indicate a role for a second, automatic mechanism in anticipatory learning, most particularly the dissociation between outcome expectancy and conditioned responding shown by Perruchet (1985). This evidence is, however, far from conclusive. It would seem unwise at this point to base a belief in a dual-system theory of learning on evidence from a very small number of experiments that are yet to be properly evaluated. If, as the propositional approach suggests, the human cognitive system is a more complex version of a similar system possessed by nonhuman animals, then animal models of human functioning would no longer be restricted to a narrow range of “associative” phenomena. We may then see animal models of reasoning or attentional control, which may, in turn, lead to the development of drug therapies for deficits in these areas. In the same vein, a single coherent approach could be developed for the treatment of learning-based clinical problems. There are, therefore, many applied benefits of this new approach. However, fundamentally, what we propose is a change in the way we think about our basic research in learning. The postulation of automatic mechanisms of link formation is pervasive in psychology; the links are used to explain phenomena as disparate as simple conditioned responding and the formation of attitudes to members of an out-group. The propositional approach suggests that these phenomena should be reinterpreted to be the consequence of propositional reasoning leading to the acquisition of new beliefs. 198

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Open Peer Commentary Associative learning requires associations, not propositions doi:10.1017/S0140525X09000867 Frank Baeyens, Debora Vansteenwegen, and Dirk Hermans Department of Psychology, University of Leuven, 3000 Leuven, Belgium. [email protected] [email protected] [email protected] http://ppw.kuleuven.be/ppw/index.htm http://ppw.kuleuven.be/ppw/english/ http://ppw.kuleuven.be/leerpsy/

Abstract: We discuss findings on evaluative conditioning (EC) that are problematic for the “conscious reasoning/propositional knowledge” account of learning, namely, dissociations between conscious beliefs and acquired (dis)liking. We next argue that, both for EC and for Pavlovian learning in general, conditioned responding cannot rationally be inferred from propositional knowledge type “CS refers to/signals US,” and that, therefore, performance cannot be explained.

There is much in this target article that we can fully endorse, but unfortunately, not the general conclusion that Pavlovian conditioning depends on high-level cognitive processes type “conscious propositional reasoning,” the core outcome of which is propositional knowledge about stimulus relations. We believe there is something fundamentally wrong with this proposal in general, but we start with the more modest claim that, both empirically and conceptually, Mitchell et al.’s approach in the target article does not capture the essence of evaluative conditioning (EC) (Baeyens et al. 2001b). There are many demonstrations of EC effects going counter to participants’ conscious propositional beliefs, including situations (1) in which unconditioned stimulus (US) occurrence is attributed to an irrelevant, non-correlated characteristic of the conditioned stimuli (CSs), but acquired (dis)liking follows the objective CS-US contingency (Baeyens et al. 1990b; 1996b; 2001a); (2) in which US occurrence is correctly believed to be conditional (modulated), but acquired (dis)liking is unconditional (Baeyens et al. 1996a; 1998); or (3) in which US occurrence is no longer expected, but conditioned (dis)liking still persists (Vansteenwegen et al. 2006). These dissociations suggest that EC falls beyond the scope of Mitchell et al.’s model. Moreover, Mitchell et al.’s account of EC faces more serious, conceptual problems: (i) How does one infer CS (dis)liking from the propositional-declarative knowledge type “CS refers to the

Commentary/Mitchell et al.: The propositional nature of human associative learning (dis)liked US”? From a logical/rational/inferential point of view, there is no causal connection at all between entertaining the propositional belief, “CS refers to (dis)liked US,” and (dis)liking a CS. “Disliking” a flavor-CS does not follow any more from entertaining the belief that “This flavor refers to the bad tasting US Tween20,” than that it would follow from it that the CS should be “liked,” or should evoke feelings of pride, envy, or plain misery. Related to this: (ii) How does one explain that a (correct) propositional belief about the CS-US relationship is not a sufficient condition for EC effects (Baeyens et al. 1990b)? Finally, (iii) if EC would be based upon a proposition with truth-value, how does one deal with the observation that EC is resistant to extinction; that is, that acquired (dis)liking is not affected by the subsequent experience (and resulting propositional belief) that the CS is no longer accompanied by the US? The alternative is to accept that EC does not depend on propositional attitudes (thinking/believing that “CS refers to US”), but reflects a less-than-propositional state (thinking of US) resulting from the operation of an associative mechanism: CS presentation activates a representation of a (dis)liked US, and the activation of this representation causes or instantiates an approach/avoidance response-tendency that phenomenologically equals a feeling of “(dis)liking,” such that “(dis)liking” the CS causally follows from mere presentation of the CS. Such an association is not true or false, but is simply established or not established; and once it has been formed, it remains there forever. One could argue that EC is just a special case, and that Mitchell et al.’s approach is valid for all instances of Pavlovian learning except for EC. We are not sympathetic to this possibility either. The problem of linking conditioned responding (“performance”) to specific “belief” states (resulting from entertaining a particular propositional mental content) is not restricted to EC, but spreads to the whole domain of Pavlovian conditioning. Mitchell et al. indeed admit that a detailed causal/mechanistic account of the translation of propositional beliefs (and concomitant/resultant expectancies) to specific physiological/behavioral responses is not the forte of their model. It is a long way from the propositional attitude, “I believe that the tone predicts a shock,” to increases in heart rate, muscle tension, breath regulation, hormone release, or the activation of escape/avoidance responses. It is not just a long way, but also a way that is not specified at all by the (content of the) propositional attitude. In principle, Pavlovian conditioned responses are not rationally connected to propositional knowledge about the CS-US relationship and cannot (logically) be inferred from it; nor do they (necessarily) result from the interaction with other propositional beliefs (i.e., from reasoning) (Shanks & Dickinson 1990). A model that can explain all but conditioned behavior, is lacking something quintessential. Again, the alternative is to accept that conditioned responding causally results from the associative activation of the US-representation – a theory that may indeed require (much) refinement, but at least offers an account that works in principle. Why is it that Mitchell et al. have arrived at this problematic account of learning? First, in some arguments purportedly favoring the “conscious reasoning/propositional knowledge” idea, two issues are erroneously mixed up. The data on the role of awareness, and the influence of cognitive load/secondary tasks, favor an account of Pavlovian learning that acknowledges the importance of “controlled” processing indeed; but these arguments do not bear at all upon the issue whether the acquired knowledge should take the form of structured mental representations, or of non-propositional associative links between representations. Second, many of the observations that do favor an account in terms of representations with combinatorial syntax and semantics (and structure sensitivity of processing), are derived from experiments that invoke processes/faculties that simply go beyond the scope of what associative learning theory reasonably could be expected to explain. The findings on the influence of verbal instructions, abstract rules, and deductive reasoning show that

people indeed can use language and reason, and can transform complex, structured mental representations in situations that require more than simple registration of stimulus co-occurrence. But how does this favor a “conscious reasoning/propositional knowledge” account of associative learning any more than does a demonstration that humans can play chess or understand poems? Mitchell et al.’s account of learning tries to get rid of a dualsystem approach, and while doing so, throws away the very notion of associations. According to our analysis, there is not much ground to justify this radical stance. Moreover, Mitchell et al. still adhere to a dual-system approach of mental processes in general. Even though the very act of learning is supposed to take place in a conscious reasoning system, this system is said to get inputs from a non-conscious, automatic perceptual/ memory system; and the only thing Mitchell et al. ultimately propose is to resect plasticity from one part of the dual system. Parsimony, where art thou?

Propositional learning is a useful research heuristic but it is not a theoretical algorithm doi:10.1017/S0140525X09000879 A. G. Baker,a Irina Baetu,a and Robin A. Murphyb a

Department of Psychology, McGill University, Montre´al, Que´bec, H3A 1B1, Canada; bCognitive Perceptual and Brain Sciences Unit, Division of Psychology and Language Sciences, University College London, London, WC1E 6BT, United Kingdom. [email protected] http://www.psych.mcgill.ca/faculty/abaker.html [email protected] [email protected]

Abstract: Mitchell et al.’s claim, that their propositional theory is a single-process theory, is illusory because they relegate some learning to a secondary memory process. This renders the single-process theory untestable. The propositional account is not a process theory of learning, but rather, a heuristic that has led to interesting research.

In a conditioning experiment, a light might signal food. There are at least two interesting questions about this conditional relationship. The first is a psychophysical question: Is the animal sensitive both to the events and to the conditional rule that describes the relationship between the events? The second question is: From what internal mechanism, or algorithm, does this conditional rule emerge? Mitchell et al. argue that learning emerges from an internal representation of the conditional rule as a proposition. This theory is contrasted with the view that the propositional representation emerges from internal associative links between the events that do not involve direct propositional mechanisms. Cognitive penetrability and the absence of automaticity?

Because it rejects the automatic link processes in learning, a propositional mechanism is more parsimonious than “dual theories” that postulate two mechanisms This parsimony is illusory because the target article authors themselves describe a second process involving memory retrieval. “Learning” is propositional, but memory is sometimes automatic. An association formed as a proposition may, in memory, become automatic. This distinction is important for Mitchell et al.’s argument since the propositional view relies on the cognitive penetrability (Brewer 1974) of conditioning experiments. That is to say, cognitive manipulations, such as instructions, can directly modulate conditioning without having direct experience with the conditioning events. Automatic memories would not be easily susceptible to cognitive manipulations. So, if a learned rule is penetrable, it is propositional; but, if the rule is not penetrable, it has become BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning automatic and thus reflects memory, not learning. Cognitive penetrability as used here is therefore circular. If people are aware of the contingency, then instructions can influence behaviour; so the mechanism is propositional. If not, the measurement tool is not sensitive enough, or the behaviour has become automatic in memory. Cognitive load manipulations designed to show that increasing load disrupts propositional reasoning provide empirical evidence for the presence of automatic links. Imposing a load, while participants are exposed to a redundant-cue blocking paradigm (Aþ AXþ), eliminates blocking because the load prevents the propositional process (De Houwer & Beckers 2003; Waldmann & Walker 2005). However, even though the propositional process is challenged, participants still respond to both A and X. If the propositional process is blocked, then this must mean the learning here is “automatic.” Mitchell et al. imply that simple pairings are automatic memory processes and that the important issues in learning are phenomena like blocking and inhibition. Even associationists have argued that such phenomena are more susceptible to modulation than excitatory pairings (Bouton 2004; Swartzentruber & Rescorla 1994). Mitchell et al. seem to be restricting their analyses to those learning phenomena that are least stable and most susceptible to modulation. The autonomic conditioning preparations might not be ideal for demonstrating strong automatic learning. Most animal conditioning preparations generate learning in nearly 100% of animals. An important finding of many of the cognitive penetrability experiments is that some people learn, and some do not, but only those who “know” the contingency show conditioned responses. Hence, in many experiments, a significant proportion of participants do not learn. This hardly makes us confident that these are biologically prepared (Seligman 1970) learning preparations suitable for evaluating the formation of strong “automatic” associations. Penetrability may reflect weak conditioning, or perhaps no conditioning at all, and therefore begs the question as to what cognitive penetrability means for human conditioning. Furthermore, unlike the rats in a conditioning experiment involving shocks, participants in these autonomic learning tasks are not naı¨ve. They have learned to expect motivational events following verbal instructions. They have learned to relax when told the teacher will not check their homework today. Is it surprising that they will feel anxious when told that a tone will be followed by a shock, or will relax when subsequently told it will no longer be followed by the shock? We believe there are learned pairings that are not cognitively penetrable. These include evaluative conditioning (Baeyens et al. 1990a) and probably strong aversions and flavour preferences. Cultural dishes evoke strong positive reactions and these are learned. Few of us have a strong positive reaction to eating live grubs, but in some cultures some people do. In a classroom demonstration, we have difficulty getting our students to salivate at the thought of crunching a grub between their teeth, as we might with students within the appropriate culture. These reactions are learned but not penetrable. This conclusion would be again open to the circular criticism that these processes were overlearned and became automatic in memory. Propositional learning as a valuable heuristic. We prefer lower-level psychological theories to explain rule learning and propositional reasoning, rather than have them as fundamental primitives of learning. If propositional learning is impossible to disconfirm and has not eliminated automatic learning, then is it valuable? We would argue that it is. High-level rule-based and symbolic cognitive views can generate important research. These cognitive views can either be a theory about internal mechanisms, or they can represent a sometimes normative description of the physical and statistical mechanisms in the world. For example, Tolman (1948) challenged the S-R (stimulus-response) psychologists’ automatic theories with his notion of the cognitive map, and this notion generated interesting research and challenged the automatic or associative approach. Now, however,

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animals’ abilities to navigate in the world to goals are beginning to be explained by lower-level theories (Diez-Chamizo et al. 1985). Elsewhere, arguing against the notion that internal mental images of three-dimensional drawings may be rotated in much the same way they are in the physical world (a notion which generated a great deal of interesting research), Pylyshyn (1973) pointed out that this is not sustainable as a cognitive theory. Mental images are computations but not direct mental representations. Our own research on contingency learning in humans and rats was originally motivated by theories (e.g., Cheng 1997) that humans and animals internalized the notion of computing contingencies (Baker & Mackintosh 1979; Baker et al. 2003). We have subsequently argued that for both animals and humans these computations of correlations, and even computations of the dependencies in multiple-event chains, emerge from a connectionist network (Baetu & Baker, in press; Murphy & Baker 2004; Wasserman et al. 1993). Around the time of the “cognitive revolution,” a number of rule-based and symbolic computational models emerged. Interest in these models has diminished because connectionist models have accounted for much of the data they initially explained, as well as for some they did not (Shultz 2003). More recent symbolic models even incorporated sub-symbolic (automatic) modules (e.g., Anderson & Lebiere 1998). Moreover, it has long been known that connectionist models can generate truth tables, logical operations, and many other linear and nonlinear rules (McCulloch & Pitts 1943). “Higher-level” cognitive processes and developmental stages emerge from connectionist or automatic architectures (e.g., Shultz et al. 1994). This brings us to propositional learning mechanisms. These propositions map the normative relationship between events in the world. Although we take issue with some of the data, we see Mitchell et al.’s work as a psychophysics of “propositional” relationships. They show the rules and mechanisms in the world people and animals can represent and pose a challenge for connectionist or other lower-level theories. We are confident that the principles of propositional learning will emerge from connectionist principles that, contrary to the authors’ claims, provide an algorithm and not a computational description (Marr 1982).

The truth and value of theories of associative learning doi:10.1017/S0140525X09000880 Tom Beckers and Bram Vervliet Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands. [email protected] http://home.medewerker.uva.nl/t.r.j.beckers/ [email protected]

Abstract: In this commentary, we assess the propositional approach to associative learning not only in terms of veridicality and falsifiability, but also in heuristic value. We remark that it has furthered our knowledge and understanding of human, as well as animal, associative learning. At the same time, we maintain that models developed from the association formation tradition continue to bear great heuristic value as well.

In their target article, Mitchell et al. present a detailed and very thoughtful evaluation of the potential evidence in favour of automatic association formation as a source of associative learning effects. They convincingly argue that there is presently very limited, if any, evidence for the existence of a separate, non-propositional association formation module. They rightfully point out

Commentary/Mitchell et al.: The propositional nature of human associative learning that, given the obvious need to postulate the existence of a propositional module in order to explain many aspects of human associative learning, an association formation module that does not add explanatory power to the propositional module is entirely redundant. Still, the claim that any instance of human associative learning (i.e., any change in performance that is a result of the presence of regularity in the relation between events in the world) must by necessity be due to the operation of controlled, propositional reasoning processes, is a strong one. Prima facie, Mitchell et al.’s claim seems ill-fitted to the existence of a phenomenon such as evaluative conditioning: If, for example, I have developed a liking for white wine because of spending many pleasant holidays in France, it would seem that this liking for white wine does not need to reflect any knowledge about the relation between white wine and anything else, beyond the fact that white wine makes me think of France. Essentially, the fact that white wine makes me think of France is non-propositional (I cannot be right or wrong for being reminded of France upon smelling white wine). As such, evaluative conditioning effects very strongly appear to result from the mind being carried from one idea or representation to another, without any intermediate processing, much like what is the presumed mode of operation of an association (Fodor 2003). It is then perhaps not surprising that some of the best evidence for automatic association formation comes from an evaluative conditioning study (Baeyens et al. 1990a). Still, the description of the propositional approach as offered by Mitchell et al. leaves open the possibility that even evaluative conditioning effects, although perhaps resulting from automatic, non-propositional memory retrieval processes (an object “automatically” making you think of something pleasant), do necessitate the conscious, falsifiable establishment, in propositional form, of a link between events (if only of the form “event A co-occurred with event B”) at some earlier point in time. That is, the fact that evaluative conditioning effects, at performance, are almost by nature non-propositional (the fact that A makes you think of B is not something that you can subsequently evaluate as correct or wrong), does not preclude that they perhaps only occur if people at some point have consciously noticed some sort of real-world relationship between A and B (such as “A has repeatedly co-occurred with B,” a statement which you can obviously evaluate to be true or false). Does this render the propositional account unfalsifiable? Surely, the fact that performance may reflect automatic memory retrieval of propositional knowledge stored earlier and may moreover reflect propositional knowledge indirectly (such as when stored propositional knowledge about the cooccurrence of two events influences your subsequent evaluation of one of both), does make falsification of the general framework difficult, but not impossible. It would suffice to convincingly demonstrate associative learning about entirely subliminally presented CSs to rule out a role for propositional reasoning altogether. The debate about whether such evidence already ¨ hman 2002 vs. exists still seems to be open (see Wiens & O Shanks & Lovibond 2002). However, the most important contribution of the propositional approach to associative learning is not to be situated in proving the association formation approach wrong. As Mitchell et al. point out, what is perhaps most important, is that it has provided a new perspective on conditioning, not only in humans (where at least a contribution of reasoning processes to learning has long been acknowledged), but also in animals. This perspective has not only enabled us to unveil the importance of rule learning in animal Pavlovian fear conditioning (Beckers et al. 2006), but also to highlight the parallels between extinction learning and rule learning in terms of context sensitivity and generalisation (Wheeler et al. 2008). As such, the propositional approach has opened up a whole new framework for the understanding and the prediction of human and animal conditioning phenomena,

the impact of which is bound to further increase over the coming years. And perhaps this is where a caveat about the propositional approach to associative learning, in turn, is warranted. Notwithstanding the impressive amount of evidence that the propositional approach is more veridical than the association formation approach, it seems beyond argument that models developed within the association formation tradition have continuing heuristic value as well. As an example, just recently Leung and Westbrook (2008), in a series of extremely elegant experiments, demonstrated that the degree of additional extinction accrued by a cue exhibiting spontaneous recovery is governed by both individual prediction error of the cue and common prediction error of all cues present during an extinction trial. Does such a finding invalidate the propositional nature of associative learning? Not necessarily (probably not, one might even argue). Still, it is obvious that experiments like these would never have been designed, and these findings never revealed, on the basis of our current understanding of propositional reasoning. As such, it may simply be too early for one truth to govern our inquiries into human and animal associative learning. Keeping our antennas open to discover empirical phenomena in the realm of associative learning and conditioning will probably necessitate a willingness to entertain a variety of models and approaches for some time to come.

What’s reason got to do with it? Affect as the foundation of learning doi:10.1017/S0140525X09000892 Eliza Bliss-Moreaua and Lisa Feldman Barrettb,c a

Department of Psychiatry and Behavioral Sciences, California National Primate Research Center, University of California –Davis, Davis, CA 95616; b Department of Psychology, Boston College, Chestnut Hill, MA 02467; and c Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129. [email protected] [email protected] www2.bc/edu/~barretli

Abstract: We propose that learning has a top-down component, but not in the propositional terms described by Mitchell et al. Specifically, we propose that a host of learning processes, including associative learning, serve to imbue the representation of the conditioned stimulus (CS) with affective meaning.

In the target article, Mitchell et al. characterize associative learning phenomena according to the relationship established between two previously unrelated stimuli (i.e., a conditioned stimulus [CS] and an unconditioned stimulus [US]). Associative learning, they suggest, occurs when the CS becomes propositionally related to the US using effortful, controlled, and rational processing. We believe this view does not account for important questions about how the representations in question (the CS and US) are modified by experience. Furthermore, this view makes assumptions about how stored representations are activated. We suggest that stimulus representations are realized by multimodal states reflecting both exteroceptive and interoceptive information brought online by a combination of top-down (e.g., propositional) and bottom-up (e.g., stimulus-driven) processes. In this view, learning occurs when the multimodal representation of a stimulus acquires an affective component. Propositional change is not necessary for learning. A growing body of evidence suggests that the human brain captures statistical regularities in sensory-motor patterns and stores them as representations. These representations are used to continuously organize incoming sensations during the process of predicting what those sensations stand for the in world (Bar 2003; 2007; Kveraga et al. 2007). External sensations BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning always occur in a context of internal sensations from the body. As a result, the sensory-motor pattern that is stored for future use will always include a representation of the interoceptive state of the body. The brain predicts what sensations refer to in the world in part based on prior experiences of how those external sensations have influenced, or changed, internal sensations from the body on prior encounters (cf. Barrett & Bar, in press). These bodily changes are referred to as “affective.” Affective states can be described hedonic (pleasure or displeasure) with some degree of arousal (for a recent review, see Barrett & Bliss-Moreau, in press). These ideas are consistent with a growing body of research demonstrating that knowledge about the world is “embodied,” or grounded, by a network of broadly distributed, diverse, multimodal states which are encoded during the experience of a given stimulus (see Barsalou 2008). What you know about an object is therefore based, in part, on its affective impact in the past. When it comes to learning, changes in a CS’s meaning can be thought of as the process by which the multimodal representation of the stimulus is changed by any experience. The most fundamental change occurs because the representation of the CS is experienced in a context of affective arousal that is derived from the representation of the US. Any number of relationships between the CS and US could serve to alter the representation of the CS. The CS and US could be paired in time or space, associated semantically, or even explicitly coupled via rule-based learning. In our view, the need to differentiate types of learning in terms of how the relationship between CS and US is established (as exemplified by Mitchell et al.’s model) is eliminated. All learning can be subsumed under the same general, basic mechanism that exists in all organisms that possess the capacity to generate affective responses to stimuli in the environment. Thus, to some extent, any change in the representation of a CS is affective learning (Bliss-Moreau et al. 2008). In typical classical conditioning paradigms, examples of USs include shocks (e.g., Vervliet et al. 2005), very loud noises (e.g., Neumann & Waters 2006), and even sexual arousal (e.g., Hoffmann et al. 2004). These USs act on the nervous system directly to generate a robust affective response in a bottom-up or stimulus-driven way that is automatic and unconscious. Other USs, such as negative words or pictures, have a less robust bottomup effect on the learner’s nervous system. Instead, such USs have top-down effects because they have propositional meaning. The difference in the bottom-up potency of different USs leads some theorists to believe that different models are required to account for learning phenomena. According to the affective learning perspective, this is not so – changes in affect can and do occur via both bottom-up and top-down processing and therefore with both types of USs. For example, evidence from instructed learning paradigms demonstrates that the representation of a CS can be changed by telling a person that a US will be presented after the CS, even if the US is never presented (e.g., Olsson & Phelps 2004). According to the affective learning perspective, the set of instructions that indicates when the (promised, but never presented) shock will occur sufficiently alters the learner’s affective state so that the interocpetive representation of this affective change is integrated into the representation of the CS. We have demonstrated that people can learn the affective value of other people when presented with propositional information about those people (e.g., seeing the phrase “hit a small child” presented with a picture of Sally) (Bliss-Moreau et al. 2008). In this example, the representation of “hit a small child” has an affective component which is integrated into the representation of Sally. It is possible that some USs are exclusively experienced either via automatic, effortless associative processing or via effortful, controlled propositional processing (but not both), as Mitchell et al. and most dual-process theories suggest (for an extensive review, see Evans 2008). A more likely scenario, however, is that the two types of processing are often active in parallel and

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serve to constrain each other to make meaning of a given stimulus in a given context. For example, the sound of gunfire is aversive and may have an automatic effect on the nervous system. But, for a person who has never experienced war, that automatic processing may be constrained by propositional information about the “shoot-’em-up” Western movies he or she remembers from childhood, resulting in a relatively neutral experience. For a war vet, the automatic processing may be constrained with propositional information gained in the experience of fighting and killing, resulting in a highly aversive experience. Propositional learning, even for a stimulus that has semantic meaning, is not required. By focusing on how stimulus representations are changed as a result of internal experience, a whole host of learning phenomena can be united under one principle. Our hope is that by approaching learning from this perspective, the field will generate new hypotheses about the way that people learn about the world.

Learning without thinking doi:10.1017/S0140525X09000909 R. A. Boakes School of Psychology (A18), University of Sydney, NSW 2006, Australia. [email protected]

Abstract: The main conclusion to draw from Mitchell et al’s article is that it is difficult to disentangle cognitive and learning processes in contingency and causal experiments. More compelling evidence for human associate learning comes from research where, because of the type of events involved, participants are unable or unlikely to think about the relationships between the events.

The conclusion Brewer (1974) drew from his review of conditioning research using human participants came as a great shock. For decades it had been very widely accepted – and not just by the many behaviorists of those times – that the processes described by S-R (stimulus-response) or reinforcement theorists based on animal evidence also provided a basis for at least simple aspects of human behavior in a manner that was independent of belief or awareness. Brewer’s conclusion was that there was no convincing evidence to support this assumption. His alternative account of what goes in a conditioning experiment is captured by the following quote: “The college sophomore does not leave his higher mental processes outside the door when he walks into the experimental room, but he uses them to try to understand what is going on and what he should do about it” (p. 2). Despite many subsequent attempts to show his conclusion to be wrong, Brewer (1974) clearly was correct about the overwhelming influence of “higher mental processes” in determining a participant’s behavior in the kind of conditioning experiment – mainly “conditioning” of autonomic responses or of small movements – that he reviewed. The article by Mitchell et al. can be seen as a successor to Brewer (1974), in which a similar argument is directed mainly at experiments from the past two decades that have used causal or predictive scenarios in experiments to test principles of associative learning. One similarity between past and present research is the overwhelming use of college students as participants, a population that has been selected on the basis of thoughtfulness and then encouraged to be curious. An odd aspect of too many causal judgment experiments is that, although the researchers want their participants’ higher mental processes to operate in order to understand the sometimes complex instructions, interpretation of the results assumes the absence of any such influence following a participant’s first response. In this respect, many points made by Mitchell et al are salutary, including the important one that associations are not expectancies. When applied to animal data, the absence from associative

Commentary/Mitchell et al.: The propositional nature of human associative learning learning theories of explanations as to how links provide the basis for expectancies is rarely problematic; but when applied to human experiments, this is a major lacuna. Mitchell et al.’s arguments are less compelling when one looks beyond the kind of study on which they focus. Odors provide an example of stimuli that people find hard to describe or identify, and therefore difficult to think about. Stevenson and I have suggested that this is the principal reason that conditioned changes in an odor’s perceptual properties are independent of the poor explicit memory participants have for the stimulus contingencies they were given during training (Stevenson & Boakes 2004). For a similar reason, changes in human flavor preferences produced, for example, by caffeine-based conditioning, also appear independent of belief or awareness (e.g., Chambers et al. 2007). Moving beyond the laboratory, the strong aversions developed by cancer patients undergoing chemotherapy appear to develop quite independently of patients’ valid beliefs about the cause of their distress; and, in a way, that shows very strong parallels with conditioned taste aversions in rats and many other animals (e.g., Bernstein 1985). Particularly interesting, and far less widely known, examples come from the study of placebo effects, an area where a similar debate has continued as to the contributions of conscious expectancies and conditioning without awareness (Stewart-Williams & Podd 2004). Here it has turned out that, whereas some placebo effects are strongly determined by patients’ beliefs about their treatment and are sensitive to the information that is provided, others depend on past treatment history in a way that is independent of belief and insensitive to information. Thus, in one major study, verbal suggestions accompanying medication had a large effect on reactions to pain and on the motor responses of Parkinson’s patients, but no detectable effect on hormonal and cortisol levels, whereas the latter could, however, be altered by a placebo treatment following a conditioning procedure (Benedetti et al. 2003). What such examples suggest is that the conclusion to draw from Mitchell et al. is that the kind of research they review is not likely to reveal much about human learning. Preventing student participants from “reflection” so that their responses in, say, a causal judgment experiment are “intuitive” (Shanks 2007) turns out to be very difficult. Mitchell et al. refer to three examples that appear to have achieved this, including our own study in which we placed participants in a causal judgment experiment under strong time pressure, so giving them “little time to think” (Karazinov & Boakes 2007). Mitchell et al. attempt to explain away the result; but in doing so, they appear to accept that the non-rational response given by the average participant under these conditions must be based on a within-compound association and not on any kind of logical inference. Nevertheless, even though a few studies of this kind appear to have been successful in reducing the influence of logical inferential thinking, this is not enough to justify a confident return to this way of studying human associative learning.

Rats and infants as propositional reasoners: A plausible possibility? doi:10.1017/S0140525X09000910 Leyre Castro and Edward A. Wasserman Department of Psychology, The University of Iowa, Iowa City, IA 52242. [email protected] [email protected] http://www.psychology.uiowa.edu/

Abstract: Mitchell et al. contemplate the possibility of rats being capable of propositional reasoning. We suggest that this is an unlikely and unsubstantiated possibility. Nonhuman animals and human infants do learn about the contingencies in the world; however, such learning

seems not to be based on propositional reasoning, but on more elementary associative processes.

Whether advanced cognitive competencies can be grounded in more elementary perceptual or associative processes is a matter of considerable current interest. Goldstone and Barsalou (1998) suggested that the distinction between low-level perception and high-level cognition is inapt; there is no chasm, but rather a continuum, from perception to conception. Similarly, Leech et al. (2008) have proposed that relational priming is a basic building block for analogical reasoning. And, in the realm of language development, sequential learning and domaingeneral mechanisms may pave the way for language (Christiansen et al. 2002). These examples illustrate how complex mental abilities could emerge from simpler behavioral mechanisms, thereby opening the door to understanding the ontogeny and phylogeny of higher-order cognition. Mitchell et al. proceed in the opposite direction. They hypothesize that “associative learning depends on high-level cognitive processes that give rise to propositional knowledge” (target article, Abstract). For Mitchell et al., a basic and general ability – learning the relations between environmental events – is not due to the formation of links between representations of the events (associative account), but to the formation of mental propositions about how the events are related (propositional account). On this view, propositions predicate some property of a subject, are generally held to be true or false, and are combined by laws of logical inference (Braine 1978). Propositional reasoning thus entails processing and storing verbal premises and assertions. For example, “blocking” results from applying the following rule (De Houwer & Beckers 2003): If cue A alone causes the outcome with a particular intensity and probability, and if cues A and B together cause the outcome with the same intensity and probability, then cue B does not cause the outcome. How organisms such as infants and nonhuman animals, who do not have language, deploy such propositions to infer relations about events is unclear; but Mitchell et al. believe that possibility and cite two studies (Beckers et al. 2006; Blaisdell et al. 2006) which hint at rats’ engaging in inferential propositional-based reasoning. Beckers et al. (2006) found that, after presenting rats with Aþ trials in Phase 1 and AXþ trials in Phase 2, blocking (e.g., low responding to X) did not take place if the rats had earlier experienced Cþ, Dþ, and CDþ trials. According to Beckers et al., rats possess (as do humans) prior knowledge that, when two potential causes are presented together, a larger effect should occur than when only one cause is presented – additivity. Mitchell et al. agree, but they never say if this prior knowledge is innate or acquired; and if it is acquired, then how all organisms have come to the same understanding. Nevertheless, armed with the additivity assumption, rats infer that X is not the cause of the outcome, when AXþ trials are presented after Aþ trials and the outcome remains the same. But, if rats are pretrained with Cþ, Dþ, and CDþ trials, then they can tell that the additivity assumption is now false. The rats consequently reassess their beliefs; when they are later presented with Aþ trials followed by AXþ trials, they deduce that X, as well as A, is a cause of the outcome. But, perhaps something simpler is happening here. Haselgrove (under review) has shown that Rescorla and Wagner’s (1972) associative model can readily explain the results of Beckers et al. (2006). Haselgrove noted that five out of the six experimental cues in Beckers et al. were from the same modality (audition); all of them were of the same duration; and all of them were trained in the same context. Under those conditions, it is conceivable that the cues used for pretraining and the cues used for blocking entailed a common element. The Rescorla and Wagner model predicts that the conditioned properties acquired by the pretraining cues can transfer to the blocking cues via this

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Commentary/Mitchell et al.: The propositional nature of human associative learning common element. Thus, generalization of the pretraining contingencies to the experimental contingencies can explain Beckers et al.’s findings – reasonably and elegantly. Mitchell et al. also consider the Blaisdell et al. (2006) project as support for propositional reasoning in animals. Here, rats could distinguish between a situation in which the outcome had been observed and a situation in which the outcome had been produced by the rats. This distinction actually reflects the dichotomy between classical and instrumental conditioning. In classical conditioning organisms learn that events in the environment are related to one another, whereas in instrumental conditioning organisms learn that their own actions change environmental events. The fact that animals can distinguish between observation (classical conditioning) and intervention (instrumental conditioning) is not new. Killeen (1981) showed that pigeons can discriminate whether their own behavior or “something else” caused changes in a light. How associative models can accommodate the specific data of Blaisdell et al. is a challenge, but it is hardly compelling evidence that rats are propositional reasoners. Mitchell et al. are curiously silent about the emergence of associative learning. Must we also assume that human infants engage in complex propositional reasoning? If so, then this proposal is difficult to reconcile with studies of human development. Clancy et al. (1976) found that use of the if-clause (the meaning, without containing the connective) emerges at 2 or 3 years of age. And, “If p, then q” propositions (the type involved in blocking) are not understood until children are 6 years old (Braine & Rumain 1983). Importantly, there seems to be consensus that children’s early-developing inferences are likely to be acquired as part of learning their language (Braine & Rumain 1983; Falmagne 1975). Does that mean that human infants who have not yet acquired language cannot learn about regularities between environmental events? Doubtful. Sobel and Kirkham (2006) found backward blocking in 8-month-old children; so, infants exhibit the same associative learning phenomena as do animals and human adults. The great advantage of associative accounts is that their mechanisms seem to be available to all species across all developmental stages. Without explaining its origin and its developmental trajectory, we cannot fully comprehend any psychological process, particularly one as essential as associative learning.

Rational models of conditioning doi:10.1017/S0140525X09000922 Nick Chater Division of Psychology and Language Sciences, University College London, London WC1E 6BT, United Kingdom. [email protected] http://www.psychol.ucl.ac.uk/people/profiles/chater_nick.htm

Abstract: Mitchell et al. argue that conditioning phenomena may be better explained by high-level, rational processes, rather than by noncognitive associative mechanisms. This commentary argues that this viewpoint is compatible with neuroscientific data, may extend to nonhuman animals, and casts computational models of reinforcement learning in a new light.

Mitchell et al. provide an important critical challenge to the presuppositions underlying current theories of human conditioning, both in psychology and the neurosciences. They suggest that human contingency learning results from reasoning processes over propositional knowledge, rather than from an elementary process of forming associations. This commentary focuses on three questions raised by this analysis, and concludes with a perspective on the origin of contradictory forces in the control of

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behavior which does not invoke a clash between a cognitive and associative system. Multiple neural systems for decision making? Mitchell et al. argue that behavioral evidence makes a case against a distinct associative learning system. Yet the idea that there are multiple, competing, neural systems underpinning decision making is very widespread within neuroscience. One line of evidence for multiple systems comes from double dissociations in human neuropsychology, and, perhaps most strikingly, from animal lesion studies (see, e.g., Coutureau & Killcross 2003; Killcross & Coutureau 2003). Yet such studies provide only tentative evidence for functionally distinct systems, rather than differential engagement of a single system (Chater 2003; Shallice 1988). Consider an analogy with allergies: Some people cannot eat prawns, but can eat pine nuts; other people can eat pine nuts, but not prawns. But we cannot, of course, conclude that there are two distinct digestive systems that process these different foods. Instead, a single digestive system deals almost uniformly with all foods, but exhibits two biochemical “quirks” leading to the selective allergies. Thus, a single processing system can in principle yield striking double dissociations of function (Chater, in press). Hence, double dissociations in humans, and animal lesion studies yielding double dissociations, are weak evidence for distinct processing systems. The same caveats apply to studies in which reinforcement learning is selectively impaired not by a lesion, but by a pharmacological intervention (e.g., a dopamine agonist, Pizzagalli et al. 2008). Similar issues arise, too, with neuroimaging studies. Such studies reveal differential neural activity under different task conditions. But such differential activity may nonetheless be entirely compatible with the existence of a single, unitary, decision-making system. Is animal conditioning associative? Mitchell et al.’s account may be correct with regard to people. But perhaps rats really do use dedicated associative learning mechanisms. Indeed, this latter assumption is widespread in the comparative literature (e.g., Mackintosh 1983). Nonetheless, there are at least three reasons to doubt this. (1) Many aspects of animal cognition are highly sophisticated and seem to go far beyond the scope of purely associative mechanisms (e.g., Wasserman & Zentall 2006). (2) Associative theories of learning typically assume gradual modifications; yet actual behavior is roughly all-or-none (Gallistel et al. 2004), just as though the animal is adopting or rejecting a hypothesis about possible environmental contingencies. The familiar smooth learning curves arise only from data averaging. (3) Putative conditioning phenomena in animals appear to be highly sensitive to rational factors (Courville et al. 2006). So, for example, blocking (Kamin 1969) can be rationally understood in terms of “explaining away” (Pearl 1988); the slower rate of extinction from partially reinforced contingencies has a natural statistical explanation; and so on. The role of computational models of reinforcement learning. There have been remarkable recent developments in

computational models of reinforcement learning (Dayan & Abbott 2001) – often implicitly or explicitly viewed as capturing the computational principles of a distinct, striatal, non-cognitive, learning system (Jog et al. 1999). If Mitchell et al. are right, then such computational models should perhaps be interpreted differently: as providing an account of rational inferences that can be drawn from data concerning actions and rewards, given minimal background knowledge. But where background knowledge is available (e.g., about likely causal connections between actions, events, and rewards), we should expect that such knowledge will be incorporated appropriately (Gopnik & Schulz 2007). According to this perspective, computational models of reinforcement learning apply to a narrow class of situations, in which background causal knowledge is restricted, rather than describing the operation of a particular neural system that drives behavior. Clash of reasons, not clash of mechanisms. One intuitive appeal of the idea of a split between associative and cognitive

Commentary/Mitchell et al.: The propositional nature of human associative learning systems, competing for the control of behavior, is a potential explanation for many paradoxical aspects of human behavior, both in laboratory studies of, for example, time-discounting and weakness-of-will and in real-world phenomena of addiction, depression, or phobias (Epstein 1994; McClure et al. 2004). If, following Mitchell et al., we reject evidence for a distinct associative system, how are we to explain the origin of internal cognitive conflict? One straightforward approach (Chater, in press) is to propose that internal conflict arises from a “clash of reasons” rather than a clash of systems. In almost all nontrivial reasoning problems, different lines of argument appear to favour different conclusions. One source of reasons, among many, may be past experience (including the “reinforcement history”). Moreover, reasons are often not equally persuasive; nor are they equally easy to evaluate. When paying close attention and given sufficient time, it may become evident one reason is valid, whereas another reason is weak. But when attention is reduced, the weaker reason may nonetheless prevail. Therefore, to choose a classic example from probabilistic reasoning, the reasoner may decide that, given information about, say, Linda’s intellectual and political background, it is more likely that Linda is a feminist bank teller, than that she is a feminist (Tversky & Kahneman’s [1983] conjunction fallacy), because there is a better overall match with the former description (for which at least the first part matches), than the second description (which seems entirely incongruous). Considered reflection on probability may, or may not, lead the reasoner to draw the opposite conclusion. More generally, it seems entirely possible that there will be systematic differences between responses when time and attention are limited and responses when time and attention are plentiful (see Cunningham & Zelazo [2007] for a similar perspective on apparent dissociations between two putative routes underpinning social cognition, as exemplified by, e.g., Bargh & Chartrand 1999); and concomitant differences in the degree to which brain areas are activated in the contemplation of different reasons. In summary, observing battles for control of the behavioral “steering wheel,” and evidence for different behavioral and neural bases for the competitors, need not be interpreted as indicating a clash between distinct mechanisms (e.g., associative vs. cognitive), but might equally arise from a clash of reasons within a unified cognitive system.

Is propositional learning necessary for human autonomic classical conditioning? doi:10.1017/S0140525X09000934 Michael E. Dawsona and Anne M. Schellb a Department of Psychology, University Southern California, Los Angeles, CA 90089-1061; bPsychology Department, Occidental College, Los Angeles, CA 90041. [email protected] [email protected] http://college.usc.edu/labs/dawson/home/index.cfm http://departments.oxy.edu/psych/index.html

Abstract: Additional support is presented for the necessity of awareness of the CS-US relation in human autonomic conditioning. However, possible limitations and exceptions regarding this general rule are discussed. Limitations include the lack of relationship between conditioned response (CR) strength and degree of awareness, and an important exception may be the finding of conditioning with backwardly masked CSs of a biologically prepared nature.

Mitchell et al., in their interesting and provocative treatment of human associative learning, link propositional learning with awareness. Specifically, they conclude that “The available evidence largely supports the propositional approach to learning.

Thus, learning does not take place outside of awareness; it requires cognitive resources, and it is affected by verbal instructions, rules, and deductive reasoning processes” (sect. 8, para. 2). Our research strongly supports this conclusion regarding human autonomic classical conditioning, as the authors noted. Therefore, our comments focus on the role of awareness of the CSUS (conditioned stimulus-unconditioned stimulus) contingency in human autonomic classical conditioning. Confirming findings. Research from our laboratories is even more supportive than Mitchell et al. indicate of the position that human autonomic classical conditioning is propositional and requires significant cognitive resources. As they point out, much of our research has embedded conditioning within a distracting cognitive masking task with the result that subjects who become aware of the CS-US contingency successfully condition, and subjects who remain unaware do not. They then raise the “devil’s advocate” possibility that an automatic associative-link mechanism might exist, but the cognitive load imposed by a masking task may act to prevent conditioning by reducing the degree to which the CS and US are processed, and hence reduce the input to the link mechanism. However, in the cognitive masking task that we have used, the CS is specifically the focus of the subject’s attention (it must be judged on some dimension and remembered), and expectancy of the US is constantly reported by the subject. For instance, subjects were presented with a series of tones on each trial, one of which was the CS, and were required to determine which tone matched a preceding tone in pitch. Subjects indicated expectancy or non-expectancy of the US by pressing a series of buttons continuously during the tones (Dawson & Biferno 1973). Hence, the failure to condition without awareness cannot be attributed to the failure to attend and process the CS and US. We have found this necessity of awareness not only with typically used CSs, such as tones or colored lights, but also with odor CSs, stimuli often thought to be capable of eliciting conditioned emotional responses without a supporting conscious memory (Marinkovic et al. 1989). Another line of evidence in support of the importance of cognitive resources not mentioned by Mitchell et al. is that performance on a secondary reaction time task performed during the conditioning session shows deterioration during the CS exactly when the conditioned responses are elicited (Dawson et al. 1982). Perplexing exceptions. Although we are strong advocates of the position that human classical conditioning cannot occur without awareness of the CS-US relationship, we find ourselves in the unusual position of noting that there may be limitations and exceptions to this general proposition. First, Dawson and Furedy (1976) reviewed evidence in support of a “necessarygate” hypothesis that included the following propositions: (1) contingency awareness is necessary, but not sufficient, for human autonomic classical conditioning (e.g., researchers in this field often observe participants who are aware of the CS-US relationship, give strong URs [unconditioned responses], but do not show conditioning); (2) the degree of contingency awareness has a gate, but not analog, relation to the strength of the conditioned response (i.e., once a critical minimum level of awareness has developed, there is little or no relationship between the strength of the conditioned autonomic response and the degree of accuracy or certainty in the learned proposition); and (3) contingency awareness is not necessary for performance of a response that has been previously conditioned. This second proposition is contrary to the position of Mitchell et al. in section 3.1 that the strength of the CR is related to the “strength of belief” in the CS-US contingency. Second, conditioning using biologically prepared CS-US relations (e.g., angry faces associated with aversive events) may be possible without awareness of the presence of the CS, as ¨ hman and his colleagues (see Esteves et al. demonstrated by O 1994). Esteves et al. (1994) used as CSs in a discrimination conditioning paradigm pictures of angry or happy faces that were BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning backwardly masked to prevent conscious awareness of their presence. These CSs were paired with an electric shock US. Conditioned skin conductance responses were observed following ¨ hman & the angry face but not the happy face CS (see also O Soares 1998; Morris et al. 2001). These results indicate that humans can be conditioned to a stimulus they do not consciously perceive, if that stimulus is evolutionarily prepared to be associated with the US. However, when biologically prepared CS-US relations were embedded in a distracting cognitive masking task, which ensured conscious perception of the individual CS, autonomic conditioning only occurred among aware subjects and only after they became aware (Dawson et al. 1986). Thus, there is conflicting evidence of whether autonomic conditioning can occur without awareness when biologically prepared CS-US relations are involved. When biologically prepared CSs are backwardly masked and subjects are presumably unaware of the CSs’ presence, there is evidence of unaware conditioning. When the CSs are part of a distracting masking task, one which ensures that the CSs are perceived and discriminated, there is no evidence of unaware conditioning. A possible explanation of these conflicting results is that when higher cortical processes become involved, as when the CSs are perceived during a distracting cognitive task, propositional learning is the dominant force in controlling autonomic conditioning. Propositional learning will be dominant even if an incorrect proposition has been tacitly learned – that the CS has no particularly predictive value. However, when these higher cortical processes concerning the CSs are prevented from occurring, as when the CSs are effectively backwardly masked, and when they are biologically prepared, then the automatic associative learning processes become the dominant force. Therefore, under conditions where the CSs are perceived, propositional learning is necessary for human autonomic classical conditioning.

Straw-men and selective citation are needed to argue that associative-link formation makes no contribution to human learning doi:10.1017/S0140525X09000946 Dominic M. Dwyer,a Michael E. Le Pelley,a David N. George,a Mark Haselgrove,b and Robert C. Honeya a

School of Psychology, Cardiff University, Cardiff CF10 3AT, United Kingdom; School of Psychology, University of Nottingham, Nottingham NG7 2RD, United Kingdom. [email protected] [email protected] [email protected] [email protected] [email protected] http://www.cardiff.ac.uk/psych/contactsandpeople/lecturing/ dwyer-dominic-m-overview_new.html http://www.cardiff.ac.uk/psych/contactsandpeople/lecturing/ le-pelley-mike-overview_new.html http://www.cardiff.ac.uk/psych/contactsandpeople/researchfellows/ george-d-n-dr-overview_new.html http://www.psychology.nottingham.ac.uk/staff/mxh/ http://www.cardiff.ac.uk/psych/contactsandpeople/lecturing/ honey-rob-overview_new.html b

Abstract: Mitchell et al. contend that there is no need to posit a contribution based on the formation of associative links to human learning. In order to sustain this argument, they have ignored evidence which is difficult to explain with propositional accounts; and they have mischaracterised the evidence they do cite by neglecting features of these experiments that contradict a propositional account.

In their target article Mitchell et al. contend that associative learning is best explained as the result of effortful cognitive processes based on propositions, and that there is no need to posit a

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contribution based on the formation of associative links. The target article greatly overstates the case for rejecting the contribution of associative links, however, because of its remarkably selective citation and interpretation of prior work. Although there are situations where propositional accounts provide good explanations of human learning, there are also many examples where the pattern of results is more easily explained by associative-link accounts. For example, if beliefs are based on post hoc reasoning about events stored in memory, changing the temporal order in which these events are stored should not affect judgments; and yet several studies have demonstrated trial-order effects in learning (Collins & Shanks 2002; Dickinson & Burke 1996; Lo´pez et al. 1998b). Similarly, the evidence relating to differences in diagnostic and predictive learning is overstated: many studies have found no impact of this manipulation (e.g., Arcediano et al. 2005; Cobos et al. 2002; Lo´pez et al. 2005; Tangen et al. 2005). Nor is mention made of cancer patients who form food aversions despite being fully aware that chemotherapy treatment, rather than any food consumed, produced their illness (e.g., Bernstein & Webster 1980). But the most surprising omission is of research on blocking by two of the target article’s authors themselves: Mitchell et al. (2006) argued that for participants to reason that a blocked cue T is non-causal requires knowledge of the outcome with which T was paired. Their results, however, revealed blocking of T without any memory of the T – outcome relationship, which led them to conclude that “associative cuecompetition would appear to be a strong candidate mechanism for noninferential forward blocking in humans” (p. 842). In the target article, Mitchell et al. also neglect dissociations in patterns of learning under different circumstances that are challenging for single-process propositional accounts, but which follow naturally from dual-process approaches to learning. For example, Lo´pez et al. (2005) demonstrated that participants were generally insensitive to the difference between predictive and diagnostic tasks but did show differences when instructions made clear the importance of this distinction in causal order. Likewise, although Mitchell et al. cite Shanks and Darby (1998) as providing evidence for propositional learning, they ignore the fact that results consistent with this account were only observed in participants who had learnt the task well; those who learnt less well showed responses consistent with associative-link theories (similar responses to novel compounds and the elements that comprise those compounds). Le Pelley et al. (2005a) demonstrated that participants show an unblocking effect when information is presented on a trial-by-trial basis, but not when presented in a questionnaire format that would facilitate the use of propositional learning. Moreover, Mitchell et al.’s analysis of this unblocking effect does not stand up to scrutiny. Participants could indeed remember what had changed between A – O1O2 and AT – O1O3 trials: Their accuracy in predicting O1 on AT – O1O3 training trials was significantly higher than that for their predictions of O3. It is unfortunate that Mitchell et al. address only a straw-man version of associative-link accounts. For example, they assert that associative theories see learning as proceeding without awareness. Few associative theorists would agree with this characterisation, however: Why should people necessarily remain unaware of links that are formed? Although the issue of awareness is orthogonal to associative accounts (Shanks 2007), the target article explicitly states that any example of learning without awareness would be highly damaging to the idea that all learning is propositional. Yet, Mitchell et al. cite two examples of dissociations between propositional knowledge and conditioned reactions in humans (flavour conditioning and the “Perruchet effect”), and the essential features of both studies have been replicated (see Lovibond & Shanks 2002). In addition, the fact that associative learning can occur in anaesthetised animals (see Lovibond & Shanks 2002) indicates that the idea that propositional mechanisms can explain all animal learning (other than

Commentary/Mitchell et al.: The propositional nature of human associative learning that produced by S-R mechanisms) must be, beyond reasonable doubt, false (see also Iselin-Chaves et al. [2005] for learning under anaesthesia in humans). It warrants repeating that any dissociation between proposition knowledge and learning is fatal to the current account, and such dissociations do exist (albeit that unambiguous evidence is not widespread). The insistence that associative accounts rely on nodes that represent whole stimuli in a symbolic manner is also a mischaracterisation. Foreshadowed by Estes’s (1950) stimulus sampling theory, associative models explicitly acknowledge that any stimulus comprises multiple features that might each be shared with other stimuli (e.g., Blough 1975; Brandon et al. 2000). This undermines attempts in the target article to characterise the concept of generalisation as an unjustified assumption by which “freedom is gained to explain results” (sect. 6.1, para. 3). In fact, this is an integral and fully specified feature of almost all current associative learning models, and flows directly from the idea that whole stimuli should be considered as collections of potentially overlapping features. Within-compound associations are also treated as “get-out clauses” despite following naturally from, and being explicitly predicted by, standard associative principles. Furthermore, there is evidence for their existence (Rescorla & Durlach 1981) and influence upon cue-competition (e.g., Batsell et al. 2001; Durlach & Rescorla 1980). Although there are examples of particular associative-link models being modified in light of an inability to account for particular results, this does not undermine the fact that principles of generalisation and within-compound associations are instantiated within associative-link models as a class. Finally, Mitchell et al. criticise associative theory for lacking parsimony because it must predicate two sources for human learning (associative-link and propositional mechanisms). However, associative-link theories are very parsimonious in other ways. Most notably, they can explain aspects of human learning (e.g., sensitization, habituation, perceptual learning) which lie beyond propositional mechanisms. Although associative models inherently require dual-process accounts of human learning, propositional accounts are inherently multiple-process with respect to other phenomena. Hence, proposition-only accounts of human learning are no more parsimonious than dual-process accounts when considered in a broader context.

Operating principles versus operating conditions in the distinction between associative and propositional processes doi:10.1017/S0140525X09000958 Bertram Gawronskia and Galen V. Bodenhausenb a Department of Psychology, The University of Western Ontario, Social Science Centre, London, Ontario N6A 5C2, Canada; bDepartment of Psychology, Northwestern University, Evanston, IL 60208-2710. [email protected] http://publish.uwo.ca/~bgawrons/ [email protected] http://www.psych.northwestern.edu/psych/people/faculty/ bodenhausen/

Abstract: Drawing on our Associative-Propositional Evaluation (APE) Model, we argue for the usefulness of distinguishing between basic operating principles of learning processes (associative linking vs. propositional reasoning) and secondary features pertaining to the conditions of their operation (automatic vs. controlled). We review empirical evidence that supports the joint operation of associative and propositional processes in the formation of new associations.

In contrast to a common assumption of dual-process models, Mitchell et al. argue that the formation of new associations in human memory is an exclusive product of controlled,

propositional inferences, and that there is no empirical evidence for automatic processes of associative linking. In response to Mitchell et al.’s conclusion, we argue that their analysis conflates the distinction between the basic operating principles of a given process (i.e., associative linking vs. propositional reasoning) and secondary features pertaining to the conditions of its operation (i.e., automatic vs. controlled). If the conceptual independence of these dimensions is taken into account, the reviewed evidence regarding features of automaticity will be diagnostic about the operation of a particular type of process only to the degree that there is perfect overlap between the two dimensions (automatic ¼ associative; controlled ¼ propositional) – which seems debatable on both conceptual and empirical grounds. Based on our own Associative-Propositional Evaluation (APE) Model (Gawronski & Bodenhausen 2006; 2007), we argue that the formation of a new association in memory should be understood as an effect that could be the result of two conceptually distinct mechanisms, associative linking and propositional reasoning. In our APE Model, we define associative linking as the creation of a new association between two concepts based on the mere co-occurrence of objects or events independent of the perceived validity of their relation. Propositional learning is defined as the creation of a new association as a result of syllogistic inferences about the validity of a given relation. The primary difference between the two processes is their dependency on subjective validity, in that only propositional learning, but not associative linking, takes the perceived validity of relations into account (see also Strack & Deutsch 2004). As such, the two mechanisms should lead to the same outcome when the cooccurrence of two objects or events is interpreted as reflecting a valid relation. However, the two mechanisms may lead to different outcomes when the co-occurrence between two objects or events is regarded as non-diagnostic or invalid. This conceptualization incorporates Mitchell et al.’s emphasis of truth values as a core feature of propositional reasoning. However, it differs from Mitchell et al.’s approach, in that assumptions about automatic features represent empirical claims about the boundary conditions of the operation of the two processes, rather than defining characteristics that could be conversely used to identify their operation in a particular case. To empirically distinguish between the two processes, we suggest that the actual operation of associative and propositional processes should be identified by means of their interactive effects on associations and beliefs. In the APE Model, we define associations as mental links between concepts independent of their subjective truth or falsity; beliefs are defined as the endorsed relations that are implied by validated or invalidated associations. This distinction has proven its usefulness in the social-cognitive literature, showing that activated associations can produce behaviors that are congruent with these associations, even when the relations implied by these associations are regarded as invalid (for a review, see Strack & Deutsch 2004). More importantly, there is suggestive evidence that such dissociations can sometimes be due to antagonistic effects of associative linking and propositional reasoning during the encoding of new information (e.g., Gawronski et al. 2008; Rydell et al. 2006), supporting the usefulness of the proposed distinction in the formation of new associations. The basic notion of these studies is that the mere co-occurrence between two objects can create a mental association between these objects, even though the validity of the implied relation is rejected at the propositional level. Empirically, these differences are often reflected in dissociations between implicit and explicit measures (Fazio & Olson 2003), such that implicit measures (e.g., sequential priming tasks) reflect the mere co-occurrence between the two objects, whereas explicit measures (i.e., self-reported judgments) reflect the perceived validity of the implied relation. Other evidence that is consistent with the notion of associative linking comes from research on spontaneous trait transference (e.g., Skowronski et al.1998), in which communicators have BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning been shown to become associated with the traits they ascribe to others. In most cases, there is no logical basis to infer that a communicator has a particular trait (e.g., tidy) simply because he or she describes that trait in another person. Hence, it seems reasonable to assume that any such associations are the product of associative linking rather than propositional reasoning (Carlston & Skowronski 2005). To be sure, such associative linking processes may still depend on perceivers’ attention, processing goals, or awareness of the co-occurrence. However, this by itself does not make the underlying learning process propositional, as defined in the proposed conceptualization. Another important issue in this context is Mitchell et al.’s concern that proposing mutual interactions between associative and propositional processes would make the distinction between the two processes obsolete. Such interactions are a core assumption of our APE Model, which assumes that mutual interactions between the two processes are reflected in different mediation patterns of experimentally induced effects on activated associations and endorsed beliefs (Gawronski & Bodenhausen 2006). Specifically, we argue that associative linking will often produce parallel effects on associations and beliefs, such that newly created associations provide the basis for explicitly endorsed beliefs. Conversely, newly created associations may be the product of propositional inferences, such that new beliefs generated in the course of validating currently accessible information may be stored in associative memory. Drawing on the abovementioned distinction between implicit and explicit measures, the first case is assumed to produce parallel effects on both kinds of measures, with effects on the explicit measure being fully mediated by the implicit measure. In contrast, the second case should produce parallel effects on both kinds of measures, with effects on the implicit measure being fully mediated by the explicit measure. An illustrative demonstration of these diverging mediation patterns is a recent study by Whitfield and Jordan (submitted), who combined an implicit evaluative conditioning (EC) procedure (Olson & Fazio 2001) with a propositional impression formation task that used descriptive information about the conditioned stimulus. Their results showed that both the EC procedure and the impression formation task produced parallel effects on both explicit and implicit measures. However, in line with the predictions of the APE Model, EC effects on the explicit measure were fully mediated by the implicit measure, whereas impression formation effects on the implicit measure were fully mediated by the explicit measure (for related findings, see Gawronski & LeBel 2008; Gawronski & Strack 2004; Gawronski & Walther 2008). Taken together, these results suggest that a conceptual distinction between associative and propositional processes in terms of their operating principles (rather than automatic vs. controlled features) has testable and empirically supported implications. More importantly, our analysis implies that the formation of new associations in memory can be the product of either associative or propositional processes, and that Mitchell et al.’s insightful review may speak only to the automatic versus controlled nature of these processes rather than to the general irrelevance of associative processes in human learning.

Rational constructivism: A new way to bridge rationalism and empiricism doi:10.1017/S0140525X0900096X Alison Gopnik Department of Psychology, University of California at Berkeley, Berkeley, CA 94704. [email protected]

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Abstract: Recent work in rational probabilistic modeling suggests that a kind of propositional reasoning is ubiquitous in cognition and especially in cognitive development. However, there is no reason to believe that this type of computation is necessarily conscious or resource-intensive.

There is a paradox at the heart of cognitive science. Human beings (and some animals) seem to have abstract, hierarchical, structured representations of the world. These representations allow us to make a wide range of novel predictions and produce a wide range of novel behaviors. And these representations seem to be accurate – they capture the structure of the world, and they improve as we learn more about the world. But the information provided by our senses, our one direct source of evidence about the world, is very different from these representations. It is a noisy, probabilistic, and chaotic set of contingencies among specific concrete inputs, apparently far removed from the true structure of the world itself. In the past 2000 years of western philosophy, and the past 50 years of cognitive science, there have been two very different approaches to resolving this paradox. One tradition (nativist, rationalist, propositional, “East Coast”) argues that cognition does indeed involve abstract, hierarchical, structured representations. It only appears, however, that we infer these representations from the evidence of our senses. In fact, these representations must be there innately, and are only slightly modified by learning. Small details may be filled in by experience, or alternative parameters may be triggered by different experiences. But the fundamental structure of the representations is there from the start. The alternative tradition (empiricist, associationist, connectionist, “West Coast”) argues that it only appears that we have abstract, hierarchical, structured representations. In fact, our novel predictions and behaviors are based on the complex contingency patterns among individual sensory inputs, patterns that we extract through associative mechanisms. There have sometimes been arguments for a kind of dismissive co-existence between these two approaches. The rationalists say that most cognition is the result of innate abstract representations, but mere associationist processes may play a role in very automatic, low-level kinds of behavior. The empiricists say that associations are responsible for most cognition, but there may be explicit, conscious, and sophisticated propositional reasoning layered on top. These two-process views both suggest that there is some relationship between the sophistication, power, and likely domain of the representations and their computational character – associations are “low-level” and propositions are “high-level.” They just disagree on whether most cognition falls on one side or the other. The target article is in this general tradition, though it endorses the idea that propositional representations can account for even classical associationist phenomena, such as conditioning. But Mitchell et al. also argue that the propositional representations they endorse are resource-intensive, subject to conscious reflection, and can be understood as beliefs – they are “high-level.” In cognitive development, going back to Piaget, there has been a long tradition of trying to elude the rationalist/empiricist dichotomy with “constructivist” theories. A constructivist account should allow us to actually infer highly structured representations accurately from patterns of contingency in the data. The most recent constructivist project has been the “theory theory” – the idea that children develop intuitive theories from evidence in the way that scientists do. But the theory theory, like earlier constructivist theories, has suffered from a lack of computational precision and specific learning mechanisms. However, in the last 10 years or so there has been increasing excitement about a new theoretical view that provides a computationally rigorous basis for the constructivist project. This approach might be called “rational probabilistic modeling.” This view, unlike classical empiricist views, proposes structured, abstract, hierarchical representations. But unlike classical

Commentary/Mitchell et al.: The propositional nature of human associative learning rationalist views, it sees those representations as probabilistic and learned. Moreover, the kind of learning that is involved is not the simple method of association, but is a form or rational probabilistic induction, often involving Bayesian methods. This general theoretical approach has been applied to a very wide range of kinds of cognition, including “low-level” automatic cognition such as vision, motor control, and syntax, as well as “high-level” conscious cognition such as category and word learning and causal learning. (For some recent examples and reviews of this work see Chater & Manning 2006; Chater et al. 2006; Gopnik & Schulz 2004; Gopnik & Tenenbaum 2007; Gopnik et al. 2004; 2007; Regier & Gahl 2004, Tenenbaum et al 2006; Xu & Tenenbaum 2007; Yuille & Kersten 2006). Causal knowledge and learning, one of the foci of the target article, has been a particularly fruitful venue for these new theories. In our own work, we have shown that even very young children represent the causal structure of the world and reason about those representations in a rational way, in accordance with the principles of causal Bayes nets (Gopnik et al. 2004). As Mitchell et al. mention, this “causal Bayes net” approach seems to be very convergent with the approach that is presented here. But there is one important difference. The computations that are involved in rational probabilistic models have no necessary link to issues of high- versus low-level, animal versus human, conscious versus unconscious, or resource-dependent versus automatic. Indeed, many of these models have their conceptual roots in vision science. At least since Irv Rock and arguably since Helmholz, vision scientists have seen vision as a process of hypothesis generation and testing. The visual system inferentially reconstructs an accurate representation of the visual world; it solves “the inverse problem.” We know that these inferential processes are much more constrained and complex than simple associations, and they have been well-modeled as a kind of Bayesian inference. But they are unconscious, automatic, and low-level (see Yuille & Kersten 2006). Thinking about the issue developmentally also makes this point vivid. We know that even very young babies are capable of sophisticated kinds of statistical and inductive reasoning – reasoning capacities that go far beyond simple associative mechanisms. In fact, arguably infants have more powerful learning capacities than adults. It seems unlikely, however, that infant resource allocation or consciousness parallels that of adults, though undoubtedly infants are conscious. From a computational point of view, propositional reasoning does indeed go all the way down, as Mitchell et al. argue. The same kinds of rational computations play an essential role in cognition from vision to causation, and from infancy to adult science. But this is orthogonal to the question of how those computations are related to resource management or phenomenology.

Cognition, consciousness, and the cognitive revolution doi:10.1017/S0140525X09000971 John D. Greenwood CUNY Graduate Center and Ph.D. Program in Philosophy, City University of New York, New York, NY 10016. [email protected] http://web.gc.cuny.edu/philosophy/people/greenwood.html

Abstract: It is argued that the cognitive revolution provided general support for the view that associative learning requires cognitive processing, but only limited support for the view that it requires conscious processing. The point is illustrated by two studies of associative learning that played an important role in the development of the cognitive revolution, but which are surprisingly neglected by Mitchell et al. in the target article.

I would like to make some historical remarks about the late twentieth century development of conditioning theory during the time of the “cognitive revolution” in psychology, that I hope will have some epistemic bearing on the theory of the propositional nature of human associative learning advanced by Mitchell et al. It is true that the cognitive revolution in psychology was marked by a general move from theories based upon stimulusresponse (S-R) or response-reinforcement connections, to theories based upon the cognitive processing of representations, including cognitive theories of classical and operant conditioning (Greenwood 2001; 2008). This progression may be said to provide general support for Mitchell et al.’s view that human associative learning is propositional in nature. However, it is not true that the cognitive revolution provided general support for the view that such cognitive processes are consciously mediated. On the contrary, a common and justified response to neobehaviorist critiques of the cognitive revolution, to the effect that it marked a return to the bad old days of “introspective psychology” (Amsel 1989; Skinner 1985), was that contemporary cognitive psychology had demonstrated that human subjects have very limited access to cognitive processes (Nisbett & Wilson 1977; Nisbett & Ross 1980). And, in general, Mitchell et al. need to distinguish evidence for the view that associative learning is based upon cognitive processing – much of which is drawn from the study of animal learning – from evidence for the view that associative learning is based upon conscious representation of association – much of which is drawn from the study of human learning. One puzzling feature of Mitchell et al.’s defense of a propositional theory of associative learning is that they embrace a feature of the associationist tradition that goes back to Hume; namely, the view that the strength of a learned association is a function of the number of times ideas or stimuli, responses, and reinforcement are observed to occur together. For if one assumes this feature, then it is natural to think of learning as an automatic function of frequency of repetition: that the connection between even cognitive or propositional representations is “stamped in,” as Edward B. Thorndike (1898) put it. Yet this feature does not appear to play any critical role in a propositional theory of associative learning. Hence, I was surprised that the authors did not mention Jose Garcia’s (Garcia & Koelling 1966) studies of conditioned taste aversion, perhaps the most powerful challenge to the traditional view. It was an axiom of the behaviorist learning tradition that the optimal temporal interval for conditioned learning of a connection between unconditioned and conditioned stimuli (in classical conditioning) or response and reinforcement (in operant conditioning) is a fraction of a second, and that conditioned learning requires the repeated association of stimuli, responses, and reinforcement. Yet Garcia’s studies demonstrated that rats could learn to avoid saccharin water after a single trial and with up to a 12-hour interval between their drinking saccharin water and the artificial inducement of radiation sickness. So antithetical was this result to the behaviorist learning tradition that one of Garcia’s professors at UC-Berkeley told him that such an outcome was frankly impossible (Bolles 1993), and Garcia had great difficulty in getting his studies published in mainstream psychology journals (Lubek & Apfelbaum 1987). These studies provided powerful evidence for cognitive processing in conditioned learning. Garcia’s rats were able to identify potential environmental causes of sickness via a cognitive process analogous to Mill’s method of difference. As Mackintosh (1978) put it, these and later studies (Kamin 1969; Revuski 1971) demonstrated that: Simple associative learning is simple in name only. Animals do not automatically associate all events that happen to occur together. If they did, they would be at the mercy of every chance conjunction of events. In fact, they behave in an altogether more rational manner. By conditioning selectively to good predictors of reinforcement at the expense of poor predictors, and by taking their past experience BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning into account, they succeed in attributing reinforcers to their most probable causes. (Mackintosh 1978, p. 54)

However, these studies did nothing to demonstrate that such forms of conditioned learning depend on consciousness of the connection between novel stimuli and their consequences. So one needs to carefully distinguish the evidence for cognitive processing in associative learning from evidence that consciousness of a connection is necessary for associative learning. Which is not to say that there is no evidence for this view, or that none was forthcoming during the course of the cognitive revolution. So I was also surprised that Mitchell et al. did not mention Dulany’s (1968) studies of verbal conditioning. Earlier studies had indicated that human subjects’ employment of linguistic items – such as use of plural nouns – could be manipulated by social reinforcement without their awareness, a form of conditioning commonly known as the “Greenspoon effect” (Greenspoon 1955). Dulany’s work suggests that in many of these studies, not only were subjects conscious of the relevant response-reinforcement connection, but that consciousness was a condition of associative learning. My own favorite example (albeit anecdotal) is the following episode described in Skinner’s (1987) book On Further Reflection, in which he reminisced about his attempt to instrumentally condition his daughter’s foot movements – when she was 3 years old – by rubbing her back: I waited until she lifted her foot slightly and then rubbed briefly. Almost immediately she lifted her foot again, and again I rubbed. Then she laughed. “What are you laughing at?” I said. “Every time you raise my foot you rub my back!” (Skinner 1987, p. 179)

Learning in simple systems doi:10.1017/S0140525X09000983 Geoffrey Hall Department of Psychology, University of York, York YO10 5DD, United Kingdom. [email protected] http://www.york.ac.uk/depts/psych/www/people/biogs/gh1.html

Abstract: Studies of conditioning in simple systems are best interpreted in terms of the formation of excitatory links. The mechanisms responsible for such conditioning contribute to the associative learning effects shown by more complex systems. If a dual-system approach is to be avoided, the best hope lies in developing standard associative theory to deal with phenomena said to show propositional learning.

After experiencing an electric shock following a squirt of water to its siphon, Aplysia will show a change in its behavior. The water squirt will come to evoke a dramatic gill withdrawal (a response readily evoked by the shock, but, previously, only weakly by the squirt itself). This is a classic example of associative learning as a phenomenon. What is the mechanism responsible for this phenomenon? Throughout most of the target article, Mitchell et al. are unambiguous in their answer to this sort of question. The associative learning effect depends, they say, on an effortful, attentiondemanding, reasoning process that produces conscious, propositional knowledge about the relationship between events. This is the only mechanism normally allowed. Why do they waver (as they do) when it comes to the case of Aplysia? (Although they focus on human learning, the authors assert that the human system is not likely to be unique and that a process of belief acquisition also underlies animal learning.) The obvious answer – that it seems implausible to attribute such processes to a mollusc equipped with just a few hundred neurons – means that they must find some other explanation for learning in Aplysia. What they offer (a reflexive,

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stimulus-response [S-R] mechanism) is entirely in accord with what we know from neurophysiological research on this animal (Carew et al. 1983) and will be widely accepted. But Mitchell et al.’s acceptance of this analysis has major implications for their central thesis, implications that they scarcely acknowledge. If classical conditioning procedures can produce S-R learning in Aplysia, might they not do so elsewhere? And the evidence currently available (see Hall 2002, for a recent review) supports the conclusion that S-R association formation plays a role in generating the conditioned responses shown by higher vertebrates even in the more complex training procedures used for these animals. In allowing the existence of this mechanism, Mitchell et al. have let in, by the back door, a version of the dual-system approach that they profess to reject entirely. It should be acknowledged, however, that modern studies of classical conditioning in animals, conducted within the associative tradition, have been concerned to show that the effect goes beyond simple S-R learning. As Mitchell et al. themselves point out, it is difficult to explain, by way of the S-R mechanism, the observation that procedures designed to change the value of an outcome (such as sating the animal for a given food) will reduce the vigor of a conditioned response evoked by a stimulus that has previously been paired with that food. This observation has been taken to indicate that the animal has learned something about the relationship between the stimulus and its outcome. Perhaps we need to turn our attention to a more modest version of Mitchell et al.’s thesis, considering its application to just this form of conditioning. Perhaps this form of conditioning, at least, is solely to be explained in terms of propositional reasoning. Standard associative learning theory offers an alternative interpretation. It suggests that the conditioning procedure establishes an excitatory link between the central representations of the signal and its consequence (i.e., it envisages an S-S [stimulus-stimulus], as opposed to an S-R association). As the signal evokes a response by way of its excitatory effect on the representation of the outcome, the effects of outcome devaluation are readily explained. How are we to choose between this account and one that allows the animal to reason that the signal results in the occurrence of the outcome? Again, we may turn to the behavior shown by (relatively) simple systems to provide an answer. Perhaps the most thoroughly worked-out S-S theory of conditioning is that developed by Wagner (e.g., 1981), and he has applied it in detail to the case of eyeblink conditioning in the rabbit. The neurophysiology of this phenomenon has been investigated extensively, and Wagner’s theoretical mechanisms map on very well to the systems identified in the cerebellum and brain stem nuclei (Wagner & Donegan 1989). It seems that S-S learning need not involve higher brain structures (decorticate rabbits maintain conditioned responding, Mauk & Thompson 1987). The argument is again one of plausibility, but it surely seems more reasonable to endow the cerebellum with the ability to form S-S associations than with the ability to reason about the relationship between events. If we accept the foregoing arguments (and thus the reality of SR and S-S excitatory links), then we must reject the central proposal of the target article: that propositional reasoning is the sole source of the associative learning phenomenon. What remains is the far less radical proposal that excitatory link mechanisms play little or no part in generating associative learning effects in human subjects. This is a question that has been worked over repeatedly and has not been resolved. Regrettably, it seems to come down to a matter of personal preference – are we more impressed by the (to me, still surprising) finding that the development of opinions about the allergenic properties of foodstuffs often seems to follow associative principles of the sort embodied in the Rescorla-Wagner model (Rescorla & Wagner 1972) or by the fact that this form of learning shows properties (e.g., the role of awareness; sensitivity to verbal instructions) that lie outside the scope of models of this sort?

Commentary/Mitchell et al.: The propositional nature of human associative learning Whatever their answers to this question, all are likely to accept the argument of the target article – that it would be a good thing if we could come up with a single theoretical analysis capable of accommodating all the data. Mitchell et al. argue for the propositional analysis. But if the arguments presented above are accepted, we must acknowledge the role of excitatory (S-R and S-S) links in some instances of associative learning. And having done so, parsimony seems to dictate that the next step should be to attempt to extend this sort of account to deal with those features that seem to call for a propositional theory. Success in this enterprise would put paid to the dual-system approach, although not in the way envisaged by Mitchell et al.

A causal framework for integrating learning and reasoning doi:10.1017/S0140525X09000995 David A. Lagnado Department of Cognitive, Perceptual, and Brain Sciences, University College London, London WC1E 6BT, United Kingdom. [email protected] http://www.psychol.ucl.ac.uk/people/profiles/lagnado_david.htm

Abstract: Can the phenomena of associative learning be replaced wholesale by a propositional reasoning system? Mitchell et al. make a strong case against an automatic, unconscious, and encapsulated associative system. However, their propositional account fails to distinguish inferences based on actions from those based on observation. Causal Bayes networks remedy this shortcoming, and also provide an overarching framework for both learning and reasoning. On this account, causal representations are primary, but associative learning processes are not excluded a priori.

The task of providing a unified framework for learning is fraught with difficulties. It must cover a wide diversity of empirical findings, mesh with theories of memory, attention, and reasoning, and be plausible from both a neural and evolutionary perspective. And all this should be achieved with a minimum of postulates and parameters. It is little wonder that numerous contenders have fallen by the wayside. Mitchell et al. launch a bold challenge to associative theories of learning. They argue that the phenomena of associative learning can be explained in terms of a propositional reasoning system, and that there is scant evidence or need for a separate link-formation system. Their thesis has many positives. One is the attempt to integrate both learning and reasoning in a unified system. This is a good thing – for too long these have been studied in relative isolation from one another, separated by different concepts, paradigms, and terminologies. This division ignores the rich interplay between learning and reasoning, and the possibility that a common framework subserves both. Another positive is the rejection of associative link-formation as automatic, unconscious, and encapsulated from higher-level cognition. There is extensive evidence against this view (De Houwer 2009; Lovibond & Shanks 2002), and it unnecessarily cuts associative theories off from other reasoning processes. Despite these positives, there are several problems with Mitchell et al.’s account, in particular their desire to replace associative theories wholesale with propositional reasoning. First, Mitchell et al. give few details about this propositional reasoning system, but the details matter a great deal. For example, none of the current models of human reasoning, whether mental models, logic, or probability-based theories, can handle causal inference (Glymour 2007; Sloman & Lagnado 2005). This is because the current models lack the formal machinery to distinguish inferences based on actions from those based on observation. This is crucial if a

representational system is to provide a guide for predicting the effects of potential actions. Causal Bayes networks (CBN) formalize the distinction between intervention and observation (Pearl 2000; Spirtes et al. 1993), and provide an overarching normative framework for both reasoning and learning. A directed link from X to Y represents a causal relation, such that potential manipulations of X can lead to changes in Y. This contrasts with associative, probabilistic, or logical connections between X and Y, which cannot capture the causal direction. Formalizing the distinction is also critical to causal learning. Associative or probabilistic information by itself is insufficient to distinguish between causal models (e.g., an association between bell and food can be generated by various causal structures, including a model where the experimenter is the common cause of both). Interventions allow the learner to discriminate between covariationally equivalent models and to identify a unique causal structure (e.g., if interventions on the bell do not produce food [nor vice versa], but interventions on the experimenter produce both bell ringing and food, then the experimenter is the common cause of both). A recent wave of psychological research suggests that people conform to the basic prescripts of CBN (Gopnik et al. 2004; Lagnado & Sloman 2004; Sloman & Lagnado 2005; Steyvers et al. 2003, Waldmann & Hagmayer 2005), and work is ongoing to identify the psychological processes that underpin this behavior. Although the accent is on causal representation, the involvement of associative mechanisms is not thereby excluded (e.g., they might be used to parameterize strengths of hypothesized causal links; Griffiths & Tenenbaum 2005). Moreover, sometimes associative connections are the most that can be established, and suffice as crude guides to prediction (e.g., when interventions are impractical, or at the early stages of inquiry). Nevertheless, in such contexts associative mechanisms will be unlike the traditional conception that Mitchell et al. rightly criticize. Thus, contingency information is not processed automatically, irrespective of prior beliefs, instructions, or other information. Rather, various sources of evidence are integrated to infer causal structure, including covariation, interventions, temporal order, and prior knowledge (Lagnado et al. 2007). Contingency information is not privileged here; in fact, the interpretation of contingency data will be modulated by other information such as temporal order (Burns & McCormack, under review; Lagnado & Sloman 2004; 2006). A further problem is that Mitchell et al., along with many learning theorists, assume that propositional and link-formation systems offer competing accounts of human learning. However, these systems need not be incompatible, as each system has distinct representational aims. Thus, a propositional causal model aims to represent how things relate in the external world, whereas an associative link models the reasoning process itself (Pearl & Russell 2001). For instance, the bell ! food link (see Fig. 1 in the target article) represents the inference from bell to food, but not how these variables relate in the world (a plausible causal model is: bell experimenter ! food). These two approaches are not exclusive; it is conceivable that people have causal representations of the world but use associative-like processes for prediction and parameter learning. Mitchell et al. risk setting up a false dichotomy – either propositions or links – without acknowledging that these concepts serve different representational aims. Another concern is Mitchell et al.’s argument from parsimony. They maintain that a dual system with two components can never be simpler than a single system made up from just one of these components. But this moves too fast, and depends heavily on how simplicity is quantified. Extending a propositional reasoning system to accommodate all learning phenomena might introduce additional complexity, such that a dual system turns out simpler overall. Despite this lacuna, the evidence that Mitchell et al. cite BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning against a dual-system approach is strong. Rather than reject associative mechanisms tout court, however, a third way remains open. Why not endorse a unified framework that takes the interaction between learning and reasoning seriously, but allows for variation in the complexity of representations and inferential processes? For example, modes of representation might range from causal models to associative networks, and computation might range from fully Bayesian to heuristic methods. These variations will be determined by task demands, as well as environmental and cognitive constraints (e.g., information availability; memory, and processing limitations). The key point is that a unified framework does not require that the same representations and computations are used for every learning problem; multiple processes are available, and are selected or integrated as required. In short, the flexibility of our cognition system is likely to permit various representational and inferential solutions, including both propositional and associative processes.

Trace conditioning, awareness, and the propositional nature of associative learning doi:10.1017/S0140525X09001009 Nanxin Li Department of Psychology, Yale University, New Haven, CT 06520. [email protected] http://pantheon.yale.edu/~nl238

Abstract: The propositional nature of human associative learning is strongly supported by studies of trace eyeblink and fear conditioning, in which awareness of the contingency of a conditioned stimulus upon an unconditioned stimulus is a prerequisite for successful learning. Studies of animal lesion and human imaging suggest that the hippocampus is critical for establishing functional connections between awareness and trace conditioning.

In the target article, Mitchell et al. argue that human associative learning requires participants to be consciously aware of contingencies between a conditioned stimulus (CS) and an unconditioned stimulus (US). This argument is strongly supported by extensive research on trace conditioning. Trace conditioning is a type of classical conditioning in which there is a temporal gap between the offset of the CS and the onset of the US. Trace conditioning is a well-established model of declarative learning that can be tested in both humans and nonhuman animals (WoodruffPak & Disterhoft 2008). Awareness of the CS-US contingency has been suggested to be a prerequisite for successful learning of trace conditioning. This idea is supported by numerous studies on differential trace eyeblink conditioning, in which one of the stimuli (CSþ) was always followed by an air puff to the eye (US), whereas another stimulus (CS2) was explicitly unpaired with the US. Clark and Squire (1998) found that only the subjects who were aware of the temporal relationships between the stimuli displayed differential responses to the CSþ and CS2. Their findings were replicated in a later study reporting that acquisition of trace eyeblink conditioning was significantly correlated with the awareness of stimulus contingencies (Knuttinen et al. 2001). Clark and Squire (1999) further showed that preventing the awareness of the contingency during conditioning disrupted differential trace conditioning, while providing knowledge about the relationship facilitated learning. Notably, the acquisition of the CS-US contingency and trace conditioning developed in a roughly parallel pattern (Manns et al. 2000). The necessity of awareness in trace conditioning has also been evident in fear conditioning studies (Carter et al. 2003; Knight et al. 2004; Weike et al. 2007). Using a differential trace fear conditioning paradigm, Weike et al. (2007) reported that the

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differential fear-potentiated startle and skin conductance responses (SCR) to the CSþ and CS2 were observed during conditioning only when subjects were aware of the contingency between the CSþ and the US. In the study by Carter et al. (2003), significant correlations between awareness and differential SCR responses to a CSþ and a CS2 were present during extinction. Altogether, these findings indicate that awareness is required for acquisition and expression of fear in trace conditioning. The importance of awareness in trace conditioning is reflected in its underlying neural mechanisms. Growing evidence suggests that trace conditioning is dependent on the hippocampus, a medial temporal lobe region which is widely believed to be critical for declarative memory (Clark et al. 2002; Shors 2004; Woodruff-Pak & Disterhoft 2008). Previous studies reported that amnesic patients who suffered from hippocampal atrophy failed to learn the CS-US contingency and, therefore, were impaired in trace eyeblink conditioning (Clark & Squire 1998; McGlinchey-Berroth et al. 1997). The performance of the amnesic patients became worse as the trace interval between the CS and the US increased. Lesion studies in animals also show that aspirative or electrolytic lesions to the hippocampus disrupted trace eyeblink conditioning (Beylin et al. 2001; Weiss et al. 1999; Solomon et al. 1986). Notably, a recent single-unit study has shown that hippocampal CA1 neurons display highly accurate timed firing to the trace period. During the CS-alone testing session, CA1 neurons maximally fired in synchronization with trace interval used in the conditioning trials (McEchron et al. 2003). This timed firing was closely associated with behavioral responses to the CS. The findings suggest that hippocampal CA1 neurons are critically involved in encoding the trace interval, which is essential for the CS-US associations in trace conditioning. With the advance of human brain imaging techniques, recent studies began to look directly into functional connections among awareness, hippocampus, and trace conditioning. A few fMRI studies have reported strong activations of the hippocampus during trace learning (Buchel et al. 1999; Cheng et al. 2008; Knight et al. 2004). The magnitude of hippocampal activation was closely associated with the accuracy of US prediction, which was a direct measurement of CS-US contingency awareness (Knight et al. 2004). Besides the hippocampus, the activations of other brain regions, including middle frontal gyrus, that support attention and working memory were also associated with trace interval. One idea is that the hippocampus contributes to awareness by interacting with the neocortex (McIntosh et al. 2003). To sum up, trace conditioning requires an active process to form an internal representation of the contingency between relevant stimuli across a temporal gap. In this respect, the requirement of awareness for trace fear learning strongly supports the single-process propositional view of associative learning. ACKNOWLEDGMENT I thank my colleague Sun Jung Bang for reading the manuscript.

Is there room for simple links in a propositional mind? doi:10.1017/S0140525X09001010 Evan J. Livesey and Justin A. Harris School of Psychology, University of Sydney, Sydney, NSW, 2006, Australia. [email protected] http://www.psych.usyd.edu.au/staff/evanl [email protected] http://www.psych.usyd.edu.au/staff/justinh

Commentary/Mitchell et al.: The propositional nature of human associative learning Abstract: Against Mitchell et al.’s assertions, we argue that (1) the concordance between learning and awareness does not support any particular learning theory, (2) their propositional approach is at odds with examples of learned behaviours that contradict beliefs about causation, and (3) the relative virtues of the two approaches in terms of parsimony is more ambiguous than Mitchell et al. suggest.

Mitchell et al. state that a demonstration of conditioning without awareness would be a major problem for their propositional approach. They claim the literature is consistent with this approach because convincing evidence for learning without awareness is scarce, and because there is a “clear concordance between conditioning and awareness” (sect. 4.1, para. 6). Lovibond and Shanks (2002) established clear principles for the appropriate assessment of awareness, and found most demonstrations of unconscious learning used awareness tests that were insufficiently sensitive or rigorous. Over time, their evaluation of the literature has fuelled the conclusion that learning cannot occur without awareness. It is worth examining that conclusion again. Aside from the observation that the dubious nature of the evidence for unconscious learning is not evidence of absence, one can make a strong case that the concordance between learning and awareness does not inform the current debate at all. This is because a realistic dual-process account should still predict that learning will be harder to observe and less likely to occur in unaware participants. First, it is wrong to assume that link formation should be observable in all situations and in all participants. Dual-process theories usually assume that conscious reasoning can have a large impact on behaviour, large enough in some circumstances to obscure other more subtle behavioural influences. This is even true of participants who reason incorrectly or erratically during an experiment. We should not be surprised if participants who were aware of an associative relationship showed behavioural evidence of learning. But those who were unaware may fail to show learning because they are deliberating on a spurious hypothesis or concentrating on something unrelated to the task. This may disrupt performance, regardless of whether learning has taken place. It is also wrong to assume that, just because a learning process is automatic, it will always occur (and always to the same degree). If link formation is affected by selective attention or memory load, then the evidence showing a close correspondence between awareness and learning is highly predictable by any account. To suppose that link formation is affected by cognitive processes is not contrary to its conception as an automatic process. Changing the input to a link-based network inevitably leads to changes in what is (or what is not) learned by that system. A model of automatic link formation would be impervious to manipulations of selective attention and working memory if, and only if, its input was restricted to the earliest levels of sensory processing. Mitchell et al. argue that if link formation is dependent on cognitive resources, then “one of the reasons for postulating the existence of an automatic link-formation mechanism has been removed” (sect. 4.2, para. 3). But this argument only holds if capacity limitations affect link formation and propositional learning in the same way. Instead, they might affect what the cognitive system does in the sense of the inferences that are drawn about the relevant events, whereas those same limitations might affect what the automatic system receives in the way of input. With this in mind, it would not be at all surprising if consistently attentive participants were more likely to consciously identify the relevant contingencies and also more likely to learn, whereas participants who attended erratically were less likely to be aware and less likely to learn. Although Mitchell et al. provide examples where learning is sensitive to rules and instructions, these examples only confirm what both approaches already assume – that conscious inferences and beliefs can influence decisions and behaviour. More importantly, some learned behaviours do not show this

sensitivity. While Mitchell et al. discuss Perruchet’s (1985) dissociation, other prominent examples include conditioned taste aversions and anticipatory nausea and vomiting elicited by cues associated with chemotherapy (Bernstein & Webster 1980; Carey & Burish 1988). Both appear to be very clear cases of uncontrollable, automatically learned responses that have nothing to do with the beliefs of the sufferer. Mitchell et al. wish to extend their approach to describe learning in other animals. But the utility of the approach is questionable given the obvious obstacles in establishing whether other animals even have awareness or beliefs, let alone in measuring either. And if we assume they do, what can we deduce from an animal’s behaviour about the content of its beliefs? As one example, when a pigeon pecks or drinks a key-light during an autoshaping procedure (e.g., Jenkins & Moore 1973), does it believe that the key-light is food or water? What reasoning process could give rise to such a proposition? Or should we conclude that the pigeon’s behaviour does not reflect the content of its belief? If the latter, then we must concede that the learning process cannot be adequately investigated by examining animal behaviour. At a superficial level, positing a single mechanism for learning seems more parsimonious than assuming two mechanisms, and Mitchell et al. encourage us to abandon link-based learning on this basis. However, conscious propositional reasoning is computationally more expensive and much less well-specified than the operations underlying the strengthening of associative links. The propositional learner is one who retrieves information from episodes and consciously rationalises which events signal other events within the same episode. This comes with its own set of assumptions that imply considerable computation. For a start, in order to calculate relationships symbolically, the propositional system needs to decide what each event is. It also needs to store events in an episodic fashion that includes temporal information. This contrasts with the simple mechanisms by which associative links are strengthened or weakened according to statistical regularities in the environment. The comparison begs the question: Is it really parsimonious to conclude that all instances of learning are the consequence of an elaborate and cumbersome set of cognitive operations just because we know these operations affect human behaviour in certain circumstances? We conclude that it is more parsimonious to assume learning is the product of a very simple link mechanism, but to evaluate exceptions to this rule where the evidence necessitates. Of course, the issue of parsimony is at the very heart of the single-versus-multiple process debate.

Saliences, propositions, and amalgams: Emergent learning in nonhumans doi:10.1017/S0140525X09001022 Heidi Lyna and Duane M. Rumbaughb,c a Department of Psychology, Agnes Scott College, Decatur, GA 30030; bGreat Ape Trust of Iowa, Des Moines, IA 50320; and cLanguage Research Center, Georgia State University, Decatur, GA 30034. [email protected] http://userwww.service.emory.edu/~whopkin/Heidipage.html [email protected] http://www.greatapetrust.org/research/drumbaugh/drumbaugh.php

Abstract: We comment on the similarities and differences of Mitchell et al.’s framework for understanding classical and operant conditioning and the theoretical framework put forth by Rumbaugh et al. (2007). We propose that all nonhuman and human learning may be based on amalgams created by co-occurring stimuli that share their responseeliciting properties and that these amalgams may be propositional in nature. BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning Rumbaugh et al. (2007) have argued that stimulus-response learning and reinforcement as constructs, as put forth in classic behaviorism over the course of the past century, are in need of redefinition. The essence of the redefinition entails contiguous stimuli sharing their response-eliciting properties as a function of their relative attributes and strengths (e.g., their saliences), forming amalgams. These amalgams are not simply linkages between stimuli; they are new entities based on the properties of the stimuli, their response-eliciting properties, and any other salient features of the environment. The ease with which amalgams form, is a function of the natural history of the species and the constructive bias of its neural system. The neural system integrates the amalgams into templates that metaphorically define a knowledge base, which includes information about the individual and its assessed capabilities in relation to the resources and risks of its ecological niche. From this knowledge base, emergent behaviors and capacities, with no history of specific training, might take form as overarching principles and rules to service adaptation rationally and creatively in both familiar and novel challenges. This theoretical stance has much in common with that of Mitchell et al. Neither theory supports the idea of reinforcement as a direct line to the CR (conditioned response); nor does either theory support the notion of simple S-S (stimulus-stimulus) links as part of a dual-learning system. This salience theory is silent on the issue of consciousness; however, its provision for emergent behaviors and capabilities allows for consciousness to emerge as a product of the complexity of the nervous system and its knowledge base. Consciousness, once functioning, might selectively interact with the systems of templates that are always subject to modification across time and experience. Thus, consciousness and symbolbased logic (and propositional knowledge) might either monitor or modify systems of templates and the amalgams they organize. Mitchell et al. make a good case against unconscious link-based learning systems, but they do admit the existence of some evidence for a learning system in which subjects react contrarily to their stated beliefs. Mitchell et al. correctly suggest these findings could be the result of an “imperfect” propositional system, where the subjects have created beliefs about certain contingencies, but those contingencies are flawed in some way. However, there is no reason to assume that all learning, even if propositional in nature, must be fully conscious and accessible. To illustrate the viability of the salience theory in these situations, we turn to some of Mitchell et al.’s examples. Mitchell et al. discuss the concept of blocking (sect. 4.2), where a pretrained CS (Aþ) blocks the association of a new CS (Tþ) and the US (ATþ). This is explainable under the salience theory, since the pre-trained CS (A) would be highly salient and would overshadow any new competitive amalgam formation unless the new CS (T) were equally or more salient. Additionally, the finding that embedded tasks (cognitively demanding tasks presented simultaneously with the presentation of the ATþ condition) result in subjects’ better learning the T condition (sect. 4.2), is supported by salience theory. In this case, the embedded/distracter task reduces salience of A during the presentation of ATþ, increasing the salience of T and resulting in some learning of the Tþ contingency. Finally, in the case when ATþ was trained, how did the presentation of A2 serve to increase the response to T? Perhaps A and T had become equally salient constituents within new amalgams, but subsequent A2 training reduced the saliency of A, in effect, increasing the relative saliency of T (sect. 4.3). Both Mitchell et al.’s and Rumbaugh’s theories are equally supported by findings from a long history of nonhuman cognitive studies, some of which were mentioned in the target article. One of the most reliable indications of learning abilities in nonhuman primates not mentioned in the target article is the transfer index

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(Rumbaugh & Washburn 2003). This index shows the ability of individuals trained in a simple one-choice test (choose between A and B) to switch a preference from a trained positive stimulus (Aþ) to a new positive stimulus (Bþ), given a certain level of ability on the first trained stimulus (67% or 84%). In other words, when your performance on the first task (always choose A) is increased from 67% to 84%, how is your performance on the second task (switch to always chose B) affected? These studies show that smaller-brained primates are adversely affected by augmented learning; that is, the more they learn in the first task, the worse they do on the second. In larger-brained primates, this finding is exactly reversed (the correlation between transfer index performance and various measures of brain size range from .79 to .82). Additionally, transfer index performance is strongly affected by early environmental conditions (see Rumbaugh & Washburn 2003, for a review). These results are difficult to explain with a straight link-based learning system and suggest that learning processes are altered by elaboration of the brain and by rearing. Further evidence for a propositional/salience-based learning system comes from language learning in chimpanzees and bonobos. These studies include findings in rapid symbolic associations (association of symbols to new referents on a one-trial basis; Lyn & Savage-Rumbaugh 2000) and “representational play” – treating a toy or toy stand-in as if it were something else (Lyn et al. 2006). Moreover, complex mental representations of symbols have been documented by detailing the errors in a vocabulary test (Lyn 2007). These errors include choosing a visual symbol (e.g., a lexigram that represents “key”) based on a photograph of a referent that has an auditory similarity (“TV”) – indicating that all levels of representation are activated in the choice task. Additionally, emergent behavior (such as the initial language learning in a bonobo; Savage-Rumbaugh et al. 1986) frequently appears during cognitive training in nonhumans, indicating that nonhumans are able to respond not just based on their learned contingencies, but rather by construction of an emergent, unlearned (self-generated) contingency, a feat difficult to explain through standard S-R (stimulus-response) link-based learning. ACKNOWLEDGMENTS The first author, Heidi Lyn, wishes to acknowledge support from NIH grant number HD 56232. Duane M. Rumbaugh’s work on this commentary was supported by NICHD 38051 to Georgia State University.

Propositional encodings are a subset of organization theory doi:10.1017/S0140525X09001034 George Mandler Department of Psychology, University of California, San Diego, La Jolla, California, 92093-0109. [email protected] http://psy.ucsd.edu/~gmandler/

Abstract: The notion that human associative learning is a usually conscious, higher-order process is one of the tenets of organization theory, developed over the past century. Propositional/sequential encoding is one of the possible types of organizational structures, but learning may also involve other structures.

The main argument of the target article is to show that associative learning is not – as reputedly generally assumed – an “automatic process that is divorced from higher-order cognition.” (sect. 1, para. 1) Instead, it is proposed that human associative learning is based mainly on the acquisition of propositions involving the

Commentary/Mitchell et al.: The propositional nature of human associative learning main two terms of the to-be-learned conjunction, from conditioning or quasi-conditioning paradigms of learning, primarily those that have been frequently characterized as formed by an associative “link.” Assuming that “learning” refers to the acquisition of new knowledge, in this commentary I show (necessarily briefly) that the notion that human associative learning is neither automatic, nor necessarily unconscious, has a venerable, nearly century-old history, missing from the target article. Furthermore, propositional structures constitute just one part of organization theories (see Mandler 2007, for a more extended history). The opposition to unconscious, automatic associative processes started in modern times with the work of G. E. Mu¨ller (e.g., Mu¨ller 1911) and proceeded rapidly with the development of Gestalt theory. Following the work of Wertheimer (1921), Duncker (1926), and Katona (1940), the time was ripe for a full-scale assault on the mechanisms of associative memory. The initial arguments were primarily presented by Asch (1962; 1969) and Asch and Ebenholtz (1962), and generated specific demonstrations of human associative learning by Bower (1970), Bower and Bryant (1991), Mandler and Mandler (1964), Mandler (1968; 1979a; 1979b), and Murdock (1966). In Mandler (1979b), I suggested three possible structures accounting for human associative phenomena: coordination (holistic, unitary organizations), subordination (hierarchical organizations), and pro-ordination (sequential organization). The last is most like the propositional structure proposed in the target article – A followed by B. Relevant to the target argument, my colleagues and I have tested human associative learning and demonstrated that holistic structures characterize the storage of verbal associations. Mandler et al. (1981) showed that in verbal human associative learning (sometimes known as paired-associates), “associations” are stored not as “links,” but by combining the two terms in a single holistic unit. Tests of free recall, cued-recall, and recognition supported that conclusion. Propositions about and tests of organizational theory describe the structure of human semantics – the mental organization of meaningful knowledge and experience. Organization defines the structure of memory. It is obvious that propositional structures depend on retrievals from memory, and, albeit without any detailed discussion of memory, Mitchell et al. too assert the centrality of memorial functions, when in section 3.1 (para. 1) they state that the encoding of an associative hypothesis in memory constitutes learning. Organization theory has generally avoided any distinction between learning and memory. The history of the organizational approach discussed the organization of mental contents, which can be seen as “learned” when established and retrieved once the organizational structure is established. Consistent with such an approach, Mitchell et al. also note that subsequent to a bell-food pairing, a bell can retrieve memories of previous pairings. More generally, it may not be initially obvious which of the possible structures applies to a particular learning experiment or paradigm. At present it is not obvious which experimental or experiential situations give rise to one organization or another. The target article seems to claim that all encodings are propositional; in contrast, we have shown that some are holistic and unitary. Specific experimental procedures and probing and testing procedures need to be developed in order to determine which particular structures eventuate from a specific “learning” situation. Finally, it does not seem obvious that “we have been heading . . . towards a propositional approach to all learning” (sect. 7.1, para. 3). The holistic encoding of word pairs or the hierarchical organization of some lists argues against a single model of underlying structures. A general organizational approach has asserted for some time that learning is indeed “not separate from other cognitive processes” (sect. 8, para. 1). Organization theory has made it possible to see the connectedness of these various functions and processes.

The Proust effect and the evolution of a dual learning system doi:10.1017/S0140525X09001046 Helena Matute and Miguel A. Vadillo Department of Psychology, Deusto University, 48080 Bilbao, Spain. [email protected] http://paginaspersonales.deusto.es/matute [email protected] http://paginaspersonales.deusto.es/mvadillo

Abstract: Proust’s madeleine illustrates the automatic nature of associative learning. Although we agree with Mitchell et al. that no compelling scientific proof for this effect has yet been reported in humans, evolutionary constraints suggest that it should not be discarded: There is no reason by which natural selection should favor individuals who lose a fast and automatic survival tool. And soon, mechanically, weary after a dull day with the prospect of a depressing morrow, I raised to my lips a spoonful of the tea in which I had soaked a morsel of the cake. No sooner had the warm liquid, and the crumbs with it, touched my palate than a shudder ran through my whole body, and I stopped, intent upon the extraordinary changes that were taking place . . . at once the vicissitudes of life had become indifferent to me, its disasters innocuous, its brevity illusory. — Marcel Proust (1913/1922), Remembrance of Things Past

The episode of the madeleine in the Proust work cited above is one of the most famous passages of universal literature of all times. Not only is it beautifully written, but the passage also describes an experience that is so personal and so ubiquitous in human nature that any psychologist, from Freud to Pavlov, would love to explain it. We will refer to it as the “Proust effect.” To our knowledge, it is the best possible description of associative learning. The beauty of the target article by Mitchell et al. is that it tries to understand the Proust effect in its entirety, not just a part of it. As such, the article is ambitious, important, and timely. It makes us rethink all the established assumptions about learning. Contrary to all intuitions, Mitchell et al. (almost) convince us that (a) there must be only one learning process, and (b) this unique process must be propositional in nature. The standard explanation for associative learning is the link approach. Because the narrator in Proust’s novel had associated the madeleines with all the happiness of childhood (even though he was not aware of this fact), then tasting one of those cakes now, after so many years, brought back the enormous happiness and all the good feelings from childhood. Thus, the Proust effect reflects a simple, automatic link that was created during childhood and is now expressed, also without effort or knowledge of the contingencies, in the form of a conditioned response (CR). According to the link proponents, there was no propositional learning here, no consciousness of the contingencies while the association was acquired; not even now that it is expressed. Indeed, it will still take the narrator many pages and a considerable amount of thinking and elaborated reasoning to discover why the madeleine was producing the CR. But the link approach is not as simple as it seems, and Mitchell et al. are correct in highlighting this point: The link approach presupposes a dual (and complex) system. Automatic links need to be complemented with some more-elaborated, rational, and time-consuming forms of learning. This complex learning is at work, for instance, after the CR has occurred and the narrator begins to consciously think about it and tries to identify its cause. Even the most enthusiastic proponents of low-level mechanisms have to admit that people are obviously capable of other forms of learning and reasoning. What Mitchell et al. suggest is that, if we all agree that propositional learning is needed, why should we maintain a belief in automatic links? Couldn’t we assume just a propositional learning BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning process that could account for both the automatic-like and the more complex processes? Are there any experiments that can only be explained by the link mechanism? That there are data to support that propositional learning exists is unquestionable, and the authors make an excellent case of it. That many of the results that have traditionally been explained using the link approach can also be explained by the propositional account is also clear in their target article. Moreover, it is well established today that there are very few experiments that can be explained solely by the link approach (Lovibond & Shanks 2002; Shanks & St. John 1994). What Mitchell et al. are showing is that both the dual and the propositional account can explain the majority of the available evidence. Scientific parsimony becomes then the central argument: If a single process can explain it all, why should science maintain two? But the argument of scientific parsimony should be confronted against that of natural selection. A simple, low-level process is vital for survival because, by definition, it can do all those things the complex process cannot do: it responds quickly, automatically, and without consciousness or effort to the demands of the environment. Even under high pressure, it provides a fast tool for survival. Its loss would be too costly. As Mitchell et al. note, natural selection has produced a continuum of complexity in the different species. At one end of this continuum, we find very simple species which have just the link system and no cognition. At the other end, we find the human species, which, according to Mitchell et al., has only the propositional system. If so, Mitchell et al. need to explain why humans (and other evolved animals) should have lost their primitive link system while developing the propositional one. There is no clear evolutionary advantage in losing a fast and automatic tool. Indeed, there is a growing body of evidence suggesting that learning is actually caused by a multiplicity of different mechanisms and that the insistence of traditional learning theory in a unique, general-purpose learning system was simply a mistake (Gallistel 2000; Tooby & Cosmides 1992; 2005). If natural selection has encouraged flexibility and adaptability, having many different forms of learning must have been favored through the course of evolution. In sum, Mitchell et al. need to explain not only why consciousness becomes so difficult in the Proust effect, but also what survival advantages a species that extinguishes the link system should have. If all the evidence for the automatic mechanism would come from novels and intuitions, Mitchell et al. would be right that science should ignore it. But we have shown good reasons to believe that the automatic mechanism must still be present in humans. Perhaps the problem is that the Proust effect has always been taken for granted and proofs have not been searched in the right places. ACKNOWLEDGMENT The authors of this commentary were supported by MICINN (Spain) Grant SEJ2007-63691.

Both rules and associations are required to predict human behaviour doi:10.1017/S0140525X09001058 I. P. L. McLaren School of Psychology, University of Exeter, Exeter, EX4 4QG, United Kingdom. [email protected]

Abstract: I argue that the dual-process account of human learning rejected by Mitchell et al. in the target article is informative and predictive with respect to human behaviour in a way that the authors’

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purely propositional account is not. Experiments that reveal different patterns of results under conditions that favour either associative or rule-based performance are the way forward.

In this target article, Mitchell et al. argue for a propositional account of human learning, rather than a dual-process model that allows for propositional and associative (what they call the “link model”) processes to operate concurrently. The issue at hand, then, is whether we need to postulate associative processes in addition to propositional ones; the converse argument, whether we need to postulate propositional processes in addition to associative processes, can be left for another time. But let me be quite clear: I am of the view that we need to appeal to both if we are to understand learning in humans. The approach taken in this commentary is to point out differences in learning and performance under conditions that should favour either propositional or associative learning. Mitchell et al. consider a number of these cases, but perhaps do not do them justice. I take as my first example their review of the Le Pelley et al. (2005a) demonstration of unblocking in humans. In these experiments, it was demonstrated that a design such as A2 . O1 followed by O2jAB2. O1 followed by O3 revealed that learning to B was greater for outcome 1 (O1) than in a conventional blocking design where the second phase had the compound followed by O1 then O2. This finding was predicted on the basis of Mackintosh’s (1975) associative theory of learning, which has received experimental support in animals other than human. To dismiss it by saying that it is possible that in a complex design the human participants had forgotten earlier trials and knew something had changed but were not sure whether it was O1 or O2, ignores this background. As an explanation of the phenomenon, it is terrifically weak. We are expected to allow that propositional learning and an automatic memory (that is definitely not associative?) are both imperfect, and so people make mistakes, which just happen to be the ones that associative theories predict. This does sound rather implausible, and it is, even though the authors reassure us that it can be tested. Their proposal is to make the outcomes more distinctive, thus reducing any confusion between them, and so the effect (unblocking) should go away. In fact, if the outcomes were made that distinct from one another, the same associative theory that predicted the original result would now predict that the effect would go away as well, as the alpha change that leads to unblocking is to some extent reinforcer-specific in this model. This result has also been found in humans in another experiment by Le Pelley and colleagues (Le Pelley et al. 2005b), in which changing outcomes from those that are generally “nice” to those that are generally “nasty” (and vice versa) prevented alpha effects that were generated by manipulating the predictiveness of certain cues during training. So we are left with a “test” of their account that fails to distinguish between it and the very associative theory that motivated the experiment in the first place. Not much of a test! Mitchell et al. also fail to take into consideration a number of other studies that demonstrate a different pattern of results when learning is dominated by either rule-based (hence propositional) or associative processes. People show a peak shift, like pigeons, when they are tested on a dimension after relatively little experience with it, and when they are unable to verbalise any rule that captures the discrimination (Jones & McLaren 1999; and see Livesey & McLaren, forthcoming). This pattern of responding changes (to a monotonic function across the dimension) after extensive experience with the stimuli and when people can verbalise the correct rule. In the spirit of the target article, I would expect the response to be that this does not demonstrate associative learning, but instead, incorrect rule induction or imperfect application of a rule in some way. If this characterisation of Mitchell et al.’s position is right, then it is impossible to defend against. There will always, with sufficient ingenuity, be some incorrect or imperfect rule that can be appealed to

Commentary/Mitchell et al.: The propositional nature of human associative learning that fits the behaviour. But it simply ignores the fact that these studies were based on predictions made before the fact by a dual-process model, not after the data had been collected. Any sufficiently complete learning system can explain any pattern of results once the pattern is known; the trick is to predict them in advance. The “imperfect rule” approach will never do that. My final example, which reinforces the point just made, concerns work on sequence learning done by myself and Rainer Spiegel (Spiegel & McLaren 2006). In this series of experiments, we show that the predictions made by the Simple Recurrent Network (SRN), even though at times quite counter-intuitive at first sight, are nevertheless borne out by the experimental results obtained with humans. For example, training on sequences of the form ABC . . . CBA and ABBC . . . CBBA where C . . . C can be one, three, or five C terms, leads to the ability to respond faster and more accurately to the term after the first B following the C terms, as it is predicted by the rule “The number of Bs after the Cs is the same as the number experienced before the Cs.” However, this rule would not predict that when tested after acquisition involving sequences with an odd number of C elements on a sequence, such as ABCCCCBA, the result would be that no learning was displayed, in that responding to the A after the Cs was not facilitated. This was predicted by the SRN, and was the case in our experiments. Many other counter-intuitive effects are reported in Spiegel and McLaren (2006) that all closely follow the predictions made by this associative model. I am not sure whether Mitchell et al. will opt for the “wrong or imperfect rule” approach here or simply rule these data out of court on the grounds that the SRN is not a standard model of associative learning, but either way the purely propositional account seems implausible to the point of incredulity when confronted by these data. The target article is an enjoyable attempt to make the case for an exclusively propositional account of human learning, but I fully expect that the learning theories of tomorrow will make use of both rules and associations, and that the attempt to restrict theorising to one or the other will quickly pass.

Associative learning without reason or belief doi:10.1017/S0140525X0900106X James D. Miles, Robert W. Proctor, and E. J. Capaldi Psychological Sciences, Purdue University, West Lafayette, IN 47907-2081. [email protected] http://www.jimmiles.org [email protected] http://www2.psych.purdue.edu/~proctor/ [email protected]

Abstract: We discuss the necessity of conscious thinking in the singlesystem propositional model of learning. Research from honeybees to humans suggests that associative learning can take place without the need for controlled reasoning or the development of beliefs of relationships between objects or events. We conclude that a single learning system is possible, but not if it depends on complex thinking.

Mitchell et al. contribute their view to a long-standing controversy in psychology: Namely, can the range of human and animal behavior be sufficiently attributed to a single processing system rather than multiple competing systems? The authors propose that a single propositional learning system drives behavior. In this learning system, “controlled reasoning processes are necessary for learning to take place, and learning results in beliefs about the relationship between events” (sect. 1, para. 8). The major aspect of this position with which we disagree is: “are necessary for learning.” In this commentary, we point out several phenomena that are difficult to explain within a single propositional learning system as it is described.

Mitchell et al. contend that associative learning in the Pavlovian situation involves propositional knowledge and not the automatic formation of links between events. The authors indicate that, in their view, Aplysia do not possess propositional knowledge. This view would presumably extend to insects, such as honeybees. Rats and people show three complex conditioning effects: backward blocking, forward blocking, and relative validity. Each of these effects involve multiple conditioned stimuli (CS) occurring concurrently or in isolation along with an unconditioned stimulus (US). Mitchell et al. argue that the resulting patterns of response behaviors in these blocking designs are difficult to explain within a strictly automatic associative-link framework. Therefore, they suggest that these phenomena involve propositional knowledge in humans and possibly in rats. Because, presumably, bees do not possess propositional knowledge but display all three effects, it follows that these effects can occur in the absence of propositional knowledge (see e.g., Blaser et al. 2004; Guez & Miller 2008). So, the authors are driven to the conclusion that highly similar and complex effects in different species can occur on the basis of highly different learning mechanisms. However, the phylogenic “cut-off” point between these mechanisms is unclear. An alternative possibility is that the same learning mechanism produces the three effects in honeybees, rats, and people. According to that view, the learning mechanism must be some form of simple links rather than propositional. It is much easier to concede that humans can learn like bees than bees can learn via propositional reasoning like humans. Whether or not the same mechanism is responsible for learning in both cases, it must be assumed that links between events occur in order to accommodate the bee data. Mitchell et al. mention that, at least in more complex animals, it is possible that their single propositional learning system may rely on the multiple connections between subcortical and cortical brain regions (sect. 5.3). As such, it is difficult to imagine an intact propositional system at work in decorticate animals in which most higher brain functions, such as those associated with reasoning skills, have been removed. However, many studies, the earliest of which include Culler and Mettler’s (1934) research, have found that conditioned learning still occurs following the general or localized removal of cortical regions. For example, rats with as much as 99% of their neocortex removed showed learning in a T-maze that was equivalent to that of fully intact controls (Thompson 1959). At the furthest extreme, the spinal cord alone is sufficient for associative learning to occur (Patterson et al. 1973). These findings do not preclude the existence of propositional learning, but they show that it is not necessary for associative learning to take place. Non-propositional learning also appears to occur within humans. The classic argument for learning without awareness is Clapare`de’s (1907) description of a woman suffering from Korsakoff’s syndrome (Nicolas 1996). Clapare`de pricked his patient’s hand with a pin hidden in his own, and, although she did not display any declarative knowledge of the experience, she later would withdraw from Clapare`de when he gestured toward her with his hand. Subsequent research with anterograde amnesiacs provides further examples of similar learning in the absence of conscious, declarative knowledge. For example, Gabrieli et al. (1995) found that eyeblink conditioning remained intact in amnesiacs and suggested that declarative information was not needed for CS-US associations to occur (for more examples, see Cohen & Squire 1980). It may be argued that aware, conscious reasoning does occur during the learning process in amnesiacs, and the patients are simply no longer aware of this learning experience during the expression of this information at a later time. Even if this were the case, there are two reasons that a propositional-system only account remains problematic based on evidence from amnesiacs. First, it is unclear how learned propositional information can be used later without conscious awareness that the information must BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning be applied. As the target article authors describe their learning system, learned propositional information is expressed through beliefs about the relationships between events, and these beliefs are very unlikely to occur at all in anterograde amnesiacs. Additionally, there is some evidence that learning processes in intact individuals may occur without the need for any conscious awareness of the associations between events in the environment. In tasks such as artificial grammar learning, implicit categorization, and implicit sequence learning, the learning and expression of associations between items or events in the environment occur without the need for awareness. For example, in four-choice tasks that contain long repeating item sequences, participants show evidence of learning the sequence often without awareness or knowledge and concurrently with or in isolation from explicit learning (Song et al. 2007). This further implies that associative learning can take place without reasoning and conscious beliefs about associations. To conclude, the authors claim that “associative learning is never automatic and always requires controlled processes” (sect. 3.1, para. 2). However, the aforementioned examples provide converging evidence that associative learning can take place without the need for reason or belief. If a single learning system is to account for all forms of learning, the system must accommodate these cases.

Undermining the foundations: Questioning the basic notions of associationism and mental representation doi:10.1017/S0140525X09001071 Ezequiel Morsella,a,b Travis A. Riddle,a and John A. Barghc a Department of Psychology, San Francisco State University, San Francisco, CA 94132-4168; bDepartment of Neurology; University of California, San Francisco, San Francisco, CA 94143; cDepartment of Psychology, Yale University, New Haven, CT 06520-8205. [email protected] http://bss.sfsu.edu/emorsella/ [email protected] [email protected] http://pantheon.yale.edu/~jab257/home.html

Abstract: Perhaps the time has come to re-examine the basic notions of cognitive science. Together with previous challenges against associationism, the target article should be viewed as a call to arms to re-evaluate the empirical basis for contemporary conceptualizations of human learning and the notion of “mental representation,” a concept that has become too imprecise for describing the elements of cognition.

Has the time arrived to rebuild the foundations of cognitive science? If Mitchell et al. had been the first to challenge the basic notion of associationism, then perhaps the field could survive this seeming coup d’e´tat by simply pointing to the mounting evidence in favor of associationism – the classic research on the changes at the synaptic level that are responsible for classical conditioning (e.g., Kandel 2000; LeDoux 2000; Thompson 2005); and investigations on the implicit/automatic processes mediating the learning of sequences, motor skills, attitudes (Stadler & Frensch 1998), and the execution of various kinds of “inputoutput” processes (Hallett 2007; Pessiglione et al. 2008). Afterwards, we could all continue to promulgate that the brain learns as it does because “cells that fire together wire together” – a concept that, like the reflex, exemplifies our switchboard intuition regarding how intelligent behavior is implemented neurally. But Mitchell et al.’s is not the only challenge, and it may not be the most ground shaking. Gallistel and Gibbon (2001) challenged associationism by demonstrating that it could not account for basic conditioning phenomena (in rats and pigeons) such as time-scale invariance or the observation that “neither the delay of reinforcement nor

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the ratio of reinforced to unreinforced presentation of the conditioned stimulus affects rates of acquisition and extinction” (p. 146). If cells that fire together do wire together, then intermittent conditioning should show less resistance to extinction than fixed schedules of conditioning; but this is not the case in classical and operant conditioning (Gallistel & Gibbon 2001; Skinner 1953). Hence, the target article should be seen as a red flag that can no longer be ignored – a wake-up call to begin to carefully take stock of what is actually known regarding the basis of human cognition. Moreover, if the time has come to rebuild the foundations of cognitive science, perhaps it is also worthwhile to clean up our terms and reexamine the value of the notion of “mental representation,” an ambiguous term that has come to mean all kinds of things to all kinds of researchers. Unlike consciousness, which has been regarded as the main “unsolved anomaly within the domain of the [scientific] approach” (Shallice 1972, p. 383), the neural mechanisms proposed to underlie conditioning reflect our intuitive understanding of how the mind/brain should work: Fear conditioning is mediated by the amygdala (LeDoux 2000) in a manner that is consonant with our switchboard intuition regarding nervous function. However, despite our intuitions, once Tolman (1948) demonstrated that there is reason to doubt that learning is due to simple stimulusresponse (SR) models (e.g., by showing that rats could solve mazes without relying on any external or prioprioceptive cues; see also Lashley 1951; Terrace 2005), doubt should have fallen over all SR explanations of behavior. But it did not. Complex behaviors continued to be explained by complex mechanisms (e.g., cognitive maps, reasoning), and simple behaviors continued to be explained by simple mechanisms (e.g., SR strength). When explaining how a pigeon pecks a button for food, one appeals to the principles of operant conditioning, but when explaining how wasps and pigeons are able to find their home in the absence of external cues, one invokes the term “cognitive map” (Gallistel 1990), which depends on neural machinery that, for some reason, is believed to not be at play during buttonpecking. It is seldom appreciated that, if all an animal possesses happens to be a sophisticated faculty of navigation, then this faculty will be used even for button pressing. The hands of evolution have been seen as behaving economically, using simple mechanisms for simple behaviors but reserving complex mechanisms for complex behaviors, which is a wrong way to think about evolution (cf. de Waal 2002). The fallacy has persisted even though, as noted by Mitchell et al., there has always been more evidence for the existence of high-level representational mechanisms than for simple mechanisms (e.g., SR strength). It remains an empirical question whether, in humans, there are no instances in which associative learning is instantiated by the kinds of synaptic-level changes that have been identified in animals (see Phelps & LeDoux 2005). If so, what is the neural foundation of human cognition? Perhaps the time has come to examine whether conditioning is mediated by higher-level processes such as “interregional synchrony” or “neural coherence” (cf., Buzsa´ki 2006; Fries 2005), processes believed to serve an important role in communication and in “binding” representations (Hummel & Gerloff 2005). Are mental representations those tokens used by the propositional system proposed by Mitchell et al.? If so, Gallistel (2001) defined a mental representation as “a system of symbols isomorphic to some aspect of the environment, used to make behavior-generating decisions that anticipate events and relations in that environment . . . [and, cognitive] psychology is the study of mental representations” (p. 9691). However, the nature of the isomorphism to the world remains unclear with respect to many “representational” processes, such as nonintentional states (e.g., moods or the experience of holding one’s breath; Gray 2004). More concretely, in light of Gallistel’s definition, it remains unclear to what the pungent flavor of hydrogen peroxide is isomorphic. This chemical differs molecularly from water only by the addition of a single oxygen atom, but

Commentary/Mitchell et al.: The propositional nature of human associative learning few would perceive it as “water with a little too much oxygen.” Instead, the toxic chemical is perceived (or “represented”) as something that should be violently expelled from the body. This may lead one to hypothesize that, as with subjective urges (Morsella 2005) and percepts (Sperry 1964), the representation of H2O2 is isomorphic with respect to how one should respond to the stimulus, but this is not in line with the traditional view (based on the cognitive map) of what a mental representation is (Hommel et al. 2001). Hence, a more precise term is needed for the tokens that furnish the contents of the propositional reasoning system proposed by Mitchell et al. Cognitive science may be far from developing its periodic table, but it can still be rigorous about delineating what is known and what is not yet known.

What is the link between propositions and memories? doi:10.1017/S0140525X09001083 Ben R. Newell School of Psychology, University of New South Wales, Sydney 2052, Australia. [email protected] http://www2.psy.unsw.edu.au/Users/BNewell/

Abstract: Mitchell et al. present a lucid and provocative challenge to the claim that links between mental representations are formed automatically. However, the propositional approach they offer requires clearer specification, especially with regard to how propositions and memories interact. A definition of a system would also clarify the debate, as might an alternative technique for assessing task “dissociations.” Propositions, memories, and their interaction. Mitchell et al. use the simple example of learning that a bell signals food to illustrate the differences between the propositional and dual-system approaches. Although useful, the simplicity of the example is potentially deceptive. Focusing on a situation in which the organism learns about a single binary cue (bell rings/does not ring) and a single binary outcome (food presented/not presented) potentially leaves out some of the devilish details of how organisms learn in multiple-cue environments. Learning to form the proposition: “When the bell rings, I expect food” does not appear too arduous (for humans and some other species at least); but is the same kind of propositional statement the only form of knowledge learned when situations become more complex? Imagine an environment where cues and outcomes are continuous and relations are probabilistic. In such an environment, do organisms form propositions of the kind: “When the bell rings for more than 5 seconds (but not over 15 seconds), the green light is at 50% brightness, and the red light is off, I expect food approximately 80% of the time”? Research into multiple-cueprobability learning (Enkvist et al. 2006; Juslin et al. 2003), multi-attribute judgment (Newell & Bro¨der 2008), and categorization and concept learning (e.g., Allen & Brooks 1991; Nosofsky et al. 1989) has suggested that humans might try to learn such propositional information (i.e., rules) up to a point, but if the environment is too complex (e.g., cue-outcome relations are nonlinear), or feedback is insufficient or inappropriate, other forms of knowledge – principally stored instances – are relied upon. Mitchell et al. acknowledge that instance memories play a role (sect. 3.1) but state that “recollections of past bell-food pairings alone cannot produce learning” (sect. 3.1, para.6). Such a conclusion implies that experiments demonstrating behaviour accounted for by an exemplar model (which relies exclusively on stored representations of stimuli; e.g., Juslin et al. 2003; Nosofsky et al. 1989) are not demonstrations of learning. This conclusion seems too extreme. Participants in such experiments have learned to classify particular objects as belonging to Category

A and others to Category B – they have learned an association between a stimulus (the to-be-classified-object) and a response (the category label). But the content of this learning appears to be instances rather than a proposition (see also Shanks & St. John 1994). The interplay (and relative influence) of instances and propositions is somewhat underspecified in Mitchell et al’s approach. However, the implication is that learning can only occur when propositions (rules) are formed. This seems a step too far, especially in situations with multiple, non-binary cues. Systems, processes, and their interaction. Mitchell et al. note that many dual-system models do not specify how systems interact with each other. This is certainly true and, moreover, it appears that there is little consensus across different areas on how such interaction might occur. For example, an influential dual-system model of category learning, COVIS (Ashby et al. 1998), proposes an initial bias towards an explicit hypothesis testing system, which is then usurped by an automatic, procedural system when the explicit system fails to learn. In contrast, popular dualsystem theories of reasoning (e.g., Evans 2008) suggest that the initial bias is towards the automatic, intuitive system, which is only corrected by the explicit system when things appear to go awry. Part of the problem in specifying these interactions is that it is often not clear what is meant by a system or a process – and whether these terms are interchangeable (Evans 2008). Mitchell et al. make a distinction (sect. 3.1), stating that their propositional approach is not a dual-system approach but that there are two types of processes (automatic processes of perception and memory and non-automatic processes of reasoning) in their single learning system. Although such specification clearly distinguishes their approach from the link-mechanism theories they wish to challenge, it blurs the distinction with many of the dual-process approaches to “higher-order” cognition. In their footnote, Mitchell et al. contrast their approach to other “dualprocess or dual-system” theories of reasoning by stating that such approaches focus on performance, not learning – but at the same time Mitchell et al. want to incorporate memory (i.e., performance) processes into their learning system. Perhaps some clarification could be achieved by defining exactly what Mitchell et al. mean by a system (cf. Sherry & Schacter 1987). New techniques for old problems. Much of the evidence for and against the propositional and link approaches reviewed in the target article comes in the form of task dissociations; that is, situations in which a variable (e.g., cognitive load, instruction) is claimed to have an effect on one system but no effect on another. However, dissociations are unable to bear the inferential weight placed upon them for several reasons. One reason is that a simple dissociation requires that a variable have no effect on a particular behavioural measure, an assertion that is impossible in principle to verify (Dunn 2003). Hence, although dissociations may be found, they are neither necessary nor sufficient for drawing inferences about the number of processes or systems underlying observed behaviour (Newell & Dunn 2008). An alternative technique which avoids the flaws of dissociation logic is state-trace analysis (Bamber 1979). This technique has already been applied successfully to many areas of cognitive science (see Newell & Dunn 2008), and its application to the areas reviewed in the target article might prove fruitful.

The new enlightenment hypothesis: All learners are rational doi:10.1017/S0140525X09001095 Rita Nolan Department of Philosophy, State University of New York at Stony Brook, Stony Brook, NY 11794-3750. [email protected] http://www.stonybrook.edu/philosophy/faculty/index.html#Nolan BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning Abstract: The proposal to recruit available formal structures to build an algorithmic model of all learning falters on close examination of its essential assumption: that the input and output of the model are propositional in structure. After giving three framework considerations, I describe three possibly fatal problems with this assumption, concluding each with a question that needs answering to avoid fatality.

I applaud Mitchell et al.’s expanded emphasis on cognition in learning theory, for our understanding pervades all we do. Nevertheless, there are fundamental problems with the propositional approach they propose. The title bills a propositional approach to human associative learning, animal learning being tucked in later as an egalitarian gesture, but the model proposed would be a standard neo-classic account of human learning in terms of a representational theory of mind except for its universal extension to all learning, human and otherwise. Such neo-classic accounts deem it explanation enough of some human behavior to hypothesize rich formal structures of inference and sentence generation internal to the organism as causes of like changes in behavior. The hypothesized structures are extrapolated from formal linguistics and formal logic. Some have found such explanations useful – not surprisingly for computer modeling of human linguistic behavior – but the target article’s bold step is to extend the neo-classic model to all animal learning. Mitchell et al. propose an algorithmic-level propositional model for all organismic learning that is sandwiched between a functional-level model and an implementation-level model. Algorithmic models of formal systems of inferences over formal structures of propositions exist, so the question is not whether what is inside the algorithmic box can be built. These inferential structures transform a propositional input into a propositional output; and they are sensitive to different conditions as constraints. Because the sandwich isolates the algorithmic-level box from any existential referents, to determine the explanatory adequacy of the model we are led to focus on the input/output structures as the locus of the psychological part of the explanation. Proposition is a term of art, a moveable vector, but there must be some retained minimal content for its artful use to be contentful. It cannot remain an undefined abstract term and bear explanatory weight. Perhaps it seems that propositional structure is a well-defined formal concept and that this is all that is required for the algorithmic model to have content. Even so, the viability of the model as psychologically explanatory still requires assessment of its assignment of propositional structure to the input and output of the algorithmic box. Human language users have a range of generalized information-bearing structures available that can be mistaken for propositional structures when they are not. So, seeing a cat up a tree differs informationally from seeing that a cat is up a tree, as seeing a red box differs from seeing that a box is red. Learning to recognize an elm differs informationally from learning that an elm has double-toothed, feather-veined leaves. Learning how to tie your shoe differs informationally from learning that to tie your shoe, you first hold the left lace in one hand [and so forth]. The input/output assumptions of the model assimilate to the propositional all structures such as these that mark off different sorts of perceptual and procedural cognitive achievements from propositional learning. How is the explanatory value of the model enhanced by trading in these finer-grained informational structures for the merely available and smooth operations in the box? Outside the algorithmic box, a key reason for hypothesizing propositions is that they are taken to be the unique bearers of truth values and this requires that they can be either true or false. Recognition of this property operates essentially in any task of drawing valid inferences since their special feature is that they preserve truth. Of course we informally anthropomorphize the mental lives of animals and certainly some analogue to belief is exhibited by them; perhaps some primal state that preceded language in Modern Humans. But to predicate any

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propositional attitude of an animal more strictly speaking, and particularly belief, requires that the animal can distinguish truth of the proposition from its falsehood. For to believe a proposition is to believe that it is true, for which feat one must be able to believe that it, one and the same proposition, is false. No explicit concept of truth is required for this ability, nor is it supposed that a belief must be an occurrent mental phenomenon. Granting that belief is used as a term of art in the description of the input/ output of the algorithmic box and thus dispensing with some of its everyday content, can its content relative to its use as a propositional attitude for the central objects that take part in inferential operations – propositions – be dispensed with, when it is exactly that use which the model aims to capture? The input and output of the algorithmic model proposed by the propositional approach exhibit the fine-grained information-bearing structures of linguistic vehicles of assertion; they are sentences of a language in the form of statements. With the resources of language at hand comes a powerful, productive vehicle for describing whatever we notice; a feature that may make the propositional approach initially attractive for representing the cognitive changes of learning for all species. But the power and productivity of language can also pose a direct challenge to the requirement of falsifiability for a model. Language makes possible a vast number of available alternative propositional descriptions of any event and any belief content, even to a limiting case of [x believes that] something happened. This feature of the propositional approach allows very high flexibility in describing the input and the output. If a model is meant to explain anything then it must admit of falsification, but it is hard to see what could falsify it given this degree of flexibility. If some result appears to falsify the model, one can always redescribe the input and output, trying out different descriptions until hitting upon ones that work. Does this high flexibility make the model merely a redescription, not an explanation, of what it is meant to model (for a discussion, see Myung & Pitt 2002)? It is essential to the proposed model that the input/output structures to the algorithmic box are propositional in structure, for these alone are the domain of inferential relations and the aim of the model is to construe all learning as inferential.

Is cultivating “biological blindness” a viable route to understanding behavioral phenomena? doi:10.1017/S0140525X09001101 ¨ hman Andreas Olsson and Ame O Department of Clinical Neuroscience, Karolinska Institute, Psychology Section, Stockholm 17177, Sweden. [email protected] http://www.psych.nyu.edu/phelpslab/pages/andreas.html [email protected] http://ki.se/ki/jsp/polopoly.jsp?d¼15053&a¼13512&l¼sv

Abstract: Mitchell et al. propose that associative learning in humans and other animals requires the formation of propositions by means of conscious and controlled reasoning. This approach neglects important aspects of current thinking in evolutionary biology and neuroscience that support the claim that learning, here exemplified by fear learning, neither needs to be conscious nor controlled.

In an era characterized by a growing convergence among evolutionary biology, neurobiology, and behavioral sciences, Mitchell et al. make a bold claim. The authors argue that in humans (and all other animals with the explicit exception of Aplysia), learning to associate stimuli requires the formation of propositions (symbolic representations with truth-value) by means of conscious and controlled reasoning. Although swimming towards the

Commentary/Mitchell et al.: The propositional nature of human associative learning (main)stream is sometimes necessary to reach the source, there are several reasons to believe that the authors are heading in the wrong direction. In principle, the proposal that the formation of propositions is necessary for learning to occur – a process subserved by protein synthesis – is not different from the absurd claim that successful protein syntheses in the digestive systems must also be preceded by propositional representations. In fact, given our current knowledge of biological systems, it is highly unlikely that humans, not to speak about other animals, are conscious or in control of the majority of processes underlying learning and memory formation. Nonetheless, they may be both (i.e., conscious and in control) in regards to the expression of the acquired learning. In an attempt to avoid linking their proposal to established biological principles of learning, the authors point towards the distinction between the psychological and the neural level of explanation and argue that their thesis applies only to the former. However, this illustrates a general problem inherent in the presented approach. By limiting the phenomena under investigation by either defining out of existence critical aspects of associative learning (e.g., their biological principles) or neglecting several lines of existing research (e.g., fear conditioning and lesions studies in humans), the authors end up proving little more than their assumptions. It is also noteworthy that the notoriously problematic terms “conscious” and “awareness,” although central to the argument, are not explicated (except that the reader is reassured that “Aplysia do not have conscious beliefs”; target article, sect. 6.3, para. 3). Here, we highlight three more specific problems with the approach presented by Mitchell et al. 1. The notion of a unitary type of associative learning resting on conscious awareness sits very uncomfortably with established ideas in evolutionary biology. Evolution is commonly conceived as a slow accumulative process, building layer upon layer of brain tissue that incorporates successful adaptations at one level into more complex functions at higher levels. As a consequence, we share many behavioral systems and their associated neural circuitry with our primitive predecessors, unlikely candidates for using awareness as their primary principle of learning. Nor does it seem a likely evolutionary feat to have reorganized the human brain for exclusive use of this principle to modify behavior. Rather, from the evolutionary perspective, many different forms of learning would be expected, as elaborated by, for example, Gregory Razran (1971). The MacLean (1993) concept of a “triune brain” is one, often discussed, example of layered evolution of this kind, which directly implies that there are at least three levels of behavioral organization, each of which may incorporate associative learning: one concerned with reflexes and instincts (brain stem and striatum), a second that incorporates emotion and autonomic control (the limbic brain), and a third level concerned with instrumental behavior and cognition (thalamus and the cerebral cortex). 2. Related to the lack of compatibility with evolutionary thinking is the omission of several lines of research within the neurosciences. A contemporary version of the MacLean concept is the model of rodent fear conditioning by LeDoux (1996), Davis (1992), Fanselow (1994), Maren and Quirk (2004), among others, which has been confirmed in human brain imaging studies (Morris et al. 1998). Because this model posits that the input to and output from the central hub in the fear network do not necessarily have to go through the cortex, it strongly implies that the fear network and its modification through fear conditioning are independent of conscious awareness. Therefore, this model (and its elaboration for human fear conditioning by ¨ hman & Mineka 2001) clearly implies two levels of learning O that are partially independent but also interacting. This model provides an articulated version of dual-process theory that integrates neuroscience and behavior and is now supported by a ¨ hman & Soares host of both behavioral (Hamm & Vaitl 1996; O

1998) and imaging work (Critchley et al. 2002), showing that conscious awareness of the associated stimuli or their contingency is not necessary for learning to be acquired and expressed. Providing further support for the independence of (at least) two kinds of learning is the work on patients with lesions on the hippocampus, a structure known to be critical for the formation of declarative memories. Following fear conditioning, these patients fail to report the contingency between two associated stimuli (e.g., a neutral tone or image and an aversive shock) in a fear conditioning paradigm, but they show a normal conditioned response as measured by the skin conductance response, SCR (Bechara et al. 1995). In contrast, patients with lesions to the amygdala, a key player in the brain’s fear network and known to be necessary for the implicit expression of learned fear, display the opposite response pattern with intact declarative memory, but an impaired conditioned response (spared conditioned response or SCR) (Bechara et al. 1995; Weike et al. 2005). These findings show a striking dissociation between explicitly (propositionally) and implicitly (SCR) expressed emotional learning. Further support along the same lines is the demonstration of fear conditioning to unseen visual stimuli in a cortically blind patient with bilateral lesions to the primary visual cortex (Hamm et al. 2003). Taken together, the findings listed above should make it clear that the psychological and neural levels of explanations are tightly coupled and that psychological models of learning can benefit tremendously by drawing from what is known in the neurosciences. 3. In their argumentation against the “dual-process” theory of learning, Mitchell et al. build a straw man around the claim that the SCR as a measure of learning is unaffected by conscious and controlled cognitions. This purported claim, ascribed to the dual-process camp, is then used to refute that emotional learning can occur without conscious and controlled reasoning. Indeed, some work has shown that the SCR can be used to index learned responses to both consciously and non-consciously per¨ hman & Mineka 2001; Olsson & Phelps 2004). ceived stimuli (O However, the authors neglect the literature which claims that the potentiation of the startle reflex may be less affected by propositional and declarative processes. Whereas the SCR has been shown to be more sensitive to cognitive processes, such as propositional reasoning, which is likely to be cortically mediated, the potentiating of the startle reflex and eyeblink conditioning draw mainly on subcortical and cerebellar mechanisms, respectively (Clark & Squire 1998; Davis 2006). This is supported by the accumulating evidence of a dissociation between the startle response and SCR, in which the SCR tracks conscious awareness of stimuli contingencies, whereas the startle response tracks nonconscious learning (Hamm & Vaitl 1996; Weike et al. 2007). To sum up, the propositional account of associative learning proposed by Mitchell et al. may be parsimonious, but it critically lacks the compatibility with current evolutionary biology and neuroscience. Cultivating this form of “biological blindness” will not advance our understanding of behavioral phenomena, such as associative learning.

There is more to thinking than propositions doi:10.1017/S0140525X09001113 Derek C. Penn,a Patricia W. Cheng,a Keith J. Holyoak,a John E. Hummel,c and Daniel J. Povinellib a

Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095; bCognitive Evolution Group, University of Louisiana at Lafayette, New Iberia, LA 70560; cDepartment of Psychology, University of Illinois, Champaign, IL 61820. [email protected] http://www.cognitiveevolutiongroup.org/ [email protected] http://reasoninglab.psych.ucla.edu/

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Commentary/Mitchell et al.: The propositional nature of human associative learning [email protected] http://reasoninglab.psych.ucla.edu/ [email protected] http://www.psych.uiuc.edu/~jehummel/ [email protected] http://www.cognitiveevolutiongroup.org/

Abstract: We are big fans of propositions. But we are not big fans of the “propositional approach” proposed by Mitchell et al. The authors ignore the critical role played by implicit, non-inferential processes in biological cognition, overestimate the work that propositions alone can do, and gloss over substantial differences in how different kinds of animals and different kinds of cognitive processes approximate propositional representations.

All the co-authors of this commentary believe that associative learning theory is dead in the water. Penn and Povinelli (2007) have argued that associative learning alone is unable to account for causal reasoning in nonhuman or human animals. Hummel and Holyoak (1997; 2003) have argued that associationist-style representations are inadequate for modeling human relational reasoning. Cheng (1997) showed that both human and nonhuman causal learning involves rational inferences that go far beyond keeping track of covariation information. And Penn et al. (2008) have recently argued that animals of many taxa employ relationally structured, functionally compositional representations and that human minds, in particular, closely approximate the higher-order features of a classical “language of thought.” Indeed, one of us actually anticipated the thesis proposed by Mitchell et al. more than a decade ago: “Is it possible,” Waldmann and Holyoak (1992) asked in the final sentence of their paper, “that lower-order associative learning should be reduced to higher order causal induction, rather than vice versa?” (p. 235). So, at first glance, it would seem that we would be strong supporters of the “propositional approach” proposed by this target article. We are not. Some cognitive processes in both human and nonhuman animals involve controlled, effortful inferences operating over highly structured relational representations, but Mitchell et al.’s claim that all learning is effortful, conscious, and propositional is unfounded and implausible. Not all learning is conscious and inferential. Certainly, some learning involves hypothesis testing and conscious propositional beliefs – at least in humans. But there is overwhelming evidence that many forms of learning are implicit and non-inferential. To cite just the most obvious examples: priming, motor-skill learning, fear conditioning, and implicit category learning. Mitchell et al.’s thesis completely fails to account for these processes. Mitchell et al. also miss the critical distinction between conscious awareness of a cognitive process and conscious awareness of the output of the process (e.g., Nisbett & Wilson 1977), a central feature of most dual-process models of cognition (Evans 2008). Instead, Mitchell et al.’s thesis seems to resurrect Descartes’ notion of a transparent mind fully conscious of its own reasoning processes. We need more processes, not fewer. Like Mitchell et al., we are not fans of dual-process theories. But our problem is the opposite of Mitchell et al.’s: In our view, there are many more than two kinds of processes involved in human and nonhuman cognition. As Evans (2008) shows, the idea that there is one system that is purely conscious, inferential, and propositional, and one other that is purely automatic, implicit, and associative, is no longer sustainable. Mitchell et al.’s one-process theory fails to explain all the phenomena that motivated dual-process models to begin with (see Evans 2008). Propositions are not enough. We are big fans of propositions. But propositions alone are ill-suited for many aspects of biological cognition – for example, pattern matching and completion, graded semantic flexibility, and stimulus generalization. Any plausible model of biological cognition must incorporate both the structural features of propositional representations and the automatic, graded flexibility of distributed, associative representations (Hummel & Holyoak 1997).

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Propositions are not just structured relations. Mitchell et al. briefly mention that propositions specify “the way in which events are related” (sect. 1, para. 5). While true, this is just the first step down a long path towards full-fledged propositions. There are many other critical features of propositions that Mitchell et al. omit, such as the capacity to systematically represent types, variables, roles, and higher-order relations, and to perform rule-governed operations over these representations in an inferentially coherent fashion (Hummel & Holyoak 1997). Crucially, these propositional features do not form a package by nomological necessity (cf. Fodor & Pylyshyn 1988). In our view, non-human animals approximate certain features of propositions and not others (Penn et al. 2008). By reducing propositions to structured relations, Mitchell et al. gloss over all the interesting computational and comparative challenges. Causal learning is not monolithic. We agree that causal learning is not purely associative in either human or nonhuman animals (Cheng 1997; Penn & Povinelli 2007; Waldmann & Holyoak 1992). But this does not mean that all kinds of causal learning or all kinds of animals employ the same degree of propositional sophistication (Penn et al. 2008). There is good evidence that nonhuman animals employ structured representations and are capable of first-order causal inferences. But this does not mean that rats employ the “higher-order reasoning processes” employed by humans (see Penn & Povinelli 2007). In the case of Beckers et al. (2006), for example, the rats’ inferences can be modeled as a kind of sequential causal learning that does not require higher-order relational representations (Lu et al. 2008). Rescorla-Wagner is not propositional. Mitchell et al. claim that the Rescorla-Wagner model can be thought of as a “simple mathematical model of propositional reasoning” (sect. 6.1, para. 10). Yes, the Rescorla-Wagner model could be implemented symbolically; but that does not make it a rational model. The Rescorla-Wagner model assumes a linear generating function and lacks a representation of causal power (Cheng 1997). Ironically, Mitchell et al. miss why associationist theories fail as rational models of causal reasoning. PDP models are better and worse than Mitchell et al. claim. Mitchell et al. have an idiosyncratic view of parallel distrib-

uted processing (PDP) models. On the one hand, they claim that traditional PDP models can account for the propositional capabilities of humans. On the other hand, they claim that “a single node in a PDP model does not represent anything” (sect. 6.2, para. 3). They are wrong on both accounts. PDP models are incapable of representing the structured relations that Mitchell et al. claim are the sine qua non of learning (Hummel & Holyoak 1997). But this does not mean they represent nothing at all. Every node in a PDP network has some equivalence class, and this equivalence class is precisely what it represents. Just because this equivalence class does not correspond to something one can point at does not mean it does not exist. Darwin and neuroscientists are not all wrong. Mitchell et al. admit that the neuroscientific evidence provides little support for their claim that all learning is propositional. But they dismiss this evidence as inconclusive. They take a similarly dismissive attitude towards the comparative evidence. They admit that Aplysia do not employ propositions. But they have no evolutionary explanation for what happened to the Aplysia’s primordial associative learning mechanisms in more sophisticated creatures such as rats and humans. Did all this pre-propositional baggage simply shrivel up and die? We think not. Many nonhuman animals have cognitive capabilities that go far beyond the automatic formation of simple links. But the degree to which propositional mechanisms are employed differs between different kinds of animals and between different kinds of cognitive process within a given individual. There are some ways in which humans are still very much like Aplysia. There are other ways in which we are unique.

Commentary/Mitchell et al.: The propositional nature of human associative learning Like Darwin, Mitchell et al. overestimate the propositional capabilities of nonhuman animals (Penn et al. 2008). But worse, Mitchell et al. ignore the incremental and cumulative fashion in which evolution crafted the various kinds of minds on this planet. Darwin did not make that mistake.

The computational nature of associative learning doi:10.1017/S0140525X09001125 N. A. Schmajuk and G. M. Kutlu Department of Psychology and Neuroscience, Duke University, Durham, NC 27516. [email protected] [email protected]

Abstract: An attentional-associative model (Schmajuk et al. 1996), previously evaluated against multiple sets of classical conditioning data, is applied to causal learning. In agreement with Mitchell et al.’s suggestion, according to the model associative learning can be a conscious, controlled process. However, whereas our model correctly predicts blocking following or preceding subadditive training, the propositional approach cannot account for those results.

In their target article, Mitchell et al. point out that, in contrast to the propositional approach, associative models cannot explain some causal learning results. Here we show that an attentionalassociative model, previously evaluated against multiple sets of classical conditioning data, provides explanations for causal learning experiments. An attentional-associative model of conditioning. Schmajuk et al. (1996; henceforth SLG) proposed a neural network model of classical conditioning (see also, Larrauri & Schmajuk 2008; Schmajuk & Larrauri 2006). The network incorporates (a) an attentional mechanism regulated not only by novelty (difference between actual and predicted magnitude) of the unconditioned stimulus (US) as in the Pearce and Hall (1980) model, but also by novelty of the conditioned stimuli (CSs) and the context (CX); (b) a network in which associations are controlled by a modified, moment-to-moment (vs. trial-to-trial) constrained version of the Rescorla and Wagner (1972) competitive rule; and (c) feedback from the associative network to the input. The attentional mechanism was designed to explain latent inhibition (Lubow & Moore 1959), and the feedback loop was included to describe inferential processes such as sensory preconditioning. Gray et al. (1997) showed that the SLG model also describes automatic (or unconscious) and controlled (or conscious) processing (Pearce & Hall 1980; Schneider & Shiffrin 1977). In the framework of the model, stimulus X might be processed in controlled or conscious mode when environmental novelty and the representation of X, XX, are large; and in automatic or nonconscious mode when novelty and XX are small. Therefore, in agreement with Mitchell et al.’s position, the SLG model suggests conditioning occurs mostly consciously. However, according to the model, in the case of latent inhibition, a preexposed X with a small XX remains unconscious. Therefore, in line with Mitchell et al.’s reference to the effects of masking on learning processes, the SLG model suggests that X pre-exposure reduces conscious processing of the X but conditioning still occurs at a slower pace. Causal learning. Several studies on causal learning were concerned with the effect of additivity information on blocking and backward blocking (e.g., Beckers et al. 2005). Blocking refers to the fact that a potential cause X is not considered a cause of a given outcome (OUT, represented by “þ”) when it is presented together with another potential cause A, if A had been previously shown to be a cause of that US (Aþ, AXþ). Two potential causes, G and H, are additive if, when presented together OUT is equal

to the sum of their OUTs when presented separately (this is represented as Gþ, Hþ, GHþþ). When the joint OUT of G and H is less than the sum of their individual OUTs, the causes are subadditive (Gþ, Hþ, GHþ). Beckers et al. (2005) demonstrated that additivity pre-training resulted in stronger blocking than subadditivity pretraining (Experiment 2); additivity pre-training resulted in stronger backward blocking than subadditivity pre-training (Experiment 3); additivity post-training resulted in stronger blocking than subadditivity post-training (Experiment 4); and blocking is stronger when OUT is weaker than the maximum OUT experienced by the subjects (Experiment 1). According to Beckers et al. (2005), their results can be explained in inferential terms: blocking is not present if either the additivity premise or the submaximal premise is not satisfied. In the following paragraphs, we describe how the model addresses two of these experimental results. Additivity training preceding blocking. Like the RescorlaWagner model, the SLG model explains blocking because, at the time of the presentation of X, A already predicts the OUT. According to the model, the compound stimulus (C) activated by G and H and associated with OUT during pre-training, is fully activated by A and X. This association, together with the blocking stimulus A, contributes to predict the OUT, thereby increasing blocking. Because the C-OUT association acquired during pre-training is stronger in the additive than in the subadditive case, blocking is stronger in the former than in the latter case (see Fig. 1, Left Panels). We assumed generalization between compounds GH and AX to be strong based in Young & Wasserman’s (2002) experimental data showing that generalization between elements is much smaller than generalization between compounds. In addition, the model implements generalization among elements and between elements and compounds through the presence of a common contextual stimulus that is always active. Additivity training following blocking. As Mitchell et al. correctly observe in the target article, in the absence of pre-training, the C compound is already associated with OUT during posttraining and, therefore, increased C-OUT associations cannot be used to explain increased blocking. Interestingly, the SLG model provides an attentional interpretation for the result. In terms of the model, during the AXþ phase of blocking, OUT-X and C-X associations are formed. During the subsequent additivity post-training, OUT-X and C-X associations predict X, but X is not there. In the additive case, the stronger OUT extinguishes its OUT-X association faster than the weaker non-additive OUT does. During additivity post-training, presentation of the novel stimuli G and H, as well as the absence of stimuli A and X, increases novelty. Thus, because the representation of X is weaker in the additive case, attention to X increases less, and blocking is stronger than in the subadditive case (see Fig. 1, Right Panels). Conclusion. In agreement with Mitchell et al.’s position, the SLG model suggests that associative learning can be a conscious, controlled process related to higher-order cognition. Furthermore, in addition to the above experiments, computer simulations show that the SLG model describes (a) the facilitatory effect of additivity training before backward blocking (Beckers et al. 2005), (b) maximality effects (Beckers et al. 2005, Experiment 1), (c) the facilitatory effect of subtractivity pre-training results on backward blocking (Mitchell et al. 2005), and (d) higher-order retrospective revaluation (De Hower & Beckers 2002). Interestingly, whereas the propositional approach predicts no blocking following subadditive pre- and post-training (see Beckers et al. 2005, pp. 241, 246), the SLG model can account for those results. Furthermore, these finely graded results are also present in the model description of latent inhibition, in which weaker conditioned responding is observed. BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Figure 1 (Schmajuk & Kutlu). Left Upper Panel: Mean causal ratings for cue A, X, and K in a blocking experiment following additivity pre-training; data from Beckers et al. (2005, Experiment 2). Right Upper Panel: Same blocking experiment followed by additivity posttraining, data from Beckers et al. (2005, Experiment 4). Lower Panels: Corresponding simulations with the SLG model. Parameters used were analogous to those used in previous papers. The Outcome is represented as a CS. Rating is given by the sigmoid R ¼ Predicted Outcome6/(Predicted Outcome6 þ b6), where b ¼ Average of predicted outcomes for all A, X, and K. Simulations for Experiment 2 included 90 additive or subadditive pre-training, 20 A þ , 40 AX þ trials, and 40 KL þ trials. Simulations for Experiment 4 included 20 A þ , 20 AX þ , 20 KLþ, and 90 additive or subadditive post-training trials. Stimulus duration was 10 time units, stimulus intensity was .6, C intensity was 1, OUT was 2 for additivity and 1 for subadditive training.

Of mice and men: Revisiting the relation of nonhuman and human learning doi:10.1017/S0140525X09001137 Holger Schultheisa and Harald Lachnitb a Transregional Collaborative Research Center Spatial Cognition, University of Bremen, 28359 Bremen, Germany; bDepartment of Psychology, PhilippsUniversity Marburg, 35032 Marburg, Germany. [email protected] http://www.cosy.informatik.uni-bremen.de/staff/schultheis/ [email protected] http://www.staff.uni-marburg.de/~lachnit/

Abstract: To support their main claim, Mitchell et al. broach the issue of the relationship between the learning performance of human and nonhuman animals. We show that their argumentation is problematic

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both theoretically and empirically. In fact, results from learning studies with humans and honey-bees strongly suggest that human learning is not entirely propositional.

Mitchell et al. argue that learning relies on propositional reasoning in humans, as well as in certain other animals such as rats. They support their view with similar results for both species in certain learning studies. At the same time, they admit that not all nonhuman animals (e.g., Aplysia) draw on propositional processes. Taking their argument seriously, we will show that their stance creates grave problems in accounting for human learning exclusively by propositions. The first problem arises from the fact that Mitchell et al.’s view effectively partitions all animals into two groups; those, most notably humans, whose learning relies entirely on propositional reasoning and those whose learning is completely without propositions (e.g., Aplysia).

Commentary/Mitchell et al.: The propositional nature of human associative learning Such partitioning implicitly establishes a “magical” point in human phylogenesis where animals stopped being equipped with non-propositional learning. But why should evolution favor individuals who throw away mechanisms that have proven adaptive for generations of ancestors? It seems much more likely that, instead of disposing of the mechanism, individuals may have complemented it by acquiring other mechanisms such as propositional reasoning. Hence, it seems more reasonable to assume that humans are equipped with two, rather than only one, learning system. Against this background, any account which claims that human learning is entirely propositional is only satisfactory if it also explains why previously existing learning systems have been shut down during phylogenesis. However, such an explanation is not provided by Mitchell et al. The second problem arises from the methodology used to establish the point of propositional learning in non-human animals. Based on behavioral similarities of nonhuman animals and humans, the learning mechanisms assumed for humans are also thought to be at work in the nonhuman animals. This method for ascribing learning mechanisms, however, cuts both ways. If nonhuman animals and humans behave similarly in learning, one can as well ascribe the nonhuman learning mechanisms to humans. Either Mitchell et al. are not aware of the twoedged nature of their method or they are not aware of results on nonhuman learning which shed doubt on their main claim. Some of these results have been obtained using honey-bees as subjects. Komischke et al. (2002), for example, trained bees on reversal problems, where a previously reinforced conditioned stimulus (CS) was subsequently not reinforced, and vice versa. The bees showed improved reversal learning performance, depending on the number of previously encountered reversals. Thus, one could argue that the bees acquired some kind of a (propositional) reversal rule. In another series of experiments, Lachnit et al. (2007) and Deisig et al. (2007) investigated the effects of trial spacing on learning performance in humans and bees, respectively. Importantly, the two studies yielded comparable results. For both, humans and bees, increasing the inter-trial interval led to a general increase of the conditioned response (CR) and an improved ability to differentiate between CSþ and CS- in learning positive patterning (PP) and negative patterning (NP) discrimination problems. Further similarities between the learning of humans and bees have been demonstrated by Kinder and Lachnit (2003) and Deisig et al. (2003). In these studies, subjects were presented with an extended NP discrimination (A/B/Cþ, AB/AC/BCþ, ABC2). Contrary to major associative learning theories available at that time, both bees and humans showed no CR difference for the single- and double-compound CSs. These five studies show substantial convergence of learning in humans and bees. Direct comparisons indicate that humans and bees behave similarly regarding several learning problems and experimental manipulations. Consequently, following the method of Mitchell et al., humans and bees can be assumed to draw on similar learning mechanisms. Mitchell et al. probably would argue that propositional reasoning is at work, because human learning relies completely on propositional reasoning. Hence, according to Mitchell et al., learning in bees also relies on propositional reasoning. Yet, the results of Komischke et al. (2003) clearly contradict this conclusion. Bees had to solve NP and PP discriminations under various conditions. In one condition, the CSs of the NP problem were presented to both antennae of the bees, whereas in another condition, the CSs were presented to only one of the antennae. A third condition simultaneously presented PP to one antenna and NP to the other antenna (with identical CSs for both problems): (a) NP was learned only in condition 1 and 3; (b) the PP discrimination was hampered in condition 2. These results contradict the idea that bees’ learning relies on propositional reasoning. According to the propositional

account, learning should degrade in dual-task situations. This was observed for PP; NP, however, improved under the dualtask condition. Hence, bees’ learning did not rely on propositional reasoning. If learning in bees is not propositional and learning in bees and humans is comparable, using the method of Mitchell et al., one can only conclude that humans – at least sometimes – also do not learn propositionally. Interestingly, this conclusion regarding non-propositional learning in humans on PP and NP tasks is further corroborated by Lachnit and Kimmel (1993) and Lachnit et al. (2002). Lachnit and Kimmel (1993), employing a design similar to that of Shanks and Darby (1998), observed an asymmetric relation of PP and NP in transfer situations: Learning PP hampers NP, but not vice versa. However, utilizing different response systems (spared conditioned response [SCR] and eye-blink) for the different patterning problems, Lachnit et al. (2002) found no such asymmetry; PP and NP were solved equally well instead. If learning to solve PP and NP discrimination problems relies on the construction of consciously available general rules, why then should the effect of learning these rules be responsespecific? In light of the evidence presented here, the account of Mitchell et al. in the target article seems untenable. Not only do the authors fail to present important information to justify their account, but empirical evidence on human and nonhuman learning also contradicts their claim that human learning is entirely propositional.

The associative nature of human associative learning doi:10.1017/S0140525X09001149 David R. Shanks Division of Psychology and Language Sciences, University College London, London WC1H 0AP, United Kingdom. [email protected] www.ucl.ac.uk/psychlangsci

Abstract: The extent to which human learning should be thought of in terms of elementary, automatic versus controlled, cognitive processes is unresolved after nearly a century of often fierce debate. Mitchell et al. provide a persuasive review of evidence against automatic, unconscious links. Indeed, unconscious processes seem to play a negligible role in any form of learning, not just in Pavlovian conditioning. But a modern connectionist framework, in which “cognitive” phenomena are emergent properties, is likely to offer a fuller account of human learning than the propositional framework Mitchell et al. propose.

We should not be too harsh on ourselves for having failed, after a century of study, fully to have worked out the basic nature (cognitive or automatic) of human learning. Psychologists have been struggling with this paradox ever since Thorndike first formulated the law of effect (Thorndike 1931; cf. Postman 1947; Spence 1950). The paradox is highlighted by the following facts: 1. As Mitchell et al. rightly point out, awareness appears to be a necessary condition for learning. Their review focuses on conditioning, but the point holds for many other forms of learning such as speeded responding to structured materials (Perruchet & Amorim 1992; Shanks et al. 2003), context-guided visual search (Smyth & Shanks 2008), grammar learning (Tunney & Shanks 2003), decision making (Maia & McClelland 2004), and many others. 2. Learning is not an automatic process. It is controlled by both bottom-up influences (by the stimuli and their relationships) and by top-down ones (how the stimuli are perceived; attention; expectancies; working memory capacity; and so on). How could learning be automatic given the evidence that BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning stimuli are not even perceived when attention is fully diverted elsewhere (Macdonald & Lavie 2008)? 3. Many aspects of learning seem to involve reasoning. For instance, after learning that two cues predict an outcome (ATþ), presentation of evidence that one of them alone predicts the outcome (Aþ) causes less predictive influence to be assigned to T (Shanks 1985; Van Hamme & Wasserman 1994). 4. If by “reasoning” one means the manipulation of symbolic representations, then much of cognition does not appear to be well described as reasoning. The embodied cognition movement has made it clear that many aspects of behaviour traditionally interpreted in terms of inferences over amodal symbolic representations are better explained via notions of mental simulation (Barsalou et al. 2003; Niedenthal 2007). Moreover, logic-based accounts of reasoning have been subjected to severe criticism even in such central domains as reasoning about conditional statements (“If A then B”) (Oaksford & Chater 2007). 5. Extraordinarily rich explanations of learning phenomena have been achieved by models built out of automatic link machinery (i.e., connectionism). Such models demonstrate massive “emergentism,” in that processes that seem cognitive and highlevel emerge from the operations and interactions of very elementary processing units. Indeed, these models can often be viewed as operating in optimal (Bayesian) ways. One way to resolve the paradox is to ignore (4) and (5) and argue, as Mitchell et al. do, that the basic processes of associative learning intrinsically embody the principles of reasoning. Indeed, it is easy to combine a logic-based system (based on a computer programming language for symbolic reasoning) with a RescorlaWagner-like rule governing belief strength (Shanks & Pearson 1987), such that inference over propositions yields behaviour with the appropriate level of strength. Yet such a propositional framework for learning only scores 3 out of 5 on the list above. An alternative resolution which scores rather better begins by noting that many things that are true of automatic links are not necessarily true of larger-scale connectionist models. Unlike automatic links, for instance, connectionist models can represent semantic information. Indeed, if there has been a single goal behind the connectionist movement, it has been to emphasize this fact. Such models can “reason.” A simple connectionist model described by Ghirlanda (2005) explains the retrospective revaluation effect described in (3) above, and some of the other reasoning-like effects described by Mitchell et al. are beginning to be modelled in connectionist systems (e.g., Schmajuk & Larrauri 2008). Unlike links, processing in connectionist models is often assumed to be related to awareness (states of settled activity – attractor states – may be just those states of which we are conscious). Unlike links, connectionist models have no difficulty in binding top-down and bottom-up influences. Many models incorporate pathways for top-down attentional control. And so on. There is a long way to go, but it is not inconceivable that such an approach will eventually make the paradox of learning dissolve. Close examination of the empirical data also adds weight to the view that at least some aspects of learning emerge from elementary link processes and questions the propositional reasoning account. Quasi-rational behaviour, such as blocking, occurs not only in intentional learning situations, but also in incidental ones in which it seems very unlikely that the individual would be motivated to “reason.” For example, in speeded reaction time tasks in which some structural property is informative about a target’s location, cue-competition effects are observed (Endo & Takeda 2004). Such effects are well modelled in connectionist systems (Cleeremans 1993). Further evidence for link-like processes emerges in experiments in which individuals judge event probabilities after exposure to a cue-learning task. As Mitchell et al. explain, such studies show that cue-outcome contingency has an impact on probability estimates even when variations in contingency do not affect the objective probabilities. Hence, if the probability

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of an outcome given a cue, P(OjC), is say 0.75, participants’ judgments will be greater when the probability of the outcome in the absence of the cue, P(Oj~C), is 0 rather than 0.75 (Lagnado & Shanks 2002). Mitchell et al. argue (sect. 5.2) that such effects arise because of participants’ confusion or uncertainty about the term “probability” in the experimental instructions. But several studies (Nosofsky et al. 1992; Shanks 1990) show the same bias in “implicit” probability estimates when probability language is absent. In these conditions, participants choose which of two outcomes is the correct diagnosis for a patient with a certain symptom pattern. The word “probability” is not even employed – participants are asked to choose which outcome they think is correct. Such studies also challenge Mitchell et al.’s suggestion that the effect is due to confusion about the absence of other cues, as it also emerges when binary dimensions are used in which there are no absent cues. These biases, which fall naturally out of link-based models, are hard to reconcile with propositional reasoning accounts.

How do we get from propositions to behavior? doi:10.1017/S0140525X09001150 Daniel A. Sternberga and James L. McClellandb a

Department of Psychology, Stanford University, Stanford, CA 94305; Department of Psychology and the Center for Mind, Brain, and Computation, Stanford University, Stanford, CA 94305. [email protected] [email protected] http://psych.stanford.edu/~jlm b

Abstract: Mitchell et al. describe many fascinating studies, and in the process, propose what they consider to be a unified framework for human learning in which effortful, controlled learning results in propositional knowledge. However, it is unclear how any of their findings privilege a propositional account, and we remain concerned that embedding all knowledge in propositional representations obscures the tight interdependence between learning from experiences and the use of the results of learning as a basis for action.

Mitchell et al. have made a number of important contributions to our understanding of human contingency learning. They cite many compelling studies that demonstrate the strong influences of verbal instruction and processing resources on learning. Along the way they have become committed to a particular representational format that may limit their ability to explain the breadth of learned human behavior. We have two main concerns with their proposal that learning is the result of controlled reasoning processes that operate on propositional representations. First, it is unclear that many of the empirical findings they describe automatically follow from a propositional approach. These would instead seem to depend on the particular models of learning and memory that one employs, not on the use of a propositional format per se. Second, if Mitchell et al. are indeed arguing that all learned knowledge is encoded as propositions with attached levels of belief – a very strong claim – they need to provide a convincing account of how we could learn to perform complex, context-sensitive behaviors using processes that operate only on propositions and beliefs. Without such an explanation, their theory can only speak to a restricted set of data drawn from standard human contingency learning paradigms, and not to human and animal learning in its broader sense. We elaborate these points below. In their discussion of cognitive load, Mitchell et al. describe a series of studies by De Houwer and Beckers (2003) who found a reduction of forward blocking in a standard contingency learning task when a moderately engaging secondary task was introduced. At the same time, participants continued to demonstrate robust learning of the explicit contingencies they experienced during training. Although Mitchell et al. do not provide an explanation

Commentary/Mitchell et al.: The propositional nature of human associative learning as to why blocking should require more cognitive resources than learning all of the other cue-outcome relationships in the task, we have been interested in whether the ability to retrieve relevant memories may play a role in generating these effects. We have recently explored cue-competition effects in two tasks using common stimuli and abstract contingencies but different task demands: a self-paced prediction task that is similar to a standard contingency learning experiment, and a fast-paced, cued reaction task (Sternberg & McClelland, in preparation). In both tasks, participants saw one or two cue objects on each trial, some of which were usually followed by the appearance of a dot. In the prediction task, participants had unlimited time to observe the cue objects before pressing a key to predict whether the dot would appear on that trial. After making their prediction, the actual outcome appeared and feedback was given. In the reaction task, on trials where the dot occurred, it appeared 350 msec after the cue object(s), and participants had to press a key within 275– 400 msec (this deadline decreased at a constant rate during training). On trials where the dot did not appear, participants had to refrain from responding. Both groups were able to learn the explicit contingencies they experienced during training, as revealed by reaction times (RTs) and contingency ratings in the reaction task, and by test-trial predictions and contingency ratings for the prediction task. However, while prediction participants showed clear competition effects, evidence for competitions effect in the reaction task was scant. Mitchell et al.’s view that knowledge of the contingencies is stored in the form of propositions does not, in our view, shed light on the differences between these conditions. The form of storage could be propositions, images, or something else – regardless, the explanation could hinge on constraints on the time available for retrieval of relevant prior episodes from memory. On this view, blocking would depend on considering both the relevant singleton and the related pair at the same time, even though these events are not presented together, whereas learning the direct events would depend only on recalling past experiences with the presented stimulus. Because the reaction task reduces the time available for recollection, we would expect to see a reduction in cue-competition effects in this task compared to those observed in the prediction task. We also consider it to be uncontroversial that the operation of any learning process should depend on the availability of cognitive resources such as attention and memory. There have been many theories of multiple interacting learning processes that make no claims about a completely autonomous and resourcefree associative learning system (e.g., McClelland et al. 1995). As it is likely that a number of interacting neural systems can spring into action when a novel or familiar stimulus is encountered, it will be important to continue to study in more detail the role of attention (see Kruschke & Blair 2000; Kruschke et al. 2005) and memory (McClelland & Thompson 2007; Vandorpe et al. 2007b) in learning and reasoning. The propositional account that Mitchell et al. advocate also may run into difficulties addressing details of situation-specific response behaviors. The same contingency may be learned in different behavioral contexts, and require different kinds of responses, and the responses may not transfer even if the contingencies remain the same. For example, we would not expect that participants who had learned cue-outcome contingencies in our prediction task would immediately show fast RTs to relevant items if tested using our reaction task, even if they were verbally instructed that the same contingencies they had previously learned still held. It seems likely that something more like a sensory-motor skill of responding to particular stimuli has been acquired by these participants. Such skills, and not simply verbal propositions, seem fairly clearly to underlie abilities like playing an instrument or driving a car. Finally, Mitchell et al.’s current theory sheds little light on the process of learning complex behaviors. Consider Cleeremans and McClelland (1991), who trained participants to make button

responses to sequences of visual stimuli that were generated by a moderately complex finite state grammar. They found that participants show graded sensitivity to the conditional probabilities between items, and that this sensitivity developed gradually across training sessions. They were able to capture the learning trajectories in the human data using a simple recurrent network (SRN) model. While the structure of the information in the task could certainly be represented in a propositional form (as it was represented in this way in the experiment script), the SRN provided an account of the process of learning through time, in addition to its final state. The key ingredient here appears to be some sort of gradual strengthening process, rather than the presence or absence of a propositional statement of contingency. Mitchell et al. conclude by presenting us with a tall order. Because they find little evidence for an automatic link-formation system, they suggest that we should recast all of the existing literature on human learning as evidence about the operation of a single propositional learning system. This conclusion seems to us to present a false dichotomy. The studies the authors present on cognitive load and verbal instruction manipulations are indeed important and useful challenges to simple associative accounts of contingency learning, but by embedding all knowledge in a propositional form, Mitchell et al. may sacrifice an account of how we learn complex behaviors.

Automatic (spontaneous) propositional and associative learning of first impressions doi:10.1017/S0140525X09001162 James S. Uleman Department of Psychology, New York University, New York, NY 10003. [email protected] http://www.psych.nyu.edu/uleman

Abstract: Contrary to the target article’s claims, social cognition research shows considerable learning (about other people) that is relatively automatic. Some of this learning is propositional (spontaneous trait inferences) and some is associative (spontaneous trait transference). Other dichotomies – for example, between learning explicit and implicit attitudes – are also important. However conceived, human conditioning is not synonymous with human learning. Alice solved the mystery halfway through the book. And Bob kicked the puppy out of his way while crossing campus.

Of course, you have no idea who Alice and Bob are, and probably have no interest in finding out. But if you are like most participants in our studies, you have already inferred that Alice is clever and Bob is cruel. Furthermore, you made these inferences without realizing it, and would likely deny them if asked. The extensive research on such spontaneous trait inferences (STIs) and related phenomena supports some of Mitchell et al.’s arguments but challenges others. STI research does not use the framework or procedures of classical conditioning, so many of the terms in the target article (unconditioned stimulus [US], conditioned response [CR], blocking, overshadowing) are not applicable. But others are – for example, learning, propositions, associations, automatic and controlled processes, conscious and unconscious processes. So STI findings have direct bearing on the target article’s more general claims. Automatic propositional learning about others occurs. In a typical study of spontaneous inferences, participants read a series of trait-implying statements about other people, with an explicit goal other than forming impressions of them. The participants may be asked to memorize the material, or just familiarize themselves with it. Then they are tested in indirect ways to see BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Commentary/Mitchell et al.: The propositional nature of human associative learning whether they made trait inferences. Cued-recall, a favorite test in early research, also showed that participants were unaware of inferring anything about the targets (see Uleman et al. 1996b, for a review). That is, these inferences are unconscious. Although certainly aware of the stimuli, participants are not aware of their inference processes or outcomes. The best evidence that STIs occur at encoding, rather than at retrieval, comes from on-line measures of concept activation. Za´rate et al. (2001) had participants read trait-implying sentences for a subsequent memory test and simultaneously do a lexical decision task. Participants were quicker to identify as words implied traits that immediately followed trait-implying sentences than control sentences. Uleman et al. (1996a) used a slightly different task. Participants had to indicate, as quickly as possible, whether probe test words that immediately followed each sentence had literally been in the sentence. As predicted, participants were slower to correctly say “No” when sentences implied the trait probes than when they did not. So even though trait inferences interfered with optimal task performance, participants made them anyway. STIs are uncontrollable, as well as unintended. Bargh (1994) identified efficiency as the fourth criterion of automaticity, and there is good evidence that STIs are highly efficient. Todorov and Uleman (2003) used a false recognition paradigm in which participants read 60 pairs of photos and sentences for a subsequent memory test. Then they judged whether particular traits had appeared in the sentences paired with particular photos. False recognition of implied traits was higher when traits were paired with actors’ photos than with other photos, even when participants only saw each photo-sentence pair for two seconds, or viewed them to count the number of nouns in each sentence (not for a subsequent memory test), or viewed them while concurrently rehearsing a six-digit number. Thus, STIs are highly efficient, occurring under speeded, and shallow processing, and concurrent cognitive load conditions. These false recognition results also show that STIs are about actors, and not just inferences about the behaviors. (See also Carlston & Skowronski 1994, for evidence from a different paradigm.) That is, STIs represent learned propositional knowledge, acquired relatively automatically (unconsciously, unintentionally, etc.). According to the target article (especially sect. 3), STIs cannot occur. Yet the evidence for spontaneous social inferences is extensive (Uleman et al. 2008). Automatic associative learning about others also occurs. Suppose you read that Carol said Dan returned the lost wallet with all the money still in it, either for a subsequent memory test or merely to familiarize yourself with such information. When this information is paired with a photo of Carol but not Dan, you are likely to (unintentionally and unconsciously) associate honesty with Carol, the communicator (Skowronski et al. 1998; Todorov & Uleman 2004). Extensive research shows that this spontaneous trait transference (STT) does not occur because you confuse Carol and Dan, or intentionally draw conclusions about Carol. Instead, the activated trait is simply associated with the communicator, affecting subsequent trait ratings and other responses to her. Carlston and Skowronski (2005) proposed that STTs represent mere associations between actors and traits, whereas STIs represent attributional inferences about actors. Mitchell et al. emphasize the centrality of truth value to propositional knowledge. This suggests that if STI involves inferring propositions and STT does not, then making participants suspicious of the veracity of behavior descriptions paired with actors or communicators should interfere with STI but not with STT. This is exactly what Crawford et al. (2007) found. These results not only support the propositional status of and relevance of truth value to STI, but they also provide another reason to distinguish between propositional and associative learning.

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Explicit learning is dissociable from implicit learning about others. This dissociation challenges Mitchell et al.’s broad claims. Rydell et al. (2006) asked participants to form an impression of Bob (so these are not spontaneous inferences). They saw 100 positive (or negative) statements describing Bob; then described how much they liked him; then saw 100 negative (or positive) statements; and then gave their explicit attitudes again. Uninterestingly, the participants liked Bob more (less) after positive (negative) descriptions, and changed their attitudes after reading inconsistent information. More interestingly, they were also subliminally primed on each trial with negative (or positive) words (e.g., love, cancer), and their implicit attitudes toward Bob were measured with a modified Implicit Association Test (IAT; Greenwald et al. 1998) after each series of 100 trials. The valence of the explicit and subliminal information always differed. After receiving positive explicit and negative subliminal information, the participants’ explicit attitude was positive and their implicit attitude was negative. The second series of trials reversed these attitudes. So, two kinds of evaluative learning occurred simultaneously about the same object (Bob): one intentional, conscious, and deliberate; the other, unintentional and unconscious. These dichotomies seem essential for describing the acquisition of explicit and implicit attitudes, even though Mitchell et al. claim that they are only useful for describing perceptual and performance processes. These comments do not challenge the target article’s claim that most, if not all, human conditioning is best understood as conscious, intentional, and effortful learning of propositions about the world. But they do challenge the authors’ claim that all human learning can be subsumed under the rich and timehonored paradigms of conditioning. These comments also challenge the conflation of conscious, intentional, effortful, and controllable processes with each other and with all propositional learning – and of their “opposites” with each other and associative learning processes. There are varieties of both human propositional and associative learning that the target article neglects. ACKNOWLEDGMENTS Thanks to students in my lab for suggestions: SoYon Rim, Laura Kressel, and Justin Mahalak.

A one-system theory that is not propositional doi:10.1017/S0140525X09001174 James E. Witnauer,a Gonzalo P. Urcelay,b and Ralph R. Millera a Department of Psychology, State University of New York –Binghamton, Binghamton, NY 13902-6000; bDepartment of Experimental Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 3EB, United Kingdom. [email protected] [email protected] [email protected] http://www2.binghamton.edu/psychology/ http://www.psychol.cam.ac.uk/

Abstract: We argue that the propositional and link-based approaches to human contingency learning represent different levels of analysis because propositional reasoning requires a basis, which is plausibly provided by a link-based architecture. Moreover, in their attempt to compare two general classes of models (link-based and propositional), Mitchell et al. refer to only two generic models and ignore the large variety of different models within each class.

Commentary/Mitchell et al.: The propositional nature of human associative learning Mitchell et al. depict propositional and associative approaches to human contingency learning as incompatible with each other. Based on a comparison between generic link-based and propositional models, Mitchell et al. conclude that the propositional approach is superior to the link-based approach. We will argue that: (1) propositional and link-based accounts are not incompatible and are concerned with separate levels of analysis; and (2) Mitchell et al. complicate their analysis by comparing two broad families of models, which has important implications for evaluating these families. We assert that the propositional and link-based approaches are concerned with different levels of analysis. Note that our argument is different from Mitchell et al.’s argument that specific link-based models speak to two different levels of analysis. The propositional approach argues that humans and animals use propositional reasoning to guide judgments about outcomes in Pavlovian and human contingency learning situations. This approach is silent concerning the cognitive architecture that supports propositional reasoning. In contrast, the link-based approach is concerned with the extent to which one representation can activate another representation and is relatively silent on the way in which animals and humans use associations. For some of the reasons outlined by Mitchell et al., one might argue that the link-based level of analysis is not useful for understanding behavior. However, we contend that, to the extent that we are able to assess changes in associations through Pavlovian conditioning and human contingency learning, the associative level of analysis is helpful in understanding many aspects of human and animal behavior. Connectionist models have been used to describe phenomena in divergent areas of cognitive psychology. Aside from the obvious examples of Pavlovian conditioning and contingency learning, connectionism has been highly influential throughout cognitive psychology, including perception, categorization, language, memory, attention, social cognition, and cognitive pathology. Evidence that these approaches are concerned with different levels of analysis comes from the literature concerning connectionist models of language processing. Connectionist models assume that connections (analogous to links or associations) between processing units provide the foundation for complex information processing. In these systems, weighted connections allow activation to pass between units and learning is presumably driven by changes in the strengths of the weights between processing units. Connectionist models of language are link-based models that can represent and process propositional knowledge. A second notable example of propositional logic being based on link-based knowledge is provided by Wynne (1995) in his associative account of transitive inference. Therefore, the existence of several link-based accounts of propositional reasoning suggests that propositional reasoning can be explained at the associative level of analysis by reductionism. Moreover, associative theories have informed us about the way the brain organizes and processes information. According to the Rescorla and Wagner (1972) model, a discrepancy between the strengths of the outcome experienced and the outcome expected based upon all cues present is necessary for changes in the strength of a CS-US association. The results of electrophysiological and neuroimaging studies suggest that the brain generates a signal that encodes the discrepancy between expected and experienced outcomes and that this signal is correlated with learning at a behavioral level (e.g., Corlett et al. 2004; Schultz 1998). Mitchell et al. aptly note that in many connectionist-like models of cognition (and in the brain), stimulus representations are distributed, meaning that units in these systems do not carry specific representational value and that information is represented by patterns of activity across arrays of units. This does not necessarily undermine the link-based level of analysis because, in systems

of distributed representation, weighted links function to bind arrays of units that together represent stimuli. Also, the results of modeling studies suggest that individual neurons are highly variable in the extent to which they locally encode information, such that some neurons function like grandmother cells (i.e., a single neuron more directly represents a stimulus) and others are more broadly tuned (Verhagen & Scott 2004). Mitchell et al. argue that the link-based approach does not explain behavior as well as the propositional approach. They base this argument on a comparison between a generic associative and a generic propositional model. This strategy has the unfortunate consequence of ignoring the great variety of associative and inferential models available. For example, Mitchell et al. point out that the propositional approach is better equipped than associative models to account for data indicating that awareness is related to learning. However, the prediction that awareness will be related to learning is not a necessary prediction from a propositional model. Bayesian models are similar to the propositional approach but do not assert that awareness is necessary for learning. It is also conceivable that an associative model might argue that awareness is necessary for learning. Similarly, data that uniquely support a specific associative model cannot be interpreted as inconsistent with the general propositional approach. The sometimes competing retrieval model (SOCR; Stout & Miller 2007) uniquely anticipated that when a target stimulus is conditioned in compound with two blocking cues, responding to the target stimulus is greater than when it is conditioned in compound with only one blocking cue (Witnauer et al. 2008). Despite SOCR being an associative model, these data do not allow us to conclude that the linkbased approach is superior to the general propositional approach. In fact, Witnauer et al.’s data were problematic for many associative models and might be consistent with revised propositional models (see Miller & Escobar [2001] for a similar argument concerning ill-conceived comparisons between generic acquisitionand performance-focused models). Differentiation between models requires identification of specific models, and the results are applicable only to the actual models that are compared. The predictions of a generic model (such as the one outlined by Mitchell et al.) or a family of models (such as acquisitionfocused models of Pavlovian learning) are necessarily less precise (and less testable) than the predictions of a specific model (e.g., SOCR). This is evident in Mitchell et al.’s application of their generic model to the effect of cognitive load on learning phenomena. In section 4.2, Mitchell et al. assert that the studies of the effect of cognitive load on learning agree with the predictions of the propositional approach. Propositional reasoning presumably requires cognitive resources and, consistent with this view, manipulations that diminish the availability of cognitive resources diminish learning. In section 4.4, they assert that propositional reasoning can occur in highly complex (demanding) situations. If it is assumed that task complexity is directly related to cognitive load, then the propositional approach (as outlined in sect. 4.2) predicts that learning should not be observed in highly complex situations. This inconsistency (and others) in Mitchell et al.’s propositional approach is the result of comparing generic models of learning rather than fully specified models. ACKNOWLEDGMENTS This research was supported by National Institute of Mental Health Grant 33881. Inquiries concerning all aspects of this research should be addressed to Ralph R. Miller, Department of Psychology, SUNY– Binghamton, Binghamton, NY 13902-6000, USA; e-mail: [email protected].

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Authors’ Response Link-based learning theory creates more problems than it solves doi:10.1017/S0140525X09001186

of the existing empirical evidence with which we disagree. Others seem to reflect a misunderstanding of the propositional approach, which suggests that we were not completely clear in the target article. From our analysis, we conclude that although the current propositional approach needs further refinement, the basic assumption that all associative learning is mediated by propositions is conceptually sound and is supported by the available data.

Chris J. Mitchell,a Jan De Houwer,b and Peter F. Lovibonda a School of Psychology, University of New South Wales, Kensington 2052, Australia; bDepartment of Psychology, Ghent University, Henn Dunantlaan 2, B-900, Ghent, Belgium. [email protected] [email protected] [email protected] http://www.psy.unsw.edu.au/profiles/cmitchell.html http://users.ugent.be/~jdhouwer/ http://www.psy.unsw.edu.au/profiles/plovibond.html

Abstract: In this response, we provide further clarification of the propositional approach to human associative learning. We explain why the empirical evidence favors the propositional approach over a dual-system approach and how the propositional approach is compatible with evolution and neuroscience. Finally, we point out aspects of the propositional approach that need further development and challenge proponents of dual-system models to specify the systems more clearly so that these models can be tested.

In our target article, we put forward the claim that all associative learning in humans is mediated by the nonautomatic truth evaluation of propositions about relations in the world. This implies that there is no need to postulate a second learning mechanism that is based on the unconscious formation of links between symbolic representations. One positive outcome of our target article was that several commentators expressed their agreement with the basic tenet of our position (Beckers & Vervliet; Chater; Morsella, Riddle, & Bargh [Morsella et al.]; Gopnik; Greenwood; Lagnado; Li; Lyn & Rumbaugh; and Newell). However, it is clear that we were unable to convince everybody. Many commentators explicitly expressed their belief in a dual-system model of learning in which both propositional and link-formation processes can produce learning (Baeyens, Vansteenwegen, & Hermans [Baeyens et al.]; Boakes; Dawson & Schell; Dwyer, Le Pelley, George, Haselgrove, & Honey [Dwyer et al.]; Gawronski & Bodenhausen; Livesey & Harris; Matute & Vadillo; McLaren; Miles, Proctor, & Capaldi [Miles et al.]; Olsson & ¨ hman; Schultheis & Lachnit; Uleman). This, too, is O a positive outcome of our target article, because it confirms that we are not fighting a straw man, but a widely held position in psychology. Few researchers had previously made explicit in writing their belief in a dual-system model of learning. The arguments made by the commentators for rejecting the propositional approach can be grouped into three categories: (1) the evidence that supports the propositional approach can also be explained by link-formation models; (2) there are empirical findings that contradict the propositional approach and thus support the idea of a second, link-based, learning mechanism; and (3) there are conceptual grounds for rejecting the possibility that all learning is propositional. Some of the arguments presented in the commentaries are based on an interpretation 230

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R1. Can evidence for the propositional approach also be explained by link-formation models? In section 3 of the target article, a large number of empirical findings were presented that confirm predictions of the propositional approach. We first describe exactly why the propositional approach predicts these findings, and then look at the extent to which these findings can be explained by link-formation models. The core assumption of the propositional approach is that associative learning depends on the non-automatic truth evaluation of propositions about relations in the world. Some commentaries revealed a misunderstanding of what we meant by “propositions” and how propositions differ from associative links. Propositions are statements about the events in the world and how they are related. These statements may be quite specific as to the nature of the relation between events (e.g., “X causes Y”). However, they can be quite general; they may specify only that particular events are related, but not how they are related. We agree with Gawronski & Bodenhausen that the crucial distinction between propositions and links is that only propositions imply a truth value. People can thus differ in the degree to which they believe that statements about the world are true or false. Although propositions are described as statements, they do not, as Shanks seems to imply, necessarily involve abstract representations. As Barsalou (2008) pointed out, propositions can also involve embodied, grounded representations. Propositions are very different from associative links. Associative links are not statements about the world. They are assumed to be states of the world, namely, states through which one representation can activate another representation. The idea that associative learning is a function of the non-automatic truth evaluation of propositions leads to two main predictions. First, everything that influences truth evaluation can influence associative learning. This means that apart from the actual occurrence of events, factors such as prior knowledge, instructions, intervention, and deductive reasoning also matter. Second, because truth evaluation depends on non-automatic processes (in the sense that they are dependent on awareness, cognitive resources, time, and goals), associative learning should also be non-automatic. There is substantial evidence to support these predictions (see our target article and De Houwer [2009] for reviews). Because of this evidence, many commentators accept the conclusion that some associative learning phenomena are the result of propositional processes (e.g., Baeyens et al.; Boakes). However, they do not agree with the more far-reaching conclusion that all learning is propositional. They argue that there are some “true” forms of learning that are driven by link formation. Although it is not clear to us

Response/Mitchell et al.: The propositional nature of human associative learning what the defining characteristic of “true” learning is, we do agree that evidence in support of the propositional approach is also compatible with dual-system models. Thus, such findings do not exclude the possibility that link formation exists as a second learning mechanism that supplements propositional learning. The reasons for why many commentators maintain a belief in the existence of link formation will be analyzed in the next section. In the remainder of this section, we will discuss a second response to the evidence in support of the propositional approach, namely, that link formation models can also account for (some of) that evidence. A first group of commentators (Dwyer et al.; Livesey & Harris; Witnauer, Urcelay, & Miller [Witnauer et al.]; see also section 2.1 of the target article) points out that link formation is not necessarily a fully automatic process. For instance, influential link-formation models do postulate that attention modulates the formation of links (e.g., Mackintosh 1975; Pearce & Hall 1980). One could also argue that link formation leads to awareness of relations in the world (e.g., Davey 1992). Hence, evidence for the non-automatic nature of associative learning could in principle be accommodated by link-formation models by adding assumptions about the way in which link formation is non-automatic. However, one of the important reasons why many researchers believe in the existence of a link-formation mechanism is precisely because it might account for seemingly automatic, irrational types of learning. This is probably why many commentators explicitly characterize link formation as primitive and automatic. Another problem with the argument that link formation might be non-automatic is that there is no a priori reason for this assumption. There are, however, good reasons to assume that the truth evaluation of propositions is a largely non-automatic process. Hence, the failure to observe automatic learning is a prediction of, and therefore supports, the propositional approach. Other commentators reject the dual-system idea, but argue that the single system is based on link formation. They suggest that the evidence for the propositional approach can be accounted for by (link-based) connectionist models of learning. In this way, propositional processes can be reduced to link formation. We did foresee this argument in our target paper when we discussed the nature of connectionist models (sect. 6.2). Our main point was that connectionist models differ from link-formation models because they involve sub-symbolic, rather than symbolic, representations. The nodes in a connectionist model do represent information (as Penn, Cheng, Holyoak, Hummel, & Povinelli [Penn et al.] point out), but they do not represent a discrete stimulus or event in the world (as they do in link-formation models). In other words, the information represented by symbolic nodes (representations) is specified by the model, but the information represented by sub-symbolic nodes (in connectionist networks) needs to be inferred from how the system operates. Our quarrel is only with symbolic link-formation models. We are aware of the fact that the brain resembles a sub-symbolic connectionist network. In fact, connectionist models can be regarded as simulated miniature brains (Clark 1990). It is also obvious that the truth evaluation of propositions must somehow be implemented in the brain. Hence, we do not exclude the possibility that truth evaluation might be implemented in

a connectionist model (although current models still seem to have a long way to go before this promise can be realized). Such an implementation would not, however, constitute evidence that propositional processes can be reduced to symbolic link formation or that there is a need to assume the existence of symbolic link formation. R2. Are there empirical findings that contradict the propositional approach? A second category of arguments focuses on evidence for associative learning that cannot be explained by propositional models. The evidence within this category can itself be divided into three classes. The first class concerns evidence for associative learning in the absence of relevant propositions (e.g., learning without awareness). Second, several commentators refer to learned behavior that is arational in that it cannot be inferred logically from the propositions that people entertain. They claim that the propositional approach does not provide an explanation for the production of Pavlovian conditioned responses (CRs). The third class includes evidence for arational beliefs about relations in the world. Some beliefs seem to result from factors other than truth evaluation and, it is argued therefore, constitute evidence for the formation of links. In this section, we explain why we are not convinced by these arguments. R2.1. Evidence for learning in the absence of relevant propositions

A number of commentators cited studies that they considered to be demonstrations of unconscious learning (Boakes; Dawson & Schell; Dwyer et al.; Gawrosnki & Bodenhausen; Livesey & Harris; McLaren; Miles ¨ hman; Penn et al.; et al.; Morsella et al.; Olsson & O Uleman). The majority of these studies have already been considered in the several reviews we cited in our target article, and shown to be artefactual, unreplicable, or subject to alternative interpretations. In many cases, direct supporting evidence for specific alternative interpretations has been reported. Other studies nominated by the commentators have been published subsequent to these reviews, but have used procedures subject to the same criticisms as the earlier studies. The commentary process provides a unique insight into the results that are considered to provide the most robust evidence for unconscious learning by dual-system theorists; therefore, we consider it important to briefly review the main difficulties with these studies and provide references with additional detail for the interested reader. Before turning to the specific research procedures, it is important to first emphasize one general point about this line of inquiry. Apparent unaware conditioning can be generated simply by the use of an insensitive or noisy measure of awareness, because some aware participants will be misclassified as being unaware. As a consequence, studies that successfully show a relationship between awareness and conditioning should be given greater weight than those that fail to find such a relationship, unless there is independent evidence that the null result was not due to lack of sensitivity of the awareness measure (Lovibond et al., submitted). BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Mitchell et al.: The propositional nature of human associative learning Studies involving fear conditioning to backwardly masked (or subliminal) fear-relevant stimuli (cited by Dawson & ¨ hman, and Penn et al.) suffer from Schell, Olsson & O three primary limitations. The first two are insensitive measures of awareness and the use of fixed stimulus durations which are suprathreshold for some participants. The third is a little more subtle. Sometimes the measurement of awareness relies on report of stimulus identity. However, participants who cannot identify the stimuli may nevertheless be aware that CSþ and CS- differ in some way – this is all that is required for differential conditioning to be observed (Cornwell et al. 2007; Lovibond & Shanks 2002; Pessoa et al. 2005). In addition, several early studies have since been shown to be an artifact of the experimental procedure; they could be explained by a specific correlated hypothesis account based on restrictions on trial order (Wiens et al. 2003). Finally, studies using a more sensitive measure of awareness, such as a trial-bytrial measure of subjective US expectancy, have shown a concordance between this measure and objective measures ¨ hman & Soares 1998). such as skin conductance (e.g., O These findings directly support a single-system account. The same is true for the resistance to extinction observed with supraliminal fear-relevant stimuli (Dawson et al. 1986; Lovibond et al. 1993). Studies involving anesthesia and spinal preparations (cited by Dwyer et al. and Miles et al.) have mostly been conducted in animals, so implications for the role of awareness are hard to draw. By contrast, there has been a great deal of research on learning in humans suffering from amnesia. Early reports of conditioning in amnesics (cited by Miles et al.) were largely anecdotal, with little detail regarding the nature of the assessment of conscious knowledge. Those studies which have been more fully reported (e.g., Bechara et al. 1995; Clark & Squire 1998) have often used a post-experimental assessment of awareness, which is particularly likely to underestimate contingency knowledge in amnesics at the time of conditioning (Lovibond & Shanks 2002). A recent study by Speekenbrink et al. (2008), however, was specifically designed to assess the relationship between task performance and relevant explicit knowledge during learning, in a probabilistic category learning task. They found a slightly slower learning rate in amnesics compared to control participants. But, importantly, there was no evidence for a qualitatively different learning process and no dissociation between task performance and explicit knowledge of cue-outcome association. ¨ hman, there As noted by Miles et al. and Olsson & O have been several reports that eyeblink conditioning with a delay conditioning procedure (in which CS and US overlap) is unrelated to awareness, unlike conditioning with a trace procedure (in which CS and US are separated by a brief interval; see Li’s commentary). However, there have been specific criticisms of some of these studies (Lovibond & Shanks 2002; Shanks & Lovibond 2002). Furthermore, other researchers have successfully demonstrated a relationship between awareness and differential eyeblink conditioning (with no evidence of conditioning in unaware participants), regardless of whether a trace or delay procedure is used (Knuttinen et al. 2001; Lovibond et al, submitted). The use of startle modulation as an index of condition¨ hman) has not, in general, ing (cited by Olsson & O 232

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yielded evidence of unconscious conditioning. The early study by Hamm and Vaitl (1996) is limited by the fact that contingency awareness was assessed after an extinction period (Lovibond & Shanks 2002), and more recent studies by Weike et al. (2007) have been criticized by Dawson et al. (2007) as using an insensitive recall-based awareness measure. More importantly, however, several studies have shown a reliable relationship between contingency awareness and startle modulation, with no evidence of modulation in the unaware group (Dawson et al. 2007; Lipp & Purkis 2005; Purkis & Lipp 2001). There have been numerous reviews of evaluative conditioning (cited by Baeyens et al.), and specifically the claim that such conditioning can occur without contingency awareness (see also Bliss-Moreau & Barrett). As noted in our target article, more recent studies have started to provide clearer evidence of an association between contingency awareness and evaluative conditioning, both for the picture-picture procedure (Pleyers et al. 2007) and the flavor-flavor procedure (Wardle et al. 2007). Other commentators (Boakes; Livesey & Harris) focused on the more general point that conditioning with tastes, odors, and internal bodily consequences, whether considered as an example of evaluative conditioning or not, may be independent of cognitive factors such as instruction and awareness. Again, much of this evidence in humans is anecdotal. Curiously, Boakes also cites placebo effects in this context, but of course this is an instructional effect that suggests bodily reactions are in fact sensitive to cognitive factors. One exception, as noted by Lovibond and Shanks (2002), is the odor-taste learning demonstrated by Stevenson et al. (1998). These findings are suggestive of an associative process that is independent of explicit contingency knowledge, and this avenue is worthy of further investigation to see if it supports the idea of a separate gustatory learning module isolated from cognitive processes. Finally, many commentators (Gawronski & Bodenhausen; Miles et al.; Morsella et al.; Penn et al.; Uleman) raised studies from the cognitive and social literatures that do not involve direct recording of conditioned responses but that are, nonetheless, clearly associative in nature. Many of these studies report effects that have been labeled implicit learning, such as sequence learning, motor learning, and artificial grammar learning. The most comprehensive review of this literature was carried out by Shanks and St. John (1994), who reported that virtually all of the studies of implicit learning at that time failed either their sensitivity or their informational criterion, or both. Subsequent research has strengthened and extended Shanks and St. John’s conclusion and extended it to other claims of implicit learning, including sequence learning (Wilkinson & Shanks 2004), continuous tracking (Chambaron et al. 2006), probabilistic category learning (Newell et al. 2007), and contextual cueing (Smyth & Shanks 2008). An alternative approach to demonstrating nonpropositional learning, taken by Perruchet (1985) and Perruchet et al. (2006), is to show effects of learning that directly contradict relevant propositions (see sect. 5.1 of the target article). Such evidence would be much more difficult to discount than the evidence for unaware learning, because demonstrating the presence of a proposition that contradicts the behavior is easier than demonstrating the absence of propositions altogether.

Response/Mitchell et al.: The propositional nature of human associative learning Some commentators argued that the Perruchet effect alone is sufficient evidence to postulate the existence of link formation as a second learning mechanism (Dwyer et al.). We believe, however, that the evidence is not yet conclusive. For instance, recent findings suggest that at least the reaction time version of the Perruchet effect does not result from the operation of two associative learning systems. Mitchell et al. (in revision) presented half of their participants with the same 50% tone-square partial reinforcement schedule used by Perruchet et al (2006). The remaining participants received the same schedule but with the tones removed. In their experiment, just as in the one Perruchet et al. (2006) conducted, reaction times were fastest following runs of square-present trials, and slowest following runs of square-absent trials. This was true for all participants, regardless of whether or not they had been presented with the tone (CS). This suggests that the pattern of reaction times does not indicate the operation of an associative learning process; it is simply the consequence of the recent square presentations. Because the behavioral effect is not due to the relation between the tone and the target event, there is no longer a contradiction between propositional beliefs about the tone-square relation and a behavioral effect of that relation. Therefore, the dissociation between RTs and expectancy cannot indicate the operation of two distinct associative learning systems. The research on the Perruchet effect shows that the propositional approach can be falsified in principle. However, it also illustrates how one should be careful in drawing conclusions prematurely. Many years of research on learning in the absence of relevant propositional knowledge has led to few (if any) convincing findings. This calls for a cautious approach toward any new piece of evidence that claims to demonstrate learning in the absence of relevant propositional knowledge. R2.2. Learning can result in arational behavior

Some commentators (e.g., Baeyens et al.; Livesey & Harris) point out that when participants do have relevant propositional knowledge about relations in the world, one cannot explain, on the basis of this knowledge, why those relations have the behavioral effect that they do. For instance, why does the proposition “the tone predicts the shock” lead to an increase in skin conductance (the CR) after the presentation of the tone? Why would a proposition such as “this flavor was sometimes followed by a bad aftertaste” lead to a disliking of the flavor when presented on its own? In the target article, we acknowledged that the propositional approach does not explain why propositions about relations in the world have certain effects but do not have others. This is because propositional models are not models of behavior. They are models of one determinant of behavior: associative learning. There are many other factors that go to determine the way in which learning translates to performance. This line of reasoning may give the impression that the propositional approach is unfalsifiable; any behavior that does not seem to follow logically from the propositions entertained can be attributed to some performance factor or other. However, although we do not have a psychological model of this translation process, we do know quite a lot about the effects that particular

propositions will have, and we do assume some stability ¨ hman in this process. For example, as Olsson & O concede, we know that (all else being equal) the proposition “the tone signals shock” will lead to an expectancy of shock when the tone is presented, and this expectancy will generate the CR of increased skin conductance (see sect. 3.2 of the target article). At some point during evolution, it would appear that the cognitive expectancy of negative events became a trigger for the activation of genetically determined defensive response patterns that include an increase in skin conductance. We can, given that we know this process is in place, make predictions about the effects of propositions on behavior. It is very important to note that, in fact, the link-formation models do not explain CR production any better than does the propositional approach. Most link-formation models remain silent about how links are expressed in behavior. Furthermore, the psychological mechanism provided by the link model for the production of CRs relies on the old idea that conditioned responses are simply indirectly activated unconditioned responses. Thus, for instance, when a tone is paired with a shock, it might evoke an increase in skin conductance because activation caused by the presentation of the tone spreads to a representation of the shock, which then activates the responses associated with shock. But, as we pointed out in our target article (see sect. 2.2), conditioned responses often differ substantially from unconditioned responses. And these differences cannot always be explained in terms of the nature of the CS. Link-formation models also do not explain why learning can be selective. For instance, why does a flavor that is paired with a bad aftertaste become negative, whereas a color paired with the same aftertaste does not (see comment of Baeyens et al.)? In sum, neither propositional models nor linkformation models provide a full account of learned behavioral responses. Hence, we see no reason why evidence for arational learned behavior should favor link-formation models. R2.3. People sometimes have arational beliefs about relations in the world

The studies discussed in sections R2.1 and R2.2 of this rejoinder can be seen as attempts to discount propositional models of learning by showing behavioral effects of learning that cannot be due to relevant propositions, either because relevant propositions are absent (sect. 2.1) or because the behavioral effects of learning cannot be inferred logically from the relevant propositions (sect. 2.2). Some commentators (Dwyer et al.; McLaren) discuss a third set of studies that followed a different approach. They suggest that some propositional beliefs about relations in the world are arational, and, therefore, might result from the operation of a link-formation mechanism. The evidence presented in the target article on this issue (sect. 4.3.2) came from the studies of Le Pelley et al. (2005a) on unblocking, Karazinov and Boakes (2007) on second-order conditioning (see Uleman for very similar studies in the context of social psychology), and from Shanks’s (2007) example of the judgment of event probabilities. Additional studies of this kind are presented in the commentaries (e.g., McLaren cites Spiegel & McLaren’s [2006] work on sequence learning). BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Mitchell et al.: The propositional nature of human associative learning As we made clear in our target article (see sect. 5.2), evidence for seemingly arational beliefs about relations in the world does not convince us of the need for a separate linkformation mechanism. First, there is a very important general point that, even if certain findings cannot be explained by propositional models, this is not evidence for the existence of link-formation processes. Many other explanations are also possible. Second, detailed research is needed before one can exclude with confidence propositional processes as a source of the observed phenomenon. Human reasoning is not necessarily normatively correct. Hence, demonstrations of arational or irrational beliefs do not fall outside of the scope of truth evaluation on the basis of reasoning. Thus, we see no need to evoke the idea of link formation in order to account for such beliefs. We do acknowledge that these concerns complicate attempts to provide evidence in support of the link-formation mechanism. Hence, we understand the complaint of certain commentators that the propositional approach runs the risk of becoming unfalsifiable (McLaren; Nolan). However, those who want to argue for the existence of link-formation processes should examine carefully all possible explanations that do not rely on link formation before they conclude that link-formation processes do operate. It is unlikely that such issues can be settled by isolated demonstrations of effects that at first sight appear to contradict the propositional approach, or (as Witnauer et al. point out) seem consistent with a particular associative model (see McLaren; Dwyer et al.). The burden of proof in the first instance is on those who want to claim the existence of link-formation processes to rule out all other possibilities. That being said, we do accept that once efforts have been made to rule out these other possibilities, proponents of propositional models cannot simply dismiss potentially problematic findings on the basis of the argument that some kind of propositional process might be crucial. They should always back up their alternative explanations with empirical evidence or sound arguments (see the work of Mitchell et al. [in revision] on the Perruchet effect). If they cannot, they should accept the conclusion that some findings fall beyond the scope of the propositional approach. R3. Are there conceptual arguments for rejecting the propositional approach? The evidence discussed in section R2 of this response relates to possible inconsistencies between the propositional approach and empirical knowledge about associative learning. In this section, we discuss comments that relate to possible inconsistencies between the propositional approach and our knowledge about evolution, the brain, and normative criteria that good theories should meet. R3.1. Propositional models are not in line with the principles of evolution

Some commentators (Miles et al.; Schultheis & Lachnit) suggested that the propositional model of human learning requires postulation of a “magical cut-point” in evolution, when propositional learning appeared and reflexive learning 234

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disappeared. Similarly, these and other commentators ¨ hman; Penn et al.) (Matute & Vadillo; Olsson & O asked why evolution would simply abandon an effective and adaptive reflexive mechanism. However, in our target article, we explicitly argued for a “continuum of cognitive complexity” (sect. 6.3) between primordial creatures, on the one hand, and humans, on the other hand. We suggested that early representational structures were likely S-R in nature, that these were elaborated to allow S-S structures that separated knowledge from action, and that further quantitative elaboration of knowledge structures provided the necessary foundation for abstract reasoning and artificial symbolic systems (language). This continuity position is entirely consistent with the known principles of evolution, which as noted ¨ hman is “a slow accumulative process . by Olsson and O . . that incorporates successful adaptations at one level into more complex functions at higher levels.” Therefore, our answer to the question of what happened to primitive reflexive mechanisms is not that they shriveled up and died (Penn et al.), but rather that they evolved into the rich representational system that humans possess. As Morsella et al. note in their commentary, this system is suitable for the solution of both complex and simple problems. Furthermore, contrary to the claims of several commentators (e.g., Hall; Matute & Vadillo), the removal of a primitive learning mechanism during the course of evolution might well have been adaptive. A primitive mechanism that creates links whenever certain inputs co-occur might be adaptive for simple organisms that register only limited and simple inputs but maladaptive for more complex organisms that receive a lot of complex input. In more complex organisms, an automatic link system would lead to an overload of associations that would result in chaotic behavior (as in the case of schizophrenia; see Carson et al. 2003). This problem can be managed if propositional encoding needs to occur before a relation can be coded in memory and influence behavior. Hence, in addition to the benefits offered by a more complex, propositionally based learning mechanism, reducing the negative effects of a primitive learning mechanism provides a second evolutionary reason for the gradual evolution from primitive to more complex learning mechanisms. Ironically, it is the dual-system model that suggests an implausible discontinuity in evolution, because it denies that earlier reflexive systems formed the precursor to representational abilities and, subsequently, to human reasoning and language. Instead, proponents of this model generally assume that reasoning emerged independently and late in mammalian evolution, for example, in primates. In the extreme case, those who propose that reasoning relies upon language (e.g., Castro & Wasserman) are forced to argue that reasoning is unique to humans (and perhaps, to a very limited extent, certain primates) and therefore emerged de novo at “one minute to midnight” in biological time. But emergence of a novel capacity in such a short time-frame is entirely implausible given the accumulative and opportunistic nature of evolution, which is much more likely to appropriate existing mechanisms to new purposes rather than develop a new one from scratch. We agree with Christiansen and Chater (2008) that it is more plausible to suppose that language was enabled by the prior evolution of a critical level of

Response/Mitchell et al.: The propositional nature of human associative learning cognitive capacity, and that language was, therefore, moulded by the operating characteristics of the cognitive system. The only alternative for dual-system theorists is to concede that propositional reasoning, or the representational capacity that preceded it, developed well before the evolution of humans. But this strategy raises another set of problems for dual-system theorists. They must now confront seriously the role that the nascent cognitive system might play in learning and other tasks in the animal laboratory. According to the propositional model, this is exactly what animal researchers have been studying successfully for many years, and indeed they sometimes describe their work as “animal cognition.” But from a dual-system perspective, it would now be necessary to force a distinction between cognitive processes and coexisting reflexive or link-formation processes. We have already reviewed the vanishingly small evidence base for such a distinction in humans, where some predictions of the dual-system model can be tested through verbal report. It seems unlikely that independent evidence will emerge from the animal laboratory that clearly supports a dual-system architecture. Rather, we consider that the learning capacities of particular species studied in the laboratory will reflect the nature and complexity of their cognitive system. Species more closely related to humans (e.g., other mammals and primates, in particular) will have cognitive systems and hence learning capacities correspondingly more similar to our own (see Lyn & Rumbaugh). It is through this lens that we approach the comments of Miles et al. and Schultheis & Lachnit concerning the learning abilities of honeybees. We suggest that during human evolution there was a continuum of development of cognitive capacity from simple organisms to modern humans. However, not all of these earlier species survive today, and of those that are extant, many are on branches of the evolutionary tree that diverged quite early from human evolution. Therefore, we are not able to make strong predictions as to the cognitive abilities of a particular species. We would not be surprised if a species such as the honeybee showed some characteristics indicative of capacity to represent the environment, such as cognitive maps, but we would also not be surprised to find strong limitations to such capacities. It is also likely that species with substantially different nervous systems will have solved the same problem in different ways, due to convergent evolution. For example, different species may show blocking on the basis of different learning mechanisms, just as they achieve other capacities such as locomotion through different mechanisms (e.g., flying, swimming, walking). Some commentators made the case for preservation of a reflexive learning system on the basis that this system is fast and automatic (Matute & Vadillo), and that evolution favors the concurrent existence of multiple, complementary systems with different strengths (Hall; Olsson & ¨ hman; Schultheis & Lachnit). The implication of this O argument is that the cognitive system is slow and effortful and hence will be out-performed by a reflexive system in situations requiring rapid reactions. However, it is not at all clear that a reflexive system, if it existed in humans, would in fact be faster in such situations. This is because situations requiring rapid responses (e.g., predator

avoidance) are ones that depend on performance based on prior learning, not on new learning per se. As we noted in our target article, performance based on existing propositional knowledge is fast and automatic – regardless of whether the knowledge is acquired through direct contingency exposure or is learned symbolically. Furthermore, when it comes to acquisition of new learning, it is debatable whether reflexive learning would be faster than propositional learning. Many S-R theories propose a relatively slow and gradual process of establishing and strengthening of links, in contrast to the rapid “insight” learning associated with propositional knowledge. What is clear, however, is that there would be considerable selection pressure to favor the ability to represent the environment in richer ways and to put together pieces of existing knowledge to draw new conclusions – precisely the characteristics we ascribe to the human learning system. R3.2. Propositional models are not in line with what is know about the brain

Several commentators considered that we had ignored or understated the importance of biological data, in particular brain data. It is true that the primary conclusions we reached were based on behavioral data. However, it is not the case that we ignored brain data. Rather, we argued that at our present state of neuroscientific understanding, the brain data are not definitive with regard to the central debate between single- and dual-system models. That is, the available brain data are to some extent open to interpretation. Of course, neuroscientists have had much more experience in fitting their data to the prevailing dual-system model of learning, so it is perhaps not surprising that several critiques were based on long-standing assumptions regarding the mapping between brain structures and psychological mechanisms. The most common assumption implicit in the present commentaries was that consciousness and higher cognitive functions are exclusively mediated by the cerebral cortex ¨ hman). Such a “cortico(Hall; Miles et al.; Olsson & O centric perspective” has been challenged recently in this journal by Merker (2007). He reviews evidence that natural or experimental loss of cortical tissue does not eliminate conscious and goal-directed behavior, and points out that mid-brain structures conform more closely than cortical structures to the limited-capacity, executive role associated with reasoning and decision making. In general terms, the graceful degradation of performance that is observed with increasing damage to brain tissue is strongly suggestive of distribution, rather than localization, of function. And, as Chater notes in his commentary, many dissociations observed in lesion and activation studies are not inconsistent with a singleprocessing system. There is certainly little reason to believe that the functions of cortical and subcortical structures map neatly onto the cognitive and reflexive systems proposed in dual-system models of learning. Our singlesystem model makes no specific claims regarding particular brain mappings. What our model does predict, however, is that those neural systems underlying reasoning (whether they are organized on a topological, chemical, or some other basis) also subserve associative learning. None of the data put forward by the commentators, either from BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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Response/Mitchell et al.: The propositional nature of human associative learning lesion studies in animals or neuropsychological case studies in humans, contradicts this prediction. R3.3. Propositional models do not meet the normative criteria that good models should meet

Some commentators argue that good models should be falsifiable, parsimonious, and formalized and that propositional models do not possess these characteristics. In this section, we explain why we believe that propositional models are falsifiable and parsimonious. They lack formalization, but that does not impede their heuristic and predictive value. R3.3.1. Propositional models are not falsifiable. As

stated in section 2.1 of this response, reliable demonstrations of associative learning in the absence of relevant propositions (i.e., propositions about the relations in the world that drive learning) would raise serious doubts about the validity of the propositional approach (but would not necessarily provide evidence for link formation). We noted that such demonstrations are only convincing if sensitive measures are used to probe all relevant propositional knowledge that participants might have. Most past research does not satisfy this criterion. A more powerful approach, taken by Perruchet (1985) is to demonstrate that participants have relevant propositional knowledge that contradicts the observed learning effects. The Perruchet effect, if it is demonstrated to be an effect of associative learning (and not due to a performance effect or some other factor) could not be accommodated within the propositional approach. Tests of the propositional approach must also satisfy one other requirement. As pointed out in section 3.1 of the target article, because propositions can be stored in memory after their truth has been evaluated, they can influence behavior as the result of memory activation even at times when their truth is not evaluated. Hence, in order to demonstrate learning in the absence of relevant propositional knowledge, one should take into account not only propositions that are truth evaluated at the time the learned behavior is emitted, but also those evaluated earlier in training, before the behavior is observed. Baker, Baetu, & Murphy [Baker et al.] argue that, because propositions can be coded in memory and can then influence later behavior in an automatic way, the propositional approach is rendered unfalsifiable. However, carefully controlled experiments can be set up in which new relations are introduced and propositions about those relations are tested online, at regular intervals during the experiment. The available evidence suggests that learned behavior will be observed only after participants report propositions about the relations that were implemented (e.g., Dawson & Biferno 1973). When retrospective rather than online measures of propositional knowledge are used, researchers should make sure that the measures are sensitive enough to pick up knowledge that participants might have held in the past. This can be done by minimizing the delay between learning and testing. Finally, in contrast to Livesey & Harris, we do believe that research on learning in the absence of relevant propositional knowledge (e.g. outside of awareness) is crucial in testing the propositional account. If convincing evidence 236

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for this type of learning can be found, it would raise serious doubts about models in which propositional processes are the only ones that can support associative learning. R3.3.2. Propositional models are not parsimonious. In

our target article (sect. 6.1), we argued that the propositional approach to associative learning is more parsimonious than a dual-process approach because “no approach that needs two systems can be more parsimonious than an approach that proposes only one of those systems, no matter how parsimonious the second system might be.” Nevertheless, some commentators have still suggested that the propositional approach is not parsimonious. Dwyer et al. argue that the propositional approach lacks parsimony because it needs additional systems in order to account for phenomena such as habituation and perceptual learning. We agree that, within the propositional approach, learning cannot take place by propositional processes alone, but also requires perception and memory. However, a very similar argument seems also to apply to link models. There may still be some researchers who assume that link formation can provide a full account of learning or behavior. It does seem more realistic, though, to assume that link models, like propositional models, need to be extended by other processes in order to explain (non-associative influences on) behavior. These necessary additional processes of perception and memory provide mechanisms for the phenomena referred to by Dwyer et al. Livesey & Harris argue that a propositional approach to learning is not parsimonious because propositional processes are effortful. A system in which all learning depends on effortful processes might end up using more resources than a system in which learning is sometimes outsourced to an effortless link-formation process. There are, however, several counterarguments to this position. First, as we pointed out earlier in this response (see sect. R3.1), having a primitive, effortless link-formation process that registers relations in the world could lead to an overload of information, resulting in chaotic behavior. It could also lead to action tendencies that are contrary to those arising from the cognitive system. The propositional encoding of relations in the world could provide the necessary buffer against such chaotic and hence wasteful behavior. Second, one can overestimate the expenditure of resources that propositional learning processes would require. Although truth evaluation is an effortful process, it can also be conducted in a “quick and dirty” manner. When there is little opportunity or motivation, not all information that is relevant for truth evaluation will be taken into account, thus minimizing the resources that are used to evaluate propositions about relations in the world. Moreover, once a truth evaluation has been completed, the resulting proposition can be stored in memory. On later occasions, this proposition can influence behavior without being evaluated again. In sum, propositional processes could provide the right balance between learning what needs to be learned and expenditure of cognitive resources. R3.3.3. Propositional models are not formalized. There

are two reasons why we do not mind the current lack of formalization of propositional models. First, we do not

Response/Mitchell et al.: The propositional nature of human associative learning believe that formalization is a normative criterion that all models should meet. Second, those who prefer formalized models are free to formalize the aspects of propositional models that can be formalized. With regard to the first argument, the quality of a model cannot be judged by its degree of formalization. It is easy to imagine a fully formalized model that is unable to account for, or correctly predict, any instance of associative learning. The quality of a model is determined by its heuristic and predictive value, that is, its ability to (a) account for and organize existing knowledge and (b) to correctly predict new empirical results. Formalization can help achieve these aims, but it is neither necessary nor sufficient. Witnauer et al. argue that, because propositional models are not formalized, precise predictions are difficult to derive from propositional models. In response, we would like to point out that formalized (link) models of associative learning also do not necessarily allow for precise, unequivocal predictions. Most of these models include a variety of free parameters. Various predictions can be derived from a model by varying the value of parameters or by adding new parameters. Despite these arguments, we acknowledge that formalization can also have benefits such as making explicit the assumptions and inferences that are made when accounting for or predicting a certain finding. It is therefore important to realize that various aspects of propositional models can and have been formalized. Formalization can be coined both in terms of logical operations (e.g., the modus tolens inference; see Beckers et al. 2005; Mitchell & Lovibond 2002) or mathematical expressions. As we pointed out in our target article, mathematical models of link formation such as the Rescorla-Wagner model can be regarded as mathematical formalizations of the operating principles of propositional processes. As Beckers & Vervliet correctly argue, such mathematical models have a heuristic and predictive value. What is important to realize, however, is that these models retain their value even when it is assumed that they describe propositional processes rather than link formation (see sect. 6.1 of the target article). Other ways to formalize propositional models is by means of connectionist models and Bayes nets. It should be noted, however, that at present none of the available avenues for formalization is able to capture all aspects of propositional processes. For instance, as Gopnik correctly points out, Bayes nets and Baysian inferences do not model the fact that truth evaluation is a non-automatic process. Some commentators (e.g., Baker et al.; Schmajuck & Kutlu; Shanks; Witnauer et al.) are optimistic that future connectionist models will be able to mimic all aspects of propositional processes, but a brief look at the current status of connectionist models suggests that much progress still needs to be made. R4. Conclusion In this rejoinder paper, we have attempted to address the main arguments that the commentators have raised against propositional models. In our opinion, the assumption that all associative learning is mediated by propositional processes is supported by the great majority of the available data, and is not significantly undermined by the

conceptual arguments that have been raised against it. We recognize that we have not responded to all of the points made in the commentaries. The diversity of views expressed, and the different disciplines from which they come (philosophy, cognitive psychology, animal learning, social psychology, and neuroscience), are testament to the central importance of learning to understanding human and animal behavior. But this diversity also means that many very interesting comments and suggestions are beyond the scope of the present response. We do believe, however, that we have addressed the main arguments raised against the central claims made in the target article, that learning is propositional in nature and there is little reason to postulate the existence of an additional link-formation mechanism. We also acknowledge that many aspects of the propositional approach require further development. As Newell and Sternberg & McClelland point out (see also Mandler), the way in which propositional processes interact with perception and memory, and whether memory processes can as such support associative learning, needs clarification. Evidence that memory processes can produce learning independently would undermine the propositional approach. However, such evidence would not constitute evidence for a link-formation mechanism. There are also other aspects of the propositional approach that need refinement. We agree with Sternberg & McClelland that research on human learning would benefit greatly from integration with reasoning research. Just as was the case with the target article, we anticipate that this response article will not have persuaded everyone. We would like to finish, therefore, by outlining what we see as some significant future challenges to the dual-system view. It is clear from the commentaries provided here that the postulated link-formation mechanism has a variety of forms. At one end of the spectrum, it is a simple S-R system that we share with Aplysia (Hall; Matute & Vadillo; Penn et al.; Schultheis & Lachnit). At the other end of the spectrum is quite a different mechanism, which is affected by attention and seems to be comparatively rich and complex (e.g., Dwyer et al.; Livesey & Harris; McLaren). It is sometimes advantageous to propose a simple mechanism (often on grounds of parsimony). At other times it is advantageous to propose a more complex mechanism (perhaps because the data demand it). But the system in which the links are formed cannot have the characteristics of both the Aplysia-like S-R mechanism and those of the more complex, attention-demanding, S-S mechanism – these characteristics are incompatible. Proponents of the dual-system view must, therefore, decide on the nature of the link-formation mechanism; otherwise, they may be accused of wanting to “have their cake and eat it too.” Those who propose the Aplysia-like system must then explain, for example, why learning does not occur outside of awareness. A different problem arises for those who opt for the more complex link-formation mechanism described by the models of Rescorla and Wagner (1972), Mackintosh (1975), Pearce and Hall (1980), Wagner (1981), and Miller and Matzel (1998). This version of the dual-system approach proposes that two very complex learning systems, which have many characteristics in common and which are sensitive to the same BEHAVIORAL AND BRAIN SCIENCES (2009) 32:2

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References/Mitchell et al.: The propositional nature of human associative learning environmental variables, operate side by side. Clarification is needed as to the crucial differences between the linkformation system and the propositional system. It is not at all clear why both of these systems are needed. Whatever the characteristics of the link mechanism, clarification is also required as to the way in which the link system and the propositional system interact. That is, how is conflict between the output of these systems resolved? In our target article, we noted the lack of detail regarding conflict resolution in dual-system theories (although see Gawronski & Bodenhausen [2006] for an exception), and regrettably many of the present commentaries continue this noncommittal tradition. For example, ¨ hman stated that the two levels of learning Olsson & O are “partially independent but also interacting.” Lack of testability therefore remains a fundamental weakness of the dual-system approach. We reiterate our challenge to dual-system theorists to clarify the laws or mechanisms by which they consider the output of the two systems to be gated to produce observed behavioral outcomes. We believe that such an exercise will reveal that major concessions are needed to allow dual-system models to fit with existing data (e.g., regarding awareness, cognitive load, and verbal instruction) and that, in this light, the coordinated propositional system we propose might be viewed more favorably. We would like to end with a comment on the evidence for the link-formation mechanism. As we pointed out in the target article and reaffirmed in this response to commentaries, a close examination of the data reveals only one or two isolated phenomena that might indicate the presence of a non-propositional (perhaps link-based) learning mechanism. These include the eyeblink version of the Perruchet effect (Perruchet 1985) and the odor-taste learning work of Stevenson et al. (1998). As Dwyer et al. concede, evidence for the link-formation mechanism is not widespread. Thus, even the proponents of the dualsystem approach accept that the link mechanism is of somewhat limited explanatory value. It seems to us that, if we do indeed possess two separate learning mechanisms, then we should see evidence for both mechanisms everywhere. Why, therefore, is the evidence for the second mechanism so weak and so vanishingly small? We keep an open mind, but there seems to be an obvious and almost unavoidable conclusion, that no such mechanism exists. ACKNOWLEDGMENTS Preparation of this paper was supported by a Discovery Project grant DP0774395 to Chris Mitchell and Peter Lovibond from the Australian Research Council, and Grant BOF/GOA2006/001 of Ghent University and Travel Grant K.1.424.06.N.01 of the Research Foundation – Flanders (FWO – Vlaanderen, Belgium) to Jan De Houwer. Thanks also goes to Gavan McNally and Fred Westbrook for discussions on issues in neuroscience.

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BEHAVIORAL AND BRAIN SCIENCES (2009) 32, 247 –248 Printed in the United States of America

doi:10.1017/S0140525X09000843

Erratum/Corrigendum

No reliable gender differences in attachment across the lifespan Marian J. Bakermans-Kranenburg and Marinus H. van IJzendoorn

doi:10.1017/S0140525X0900003X, Published by Cambridge University Press, 19 February 2009 In the first sentence of the abstract in Bakermans-Kranenburg & van IJzendoorn’s commentary on the target article by Del Giudice in the Behavioral and Brain Sciences 32(1) (February 2009), the word “would” was deleted. The abstract should read as follows: Abstract: In middle childhood boys would show more avoidant attachments and girls more ambivalent attachments as a prelude to gender differentiation in reproductive strategies. However, we failed to find systematic and method-independent gender differences in middle or late childhood attachments, nor in adult attachment representations. We conclude that Del Giudice’s model rests on a brittle empirical basis.

There are also several errors in Table 1 of the commentary. Due to an error on the part of the typesetter, several values in the table were set in boldface when they should not have been. These include: . – 0.2 in the “Avoidant” column below “Haberman’s adjusted standardized residuals” and the “Boys” row below “Observations (separation-reunion)” . – 0.2 in the “Secure” column below “Haberman’s adjusted standardized residuals” and the “Girls” row below “Observations (separation-reunion)” . – 1.8 in the “Avoidant” column below “Haberman’s adjusted standardized residuals” and the “Girls” row below “AAI (modified for younger ages)” . – 1.0 in the “Secure” column below “Haberman’s adjusted standardized residuals” and the “Boys” row below “AAI” . – 1.8 in the “Avoidant” column below “Haberman’s adjusted standardized residuals” and the “Girls” row below “AAI”

# 2009 Cambridge University Press

0140-525X/09 $40.00

“Haberman’s adjusted standardized residuals” should extend across the last three columns of the table, below the “Secure,” “Avoidant,” and “Ambivalent” column heads. In the issue, this only extends below the “Secure” and “Avoidant” column heads. Also, the word “residuals” followed by an asterisk should not have appeared below the word “Ambivalent” in the last column head. We regret these errors. In the “Ambivalent (n; %)” column in the “Girls” row below “Doll play narratives,” the “20%” should be “13%.” This value was listed as “20%” on the manuscript submitted to the publisher. The table as corrected appears on page 248.

Reference Bakermans-Kranenburg, M. J. & van IJzendoorn, M. H. (2009) No reliable gender differences in attachment across the lifespan. Behavioral and Brain Sciences 32(1):22 – 23.

247

Erratum/Corrigendum Table 1 (Bakermans-Kranenburg & Van IJzendoorn). Distributions of attachment in middle childhood and adulthood Secure (n; %) Total, 6 – 14 years Boys Girls Doll play narratives1 Boys Girls Observation (separation-reunion)2 Boys Girls AAI (modified for younger ages)3 Boys Girls AAI4 Males Females

Avoidant (n; %)

Ambivalent (n; %)

Total (N)

Secure

Avoidant

Ambivalent

n%

n%

n%

N

179; 49% 342; 64%

134; 37% 117; 22%

53; 14% 72; 14%

366 531

Haberman’s adjusted standardized residuals 24.6 4.8 0.4 4.6 24.8 20.4

123; 51% 210; 67%

88; 36% 61; 20%

31; 13% 41; 13%

242 312

23.9 3.9

4.4 24.4

20.1 0.1

27; 69% 84; 69%

7; 18% 24; 20%

5; 13% 13; 11%

39 121

0.2 20.2

20.2 0.2

0.4 20.4

29; 34% 48; 49%

39; 46% 32; 33%

17; 20% 18; 18%

85 98

22.0 2.0

1.8 21.8

0.3 20.3

331; 48% 1507; 50%

230; 33% 901; 30%

126; 18% 589; 20%

687 2997

21.0 1.0

1.8 21.8

20.8 0.8

 Significant 1

adjusted standard residuals in bold Del Giudice (2008); Gilissen et al. (2008); Gloger-Tippelt et al. (2007); Granot & Mayseless (2001); Kerns et al. (2007); Toth et al. (2006) Bureau et al. (2006); Pannebakker (2007) 3 Beijersbergen et al. (in press); Ammaniti et al. (2000) 4 Van IJzendoorn & Bakermans-Kranenburg (in preparation) 2

248

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Journal of Neurophysiology 2:644–66. http://cogprints.soton.ac.uk/abs/ neuro/199806009 Dennet, D. C. (1991) Two contrasts: Folk craft versus folk science and belief versus opinion. In: The future of folk psychology: Intentionality and cognitive science, ed. J. D. Greenwood, pp. 26–7. Cambridge University Press. http:// cogprints.soton.ac.uk/abs/phil/199804005 Bateson, P.P.G. & Hinde, R.A., eds. (1978) Growing points in ethology. Cambridge University Press. Editing: The publishers reserve the right to edit and proof all articles and commentaries accepted for publication. Authors of target articles will be given the opportunity o review the copy-edited manuscript and page proofs. Commentators will be asked to review copy-editing only when changes have been substantial; commentators will not see proofs. Both authors and commentators should notify the editorial office of all corrections within 48 hours or approval will be assumed. Author response to commentaries: All invited commentaries received before the deadline are only accessible to the Authors and Editors. Please note that no commentary is officially accepted until the Editor in charge has formally reviewed it and notified both the authors and the Editorial Administrator. Please refer to and follow exactly the BBS Commentary Response Instructions at http://www.editorialmanager.com/ bbs/account/BBS_ifc.pdf before submitting your response. Authors of target articles receive 50 offprints of the entire treatment, and can purchase additional copies. Commentators will also be given an opportunity to purchase offprints of the entire treatment.

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Offprints of the following forthcoming BBS treatments can be purchased for educational purposes if they are ordered well in advance. For ordering information, please write to Journals Department, Cambridge University Press, 32 Avenue of the Americas, New York, NY 10013-2473.

How we know our own minds: The relationship between mindreading and metacognition Peter Carruthers

The propositional nature of human associative learning Chris J. Mitchell, Jan De Houwer & Peter F. Lovibond

To appear in Volume 32, Number 3 (2009)

Behavioral and Brain Sciences

In this issue

Does sexual selection explain human sex differences in aggression? John Archer, University of Central Lancashire

With commentary from DH Bailey, JK Oxford & DC Geary; C Behme; JF Benenson; JM Boden; KR Browne; DM Buss; A Campbell; E Cashdan; PJ Corr & AM

April 2009

I argue that the magnitude and nature of sex differences in aggression, and their development, causation, and variability, can be better explained by sexual selection than by the alternative social role theory. Other sex differences – in variance in reproductive output, threat displays, size and strength, maturation rates, and mortality and conception rates – all indicate that male aggression is part of a sexually selected adaptive complex. Physical aggression between partners can be explained by using different evolutionary principles, arising from the conflict of interests between males and females entering a reproductive alliance, combined with variability following differences in societal gender roles.

Perkins; TE Dickins & MJT Sergeant; AH Eagly & W Wood; AJ Figueredo, PR Gladden & BH Brumbach; EJ Finkel & EB Slotter; SJC Gaulin; DP Johnson & M Van Vugt; F Kaighobadi & TK Shackelford; B Kempenaers & W Forstmeier; DT Kenrick & V Griskevicius; AD Pellegrini; N Pound, M Daly & M Wilson; M Schredl; JA Sefcek & DF Sacco; A Sell; D Terburg, JS Peper, B Morgan & J Van Honk; RE Tremblay & SM Côté; PL van den Berghe;

Jacob Vigil, University of North Florida Despite extensive empirical demonstrations of sex differences in expressed emotion, no critical examination and account of their evolution and development yet exists. There is a consistent difference in the typical social setting in which each sex has functioned and evolved; that is, the predominance of male philopatry in past and present human societies. I argue that, because of the requirements of sex-typical social settings, emotional systems that differentially project and assess perceived capacity will be favored in males contrasted with those related to perceived trustworthiness in females. A sociorelational framework to account for the advertisement and perception of these basic social predispositions in conjunction with situational factors is described.

With commentary from F Basso & O Oullier; AH Fischer; JMB Fugate, H Gouzoules & LF Barrett; S Goldstein Ferber; CE Izard, KJ Finlon & SR Grossman; P Li & D Balliet; V LoBue & JS DeLoache; GA Lozano; M Lyons; G Madison; JE Swain; N Vermeulen; A Wiefel & R Schepker; V Zayas, JA Tabak, G Günaydýn & JM Robertson

Among the articles to appear in forthcoming issues of BBS: J. Archer, “Does sexual selection explain human sex differences in aggression?” J. Vigil, “A sociorelational framework of sex differences in the expression of emotion” R. Cohen Kadosh & V. Walsh, “Numerical representation in the parietal lobes: Abstract or not abstract?" N. Evans & S. Levinson, “The myth of language universals: Language diversity and its importance for cognitive science”

http://www.bbsonline.org/Preprints/Archer-05142007/Referees http://www.bbsonline.org/Preprints/Vigil-02212008/Referees

Volume 32, Number 2

A sociorelational framework of sex differences in the expression of emotion

http://www.bbsonline.org/Preprints/CohenKadosh-05062008/Referees http://www.bbsonline.org/Preprints/Evans-08042008/Referees

Pages 121–248

Cambridge Journals Online For further information about this journal please go to the journal website at: journals.cambridge.org/bbs

An International journal of current research and theory with open peer commentary Volume 32 | Issue 2 | April 2009 | ISSN: 0140-525X