Behavioral and Brain Sciences, Volume 33, Issue 4, August 2010

An International journal of current research and theory with open peer commentary Volume 33 | Issue 4 | August 2010 | 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 33:4

August 2010

Anderson, M. L. Neural reuse: A fundamental organizational principle of the brain Open Peer Commentary Aisenberg, D. & Henik, A. Reuse or re-function? Bargh, J. A., Williams, L. E., Huang, J. Y., Song, H. & Ackerman, J. M. From the physical to the psychological: Mundane experiences influence social judgment and interpersonal behavior Bergeron, V. Neural reuse and cognitive homology Bridgeman, B. Neural reuse implies distributed coding Brincker, M. Sensorimotor grounding and reused cognitive domains Dekker, T. M. & Karmiloff-Smith, A. The importance of ontogenetic change in typical and atypical development Donnarumma, F., Prevete, R. & Trautteur, G. How and over what timescales does neural reuse actually occur? Fishbein, W., Lau, H., DeJesu´s, R. & Alger, S. E. Sleep, neural reuse, and memory consolidation processes Foglia, L. & Grush, R. Reuse (neural, bodily, and environmental) as a fundamental organizational principle of human cognition Gomila, A. & Calvo, P. Understanding brain circuits and their dynamics Immordino-Yang, M. H., Chiao, J. Y. & Fiske, A. P. Neural reuse in the social and emotional brain Iriki, A. Neural reuse: A polysemous and redundant biological system subserving niche-construction Junge´, J. A. & Dennett, D. C. Multi-use and constraints from original use Katz, P. S. Comparative studies provide evidence for neural reuse

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Kiverstein, J. No bootstrapping without semantic inheritance Klein, C. Redeployed functions versus spreading activation: A potential confound Lia, B. Implications of neural reuse for brain injury therapy: Historical note on the work of Kurt Goldstein Lindblom, B. Reuse in the brain and elsewhere Michaux, N., Pesenti, M., Badets, A., Di Luca, S. & Andres, M. Let us redeploy attention to sensorimotor experience Moore, D. S. & Moore, C. Neural reuse as a source of developmental homology Niven, J. E. & Chittka, L. Reuse of identified neurons in multiple neural circuits Petrov, A. A., Jilk, D. J. & O’Reilly, R. C. The Leabra architecture: Specialization without modularity Rabaglia, C. D. & Marcus, G. F. Neural reuse and human individual differences Reimers, M. Reuse of molecules and of neural circuits Ritchie, J. B. & Carruthers, P. Massive modularity is consistent with most forms of neural reuse Rozin, P. More than modularity and metaphor: The power of preadaptation and access Speed, A., Verzi, S. J., Wagner, J. S. & Warrender, C. Optical holography as an analogue for a neural reuse mechanism Toskos Dils, A. & Flusberg, S. J. Massive redeployment or distributed modularity? Vilarroya, O. Belling the cat: Why reuse theory is not enough Author’s Reponse Anderson, M. L. Cortex and context: Response to commentaries on neural reuse

Knobe, J. Person as scientist, person as moralist Open Peer Commentary Alexander, J., Mallon, R. & Weinberg, J. M. Competence: What’s in? What’s out? Who knows? Alicke, M. & Rose, D. Culpable control or moral concepts? Baldo, M. V. C. & Barberousse, A. Person as moralist and scientist Bartsch, K. & Young, T. N. Reasoning asymmetries do not invalidate theory-theory

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Brogaard, B. “Stupid people deserve what they get”: The effects of personality assessment on judgments of intentional action Carpendale, J. I. M., Hammond, S. I. & Lewis, C. The social origin and moral nature of human thinking Cova, F., Dupoux, E. & Jacob, P. Moral evaluation shapes linguistic reports of others’ psychological states, not theory-of-mind judgments

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Egre´, P. Qualitative judgments, quantitative judgments, and norm-sensitivity Gintis, H. Modalities of word usage in intentionality and causality Girotto, V., Surian, L. & Siegal, M. Morals, beliefs, and counterfactuals Guglielmo, S. Questioning the influence of moral judgment Hindriks, F. Person as lawyer: How having a guilty mind explains attributions of intentional agency Humphrey, N. Person as moral scientist Kang, M. J. & Glassman, M. The cultural capital of the moralist and the scientist Kreps, T. A. & Monin, B. Are mental states assessed relative to what most people “should” or “would” think? Prescriptive and descriptive components of expected attitudes Kushnir, T. & Chernyak, N. Understanding the adult moralist requires first understanding the child scientist Levy, N. Scientists and the folk have the same concepts Lombrozo, T. & Uttich, K. Putting normativity in its proper place

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Mandelbaum, E. & Ripley, D. Expectations and morality: A dilemma Menzies, P. Norms, causes, and alternative possibilities Nanay, B. Neither moralists, nor scientists: We are counterfactually reasoning animals Scanlon, T. M. Ambiguity of “intention” Sinnott-Armstrong, W. Alternatives and defaults: Knobe’s two explanations of how moral judgments influence intuitions about intentionality and causation Spurrett, D. & Martin, J. “Very like a whale”: Analogies about the mind need salient similarity to convey information Stich, S. & Wysocki, T. Are we really moralizing creatures through and through? Terroni, L. & Fraguas, R. Depression affecting moral judgment Ulatowski, J. & Johnson, J. Fixing the default position in Knobe’s competence model

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Author’s Response Knobe, J. The person as moralist account and its alternatives

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BEHAVIORAL AND BRAIN SCIENCES (2010) 33, 245 –313 doi:10.1017/S0140525X10000853

Neural reuse: A fundamental organizational principle of the brain Michael L. Anderson Department of Psychology, Franklin & Marshall College, Lancaster, PA 17604, and Institute for Advanced Computer Studies, Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742 [email protected] http://www.agcognition.org

Abstract: An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design. Keywords: brain; development; evolution; exaptation; functional architecture; localization; modularity Although an organ may not have been originally formed for some special purpose, if it now serves for this end we are justified in saying that it is specially contrived for it. On the same principle, if a man were to make a machine for some special purpose, but were to use old wheels, springs, and pulleys, only slightly altered, the whole machine, with all its parts, might be said to be specially contrived for that purpose. Thus throughout nature almost every part of each living being has probably served, in a slightly modified condition, for diverse purposes, and has acted in the living machinery of many ancient and distinct specific forms. — Charles Darwin (1862), p. 348.

1. Introduction and background Research in the cognitive neurosciences has long been guided by the idealization that brain regions are highly selective and specialized, and that function can be mapped to local structure in a relatively straightforward way. But the degree of actual selectivity in neural structures is increasingly a focus of debate in cognitive science (Poldrack 2006). It appears that many structures are activated by different tasks across different task categories and cognitive domains. For instance, although Broca’s area has been strongly associated with language processing, it turns out to also be involved in many different action- and imagery-related tasks, including movement preparation (Thoenissen et al. 2002), action sequencing (Nishitani et al. 2005), action recognition (Decety et al. 1997; Hamzei et al. 2003; Nishitani et al. 2005), imagery of human motion (Binkofski et al. 2000), and action # Cambridge University Press 2010

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imitation (Nishitani et al. 2005; for reviews, see Hagoort 2005; Tettamanti & Weniger 2006). Similarly, visual and motor areas – long presumed to be among the most highly specialized in the brain – have been shown to be active in various sorts of language processing and other higher cognitive tasks (Damasio & Tranel 1993; Damasio et al. 1996; Glenberg & Kaschak 2002; Hanakawa et al. 2002; Martin et al. 1995; 1996; 2000; Pulvermu¨ller 2005; see sect. 4 for a discussion). Excitement over the discovery of the Fusiform Face Area (Kanwisher et al. 1997) was quickly tempered when it was discovered that the area also responded to cars, birds, and other stimuli (Gauthier et al. 2000; Grill-Spector et al. 2006; Rhodes et al. 2004). MICHAEL L. ANDERSON , Assistant Professor of Cognitive Science in the Department of Psychology at Franklin & Marshall College, is author or co-author of more than sixty scholarly and scientific publications in cognitive science, artificial intelligence, and philosophy of mind. His papers include: “Evolution of cognitive function via redeployment of brain areas,” “Circuit sharing and the implementation of intelligent systems,” “Investigating functional cooperation in the human brain using simple graph-theoretic methods,” “A self-help guide for autonomous systems,” and “Embodied cognition: A field guide.” Anderson was recently nominated for the Stanton Prize, recognized as an “emerging leader under 40” by the Renaissance Weekend, and was an invited participant in the McDonnell Project in Philosophy and the Neurosciences workshop for early career researchers.

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Anderson: Neural reuse: A fundamental organizational principle of the brain The ensuing debates over the “real” function of these areas have still not been resolved. This is just a short list of some highly-studied regions for which the prospect of a clear-cut mapping of function to structure appears dim. In this target article, I will review a great deal more evidence that points in a similar direction. But if selectivity and localization are not in fact central features of the functional organization of the brain, how shall we think about the function-structure relationship? This target article reviews an emerging class of theories that suggest neural circuits established for one purpose are commonly exapted (exploited, recycled, redeployed) during evolution or normal development, and put to different uses, often without losing their original functions. That is, rather than posit a functional architecture for the brain whereby individual regions are dedicated to large-scale cognitive domains like vision, audition, language, and the like, neural reuse theories suggest instead that low-level neural circuits are used and reused for various purposes in different cognitive and task domains. In just the past five years, at least four different, specific, and empirically supported general theories of neural reuse have appeared. Two of these theories build on the core notion of the sensorimotor grounding of conceptual content to show how it could implicate many more aspects of human cognitive life: Vittorio Gallese’s “neural exploitation” hypothesis (Gallese 2008; Gallese & Lakoff 2005) and Susan Hurley’s “shared circuits model” (Hurley 2005; 2008). Two other theories suggest that reuse could be based on even more universal foundations: Dehaene’s “neuronal recycling” theory (Dehaene 2005; 2009; Dehaene & Cohen 2007) and my own “massive redeployment” hypothesis (M. L. Anderson 2007a; 2007c).1. These latter two suggest reuse might in fact constitute a fundamental developmental (Dehaene’s recycling theory) or evolutionary (my redeployment hypothesis) strategy for realizing cognitive functions. Others are clearly thinking along similar lines, for example, Luiz Pessoa (2008), Gary Marcus (2004; 2008), Steven Scher (2004), William Bechtel (2003), and Dan Lloyd (2000). These models have some interesting similarities and equally interesting differences, but taken together they offer a new research-guiding idealization of brain organization, and the potential to significantly impact the ongoing search for the brain basis of cognition. I discuss each model, and what these models might collectively mean for cognitive science, in sections 6 and 7, after reviewing some of the broad-based evidence for neural reuse in the brain (sects. 4 and 5). In order to better appreciate that evidence and its implications, however, it will be useful to have before us a more concrete example of a theory of neural reuse, and some sense of where such theories fit in the landscape of cognitive science. To this end, the next subsection briefly details one of the theories of reuse – the massive redeployment hypothesis – and sections 2 through 5 serve to situate reuse with respect to some other well-known accounts of the functional structure of the brain. 1.1. The massive redeployment hypothesis

The core of the massive redeployment hypothesis is the simple observation that evolutionary considerations might often favor reusing existing components for new 246

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tasks over developing new circuits de novo. At least three predictions follow from this premise. Most generally, we should expect a typical brain region to support numerous cognitive functions in diverse task categories. Evidence to the contrary would tend to support the localist story that the brain evolved by developing dedicated circuits for each new functional capacity. More interestingly, there should be a correlation between the phylogenetic age of a brain area and the frequency with which it is redeployed in various cognitive functions; older areas, having been available for reuse for longer, are ceteris paribus more likely to have been integrated into later-developing functions. Finally, there should be a correlation between the phylogenetic age of a cognitive function and the degree of localization of its neural components. That is, more recent functions should generally use a greater number of and more widely scattered brain areas than evolutionarily older functions, because the later a function is developed, the more likely it is that there will already be useful neural circuits that can be incorporated into the developing functional complex; and there is little reason to suppose that the useful elements will happen to reside in neighboring brain regions. A more localist account of the evolution of the brain would instead expect the continual development of new, largely dedicated neural circuits, and would predict that the resulting functional complexes would remain tightly grouped, as this would minimize the metabolic cost of wiring the components together and communicating among them. In a number of recent publications (M. L. Anderson 2007a; 2007c; 2008a) I report evidence for all of these predictions. Consider, for instance, some data demonstrating the first prediction, that a typical brain region serves tasks across multiple task categories. An empirical review of 1,469 subtraction-based fMRI experiments in eleven task domains reveals that a typical cortical region2 is activated by tasks in nine different domains. The domains investigated were various – action execution, action inhibition, action observation, vision, audition, attention, emotion, language, mathematics, memory, and reasoning – so this observation cannot be explained by the similarity of the task domains. And because the activations were post-subtraction activations, the finding is not explained by the fact that most experimental tasks have multiple cognitive aspects (e.g., viewing stimuli, recalling information, making responses). Control tasks would (mostly) ensure that the reported brain activity was supporting the particular cognitive function under investigation. Finally, the observation is not explained by the size of the regions studied. As recounted in more detail in section 5, below, one gets the same pattern of results even when dividing the cortex into nearly 1,000 small regions.3 In evaluating the second prediction, one is immediately faced with the trouble that there is little consensus on which areas of the brain are older. I therefore employed the following oversimplification: All things being equal, areas in the back of the brain are older than areas in the front of the brain (M. L. Anderson 2007a). Thus, the prediction is for a relationship between the position of a brain region along the Y-axis in Talairach space (Talairach & Tournaux 1988) and the frequency with which it is used in cognitive functions. The study reports the expected negative correlation4 between the Y-position and the number of tasks in which it is active (r ¼ 20.412, p ¼ .003, t ¼ 23.198, df ¼ 50). A similar analysis using

Anderson: Neural reuse: A fundamental organizational principle of the brain the data set mentioned above reveals a negative correlation between the number of domains in which an anatomical region is activated and the Y-position of the region (r ¼ 20.312, p ¼ 0.011, t ¼ 22.632, df ¼ 65). Although the amount of variance explained in these cases is not especially high, the findings are nevertheless striking, at least in part because a more traditional theory of functional topography would predict the opposite relation, if there were any relation at all. According to traditional theories, older areas – especially those visual areas at the back of the brain – are expected to be the most domain dedicated. But that is not what the results show. As for the last prediction, that more recently evolved functions will be supported by more broadly scattered regions of activation, in (M. L. Anderson 2007a), I reported that language tasks activate more and more broadly scattered regions than do visual perception and attention. This finding was corroborated by a larger study (M. L. Anderson 2008a), which found that language was the most widely scattered domain of those tested, followed (in descending order) by reasoning, memory, emotion, mental imagery, visual perception, action, and attention. The significant differences in the degree of scatter were observed between attention and each of the following domains: language, reasoning, memory, emotion, and mental imagery; and between language and each of the following domains: visual perception, action, and attention. No other pair-wise comparisons showed significant differences. Note that, in addition to supporting the main contentions of the massive redeployment hypothesis, this last finding also corroborates one of the main assumptions behind most theories of neural reuse: that cortical regions have specific biases that limit the uses to which they can be put without extensive rewiring. If neural circuits could be easily put to almost any use (that is, if small neural regions were locally poly-functional, as some advocates of connectionist models suggest), then given the increased metabolic costs of maintaining long-distance connections, we would expect the circuits implementing functions to remain relatively localized. That this is not the observed pattern suggests that some functionally relevant aspect of local circuits is relatively fixed. The massive redeployment hypothesis explains this with the suggestion that local circuits may have low-level computational “workings” that can be put to many different higher-level cognitive uses.5 If this is the right sort of story, it follows that the functional differences between task domains cannot be accounted for primarily by differences in which brain regions get utilized – as they are reused across domains. And naturally, if one puts together the same parts in the same way, one will get the same functional outcomes. So, the functional differences between cognitive domains should reveal themselves in the (different) ways in which the (shared) parts are assembled. I explored this possibility using a co-activation analysis – seeing which brain regions were statistically likely to be co-active under what task conditions. The results indicated that although different domains do indeed tend to be supported by overlapping neural regions, each task domain was characterized by a distinctive pattern of co-activation among the regions (M. L. Anderson 2008a). This suggests an overall functional architecture for the brain that is quite different from that proposed by anatomical modularity and functional localization (see Fig. 1).

Keeping this substantive introduction to the concept of neural reuse in view, I will devote the next three sections to situating neural reuse with respect to three relevant classes of theory in cognitive science, and return to both neural reuse theory and supporting data in sections 5 and 6. For the purposes of this review, it is important to note that neural reuse theories are not full-fledged theories of how the brain (or mind) works. Rather, they are theories of how neural resources are (typically) deployed in support of cognitive functions and processes. Given this, there are at least three relevant comparison classes for neural reuse, each of which I discuss in turn in the sections that follow. First, in section 2, I briefly discuss some other theories – anatomical modularity and global wiring optimization theory – for how neural resources are typically deployed in support of the brain’s function. Then, in section 3, I turn to some theories of overall cognitive architecture – ACT-R, massive modularity, and both classic and contemporary parallel distributed processing models – and what they may imply for neural reuse and vice versa. And finally, in section 4, I examine at some length some other theories that predict neural reuse, notably concept empiricism and conceptual metaphor theory, as part of an argument that these established theories are not adequate to account for the full range of neural reuse that can be observed in the brain. 2. How are neural resources deployed in the brain? There are two prominent theories for how neural resources are deployed in the function and structure of the brain: anatomical modularity and global wiring optimization theory. We will see that neural reuse is deeply

Figure 1. Expected patterns of co-activation in a simple six-region brain for two cognitive functions (solid vs. dashed lines). Anatomical modularity and localization (top) predicts largely non-overlapping sets of regions will contribute to each function, whereas reuse (bottom) suggests that many of the same cortical regions will be activated in support of both functions, but that they will co-activate (cooperate) in different patterns. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Anderson: Neural reuse: A fundamental organizational principle of the brain incompatible with anatomical modularity, but compatible with wiring optimization theory. In fact, in combination neural reuse and wiring optimization theory make some novel predictions for cortical layout.

2.1. Anatomical modularity

Anatomical modularity is functional modularity plus a strong thesis about how the functional modules are implemented in the brain. Functional modularity is (minimally) the thesis that our cognitive systems are composed of separately modifiable (or “nearly decomposable”; Simon 1962/1969) subsystems, each typically dedicated to specific, specialized functions (see sect. 3.1 for a discussion). Anatomical modularity is the additional thesis that each functional module is implemented in a dedicated, relatively small, and fairly circumscribed piece of neural hardware (Bergeron 2007). Simply put, neural reuse theories suggest anatomical modularity is false. According to the picture painted by reuse, even if there is functional modularity (see sect. 3.1), individual regions of the brain will turn out to be part of multiple functional modules. That is, brain regions will not be dedicated to single high-level tasks (“uses”), and different modules will not be implemented in separate, small, circumscribed regions. Instead, different cognitive functions are supported by putting many of the same neural circuits together in different arrangements (M. L. Anderson 2008a). In each of these arrangements, an individual brain region may perform a similar information-processing operation (a single “working”), but will not be dedicated to that one high-level use. Although there are few defenders of a strong anatomical modularity hypothesis, Max Coltheart (2001) goes so far as to include it as one of the fundamental assumptions guiding cognitive neuropsychology. The idea is that the success of neuropsychological research – relying as it does on patients with specific neurological deficits, and the discovery of double-dissociations between tasks – both requires and, in turn, supports the assumption that the brain is organized into anatomical modules. For if it were not, we wouldn’t observe the focal deficits characteristic of some brain injuries, and nor would we be able to gather evidentiary support for double-dissociations between tasks. If this argument were sound, then the success of neuropsychology as a discipline would itself be prima facie evidence against neural reuse. In fact, the inference is fairly weak. First, it is possible for focal lesions to cause specific functional deficits in non-modular systems (Plaut 1995), and double-dissociations do not by themselves support any inference about the underlying functional architecture of the brain (Van Orden et al. 2001). In any event, such deficits are the exception rather than the rule in human brain injuries. Even some of the patients most celebrated for having specific behavioral deficits often have multiple problems, even when one problem is the most obvious or debilitating (see Bergeron 2007; Prinz 2006 for discussions). The evidence coming from neuropsychology, then, is quite compatible with the truth of neural reuse. But is neural reuse compatible with the methodological assumptions of cognitive neuropsychology? Section 7 will discuss some of the specific methodological changes that will be needed in the cognitive neurosciences in light of widespread neural reuse. 248

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2.2. Optimal wiring hypotheses

The layout of neurons in the brain is determined by multiple constraints, including biomorphic and metabolic limitations on how big the brain can be and how much energy it can consume. A series of studies by Christopher Cherniak and others has reported that the layout of the nervous system of C. elegans, the shape of typical mammalian neuron arbors, and the placement of large-scale components in mammalian cortex are all nearly optimal for minimizing the total length of neurons required to achieve the structure (Cherniak et al. 2004; see also Wen & Chklovskii 2008). The last finding is of the highest relevance here. Cherniak et al. examined the 57 Brodmann areas of cat cortex. Given the known connections between these regions, it turns out that the Brodmann areas are spatially arranged so as to (nearly) minimize the total wiring length of those connections. This is a striking finding; and even though this study examined physical and not functional connectivity, the two are undoubtedly related – at least insofar as the rule that “neurons that fire together wire together” holds for higher-level brain organization. In fact, Cherniak et al. (2004) predict that brain areas that are causally related – that co-activate, for instance – will tend to be physically adjacent. The data reviewed above did not exactly conform to this pattern. In particular, it seems that the neural regions supporting more recent cognitive functions tended to be less adjacent – farther apart in the brain – than those supporting older cognitive functions. Neverthless, neural reuse and the global optimization of component layout appear broadly compatible, for four reasons. First, wiring length can hardly be considered (and Cherniak et al. do not claim that it is) the only constraint on cortical structure. The total neural mass required to achieve the brain’s function should also be kept minimal, and reuse would tend to serve that purpose. Second, it should be kept in mind that Cherniak et al. (2004) predict global optimization in component layout, and this is not just compatible with, but also positively predicts that subsets of components will be less optimal than the whole. Third, there is no reason to expect that all subsets will be equally suboptimal; global optimality is compatible with differences in the optimality of specific subsets of components. Fourth, when there is a difference in the optimality of component subsets, neural reuse would predict that these differences would track the evolutionary age of the supported function. That is, functionally connected components supporting recently evolved functions should tend to be less optimally laid out than those supporting older functions. More specifically, one would expect layout optimality to correlate with the ratio of the age of the cognitive function to the total evolutionary age of the organism. When functional cooperation emerged early in the evolution of the cortex, there is a greater chance that the components involved will have arrived at their optimal locations, and less chance of that for lower ratios, as overall brain morphology will not have had the same evolutionary opportunity to adjust. This notion is not at all incompatible with the thesis of global (near-) optimality and indeed might be considered a refinement of its predictions. Certainly, this is a research direction worth pursuing, perhaps by merging the anatomical connectivity data-sets from Hagmann et al. (2008) with

Anderson: Neural reuse: A fundamental organizational principle of the brain functional databases like BrainMap (Laird et al. 2005) and the NICAM database (M. L. Anderson et al. 2010). In fact, I am currently pursuing a related project, to see whether co-activation strength between regions predicts the existence of anatomical connections. 3. Cognitive architectures In this section, I review four of the most commonly adopted approaches to understanding how the mind is functionally structured, and the implications of these approaches for the functional structure of the brain: massive modularity; ACT-R; and classic and contemporary parallel distributed processing models. Neural reuse appears to undermine the main motivation for positing massive modularity, and although reuse is broadly compatible with the other three theories, it seems likely to somewhat modify the direction of each research program. 3.1. Massive modularity

As noted above, functional modularity is minimally the thesis that the mind can be functionally decomposed into specialized, separately modifiable subsystems – individual components charged with handling one or another aspect of our mental lives. Carruthers (2006) follows this formulation: In the weakest sense, a module can just be something like: a dissociable functional component. This is pretty much the everyday sense in which one can speak of buying a hi-fi system on a modular basis, for example. The hi-fi is modular if one can purchase the speakers independently of the tapedeck, say, or substitute one set of speakers for another with the same tape deck. (Carruthers 2006, p. 2)

Massive modularity, which grows largely out of the modularity movement in evolutionary psychology (Pinker 1997; Sperber 1996; Tooby & Cosmides 1992) is the additional thesis that the mind is mostly, if not entirely, composed of modules like this – largely dissociable components that vary independently from one another. Is such a vision for the mind’s architecture compatible with widespread neural reuse? Carruthers (2006) certainly thinks so: If minimizing energetic costs were the major design criterion, then one would expect that the fewer brain systems that there are, the better. But on the other hand the evolution of multiple functionality requires that those functions should be underlain by separately modifiable systems, as we have seen. As a result, what we should predict is that while there will be many modules, those modules should share parts wherever this can be achieved without losing too much processing efficiency (and subject to other constraints: see below). And, indeed, there is now a great deal of evidence supporting what Anderson [2007c] calls “the massive redeployment hypothesis”. This is the view that the components of brain systems are frequently deployed in the service of multiple functions. (Carruthers 2006, pp. 23 –24; emphasis his)

As much as I appreciate Carruthers’ swift adoption of the redeployment hypothesis, I am troubled by some aspects of this argument. First, it appears to contain a false premise: Energetic constraints predict more compact or localized, not necessarily fewer brain systems. Second, it may be logically invalid, because if functions must be underlain by separately modifiable systems, then they cannot be built from

shared parts. That is, it appears that this apparently small concession to neural reuse in fact undermines the case for massive modularity. Consider Carruthers’ hi-fi system analogy. There it is true that the various components might share the amplifier and the speakers, the way many different biological functions – eating, breathing, communicating – “share” the mouth. But if neural reuse is the norm, then circuit sharing in the brain goes far beyond such intercommunication and integration of parts. The evidence instead points to the equivalent of sharing knobs and transistors and processing chips. A stereo system designed like this would be more like a boom-box, and its functional components would therefore not be separately modifiable. Changing a chip to improve the radio might well also change the performance of the tape player.6 To preview some of the evidence that will be reviewed in more detail in section 4, the brain may well be more boom-box than hi-fi. For instance, Glenberg et al. (2008a) report that use-induced motor plasticity also affects language processing, and Glenberg et al. (2008b) report that language processing modulates activity in the motor system. This connection is confirmed by the highly practical finding that one can improve reading comprehension by having children manipulate objects (Glenberg et al. 2007). And of course there are many other such examples of cognitive interference between different systems that are routinely exploited by cognitive scientists in the lab. This does not mean that all forms of functional modularity are necessarily false – if only because of the myriad different uses of that term (see Barrett & Kurzban 2006 for a discussion). But it does suggest that modularity advocates are guided by an idealization of functional structure that is significantly at odds with the actual nature of the system. Instead of the decompose-and-localize approach to cognitive science that is advocated and exemplified by most modular accounts of the brain, neural reuse encourages “network thinking” (Mitchell 2006). Rather than approach a complex system by breaking functions into subfunctions and assigning functions to proper parts – a heuristic that has been quite successful across a broad range of sciences (Bechtel & Richardson 1993; 2010) – network thinking suggests one should look for higherorder features or patterns in the behavior of complex systems, and advert to these in explaining the functioning of the system. The paradigm exemplars for this sort of approach come from the discovery of common, functionally relevant topological structures in various kinds of networks, ranging from human and insect social networks to the phone grid and the Internet, and from foraging behaviors to the functioning of the immune system (Baraba´si & Albert 1999; Baraba´si et al. 2000; Boyer et al. 2004; Brown et al. 2007; Jeong et al. 2000; Newman et al. 2006). Although it is hardly the case that functional decomposition is an ineffective strategy in cognitive science, the evidence outlined above that patterns of neural co-activation distinguish between cognitive outcomes more than the cortical regions involved do by themselves suggests the need for a supplement to business as usual. Even so, there are (at least) two objections that any advocate of modularity will raise against the picture of brain organization that is being painted here: Such a brain could not have evolved, because (1) the structure BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Anderson: Neural reuse: A fundamental organizational principle of the brain would be too complex, and (2) it would be subject to too much processing interference and inefficiency. Carruthers (2006) follows Simon (1962/1969) in making the first argument: Simon [1962/1969] uses the famous analogy of the two watchmakers to illustrate the point. One watchmaker assembles one watch at a time, attempting to construct the whole finished product at once from a given set of micro components. This makes it easy for him to forget the proper ordering of parts, and if he is interrupted he may have to start again from the beginning. The second watchmaker first builds a set of sub-components out of given micro component parts and then combines those into larger sub-component assemblies, until eventually the watches are complete . . . . Simon’s argument is really an argument from design, then, whether the designer is natural selection (in the case of biological systems) or human engineers (in the case of computer programs). It predicts that, in general, each element added incrementally to the design should be realized in a functionally distinct sub-system, whose properties can be varied independently of the others (to a significant degree, modulated by the extent to which component parts are shared between them). It should be possible for these elements to be added to the design without necessitating changes within the other systems, and their functionality might be lost altogether without destroying the functioning of the whole arrangement. (Carruthers 2006, pp. 13, 25; emphasis in original)

The argument from design set forth here is more convincing when it is applied to the original emergence of a complex system than when it is applied to its subsequent evolutionary development. What the argument says is that it must be possible for development to be gradual, with functional milestones, rather than all-or-nothing; but neural reuse hardly weakens the prospect of a gradual emergence of new functions. And the possibility that new functionality can be achieved by combining existing parts in new ways – which undermines independent variation and separate modifiability, as Carruthers (2006) admits, here – suggests that a modular architecture is only one possible outcome from such gradualism. Moreover, the strong analogy between natural selection and a designer may not be the most helpful conceptual tool in this case. When one thinks about the brain the way a human designer would, the problem that neural reuse presents is one of taking a given concrete circuit with a known function and imagining novel uses for it. That this process can be very difficult appears to place a heavy burden on reuse theories: How could such new uses ever be successfully designed? But suppose instead that, in building a given capacity, one is offered a plethora of components with unknown functions. Now the task is quite different: Find a few components that do something useful and can be arranged so as to support the current task – whatever their original purpose. Thus is a problem of design imagination turned into a problem of search. Evolution is known to be quite good at solving problems of the latter sort (Newell & Simon 1976), and it is useful to keep this alternate analogy for the evolutionary process in mind here. This brings us to the second objection, that non-modular systems would suffer from disabling degrees of interference and processing inefficiency. Here, it may be useful to recall some of the main findings of the situated/embodied cognition movement (M. L. Anderson 2003; Chemero 2009; Clark 1997; 1998). Central to the picture of cognition offered there is the simple point that organisms evolve in 250

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a particular environment to meet the particular survival challenges that their environment poses. Situated/embodied cognition emphasizes that the solutions to these problems often rely in part on features of the environments themselves; for example, by adopting heuristics and learning biases that reflect some of the environments’ structural invariants (Gigerenzer et al. 1999; Gilovitch et al. 2002). One such useful feature of most environments is that they don’t pose all their problems all at once – inclement weather rarely comes along with predator abundance, pressing mating opportunities, and food shortages, for instance. And often when there are competing opportunities or challenges, there will be a clear priority. Thus, an organism with massively redeployed circuitry can generally rely on the temporal structure of events in its environment to minimize interference. Were this environment-organism relationship different – or if it were to change – then neural reuse does predict that increased interference will be one likely result. Interestingly, contemporary humans encounter just such a changed organism-environment relationship in at least two arenas, and the effect of reused circuitry can often be seen as a result: First, in the labs of some cognitive scientists, who carefully engineer their experiments to exploit cognitive interference of various sorts; and, second, at the controls of sophisticated machinery, where the overwhelming attentional demands have been observed to cause massive processing bottlenecks, often with dangerous or even deadly results (Fries 2006; Hopkin 1995). It is no coincidence that, in addition to designing better human-machine interfaces, one important way of minimizing the problems caused by processing bottlenecks is to engineer the environment, including, especially, changing its task configuration and social structure, for instance by designing more efficient teams (Hutchins 1995). 3.2. ACT-R

At the core of ACT-R is the notion of a cognitive architecture, “a specification of the structure of the brain at a level of abstraction that explains how it achieves the function of the mind” (J. R. Anderson 2007, p. 7). ACT-R is explicitly modular. As of ACT-R 6.0, it consisted of eight functionally specialized, domain-specific, relatively encapsulated, independently operating, and separately modifiable components. Given J. R. Anderson’s definition of a cognitive architecture, it might seem to directly follow that ACT-R is committed to the notion that the brain, too, consists of functionally specialized, domain-specific, relatively encapsulated, independently operating, and separately modifiable regions that implement the functional modules of the ACT-R model. Certainly, recent experiments meant to associate ACT-R components with specific brain regions encourage this impression (J. R. Anderson 2007; J. R. Anderson et al. 2007). As he argues: As discussed above, modular organization is the solution to a set of structural and functional constraints. The mind needs to achieve certain functions, and the brain must devote local regions to achieving these functions. This implies that if these modules reflect the correct division of the functions of the mind, it should be possible to find brain regions that reflect their activity. Our lab has developed a mapping of the eight modules . . . onto specific brain regions . . . (J. R. Anderson 2007, p. 74)

Anderson: Neural reuse: A fundamental organizational principle of the brain Given that neural reuse implies that anatomical modularity is false (see sect. 2.1), success in assigning ACT-R modules to specific brain regions would seem to be a problem for neural reuse, and evidence for neural reuse would appear to create problems for ACT-R. But the conclusion does not follow quite so easily as it seems. First, ACT-R does not strictly imply anatomical modularity. ACT-R is committed to the existence of functional modules, and to the existence of elements of the brain that implement them. If it turned out that activity in the ACT-R goal module was a better fit to the coordinated activity of some non-contiguous set of small brain regions than it was to the anterior cingulate (to which they currently have the goal module mapped), then this would count as progress for ACT-R, and not a theoretical setback. Similarly, if it turned out that some of the brain regions that help implement the goal module also help implement the imaginal module, this would pose no direct challenge to ACT-R theory.7 Therefore, although J. R. Anderson is at pains to deny he is a functionalist – not just any possible mapping of function to structure will count as a success for ACT-R – there is a good deal of room here for alternatives to the simple 1 : 1 mapping that he and other ACT-R theorists are currently exploring. For its part, neural reuse predicts that the best fit for ACT-R modules, or any other highlevel functional components, is much more likely to be some cooperating complex of multiple brain regions than it is a single area, and that brain regions involved in implementing one ACT-R function are likely to be involved in implementing others as well. Interestingly, this is more or less what J. R. Anderson et al. (2007) found. For every task manipulation in their study, they found several brain regions that appeared to be implicated. And every one of their regions of interest was affected by more than one factor manipulated in their experiment. Thus, despite their methodological commitment to a 1:1 mapping between modules and brain regions, J. R. Anderson et al. (2007) are quite aware of the limitations of that approach: Some qualifications need to be made to make it clear that we are not proposing a one-to-one mapping between the eight regions at the eight functions. First, other regions also serve these functions. Many areas are involved in vision and the fusiform gyrus has just proven to be the most useful to monitor. Similarly, many regions have been shown to be involved in retrieval, particularly the hippocampus. The prefrontal region is just the easiest to identify and seems to afford the best signal-to-noise ratio. Equally, we are not claiming these regions only serve one function. This paper has found some evidence for multiple functions. For instance, the motor regions are involved in rehearsal as well as external action. (J. R. Anderson et al. 2007, pp. 213– 14)

Here, the regulative idealization promoted by decomposition and localization may have unduly limited the sorts of methodological and inferential tools that they initially brought to bear on the project. As noted already in section 1, one of the contributions neural reuse may be able to make to cognitive science is an alternate idealization that can help guide both experimental design and the interpretation of results (M. L. Anderson et al. 2010). Going forward there is at least one other area where we can expect theories of neural reuse and modular theories like ACT-R to have significant, bidirectional critical

contact. Right now, ACT-R is not just theoretically, but also literally modular: it is implemented as a set of independent and separately modifiable software components. It does not appear, however, that separate modifiability is theoretically essential to ACT-R (although it is no doubt a programming convenience). Therefore, implementing overlaps in ACT-R components in light of the evidence from neuroimaging and other studies of the sort recounted here is likely to offer scientific opportunities to both research communities (see Stewart & West 2007 for one such effort). For example, overlaps in implementation might offer a natural explanation and a convenient model for certain observed instances of cognitive interference, such as that between language and motor control (Glenberg & Kaschak 2002) or between memory and audition (Baddeley & Hitch 1974), helping to refine current hypotheses regarding the causes of the interference. The ACT-R community is already investigating similar cases, where different concurrent tasks (dialing the phone while driving) require the use of the same ACT-R module, and thus induce performance losses (Salvucci 2005). Altering ACT-R so that different modules share component parts might enable it to model some cognitive phenomena that would otherwise prove more difficult or perhaps impossible in the current system, such as the observation that object manipulation can improve reading comprehension (Glenberg et al. 2007). Finally, observations of interference in a modified ACT-R but not in human data, might suggest that the ACT-R modules did not yet reflect the correct division of the mind’s functions. Such conflicts between model and data could be leveraged to help ACT-R better approximate the high-level functional structure of the mind.

3.3. Classic parallel distributed processing

It is of course true that from a sufficiently abstract perspective, the idea of neural reuse in cognitive functioning is nothing new. It has been a staple of debates on brain architecture at least since the advent of parallel distributed processing (PDP) models of computation (Rummelhart & McClelland 1986). For one widely cited example, consider the following from Mesulam (1990). He writes: A central feature of networks is the absence of a one-to-one correspondence among anatomical site, neural computation and complex behavior . . . Figure [2] implies that each behavior is represented in multiple sites and that each site subserves multiple behaviors, leading to a distributed and interactive but also coarse and degenerate (one-to-many and many-toone) mapping of anatomical substrate onto neural computation and computation onto behavior. This distributed and degenerate mapping may provide an advantage for computing complex and rapid cognitive operations and sets the network approach sharply apart from theories that postulate a nondegenerate one-to-one relationship between behavior and anatomical site. (Mesulam 1990, pp. 601– 602)

Broadly speaking, neural reuse theories are one of a family of network approaches to understanding the operation of the brain. They share with these an emphasis on cooperative interactions, and an insistence on a nonmodular, many-to-many relationship between neural-anatomical sites and complex cognitive functions/behaviors. But there are also some important differences that set neural reuse apart. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Figure 2. Detail of Figure 3 from Mesulam (1990). Reprinted with permission of the author.

First is a better appreciation of the computational work that can be done by very small groups of, or even individual, neurons (Koch & Segev 2000). Neural reuse theories all agree that most of the interesting cognitive work is done at higher levels of organization, but they also emphasize that local circuits have specific and identifiable functional biases. In general, these models make a strong distinction between a “working” – whatever specific computational contribution local anatomical circuits make to overall function – and a “use,” the cognitive purpose to which the working is put in any individual case. For neural reuse theories, anatomical sites have a fixed working, but many different uses. In contrast, note that in Figure 2 “neural computations” are located at Plane 2, parallel distributed processing. This reflects the belief that computational work can only be done by fairly large numbers of neurons, and that responsibility for this work can only be assigned to the network as a whole. Put differently, on PDP models there are no local workings. Classic PDP models are indeed a powerful way to understand the flexibility of the brain, given its reliance on relatively simple, relatively similar, individual elements. But the trouble for PDP models in this particular case is that there is no natural explanation for the data on increasing scatter of recently evolved functions, nor for the data on the cross-cultural invariance in the anatomical locations of acquired practices (see sect. 6.3). Indeed, on PDP models, investigating such matters is not even a natural empirical avenue to take. This represents a significant distinction between PDP and neural reuse. Other important differences between neural reuse and classic PDP models flow from the above considerations, including the way neural reuse integrates the story about the cognitive architecture of the brain into a natural story about the evolution and development of the brain. In a sense, neural reuse theories make some more specific claims than generalized PDP – not just that the brain is a kind of network, but that it is a kind of network with functional organization at more levels than previously thought. 252

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As can be seen already in the evidence outlined above, and will be seen in greater detail in sections 5 and 6, this specificity has led to some interesting and empirically testable implications for the brain’s overall functional organization. 3.4. Contemporary parallel distributed processing models

More contemporary versions of network models, such as Leabra (O’Reilly 1998; O’Reilly & Munakata 2000) tend to be composed of densely connected, locally specialized networks that are sparsely connected to one another (see Fig. 3).

Figure 3. Overview of the Leabra architectural organization. Reprinted from Jilk et al. (2008) with permission of the authors.

Anderson: Neural reuse: A fundamental organizational principle of the brain In one sense, Leabra appears to be more compatible with neural reuse than classic PDP models are, as Leabra explicitly allows for regional functional biases. But insofar as this new architecture reflects the influence of the selectivity assumption, and represents a more modularist approach to understanding the brain, then there are potentially the same points of conflict with Leabra as there are with those theories. Consider the following, from a recent paper describing Leabra:

connectionist – and whether theories falling on one or another side of each dichotomy are compatible with the notion of neural reuse will ultimately depend on how their advocates interpret the theories, and how flexible their implementations turn out to be.

The brain is not a homogenous organ: different brain areas clearly have some degree of specialized function. There have been many attempts to specify what these functions are, based on a variety of theoretical approaches and data. In this paper, we summarize our approach to this problem, which is based on the logic of computational tradeoffs in neural network models of brain areas. The core idea behind this approach is that different brain areas are specialized to satisfy fundamental tradeoffs in the way that neural systems perform different kinds of learning and memory tasks. (Atallah et al. 2004, p. 253)

One of the most successful theoretical paradigms in cognitive science has been the conceptual metaphor theories originating with Lakoff and Johnson (1980; 1999) and extended by many others, perhaps most notably Fauconnier and Turner (2002).8 As is well known, conceptual metaphor theories suggest that cognition is dominated by metaphor-based thinking, whereby the structure and logical protocols of one or more domains, combined in various ways, guide or structure thinking in another. For a simple case, consider the Love Is War mapping taken from Lakoff and Johnson (1980; 1999). When employing this metaphorical mapping, people use their understanding of war – of how to interpret events and how to respond to them – to guide their thinking about love: One fights for a partner, makes advances, fends off suitors, or embarks on a series of conquests. Similarly, the Life Is a Journey mapping allows people to leverage their extensive experience and competence in navigating the physical world in order to facilitate planning for life more generally: We plan a route, overcome obstacles, set goals, and reach milestones. The theory has been widely discussed and tested, and enjoys a raft of supporting evidence in linguistics and cognitive psychology. A natural question that arises for such theories, however, is how the structured inheritance from one domain to another is actually achieved by the brain. Is it done abstractly, such that mental models (Gentner & Stevens 1983; Johnson-Laird 1983) of war or navigation are used as prototypes for building other models of love or life? Or is there a more basic biological grounding, such that the very neural substrates used in supporting cognition in one domain are reused to support cognition in the other? Although some researchers favor the first possibility – notably Lera Boroditsky (e.g., Boroditsky & Ramscar 2002) – it seems fair to say that the greater effort has been focused on investigating the second. This is at least in part because the debate over the biological basis of conceptual metaphors dovetails with another over the nature and content of cognitive representations – symbols, concepts, and (other) vehicles of thought – that has also played out over the last twenty years or so. At issue here is the degree to which the vehicles of thought – our mental carriers of meaning – are tied to sensory experience (Barsalou 2008; 1999). Concept empiricists (as they are called in philosophy) or supporters of modal theories of content (as they are called in psychology) are generally committed to some version of the thesis that “the vehicles of thought are reactivated perceptual representations” (Weiskopf 2007, p. 156). As one of the core statements of the modal position puts it, perceptual symbols, which “constitute the representations that underlie cognition,” are “record[s] of the neural activation that arises during perception” (Barsalou 1999, pp. 578, 583; see Prinz 2002 for a general discussion). This position is meant to contrast

There is nothing here that explicitly commits the authors to the idea that large brain regions are dedicated to specific tasks or cognitive domains – something the data presented here throw into question – although that is certainly one possible reading of the passage. Moreover, O’Reilly (1998) tends to focus more on modeling processes over modeling parts, an approach that need not commit one to a specific story about how and where such processes are implemented in the brain – it needn’t be the case that individual brain regions implement the processes being modeled, for instance. And yet, O’Reilly and his collaborators have assigned these processes to specific regions: The large-scale architectural organization of Leabra includes three major brain systems: the posterior cortex, specialized for perceptual and semantic processing using slow, integrative learning; the hippocampus, specialized for rapid encoding of novel information using fast arbitrary learning; and the frontal cortex/basal ganglia complex, specialized for active and flexible maintenance of goals and other context information, which serves to control or bias processing throughout the system. (Jilk et al. 2008, p. 204)

And, in fact, the Leabra team has gone further than this by recently integrating Leabra with ACT-R to form the SAL architecture: When the ACT-R and Leabra research teams started to work together in 2006, they came to a startling realization: the two theories, despite their origins in virtually opposite paradigms (the symbolic and connectionist traditions, respectively) and widely different levels of abstraction, were remarkably similar in their view of the overall architecture of the brain. (Jilk et al. 2008, p. 205)

So it is not clear just what commitments Leabra has to modularity and localization. As with ACT-R, there doesn’t seem to be anything essential to Leabra that would prevent it from explicitly incorporating neural reuse as one of its organizing principles. In particular, the functional specializations being ascribed to the brain regions mentioned are general enough to plausibly have many different cognitive uses, as predicted by neural reuse theories. But, as with ACT-R, more research will be needed before it becomes clear just how compatible these visions for the functional organization of the brain in fact are. The notion of neural reuse cuts across some old divisions – localization versus holism; modular versus

4. Other theories predicting forms of neural reuse

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Anderson: Neural reuse: A fundamental organizational principle of the brain with a rationalist or amodal one in which the vehicles of thought are inherently nonperceptual, abstract, logical, linguistic, or computational structures for which (as the classic semiotics line goes) the relation between signifier and signified is established arbitrarily (see, e.g., Fodor 1975; Fodor & Pylyshyn 1988). In the case of both debates, it looked as if information about what neural resources were actually deployed to support cognitive tasks could provide evidence favoring one side or another. If planning a task used brain regions different from those used in planning (or imagining) a journey, then this would be prima facie evidence against the notion that the two were related via direct neural grounding. Similarly, if perceptual tasks and cognitive tasks appeared to be handled by distinct brain regions, this would appear to favor the amodal view. In the event, a series of early findings bolstered the case for modal concepts, on the one hand, and for the idea that direct neural substrates supported metaphorical mappings, on the other. For example, a series of papers from the labs of Antonio Damasio and Alex Martin offered evidence that verb retrieval tasks activated brain areas involved in motor control functions, and naming colors and animals (that is, processing nouns) activated brain regions associated with visual processing (Damasio & Tranel 1993; Damasio et al. 1996; Martin et al. 1995; 1996; 2000). Similarly, it was discovered that perceiving manipulable artifacts, or even just seeing their names, activates brain regions associated with grasping (Chao & Martin 2000). All this suggested that class concepts like HAMMER, RED, and DOG might be stored using a sensory and/or motor code, and, more generally, that high-level, conceptual-linguistic understanding might involve the reactivation of perceptuomotor experiences. This dovetailed nicely with the general idea behind direct neural support for metaphorical mappings, whereby understanding in one domain would involve the reactivation of neural structures used for another. Thus, the finding that mental planning can activate motor areas even when the task to be planned itself involves no motor activity (Dagher et al. 1999) has long been taken to support the case that mappings like Life Is a Journey are mediated by the direct sharing of neural resources by both domains.9 It seems fair to say that these early discoveries prompted a much larger effort to uncover the neural underpinnings of high-level cognitive functions, one specifically focused on revealing the ways in which these underpinnings were shared with those of the sensorimotor system. The result is literally hundreds of studies detailing the various ways in which neural substrates are shared between various cognitive functions. A representative sample of these studies will be reviewed further on in sections 4.1 through 4.6, but to presage the argument to follow: The effort to uncover instances of neural reuse has been so successful that even a cursory examination of the breadth and frequency of reuse suggests that there is much more reuse than can be accounted for by modal concepts or conceptual metaphor theory. Any explanation of the phenomenon must therefore articulate a broader framework within which the prevalence of reuse naturally fits, and which in turn can explain such individual cognitive phenomena.10 We will review some of the evidence for this claim in the next subsections. 254

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4.1. Reuse of motor control circuits for language

A great deal of the effort to discover the specific neural underpinnings of higher cognitive functions has focused on the involvement of circuits long associated with motor control functions. In a typical example of this sort of investigation, Pulvermu¨ller (2005) reports that listening to the words “lick,” “pick,” and “kick” activates successively more dorsal regions of primary motor cortex (M1). The finding is consistent both with the idea that comprehending these verbs relies on this motor activation, insofar as the concepts are stored in a motoric code, and also with the idea that understanding these verbs might involve (partial) simulations of the related actions. Either interpretation could easily be used as part of the case for concept empiricism. Similarly, Glenberg and Kaschak (2002) uncover an interesting instance of the entanglement of language and action that they call the “action-sentence compatibility effect” (ACE). Participants are asked to judge whether a sentence makes sense or not and to respond by pressing a button, which requires a move either toward or away from their body. In one condition “yes” is away and “no” is toward; another condition reverses this. The sentences of interest describe various actions that would also require movement toward or away, as in “put a grape in your mouth,” “close the drawer,” or “you gave the paper to him.” The main finding is of an interaction between the two conditions, such that it takes longer to respond that the sentence makes sense when the action described runs counter to the required response motion. More striking, this was true even when the sentences described abstract transfers, such as “he sold his house to you,” which imply a direction without describing a directional motor action. Following the reasoning originally laid out by Sternberg (1969), an interaction between two manipulated factors implies at least one shared component between these two different processes – movement and comprehension. A likely candidate for this component would be a neural circuit involved in motor control, a supposition confirmed by Glenberg (2008b).11 Thus, this seems another clear case in which motor control circuits are involved in, and perhaps even required for, language comprehension, whether via simulation (e.g., in the concrete transfer cases), metaphorical mapping (e.g., in the abstract transfer cases), or by some other mechanism. Glenberg has suggested both that the effect could be explained by the activation of relevant action schemas (Glenberg et al. 2008b) and by the activation and combination of appropriate affordances (Glenberg & Kaschak 2002; Glenberg et al. 2009). Whatever the precise mechanism involved, the finding has been widely interpreted as support for both concept empiricism and for conceptual metaphor theory (although see M. L. Anderson 2008c for a dissent).

4.2. Reuse of motor control circuits for memory

Another interesting description of the motor system’s involvement in a different cognitive domain comes from Casasanto and Dijkstra (2010), who describe bidirectional influence between motor control and autobiographical memory. In their experiment, participants were asked to retell memories with either positive or negative valence, while moving marbles either upward or downward from

Anderson: Neural reuse: A fundamental organizational principle of the brain one container to another. Casasanto and Dijkstra found that participants retrieved more memories and moved marbles more quickly when the direction of movement was congruent with the valence of the memory (upward for positive memories, downward for negative memories). Similarly, when participants were asked simply to relate some memories, without prompting for valence, they retrieved more positive memories when instructed to move marbles up, and more negative memories when instructed to move them down. Because the effect is mediated by a mapping of emotional valence on a spatial schema, the finding seems to most naturally support conceptual metaphor theory. The fact that the effect was bidirectional – recounting memories affected movement and movement affected memory retrieval – is a striking detail that seems to suggest direct neural support for the mapping.12 4.3. Reuse of circuits mediated by spatial cognition

Many of the apparent overlaps between higher-order cognition and sensorimotor systems appear to be mediated by spatial schemas in this way. For example, Richardson et al. (2003) report that verbs are associated with meaningspecific spatial schemas. Verbs like “hope” and “respect” activate vertical schemas, whereas verbs like “push” and “argue” activate horizontal ones. As the authors put it, “language recruits spatial representations during realtime comprehension.” In a similar vein, Casasanto and Boroditsky (2008) suggest that our mental representations of time are built upon the foundations of our experience with space. These findings appear to provide strong and relatively unproblematic support for conceptual metaphor theory, and perhaps also for a generic theory of concept empiricism, according to which the content of our concepts is grounded in (but does not necessarily constitute a simulation or reactivation of) sensorimotor experiences. On the other hand, even when simulation is an important aspect of the reuse of resources between different domains, it does not always play the functional role assigned it by concept empiricism or conceptual metaphor theory. For some time, there has been growing evidence that doing actions, imagining actions, and watching actions done by others all activated similar networks of brain regions (Decety et al. 1990; Decety et al. 1997; Jeannerod 1994). This has suggested to many that social cognition – understanding the actions and intentions of others – could involve simulating our own behaviors, a notion that attracted even more widespread interest after the discovery of mirror neurons (Decety & Gre`zes 1999; Gallese et al. 1996; Gallese & Goldman 1998; Rizzolati et al. 1996). The trouble for concept empiricism and conceptual metaphor theory is that the logic governing the reuse of resources for multiple purposes is quite different in this case. Here, the idea is that circuits associated with behavioral control can be used to build predictive models of others, by inputting information about another agent into the system that would normally be used to guide one’s own actions (and reactions). Although it could be argued that using simulation in support of such “mindreading” (Gallese & Goldman 1998) requires a kind of metaphorical mapping (he is like me in relevant ways), in fact this is simply a necessary assumption to make the strategy sensible, and does not play the role of a domainstructuring inheritance.

Even some of the evidence for the reuse of spatial operations in other cognitive domains – which has been a mainstay of research into concept empiricism and conceptual metaphor theory – suggests the existence of more kinds of reuse than can be accounted for by these theoretical frameworks. Consider just a few of the various manifestations of the spatial-numerical association of response codes (SNARC) effect (Dehaene et al. 1993): (1) When participants are asked to judge whether numbers are even or odd, responses are quicker for large numbers when made on the right side of space (canonically with the right hand, although the effect remains if responses are made while hands are crossed) and quicker for smaller numbers when responses are made on the left side of space. (2) Participants can accurately indicate the midpoint of a line segment when it is composed of neutral stimuli (e.g., XXXXX), but are biased to the left when the line is composed of small numbers (e.g., 22222 or twotwotwo) and to the right when the line is composed of large numbers (e.g., 99999 or nineninenine). (3) The presentation of a number at the fixation point prior to a target detection task will speed detection on the right for large numbers and to the left for small numbers. Hubbard et al. (2005) hypothesize that the SNARC effect can be accounted for by the observed reuse in numeric cognition of a particular circuit in left inferior parietal sulcus that plays a role in shifting spatial attention. Briefly, the idea is that among the representational formats we make use of in numerical cognition there is a mental “number line,” on which magnitudes are arrayed from left to right in order of increasing size. Once numerals are arrayed in this format, it is natural to reuse the circuit responsible for shifting spatial attention for the purpose of shifting attention between positions on this line. The resulting magnitude-influenced attentional bias can explain the SNARC effect. This redeployment of visuo-spatial resources in support of alternate cognitive uses is somewhat difficult to explain from the standpoint of either concept empiricism or conceptual metaphor theory. In these examples, the effects would not be accounted for by the fact that numbers might be grounded in or involve simulations of basic sensorimotor experience, nor is it immediately obvious what metaphorical mapping might be implicated here. In fact, if the reuse of spatial schemas were in support of some semantically grounding structural inheritance from one domain to the other, we would expect the numbers to be arrayed vertically, with magnitude increasing with height. Instead, the reuse in this case appears driven by more abstract functional considerations. When doing certain numerical tasks, a number line is a useful representational format, and something like the visuo-spatial sketchpad (Baddeley 1986) offers a convenient and functionally adequate storage medium. Similarly, reusing the spatial shifting mechanism is a sensible choice for meeting the functional requirements of the task, and need not ground any semantic or structural inheritance between the domains. 4.4. Reuse of circuits for numerical cognition

In fact, several such examples can be found in the domain of numerical cognition. Zago et al. (2001) found increased activation in the premotor strip in a region implicated in BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Anderson: Neural reuse: A fundamental organizational principle of the brain finger representation during multiplication performance compared to a digit reading condition. Similar findings were reported by Andres et al. (2007), who found that hand motor circuits were activated during adults’ number processing in a dot counting task. That these activations play a functional role in both domains was confirmed by Roux et al. (2003), who found that direct cortical stimulation of a site in the left angular gyrus produced both acalculia and finger agnosia (a disruption of finger awareness), and by Rusconi et al. (2005), who found that repetitive Transcranial Magnetic Stimulation (rTMS) over the left angular gyrus disrupted both magnitude comparison and finger gnosis in adults. Here again, this reuse of a basic sensorimotor function in an alternate cognitive domain does not seem to follow the logic of conceptual metaphor theory or concept empiricism. These theories are not making the claim that magnitudes inherit their meanings from finger representations, nor is any mathematical metaphor built in any straightforward way on our finger sense. Rather, the idea is that this neural circuit, originally developed to support finger awareness, is offering some functionally relevant resource in the domain of numerical cognition. For instance, Butterworth (1999c) suggests that the fingers provide children a useful physical resource for counting, with the neural result that the supporting circuits now overlap, while Penner-Wilger and Anderson (2008; submitted) suggest instead that the circuit in question might itself offer useful representational resources (such as a storage array).13 This is not to question the notion that mathematical concepts and procedures are in some way grounded in sensorimotor experience (Lakoff & Nu´n˜ez 2000), but this specific overlap in neural circuitry isn’t straightforward to explain in the context of such grounding, nor is it anything that would have been predicted on the basis of either conceptual metaphor theory or concept empiricism. In fact, proponents of conceptual metaphor theory in mathematics tend to focus on relatively higher-level concepts like sets and investigate how our understanding of them is informed by such things as our experience with physical containers. A similar argument can be made when considering the interrelations of speech and gesture, and the cognitive importance of the latter (see, e.g., Goldin-Meadow 2003). According to Goldin-Meadow (2003), gesture is typically used not just to signal different moments in the learning process (e.g., to index moments of decision or reconsideration in a problem-solving routine), but also appears to have utility in advancing the learning process by providing another representational format that might facilitate the expression of ideas currently unsuited (for whatever reason) to verbal expression. The motor control system is here being used for a specific cognitive purpose not because it is performing semantic grounding or providing metaphorically guided domain structuring, but because it offers an appropriate physical (and spatiotemporal) resource for the task. 4.5. Reuse of perceptual circuits to support higher-order cognition

There are examples of the reuse of circuits typically associated with perception that also make the same point. Although there have certainly been studies that appear 256

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to unproblematically support concept empiricism – for example, Simmons et al. (2007) report the discovery of a common neural substrate for seeing colors, and for knowing about (having concepts for) color – other studies suggest that such cases represent only a small subset of a much broader phenomenon. Consider one of the earliest and most discussed cases of the reuse of neural circuits for a new purpose, the Baddeley and Hitch model of working memory (Baddeley & Hitch 1974; 1994; Baddeley 1986; 1995). One strategy for remembering the items on a grocery list or the individual numbers in a phone number involves (silently) saying them to one’s self (producing a “phonological loop”), which engages brain areas typically used both in speech production and in audition. A pattern of findings supports the existence of a phonological loop, and the engagement of both inner “speaking” and inner “hearing” to support working memory (see Wilson 2001 for a review). First, there is poor recall of similar sounding terms; second, there is poor recall of longer words; third, there is poor recall if the subject is made to speak during the maintenance period; and fourth, there is poor recall when the subject is exposed to irrelevant speech during the maintenance period. Moreover, imaging studies have found that such memory tasks cause activation in areas typically involved in speech production (Broca’s area, left premotor cortex, left supplementary motor cortex, and right cerebellum) and in phonological storage (left posterior parietal cortex) (Awh et al. 1996). In this interesting and complicated case, we have something of a triple borrowing of resources. First is the use of a culturally specific, acquired representational system – language – as a coding resource, and second is the application of a particular learned skill – silent inner speech – as a storage medium. These two together imply the third borrowing – of the neural resources used to support the first two functions. And note that all of this borrowing is done in support of what is likely an enhancement of a basic evolved function for storing small amounts of information over short periods. This raises the obvious question of whether and to what degree evolutionary pressures might have shaped the language system so that it was capable of just this sort of more general cognitive enhancement (Carruthers 2002). In any case, it seems clear that this sort of borrowing is very hard to explain in terms of concept empiricism or conceptual metaphor theory. In the case of sensorimotor coding in working memory, the phonological loop is not metaphorically like speech; rather, it is a form of speech. In this, it is another instance of a straightforward functional redeployment – the reuse of a system for something other than its (apparent) primary purpose because it happens to have an appropriate functional structure. 4.6. Reuse is not always explained by conceptual metaphor theory or concept empiricism

These various examples suggest something along the following lines: One of the fundamental principles guiding reuse is the presence of a sufficient degree of functional relatedness between existing and newly developing purposes. When these functional matches result in the reuse of resources for both purposes, this history

Anderson: Neural reuse: A fundamental organizational principle of the brain sometimes – but not always – reveals itself in the form of a metaphorical mapping between the two task domains, and sometimes, but not always, results in the inheritance or grounding of some semantic content. This way of thinking makes conceptual metaphors and “grounded” symbols into two possible side-effects of the larger process of reuse in cognition. It also muddies the causal story a bit: Planning is like locomotion because it inherits the structure of the existing domain via neural overlap; but planning also overlaps with the neural implementation base of locomotion to the degree that it is like locomotion. The suggestion here is not that planning or communication or any other cognitive function has some predetermined Platonic structure that entirely reverses the causal direction typically supposed by conceptual metaphor theory. Rather, the idea is to point out the need to be open to a more iterative story, whereby a cognitive function finds its “neural niche” (Iriki & Sakura 2008) in a process codetermined by the functional characteristics of existing resources, and the unfolding functional requirements of the emerging capacity (Deacon 1997). Consider, in this regard, the particular phonemic character of human speech. A phoneme is defined by a certain posture of the vocal apparatus, and is produced by moving the apparatus toward that posture while making some noise (Fowler et al. 1980). Why should speech production be this way? In an article outlining their discoveries regarding the postural organization of the motor-control system, Graziano et al. (2002b) write: One possibility is that the mechanisms for speech were built on a preexisting mechanism for motor control, one that emphasized the specification of complex, behaviorally useful postures. When we stimulated the ventral part of the precentral gyrus, in the mouth and face representation, we often caused the lips and tongue to move toward specific postures (Graziano et al. 2002a). For example, at one site, stimulation caused the mouth to open about 2cm and the tongue to move to a particular location in the mouth. Regardless of the starting posture of the tongue or jaw, stimulation evoked a movement toward this final configuration. This type of posture may be useful to a monkey for eating, but could also be an evolutionary precursor to the phoneme. (Graziano et al. 2002b, p. 355)

There are certainly functional characteristics that a unit of acoustic communication must have in order to adequately perform its communicative purpose, and not just any neural substrate would have had the required characteristics. But there remain degrees of freedom in how those characteristics are implemented. Speech production, then, developed its specific phonemic character as the result of the circuits on which it was built. Had the motor control system been oriented instead around (for example) simple, repeatable contractions of individual muscles – or had there been some other system with these functional characteristics available for reuse as acoustic communication was evolving – the result of the inheritance might have been a communication code built of more purely temporal elements, something closer to Morse code.14 Finally, consider what may be a case not of the reuse of a basic sensorimotor area for higher cognitive functions, but rather the reverse. Broca’s area has long been associated with language processing, responsible for phonological processing and language production, but what has recently begun to emerge is its functional complexity (Hagoort

2005; Tettamanti & Weniger 2006). For instance, it has been shown that Broca’s area is involved in many different action- and imagery-related tasks, including movement preparation (Thoenissen et al. 2002), action sequencing (Nishitani et al. 2005), action recognition (Decety et al. 1997; Hamzei et al. 2003; Nishitani et al. 2005), imagery of human motion (Binkofski et al. 2000), and action imitation (Nishitani et al. 2005). Note that Mu¨ller and Basho (2004) suggest that these functional overlaps should not be understood as the later reuse of a linguistic area for other purposes, but are rather evidence that Broca’s area already performed some sensorimotor functions that were prerequisites for language acquisition, and which made it a candidate for one of the neural building blocks of language when it emerged. That seems reasonable; but on the other hand, Broca’s area is also activated in domains such as music perception (Tettamanti & Weniger 2006). While it is possible that this is because processing music requires some of the same basic sensorimotor capacities as processing language, it seems also possible that this reuse was driven by functional features that Broca’s acquired as the result of its reuse in the language system, and thus by some more specific structural similarity between language and music (Fedorenko et al. 2009). Whatever the right history, this clearly represents another set of cases of functional reuse not explained by conceptual metaphor theory or concept empiricism. Assuming the foregoing is sufficient to establish the existence of at least some cases of neural reuse that cannot be accounted for by these theoretical frameworks alone, the question naturally arises as to whether these anomalous cases should be dealt with by post-hoc elaborations of these theories (and/or by generating one or a few similarly specific theories), or whether this is a situation that calls for a global theory of reuse that supersedes and at least partially subsumes these existing frameworks. Far be it from me to argue a priori that one tack must be the correct one to take – science works best when we pursue multiple competing research paths – but one thing it might be useful to know when deciding how to spend one’s research time is exactly how widespread neural reuse is. That is, the more widespread reuse appears, and the more instances of reuse that can be identified that do not involve the sensorimotor system, the stronger the justification would seem for trying to formulate a more global theory of neural reuse. 5. Further evidence that neural reuse is a pervasive feature of brain organization Given the success of the theoretical frameworks just mentioned, as well as the growing interest in embodied cognition (M. L. Anderson 2003; Chemero 2009; Clark 1997; 1998), it is quite easy to find studies reporting that the neural implementations of higher cognitive functions overlap with those of the sensorimotor system. Indeed, this was the theme of a recent Attention and Performance Symposium, culminating in the 27-essay volume Sensorimotor Foundations of Higher Cognition (Haggard et al. 2008). In contrast, there are only a few examples of reuse not involving the sensorimotor system that are reported as such in the literature. This fact would seem to favor the post-hoc elaboration approach to explaining BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Anderson: Neural reuse: A fundamental organizational principle of the brain the sorts of cases outlined above. On the other hand, the lack of such reports could simply be because people are not looking in the right place, or looking in the right way; after all, nobody is trying to establish a theory of attention-grounded, mathematics-grounded, or musicgrounded cognition (as interesting as that sounds!). Absence of evidence of these cases, this is to say, is not evidence of absence. A typical literature search, then, will not help answer our question. The literature can, however, be used in a somewhat different way. There are many, many thousands of studies in the neuroimaging literature that purport to uncover the neural underpinnings of various cognitive functions. If one were to compile a number of these studies in various task domains, one could ask, for each region of the brain, whether it supported functions in multiple domains, and whether such reuse was typically limited to regions of the brain implicated in supporting sensorimotor tasks. The NICAM database (M. L. Anderson et al. 2010) currently contains information from 2,603 fMRI studies reported in 824 journal articles. All the studies involve healthy adults and use a within-subjects, subtractionbased, whole-brain design. That is, for all the studies in the database, brain activity during an experimental task was observed over the whole brain (not just a region of interest), and then compared to and subtracted from activity observed in the same participant during a control task. The logic of subtraction method is such that it should uncover only the regions of activation that support the specific mental function that best captures the difference between the experimental and control task. The neural activations supporting the mental operation that the two tasks have in common – the visual process allowing one to see the stimuli in a language task, for example – should be subtracted out. The database lists, among other things, the locations of the 21,553 post-subtraction fMRI activations observed during those 2,603 studies – that is, the regions of activation that are purported to specifically support those 2,603 mental operations. These features make the database ideal for investigating whether and to what degree specific brain regions support multiple functions across various task domains. The general methodology for this sort of study is simple and straightforward. First, choose a spatial subdivision of the brain, then see which experiments, in which (and how many) domains, showed activity in each of the regions. To get the results reported in the next paragraph, below, I used the same 998 anatomical regions of interest (ROIs) used by Hagmann et al (2008).15 The study was restricted to the following eleven task domains: three action domains – execution, inhibition, and observation – two perceptual domains – vision and audition – and six “cognitive” domains – attention, emotion, language, mathematics, memory, and reasoning.16 Any study that was assigned to more than one domain was excluded. Activations were assigned to the ROI with the closest center; any activation that was more than 13mm from the center of one of the ROIs was excluded. This left 1,469 experiments collectively reporting 10,701 eligible activations.17 There were 968 regions that were active in at least one experiment (and thus in one domain). Of these, 889 (91.8%) were active in at least two domains – that is, were reused at least once. On average, these 968 regions were active in 4.32 different domains (SD 1.99), and 555 258

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of the regions were active in action tasks, with 535 of these “action” areas also active in an average of 3.97 (SD 1.58) non-action domains, and 530 active in an average of 3.16 (SD 1.23) cognitive domains. There were 565 regions active in perception tasks; 555 of these “perception” regions were also active in an average of 4.00 (SD 1.61) non-perception domains, and 550 were active in an average of 3.20 (SD 1.24) cognitive domains. There were 348 regions active in both action and perception tasks. On average, these were reused in 3.33 (SD 1.22) cognitive domains. There were also 196 regions not active in either perception or action tasks; 143 of these (72.96%) were active in two or more domains and averaged 2.97 (SD 0.95) domains. With all 196 regions included, the average is 2.43 (SD 1.19) of the six cognitive domains.18 Naturally, if one uses larger regions – for instance, the 66 cortical ROIs19 used by Hagmann et al (2008) – the average amount of reuse increases accordingly. All 66 regions were active in at least one domain; 65 (98.5%) were active in two or more domains.20 As noted already above, the 66 regions were active in an average of 9.09 (SD 2.27) different domains. The 60 regions active in action tasks were also active in an average 7.38 (SD 0.98) non-action domains and 5.5 (SD 0.81) cognitive domains. The 64 regions active in perception tasks were also active in 7.39 (SD 1.87) non-perceptual domains and 5.34 cognitive domains. The 59 regions active in both perception and action tasks were also active in an average of 5.53 (SD 0.80) other domains, and the 7 regions not active in both perception and action tasks were active in an average of 3.00 (SD 1.41) of the cognitive domains. Only one region was active in only cognitive tasks, and that region was active only in memory. These data appear to support the following claims: (1) Regions of the brain – even fairly small regions – are typically reused in multiple domains. (2) If a region is involved in perception tasks, action tasks, or both, it is more likely to be reused than if it is not involved in such tasks.21 (3) Regions not involved in such tasks are nevertheless more likely than not to be reused in multiple domains. Note that the way of counting adopted above makes the best possible case for the “action and perception are special” position, by classifying as an “action” or “perception” region every region that is active in any such task. But it seems unlikely that there are 60 large cortical “action areas” and 64 “perception areas” in the way this term is usually understood. If instead some of these regions in fact contain instances of the reuse of “cognitive” circuits22 for action or perception tasks, then this way of counting likely overestimates the relatively higher reuse frequency of action and perception circuits. That is, neural reuse appears to be a pervasive feature of the functional organization of the brain, and although circuits that support action and perception may be favored targets for reuse, reuse is by no means restricted to sensorimotor circuits. Therefore, the situation appears to call for an assimilative, global theory, rather than the elaboration of existing theoretical frameworks. 6. Global theories of neural reuse As mentioned at the outset, there are currently four candidates for a broad, general theory of neural reuse (or for the

Anderson: Neural reuse: A fundamental organizational principle of the brain core around which such a theory could be built): Gallese’s neural exploitation hypothesis, Hurley’s shared circuit model, Dehaene’s neuronal recycling hypothesis, and my massive redeployment hypothesis (already outlined in sect. 1.1 of this article). In this section, I will discuss each theory in turn and explore some of their similarities and differences. 6.1. Neural exploitation hypothesis

The neural exploitation hypothesis is a direct outgrowth of conceptual metaphor theory and embodied cognition, and largely sits at the intersection of these two frameworks. The main claim of the framework is that “a key aspect of human cognition is . . . the adaptation of sensory-motor brain mechanisms to serve new roles in reason and language, while retaining their original function as well.” (Gallese & Lakoff 2005, p. 456) This claim is the conclusion of an argument about the requirements of understanding that runs roughly as follows: 1. Understanding requires imagination. In the example most extensively developed by Gallese and Lakoff (2005), understanding a sentence like “He grasped the cup” requires the capacity to imagine its constituent parameters, which include the agent, the object, the action, its manner, and so on. 2. Imagination is simulation. Here, the neural exploitation hypothesis dovetails with concept empiricism in arguing that calling to mind individuals, objects, actions, and the like involves reactivating the traces left by perceiving, doing, or otherwise experiencing instances of the thing in question. 3. Simulation is therefore neural reuse. Simulation involves reuse of the same functional clusters of cooperating neural circuits used in the original experience(s). As much of the evidence for these claims has been laid out already in earlier sections, it won’t be recounted here. The reader will of course notice that the theory as stated is limited to the adaptation of sensorimotor circuits, and we have already seen that reuse in the brain is much more broad-based than this. This is indeed a drawback of the theory, but it is nevertheless included here for two reasons: first, because it has been expanded to include not just the case of concept understanding, but also of human social understanding (Gallese 2008); and, second, because it incorporates a detailed computational model for how the reuse of circuitry might actually occur, based on work by Feldman and Narayanan (2004). This model has broader applicability than is evidenced in the two main statements of the neural exploitation hypothesis (Gallese 2008; Gallese & Lakoff 2005). The core of the computational model is a set of schemas, which are essentially collections of features in two layers: descriptions of objects and events and instructions regarding them. These two layers are systematically related to one another and to the sensorimotor system, such that event schemas can be used both to recognize events and to guide their execution, and object schemas can be used both to recognize objects and also to guide actions with respect to them.23. The schemas are also connected to the conceptual system, such that the contents of our concepts are built from the same features that form the schemas. The general idea is that the features’ connections to the sensorimotor system give semantic substance to the

concepts, as well as a natural model for understanding as the activation of neurally (or, in the current case, neuralnetwork-ly) instantiated features and schemas. Like Gallese and Lakoff (2005), Feldman and Narayanan (2004) focus primarily on cases of understanding that can be directly (“He grabbed the cup”) or metaphorically (“He grabbed the opportunity”) mapped to basic perception-action domains. But there is no reason in principle that the model need be limited in that way. As the authors note, by adding layers of abstraction, one can move from concrete action execution plans to abstract recipes like mathematical algorithms. Given this flexibility, it seems that action schemas need not be limited to providing guidance for the manipulation of independent objects (whether concrete or abstract) but could presumably also become control systems for the manipulation of neural circuits. That is, the same action schema that might normally be used to control rhythmic speech production, could be reused to guide silent memory rehearsal, and more abstract schemas might form the basis of control systems for predictive modeling or other applications.24 Of course, this emendation would constitute a significant departure from the model as originally formulated.25. In particular, it would turn a system in which neural reuse was driven by grounding – the inheritance of semantic content from one level to another – into one in which reuse was driven by the need to create control systems for functionally relevant outcomes. Although it is far from clear that this switch precludes the possibility that grounding plays a role in driving neural reuse, it certainly moves it from center stage, which may have undesirable theoretical consequences for the theory as a whole, and for the way it interfaces with related ideas in linguistics, philosophy, and psychology. On the other hand, without some emendation that significantly broadens the kinds of cases that it can cover, the neural exploitation hypothesis risks being inadequate to the full range of available empirical evidence. We will return to these issues when we come to our general discussion of the four candidate theories. 6.2. The shared circuits model

The shared circuits model (Hurley 2005; 2008) is organized around five control layers of similar structure, which are differentiated by the increasing abstraction of inputs and outputs. Each layer consists of an adaptive feedback loop that takes state information as input and generates control information as output. The first, lowest layer is a simple perception-action feedback loop that monitors progress toward action goals (reaching a target) and adjusts motor output in light of perceptually generated state information. It is, in this sense, a model of the simplest sort of thermostat; and the idea is that behavioral control systems might consist, at the most basic level, of multiple such control systems – or circuits. Layer 2 takes input from the external world, but also from layer 1, and becomes in essence an adaptive feedback loop monitoring the original layer. That is, layer 2 is in essence a forward model of layer 1. As is well known, incorporating such models into adaptive control systems tightens overall control by allowing for the prediction of state information, so appropriate action can be taken without waiting for the (typically slower) external feedback signal.26. The more BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Anderson: Neural reuse: A fundamental organizational principle of the brain hysteresis in the system – the longer it takes control interventions to produce expected results – the more improvement forward models can offer. Circuit sharing really begins with layer 3, in which the same control circuits described by layers 1 and 2 take as input observations of the actions (or situations) of other agents. Hurley’s suggestion is that the mirror system (Decety & Gre`zes 1999; Gallese et al. 1996; Rizzolati et al. 1996) should be modeled this way, as the activation of basic control circuits by state information relevant to the situations of other agents. Layer 3 also implements output inhibition, so agents don’t automatically act as if they were in another agent’s situation whenever they observe another agent doing something. Layer 4 incorporates monitoring of the output inhibition, supporting a selfother distinction; and layer 5 allows the whole system to be decoupled from actual inputs and outputs, to allow for counter-factual reasoning about possible goals and states and about what actions might follow from those assumptions. The idea is that the same circuits normally used to guide action in light of actual observations can also be fed hypothetical observations to see what actions might result; this can be the basis of predictive models. By the time we achieve the top layer, then, we have the outline for a model both of deliberation about possible actions, and also of multi-agent planning, which could serve as the basis for high-level social awareness and intelligence. Like the neural exploitation hypothesis, one of the main explanatory targets of the shared circuits model is the possibility of mindreading and intelligent social interaction. And like the neural exploitation hypothesis, it is built entirely on the foundation of sensorimotor circuits. However, unlike the neural exploitation hypothesis, the shared circuits model does not revolve around the inheritance of semantic content from one level to another, but rather around the inheritance of function. The core capacities of the higher layers are based on exploiting the functional properties of the lower layers; all the layers are essentially control loops containing predictive models because they are reusing the basic implementation of the lowest levels. This is an advantage in that it is easier to see how the shared circuits model could be used to explain some of the specific instances of function-driven inheritance canvassed above; for, although Hurley models layer 1 on low-level sensorimotor circuits, there seems no reason in principle that the general approach couldn’t allow for other kinds of basic circuits, on which other functional layers could be built.27 It is also a potential weakness, in that it is less easy to see how it could be used to account for the central findings of concept empiricism or conceptual metaphor theory; can the sort of functional inheritance allowed by this model also allow for semantic inheritance? The inheritance of a basic feedback structure does not seem to lend itself to any obvious examples of this sort. This is not a criticism of the model as it stands – it was meant only to account for our understanding of instrumental actions; but it suggests that there is no very simple way to generalize the model to a wider set of cases. On the other hand, there seems no fundamental conflict between inheriting a function and thereby inheriting semantic content or domain structure. I mentioned at the outset that the hierarchy of levels was characterized by an increasing abstraction of input and output. Consider layer 3 in this regard – at this level, input 260

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will be both impoverished and abstract as compared with lower layers. It will be impoverished because it will be missing a great deal of the richness of embodied experience – tactile experience, proprioceptive feedback, and efference copy are all absent when observing as opposed to acting. One is left with the visual experience of an action. And note that an action viewed from the firstperson perspective looks different from the same action viewed from the third-person perspective. This points to one reason that the information must be abstract: since the visual experience of another agent’s action will differ in most, if not all, of its low-level particulars, the system must be sensitive not to these, but to high-level features of the action that are common to the two situations.28 Moreover, by hypothesis, layer 3 responds not just to actions, but to situations in which actions are possible – not just to another agent reaching for a banana, but to the banana being within the reach of another agent. This requires imputing possible goals to the observed agent, as well as encoding the high-level features of situations (relations between other agents, their capacities, and the objects in a scene). Here, the shared circuits model may need to be supplemented with something like the feature schemas from the neural exploitation model, itself expanded to allow for situation schemas, and not just object-action ones. Similarly, if layer 4 is to appropriately and selectively inhibit the control outputs, it must take as input information about the relationships among the actions, agents, goals, and situations – who is in which situation doing what – which requires at least a rudimentary self/other distinction. And if layer 5 is going to be useful at all, the predictions it provides as output must be abstract, high-level action descriptions, not low-level motor commands. These facts might seem to be nothing more than interesting and functionally useful features of the model, but in fact the requirement for abstraction at higher levels raises a puzzle: If low-level circuits respond to high-level features as inputs, and can produce high-level commands as outputs, might this not imply that layers 1 and 2 are more abstract than the model assumes? The trouble this raises is not with the coherence of the model, but with the evidence for it: All the evidence for layer 1 and 2 type controllers comes from on-line control systems dealing with realtime effector-specific, low-level feedback and control information, and not with abstract, feature-based information. One obvious way to address this puzzle is to say that each layer is in fact a separate control structure that takes input from and delivers output to the layer below it, but this would undercut the entire premise of the model, since it would no longer be clear in what sense circuits were being “shared.” That high-level control systems are structurally like low-level ones is a perfectly reasonable hypothesis, but this is not the hypothesis put forward by this model, nor is it one for which there is a great deal of biological evidence. A different approach would be to retain the central hypothesis that control circuits are shared among layers – that layer 3 reuses the control circuit defined by layers 1 and 2, and layer 5 reuses the control circuit defined by layers 1 – 4 – but suggest that the inputs between layers must be mediated by translators of various kinds. That is, layer 3 takes high-level feature information and translates this into the low-level information favored by layers 1 and 2 before passing it on. Indeed, one might hypothesize it does this by reusing other circuits, such as those that

Anderson: Neural reuse: A fundamental organizational principle of the brain translate abstract plans into successive low-level motor actions. Similarly, layer 5 accepts the low-level motor commands natively output by layer 1, but translates them into high-level action descriptions. This picture is pretty plausible in the case of layer 3 observations of abstract action features, but it is much less clear how situations might get translated appropriately; and it is especially unclear how the reverse inference from low-level motor commands to high-level action descriptions might work. Just as a highlevel action might be implemented any number of ways, a specific motor movement might be put in the service of innumerable high-level actions. The fact that part of the sensory information used to retroduct the action/intention from motor movement is the observed effect of the motor movement will help somewhat, but the basic problem still remains: There is a many-to-many relationship between movement and actions, so the valid deduction of a movement from an intention, and the valid retroduction of an intention from a movement need not follow the same paths in opposite directions. These are hard problems to address; and because they originate from the fact that the shared circuits model requires that different kinds of inputs be fed to the same neural circuits, they may be problems that will surface for any theory of neural reuse (see discussion in section 6.4). Hence, it seems that the best approach to this puzzle may be to bite the bullet and say that, in at least some cases, circuit reuse is arranged such that different data – both information pertaining to different targets, as well as information about the same targets but at different levels of abstraction – can be fed without translation to the same circuits and still produce useful outputs.29 Many sorting algorithms can just as easily sort letters as numbers; and if you feed a given algorithm pictures instead, it will do something with them. Naturally, this raises some pressing questions that seem ready-made for an enterprising theorist of neural computation: Under what conditions might useful things be done by circuits working with non-standard data? What kinds of implementations increase the chances of functionally beneficial outcomes given the fact of reuse? We will return to these issues in sections 6.4 and 7. At its core, the shared circuits model offers an approach to understanding how high-level function could possibly be enabled by low-level circuits – and specifically by the reuse of low-level circuits for various purposes. Unfortunately, it is left fairly unclear exactly how they might actually be so enabled, given the different input-output requirements for each level; I have tried to sketch a solution that does the least damage to the intentions of the model, but I have to admit that some deep puzzles potentially remain. Nevertheless, the model is interesting as an example of what might come from adopting a fairly classical “boxological” approach to cognitive modeling – understanding information processes via decomposition and interrelation – but without the underlying assumption of anatomical modularity.30 If neural reuse is indeed a pervasive feature of the functional organization of the brain – as the current article is arguing – we will need to see more such work in the future. 6.3. The neuronal recycling hypothesis

The neuronal recycling hypothesis (Dehaene 2005; Dehaene & Cohen 2007) originates from a set of

considerations rather different from those motivating the two theories just discussed (i.e., the neural exploitation hypothesis and the shared circuits model). While those are neutral on the question of how and over what timescales the brain organization they propose came about, Dehaene is interested specifically in those cognitive capacities – such as reading and mathematics – that have emerged too recently for evolution to have generated cortical circuits specialized for these purposes. Such cultural practices must be learned, and the brain structures that support them must therefore be assigned and/or shaped during development. There are two major ways to explain how recent cultural acquisitions, which emerge and are maintained in a population only by learning and not via genetic unfolding, can be supported by neural structures, as of course they must partly be. One way is to take our capacity to acquire such practices as reading and arithmetic as evidence for domain-general learning mechanisms (Barkow et al. 1992) and fairly unconstrained neural plasticity (Quartz & Sejnowski 1997). The other way is to suggest that cultural acquisitions must find a “neuronal niche”– a network of neural structures that already have (most of) the structure necessary to support the novel set of cognitive and physical procedures that characterize the practice. The neuronal recycling hypothesis is of the latter sort. Note the interesting implication that the space of possible cultural acquisitions is partly constrained by cortical biases. The phrase “neuronal niche” is clearly meant to echo the idea of an ecological niche, and suggests both that acquired cognitive abilities “belong” in specific neural locations (i.e., can only survive where the neural climate is appropriate) and that the neural ecology may partly determine the characteristics that these cultural acquisitions possess, by limiting what is even possible to learn (and therefore which cognitive animals survive). Assuming the set of evolutionarily determined cortical biases is consistent across the species, we should expect to find evidence of at least three things: First, the neural manifestations of acquired abilities should be relatively consistent across individuals and even cultures; second, these practices should have some common cross-cultural characteristics; and third, the same sorts of cortical biases, as well as some of the same possibilities for learning, should be present in nonhuman primates. As evidence for the first expectation, Dehaene and Cohen (2007) note that the visual word form area, functionally defined as a region specifically involved in the recognition and processing of written words, appears in the same location in the brain across participants, whether the participants in question are using the same language and writing system or using different ones. Similarly, the intraparietal sulcus has been implicated in numeric tasks, regardless of the culture or number representation system used by the participants. As evidence for the second expectation, they point to work by Changizi and colleagues (Changizi & Shimojo 2005; Changizi et al. 2006) that writing systems are characterized by two cross-cultural invariants: an average of three strokes per written letter; and a consistent frequency distribution for the types of contour intersections among the parts of those letters (T, Y, Z, etc.). Finally, the third expectation has been supported by some interesting and groundbreaking work by Atsushi Iriki and colleagues (Iriki 2005; Iriki & BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Anderson: Neural reuse: A fundamental organizational principle of the brain Sakura 2008) who uncover evidence for real-time neural niche construction in primate brains (specifically Macaca fuscata) as the result of learning to use simple tools. The location of the observed neuro-morphological changes following tool training is roughly homologous to the regions associated with tool-use in the human brain (Culham & Valyear 2006). Thus, the theory suggests a novel pathway by which Homo sapiens may have achieved its current high-level cognitive capacities. The neuronal recycling hypothesis outlines a universal developmental process that, although illustrated with specific examples, is meant to describe the way any acquired ability would come to have a neural instantiation. In this sense, it is broader in conception than the neural exploitation and shared circuits theories described in sections 6.1 and 6.2, respectively (although as noted their scope might well be increased with a few modifications). How much neural plasticity would be required in any given case will vary with the specifics of the acquisition, but one strength of the neuronal recycling theory is it makes clear some of the limits and costs that would be involved. The greater the distance between the function(s) required by a given practice, and the existing cortical biases, the harder the learning process will be, and the more likely that the learning process will disrupt whatever other functions the affected brain regions support. On the other hand, the more the requirements of the acquisition match what is already possible, the less novel and potentially less valuable the cultural practice is likely to be – unless, that is, it is possible to combine existing capacities in new ways, to use old wheels, springs, and pulleys to form new machines. It is interesting to note in this regard that while the neural exploitation and shared circuits theories outlined earlier tend to envision neural circuits being put to fairly similar new uses – for example, forward models in motor control being used to support forward models in social interaction – the neuronal recycling hypothesis suggests that neural circuits might be put to uses quite other than the ones for which they were originally developed. As already noted above, this notion is central to the massive redeployment hypothesis, which we will briefly review next. 6.4. The massive redeployment hypothesis

Since the massive redeployment hypothesis has already been discussed in section 1.1, I will only review the main idea here. The primary distinction between massive redeployment and neuronal recycling is the time course over which each is supposed to operate. Massive redeployment is a theory about the evolutionary emergence of the functional organization of the brain, whereas neuronal recycling focuses on cognitive abilities for which there has been insufficient time for specialized neural circuits to have evolved. Both, however, suggest that the functional topography of the brain is such that individual circuits are put to various cognitive uses, across different task domains, in a process that is constrained in part by the intrinsic functional capacities (the “workings” or “cortical biases”) of local circuitry. It is worth noting that the concepts of a “working” and of a “cortical bias” are not identical. The workings envisioned by the massive redeployment hypothesis commit that theory to the existence of cortical biases – that is, 262

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limitations on the set of functions it is possible for the circuit to perform in its present configuration. However, Dehaene is not committed to the notion of a local working in virtue of belief in cortical biases. Although it would be natural to understand cortical biases as the result of fixed local workings, a region could in fact perform more than one working and still have a cortical bias. However, the more flexible regions are, the less their individual biases will differ, and the harder it will be to explain the findings that recently evolved cognitive functions use more and more widely scattered neural components. On the other hand, as noted already above, the data are consistent with a number of functionally relevant constraints on local operation. For example, it could be that the dynamic response properties of local circuits are fixed, and that cognitive function is a matter of tying together circuits with the right (relative) dynamic response properties (for a discussion, see Anderson & Silberstein, submitted). In this sense, “cortical bias” is perhaps useful as a more generic term for denoting the functional limitations of neural regions. In any event, both theories are committed to the notion that putting together the same neural bits in different ways can lead to different – in some cases very different – functional outcomes. In the discussion of the shared circuits model (sect. 6.2), I raised the issue of whether and how a single circuit could be expected to deal with various different kinds of data, as reuse theories seem to require. The question arises here as well: Exactly how is such reuse possible? It must be considered a weakness of both the massive redeployment and the neuronal recycling hypotheses that they lack any semblance of a functional model. In describing my theory (M. L. Anderson 2007a; 2007b; 2007c), I have used the metaphor of component reuse in software engineering, which may be useful as a conceptual heuristic for understanding the proposed functional architecture but cannot be taken as a model for the actual implementation of the envisioned reuse. In software systems, objects are reused by making virtual copies of them at run-time, so that there can be multiple, separately manipulable tokens of each object type. With wetware systems, no such process is possible. What is reused is the actual circuit. In general, how such reuse is actually effected must be considered an open question for the field. Going forward, supporters of recycling and redeployment need to provide at least three things: specific models of how information could flow between redeployed circuits; particular examples of how different configurations of the same parts can result in different computations; and a more complete discussion of how (and when and whether) multiple uses of the same circuit can be coordinated. PennerWilger and Anderson (2008; submitted) have taken some tentative steps in this direction, but much more such work is needed. It is to the credit of both Hurley and Gallese that they each offer a (more or less concrete) proposal in this regard (see Gallese 1996; 2008; Hurley 2005; 2008). That neither seems wholly adequate to the task should not be surprising nor overemphasized; the neurosciences are replete with what must be considered, at best, partial models of the implementation of function by neural structure. More important by far is that neural reuse offers a unique guide to discovery – a sense of what to look for in understanding brain function, and

Anderson: Neural reuse: A fundamental organizational principle of the brain how to put the pieces together into a coherent whole. If neural circuits are put to many different uses, then the focus on explaining cognitive outcomes should shift from determining local circuit activity and single-voxel effects to uncovering the complex and context-dependent web of relations between the circuits that store, manipulate, or otherwise process and produce information and the functional complexes that consume that information, putting it to diverse purposes.31 One way this effort might be abetted is via the formulation of an even more universal theory of neural reuse than is offered by any of the four theories canvassed above. As should be clear from the discussion, none of the four proposals can explain all the kinds of reuse in evidence: reuse supporting functional inheritance, reuse supporting semantic inheritance, reuse that occurs during development, and reuse that occurs during evolution. In fact, each is strongest in one of these areas, and weaker in the others. This opens the obvious possibility that the four theories could be simply combined into one.32 While it is true that there seems no obvious barrier to doing so, in that none of the theories clearly contradicts any of the others, this lack of conflict is in part an artifact of the very under-specification of the theories that leaves them covering distinct parts of the phenomenon. As mentioned already, it may turn out that the kind of functional inheritance required by the shared circuits model precludes the kinds of semantic inheritance required by the neural exploitation hypothesis, or that the schemas envisioned by neural exploitation cannot be modified and expanded along the necessary lines. Likewise, it could turn out that the processes driving massive redeployment are in tension with those driving neuronal recycling; or that one, or the other, but not both can explain semantic and/or functional inheritance. Should such problems and conflicts arise, no doubt solutions can be found. The point here is simply: We don’t yet even know if there will be problems, because no one has yet even tried to find a solution. I would encourage all those interested in the general topic of brain organization to ponder these issues – how does the fact of reuse change our perspective on the organization, evolution, development, and function of the brain? Within what framework should findings in neuroscience ultimately be placed? There is enough work here for many hands over many years. 7. Implications Although the question of how neural reuse is actually effected must be considered open, the question of whether there is significant, widespread, and functionally relevant reuse must be considered closed. In light of all the evidence discussed above, it is clear that there is neural reuse, and there is a lot of it. Neural reuse is a real feature of brain organization, but it is also a novel concept – something about the brain that we are just now beginning to notice. What might it mean? What is the way forward? I close the article with a few thoughts on these topics. First, and most obviously, the fact of widespread neural reuse seems to favor modal and “embodied” accounts of cognition – and of representational content, in particular – over amodal or more abstract accounts. On the other

hand, the neuroscientific evidence for these theories has generally been over-read (M. L. Anderson 2008c). Especially in light of the many different kinds of reuse, and the many potential mechanisms by which it may have come about, the claims made on behalf of concept empiricism and embodied cognition need close examination. Although a lack of neural reuse would have been evidence against embodied cognition, concept empiricism, and conceptual metaphor theory, the fact that it is even more widespread than these theories predicted means that neural overlaps are not by themselves evidence for these theories, and do not fully explain the relationships between cognitive domains that are at the heart of these ideas. In particular, it needs to be asked what kinds of reuse will, and will not, support the kinds of inheritance of structure and content these theories require; and whether the evidence actually points specifically to that sort of reuse. In fact, this is one of the main open areas of research for neural reuse: How is functional inheritance possible, and what kinds of implementations of reuse can lead to semantic inheritance of the sort described in concept empiricism, conceptual metaphor theory, and other theories of cognitive grounding? Providing this sort of story would offer the possibility of unifying these different theories of grounding with one another, under the umbrella of general neural reuse. In the absence of such a story, general neural reuse instead threatens to undermine some of the justification for these accounts. If regions of the cortex are indeed put to many different cognitive uses, this suggests that cortical parcellation and function-to-structure mapping should be approached via multiple- or cross-domain investigations (Penner-Wilger & Anderson 2008; submitted). One way to move forward on this task is via the increased use of effect location meta-analysis, in which multiple imaging studies, each reporting significant effects, are analyzed together to get more accurate information about the brain locations of mental operations (Fox et al. 1998). Although such studies are increasingly common, they are also typically limited to one task domain. There is nothing intrinsic to effect-location meta-analysis or cognitive modeling in general that militates against cross-domain modeling, but in practice it is very rarely done. This is, I presume, because there remains a very strong, and perhaps typically unconscious, assumption that brain regions are both unifunctional and domain dedicated.33 Widespread neural reuse suggests that this assumption must be given up. Neural reuse offers an alternative to these assumptions, as well as to the more general selectivity and localization assumptions that have long been the guiding idealization for research in the cognitive neurosciences. In their place, neural reuse offers the strong distinction between working (or local cortical bias) and cognitive use, which can help guide the (re-)interpretation of experimental results, especially those based on single brain-imaging experiments. It also offers the suggestion that attention paid to the interactions of multiple regions over the activity of single ones will be well rewarded. Methodological tools that take us beyond single-voxel effects – such as functional connectivity analysis and multi-voxel pattern analysis – may have an important role to play in supporting these efforts (Anderson & Oates 2010; M. L. Anderson et al. 2010; Honey et al. 2007; Pereira et al. 2009; Sporns et al. 2000; 2004). BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Anderson: Neural reuse: A fundamental organizational principle of the brain Once we give up these assumptions, our vocabulary of cognitive function might need specific revision to include fewer domain-specific concepts. In current practice, cortical regions are assigned visual functions by vision researchers, memory functions by memory researchers, attention functions by attention researchers, and so on (Cabeza & Nyberg 2000). But if cortical circuits contribute to multiple task domains, then this practice will not lead to the accurate attribution of workings to these circuits. In light of neural reuse, it appears that this practice can at best reveal one of the uses to which a region is put, but is unlikely to hit upon the actual local working (see M. L. Anderson 2007b; Bergeron 2008 for discussions). This best-case scenario requires that the process models are themselves accurate, but it seems implausible to suppose that these models – also typically generated on the basis of domain-focused experimentation – will themselves survive widespread acceptance of neural reuse without significant revision. In this sense neural reuse is a potentially disruptive finding, although hopefully in the service of increased theoretical fertility. Widespread neural reuse makes it quite clear that there is not and cannot be anatomical modularity in the brain. Whether this means there is no functional modularity is an open question. Can cognitive functions be independent when they have overlapping neural implementations? Questions about what functional modularity requires are vexed, and different researchers have come to many different conclusions on the matter (Barrett & Kurzban 2006; Carruthers 2006). Whether and precisely how neural reuse constrains this debate is a matter that deserves careful attention. There are some practical upshots as well. Maps of the overlaps among the circuits supporting cognitive function will support robust predictions regarding cognitive processes and tasks that are likely to interfere with one another. Not only does this offer leverage to the experimentalist in designing inquiries into brain function, it also offers advice to the system designer in designing work flows and machine interfaces. As consumer devices, medical instruments, and heavy machinery become more sophisticated and powerful, increasing attention will need to be paid to the cognitive demands of operating them, and information about neural overlaps will be one important tool in the designers’ toolbox (Rasmussen & Vicente 1989; Ritter & Young 2001), especially as leading cognitive models start incorporating information about reuse into their systems (Stewart & West 2007). Similarly, knowledge of neural overlaps might suggest novel therapies for brain injury. Many therapies for traumatic brain injury are based on the “use it or lose it” principle – the more tasks that stimulate a brain region, the more likely patients are to recover function. Knowledge about the range of different tasks that potentially stimulate each region may serve as the basis for unexpected therapeutic interventions, ways of indirectly recovering function in one domain by exercising capacities in another. Indeed, there is evidence from healthy subjects that such indirect approaches to strengthening neural function can in fact work – for example, the finding that object manipulation can increase reading comprehension in school-age children (Glenberg et al. 2007). 264

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Finally, given that brain functions are apparently supported by multiuse components, there are possible implications for how cognition might be engineered and reproduced in robotic artificial intelligence (AI) (M. L. Anderson 2008a). That is, neural reuse might recommend a shift from building intelligent systems out of separate, specialized modules dedicated to language, motor-control, vision, and such, to engineering low-level multi-use components that offer services to many different high-level functions. There has been some theoretical and practical work in this direction (Hall 2009; Stewart & West 2007), but much more is needed. Such work is probably the necessary precursor to any satisfactory theory of how it is that component reuse can engender both functional and semantic inheritance. I hope the present article gives some sense that such efforts will be rewarded. ACKNOWLEDGMENT

Several students have played important roles in building the NICAM database used for some of the analyses reported here: Joan Brumbaugh, Kristen Calapa, Thomas Ferro, Justin Snyder, and Aysu S¸uben. This project would not have been possible without their efforts. Many colleagues made helpful remarks on earlier drafts of the essay, including Paco Calvo, Cristo´bal Paga´n Ca´novas, Tony Chemero, Andy Clark, Antoni Gomila, Julian Kiverstein, Marcie Penner-Wilger, Michael Silberstein, and Terry Stewart. The anonymous reviewers for BBS also made detailed, extensive, and helpful comments. The preparation of this article was made possible by several kinds of support from Franklin & Marshall College, including generous lab start-up funds, support for student research assistants, and a junior faculty research leave. All of this support is gratefully acknowledged.

NOTES 1. It is perhaps worth mentioning that, although the first publications on the massive redeployment hypothesis did not appear in print until 2007, the original article detailing the theory was received by Philosophical Psychology in 2005. It hence appears likely that all the neural reuse theories of cognition discussed here were independently developed in the very same year. 2. The cortical regions studied were the same as those used in Hagmann et al (2008): “The 66 cortical regions are labeled as follows: each label consists of two parts, a prefix for the cortical hemisphere (r ¼ right hemisphere, l ¼ left hemisphere) and one of 33 designators: BSTS ¼ bank of the superior temporal sulcus, CAC ¼ caudal anterior cingulate cortex, CMF ¼ caudal middle frontal cortex, CUN ¼ cuneus, ENT ¼ entorhinal cortex, FP ¼ frontal pole, FUS ¼ fusiform gyrus, IP ¼ inferior parietal cortex, IT ¼ inferior temporal cortex, ISTC ¼ isthmus of the cingulate cortex, LOCC ¼ lateral occipital cortex, LOF ¼ lateral orbitofrontal cortex, LING ¼ lingual gyrus, MOF ¼ medial orbitofrontal cortex, MT ¼ middle temporal cortex, PARC ¼ paracentral lobule, PARH ¼ parahippocampal cortex, POPE ¼ pars opercularis, PORB ¼ pars orbitalis, PTRI ¼ pars triangularis, PCAL ¼ pericalcarine cortex, PSTS ¼ postcentral gyrus, PC ¼ posterior cingulate cortex, PREC ¼ precentral gyrus, PCUN ¼ precuneus, RAC ¼ rostral anterior cingulate cortex, RMF ¼ rostral middle frontal cortex, SF ¼ superior frontal cortex, SP ¼ superior parietal cortex, ST ¼ superior

Anderson: Neural reuse: A fundamental organizational principle of the brain temporal cortex, SMAR ¼ supramarginal gyrus, TP ¼ temporal pole, and TT ¼ transverse temporal cortex.” 3. If cognitive scientists are very bad at categorizing their experiments – at knowing what cognitive domains or tasks their experiments in fact explore – that could explain the simple finding that regions are activated by multiple tasks, because some experiments that belonged in one category would have instead been placed in another. I don’t doubt we are pretty bad at this. But this fact alone would not explain the specific patterns of findings reported in support of the other predictions of redeployment. Moreover, Tony Chemero and I have performed a clustering analysis on the data to see if there is a way of dividing experiments into groups so that the neural activations do not overlap. There does not seem to be any clustering that avoids overlaps (unpublished data). We have not yet determined whether and to what degree it is possible to minimize overlap with alternate clusterings of the experiments. 4. In Talairach space, the origin is located deep in the center of the brain, and regions anterior of that are increasingly positive, and posterior to that are increasingly negative. 5. The terms “working” and “use” are adopted from Bergeron (2008). That brain regions have fixed low-level functions (“workings”) that are put to many high-level “uses” is the assumption followed by most work on the massive redeployment hypothesis (M. L. Anderson 2007a; 2007b; 2007c; 2008a; Penner-Wilger & Anderson 2008), but it should be noted that there are other possibilities consistent with the data. For example, it could be that the dynamic response properties of local circuits are fixed, and that cognitive function is a matter of tying together circuits with the right (relative) dynamic response properties. See Anderson and Silberstein (submitted) for a discussion. 6. Terry Stewart (personal communication) suggests that an even better analogy might be modern Graphics Processing Units (GPUs). GPUs were initially intended as specialized devices to offload computationally intensive graphics rendering from the main CPU, but it has turned out they are useful for many other tasks. He writes: “it’s turning out that they’re extremely useful for general parallel processing, and lots of people (including us) are using them to run neural simulations. And, it’s an interesting balancing task for the GPU developers to support this new use of the same working while maintaining the graphics use as well.” (See, e.g., Ho et al. 2008; Nvidia 2007, sect. 1.1.) 7. ACT-R modules are separately modifiable, and, if neural reuse is true, the functional components of the brain will often not be. But separate modifiability does not appear to be an essential aspect of ACT-R theory, the way it is at the core of massive modularity (see sect. 3.1). 8. Some proponents of blending have argued to me that Conceptual Blending Theory (CBT) and Conceptual Metaphor Theory (CMT) are much more different than this brief description allows. For instance, Cristo´bal Paga´n Ca´novas (personal communication) writes that: Fauconnier and Turner argue that double-scope blending is a defining capacity of our species, of which metaphor is just a surface product, emergent from complex integration network that cannot be described by binary unidirectional mappings. I think that: a) this makes CBT and CMT hardly compatible; b) CBT (unlike CMT) accounts for frame shifting, bidirectional or multidirectional conceptual mappings, emergence of new meanings not present in their inputs, opportunistic re-use of conceptual materials, etc. and thus constitutes a change of paradigm; c) CBT is much more compatible with the massive redeployment hypothesis; d) a deeper debate about CMT and CBT is necessary. (For more on this, see Paga´n Ca´novas 2009.) This is certainly a very interesting issue, and I would be especially pleased if Conceptual Blending turned out to be more compatible with the observed extent of neural reuse than CMT appears to be (although whether it could account

for all of it is a different matter), but space constraints dictate that we leave the matter for future discussion. 9. Note the reuse of the same neural circuits to support abstract planning wouldn’t necessarily mean that one simulates motor experience as part of the planning process. Rather, for conceptual metaphor theory, the neural overlap would support the inheritance of elements of one domain (e.g., its inferential structure) by the other. The discovery of such circuit reuse therefore does offer support for both theories – although, as I have complained elsewhere (M. L. Anderson 2008c), little attention has been paid to the fact that concept empiricists and conceptual metaphor theorists in fact need to interpret this evidence in quite different ways for it to support their specific claims. 10. Apropos of which it should be noted that this approach is broadly compatible with the developmental theories of Piaget, according to which abstract thought depends on the acquisition of sensorimotor skills and concrete operations (e.g., Piaget 1952). 11. Glenberg et al. (2008b) confirmed that motor regions were involved by applying TMS over the motor areas and measuring a motor-evoked potential (MEP) at the hand while having a subject judge both action sentences, describing concrete and abstract transfers, and neutral sentences. A larger MEP response was seen during transfer sentences as compared with non-transfer sentences, consistent with the notion that the motor areas are specifically activated by action sentences. 12. If it were the case that emotional valence was metaphorically mapped to movement in space without direct neural sharing, we would be more likely to see that emotions affected movement, but not the reverse, for presumably movement is not metaphorically mapped to anything. The fact that the effect is bidirectional suggests that it is mediated by the activation of something shared by and necessary to both systems, and a shared neural circuit seems a likely (although naturally not the only) possibility. 13. Note that on both views the neural overlaps could remain even if numbers were entirely amodally represented. A complete review of the evidence for and the various theories regarding the nature of this resource would take us too far afield to include here. For a discussion, see (Penner-Wilger 2009; PennerWilger & Anderson, submitted). 14. Interestingly, this inheritance by the language system of the postural organization of motor control circuits also has the potential to help explain why even American Sign Language (ASL) seems to have a phonemic structure, despite differences in modality that might otherwise have predicted a rather different organization (Sandler & Lillo-Martin 2006). 15. The advantages of using this subdivision are that it ensures a neutral choice of ROIs, and lays the groundwork for future studies in which the domain-related topology of the cortex can be directly compared to the cortical connection matrix reported in that study. Thanks to the authors for sharing their ROI data. 16. The domains follow the standards defined by the BrainMap database (Fox & Lancaster 2002; Laird et al. 2005), and are generally determined by the authors of the study. Where available, we adopted the classification entered into the BrainMap database itself. 17. The disadvantage of using this set of ROIs is that it is based on 1.5cm2 regions of the cortical surface; hence, many activations deeper in the brain are not captured by this subdivision. One can mitigate this problem by defining a set of cubes of roughly the same size as those from Hagmann et al. (2008) – 12mm on a side – but distributed equally through the entire brain. This brings the eligible total of 12,279 activations in 1,486 experiments. For the sort of counting we are presenting here, this addition of only 17 new experiments does not materially change the results. 18. These are averages of the raw counts. If the averages are normalized to 11 (the number of possible domains in the overall average), the numbers are as follows: Action areas are active in the equivalent of 5.46 (SD 2.17) nonaction domains and 5.79 BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain (SD 2.26) cognitive domains; perception areas are active in 4.90 (SD 1.97) non-perception domains and 5.87 (SD 2.28) cognitive domains; perception-action areas are active in the equivalent of 6.11 (SD 2.23) cognitive domains; and cognitive areas are active in 4.46 (SD 2.18) cognitive domains. 19. See Note 2. 20. The one region active in only one domain was left Frontal Pole, which was active only in memory. 21. The differences are indeed significant, 2-tailed student’s ttest, p ,, 0.01, whether one uses the raw or normalized counts. Note that the massive redeployment hypothesis would explain this finding in terms of the relative age of the brain regions involved. Perceptual and motor circuits are more frequently reused because they are older, and not necessarily because they are functionally special. 22. Note that for the purposes of this article, the term “circuit” is more-or-less interchangeable with “small neural region.” I take the evidence of this section to indicate that small neural regions are activated in multiple tasks across multiple domains, which for current purposes is interpreted to indicate that local neural structures – that is, neural circuits – are reused in these tasks and domains. One certainly could reserve the term “circuit” for larger neural structures, such as might be revealed by combining fMRI results with Diffusion Tensor Imaging data that can reveal the physical connectivity underlying function (see, e.g., Behrens & Johansen-Berg 2005; Honey et al. 2009; Sporns et al. 2000), but this lexical preference would not materially alter the claims of this section. And although I do believe that one of the upshots of this article as a whole is that much more attention should be paid to functional connectivity and other measures of the cooperation between cortical regions, rather than making functional attributions primarily on the basis of differential activation, following out this implication in detail will have to wait for some future paper (but see, e.g., M. L. Anderson 2008a). 23. The authors explicitly relate this latter aspect to the concept of affordances (Gibson 1979), the perceived availability of objects for certain kinds of uses or other interactions. 24. Hurford (2003) suggested something like this when he hypothesized that the division between ventral stream and dorsal stream vision provide the biological basis for predicateargument structure. 25. There is the further drawback that, in contrast to the model actually built by Feldman and Narayanan (2004), there is no proof it is actually possible to build such a control system. 26. In fact, most household electronic thermostats contain such forward models, one reason they are more efficient than the older mercury-switch models. 27. This might push the architecture in the direction of something like the “servo stacks” concept (Hall 2009), which imagines building diverse high-level cognitive components from the iterative combination of simple, relatively homogenous, low-level building blocks. 28. The problem remains even given (1) observed actions will be associated with motor commands and those commands may be simulated by the observer, and (2) part of the observation is not just the movements of an agent, but also the effects of the agent’s actions. Even if motor simulations kick in, enriching our observational experience, one must begin with the visual experience of the action – it is that which drives the initial categorization. And the sensory effects of an action will still differ for actor and observer, so that abstraction – attention to high-level features – will still be required. 29. A somewhat different approach to this problem is offered by Wolpert et al. (2003). In this model, there are multiple predictors and multiple controllers arranged in an abstraction hierarchy. Actions and observations activate different controllers and predictors to different degrees, and the ones that generate the fewest errors (of prediction or of movement) over time are the ones that come to dominate. That is, they are the ones that

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come to drive action, or action understanding. Miali (2003) describes how such a model might be instantiated in a large brain circuit involving F5 mirror neurons cooperating with cerebellum and cortical motor areas. In this model, there is no need for translation between levels because there are multiple specialist modules, each corresponding to some (class of) actions or situations, already arranged in an appropriate hierarchy; but there is also little need for reuse. 30. Hurley appears to accept functional modularity, but explicitly denies anatomical modularity. 31. The producer/consumer distinction is based on Millikan’s (1984) as it pertains to the content of representations. It will surely need to be part of the model for how circuit reuse is possible. The “same” representation can have different content depending on the characteristics of the representation consumer. Similarly, the same neural activity or output can have different functional significance depending on the nature of the neural partners. 32. My colleague Tony Chemero and I are developing one such model, by adapting and combining insights from the literature on niche construction (Odling-Smee et al. 2005) and the evolution of food-webs (Quince et al. 2002), but the space of illuminating models of this process is surely quite large. 33. Consider the titles of some recent meta-analyses of imaging data: “Functional neuroanatomy of emotion: A metaanalysis of emotion activation studies in PET and fMRI” (Phan et al. 2002); “Meta-analysis of the functional neuroanatomy of single-word reading: Method and validation” (Turkeltaub et al. 2002); “Functional neuroanatomy of emotions: A meta-analysis” (Murphy et al. 2003); “The functional neuroanatomy of autobiographical memory: A meta-analysis” (Svoboda et al. 2006); “A systematic review and quantitative appraisal of fMRI studies of verbal fluency: Role of the left inferior frontal gyrus” (Costafreda et al. 2006). In fact, of the 51 papers that cite Fox et al. (1998), the only one to consider studies in more than one task domain was a paper analyzing the functional connectivity of the basal ganglia (Postuma & Dagher 2006).

Open Peer Commentary Reuse or re-function? doi:10.1017/S0140525X10000981 Daniela Aisenberg and Avishai Henik Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel. [email protected] [email protected] http://www.bgu.ac.il/henik

Abstract: Simple specialization cannot account for brain functioning. Yet, we believe Anderson’s reuse can be better explained by refunction. We suggest that functional demands shape brain changes and are the driving force behind reuse. For example, we suggest that the prefrontal cortex (PFC) is built as an infrastructure for multi-functions rather than as a module for reuse.

Anderson is impressed by reuse; namely, by the fact that the same brain structures are used in different tasks and contexts. He points out that “in combination neural reuse and wiring optimization theory make some novel predictions for cortical layout” (sect. 2, para. 1). We agree that theories assuming simple structural specialization cannot account for all brain functioning. Yet, we suggest that functional demands drive reuse. More than thirty years ago, Paul Rozin suggested that the evolution of intelligence is marked by exploiting routines designed

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain for a special task or goal, to achieve other goals (Rozin 1976). Namely, routines (programs, served by specific brain tissue) that were designed to provide specific solutions to unique problems become accessible to other systems through evolution and within the individual lifetime. Such routines are also examples for reuse, but they are better described as a change or expansion of function, rather than reuse, because we “make these (adaptive specializations) more generally available or accessible. This would have adaptive value when an area of behavioral function could profit from programs initially developed for another purpose” (Rozin 1976, p. 256). Rozin connects such changes in accessibility to genetic programs in which “A specialization [circuit] could be extended by releasing (or depressing) the appropriate genetic program at the appropriate time in appropriate neural context. Such extensions have probably occurred many times in the evolution of organisms” (Rozin 1976, p. 260). Dehaene’s neuronal recycling hypothesis (Dehaene 2005; Dehaene & Cohen 2007) fits with this conceptualization; “ ‘neuronal recycling’. . . refer[s] to the putative mechanism by which a novel cultural object encroaches onto a pre-existing brain system . . . (which) occurs during the life span as a result of brain plasticity” (Dehaene & Cohen 2007, p. 384). We suggest that functional demands are the driving force behind reuse and that these demands shape brain changes. Frontal control and brain connectivity. Anderson’s second assumption in his massive redeployment hypothesis (MRH) is that older areas in the brain would be more subjective to reuse (sect. 1.1, para. 1). In contrast, the frontal lobes are able to perform more functions (or are more reused, in Anderson’s words) than lower and older areas (Miller 2000). The assumption that higher and more novel areas in the brain perform more functions can be explained by their connectivity. Specifically, it has been suggested that the prefrontal cortex (PFC) is “built for control,” because it is composed of several interconnected areas that are linked to cortical sensory and motor systems and to a wide range of subcortical structures, so that it is provided with the ability to synthesize a wide range of information. Miller (2000) and Duncan (2001) suggested that the characteristics of the system and its connections allow flexibility that enables the system to adjust and control different situations. In addition, the PFC has widespread projections back to lower systems, which allow for a top-down influence. These features make it reasonable to assume that the PFC is built as an infrastructure for multi-functions, rather than as a module to be reused. Attention. In visuo-spatial attention, responding is commonly faster and more efficient at cued (valid) than non-cued (invalid) locations. In exogenous-reflexive orienting of attention this validity effect is replaced, after 300 msec from cue onset, by faster responding to non-cued locations. This was described as inhibition of return (IOR), which helps to avoid automatic returning to already searched locations and is dependent on involvement of the midbrain superior colliculus (Klein 2000; Posner & Cohen 1984; Sapir et al. 1999). It has been suggested that the evolutionarily older retinotectal visual system developed a mechanism (IOR) which, through connections with higher brain structures (e.g., parietal lobe; Sapir et al. 2004), enabled coordination of reflexive and voluntary attentional systems (Sapir et al. 1999). Connectivity with other brain areas helped to transfer control to higher brain centers. Anterior cingulate cortex (ACC) – “Dedicated to one highlevel use.” In his target article, Anderson (suggests that “an indi-

vidual brain region . . . will not be dedicated to . . . one high-level use” (sect. 2.1, para. 2). Anterior cingulate cortex (ACC) function is of interest here. There is wide agreement that the ACC is involved in conflict monitoring (Botvinick et al. 2004; Kerns et al. 2004). However, recent reports indicate that the ACC and close structures are also involved in outcome evaluation and in reward-based action (Botvinick et al. 2004; Ridderinkhof et al. 2004). Such results suggest that conflict monitoring may be a manifestation of a more general function of the ACC.

Specifically, the ACC is involved in monitoring and evaluating the outcomes of actions, and, in turn, serves to mold goaldirected behavior and achievement of planned behavior. Numerical cognition. In the area of numerical cognition, many assume that the ability to grasp the number of displayed objects (e.g., counting) is an essential part of the core system that enables the development of the number sense and arithmetic skills. However, there are clear indications for a connection between numerical processing and size perception and judgment (Ashkenazi et al. 2008; Henik & Tzelgov 1982). Accordingly, it is possible that another system, heavily dependent on the processing of size, is the antecedent for the human numerical system. Namely, routines and neural structures built for size judgments were made available, through evolution, due to the need to develop an exact numerical system. Cantlon and colleagues (Cantlon et al. 2009) presented a similar idea: “a system that once computed one magnitude (e.g., size) could have been hijacked to perform judgments along a new dimension (e.g., number)” (p. 89). Summary. We suggest that functional demands shape brain changes and are the driving force behind reuse. This is a different point of view, rather than just a terminology change.

From the physical to the psychological: Mundane experiences influence social judgment and interpersonal behavior doi:10.1017/S0140525X10000993 John A. Bargh,a Lawrence E. Williams,b Julie Y. Huang,a Hyunjin Song,a and Joshua M. Ackermanc a

Department of Psychology, Yale University, New Haven, CT 06520; bLeeds School of Business, University of Colorado at Boulder, Boulder, CO 803090419; cSloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142. [email protected] [email protected] [email protected] [email protected] [email protected] www.yale.edu/acmelab leeds-faculty.colorado.edu/lw/ web.mit.edu/joshack/www/

Abstract: Mere physical experiences of warmth, distance, hardness, and roughness are found to activate the more abstract psychological concepts that are analogically related to them, such as interpersonal warmth and emotional distance, thereby influencing social judgments and interpersonal behavior without the individual’s awareness. These findings further support the principle of neural reuse in the development and operation of higher mental processes.

The principle of neural reuse and the various competing theories regarding its underlying mechanisms are of great value to the understanding of a growing body of findings within social psychology – those in which concrete physical sensations are shown to influence higher-order processes involved in trust, interpersonal and situational evaluation, and interpersonal behavior. For example, briefly holding a cup of hot (versus iced) coffee just before an impression formation task involving the identical set of information about a given target person changes that impression (Williams & Bargh 2008a): those who had contact with the warm cup subsequently judged the person as warmer (more prosocial, generous, helpful; see Fiske et al. 2007) than did those in the cold-coffee condition. (The effect was specific to variables related to interpersonal warmth, and not an overall positivity effect, as the coffee-temperature manipulation did not affect impression judgments on dimensions unrelated to prosocial behavior.) In a second study, those in the warm-coffee condition were more likely to give their compensation for being in the experiment to a friend (in the form of a BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain gift certificate), whereas those in the cold-coffee condition were more likely to keep it for themselves. Thus, physical experiences of warmth directly influence perceptions of psychological warmth in another person, as well as the participant’s own behavioral warmth towards others (see also IJzerman & Semin 2009; Zhong & Leonardelli 2008). Similarly, perceptions of physical distance produce corresponding analogous influences on perceptions of psychological and emotional distance. Merely plotting two points on Cartesian graph paper that are relatively far versus close together on the page causes participants to feel more psychologically distant from their friends and family, and, in further studies, to show less physiological reactivity to emotionally laden photographs (i.e., more emotionally distant; see Williams & Bargh 2008b; Williams et al. 2009a). In both cases, these effects were predicted in part from the observed ubiquity of priming effects in social psychology in which incidental stimuli are shown to influence higher-order cognitive and behavioral outcomes without the individual’s awareness or appreciation of this influence (see, e.g., Dijksterhuis et al. 2007). These priming effects have become so prevalent that the prevalence itself requires an explanation (Bargh 2006). Ours (Bargh & Morsella 2008; Williams et al. 2009b) involved the notion of scaffolding, in which the development of more abstract concepts is said to be grounded in earlier-formed concrete concepts (such as spatial concepts that form in infancy and young childhood out of the comprehension of the physical world; Clark 1973; Mandler 1992), or exapted from pre-existing innate structures such as evolved motivations for reproduction and survival (Huang & Bargh 2008). In this manner, associative connections develop between the original physical and the analogous later psychological versions of the concept (warmth, distance), creating multiple physical avenues for psychological priming effects in adults. It is also possible that such warmth and distance effects have an innate basis. The attachment theorist John Bowlby (1969) notably argued that distance information was of survival relevance to many, if not all, organisms, because it facilitates both keeping close to caretakers when young and vulnerable, as well as the dispersal of conspecifics to reduce competition for scarce resources, as in territoriality behavior. And, at least in the case of primates, Harlow’s (1958) pioneering studies of monkeys raised alone showed the importance of early warmth experiences in infancy for successful social functioning as adults; those raised with a cloth mother, with a 100-watt light bulb behind the cloth, adapted much better than did the other parent-less monkeys. The physical-to-psychological effects are not limited to warmth and distance, and may instead represent a general phenomenon involving many forms of sensory experience. For example, six experiments reported recently by Ackerman et al. (2010) reveal how the sense of touch influences analogously related psychological variables. Holding a relatively heavy (versus light) clipboard on which to evaluate a job candidate causes evaluators to see the candidate as more serious (heavy ¼ serious) about his or her work and also causes the evaluators to take their own judgment task more seriously (they spend significantly longer on it). Working on a jigsaw puzzle with a rough versus smooth surface causes participants to subsequently rate an interpersonal interaction as going less (versus more) smoothly. Likewise, sitting on a hardwood versus cushioned chair produced greater rigidity (less attempt to compromise) in an interpersonal negotiation task. Taken together, these demonstrations suggest a cognitive architecture in which social-psychological concepts metaphorically related to physical-sensory concepts – such as a warm person, a close relationship, and a hard negotiator – are grounded in those physical concepts, such that activation of the physical version also activates (primes) the more abstract psychological concept. Again, as in most priming research involving these social-psychological concepts and variables, the experimental participants are unaware of these potentially biasing

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influences on their social judgments and behavior and so do not correct or adjust for them (Wilson & Brekke 1994). The principle of neural reuse – specifically, that “local circuits may have low-level computational ‘workings’ that can be put to many different higher-level cognitive uses” (sect. 1.1, para. 5) – also helps to explain how activation of presumably evolved motivations, such as the mating (reproduction) goal, can exert influences outside of its focal domain of mating – effects that are difficult to understand under the principles of anatomical modularity or functional localization. For example, priming the mating goal influences the evaluation of other living kinds (flowers, fruits), as well in terms of “prime” life stages (Huang & Bargh 2008). Viewed in terms of the principle of reuse, this finding suggests that the mating goal makes use of a “prime lifestage” appraisal circuit, which is activated when the mating goal is primed and is thus influential in other domains as well, not exclusively mate selection. Overall, these findings are in harmony with Anderson’s central point that our mental carriers of meaning are tied to sensory experience to such an extent that one’s physical state exerts a pervasive and often unconscious influence over the workings of the mind.

Neural reuse and cognitive homology doi:10.1017/S0140525X10001111 Vincent Bergeron Department of Philosophy, University of Ottawa, Ottawa, ON K1N 6N5, Canada. [email protected]

Abstract: Neural reuse theories suggest that, in the course of evolution, a brain structure may acquire or lose a number of cognitive uses while maintaining its cognitive workings (or low-level operations) fixed. This, in turn, suggests that homologous structures may have very different cognitive uses, while sharing the same workings. And this, essentially, is homology thinking applied to brain function.

The study of human cognition is, in many ways, linked to the study of animal cognition. This is perhaps most apparent if one considers the large number of animal models of human cognitive functions developed in the past few decades. In memory research, for example, various forms of memory or memory systems have been modeled extensively in other species – for example, long-term memory in rats, working memory in nonhuman primates. Vision research provides another good example, where a great deal of our current knowledge of the human visual system comes from an extensive mapping of the macaque monkey’s visual system. A less obvious candidate is the study of language. Despite it being a uniquely human cognitive capacity, there is mounting evidence that experimental work in nonhuman primates may illuminate various aspects of language processing (Petrides et al. 2005; Rauschecker & Scott 2009; Schubotz & Fiebach 2006). In using animal data to explain human cognitive functions, one must assume that there is sufficient evolutionary continuity between the human brain and that of other species. Not all animal data are equally relevant, of course, and whether a piece of data in a given species appears to be relevant to human studies depends on the interplay between several different factors, such as the kind of cognitive systems involved, the evolutionary distance between the two species, and the particular experimental methods used. For example, basic neurobiological mechanisms like long-term potentiation can be studied in evolutionarily distant animals such as Aplysia and rats, whereas higher cognitive functions like executive functions are best studied in evolutionarily closer species such as nonhuman primates. In its simplest form, this evolutionary continuity assumption is uncontroversial. The human brain shares many of its principles

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain and functions with that of other species; and for any human cognitive function, we can expect that (at least) some component(s) of it could be found in the cognitive repertoire of another species. What is less clear, however, is how best to exploit this evolutionary continuity in building models of human cognition. This is the challenge of finding precisely which components of human cognitive functions can be successfully studied in other species. Anderson’s target article, and neural reuse theories in general, provide a unique perspective on how to accomplish this task. Central to the concept of neural reuse is a distinction between two concepts of function, namely, “working” and “use.” The cognitive workings of a brain structure (e.g., Broca’s area) are the low-level operations that it performs, whereas the cognitive uses of that structure are the higher-level operations (or capacities) to which it contributes. What neural reuse theories suggest is that, in the course of evolution, a brain structure may acquire or lose a number of cognitive uses while maintaining its cognitive workings fixed. This, in turn, suggests that homologous structures may contribute to very different cognitive capacities, and thus have very different cognitive uses, while sharing essentially the same low-level internal operations or workings. And this, one might think, is homology thinking applied to brain function. The idea of functional homology may seem confused at first (Love 2007). After all, the concept of homology was originally defined as “the same organ in different animals under every variety of form and function” (Owen 1843, p. 379), where sameness is defined by common phylogenetic origin. And in fact, homologous brain structures will often have very different functions. For example, Broca’s area, unlike its homologue in the macaque monkey (Petrides et al. 2005), is heavily involved in language and music processing (Patel 2003). However, as we have just seen, the fact that these two structures appear functionally dissimilar based on a comparison of their cognitive uses obscures the fact that they may share the same workings. By specifying the workings of the two structures independently of their specific uses, as neural reuse theories suggest we do, one could test whether this is in fact the case. Recent models of Broca’s area’s workings (Schubotz & Fiebach 2006) provide a first step. For example, Fiebach and Schubotz (2006) propose that Broca’s area may function as a hypersequential processor that performs the “detection, extraction, and/or representation of regular, rule-based patterns in temporally extended events” (p. 501). As the model attempts to explain Broca’s area’s contribution to complex, behaviorally relevant sequences that are also present in nonhuman primates (e.g., action sequencing and the manipulation of objects), and because there is a homologue of the area in the macaque monkey, Fiebach and Schubotz’s account of Broca’s area’s workings appears be a good candidate for a cognitive homology – that is, the same workings in different animals regardless of cognitive use, where sameness is defined by the common phylogenetic origin of the associated structures (see also Love 2007 for a similar proposal regarding “homology of function”). Anderson’s discussion of the spatial-numerical association of response codes (SNARC) effect (Dehaene et al. 1993) provides another illustration of how homology thinking might apply to cognitive function. When subjects are asked to classify numbers as even or odd by making their responses on either the right or the left side of space, their responses to larger numbers are faster when made on the right side of space, whereas responses to smaller numbers are faster when made on the left side of space. Hubbard et al. (2005) review several lines of evidence in monkeys and humans that point to a region in the intraparietal sulcus as the site of this interaction between numerical and spatial cognition. They hypothesize that the interaction arises because of the common involvement, in both attention to external space and internal representations of numbers, of a particular circuit in this region. Here again, we can think of their account of the workings of this brain structure in both monkeys and humans as a cognitive homology.

Homology thinking applied to brain structures is already an integral part of cognitive neuroscience. The perspective offered by neural reuse theories allows us to extend homology thinking to brain function.

Neural reuse implies distributed coding doi:10.1017/S0140525X10001007 Bruce Bridgeman Department of Psychology, University of California at Santa Cruz, Santa Cruz, CA 95064. [email protected] http://people.ucsc.edu/bruceb/

Abstract: Both distributed coding, with its implication of neural reuse, and more specialized function have been recognized since the beginning of brain science. A controversy over imageless thought threw introspection into disrepute as a scientific method, making more objective methods dominate. It is known in information science that one element, such as a bit in a computer, can participate in coding many independent states; in this commentary, an example is given.

The tension between interpreting the brain as a collection of specialized areas and as a distributed network is as old as brain research itself. Rejecting the medieval idea that the brain was unitary because the soul was indivisible, nineteenth-century phrenologists emphasized modularity. Although these phrenologists got the details wrong because of inadequate methods, they introduced the idea of different functions being handled by distinct cortical areas. The idea was made concrete by neurologists such as Fritsch and Hitzig (1870/1960) (sensory and motor areas), Broca (1861) and Wernicke (1874) (language areas), and many others. Distributed coding and the neurological evidence for it came from Karl Lashley’s (1929) mass action, through his student Karl Pribram’s (1971) distributed coding, to presentday parallel distributed processing. The contrast between the “concept empiricists” and the rational or amodal concept also has a long history, far more than “the last twenty years or so” (as Anderson writes in sect. 4, para. 3) and unknown to most philosophers. The idea that “the vehicles of thought are re-activated perceptual representations” (Weiskopf 2007, p. 156) – which Anderson refers to in this section (same paragraph) – was championed at Cornell by Titchner, a student of Wilhelm Wundt. He fought a long battle with the followers of Ku¨lpe at Wu¨rzburg, who saw mental life as built of a great hierarchy of ideas. The controversy was defined as an evaluation of the role of imageless thought. Ku¨lpe insisted that some ideas had no associated images, and that Titchner just hadn’t found those ideas yet. Titchner, in turn, held that all ideas included images, and that Ku¨lpe hadn’t found the images yet. Each founded a school to press his introspection, and the battle raged around the turn of the twentieth century. Eventually, the whole controversy blew up in their faces, as it became clear that the introspective method could not resolve the issue. Objective data, not private opinions, were necessary for psychology to become scientific. Tragically, philosophers of mind continue to use the discredited method, disguised in phrases such as “it is obvious that” or “a moment’s thought will reveal that.” Introspection is a good starting point for an investigation, but it can never be an ending point. The essential features of the “action-sentence compatibility effect” are also older than Glenberg and Kaschak (2002) (referred to in section 4.1, para. 2, of the target article). An obvious example is the Stroop effect (Stroop 1935), well known to cognitive psychologists. Color naming is easy when a printed color name is in the corresponding color, but difficult when the color and name are incompatible, such as the word “blue” printed in red ink. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain There are really two parts to the reuse hypothesis: First, a given brain area can be involved in processing functions of more than one kind; and second, a brain area that evolves to perform one function can later be pressed into service to participate in performing other related functions as well. Models like Hurley’s (2008, p. 41) may be too specific in assigning hardwired logic to each problem. It is like tracing the functions of my word processor, or my spreadsheet, through the hardware of my computer. Reuse implies that the hardware can be more flexible, like the general-purpose hardware in my computer that supports a variety of software in the same array of logic elements. In this light, can cognitive functions be independent when they have overlapping neural implementations? Of course. For example, numbers in a computer’s register do not gain their meaning from any particular bit. Rather, it is the combination of bits, 16 or 32 at a time, that determines what is represented. With 16 bits operating as independent detectors, a brain could store 16 different events. But when combined as a binary number, the same 16 bits can code more than 64,000 distinct states. As the number of elements available increases, the combinatoric advantage of this distributed coding becomes overwhelming. Given the large swaths of brain involved in almost any mental operation, neural reuse becomes inevitable.

Sensorimotor grounding and reused cognitive domains doi:10.1017/S0140525X10001123 Maria Brincker Department of Philosophy, Graduate Center, City University of New York, New York, NY 10016. [email protected] sites.google.com/site/mariabrincker/

Abstract: Anderson suggests that theories of sensorimotor grounding are too narrow to account for his findings of widespread “reuse” supporting multiple different cognitive “task domains.” I call some of the methodological assumptions underlying this conclusion into question, and suggest that his examples reaffirm rather than undermine the special status of sensorimotor processes in cognitive evolution.

Anderson’s massive redeployment hypothesis (MRH) proposes that “reuse” of local cognitive circuits is a general evolutionary principle. “Reuse” is understood as the exaptation of cognitive circuits to new cognitive uses, while retaining prior but separate functions. The evidence for widespread reuses is based on statistical analyses of overlapping activations across predetermined task domains in a wide array of fMRI studies. On this basis, Anderson raises a two-pronged objection to theories of sensorimotor grounding: (1) That they cannot explain all his findings of reuse, and (2) that the functional properties of sensorimotor circuits are not special in regard to evolutionary reuse, nor in grounding higher cognition; these are simply older circuits and hence reused more in evolution. While I am deeply sympathetic to the project of questioning modularity and investigating neural co-activations and overlaps, I am puzzled by Anderson’s approach and suspicious of his conclusions. I propose that his assumptions about “reuse” and “task domains” seem implausible from such a sensorimotor grounding point of view – and hence that his arguments against such theories lose their bite. Anderson analyzes findings of fMRI activation overlaps in terms of predefined “task domains” (such as visual perception, action execution, inhibition, emotion, memory, attention, language, etc.); and given this methodology, he finds significant activation overlaps in regions beyond typical perceptual or motor areas for multiple, typically “cognitive” tasks domains.

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He concludes that sensorimotor theories are too narrow to accommodate such findings of “reuse.” In spite of many admittedly ambiguous expressions, the idea of sensorimotor grounding is not that all cognitive processes are localized in areas supporting typical action output or perception input. Rather, generally the core claim is that brains develop and have evolved on the basis of and in support of sensorimotor engagements between animal and environment (Clark 1997; Glenberg 2010; Haggard et al. 2008; Hurley 1998; Nunez & Freeman 2000). In short, it is not simply about location, but also about evolution and development. But how can we tell whether fMRI activation overlaps are due to evolutionary “reuse,” rather than simply repeated use of the same functional circuit? Anderson’s answer seems to be that, “For neural reuse theories, anatomical sites have a fixed working, but many different uses” (sect. 3.3, para. 3). That is, exaptation does not imply an evolutionary change in the local circuit, but simply a reuse of this very circuit to form a new combination with other circuits to support a new cognitive function. This sort of atomistic combinatorial idea features prominently in Anderson’s methodological equation between fMRI activation overlaps and evolutionary reuse: “Reuse” simply is repeated use of the same anatomical circuit across task domains (sect. 4.4., paras. 4 – 5). Anderson himself notes his theory does not address how the brain circuits have expanded and changed over the course of evolution. This is, however, a central issue for sensorimotor grounding theories, and such a perspective precisely calls Anderson’s notion of reuse and methodology of counting task domains into question. First, primitive cognitive circuits might be multifunctional at the outset – that is, supporting not only action and perception, but also other of Anderson’s “task domains” such as, for example, primitive attention, emotion, and memory functions. Secondly, differentiation from within, in concert with newer cognitive circuits, could form cognitive support systems for increasingly more complex organism-environment engagements. Accordingly, cognitive exadaptions could involve both old and new anatomical regions, and local activation overlaps might be the result of either “repeated use” of already evolved processes, or of evolutionary “reuse.” Anderson’s key assumptions that (1) neural activation overlaps ¼ evolutionary “reuse” and (2) his statistical use of predefined “task domains” are therefore questionable. And, given a sensorimotor grounding of reuse and “task domains,” there is no obvious incompatibility between findings of areas outside the sensorimotor system, say, medial prefrontal regions, being involved in multiple higher cognitive tasks such as memory, imagery, or motivation – or that other additional “cognitive domains” such as attention would interact with these “default network” processes (Bruckner et al. 2008). Anderson uses the phonemic character of human speech as an example of a reuse exadaption. His discussion is illustrative in that it shows how he assumes that certain abilities or “task domains” as functionally independent and to a certain extent reified by their cognitive purpose independently of the actual neurobiological instantiation that they happened to get. He describes (via Graziano et al. 2002b) how the evolution of phonemic speech piggybacked on the specifics of the preexisting motor control mechanism organized around endpoint postures. So far so good. But then he writes: “Had the motor control system been oriented instead around (for example) simple, repeatable contractions of individual muscles . . . the result of the inheritance might have been a communication code built of more purely temporal elements, something closer to Morse code”(sect. 4.6, para. 4). Anderson here assumes that complex symbolic and structured language could have evolved absent a motor system organized around perceptual end-goals in abstraction from the precise physical vectors of the kinetic movements. Maybe so, but he makes the tacit assumption that one can separate the sophisticated cognitive function of language not only from its

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain phonetic character and the concrete physical constraints of the vocal system, but also from what might be a core organizing principle of motor control, namely, sensorimotor goal or end-state representations (Gallese 2003; Hommel et al. 2001; Rizzolatti et al.1988). In my work on mirror neurons and sensorimotor integration (Brincker, forthcoming), I argue that this organization of the motor system exactly presents a seed for abstraction that can be exploited for higher cognitive processes, including language. Accordingly, one might think that sign language could have evolved without our specific vocal system but probably not without sensorimotor end-state organizations. In summary, Anderson’s assumptions differ significantly from the essential ideas of sensorimotor grounding, namely, that there is something about the basic biological acting and perceiving organism that structures the evolution and development of higher cognition. His findings of neural activation overlaps are not incompatible with sensorimotor grounding per se, as these statistical findings simply suggest that neural regions are used independently of sensorimotor engagements and say nothing about whether their evolution and primary function can be understood independently of such.

The importance of ontogenetic change in typical and atypical development doi:10.1017/S0140525X10001019 Tessa M. Dekker and Annette Karmiloff-Smith Centre for Brain and Cognitive Development, Birkbeck College, University of London, London WC1 7HX, United Kingdom. [email protected] [email protected] http://www.psyc.bbk.ac.uk/research/DNL/personalpages/tessa.html http://www.bbk.ac.uk/psyc/staff/academic/annettekarmilofsmith

Abstract: The compelling case that Anderson makes for neural reuse and against modularity as organizing principle of the brain is further supported by evidence from developmental disorders. However, to provide a full evolutionary-developmental theory of neural reuse that encompasses both typical and atypical development, Anderson’s “massive redeployment hypothesis” (MRH) could be further constrained by considering brain development across ontogeny.

Neural reuse is the notion that new cognitive skills are comprised of recombined and reused neural solutions, rather than independently evolved modules. In Anderson’s version of such theories, the “massive redeployment hypothesis” (MRH), he predicts that newer cognitive functions will be more scattered across the brain. His reasoning is that local neural circuits have fixed internal workings across evolutionary time, which enables solutions to newer evolutionary problems to draw upon a more widely spread out set of neural building blocks. By providing evidence that all cognitive domains overlap and are distributed across the brain, Anderson convincingly negates the need for implementation of cognitive functions as sets of independently evolved, localized modules in the brain, and, at the same time, makes a compelling case for neural reuse. In our view, however, the MRH falls short of providing a full evolutionarydevelopmental explanation of brain organization because the roles of ontogenetic change and plasticity across the life span are overlooked. In fact, one of the strongest lines of evidence against modular organization in the brain comes from in-depth analyses of developmental disorders across ontogeny. Although impairments in developmental disorders seem to be specific to particular cognitive domains and are often taken as evidence for innately specified modularity, this turns out not to be the case. On closer inspection, claims about intact and impaired cognitive modules have consistently overlooked subtle deficits in “intact” domains and have failed to trace cognitive-level impairments in the

phenotypic outcome back to their basic-level origins in infancy; that is, they do not account for the full atypical cognitive spectrum over developmental time (see discussions in KarmiloffSmith 1998; 2009; Southgate & Hamilton 2008). Take, for example, the case of Williams syndrome (WS), caused by a hemizygous deletion of genes on chromosome 7, resulting in decreased expression of affected gene products throughout the brain from conception onwards. Although the effects of the deletion may be superficially more apparent in certain cognitive domains, in fact they turn out to be widespread across the multiple cortical regions where the genes are expressed and are therefore highly unlikely to be specific to single domain-specific modules. Indeed, in WS, impairments across several domains such as face processing, number, auditory and spatial perception (Brown et al. 2003; Elsabbagh et al., in press; Paterson et al. 1999; Van Herwegen et al. 2008) can be traced to a featural processing bias in infancy (Karmiloff-Smith et al. 2004), which itself is likely to be due to very early atypical saccadic eye movement planning (Karmiloff-Smith 2009). Theories that explain WS in terms of intact and impaired, innately specified modules are based on static descriptions of the phenotypic end state (Bellugi et al. 1999; Pinker 1994; Rossen et al. 1996), ignoring the complex dynamics of development. In contrast to modular theories of the brain, theories of neural reuse are far more likely to explain why pure cognitive deficits in specific brain regions have been so difficult to identify. How the massive redeployment theory of neural reuse could give rise to adult-like brain organization across the life span needs to be specified further, however. Firstly, it remains unclear whether Anderson considers locally fixed internal workings to be already present at birth – in which case one innately specified mechanism (modules) is simply being replaced by another (fixed internal neuronal workings) – or whether his approach encompasses the development of such neural functions over ontogeny. On the one hand, aspects of neuronal differentiation may indeed emerge early in development through intrinsic factors that determine cortical connections, causing cortically localized functions to be highly preserved across individuals, cultures, and even species (but see Han & Northoff 2008; Orban et al. 2004). On the other hand, research on brain plasticity shows that developmental pressures can dramatically reshape the inner workings of neurons. Most strikingly, this is illustrated by classic studies in which developing patches of cortex received abnormal sensory input. For example, when ferret auditory cortex neurons were rewired to receive visual input, and visual cortex neurons to receive auditory input, the inner workings of both types of neurons changed. The auditory cortex took on characteristics and assumed functions of the visual cortex and vice versa (von Melchner et al. 2000). A neuroconstructivist approach to brain development reconciles these two apparently contradicting sets of findings by suggesting that early differentiation may render certain parts of the cortex more relevant to performing certain functions. However, these initial systems are coarsely coded, and competition between regions gradually settles which regions with domain-relevant biases become domain-specific over time, ultimately giving rise to the structured adult brain (e.g., Johnson 2001; Karmiloff-Smith 1998; 2009). A second issue that remains unclear is whether recombination of connections between specialized regions is the only mechanism that Anderson considers relevant, leaving no role for localized plasticity of neural computation in response to newly learnt tasks such as mathematics and reading. Dehaene’s neuronal recycling hypothesis (2005) proposes that such culturally transmitted skills invade neural systems that are already present and that lend themselves well to performing these new tasks. If there is any difference between functions, optimizing a neural circuit with an existing function for a new task will consequently affect tasks that already relied on the same circuit. It remains unclear whether Anderson accepts this possibility or whether he maintains that inner neuronal workings are truly BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain fixed, which would imply that learning a new task (e.g., reading) should never adversely affect other tasks that depend on the shared neuronal circuitry (e.g., object processing). To summarize, we maintain that the consideration of ontogenetic change and developmental disorders can provide vital evidence for the organizational principles of the brain, principles that run counter to modular views. We agree with Anderson that neural reuse is a promising organizing principle of the brain, as opposed to the notion that the brain has evolved into a Swiss army knife with innately specified modules uniquely designed for each new cognitive function. However, we suggest that Anderson’s massive redeployment hypothesis could be further constrained by considering brain development across ontogeny in order to provide a full evolutionary-developmental theory of neural reuse that encompasses both typical and atypical development.

How and over what timescales does neural reuse actually occur? doi:10.1017/S0140525X10001184 Francesco Donnarumma, Roberto Prevete, and Giuseppe Trautteur Dipartimento di Scienze Fisiche, Universita` di Napoli Federico II, Complesso Universitario Monte Sant’Angelo, I-80126 Napoli, Italy. [email protected] [email protected] [email protected] http://vinelab.na.infn.it

Abstract: We isolate some critical aspects of the reuse notion in Anderson’s massive redeployment hypothesis (MRH). We notice that the actual rearranging of local neural circuits at a timescale comparable with the reactivity timescale of the organism is left open. We propose the concept of programmable neural network as a solution. Reuse, working, function. Merriam-Webster’s Collegiate Dictionary, 11th edition, gives the definition of reuse as: “to use again, especially in a different way or after reclaiming or reprocessing.” Thus, for example, the well-known evolutionary sequence from jaw bones of reptiles to the ossicles of mammalian ears may be taken as an instance of an acoustic reuse of the manducatory reptilian jaw bones after an (extensive and) exaptive “reprocessing.” Is this the use of “reuse” (no pun intended) in the target article? Notice that, in the above example, reuse completely obliterates original use. On the contrary, the overwhelming connotation of the term one gleans from an overview of the target article is: “new use or uses, without losing the original function.” In the article’s Note 5, Anderson clarifies the meaning of working: “brain regions have fixed low-level functions (‘workings’) that are put to many high-level ‘uses’.” “Function” or “functionalities” occur in contexts in which it is difficult to separate their meanings from working, or cortical bias, except on the basis of the granularity of the neural circuits considered. “Working” is used for local circuits; “function,” for overall cortical, cognitive behavior. Drawing on numerous excerpts of the article, we summarize the gist of the reuse idea in the massive redeployment hypothesis (MRH), and stress the timescale aspect, as follows: The brain – at least, but not exclusively, in sensorimotor tasks – obtains its enormously diversified functional capabilities by rearranging in different ways (i.e., putting to different uses) local, probably small, neural circuits endowed with essentially fixed mini-functionalities, identified as “workings,” and does so on a timescale comparable with the reactivity timescale of the organism. There is one exception where reuse seems to originate in the circuit itself – as contrasted with the empirical rejection of

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“small neural regions [were] locally polyfunctional” (sect. 1.1, para. 5) – and not in the putting together of circuits: “in at least some cases, circuit reuse is arranged such that different data – both information pertaining to different targets, as well as information about the same targets but at different levels of abstraction – can be fed without translation to the same circuits and still produce useful outputs” (sect. 6.2, para. 9; emphasis Anderson’s). A surprising disconnection occurs, though, with respect to timescales. Indeed, Anderson states: “Massive redeployment is a theory about the evolutionary emergence of the functional organization of the brain” (sect. 6.4, para. 1). But the actual reuse of neural circuits must occur at the timescale of the organism’s intercourse with the environment, as we stressed above. Any “evolutionary emergence” does not explain how the mechanism of reuse is deployed at real time. Synaptic plasticity is of no use here, both because of its slower timescale with respect to the reactivity timescale and because the synaptic structure of the neural tissue gets altered and the previous function is lost. Indeed, plasticity is very aptly distinguished, in the target article’s Abstract, from reuse and, therefore, from learning. Need of programming. The conundrum implicit in the MRH, succintly stated in the quote we chose for our title, is as follows: Evolutionary or exaptive processes have determined a structure of synaptic connections, which must be considered as fixed over current reactivity timescales, bringing about all possible useful “arrangements” of local circuits which give rise to the multiplicity of cognitive functions. But how can a fixed structure deploy at reactivity time selectivity over the specific prewired arrangements? How can a specific routing of connections be selectively enabled at reactivity time, if the connections are fixed? The answer is, by programming. Anderson almost says so: “I have used the metaphor of component reuse in software engineering” (he writes in sect. 6.4, para. 3; our emphasis) – but then he argues against assuming the metaphor as literal. Fixed-weight programmable networks. We propose a model that allows real-time programmability in fixed-weight networks, thus solving the conundrum. The model is realized in the Continuous Time Recurrent Neural Networks (CTRNNs) environment. CTRNNs are well known, neurobiologically plausible, modeling tools – as attested, for instance, by Dunn et al. (2004). The architecture we developed sustains a programming capability which is usually associated with algorithmic, symbolic systems only. By means of this architecture one can design either local circuits or networks of local circuits having the capability of exhibiting on-the-fly qualitative changes of behavior (function) caused and controlled by auxiliary ( programming) inputs, without changing either connectivity and weights associated with the connections. The main idea underlying this approach is as follows: The post-synaptic input to biological neurons is usually modeled in artificial neural networks – and it is so in CTRNNs – as sums of products between pre-synaptic signals originating from other neurons, and the weights associated with the synapses. So, the behavior of a network is grounded into sums of products between presynaptic signals and weights. In the proposed architecture, we “pull out” the multiplication operation by using auxiliary (interpreting) CTRNN sub-networks providing the outcome of the multiplication operation between the output of the pre-synaptic neuron and the synaptic weight. In this way, one obtains a Programmable Neural Network (PNN) architecture with two kinds of input lines: programming input lines fed to the interpreting CTRNN subnetworks, in addition to standard data input lines. As a consequence, a PNN changes on the fly the mapping (working/function) it is performing on standard input data, on the basis of what is being fed into its programming input lines. Notice that a PNN is strictly fixed-weight. More importantly, notice that the two kinds of input signals are different only on a contextual basis. If input signals are

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain fed to the appropriate lines, then they will be interpreted as code, but – as in programming practice – they have the nature of data, and, as such, can be processed or originated by other parts of a complex network. The proposed solution. By using PNNs, one can develop an artificial neural network composed of fixed, that is, non-programmable, local neural circuits which can be rearranged in different ways at “run-time” by programmable, and still fixed-weight, routing networks. The local circuits will be thus reused in different arrangements, giving rise to different overall functions and cognitive tasks. But PNNs are also hypothetical models for fully programmable local networks, thus suggesting an answer to the “exception” we mentioned above. There we take “without translation” to mean that those data are fed to the programming inputs – an enticing possibility. Bibliographical notice. The seminal motivations for programming neural networks, in a more general setting than that of reuse, are expounded in Tamburrini and Trautteur (2007) and Garzillo and Trautteur (2009); some toy realizations were presented in Donnarumma et al. (2007), and a full implementation of the concept is reported in Donnarumma (2010).

Sleep, neural reuse, and memory consolidation processes doi:10.1017/S0140525X10001135 William Fishbein, Hiuyan Lau, Rafael DeJesu´s, and Sara Elizabeth Alger Laboratory of Cognitive Neuroscience and Sleep, The City College and Graduate Center, The City University of New York, New York, NY 10031. [email protected] [email protected] [email protected] [email protected]

Abstract: Neural reuse posits development of functional overlap in brain system circuits to accommodate complex evolutionary functions. Evolutionary adaptation evolved neural circuits that have been exploited for many uses. One such use is engaging cognitive processes in memory consolidation during the neurobiological states of sleep. Neural reuse, therefore, should not be limited to neural circuitry, but be extended to include sleep-state associated memory processes.

Anderson’s neural reuse hypothesis posits the development of functional overlap in brain system circuits to accommodate increasingly complex and evolutionarily more advanced functions. The notion of reuse is also consistent with many researchers’ thinking regarding multiple functions of brain circuitry. The work in our laboratory centers around the ongoing processes of cognitive functions during sleep, and its various stages, that might be associated with different forms of memory, including implicit, explicit, and emotional salient memories with specific yet overlapping or instantiated neural circuits associated with each. Yet we operate with the implied assumption that memory is not the sole function of sleep, but an evolutionary epiphenomena that has played a central role in the development and retention of complex and advanced cognitive abilities. Species adaptation of the basic rest-activity cycle seen in plants and animals suggest an evolutionary adaptive aspect to this universal behavior. The development of this universal process appears to fulfill a myriad of ancillary activities. There has certainly been much debate about the functional purpose of sleep – the rest aspect of the rest-activity cycle. While there is no debate about the functional importance of eating, drinking, and engaging in sexual behavior, a clear conclusion regarding the biological state of sleep has yet to be determined. Many theories abound, and memory consolidation is one such theory. Although sleep is more likely to be an adaptive state for more vital purposes such as energy conservation, the sleeping state depends upon essential neural circuitry that hosts neurophysiological and

neurochemical dynamics important for memory processing. One such example, the cortical cycle of synchronized and desynchronized neural firing during slow wave sleep (SWS) may serve to globally reduce and restrict unsustainable synaptic growth resultant of learning experiences in wakefulness (Tononi & Cirelli 2003; 2006). At the same time, the reduction of weak synaptic connection may inadvertently enhance the signal-tonoise ratio for more significant connections that are strong enough to survive this global downscaling. Another example might be the neurophysiological and neurochemical dynamics occurring during the various stages of sleep, involving brainstem activation or hippocampal to cortical off-line activation (Busza´ki 1998; Hasselmo 1999), acting upon newly acquired information, thereby facilitating long-term memory consolidation. Several laboratories, including our own (Alger et al. 2010; Tucker et al. 2006), have provided evidence demonstrating that the neurobiological state of sleep plays an essential role in facilitating the formation of direct associative and non-associative memories. We (Lau et al. 2010), along with Wagner et al. (2004), Ellenbogen et al. (2007), and Payne et al. (2009), have extended these findings demonstrating that sleep also facilitates the formation of relational memories – the flexible representation and expression of items not directly learned. The mechanisms underlying the processing of direct associative and relational memory appear related to the physiological events occurring during SWS (Lau et al. 2010). Besides temporally coordinated physiological activities specific to the hippocampal-neocortical circuitry (Busza´ki 1998), SWS is also characterized by global synchronized oscillatory activities (Tononi & Cirelli 2003; 2006) and depressed acetylcholine level (Hasselmo 1999). Perhaps associations between items learned before sleep are strengthened and reorganized inadvertently through these widespread activities during sleep to form more energy-efficient and functionally flexible networks among existing neural substrates. Similarly, once treated as distinct and separate from that of cognition, emotions involve neural circuitry that host neurophysiological and neurochemical dynamics. The traditional limbic system theory supports the idea that neural resources (e.g., physiological or somatic) were carried out by the evolutionarily old cortex (i.e., the so-called reptilian brain), whereas cognitive processes (i.e., higher-order functions) were subserved by the neocortex. The present view, however, integrates both the limbic system and the neocortex as separate but interacting brain systems functioning in parallel. The processes of long-term potentiation (LTP), long-term depression (LTD), and neural plasticity are just some of the ways that the brain can reorganize and change its pattern of activity across cortical regions (and across modalities) in response to experiences. Following this logic, one can imagine that neural reuse, whether evolutionary old or new, also follows a similar trend whereby mental functions are mediated by separate but interdependent brain processes. In the context of emotional arousal, the domain highly implicated for such processing is the amygdala, interacting with the hippocampus, thereby playing a role in supporting the formation and storage of emotionally salient forms of declarative memories. The brain state supporting such a process appears to occur primarily during the low-voltage, fast activity of rapid eye movement (REM) and stage II sleep (DeJesu´s et al., in preparation). Therefore, the processes ongoing during the different sleep stages, stage II, SWS and REM sleep, might serve to consolidate distinct aspects of emotionally salient declarative memories. Whether it is one process or mechanism or another, it would appear that evolutionary adaptation evolved neural circuits that may have been exploited for different uses, and one such use may be the cognitive processes engaged in memory consolidation that occur during the neurobiological states of sleep. Therefore, the notion of neural reuse should not be limited to recycling of neural circuitry, but should extend to recycling of neurobiological processes that may have well served the evolutionary advancement in mammalian intelligence. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain

Reuse (neural, bodily, and environmental) as a fundamental organizational principle of human cognition doi:10.1017/S0140525X10001147 Lucia Fogliaa and Rick Grushb a Dipartimento di Studi Storico, Sociali e Filosofici, Universita` degli Studi di Siena, 52100 Arezzo, Italy; bPhilosophy Department, University of California – San Diego, La Jolla, CA 92093-0119. [email protected] [email protected] http://mind.ucsd.edu

Abstract: We taxonomize the varieties of representational reuse and point out that all the sorts of reuse that the brain engages in (1) involve something like a model (or schema or simulator), and (2) are effected in bodily and external media, as well as neural media. This suggests that the real fundamental organizational principle is not neural reuse, but model reuse.

The target article discusses a number of proposals concerning the reuse of neural mechanisms, and these fall broadly into two categories: those which are motivated primarily by representational considerations, and those which are motivated by purely neurophysiological considerations (e.g., cortical areas determined to be active during a variety of tasks). We won’t discuss the latter sort of proposals, but will focus on the former. These all involve the reuse of something like a model of some domain. They differ on how the model is reused. In one sort of case, a model of some domain D1 is used to represent, or model, some distinct domain D2. An example would be using models of space, or movement through space, to represent time. Call this domain reuse. The other sort of case is where a model of D1 is still representing the same domain D1, but serves a different function. For example, a model used for perceptual processing of environmental scenes is used to generate imagery of those same scenes. In this case, the domain represented is the same, but the function (perception, imagery, memory, planning, language comprehension) may be different. Call this functional reuse. It isn’t obvious what other sort of reuse there could be. We want to point out that, remarkably, both these sorts of reuse are not limited to neural models. Domain reuse is evident in using physical lines (or circles on clocks) to represent time, or using parts of one’s body, such as fingers, to represent numbers. Functional reuse occurs, for instance, when one uses a chess-board to not only play a game, but to plan moves by physically implementing mock sequences of moves on the board. Another example would be cultural rituals where important prior events are remembered, as opposed to performed, through re-enactment (reenactments of Civil War battles are not battles, any more than a memory of a birthday party is itself a birthday party). This suggests that what is most interesting about the human brain is not neural reuse per se, but the fact that the brain is able to use things as models, and then submit those models to both domain and functional reuse. The deep interesting principle here is model reuse. That some of these models are implemented neurally is obviously interesting, but it may not be the crucial organizational principle. Domain reuse includes, among other things, the examples falling under the heading of conceptual metaphor theory. Most generally, a representation of the source domain is used to model the target domain. Familiar examples are space being used to represent time (or money, or state transitions). But the entity reused need not be neural: Fingers can be used to model numbers; drawn circles on the ground to represent logical inclusion relations, or possible state transitions. Interestingly, the latter is a strategy widely used in cognitive-behavioral therapy where drawn diagrams can represent emotional state transitions to help patients understand their situation and possible remedies. Functional reuse includes the examples of so-called concept empiricism, among others. In concept empiricism, the idea is that some model or scheme that is used in perception, say,

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perceiving a spatial relationship such as spatial inclusion, is reused for a different function, such as imagery, information retrieval, or language comprehension (e.g., the word “in”). A related view is Grush’s emulation theory of representation (Grush 2004), which describes in detail how models used for perceptual functions can be reused for visual imagery, motor planning, and many others. Other examples include making a sensibility judgment (whether the sentences such as “kick a ball” or “kick a cloud” convey a feasible body movement), which, as the target article discusses, requires the activation of the motor circuits usually involved with modeling the body for planning and guidance of real actions. Here, a model of the body does not serve one of its primary functions, like motor planning, but is reused for a totally different purpose: language comprehension. This ability, however, seems to transcend neural models. We can take a chess-board, from its primary use of an arena in which to make moves and play a game, and reuse it to plan moves, or even to help understand why someone might have made a certain move. Of course, we could also use a neural model for the purpose. In some cases, it is not obvious whether functional or domain reuse is the best analysis. Mirror neurons, for example, could be analyzed either way. If one takes it that their proper domain is the agent’s own behavior, then using mirror neurons to model or understand another agent’s behavior would be domain reuse. On the other hand, if one takes their proper domain to be motor behavior generally, then using mirror neurons to execute behavior versus to understand another agent’s motor behavior would be functional reuse. And sometimes there are combinations of both kinds. We can use an external spatial arrangement, like a calendar, to represent time, but we can also use it for different functions: to keep a record of what actually happened at various times, to plan what we might do at different times, to communicate to someone what we want them to do at some time, and so forth. We can imagine that some might quibble with our use of the expression “model,” but in the relevant sense, what others call schemas or simulators are models, or degenerate cases of models. It also might be maintained that other kinds of reuse do not involve anything like a model – for example, some have claimed that merely reusing non-model-involving motor areas is sufficient for generating imagery. We have explained elsewhere (Foglia & Grush, in preparation) that such “simulation” or “enactive” accounts require a model in order to do their job, and we won’t rehash those points here. Our present points are that while we agree that neural reuse is interesting, it seems to us that (1) they are all examples of the reuse of one or another kind of a model, and (2) the human brain is not limited to neural models. Accordingly, we suggest that investigations into the architectural requirements for constructing, using, and reusing models, whether in neural or nonneural media, will teach us much about the brain.

Understanding brain circuits and their dynamics doi:10.1017/S0140525X10001238 Antoni Gomilaa and Paco Calvob a Department of Psychology, University of the Balearic Islands, 070XX Palma, Spain; bDepartment of Philosophy, University of Murcia, 30003 Murcia, Spain. [email protected] [email protected]

Abstract: We argue that Anderson’s “massive redeployment hypothesis” (MRH) needs further development in several directions. First, a thoroughgoing criticism of the several “embodied cognition” alternatives is required. Second, the course between the Scylla of full holism and the Charybdis of structural-functional modularism must be plotted more distinctly. Third, methodologies better suited to reveal brain circuits must be brought in. Finally, the constraints that naturalistic settings provide should be considered.

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain In his target article, Anderson points to the fact that currently available fMRI neuroimaging data clearly show that “neural reuse” or, more precisely, anatomical polyfunctionality, is a pervasive feature of brain organization. He further argues that this polyfunctionality makes it impossible to distinguish which of the various versions of cognitive embodiment proposed so far is more plausible. His main point is that the evidence just shows that multiple cortical regions are involved in multiple tasks, whereas the different theories of embodied cognition conceive in different ways the functional import of such “reuse” or polyfunctionality: as semantic grounding, as simulation of experience, or as anticipation of feedback. However, Anderson does not develop a sustained criticism of such approaches; rather, he insists in their shortcomings as general approaches to brain function. In this regard, much more could be said, precisely on the grounds of the neurophysiological evidence he discusses. Thus, for instance, “simulationist” accounts that appeal to internal models in the brain as grounding for higher cognitive functions, ought to consider the evidence that the “efference copies” are fed to a distinct brain region, as in the case of motor control, where modelling appears to take place in the cerebellum (Kawato et al. 2003), not in the motor cortex. Conversely, both simulationist and conceptual metaphor theories should explain how it is possible for the activation of the same circuits to correspond to different tasks and levels of cognitive abstraction (Gomila 2008). Second, Anderson’s approach has the potential to avoid the Scylla of full holism and the Charybdis of structural-functional modularism, but maybe not as it stands. In the article, full holism is represented by connectionist neural networks, although it refers to the more general idea that function emerges out of the interaction of basic equipotent units. Modularism, by contrast, views the brain as an aggregate of independent, decomposable, functional units with their own proprietary anatomic (maybe even genetic) structure. Anderson’s proposal requires that basic units of brain circuitry be identifiable, both structurally (say, in terms of cell assemblies) and functionally, in order to look for the different “higher-level” circuits in which they can be “used,” again both structurally (how this basic functional units can be multiply connected with many others) and functionally (what they do depending on which connectivity gets activated). The problem here is whether the requirement of a basic, independent “functionality” – in Anderson’s terminology, the “work” of the circuit, distinguishable from the “uses” to which it is put through its “redeployment” – makes any neuronal sense. In principle, it could even happen that it is not the whole basic unit that gets reused, but rather that different components are differentially involved across functions. In other words, the challenge resides in the very possibility of specifying such elementary components in the brain, given that the individuation of circuits cannot be made in the abstract, but always within a functional setting. Moreover, third, although Anderson widely uses the expression “brain circuit,” standard fMRI-based methodologies simply uncover differential, regional, metabolic activity, and are therefore inadequate to unearth brain connectivity as such. Towards the end of the target article, Anderson calls for new methodological approaches, such as multiple- or cross-domain studies; but these should avoid the limitations of subtractive methodologies. An alternative methodology in this regard is to look for common patterns of activity through different tasks. Inspired by a complex systems approach to the brain, this approach applies the analytical techniques of network analysis to find out which nodes are shared by multiple tasks (Eguiluz et al. 2005; Sporns et al. 2004). This approach initially confirms a hierarchy of levels of structural organization, suggesting that neural reuse does not characterize equally all network nodes: brain connectivity takes the structure of scale free networks. Another interesting option is tensor diffusion, a method based on structural magnetic resonance, which uncovers white matter interregional connectivity, and whose functional import has

already been shown (Behrens & Johansen-Berg 2005; Fuentemilla et al. 2009). Lastly, fourth, one may wonder how we can discover whether neural reuse does constitute an “evolutionary [. . .] strategy for realizing cognitive functions” (sect. 1, para. 3), when the data reported in support of Anderson’s framework is not ecological after all. It is noteworthy that in order to enhance neural specificity, experimental designs require a high degree of sophistication; a form of manipulation that, although needed, prevents us from knowing whether the results thus obtained still hold true under naturalistic settings. For example, we do not know if, say, the Fusiform Face Area responds to “ecological” faces in the wild (Spiers & Maguire 2007). Hence, in our view, beyond the exploitation of methodologies other than fMRI in order to be able to properly speak of “brain circuits,” the explanation of how structure relates to function requires paying closer attention to the way the environment and the body constrain the sensory and cognitive structure and function in naturalistic, non-taskevoked, settings. In fact, task-evoked responses promote a static interpretation of brain function, which is orthogonal to the spirit of the version of embodiment that underlies Anderson’s MRH. Anderson presents his MRH as a general account of how structure relates to function in the brain. His view somehow reminds us of Simon’s (1962/1982) monograph, “The architecture of complexity.” Even if Anderson does not mention the term “hierarchy” in his proposal, it seems to be implicit in his conception of a set of basic anatomical circuits, with distinctive functionalities, that constitute several “second-order” circuits by re-wiring, thus giving rise to new functionalities, and so on and so forth. Each new level of organization inherits the capabilities of the circuits involved. In addition, the same basic circuitry can become part of multiple higher-level circuits/functions. In Anderson’s proposal, this process of amplifying capabilities by re-wiring of circuits is thought to be characteristic of evolution. However, it doesn’t need to be so restricted. It could also account for the possibility of new circuits appearing in phylogenesis (that is, new circuits, not just reuse of the ones available, as it was the case in human brain evolution), as well as of functional reorganization in ontogenetic development (Casey et al. 2005), in learning, and in cases of brain plasticity after stroke, for instance. But, if neuroimaging data are to help us choose among competing views of cognition, the set of issues raised in this commentary must be addressed with an eye to furthering Anderson’s project. ACKNOWLEDGMENT This work was supported by a grant from the Spanish Government through project FFI2009-13416-C02-01 and from Fundacio´n Se´necaAgencia de Ciencia y Tecnologı´a de la Regio´n de Murcia, through project 11944/PHCS/09.

Neural reuse in the social and emotional brain doi:10.1017/S0140525X10001020 Mary Helen Immordino-Yang,a Joan Y. Chiao,b and Alan P. Fiskec a

Brain and Creativity Institute and Rossier School of Education, University of Southern California, Los Angeles, CA 90089; bPsychology Department, Northwestern University, Evanston, IL 60208; cAnthropology Department, University of California Los Angeles, Los Angeles, CA 90095. [email protected] [email protected] [email protected] http://rossier.usc.edu/faculty/mary_helen_immordinoyang.html http://culturalneuro.psych.northwestern.edu http://www.sscnet.ucla.edu/anthro/faculty/fiske/

Abstract: Presenting evidence from the social brain, we argue that neural reuse is a dynamic, socially organized process that is influenced ontogenetically and evolutionarily by the cultural transmission of

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain mental techniques, values, and modes of thought. Anderson’s theory should be broadened to accommodate cultural effects on the functioning of architecturally similar neural systems, and the implications of these differences for reuse.

Reuse of tissues, organs, and systems is a key adaptive strategy in all phyla across evolution and through development. Neural systems are reused in the evolution and development of complex human behaviors, including social emotion and the representation of social status. Research shows: (1) evolutionary and developmental reciprocal reuse between social and nonsocial neural systems; (2) the importance of cultural transmission as a mode for learning evolutionarily and ontogenetically new uses and combinations of neural systems; and (3) the possibility that socially mediated reuse may affect the original, primitive function of a neural system, either developmentally or evolutionarily. In short, although Anderson’s approach maps distinct cognitive functions to unique networks, neural reuse within and between networks is a dynamic process involving culture and sociality. Compassion and admiration: Neural reuse between a social and a somatosensory system. A growing body of evidence

points to developmental and evolutionary reuse between a social and a somatosensory system in the feeling of social emotions. Brain systems involved in the direct sensation of physical pain in the gut and viscera (e.g., during stomach ache), are also involved in the feeling of one’s own social or psychological pain (Decety & Chaminade 2003; Eisenberger & Lieberman 2004; Panksepp 2005). These systems are also involved in the feeling of late-developing social emotions about another person’s psychologically or physically painful, or admirable, circumstances (ImmordinoYang et al. 2009). These systems most notably involve the anterior insula, anterior middle cingulate, and ascending somatosensory systems in the dorsal midbrain, most directly associated with regulation of arousal and homeostasis. Comparative social status: Neural reuse between a social and a cognitive system. The intraparietal sulcus (IPS) is important in

representing comparative numerosity, quantity, magnitude, extent, and intensity (Cohen et al. 2008; Dehaene et al. 2003); it is also involved in representing social status hierarchy (Chiao et al. 2009b). Particularly when comparisons are close, neural activations observed within the IPS for numerical and social status comparisons parallel behavioral distance effects in reaction time and error rates, and are thought to reflect a domain-independent spatial representation of magnitude, including the “magnitude” of social rank. All animals are responsive to magnitudes, distances, temporal intervals, and intensities (Gallistel 1993). The neurocognitive systems that support this seem to have been reused in evolution to represent the linear dominance hierarchies that are ubiquitous in both vertebrates and invertebrates. Social dominance hierarchies existed long before the invention of symbols to mediate mathematical calculation, so it is likely that the neural systems modern humans use for analog processing of numerical symbols reflect this phylogenetic history.

processes operate in neurochemistry. For example, oxytocin, whose original functions were to mediate birth and lactation, was evolutionarily reused to bond infants and mothers, then further reused in a small proportion of mammals for parental pair-bonding (Lee et al. 2009). Subsequently, oxytocin systems were culturally reused in diverse social bonding rituals and recently exploited in recreational ingestion of MDMA (ecstasy). The function of culture in shaping the use of neural systems is demonstrated by cultural variation in the neural correlates of visual attention (Lin et al. 2008) and self-representation (Chiao et al. 2009a), including differential activation patterns within the same neural systems, which can be manipulated by cultural priming in bicultural individuals (Chiao et al. 2010). Together, these findings suggest that Anderson’s assertion that putting “together the same parts in the same way [will lead to] the same functional outcomes” (sect. 1.1, para. 6) may not adequately account for the dynamic effects of socialization on neural reuse. Conversely, the reuse of a neural system for a more complex, culturally organized task apparently can affect its recruitment for a phylogenetically or ontogenetically earlier use. Cross-cultural psychiatric research shows that various Asian populations tend to manifest psychosocial distress somatically, in medically unexplained bodily symptoms, whereas Westerners tend to express depression psychologically (Parker et al. 2001). Cross-cultural work in progress by Immordino-Yang and colleagues suggests that such tendencies may be associated with cultural differences in the recruitment of neural systems for somatosensation in the cortex and brain stem during social processing, extending even into midbrain nuclei that regulate basic bodily functions. From use to reuse and back: Toward a dynamic, sociocultural theory of reuse. Anderson’s theory proposes that neural reuse is

mainly a process of organizing low-level circuits with relatively fixed functions into interconnected networks, and that functional differences between cognitive domains correspond to differences in the architecture or organization of these networks. Here, we argue that Anderson’s model should be expanded to account for the possibilities that social learning produces distinct culturally informed operations within architecturally similar complex networks, and that the reuse of a low-level neural circuit may, in turn, influence its original, primary function. Future research should investigate how socioculturally shaped ontogenetic processes interact with the constraints and potentials of neural subsystems, connectivity, and chemistry. Are there (as Anderson assumes) fundamental components of neurocognition that are not decomposable – or how modifiable are the functions of such basic components? What biologically and culturally transmitted processes, and what social and nonsocial experiences at what stages of development, determine how neurocognitive components are combined? In humans, neural reuse involves dynamic interplay among social and nonsocial (re)uses over developmental, cultural-historical, and evolutionary timescales.

The social chicken or the useful egg? Learning cognitive skills through cultural transmission. In addition to demonstrat-

ing neural reuse in the social brain, the juxtaposition of these examples demonstrates the importance of considering the social sources and functions of the complex skills underlain by neural reuse. Many of modern humans’ complex mental functions, both social and nonsocial, are learned through cultural transmission of practices and cognitive techniques, and are further shaped by social values, emotional relevance, and cultural modes of thought. For example, the use of numeral symbols to represent, remember, and communicate magnitude depends on the cultural invention and transmission of such symbols. Learning to use a number board or abacus allows the reuse of systems in the motor and visual cortices to calculate and remember quantities. Similarly, the cultural invention and transmission of calendars and later digital PDAs entails the reuse of perceptual object recognition and spatial relations systems, in conjunction with fine motor control skills, for temporal mnemonics. Similar

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Neural reuse: A polysemous and redundant biological system subserving niche-construction doi:10.1017/S0140525X10001159 Atsushi Iriki Laboratory for Symbolic Cognitive Development, RIKEN Brain Science Institute, Wako 351-0198, Japan. [email protected] http://www.brain.riken.jp/en/a_iriki.html

Abstract: Novel functions, which emerge by reusing existing resources formerly adapted to other original usages, cannot be anticipated before the need eventually arises. Simple reuse must be accidental. However, to survive the evolutionary race, one cannot merely keep hoping for a

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain string of good fortune. So, successful species might be gifted with “rational” and “purposeful” biological mechanisms to prepare for future reuse. Neural reuse must be extrapolated from such mechanisms.

Anderson thoroughly reviews neural reuse as a common brain mechanism for human cognitive functions to emerge. During evolution, whenever organisms were faced with a novel unforeseen environment, they had no other means to overcome any immediate problems than by reusing any materials at hand. So, neural reuse appears to be a truly “fundamental organization principle” (target article title). However, it remains an open question as to how human higher-cognitive functions appear as though they are “ensured” to “evolve” much quicker than via ordinary biological evolutionary processes (sect. 6.3, para. 1). To bridge this gap, I try to propose here a “more universal theory of neural reuse” (sect. 6.4, para. 5) grounded in a broader evolutionary framework. Anderson’s “massive redeployment hypothesis” (MRH) stands on two major observations – selectivity and localization are not central features of the brain (sect. 1, para. 1), and newer brain networks of cognitive functions tend to involve more brain areas than older ones (sect. 1.1, para. 1). Four other premises could be recognized further: (1) Biological systems are never ultimately efficient – systems require some redundancy to be stable, adaptable, and sustainable, leading extreme (over-adapted) efficiency to risk flexibility to survive novel situations. (2) A somewhat redundant brain structure would allow representational bistablility, for both the original and adapted functions. Such bistability, or “polysemy,” would support the use of metaphor in conceptual structure (sect. 4, para. 1). In addition, gains of further redundancy to stabilize this adapted alternative usage, perhaps by rapid brain expansion, would allow rapid construction of new neural-niche (sect. 4.6, para. 2). (3) Humans have attained unusually long postreproductive life spans, particularly for females. Reuse-based acquisition of cognitive functions, and resulting accumulation of knowledge, continues over the whole lifespan, tending to peak in middle to old age. Hence, for semantic inheritance (sect. 7, para. 2) over generations to happen, some extra-genetic mechanisms are indispensable. Finally, (4) a “novel concept” (sect. 7, para. 1) that realizes neural reuse should not be found only in Homo sapiens, but precursors must exist in nonhuman primates (sect. 6.3, para. 3) and are perhaps also present in other extant taxa. Evolution ushers diversity and complexity (or, adaptive radiation), perhaps through two major different paths: Species with short life spans and mass-reproduction adapt to environmental changes through variations in their numerous offspring, expecting at least a few to survive. Species with long life spans and low birth rates do so through an individual capacity to adapt. This is readily carried out through expansion of an organ to control adaptive behaviors – the primate brain, and that of humans in particular, is the extreme example. The latter evolutionary process may not be naı¨ve mutation and selection, but rather like the Baldwin effect that initially induced modification, within the range of preprogrammed adaptation, stands by for later mutations to optimize it – modular structures and their exploratory behaviors are proposed to be essential to realize such a phenomenon (Kirshner & Gerhart 2005). The concept of reuse would reinforce this path. That is, slightly excessive redundancy of the brain, initially designed to stabilize a system against unexpected environmental noise, occasionally allowed the system to be polysemous. This newly acquired bistable state enabled systems to be reused for completely different functions in the future, maybe in combination with other parts of the brain. Such novel networks could wait for optimization through later genetic changes, perhaps induced by an emergent epigenetic factor, and become embedded in the environment as a result of the function of the network itself – thus, enabling post-reproductive inheritance. This hypothetical mechanism, referred to as “real-time neural niche-construction” (sect. 6.3, para. 4), seems to be supported by recently discovered concrete biological phenomena, which are described below.

Monkey intraparietal neurons normally coding body image could be trained to code a tool in a way equivalent with the hand holding it (sect. 6.3, para. 4; Iriki et al. 1996) – thus, bistable or polysemous for the hand or the tool. This functional plasticity might range within a fringe of the system prepared for body growth, which came across adaptable to “sudden elongation” by the tool. This accidentally established equivalence between body parts (hands) and tools, in turn demonstrated additional polysemic interpretations, that is, hands were extended towards tools (externalization of innate body), or alternatively, tools were assimilated into the body schema (internalization of external objects). This “self-objectification” process happened to adapt further for the mind and the intention to emerge (Iriki 2006). However, if this new function stays limited within existing neural machinery, it is merely plasticity, or a learning process. But the evidence suggests this is not the case – monkeys exhibited substantial expansion (detectable in each individual monkey) of the grey matter, including above cortical areas, during only a two-week tool-use training period (Quallo et al. 2009). This directly proves the phenomena previously suggested (and detected statistically in groups) that humans who are experts in certain cognitive domains tend to have slightly thicker grey matter in the part corresponding to the area subserving such mental functions. Concrete biological and genetic mechanisms realizing this expansion could be studied using the monkey paradigm in the near future. Once a novel, alternative, bistable state was found to be useful, additional resources will be invested to stabilize the system, perhaps allowing further redundancy. Humans can induce such expansion intentionally, to create a better, more comfortable environmental niche. Subsequently, triggered by (extra-genetic, or epigenetic) factors embedded in such an environment, the corresponding neural niche in the brain could be reinforced further – thus, comprising recursive intentional niche construction (Iriki & Sakura 2008). Indeed, human-specific cognitive characteristics (or polysemous bias) seem to be subserved mainly by these “expanded” brain areas (Ogagwa et al. 2010; in press). Some aspects of recently evolved cognitive functions resulting from such neural reuse could be the mind (as described above; Iriki 2006), language, or culture, all of which contribute remarkably to semantic inheritance of the benefits acquired during the unusually elongated human post-reproduction period. “Thus, the theory suggests a novel pathway by which Homo sapiens may have achieved its current high-level cognitive capacities” (target article, sect. 6.3, para. 4).

Multi-use and constraints from original use doi:10.1017/S0140525X1000124X Justin A. Junge´ and Daniel C. Dennett Center for Cognitive Studies, Tufts University, Medford, MA 02155. [email protected] [email protected] http://www.tufts.edu

Abstract: Anderson’s theory is plausible and largely consistent with the data. However, it remains underspecified on several fronts, and we highlight areas for potential improvement. Reuse is described as duplicating a functional component, preserving one function and tinkering to add another function. This is a promising model, but Anderson neglects other reasonable alternatives and we highlight several. Evidence cited in support of reuse fails to uniquely support it among a broader set of multi-use theories. We suggest that a more stringent criterion for direct support of reuse may be satisfied by focusing on previous adaptive functions (original use).

On the whole, Anderson’s theoretical framework appears plausible and advances a flexible computational architecture for brains. Although this framework works well in the abstract, there are several points for further refinement and investigation. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain Our first suggestion is to better constrain the concept of reuse in order to set clear criteria for evidential support. One way to do this is by focusing on previous adaptive functions, original use. Until we have some sense of the functions that specific parts were optimized to perform in the past, it remains unclear how such parts might (or might not) be reused. Reuse promises (among other things) to go beyond original use. But how do former functions of neural components constrain the possibilities for reuse, if at all? Anderson is largely silent on this account, perhaps advantageously at this stage. Casting the theory abstractly leaves plenty of room for it to be generally accurate, and avoids objections to uncertain particulars. However, filling in more details will eventually be required for the theory to gain explanatory and predictive traction. Anderson’s discussion of modularity could benefit from additional examples, narrowing in the specific thesis of reuse. Modularity is a versatile – perhaps too versatile – concept. Is “massive modularity” a thesis about the size (crudely analogous to mass) or scale of the modules, the large number of modules (whatever their size), or the ubiquity of modular architecture in brains? Carruthers’ (2006) comparison with hi-fi components may have misled Anderson. A better parallel might be the random number generator and the graphics processing card in a laptop, which can vary independently, and interact in many different applications. However, probably any parallel with technological modules is of very limited utility, since no such module exhibits the sorts of plasticity that neural tissue is known to enjoy. Sperber (1996), for instance, is a proponent of modularity, but he insists that modules are there to be exploited to meet new demands. Anderson might categorize Sperber’s (1996; 2001) views as more closely aligned with reuse than massive modularity, but this suggests fuzzy boundaries between modularity and potential alternatives. A software theory of massive modularity – programs evolved to serve particular adaptive functions within brains – without commitments about implementation (unlike anatomical modularity), could survive Anderson’s critique largely untouched. The grain and level of analysis where modularity is applied can make all the difference. An important point for clarification concerns Anderson’s occasional conflation of two partially overlapping (classes of) hypotheses. Reuse and multi-use should be better distinguished. Reuse theories form a set of related hypotheses. Multi-use is a larger set, including cases where original function is lost, as well as cases where original function is preserved (preservation is a defining attribute of Anderson’s reuse theory). The term “reuse” strongly suggests exaptation, and Anderson is explicit that his reuse differs from typical exaptation by proposing that components continue to serve some previous adaptive function while also becoming available to “time share” new functions (though he doesn’t put it in exactly those terms). Anderson takes the multiplicity of functions – a brain area being activated by multiple different tasks – as evidence for reuse. However, if multi-use is an available move in design space, what reason do we have to assume that original function is preserved? Without preserving original function, reuse is an inaccurate account, and adaptation to multi-use is more accurate. The case for multi-use is strong, but all of the evidence cited implicating multi-use, while consistent with the reuse hypothesis, is not evidence for the more specific hypotheses of reuse. This ties in with our first point. Until the original use of components is specified, along with examples, Anderson hasn’t yet made the strong case for reuse. To illustrate our suggestion that Anderson’s theory should be fleshed out with details, we conclude with a specific example. As mentioned above, the picture of reuse that Anderson offers appears analogous to a time-sharing model: (1) At any given time, one high-level process uses the “workings” of multiple lower-level areas, and (2) numerous high-level processes are hypothesized to alternately access a common pool of specialized lower-level resources. While this account may be accurate, we wish to highlight an alternative that focuses on a finer mechanical

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grain, such as individual neurons (or perhaps small collections of neurons, such as minicolumns). It is possible that specialized brain areas contain a large amount of structural/computational redundancy (i.e., many neurons or collections of neurons that can potentially perform the same class of functions). Rather than a single neuron or small neural tract playing roles in many high-level processes, it is possible that distinct subsets of neurons within a specialized area have similar competences, and hence are redundant, but as a result are available to be assigned individually to specific uses (similar to the way that redundancies due to gene duplication provide available competences for reassignment, leaving one copy to perform the original function). Over development or training, subsets of neurons in a specialized brain area could then be recruited for involvement in distinct high-level processes. This model emphasizes multipotential of neurons, but single-use of individual neurons, as determined in the course of development and learning. In a coarse enough grain, this neural model would look exactly like multi-use (or reuse). However, on close inspection the mechanism would be importantly different. In an adult brain, a given neuron would be aligned with only a single high-level function, whereas each area of neurons would be aligned with very many different functions. This model of multi-potential and single-use may account for all the data that Anderson cites in support of reuse, and it also avoids time-sharing for specific neurons. Whether or not the model sketched here is accurate, it illustrates the kind of refinement that could make Anderson’s abstract theoretical proposal more concrete, and perhaps subtly improved.

Comparative studies provide evidence for neural reuse doi:10.1017/S0140525X10001032 Paul S. Katz Neuroscience Institute, Georgia State University, Atlanta, GA 30302-5030. [email protected] http://neuroscience.gsu.edu/pkatz.html

Abstract: Comparative studies demonstrate that homologous neural structures differ in function and that neural mechanisms underlying behavior evolved independently. A neural structure does not serve a particular function so much as it executes an algorithm on its inputs though its dynamics. Neural dynamics are altered by a neuromodulation, and species-differences in neuromodulation can account for behavioral differences.

Anderson begins his article by quoting one of Darwin’s explanations about how homologous structures can differ in function across species. Such a realization was clear even to Richard Owen who, although not accepting Darwin’s theory of evolution, defined homology as “the same organ in different animals under every variety of form and function” (Owen 1843). It is therefore surprising that Anderson uses very little comparative data to support his theory of neural reuse through “massive redeployment.” Comparative research examining neural circuitry across species, which has led to important insights into the evolution of neural circuits, needs to be included in any global theory about the evolution of human cognitive abilities. By concentrating solely on humans and extending analogies only to primates, one misses the strength of the comparative approach. Evolutionary principles can be generalized across species; humans are not more special for their cognitive abilities than bats are for their sonar abilities or song birds are for vocal learning abilities. Even the more distantly related invertebrates can provide lessons about how nervous systems evolved. As a structure, the cortex is very adaptable; similar circuitry can be used for different functions. For example, in the

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain absence of auditory afferents, primary auditory cortex in ferrets can be experimentally induced to process visual information (Sur et al. 1988), and the ferrets respond to visual stimuli as being visual in nature (Von Melchner et al. 2000). Such a situation may occur naturally in congenitally blind humans; primary visual cortex, which lacks visual input, is instead responsive to somatosensory input and is necessary for reading Braille (Cohen et al. 1997). Therefore, the “function” of cortex is very much determined by the inputs that it receives. It may be better to refer to the algorithm that cortex performs on its inputs than on its innate function. Because of cortical plasticity, it can be problematic to call one area of cortex “homologous” to a region in other species based on its function (Kaas 2005). Evidence suggests independent evolution of higher-order cortical areas, indicating that there may be innate directions for evolutionary change (Catania 2000; Krubitzer 2007; 2009; Padberg et al. 2007). In discussing the “neuronal recycling hypothesis,” Anderson refers to changes following tool training in an area of the macaque brain that is “roughly homologous to the regions associated with tool-use in the human brain” (sect. 6.3, para. 4). It is difficult to develop any theory about the evolution of a structure without being able to unambiguously identify homologous structures in other species. Homology of neural structures can be more precisely determined in invertebrates, where individual neurons are uniquely identifiable and can be recognized as homologous across species (Comer & Robertson 2001; Croll 1987; Meier et al. 1991). This allows the role of homologous neurons across species exhibiting different behaviors to be assessed. For example, homologous neurons in nudibranch molluscs have different effects and are involved differently in the production of different types of swimming behavior (Newcomb & Katz 2007; 2008). There is also evidence to suggest that homologous neurons have independently been incorporated into circuits that perform analogous swimming behaviors (Katz & Newcomb 2007). This is reminiscent of the reuse of cortical areas across mammals for similar tasks (Catania 2000; Krubitzer 2007; 2009; Padberg et al. 2007). Thus, a corollary of neuronal reuse may be that constraints on neuronal structure preclude some potential avenues and allow evolution to proceed in only particular directions, which leads to reuse. Work on invertebrates has established the existence of multifunctional neural circuits, in which the same set of neurons in a single animal produces different types of behaviors at different times (Briggman & Kristan 2008). One mechanism for shifting activity of neurons is neuromodulatory inputs, which alter cellular and synaptic properties (Calabrese 1998; Katz 1999; Katz & Calin-Jageman 2008; Marder & Thirumalai 2002). This has been particularly well studied in circuits that produce rhythmic motor patterns. Cortex has been likened to such a circuit in that it can exhibit different dynamic activity states depending upon its neuromodulatory input (Yuste et al. 2005). It has been proposed that phylogenetic differences in neuromodulation could be a mechanism by which neural circuits exhibit different behaviors across species (Arbas et al. 1991; Katz & HarrisWarrick 1999; Meyrand et al. 2000; Wright et al. 1996). This would allow core functions of a neural circuit to remain intact, while enabling the circuit to produce different dynamic states, corresponding to the neural exploitation theory. A nice example of changes in neural modulation that leads to large changes in behavior has been documented in the social behavior of voles (Donaldson & Young 2008; McGraw & Young 2010). Prairie voles pair-bond after mating, whereas meadow voles do not. In addition to displaying partner preference, pairbonding involves a number of complex behavioral traits, including increased territoriality and male parental care. The difference in the behavior of male voles can largely be accounted for by the neural expression pattern of vasopressin V1a receptors. These receptors are highly expressed in the ventral pallidum of prairie voles, but not in non-monogamous species. Using viral

gene expression to express the V1a receptor in the ventral forebrain of the meadow vole substantially increased its partner-preference behavior (Lim et al. 2004). The evolutionary mechanism for differences in gene expression patterns in voles has been traced to an unstable stretch of repetitive microsatellite domains upstream from the coding region of the V1a receptor gene (Hammock & Young 2005). Although similar genetic mechanisms do not play a role in the expression pattern in primates (Donaldson et al. 2008), monogamous primate species such as the common marmoset display high levels of V1a receptor expression in ventral forebrain regions, whereas non-monogamous species such as rhesus macaques do not (Young 1999). This suggests that similar social behaviors have arisen independently through changes in the expression of V1a receptors in the ventral forebrains of rodents and primates. Once again, this supports the neural exploitation model: The basic connectivity of the brain has not been altered; instead, there is change in the expression of a particular receptor, which can modulate the dynamics of the activity through that area. The ventral forebrain areas are involved in more than pair-bonding; they also play a role in addiction and reward-based learning ( Kalivas & Volkow 2005; Schultz et al. 1997). Pair-bonding results from these types of reward-learning processes being applied to a mate. This further supports the neural exploitation theory. Anderson expresses several ideas relating to the “age” of a particular brain area influencing its ability to undergo evolutionary change. This notion smacks of Scala Natura because it assigns youngest age to structures that are found in humans and not in other animals. The fallacy of this line of thinking can be seen with the above example. By all accounts, the ventral forebrain areas predate mammals. Yet, even closely related voles can exhibit behavioral differences caused by evolutionary change to this “older” region of the forebrain. Furthermore, the ventral forebrain area is also involved in learning in birds (Jarvis et al. 2005; Perkel 2004). In summary, comparative studies offer important insights into how brains evolved. There are surely many mechanisms that can be found. It is clear, however, that assigning a function to a particular brain structure is a gross simplification and can lead to false conclusions about its evolution. Neural circuitry is multifunctional and dynamic. Anything that changes the dynamics of the circuit will alter the behavioral output.

No bootstrapping without semantic inheritance doi:10.1017/S0140525X10001196 Julian Kiverstein School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh EH8 7PU, Scotland, United Kingdom. [email protected] http://www.artandphilosophy.com/philosophy.html

Abstract: Anderson’s massive redeployment hypothesis (MRH) takes the grounding of meaning in sensorimotor behaviour to be a side effect of neural reuse. I suggest this grounding may play a much more fundamental role in accounting for the bootstrapping of higher-level cognition from sensorimotor behaviour. Thus, the question of when neural reuse delivers semantic inheritance is a pressing one for MRH.

Evolution has devoted by far and away the largest part of its history to building organisms that can move around in a dynamic environment, sensing their environments in ways conducive to their own survival and reproduction (Brooks 1991). The challenge to cognitive scientists is to explain how the strategies organisms use to solve these basic problems of perception BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain and action scale up to the strategies humans use in solving abstract higher-level problems. I call this the “bootstrapping challenge.” Embodied cognitive science offers a programmatic response to the bootstrapping challenge that attempts to show how high-level problem solving might have been built upon the foundation of a substrate of perception and sensorimotor control. The ascent from sensing and moving to thinking, planning, and language understanding is an incremental and gradual one, and a key strategy may have been the redeployment of sensorimotor capacities to perform high-level cognitive tasks. Anderson has done the embodied cognition community the enormous service of framing a global hypothesis about how these incremental changes might have taken place in our brains over the course of evolution. The central claim of his massive redeployment hypothesis (MRH) is that more recent cognitive functions such as those involved in abstract problem solving might have their origin in the reuse of evolutionarily older neural circuits that served biologically basic functions. In this commentary, I want to take up Anderson’s claim that the principle guiding reuse is “functional inheritance” and not “semantic inheritance.” By “semantic inheritance,” I mean the kind of relation that concept empiricists and conceptual metaphor theories take to hold between concepts and sensorimotor representations. What connects both theories is the use of our experience and competence in one domain to guide our thinking in a distinct domain. Anderson describes very many instances of neural reuse that do not obviously involve the sensorimotor system, and hence do not involve semantic inheritance. He takes this to show that semantic inheritance may be a “side effect” (see sect. 4.6) of neural reuse. I will argue that it is only when reuse is accompanied by semantic inheritance that you find any bootstrapping from low-level cognitive functions to high-level cognitive functions. This follows from an argument Anderson himself makes against Susan Hurley’s (2008) shared circuits model (SCM). Therefore, the question of what kinds of reuse support semantic inheritance (a question Anderson himself raises in sect. 7) becomes a particularly pressing issue for the embodied cognition research programme. I will finish up by suggesting that neural reuse and semantic inheritance may actually be much more closely tied than Anderson suggests. We can see how semantic inheritance is required for bootstrapping by considering Anderson’s discussion of Susan Hurley’s (2008) shared circuits model (SCM). The model is complex, and I shall restrict my discussion to layer 3 of SCM, which describes how functional mechanisms used to predict sensory feedback in the control of motor behaviour might be reused to “simulate” the motor processes that stand behind the observed behaviour of another. This simulation is hypothesised to take the form of “mirroring” that can underwrite the copying of instrumental behaviour either in the form of priming, emulation, or imitation. Anderson worries that the inputs and outputs required for mirroring are “impoverished” and “abstract” when compared to those inherited from layer 2. When I perform an action myself, for instance, the action is represented from my own point of view. Anderson supposes that when I observe another’s action, I must represent the other’s action from a third-person point of view. Hence, the progression from layer 2 to layer 3 would seem to require a translation of a first-person representation of action into a third-person representation. Without some explanation of how this translation gets effected, we will not have shown how high-level cognitive abilities like imitative learning can have their basis in the reuse of low-level sensorimotor representation. This problem Anderson has identified for SCM would however seem to apply equally to MRH. What allows the control mechanisms found at layer 2 to be reused at layer 3 are the functional properties of those control mechanisms. According to MRH, it is a neural region’s functional properties that allow a region used in one domain to get reused in a distinct domain. The inheritance of functional properties falls some way short of

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guaranteeing semantic inheritance. Functional inheritance doesn’t on its own explain the abstraction and informational impoverishment you find as you move from lower-level sensorimotor behaviour to higher-level cognition. If this is right, it seems to follow that neural reuse won’t suffice for bootstrapping. Hurley’s SCM may however have resources for responding to this problem that are different from those outlined by Anderson in his target article. What is missing from Anderson’s framing of the problem is any mention of the sensorimotor associations that drive the predictions at layers 2 and 3 of SCM. Predictions of the sensory effects of movement are possible at layer 2 only because the motor system has learned that movements of a given type are correlated with certain sensory effects. Hurley followed Cecilia Heyes in thinking of this learning as arising in development through associations that wire sensory neurons (in superior temporal sulcus, for example) together with motor neurons (in premotor and parietal cortices; see Heyes [2010] for more on a recent presentation of this hypothesis). Crucially, Hurley is assuming that the sensory inputs from one’s own movement and from the movement of others are similar enough for sensory neurons to respond to both without distinguishing them. Thus, sensorimotor associations can underwrite an “inference” from the sensory effects of observed behaviour to the motor processes that tend to cause behaviour. In this way, sensorimotor associations can be used both to control the sensory effects of movement and to simulate the movements that have similar sensory effects when carried out by others. For SCM then, it is associative learning that delivers the kind of semantic inheritance required for bootstrapping. I finish by drawing a tentative moral for MRH. The functional inheritance that underpins neural reuse might bear cognitive fruit only when it is accompanied by semantic inheritance. Reuse of functional mechanisms in SCM is understood as simulation that establishes a space of shared meaning. Semantic inheritance, as appealed to in concept empiricism and conceptual metaphor theories, is also naturally understood as a form of simulation which opens up a space of shared meaning. While neural reuse could well turn out to be a “fundamental organisational principle” of the brain, the pressing question that remains is how neural reuse could deliver a shared space of meaning of a kind that supports bootstrapping.

Redeployed functions versus spreading activation: A potential confound doi:10.1017/S0140525X1000097X Colin Klein Department of Philosophy, University of Illinois at Chicago, Chicago, IL 60607. [email protected] http://tigger.uic.edu/cvklein/links.html

Abstract: Anderson’s meta-analysis of fMRI data is subject to a potential confound. Areas identified as active may make no functional contribution to the task being studied, or may indicate regions involved in the coordination of functional networks rather than information processing per se. I suggest a way in which fMRI adaptation studies might provide a useful test between these alternatives.

That there is a many-to-one mapping between cognitive functions and brain areas should now be beyond dispute. The tricky part is figuring out what to say about it. Anderson’s massive redeployment hypothesis (MRH) is a plausible position in the debate. Good engineers often find new uses for old tricks; we should expect nature to be no less clever. A crucial piece of evidence for the MRH is Anderson’s impressive meta-analyses of fMRI experiments (Anderson 2007b; 2007c). These show that phylogenetically older areas

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain tend to be more active, across a variety of tasks, than phylogenetically newer ones. Crucially, Anderson assumes that the areas identified as active make a functional contribution to the experimental tasks being studied. That is often assumed in fMRI experiments, and so may seem unproblematic. This assumption is subject to a potential confound, however, and one that becomes especially troublesome when doing large-scale metaanalyses. The BOLD response on which fMRI depends is a measure of physiological change. Which physiological change fMRI tracks is a matter of considerable debate. There is increasing evidence that the BOLD response better tracks regional increases in synaptic activity, rather than increased output of action potentials (Logothetis et al. 2001; Nair 2005, sect. 2.2 reviews; Viswanathan & Freeman 2007). Crucially, this means that observed BOLD activity may represent a mix of both excitatory and inhibitory inputs. A region which receives subthreshold excitatory input, or one which is both excited and inhibited enough to suppress further activation, may nevertheless show a measurable – even strong – BOLD response (Logothetis 2008). However, these “active” regions would make no functional contribution to the experimental task. Hence the potential confound. The fact that phylogenetically older areas are more often active may be explained by redeployment. It may also be explained by assuming that older areas simply receive more input than do newer ones. This potential confound may be manageable in individual fMRI experiments. Meta-analyses increase statistical power, however, making even small effects more likely to be noticed. Further, meta-analyses necessarily lack the fine-grained detail that might normally allow these functional by-products to be explained away. This is not a merely academic worry. To give one example: Mahon and Caramazza (2008) recently reviewed the fMRI evidence for the sensorimotor account of conceptual grounding (including many of the studies reviewed by Anderson in sect. 4). They conclude that the evidence is consistent with a view on which the semantic analysis of a sentence activates motor areas as an inevitable consequence of spreading activation within a complex neural system. Hence, although the motor system may often be activated during semantic analysis tasks, this activation need not represent a functional contribution to semantic analysis itself. It would instead be the natural consequence of a system in which the typical consumers of representations were primed for action, but inhibited (or simply under-excited) if their further, functionally specific, contribution was unnecessary. Note that a reliance on subtraction-based imaging does not obviate this problem: distinct semantic terms may well prime distinct motor regions. Spreading activation and massive redeployment are not mutually exclusive hypotheses. Indeed, it seems to me that the redeployment model should accept some version of the former. If the brain does consist of pluripotent regions that flexibly combine into functional networks, problems of coordination – and especially the necessity of inhibiting preponent but contextually inappropriate dispositions – become paramount. Further, phylogenetically newer areas evolved in the context of organisms which already had well-functioning brains. We should expect newer areas to project heavily to older areas, both because the information they provide might be relevant to these older adaptive repertoires and because those older functions will need to be coordinated in light of newer capacities. The crucial question, then, is how we might get experimental evidence that favors redeployment over the alternatives. Anderson suggests several plausible possibilities for testing his hypothesis. I suggest a further possibility: the use of fMRI adaptation. This technique exploits the fact that recently active neurons tend to show a decreased response to further stimulation; a decreased BOLD response across experimental conditions thus provides evidence that a region is making the same contribution to both tasks. Adaptation would allow one to distinguish areas

which are truly redeployed from those which have simply parcellated into functionally specific areas that are smaller than the resolution of fMRI (an open evolutionary possibility; Streidter 2005, Ch. 7 reviews). Further, adaptation would allow us to distinguish areas that are truly reused from areas that are involved in the coordination of complex networks. Crinion et al. (2006) used this technique to distinguish the contribution of various cortical and subcortical areas in language processing. Proficient bilingual speakers showed both within- and cross-language priming in the left anterior temporal lobe, suggesting a shared substrate for semantic information (and thus supporting a form of reuse). Activation in the left caudate, in contrast, did not show a priming effect. This supports a hypothesized role for the caudate in language control: Plausibly, the caudate helps inhibit contextually inappropriate responses, a real problem when distinct languages partially share the same substrate. fMRI adaptation might thus allow us to disentangle the contribution of frequently activated areas in a variety of tasks, and so provide a further test of Anderson’s intriguing hypothesis.

Implications of neural reuse for brain injury therapy: Historical note on the work of Kurt Goldstein doi:10.1017/S0140525X10001202 Barry Lia Dizziness and Balance Center, Otolaryngology/Head and Neck Surgery, University of Washington Medical Center, Seattle, WA 98195-6161. [email protected]

Abstract: This commentary suggests how the target article raises new implications for brain injury therapies, which may have been anticipated by the neurologist Kurt Goldstein, though he worked in and earlier era of fervent localization of brain function.

I first took interest in Anderson’s article by dint of the notion that neural circuits established for one purpose may be exapted for new functions during evolution and development. In a previous BBS commentary (Lia 1992), I had proposed an exaptation of the peripheral visual system for the adaptive evolution of enactive focal vision and praxic use of the forelimbs in primates, a crucial feature of our cognitive niche. I applaud Anderson’s discussions of the co-determination of organism and environment and of the idea of “neural niche” within the organism itself as most welcome for cognitive science. But it was implications for therapies for brain injury – which this article raises in closing – which brought to mind the work of Kurt Goldstein (1963) for comment. Anderson refers to “network thinking” which “suggests one should look for higherorder features or patterns in the behavior of complex systems, and advert to these in explaining the functioning of the system” (sect. 3.1, para. 6). Writing in 1939, Goldstein was a neurologist primarily occupied with patients’ recovery from brain injury. Similar to Anderson, Goldstein was also concerned with method in biological research and ways of conceptualizing the empirical material. The influence of the Gestalt school of psychology upon Goldstein is reflected in the following passage from Goldstein (1963), which refers to the “figure” of a performance: Localization of a performance no longer means to us an excitation in a certain place, but a dynamic process which occurs in the entire nervous system, even in the whole organism, and which has a definite configuration for each performance. This excitation configuration has, in a certain locality, a special formation (“elevation”) corresponding to the figure process. This elevation finds its expression in the figure of the performance. A specific location is characterized by the influence which a particular structure of that area exerts on the total process, i.e., by the contribution which the excitation of that area, by virtue of its structure, makes to the total process. (pp. 260–61)

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain This foreshadows the dynamic view of functional recruitment and brain organization which neural reuse theories present. Goldstein would likely have appreciated Anderson’s hope that “Knowledge about the range of different tasks that potentially stimulate each region [akin to Goldstein’s notion of ‘excitation configuration’] may serve as the basis for unexpected therapeutic interventions, ways of indirectly recovering function in one domain by exercising capacities in another” (sect. 7, para. 8, emphasis Anderson’s). Such specific knowledge of the “excitation configuration” was unknown and unavailable to Goldstein; he could only infer it. But by taking a holistic, organismal perspective, somewhat akin to Anderson’s “network thinking,” Goldstein intuited such an understanding and postulated such an indirect recovery of function in his work with rehabilitation of brain injury. Goldstein’s outlook echoes Anderson’s “call for an assimilative, global theory, rather than the elaboration of existing theoretical frameworks” (sect. 5, para. 7). This target article may point toward advances which a Goldstein would be striving toward today had he had our modern tools for studying the brain and cognitive function.

Reuse in the brain and elsewhere doi:10.1017/S0140525X10001044 Bjo¨rn Lindblom Department of Linguistics, Stockholm University, 10691 Stockholm, Sweden. [email protected] http://www.ling.su.se

Abstract: Chemistry, genetics, physics, and linguistics all present instances of reuse. I use the example of how behavioral constraints may have contributed to the emergence of phonemic reuse. Arising from specific facts about speech production, perception, and learning, such constraints suggest that combinatorial reuse is domain-specific. This implies that it would be more prudent to view instances of neural reuse not as reflecting a “fundamental organizational principle,” but as a fortuitous set of converging phenomena. Hallmark of true phonology. It is easy to forget that the words we use everyday are built rather “ingeniously.” They code meanings in a combinatorial way, arranging a limited number of phonetic properties in various combinations (phonetic segments, phonemes) and permutations (syllables, morphemes, words). This method of reuse provides tremendous expressive power and creates the means for developing large and open-ended vocabularies. In the realm of animal communication, it appears to be unique to humankind. How did it come about? Combinatorial structure is hardly a product of humankind’s ingenuity, a cultural invention. It is more likely to have evolved. But how? Is it an idiosyncrasy pre-specified in our genetic endowment for language? Or did performance factors drive language towards phonemically structured signals? If, as Anderson claims, neural reuse is a general principle of brain organization, did perhaps this process play a role in the emergence of linguistic reuse? On-line speaking. Assuming that phonetic reuse evolved from existing capacities, we are led to ask: What were those capacities? Recent work (Lindblom et al., in press) suggests three factors. The first two identify general characteristics of motor control (not specific to speech). The third highlights expressive needs arising from the growth of humankind’s cognitive capacity. 1. Positional control (targets . discrete units). 2. Motor equivalence (movement trajectories . recombination). 3. Cognitive processes (expressive needs . sound-meaning link . vocabulary size). Voluntary non-speech motions are output-oriented, that is, organized to produce desired results in the subject’s external environment. So is speech. Experiments indicate that speech

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movements are controlled by commands specifying a series of positions (targets) in articulatory space. Goals can be attained from arbitrary initial conditions, and the system compensates in response to obstacles and perturbations. Transitions between targets are typically smooth and show stable velocity profiles reminiscent of point-to-point reaching motions. We conclude that speech is in no way special. Both speech and non-speech show positional (target-based) control and motor equivalence (mechanisms for deriving trajectories from arbitrary initial to arbitrary final locations within the work space). A difference worth pointing out is that, since we speak to be understood, perceptual factors play a role in determining the extent to which targets are reached. But, although information dynamics may modulate the speaker’s performance (cf. clear/ casual speech), its motor organization is basically the same. Significantly, “target” is a context-independent notion, whereas its associated articulatory movements are highly context-sensitive. Evo/devo implications. The above account implies that the end-state of phonetic learning is a mastery of targets and motor equivalent trajectory formation. What the learner does in imitating ambient speech is to find the sparsest way of activating the dynamics of the speech effectors. Using a least-action strategy, the child residually ends up with targets. The context-free nature of target implies that once a target is learned in one context, it can immediately be recruited in another. There lies the key to reuse in the present account. Learning targets speeds up the acquisition process, compared with learning contextually variable movements. For evolution, this means that lexical inventories that are phonemically coded are easier to learn than systems consisting of Gestalt (holistic) sound patterns. Seen in this light, phonetic reuse appears to be an adaptation linked to ease of acquisition. If discrete units are to be derived from positional control and recombination from motor equivalence – two general cross-species motor characteristics – we must ask why other animals do not end up speaking. This is where the third factor comes in. Humankind’s cognitive capacity has developed dramatically from skills not unlike those of present-day apes. It makes it possible to use language to encode a virtually infinite set of meanings. For an account of how that may have happened, see Donald’s (1991) synthesis of a broad range of evidence. Donald assumes that, as gestural messages grew more elaborate, they eventually reached a complexity that favored faster and more precise ways of communicating. The vocal/auditory modality offered an independent, omnidirectional channel useful at a distance and in the dark. It did not impede locomotion, gestures, or manual work. The vocal system came to be exploited more and more as the growing cognitive system pushing for lexical inventions and sound-meaning pairs. The reuse capability implicit in discrete targets and motor equivalence conveniently provided the expressive means for these growing semantic abilities to interact in a process of mutual reinforcement. Accordingly, the reason why no other species has extensive reuse lies in the felicitous convergence of all three factors. According to the present account, one would expect reuse not to be limited to the vocal/auditory modality. The formal organization of sign language corroborates that prediction. Neural reuse: Organizational principle or widespread phenomenon? While it may be the case that true phonology is

uniquely human, combinatorial reuse is known to occur in other domains. Studdert-Kennedy (2005) draws attention to the work of Abler (1989) who “recognized that a combinatorial and hierarchical principle is a mathematically necessary condition of all natural systems that ‘make infinite use of finite means’, including physics, chemistry, genetics, and language. He dubbed it ‘the particulate principle’.” (Studdert-Kennedy 2005, p. 52). I take the word “principle” here to be used descriptively, rather than as referring to the possibility that there is a hidden abstract formal condition to be discovered which can be used for explaining all instances of combinatorial and hierarchical

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain coding. In other words, each case of reuse is likely to have its own history. Which takes us back to neural reuse. If the central nervous system (CNS) exhibits massive reuse of neural circuitry, we may, as Anderson does, choose to talk about a fundamental organizational principle of the brain. Or we might prefer saying that massive reuse of neural circuitry is a widespread phenomenon, bearing in mind that every example of reuse may have its own story.

Let us redeploy attention to sensorimotor experience doi:10.1017/S0140525X10001251 Nicolas Michaux,a Mauro Pesenti,a Arnaud Badets,b Samuel Di Luca,a and Michael Andresa a Institut de Recherche en Sciences Psychologiques, Universite´ catholique de Louvain, 1348 Louvain-la-Neuve, Belgium; bCentre de Recherches sur la Cognition et l’Apprentissage, CNRS UMR 6234, France. [email protected] [email protected] [email protected] [email protected] [email protected] http://www.uclouvain.be/315041.html http://cerca.labo.univ-poitiers.fr

Abstract: With his massive redeployment hypothesis (MRH), Anderson claims that novel cognitive functions are likely to rely on pre-existing circuits already possessing suitable resources. Here, we put forward recent findings from studies in numerical cognition in order to show that the role of sensorimotor experience in the ontogenetical development of a new function has been largely underestimated in Anderson’s proposal.

With his massive redeployment hypothesis (MRH), Anderson proposes an attractive view of neural reuse by claiming that neural circuits initially dedicated to a specific function can be reused in the course of human evolution to support novel cognitive functions. Because this is meant to occur whenever a preexisting circuit already possesses useful mechanisms for a novel function, Anderson’s proposal challenges the assumption of concept empiricism that neural reuse is causally related to sensorimotor experience. Here, we question the idea that the mere availability of neural resources is sufficient to explain how new functions emerge from neural reuse, and we highlight the role of sensorimotor experience during the ontogenetical development of a new function by reviewing recent findings from studies in numerical cognition. In the past few years, finger control and numerical cognition have been shown to share common areas in the parietal and premotor cortices (Andres et al. 2007; Pesenti et al. 2000; Zago et al. 2001). This common ground for finger movements and number processing may be a developmental trace of the use of fingers when learning to count (Butterworth 1999a). In contrast, Anderson and colleagues (see Penner-Wilger & Anderson 2008) propose that the neural network originally evolved for finger representation has been redeployed to serve numerical cognition only because it offers suitable resources to represent numbers, such as a register made of switches that can be independently activated. Accordingly, sensorimotor experience would play no role in the development of numerical cognition. However, a growing body of empirical data makes this perspective untenable. Indeed, finger use was found to deeply impact the acquisition of numerical skills in at least four different ways. First, developmental studies indicate not only that children with poor abilities to discriminate their fingers are more likely to experience difficulties in mathematical tests (Fayol et al. 1998; Noel 2005), but also that an extensive training in finger differentiation, via sensorimotor exercises, improves both finger

gnosis and numerical abilities (Garcia-Bafalluy & Noe¨l 2008). This shows that sensorimotor experience critically contributes to reaching an optimal performance during the acquisition of new numerical skills and, more generally, to making neural reuse effective in supporting new functions. Second, a cross-cultural brain imaging study with participants from Eastern and Western cultures showed that cultural and educational habits can shape neural resources (Tang et al. 2006). Various numerical tasks activated similar networks in occipito-parietal, perisylvian, and premotor areas in both cultures, but English participants showed higher activity in the perisylvian areas, whereas Chinese participants showed higher activity in premotor areas, a finding difficult to explain unless one considers their high level of practice in calculations with an abacus which requires a fine control of finger movements (Cantlon & Brannon 2007; Tang et al. 2006). The cerebral network underlying numerical cognition can thus be shaped by the constraints that culture and/or education exert on the way individuals physically represent and manipulate numbers, thereby providing key evidence against the deterministic view conveyed by the MRH. Third, even if Anderson’s proposal makes it clear why preexisting neural resources may underlie new representations, such as numbers, it remains confusing how these representations acquire their conceptual meanings. The idea that number semantics could also pre-exist in the brain is still disputed (see Rips et al. 2008; and our comment, Andres et al. 2008). We argue that the use of finger counting can account for conceptual properties of numbers that are left undefined in the initial redeployment of pre-existing neural resources. For instance, the stable sequence of finger movements performed by children while counting, presumably under the combined influence of motor constraints and cultural habits, may lead them to understand that natural numbers include a unique first element, and that each number in a sequence has a unique immediate successor and a unique immediate predecessor, except the first (Wiese 2003). This suggests that neural reuse involves domain-structuring inheritance, as predicted by concept empiricism, but not by a strong version of the MRH. Furthermore, the recurrent use of a stable finger-counting strategy during childhood keeps on influencing the way numbers are represented and processed in adults. Indeed, we recently showed that, when participants are asked to identify Arabic digits by pressing keys with their ten fingers, a fingerdigit mapping congruent with their prototypical finger-counting strategy leads to a better performance than any other mapping, suggesting that number semantics of educated adults is grounded in their personal experience of finger counting (Di Luca et al. 2006). The finding that, in long-term memory, the structure of newly acquired concepts reflects idiosyncratic aspects of sensorimotor experience challenges Anderson’s proposal that neural reuse anticipates concept formation. One may argue that neural redeployment may constrain or predispose individuals to count the way they do. However, this alternative explanation cannot account for the multiplicity of finger-counting strategies observed across individuals and cultures (Butterworth 1999b; Wiese 2003). It is also incompatible with the results of an unconscious priming study showing that number semantics are linked not only to finger-counting, but also to finger-monitoring configurations (i.e., finger configurations used to show numerosities to other people; Di Luca & Pesenti 2008). Finally, recent findings show that object-directed actions mediate some aspects of the functional relationship between fingers and numbers. For example, observing grip closure movements interferes with numerical magnitude processing, suggesting the automatic activation of a magnitude code shared by numbers and finger movements (Badets & Pesenti 2010). Critically, this interference is not observed when viewing nonbiological closure movements, which suggests that it does not result from a general system for processing movement amplitude. This finding rather underlines the need to postulate a BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain grounding mechanism, as predicted by empiricist accounts only. In conclusion, although pre-existing circuits might be reused to provide representational resources for novel functions, we propose that these resources remain insufficient, and possibly unspecified, without the involvement of sensorimotor experience. In order to obtain a more universal theory of neural reuse, future studies now have to clarify how representational resources are shaped by cultural and educational constraints and how they interact with the functions they support.

Neural reuse as a source of developmental homology doi:10.1017/S0140525X10001056 David S. Moorea and Chris Mooreb a Department of Psychology, Pitzer College and Claremont Graduate University, Claremont, CA 91711; bDepartment of Psychology, Dalhousie University, Halifax, NS B3H4J1, Canada. [email protected] [email protected] http://pzacad.pitzer.edu/dmoore/ http://myweb.dal.ca/moorec/index.html

Abstract: Neural reuse theories should interest developmental psychologists because these theories can potentially illuminate the developmental relations among psychological characteristics observed across the lifespan. Characteristics that develop by exploiting preexisting neural circuits can be thought of as developmental homologues. And, understood in this way, the homology concept that has proven valuable for evolutionary biologists can be used productively to study psychological/behavioral development.

Conventional wisdom in the neurosciences has long held that specific brain regions have specific functions. However, several recent studies have undermined the claim that cognitive functions can typically be mapped in straightforward ways to highly specialized brain areas, leading Anderson (2007c) to propose his massive redeployment hypothesis (MRH). In the target article, Anderson has considered his theory, along with others that posit similarly, that existing neural structures are normally reused/recycled/ redeployed as new brain functions develop. This new approach has enormous potential for helping neuroscientists rethink the relationship between brain structures and their functions, as well as for helping those interested in the development and/or evolution of behavioral organization to understand changes in that organization across ontogeny and phylogeny. Anderson uses the MRH to predict that a brain area’s phylogenetic age should correlate with how often that area is deployed for various cognitive functions, and that a cognitive function’s phylogenetic age should correlate with how localized that function is in the brain. However, although Anderson recognizes that neural reuse theories bear on questions of development, his article focuses on phylogeny to the virtual exclusion of ontogeny. Brief mentions of development are made, and a note points out that neural reuse “is broadly compatible with the developmental theories of Piaget” (target article, Note 10); but, in fact, neural reuse should interest all developmental psychologists because the approach is compatible with most current theories of development and could contribute to theoretical progress in the field in general. Anderson cites Dehaene’s “neuronal recycling” theory as having potentially identified a “fundamental developmental . . . strategy for realizing cognitive functions” (sect. 1, para. 3); but, like other promissory notes in Anderson’s text, this one is never fully redeemed. Neither Anderson nor Dehaene and Cohen (2007) fully consider the implications of neural reuse theories for understanding development. The idea of neural reuse could have profound and general implications for the understanding of behavioral development.

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In particular, we believe that neural reuse produces a type of developmental homology, and that just as evolutionary biology has profited from the discovery and analysis of evolutionary homologies (Hall 2003), so developmental psychology may profit from the identification of developmental homologies, some of which likely arise as a result of neural reuse. Because two or more psychological characteristics present at a given point in development might both (re)use neural circuits formed much earlier in development, thinking about such characteristics in terms of developmental homology could well illuminate their relationship to each other (as well as to other psychological characteristics present earlier in development that also depend on these circuits). Consequently, we believe that importing the concept of homology into developmental psychology has the potential to help behavioral scientists understand when, how, and why specific traits have common developmental origins. Within biology, several types of homology have been identified, including among others (1) taxic homology (Griffiths 2007), in which characteristics in different species (e.g., bat wings and human forearms) have derived from a characteristic present in a common ancestor; (2) serial homology (Rutishauser & Moline 2005), in which parts of an individual organism are of the same type (e.g., the corresponding bones in a person’s right hand and right foot, or any two vertebrae in mammalian spinal columns); and (3) ontogenetic homology (Hoßfeld & Olsson 2005), in which distinct individuals of the same species have differing features that nonetheless derive from common embryonic tissues (e.g., human ovaries and testes). Developmental homologies arising from neural reuse would be most similar to the kinds of homologies identified by Bertalanffy in 1934 (described in Hoßfeld & Olsson 2005), and would include pairs of psychological characteristics, both of which emerged from a common characteristic present earlier in development. In addition, much as human forearms are homologous to the forearms of extinct Australopithecines, psychological characteristics of adults could be recognized as homologues of psychological characteristics present in juveniles in various developmental stages. Such homologues could arise in ways that would not require neural reuse – after all, “a structure that is homologous across species can develop based on non-homologous genes and/or developmental processes, and vice-versa” (Brigandt & Griffiths 2007, p. 634) – but any characteristics known to emerge following the redeployment of a specific neural circuit would seem prima facie to be homologous, at least structurally if not functionally. Several examples of possible developmental homologies may be identified. Temporal cognition in the form of episodic thinking develops later than spatial cognition and makes use of related conceptual structures (Clayton & Russell 2009). The discovery that these mental processes also make use of certain shared neural circuits would indicate that they are homologous, thereby shedding light on the nature of their developmental relationship. Linguistic structures, likewise, may well depend upon earlier-developing social interactive communicative structures. Tomasello (2003), for example, argues that syntax can be understood as a form of joint attention, a conceptualization that implies that these are homologous psychological characteristics, their different appearances notwithstanding. Still other psychological characteristics that appear similar across age have been assumed to be homologues, such as the neonatal imitation reported by Meltzoff and Moore (1977) and later-developing forms of imitation observed in older children and adults. Even so, studies of the neural circuits that contribute to neonatal and later imitation might or might not support this conclusion; a finding that adult imitation normally recruits neural circuits previously used during neonatal imitation would certainly qualify as support for the contention that these behaviors are homologous. As Anderson suggests, neural reuse might be a fundamental organizational principle of the brain; and just as this idea can be used to formulate testable hypotheses about the evolution of

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain both the brain and its function, we think it could also influence the study of psychological development in significant ways. Similarly, importing the idea of homology from evolutionary biology into developmental psychology could help researchers conceptualize behavioral development in new and potentially informative ways. Taken together, the concepts of neural reuse and developmental homology could be used to further our understanding of brain development, psychological development, and the relationships between these phenomena.

Reuse of identified neurons in multiple neural circuits doi:10.1017/S0140525X10001068 Jeremy E. Nivena and Lars Chittkab a Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, United Kingdom; bResearch Centre for Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom. [email protected] [email protected] http://www.neuroscience.cam.ac.uk/directory/profile.php?jen22 http://chittkalab.sbcs.qmul.ac.uk/

Abstract: The growing recognition by cognitive neuroscientists that areas of vertebrate brains may be reused for multiple purposes either functionally during development or during evolution echoes a similar realization made by neuroscientists working on invertebrates. Because of these animals’ relatively more accessible nervous systems, neuronal reuse can be examined at the level of individual identified neurons and fully characterized neural circuits.

The principle of neural reuse is widespread within peripheral sensory and motor circuits in both vertebrates and invertebrates. Peripheral sensory circuits, such as those in the retina, extract and process information that is used in many behaviors. Indeed, the coding of visual scenes or odors requires that overlapping sets of sensory neurons are activated in response to different scenes or odors. Likewise, overlapping sets of premotor and motor neurons may be activated in disparate behaviors that require activation of overlapping sets of muscles. The detailed characterization of invertebrate neurons and neural circuits has demonstrated that neurons can be reused to form neural circuits that perform multiple functions. One striking example comes from the stomatogastric ganglion (STG) of the crab Cancer borealis. The 30 neurons of the STG control rhythmic muscle activity involved in chewing and digestion of food – the gastric mill and pyloric rhythms, respectively. Individual identified neurons may contribute to the production of more than one rhythm. The VD neuron, for example, is involved in the generation of both the gastric mill and pyloric rhythms (Weimann & Marder 1994). Thus, the dynamic restructuring of neural circuits within the STG provides a clear example of the reuse of neurons for the production of different behaviors. Reuse may also be found in neurons involved in learning and memory. In the pond snail (Lymnea stagnalis), the breathing rhythm is generated by three synaptically connected neurons that form a central pattern generator. One of these neurons, RPeD1, is also necessary for many aspects of learning and memory; and removing the RPeD1 cell body can prevent the formation or reconsolidation of long-term memories (Sangha et al. 2003). In honeybees (Apis mellifera), a single identified neuron (VUMmx1) in the suboesophageal ganglion mediates the reward pathway in associative olfactory learning, but this neuron has also been implicated in learning phenomena as diverse as second-order conditioning and blocking (Menzel 2009). The above examples emphasize that within the adult nervous system neurons are reused for different functions; but as Anderson points out, neurons may also be reused during development.

One such example is the reuse of larval motor neurons in the adult nervous system of the tobacco hornworm moth (Manduca sexta). Manduca caterpillars, like those of all moths and butterflies, undergo a metamorphosis that involves restructuring of the nervous system. Motor neurons that innervate leg muscles in the caterpillar have been shown to remodel their axons and dendrites during metamorphosis before innervating newly developed leg muscles (Kent & Levine 1993). Memories can also be retained between larval and adult forms of insects, despite the remodeling of neural networks during metamorphosis. For example, adult fruit flies (Drosophila melanogaster) retain memories of odors associated with aversive stimuli formed as third instar larvae (Tully et al. 1994). Memory retention between developmental stages suggests that those elements of neural circuits that are the loci of these stored memories are reused in adult animals. Anderson also suggests that neurons may be reused during evolution, acquiring novel functions and possibly losing their original function. Again, invertebrate neural networks provide examples of such reuse during evolution. In the desert locust (Schistocerca gregaria), more than 20 interneurons have been identified from the neural networks controlling the flight muscles. Some of these interneurons have homologues in abdominal neuromeres, which innervate segments that do not bare wings or contain motor neurons innervating flight muscles (Robertson et al. 1982). Yet, these interneurons can reset the flight rhythm in the locust, showing that despite their location they are components of the flight control machinery. Indeed, their role in the flight control circuitry may have influenced the structure of the insect ventral nerve cord (Niven et al. 2006). Robertson et al. (1982) have suggested that these interneurons are remnants of control circuits for ancestral appendages that have been lost. Neural reuse may be more prevalent in invertebrate brains, especially those of insects, which contain relatively few neurons compared to those of many mammals. Many insects possess small brains that have been miniaturized during evolution (Beuthel et al. 2005). Their small size means that insects are under selective pressure to reduce energetic costs and brain size (Chittka & Niven 2009). Anderson suggests that energy minimization in the absence of behavioral constraints would promote the reduction of neural structures and, thereby, the reuse of neural substrates. The possibility of reusing neurons for different behaviors through the dynamic restructuring of neural circuits means that the consequences of miniaturization may not be as severe as is often assumed. Anatomical modularity is clear within invertebrate nervous systems (e.g., Niven et al. 2006) but, as Anderson mentions, neural reuse may blur the boundaries between anatomical modules. Indeed, most behaviors involve sensory and motor circuits that are overlapping anatomically, and it seems unlikely that the majority of behaviors are localized entirely within specific anatomical modules. As discussed above, the locust neurons involved in wing control, which include examples of evolutionary reuse, are spread across six neuromeres although only two segments bear wings (Robertson et al. 1982). Indeed, even reflex arcs confined to a single neuromere can be modified by descending and local control, allowing the neurons to be reused in different behaviors (Burrows 1996). Anatomical modularity has been suggested to reduce the energy consumption of neural processing by reducing the length of relatively common local connections and increasing the length of relatively rare long-distance connections. Thus, although modularity may be beneficial for efficiency, it may be opposed by neural reuse, which may not minimize the lengths of connections within neural circuits. In small brains, the low number of neurons and the short distances of most connections may promote further functional reuse, even when some components of neural circuits are in different anatomical segments. Thus, in small brains there may be an increased prevalence of neural reuse. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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The Leabra architecture: Specialization without modularity doi:10.1017/S0140525X10001160 Alexander A. Petrov,a David J. Jilk,b and Randall C. O’Reillyc a

Department of Psychology, Ohio State University, Columbus, OH 43210; eCortex, Inc., Boulder, CO 80301; cDepartment of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309. [email protected] [email protected] [email protected] http://alexpetrov.com http://www.e-cortex.com http://psych.colorado.edu/oreilly

b

Abstract: The posterior cortex, hippocampus, and prefrontal cortex in the Leabra architecture are specialized in terms of various neural parameters, and thus are predilections for learning and processing, but domain-general in terms of cognitive functions such as face recognition. Also, these areas are not encapsulated and violate Fodorian criteria for modularity. Anderson’s terminology obscures these important points, but we applaud his overall message.

Anderson’s target article adds to a growing literature (e.g., Mesulam 1990; Prinz 2006; Uttal 2001) that criticizes the recurring tendency to partition the brain into localized modules (e.g., Carruthers 2006; Tooby & Cosmides 1992). Ironically, Anderson’s critique of modularity is steeped in modularist terms such

as redeployment. We are sympathetic with the general thrust of Anderson’s theory and find it very compatible with the Leabra tripartite architecture (O’Reilly 1998; O’Reilly & Munakata 2000). It seems that much of the controversy can be traced back to terminological confusion and false dichotomies. Our goal in this commentary is to dispel some of the confusion and clarify Leabra’s position on modularity. The target article is vague about the key term function. In his earlier work, Anderson follows Fodor (2000) in “the pragmatic definition of a (cognitive) function as whatever appears in one of the boxes in a psychologist’s diagram of cognitive processing” (Anderson 2007c, p. 144). Although convenient for a meta-review of 1,469 fMRI experiments (Anderson 2007a; 2007c), this definition contributes little to terminological clarity. In particular, when we (Atallah et al. 2004, p. 253) wrote that “different brain areas clearly have some degree of specialized function,” we did not mean cognitive functions such as face recognition. What we meant is closest to what Anderson calls “cortical biases” or, following Bergeron (2007), “working.” Specifically, the posterior cortex in Leabra specializes in slow interleaved learning that tends to develop overlapping distributed representations, which in turn promote similarity-based generalization. This computational capability can be used in a myriad of cognitive functions (O’Reilly & Munakata 2000). The hippocampus and the surrounding structures in the medial temporal lobe (MTL) specialize in rapid learning of sparse conjunctive

Figure 1 (Petrov et al.) Information encapsulation is a matter of degree. Four neuronal clusters are shown, of which A is the most and D the least encapsulated. Black circles depict exposed (input/output) units that make distal connections to other cluster(s); grey circles depict hidden units that make local connections only.

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain representations that minimize interference (e.g., McClelland et al. 1995). The prefrontal cortex (PFC) specializes in sustained neural firing (e.g., Miller & Cohen 2001; O’Reilly 2006) and relies on dynamic gating from the basal ganglia (BG) to satisfy the conflicting demands of rapid updating of (relevant) information, on one hand, and robust maintenance in the face of new (and distracting) information, on the other (e.g., Atallah et al. 2004; O’Reilly & Frank 2006). Importantly, most1 of this specialization arises from parametric variation of the same underlying substrate. The components of the Leabra architecture differ in their learning rates, the amount of lateral inhibition, and so on, but not in the nature of their processing units. Also, they are in constant, intensive interaction. Each high-level task engages all three components (O’Reilly et al. 1999; O’Reilly & Munakata 2000). We now turn to the question of modularity. Here the terminology is relatively clear (e.g., Carruthers 2006; Fodor 1983; 2000; Prinz 2006; Samuels 2006). Fodor’s (1983) foundational book identified nine criteria for modularity. We have space to discuss only domain specificity and encapsulation. These two are widely regarded as most central (Fodor 2000; Samuels 2006). A system is domain-specific (as opposed to domain-general) when it only receives inputs concerning a certain subject matter. All three Leabra components are domain-general in this sense. Both MTL and PFC/BG receive convergent inputs from multiple and variegated brain areas. The posterior cortex is an interactive multitude of cortical areas whose specificity is a matter of degree and varies considerably. The central claim of Anderson’s massive redeployment hypothesis (MRH) is that most brain areas are much closer to the general than the specific end of the spectrum. This claim is hardly original, but it is worth repeating because the subtractive fMRI methodology tends to obscure it (Uttal 2001). fMRI is a wonderful tool, but it should be interpreted with care (Poldrack 2006). Any stimulus provokes a large response throughout the brain, and a typical fMRI study reports tiny differences2 between conditions – typically less than 1% (Huettel et al. 2008). The importance of Anderson’s (2007a; 2007c) meta-analyses is that, even if we grant the (generous) assumption that fMRI can reliably index specificity, one still finds widespread evidence for generality. MRH also predicts a correlation between the degree of generality and phylogenetic age. We are skeptical of the use of the posterior-anterior axis as a proxy for age because it is confounded with many other factors. Also, the emphasis on age encourages terms such as reuse, redeployment, and recycling, that misleadingly suggest that each area was deployed for one primordial and specific function in the evolutionary past and was later redeployed for additional functions. Such inferences must be based on comparative data from multiple species. As the target article is confined to human fMRI, the situation is quite different. Given a fixed evolutionary endowment and relatively stable environment, each human child develops and/or learns many cognitive functions simultaneously. This seems to leave no room for redeployment but only for deployment for multiple uses. Anderson’s critique of modularity neglects one of its central features – information encapsulation. We wonder what predictions MRH makes about this important issue. A system is encapsulated when it exchanges3 relatively little information with other systems. Again, this is a matter of degree, as our Figure 1 illustrates. The degree of encapsulation depends on factors such as the number of exposed (input/output) units relative to the total number of units in the cluster, and the density and strength of distal connections relative to local ones. Even when all units are exposed (as cluster D illustrates), the connections to and from each individual unit are still predominantly local because the units share the burden of distal communication. Longrange connections are a limited resource (Cherniak et al. 2004) but are critical for integrating the components into a coherent whole. The Leabra components are in constant, high-bandwidth interaction, and parallel constraint satisfaction among them is

a fundamental implicit processing mechanism. Hence, we eschew the terms module and encapsulation in our theorizing. This is a source of creative tension in our (Jilk et al. 2008) collaboration to integrate Leabra with the ACT-R architecture, whose proponents make the opposite emphasis (J. R. Anderson 2007; J. R. Anderson et al. 2004). Much of this tension is defused by the realization that the modularist terminology forces a binary distinction on what is fundamentally a continuum. NOTES 1. There are exceptions, such as the use of a separate neurotransmitter (dopamine) in the basal ganglia. 2. Event-related designs do not escape this criticism because they too, via multiple regression, track contingent variation around a common mean. 3. Encapsulation on the input side is usually distinguished from inaccessibility on the output side. We discuss them jointly here because of space limitations. Also, the reciprocal connectivity and the task-driven learning in Leabra blur the input/output distinction.

Neural reuse and human individual differences doi:10.1017/S0140525X1000107X Cristina D. Rabaglia and Gary F. Marcus Department of Psychology, New York University, New York, NY 10003. [email protected] [email protected]

Abstract: We find the theory of neural reuse to be highly plausible, and suggest that human individual differences provide an additional line of argument in its favor, focusing on the well-replicated finding of “positive manifold,” in which individual differences are highly correlated across domains. We also suggest that the theory of neural reuse may be an important contributor to the phenomenon of positive manifold itself.

Anderson’s compelling case for neural reuse is well motivated by empirical results and evolutionary considerations and dovetails nicely with the “descent with modification” perspective put forward by our lab (Marcus 2006; Marcus & Rabagliati 2006). An important additional line of support comes from the study of human individual differences. In an entirely modular brain, one might predict that individual differences in specific cognitive domains would be largely separate and uncorrelated, but the opposite is in fact true: An extensive literature has shown that performance on separate cognitive tasks tends to be correlated within individuals. This “positive manifold,” first noted by Spearman (1904), is arguably one of the most replicated findings in all of psychology (e.g., Deary et al. 2006). At first glance, such correlations might appear to be a statistical by-product of the fact that any individual cognitive task draws on multiple underlying processes. However, even when the impurity of individual tasks is taken into account, using more sophisticated structural equation models that form latent cognitive constructs (representing a cognitive ability, such as short-term memory, by the shared variance among performance on diverse tasks with different specific task demands), clear correlations between cognitive capacities within individuals remain. Positive manifold is not an artifact, but a fact of human cognitive life. (Our point here is reminiscent of Anderson’s observation that patterns of co-activation in fMRI remain even after subtraction, and are therefore not attributable solely to mechanistic impurities at the task level.) These correlations between cognitive domains have now been shown in hundreds of separate data sets, and at many levels, ranging from parts of standardized tests such as SAT math and SAT verbal, to broad ability domains such as memory and spatial visualization (see Carroll 1993), to more specific links BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain such as susceptibility to memory interference and sentence processing (Rabaglia & Marcus, in preparation). Recently, it has been pointed out that “the existence of g creates a complicated situation for neuroscience” (Deary et al. 2010). Adequate theories of brain organization and functioning will have to be consistent with the robust finding of positive manifold, and Anderson’s theory of neural reuse is one of the few that is. Strictly modular theories would not predict such between-domain correlations, and nor would theories that are driven purely by experience (since experience is likely to differ heavily between domains). At the same time, the concept of neural reuse (or decent with modification) may help to shed some light on the interpretation of positive manifold itself. Despite being noted for more than 100 years, there is not yet a consensus on how to explain the phenomenon. Spearman’s view was that positive manifold reflected the operation of a general intelligence factor, referred to as “g.” Since then, proposed causes range from biological factors such as overall mental speed (Jensen 1998) or myelination (Chiang et al. 2009), to some special rudimentary cognitive ability influencing the operation of others, such as the optimal allocation of resources or a limited central memory capacity (e.g., Kyllonen & Christal 1990); but each of these individually only accounts for (at most) a portion of the variance. If neural reuse characterizes brain functioning for most of human cognition, overlap in the neural substrates recruited by separate cognitive capacities could, in fact, be another factor contributing to positive manifold. One finding that could lend support to this notion is the fact that Ravens Progressive Matrices – arguably the gold standard for tapping into “g” – is an abstract reasoning task, and, as Anderson points out, reasoning tasks are among the most widely distributed in terms of neural areas of activation. Indeed, the most heavily “gloaded” tasks, or, in other words, the tasks that seem most related to what a range of cognitive abilities tend to share, are usually those involving frontal-lobe type abilities (see, for example, Jung & Haier 2007) – the very same abilities that are presumably latest-evolving and thus perhaps most characterized by reuse.

Reuse of molecules and of neural circuits doi:10.1017/S0140525X10001172 Mark Reimers Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23284. [email protected] http://www.people.vcu.edu/mreimers

Abstract: Reuse is well established in molecular evolution; some analogies from this better understood field may help suggest specific aspects of reuse of neural circuits.

Reuse is a settled issue in molecular evolution: most functions in modern cells reuse proteins, or parts of proteins, which previously evolved under different selective pressures. This commentary on Anderson’s target article draws analogies between specific aspects of molecular evolution and the ideas he presents about neural reuse, and suggests how the better understood field of molecular evolution may illuminate aspects of and inform hypotheses about neural reuse. 1. Analogy between protein domains and local neural architecture. A protein domain is a chain of typically 20 to 70

amino acids, which folds consistently into a specific compact shape (under normal cellular conditions). Early in protein evolution, a set of useful folds emerged; these domains are essential components of almost all known proteins (Caetano-Anolles et al. 2009a, Finn et al. 2010). Most proteins contain several domains, many contain dozens, and some large proteins contain hundreds of domains. These domains typically perform similar physical

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functions, such as binding specific protein partners or catalyzing specific reactions, in most of the proteins in which the domains occur. However, the role of each of these domains in the overall economy of the cell has diverged over evolutionary time. Thus domains are prime examples of molecular reuse, reflecting the general evolutionary principle that it is hard to invent something new. We may think of specific types of neural circuitry as analogous to protein domains. For example, the six-layer local circuit is broadly similar across the cortex, and yet this relatively narrow range of circuit architectures has become involved with almost every aspect of behavior. The typical striatal circuit with inhibitory output cells has also been reused in regions such as the central nucleus of the amygdala (Ehrlich et al. 2009). As the phylogeny of neurodevelopment is uncovered, we might expect to find more examples of newer brain regions borrowing (and mixing) developmental programs from older brain regions. 2. Analogy between metabolic networks and functional circuits. A metabolic network is a set of metabolites, each of

which may be readily inter-converted with one of several neighboring metabolites (by gain or loss of some atoms) through the catalytic action of a specific enzyme. The principal metabolic reactions have evolved with the enzymes that catalyze them. Early enzymes catalyzed a set of analogous chemical reactions inefficiently on a wide variety of chemically similar substrates. During the course of early evolution, these enzymes were duplicated by DNA copying errors, and each of the descendant enzymes came to act much more effectively on a narrower range of substrates (Caetano-Anolles et al. 2009b, Yamada & Bork 2009). There was for some years a controversy over how novel metabolic pathways are assembled, which is analogous to the controversy in cognitive science between dedicated modules and ad hoc neural reuse. An early theory suggested that when genes for enzymes duplicated, they acted on the same kinds of substrates, but catalyzed novel reactions. The major alternative theory, now widely accepted, is that novel metabolic pathways are assembled by duplication of genes for enzymes that perform relevant biochemistry with different substrates; these enzymes then adapt to the substrates of the novel pathway (Caetano-Anolles et al. 2009b; Yamada & Bork 2009). The enzymes that structure novel metabolic functions or pathways are therefore a “patchwork” of adapted pieces from previously existing functions. Thus, many important pathways of recent vintage are constructed mostly of unrelated proteins. Some of these pathways are crucial to most current forms of life. For example, many of the proteins of the Krebs cycle are distant cousins of proteins that catalyze amino acid metabolism, which evolved earlier in the history of life (Gest 1987; Melendez-Hevia et al. 1996). This “patchwork” model is analogous to Anderson’s prediction that more recently evolved pathways invoke more distal brain regions. These themes in metabolic evolution suggest by analogy that during development many brain regions both become more specialized – dealing with a subset of functions performed previously – and also paradoxically acquire novel functions in the expanding repertoire of behavior. Although a particular behavior may elicit broad brain activity in early life, the same behavior would recruit only a subset of those early regions in later life. However, each individual region active in the original behavior would later become active in many related behavioral functions, in which the region was not originally active. This kind of idea could be tested using chronic implants in animals or using fMRI at several points during child development. 3. Analogy comparing neural reuse to proteins that acquire novel functions very different from their earlier functions. A

well-known example concerns cell adhesion molecules (CAMs), whose sticky domains were crucial in forming multi-cellular bodies early in animal life. These same sticky domains have been reused in a variety of other contexts, notably as immunoglo-

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain bulins in the adaptive immune system, a key to the evolutionary success of vertebrates (Edelman 1987). By analogy, we would expect that during development some brain regions would become important in functions unrelated to those for which they had been used. This idea could be tested as described in the previous paragraph. 4. Analogy between neural circuits and signaling proteins. The majority of proteins in mammals are not enzymes

catalyzing reactions, nor even structural components of our diverse tissues, but rather regulators and organizers of small molecules or other proteins. Some of these proteins sequester or transport small molecules, while others modify other proteins (often by adding a small molecular group such as phosphate or methyl), or regulate access to DNA. These “classical” signaling pathways are well-studied because they are reused in many situations. For example, the Wnt signaling pathway is widely reused throughout animal development (Croce & McClay 2008). (Wnt is an example of a protein with three unrelated mechanisms of action, which seem to have been acquired independently.) The fibroblast growth factor (FGF) family of proteins plays a crucial role in the emergence of limb buds, and individual members of the family are reused at several points in mammalian development (Popovici et al. 2005). In all these cases, the specific protein interactions have been preserved while being adapted to a novel function. By analogy then, we might expect different brain regions to preserve the dynamics of their interactions as these regions become co-opted to new functions. This idea might be tested by identifying pairs of brain regions with multiple behavioral functions and recording from these regions simultaneously during several types of behavior in which both regions are active. Several families of DNA-binding proteins regulate transcription of genes by attracting or blocking the transcription machinery (RNA polymerase complex) at the locations on DNA where they bind. Reuse of these proteins is at the core of some of the most exciting current work in molecular biology: evolutionary developmental biology (“evo-devo”) (Carroll et al. 2005). The homeobox genes are famous for their role in early patterning of the front-to-back axis of the embryos of vertebrates and many invertebrates, and these functions are believed to date to the original bilaterian ancestor. However, most of these proteins have lesser-known roles in patterning limbs or digits or epithelia of organs, using the same mechanisms but responding to different signals. By analogy, we might expect that brain regions involved in early aspects of planning actions may also play a role in the fine-tuning of a subset of actions. This suggestion might be tested by recording from “executive” regions of the prefrontal cortex (PFC) during a variety of tasks. Molecular evolution provides many specific examples of reuse, of which I have only scratched the surface. By analogy, these may provide some concrete inspiration for further research in the evolution and development of mental function.

Massive modularity is consistent with most forms of neural reuse doi:10.1017/S0140525X10001081 J. Brendan Ritchie and Peter Carruthers Department of Philosophy, University of Maryland, College Park, MD 20742. [email protected] [email protected] https://sites.google.com/site/jbrendanritchie/Home http://www.philosophy.umd.edu/Faculty/pcarruthers/

Abstract: Anderson claims that the hypothesis of massive neural reuse is inconsistent with massive mental modularity. But much depends upon how each thesis is understood. We suggest that the thesis of massive

modularity presented in Carruthers (2006) is consistent with the forms of neural reuse that are actually supported by the data cited, while being inconsistent with a stronger version of reuse that Anderson seems to support.

Carruthers (2006) characterizes the mind as composed out of the interactions of a large set of mental modules, utilizing the “global workspace” provided by perception and working memory to recruit the resources of multiple specialized systems in the service of cognition and behavior. The sense of “module” in question is quite weak, however. Modules are functionally dissociable, intentionally characterized processing systems, each with its own neural realization. Modules need not be encapsulated, domainspecific, or innate (although many probably are). And the neural systems that realize them certainly need not be anatomically localized. On the contrary, modules can be realized in spatially distributed interconnected networks of brain regions. Moreover, many modules are constructed out of, and share parts with, other modules. Hence, the distinctness of different modules and their neural realizers will only be partial. Anderson claims that the thesis that modules can share parts is inconsistent with the idea that modules are functionally dissociable and separately modifiable, committing Carruthers to a strong version of anatomical modularity. But this is a mistake. Provided that two modules sharing a part differ from one another in other respects, it will be possible to disrupt the operations of one without having any impact on the other (by disrupting only the parts of the former that are not shared), and it will be possible for natural selection to make modifications in the one without changing the other (again, by making improvements in the parts that are not shared). Indeed, at the limit, two modules could share all of their processing parts while still remaining dissociable and separately modifiable. For the differences might lie entirely in the patterns of connectivity among the parts, in such a way that those connections could be separately disrupted or improved. In short, the functional dissociation and separate modifiability of modules do not preclude the possibility of neural reuse. The shared-parts doctrine provides a clear sense of neural reuse that is consistent with massive modularity. Moreover, each shared part can be given a dual functional characterization. Its function can either be described in univocal local semantic terms, or it can be said to be multi-functional, characterized in terms of the different longer-range uses for which its outputs are employed. (This seems to correspond to one way of understanding Anderson’s distinction between “workings” and “functions,” respectively, although he himself characterizes the former in “low-level computational” rather than intentional terms [sect. 1.1, para. 5].) Consider, for example, the region of fusiform gyrus that is often characterized as a face-recognition area (Coltheart 1999; Kanwisher et al. 1997). At one level of description, this is a module that recognizes faces. But it will contribute to, and be a part of, a number of larger systems. One is a person-file system, which uses face-recognition to help collect and store information about the individuals in one’s community (especially their personality traits and mental states). Another is an affiliative, social-bond-building, module which uses face-recognition as part of the process of creating and activating positive affective reactions to specific others. And a third is a Westermarck-style incest avoidance module (Fessler & Navarrete 2004), which uses the face-recognition module during childhood to track the extent to which other children are co-present in the home, and then above a certain threshold of cohabitation produces sexual disgust at the prospect of intercourse with those individuals post-adolescence. We can then say that the fusiform gyrus is a module with one local function (face-recognition) which is part of at least three other larger-scale modules (and hence is at the same time multi-functional). Notice that nothing much needs to change in this account if one thinks that the fusiform gyrus isn’t a face area, but is rather a holistic shape-processing area, which can be used for

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Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain recognizing any type of object that requires a combination of local detail and overall form (Gauthier et al. 2000; 2003). For we can now characterize its local function in just such semantic terms; and yet on this account, there will be an even larger set of systems of which it constitutes a modular part. However, the massive modularity hypothesis is inconsistent with a distinct, stronger, doctrine of neural reuse. This would claim that a neural region can be implicated in multiple longrange functions without there being a single semantic characterization of its local function. Perhaps Anderson endorses this stronger view. He emphasizes, for example, how the same brain regions can be involved in very different tasks like reading comprehension and manual object-manipulation (sect. 3.1, para. 5). And he thinks that local functions (or “workings”) are “low-level” and computational rather than intentional. But nothing in the evidence that Anderson presents actually supports such a view over the weaker account sketched above. Moreover, it strikes us as quite implausible. It is hard to see how the same set of computations could realize distinct representational properties on different occasions of use. For the consumer, systems for those computations would have no way of knowing which representational properties are involved on a given occasion, and hence no way of determining how the outputs should be used. Anderson might accept a more modest position with which the data are equally consistent: Under such a view, the neural region of interest subdivides into a number of more fine-grained areas (too fine-grained to show up in fMRI data, for example), each of which has a specialized semantically characterizable function. Furthermore, for all that the data show, distinct local modules might spatially interpenetrate one another, with the neurons involved in one being interspersed among neurons involved in the other, in something like the way that mirror neurons are interspersed among purely motor-related neurons in premotor regions of macaque cortex (Rizzolatti & Craighero 2004). However, such a position would also be consistent with the thesis of massive modularity. We conclude that to the extent that the data reviewed by Anderson support a thesis of massive neural reuse, the resulting thesis is fully consistent with the hypothesis of massive mental modularity, as characterized by Carruthers (2006).

More than modularity and metaphor: The power of preadaptation and access doi:10.1017/S0140525X10001093 Paul Rozin Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104-6241. [email protected]

Abstract: Neural reuse demonstrates preadaptation. In accord with Rozin (1976), the process is an increase in accessibility of an originally dedicated module. Access is a dimension that can vary from sharing by two systems to availability to all systems (conscious access). An alternate manifestation is to reproduce the genetic blueprint of a program. The major challenge is how to get a preadaptation into a “position” so that it can be selected for a new function.

For more than ten years, I have intended to submit an article to Behavioral and Brain Sciences on the power of preadaptation and access. The excellent article by Anderson on neural reuse provides strong evidence for preadaptation and access, and I jump at this opportunity. Preadaptation is a basic principle in twentieth-century evolutionary biology (Bock 1959; Mayr 1960). As Ernst Mayr points out: “The emergence of new structures is normally due to the acquisition of a new function by an existing structure . . . the resulting ‘new’ structure is merely a modification of a preceding

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structure” (Mayr 1960, p. 377). The basic idea is that something that evolved for one function is used for another. Occasionally the original structure is not itself an adapted entity, falling under the broader category of exaptation (Buss et al. 1998; Gould 1991; Gould & Vrba 1982). The human brain is surely a preadaptation: a very large processing system selected to solve a wide range of problems, then adapted to solve (or create!) problems other than those for which it was originally selected. In 1976, in response to the view that learning was accomplished by a few general-purpose and domain-insensitive mechanisms, I put forth some ideas in a paper entitled “The Evolution of Intelligence and Access to the Cognitive Unconscious,” ideas that were related to preadaptation and to the issues raised by Anderson (Rozin 1976). Below, I list a few points made in this 1976 paper and in some subsequent work (Rozin 1999; 2006) that anticipate some of the later findings and/or suggest directions for future work. 1. The building blocks for innovations in evolution, and particularly the brain, are adaptive specializations (called modules by Fodor) which are circuits or structures specifically dedicated to performing a specific function. These can be considered preadaptations. 2. In the course of evolution, these modules may be accessed by other systems, and thus acquire a new function. The original function may remain (e.g., shared circuitry – neural reuse), or the original function may disappear. 3. Accessibility is a dimension, varying from a dedicated module at one extreme to attainment of consciousness, which usually means total access to all systems. The brain (mind) is neither totally modular nor totally a general processor. It is both and everything in between. 4. A parallel process of increasing access occurs in development (e.g., Piaget’s de´calage), and an inversion of this process is associated with degeneration of the nervous system. 5. Alphabetic writing and reading presumes some level of access (or even “insight”) into the fact that “bat” has three sounds. This can be framed as gaining access to the phonological processing “module.” 6. In addition to the idea of reuse, there is an alternate preadaptive pathway (Rozin 1976): that is, to reproduce the genetic/ developmental plan for a particular neural circuitry in another part of the brain. This presumably happened, for example, with multiple topographic representations of space in different visual areas of the brain. The impressive recent data supporting the idea of a literally embodied mind are an instance of preadaptation and access, in the use of sensory and motor cortical structures to represent “higher” functions. The framework I present highlights the critical developmentalevolutionary problem with this whole class of models. As formulated by Mayr, the problem is: “How can an entirely new structure be gradually acquired when the incipient structure has no selective advantage until it has reached a considerable size and complexity?” (Mayr 1960, p. 350). How do we get from a photosensitive cell to an eye, from a fin to a limb, from a jaw articulation to middle ear bones? Many of the imaginable intermediate stages are not adaptive. In terms of the reuse (as opposed to duplicate circuitry) model, physical contact is necessary between a brain area whose function could be improved and the other circuitry that could enhance its function, in order for selection pressure to operate. Getting closer is not more adaptive; it is contact that is needed. One must identify the selective force that leads to contact, as demonstrated beautifully by Bock (1959) in his analysis of how an enlarging muscle insertion point on the mandible of a particular bird species becomes a jaw articulation after it contacts the skull. There is no doubt that some type of contact has been established in many important examples of preadaptation in evolution, as, for example, the invasion of land by aquatic vertebrates. There are examples of preadaptation where the new adaptation replaces the old (reptile jaw

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain articulation to middle ear bones) and others more like reuse, where a structure maintains its old function and acquires a new one (such as the human tongue functioning both in ingestion and in the articulation of speech sounds). Brain anatomy, and developmental constraints, probably make it difficult to physically co-opt circuits that are not in close proximity. One possibility, implied by some of the work of Dehaene and Cohen (2007), is that expansion of a particular area of the brain brings it into contact with neural tissue that can improve its function by integrating this circuitry. Natural selection is powerful when there is transmission. But it can only act on the available variants, and it can be trapped by local optima and the necessity to bridge maladaptive intermediate phases. And here is where something wonderful comes in to speed up and expand the process immensely. Culture! Preadaptation, however impressive in biological evolution, is massively important in cultural evolution, because the variants can be generated purposively, and there is tolerance for maladaptive intermediate stages, motivated by the desire to reach a goal. The extraordinary power and speed of cultural evolution is well documented (Girifalco1991; Newson et al. 2007). Natural selection can work without constraints! The results are computers, memory storage systems that evolve by the year, Mozart symphonies, and the like. I am astonished that evolutionary psychologists are not excited by the application of the principle of natural selection to the study of cultural evolution, given that they can watch it happen (Rozin, in press). I was excited to learn from Anderson that Dehaene and Cohen (2007) have been examining how processes like access can occur in the developing brain under the selective guidance of cultural selection. I think this is what I was talking about in 1976 as accessibility in development and in cultural evolution (Rozin 2006). But we still have to figure out how Mother Nature built such an extraordinary creature as the human before intentional cultural actions made abilities and artifacts available as preadaptations.

Optical holography as an analogue for a neural reuse mechanism1 doi:10.1017/S0140525X10001214 Ann Speed, Stephen J. Verzi, John S. Wagner, and Christina Warrender Sandia National Laboratories,2 Albuquerque, NM 87185-1188. [email protected] www.sandia.gov [email protected] [email protected] [email protected]

Abstract: We propose an analogy between optical holography and neural behavior as a hypothesis about the physical mechanisms of neural reuse. Specifically, parameters in optical holography (frequency, amplitude, and phase of the reference beam) may provide useful analogues for understanding the role of different parameters in determining the behavior of neurons (e.g., frequency, amplitude, and phase of spiking behavior). Optical holography hypothesis. In this commentary, we highlight a possible physical mechanism for neural reuse. Importantly, how reuse is implemented in neural tissue is one of the primary open questions, as the author states in section 6.4, paragraph 4, of the target article. Specifically, we wonder if there might be utility in a theory of reuse (i.e., recruitment of the same cortical area for multiple cognitive functions) based on an analogy to optical holography. This analogy has been proposed by several authors as early as the late 1960s and early 1970s (e.g., Westlake 1970) and as recently as 2008 (Wess 2008). It has influenced work in distributed associative memory, which involves neural reuse in the form of individual processors

contributing to multiple distributed representations (Plate 1995; Sutherland 1992). However, the full potential of the analogy does not appear to have been realized. Therefore, we describe optical holography and the neural analogy, state some predictions about neural function based on this analogy, and propose means for testing these predictions. (See our Fig. 1.) Optical holography was developed by Dennis Gabor, for which he won the 1971 Nobel Prize in Physics. It is a method for encoding, and then retrieving, multiple images onto a single (color-sensitive) photographic plate using different wavelengths of light emitted by a laser. Illustrated in Figure 1, laser light is split into two equally coherent beams of light by a beam splitter. One path goes through the beam splitter and reflects off of the target (real three-dimensional) object; some of this reflected light hits the storage media (photographic film). The other path is reflected by the beam splitter directly towards the storage media. The difference in path length of the two coherent beams of light from the beam splitter to the storage media creates a phase difference that exposes the photographic film with an intoferogram image (inset, Fig. 1). To retrieve the stored image, the real object can be removed and the laser light is sent through the beam splitter, all of which is reflected to the photographic film. After passing through the photographic film, the optical holographic image is reconstructed and visible to the eye. Importantly, if lasers of different wavelength are used, different holograms can be encoded on the same photographic film, essentially allowing reuse of that film. Reuse. That multiple images can be encoded in a distributed manner on a single plate at different wavelengths is the foundation of the applicability to the neural reuse hypothesis, although we imagine it would apply to more than just storage of memories. Specifically, optical holography has fundamentally three parameters that can be varied to encode each unique hologram onto a photographic medium: (1) frequency of the laser, (2) amplitude of the laser, and (3) phase relationships to other stored representations. On the surface, these three variables might be analogous to frequency, amplitude, and phase relationships in firing of individual neurons and neural circuits or ensembles. However, there are additional variables affecting neural behavior, including: (i) involvement of various neurotransmitters; (ii) afferent, lateral, and feedback connectivity; and (iii) temporal relationships between thousands of inputs. This implies a large variety of ways in which an individual neuron, circuit, or area can be recruited for different functions. One prediction that follows from this part of the analogy is that one should be able to elicit fundamentally different behaviors from a neuron, circuit, or even a cortical region by changing the input or the larger context in which the input occurs. This could take the form of electrical stimulation with different properties or the presentation of different neurotransmitters to the same neuron or circuit and measuring the resulting outputs. If the electrical stimulation or neurotransmitter acts as an analogue to the wavelength of the reference beam, different behaviors should result. Such testing could be done in slice preparations, in engineered networks, or in simple animal models such as Drosophila. Interference. Harmonic interference, or holographic aliasing, leads to errors that may have analogues in human memory and learning. Specifically, aliasing in holography may be analogous to confabulation or abstraction into a schema representation. Aliasing can occur as a result of two objects being encoded onto the same plate using lasers with similar wavelengths, and results in retrieval of an image that is an amalgamation of the two original objects. An analogue in human behavior would be two skills learned under similar contextual conditions. When those skills are functionally similar, they are considered to positively transfer to one another, and can result in a generalized representation of that particular class of problems. When they are functionally dissimilar, this is considered to be an example of negative transfer (e.g., Cormier 1987; Gick & Holyoak 1983; BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Figure 1 (Speed et al.) Holographic data storage and retrieval. Inset illustrates an interference pattern on film that is the physical storage of the holographic image.

Novick 1988). This might also cause enhancement of a particular memory (e.g., the von Restorff effect; Hunt 1995). Additional implications of the holographic analogy include: 1. The fact that a single beam of a particular frequency recalls the entire image may be analogous to redintegration (Roodenrys & Miller 2008). 2. The capacity for storage and reuse increases with the number of variables used in defining neural and circuit behavior (Kalman et al. 2004; Plate 1995; Psaltis & Burr 1998; Sutherland 1992). 3. The number of parameters defining neural and circuit behavior in a given organism should predict behavioral/cognitive complexity, and such complexity should scale similarly to the predicted capacity. As indicated above, tests of predicted capacity and interference can be done using computational simulations, experiments with networks in preparation, or engineered networks of neurons. In the past, the optical holography analogy has been criticized (e.g., Wilshaw et al. 1969). Certainly, the analogy does break down in certain places – for example, the fact that any piece of the photographic plate encodes the entire image, thus destroying some parts of the plate, merely degrades image quality rather than creating an analogue to aphasias seen in humans. However, using the holographic analogy as a starting point for hypothesis development might provide a foundation from which the physical mechanisms of neural reuse might be identified.

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NOTES 1. The authors of this commentary are employed by a government agency, and as such this commentary is considered a work of the U.S. government and not subject to copyright within the United States. Each commentator contributed equally to this response and are thus listed in alphabetical order. 2. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94AL85000.

Massive redeployment or distributed modularity? doi:10.1017/S0140525X10001226 Alexia Toskos Dils and Stephen J. Flusberg Department of Psychology, Stanford University, Stanford, CA 94305. [email protected] [email protected]

Abstract: In distinguishing itself from other distributed approaches to cognition, Anderson’s theory of neural reuse is susceptible to some of the same criticisms that have been leveled at modular approaches. Specifically, neural reuse theories state that: (1) the “working” of a given brain circuit is fixed, rather than shaped by its input, and (2) that high-level cognitive behaviors can be cleanly mapped onto a specific set of brain circuits in a non-contextualized manner.

Commentary/Anderson: Neural reuse: A fundamental organizational principle of the brain The target article does an excellent job of exploring the behavioral, neural, and theoretical evidence supporting the idea that brain regions are reused in the service of many different cognitive functions and that traditional, modular approaches to neural architecture may be misguided. This viewpoint echoes other recent critics of contemporary cognitive neuroscience (e.g., Uttal 2001) and fits well alongside related distributed, emergent approaches to cognitive functioning (Rumelhart & McClelland 1986; Thelen & Smith 1994; Varela et al. 1991). A distinguishing feature of Anderson’s neural reuse framework is that it highlights how local neural circuits with fixed “workings” may be combined in evolutionary (or developmental) time to support new cognitive “uses.” However, we are concerned that some of the same criticisms that have been leveled at modular approaches to the mind may also pose problems for the current formulation of the neural reuse theory. First, much like classical modular views of mind, Anderson’s theory of neural reuse de-emphasizes the role that the immediate environment plays in the development of the functional properties of a particular neural circuit (Fodor 1983; Pinker 1997). In fact, the target article explicitly claims that the working of any given anatomical brain site is fixed, in stark contrast to classical PDP (parallel distributed processing) models. However, there is evidence that the function of a given neural circuit may be largely shaped by the structure of its input. For example, Sur and colleagues (Sharma et al. 2000; von Melchner et al. 2000) surgically rewired the optic tract of a ferret so that primary auditory cortex received visual input from the eyes of the animal. Not only did the ferret seem to develop normal visual (and auditory) behavior, but also the circuitry in auditory cortex exhibited many of the properties traditionally associated with visual cortex, such as orientation selective cortical columns. This suggests that the working of circuits even in the most evolutionarily ancient cortical regions is not restricted to any particular modality, let alone any specific function. Such flexibility provides evidence in favor of computational mechanisms that derive their function based in part on the statistical structure of the input (Rumelhart & McClelland 1986). Second, while Anderson’s theory of neural reuse rejects the idea that high-level cognitive functions (e.g., “language comprehension”) can ultimately be mapped onto any single brain module, the approach still calls for the one-to-one mapping between these high-level functions and a specific, distributed set of neural circuits. However, it may be the case that distinct instances of what we would label as the same cognitive behavior might actually emerge from the distributed activation of different, contextually variable sets of neural circuits. For example, although visual object recognition has been shown to automatically activate motor brain regions (Chao & Martin 2000; Tucker & Ellis 1998), very different motor circuitry might be recruited to recognize a chair when you are tired and want to sit down than when you need to reach something on a high shelf. There may also be individual differences across a population in what neural resources are recruited for a particular cognitive task. For example, some people seem to readily recruit direction-selective neurons when listening to stories describing both literal and metaphorical motion, whereas others do not, even though both groups comprehend the story (Toskos Dils & Boroditsky, forthcoming). Thus very different neural representations might subserve the very same high-level cognitive behavior (i.e., “object perception” and “language comprehension”) both within and across individuals. This suggests that it may be a category mistake to try to reduce complex, person-level cognitive phenomena to a unique set of neural circuits (Ryle 1949). Rather, these mental operations are always a contextually bound, emergent function of the history of the organism, the immediate environment, and the bodily state of the organism (Thelen & Smith 1994). In sum, while Anderson’s theories of neural reuse offer a much-needed counterpoint to traditional, modular views of neural architecture, they still suffer from some of the same

difficulties these modular views have in accounting for complex cognitive behaviors that develop over the course of learning and experience. Dynamic models of cognitive function preserve many features of the neural reuse framework that account for data unexplained by massive modularity models. They should be preferred because, unlike neural reuse models, they also predict that the function of a given circuit should change as the structure of its input changes, and they do not require that high-level cognitive functions cleanly map onto specific cortical circuits. These approaches currently provide the additional benefit of computational models that can be used to make precise predictions about the development of cognition function. Proponents of neural reuse should point to specific ways in which they can accommodate the limitations of the current formulation of neural reuse theory.

Belling the cat: Why reuse theory is not enough doi:10.1017/S0140525X1000110X Oscar Vilarroya Unitat de Recerca en Neurocie`ncia Cognitiva, Departament de Psiquiatria i Medicina Legal, Universitat Auto`noma de Barcelona, and Fundacio´ IMIM, Barcelona 08193, Spain. [email protected]

Abstract: I agree with Anderson’s approach to reuse theories. My main concern is twofold. Anderson assumes certain nomological regularities in reuse phenomena that are simply conjectures supported by thin evidence. On the other hand, a biological theory of reuse is insufficient, in and of itself, to address the evaluation of particular models of cognition, such as concept empiricism or conceptual metaphor.

I would first like to welcome Anderson’s target article. Extant cognitive neuroscience and neuroimaging studies, as well as the growing importance of biological analyses in cognitive science, increasingly show the unsuitability of a modular approach to cognition. In this situation, a new framework is required to model the functional architecture of cognitive processes in the nervous system. Anderson’s article is a remarkable effort in this direction. I agree with his general approach to the issue. My main concern, though, is twofold. On the one hand, Anderson assumes certain nomological regularities in reuse phenomena that are simply conjectures supported by shaky evidence. On the other hand, a biological theory of reuse by itself is inadequate for the task of evaluating particular models of cognition, such as concept empiricism or conceptual metaphor. We need an independent characterization of cognitive phenomena, a model that we currently lack. First, extracting biological regularities from evolutionary phenomena is not a straightforward issue. Elsewhere (Vilarroya 2001), I have suggested that cognitive systems are constrained by what I called “bounded functionality,” which accounts for the dynamics of the functional paths leading to solutions to adaptive problems. One of the bounded functionality constraints is what I call the “bricoleur constraint,” defined as the fact that natural selection favors the shortest design path. In other words, the solutions to adaptive problems have to take into account the resources that were available to the system before the adaptive problem appeared. The bricoleur constraint is the evolutionary characterization of the reuse approach. However, the bricoleur constraint can be realized in many ways for any evolutionary phenomenon. For instance, Anderson’s principle, that “older areas, having been available for reuse for longer, are ceteris paribus more likely to have been integrated into later-developing functions” (sect. 1.1, para. 1), can be a good starting point, but it cannot be taken as an evolutionary law. Evolutionary biology is full of surprises; older areas can serve a small BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Anderson: Neural reuse: A fundamental organizational principle of the brain range of functions at the same time that an intermediately incorporated area which proved more useful in later functions results in more pervasive implications. Evolutionary tinkering is, in itself, not susceptible to lawlike regularities (see, e.g., Jacob 1977). Additionally, the evidence by which Anderson tries to sanction the abovementioned principle is based on the hypothesis that “the older the areas, the more back in the brain they are” (see sect. 1.1, para. 3), which is, to say the least, highly contentious. The foundation of his entire argument is therefore a shaky one. Second, in order to address the evaluation of particular models of cognition, we require, apart from reuse theory, a characterization of the cognitive processes the nervous system actually carries out; and the jury is still out on nearly all the available hypotheses. Indeed, Anderson examines cognitive models while taking for granted some functional attributions, for example, of fMRI studies, to form the basis of his argumentation, but such characterizations are under discussion precisely in part because of reuse theories. For example, in section 4.4, Anderson uses neuroimaging studies to argue against conceptual metaphor. However, the functional interpretation of such studies (e.g., “finger representation”) are prone to criticism, as is any other neuroimaging study, precisely on account of reuse theories, and therefore cannot be used as arguments against conceptual metaphor or any other hypotheses. Neuroimaging studies are task-oriented, and the interpretations are reverse-engineering biased. Previously (Vilarroya 2001), I addressed the issue of “functional mesh,” that is, the assumed tight fit between a cognitive trait’s design and the adaptive problem it is supposed to solve. It is now widely assumed, even by Anderson, that the “optimality move” that creeps in behind functional mesh is misplaced – namely, that cognitive mechanisms need not be specially designed to solve the adaptive problems for which they were selected. Even if Anderson seems to agree with such an approach, my impression is that he eventually falls into the functional mesh trap, by assuming the functions of certain areas. I have also defended (Vilarroya 2002) a dual reverse-engineering and biological analysis to characterize cognitive functioning. However, biological analyses in cognitive science are of a particular type. Usually, biological explanations are teleonomic explanations that first identify the trait that is likely to be under selection, and then identify the adaptive problem that the trait is supposed to solve. Yet, certain aspects of cognitive science force a change in this methodology. In trying to explain the cognitive mechanisms of a biological organism, the researcher can identify the adaptive problem that the brain is supposed to solve, but in reality it is difficult to identify the actual trait itself, because the trait is not as self-evident as, say, an eye, a liver, or a wing. Moreover, the explanatory strategy of cognitive science cannot simply be an inversion of the first steps of the teleonomic explanation. It is not enough to identify the adaptive problem and then infer the mechanism. Rather, we need to complement an initial assumption about a trait’s design with a characterization of how the adaptation might have appeared over evolutionary time – first characterizing the adaptive problem that the organism is supposed to solve, then the fitness-maximization process, as well as showing that the trait is specialized for solving the adaptive problem, unlikely to have arisen by chance alone, and not better explained as the byproduct of mechanisms designed to solve some alternative adaptive problem. In summary, functional attribution in cognitive science is not a straightforward operation, but rather, requires an independent characterization from the functional mesh assumption; reuse theory alone cannot provide this type of tool. Hence, in my opinion, Anderson lacks the basis to apply his functional characterizations as arguments against specific models of cognition. Once we have the necessary tools to account for functional characterization in cognitive science, of course, reuse theory will prove extremely useful.

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Author’s Response Cortex in context: Response to commentaries on neural reuse doi:10.1017/S0140525X10002049 Michael L. Anderson Department of Psychology, Franklin & Marshall College, Lancaster, PA 17603, and Institute for Advanced Computer Studies, Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742. [email protected] http://www.agcognition.org

Abstract: In this response, I offer some specific examples of neural workings, discuss the uncertainty of reverse inference, place neural reuse in developmental and cultural context, further differentiate reuse from plasticity, and clarify my position on embodied cognition. The concept of local neural workings is further refined, and some different varieties of reuse are identified. Finally, I lay out some opportunities for future research, and discuss some of the clinical implications of reuse in more detail.

Behavioral and Brain Sciences (BBS) is a unique and extremely valuable resource, and so I would like to begin this response by thanking the editors for their continued service to our field. BBS has been an important part of my intellectual life since I was an undergraduate. I vividly remember my first encounter with the journal in the library stacks. Its debates were deeply helpful to me in preparing my senior thesis, and have remained crucial to my intellectual development since. I know many of us in the cognitive sciences are similarly indebted. Naturally, this arena for discussion would serve no purpose without willing participants, who spend their time and energy to help improve the ideas of others. For this gift from my commentators, I am truly grateful. The commentaries cover an astonishingly broad range of issues – from history to holograms, modularity to memory consolidation – and I will do my best to at least touch on all of the many ideas they contain. Many commentators are especially concerned about the core notion of cortical “workings,” and about my emphasis on neural context as the main determiner of cognitive function, to the apparent exclusion of the social, environmental, and developmental contexts that also help determine functional outcomes. A few commentators take issue with my stance on embodied/grounded cognition. Some commentators have concerns about the general adequacy of the theory; others, about the adequacy of the data; and a few offer some alternate hypotheses to account for the data I review. Very many commentators offered specific advice for ways to improve the theory – proposals for better integrating neural reuse with evolutionary theory, for specifying the mechanisms driving reuse, and for some experimental approaches that could further elucidate the functional organization of the brain. I try to treat each of these topics in the following sections. R1. What neural reuse is Before getting to those specific responses, let me begin with a short section in which I discuss two specific

Response/Anderson: Neural reuse: A fundamental organizational principle of the brain examples of what I take a “working” to be, as it might help clarify the theory of neural reuse more generally. As was hopefully made clear in the target article, the basic idea behind neural reuse is that neural circuits, which may have initially served one specific purpose, have come over evolutionary and developmental time to play many different roles. Because terms like role, purpose, and function have many different meanings, and were in fact being used in conflicting ways in the literature on function-tostructure mapping, Bergeron (2008) introduced the terms use and working. Neural reuse holds that the “workings” of local neural circuits are put to many different higher-level “uses,” and that the flexibility and variety of our cognitive repertoire results in part from the ability to put together the same parts in different configurations to achieve different behavioral outcomes. From the perspective of neural reuse, it appears that the field of psychology has typically concerned itself with investigating uses, which is of course a necessary part of any investigation of the mind. Nevertheless, given the apparent many-to-many mapping between uses and brain regions, it behooves the cognitive scientist interested in the neural basis of cognition to think about workings, as well. What, then, is a working? Abstractly, it is whatever single, relatively simple thing a local neural circuit does for or offers to all of the functional complexes of which the circuit is a part. Concretely, consider two examples: In Penner-Wilger and Anderson (2008), we suggested that a brain circuit known to subserve both finger and number representations might be offering to both a kind of ordered storage. The idea was that a register – an ordered set of containers in which to store specific values – offered a functional structure useful to both finger and number representation, and so that structure might have been deployed for both uses.1 Somewhat more speculatively, consider the ability to fixate the eye on a specific region of the visual field. This is known as foveation, because its purpose is to move the eye so that the fovea (the retinal region offering the greatest acuity) is receiving the desired input. Foveation is important to many visual capacities, including the visual grasp reflex, smooth ocular pursuit, and reading. One component of the foveation function might be the ability to map any arbitrary element in a matrix (a two-dimensional grid that could represent the retina) onto the center of that matrix, that is, the ability to re-center the matrix around any of its elements.2 Such a working could play a functional role not just in the visual uses mentioned above, but also in such things as shifting spatial attention and Braille reading – and even in the “tactile foveation” exhibited by the star-nosed mole (Catania & Remple 2004). Hence, we should not be surprised to find that parts of the foveation circuit are deployed not just in visual tasks, but in these other tasks as well. These are, of course, just examples of the kinds of thing that workings could be. As noted in the target article, the discovery and definition of specific neural workings can only come at the end of a long process of investigation and testing. Nevertheless, I hope these examples – however speculative or provisional – can serve to clarify the basic notion, and improve understanding of the theory as a whole.

R2. Context, context, context One of the central implications of neural reuse that did not come out as clearly in the target article as I would have liked is the deep uncertainty of reverse inference as a strategy of functional explanation in cognitive neuroscience (Poldrack 2006). If brain regions contribute to multiple uses – if, that is, they fail to be “use-selective” – then the mere observation of activity in a given region provides very little information about what the brain is up to. Certainly, one cannot assume that a region is being put to the same use in an attention task as it was in a language task or an emotion task. This goes also, and perhaps especially, for inferences based on seeing more (or less) activation in a region under different task conditions. If one doesn’t know what the brain is doing just in virtue of observing regional activity, then one cannot know it is doing more of some specific thing (more attention, more control, more calculation) in virtue of any observed increase in that activity. Differences in activity level could equally well be a sign of being in a different information state.

R2.1. The importance of neural context

For many, these observations will simply add fuel to the skeptical fire being built under the use of neuroimaging in cognitive science (see Coltheart 2006; Klein 2010; Roskies 2007 for discussions of the general issue). Certainly there is reason to be cautious, but the potential value of neuroimaging is so vast that it would be foolish to forego its use. So how should we address this issue? The target article emphasizes that although neural regions are not use-selective, they may be “working selective,” and so clearly one promising empirical project is to begin to define local workings with various sorts of crossdomain modeling. What was less clear in the target article, but helpfully raised by Gomila & Calvo and Reimers, is that there is another, complementary empirical project: Although local regions are apparently not useselective, networks of regions may well be use-selective. That is, it might be possible to recover selectivity by attending to larger scale patterns of regional co-activation and coherence. Cognitive tasks and task categories may turn out to have characteristic signatures at the network level (for discussion, see Anderson et al. 2010; Poldrack 2006). Whether and to what degree specific identifiable networks of interacting regions turn out to be use-selective is an empirical question, one that is only now starting to be investigated. But it seems clear that this is currently the most promising, and perhaps the only viable way to uncover use selectivity in the brain. Note the implication that knowing what activity in a given region means for the tasks engaging the brain will require careful attention to the neural context of that activation – to what else the brain is doing when it exhibits the activation of interest. Seeing activation in Broca’s area may give one confidence that it is doing something of importance (although see Klein’s commentary and Klein [2010] on the uncertainty of this inference), but knowing what else is active along with Broca’s may tell us what that something is, the use to which Broca’s is being put.

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Response/Anderson: Neural reuse: A fundamental organizational principle of the brain R2.2. Bodily, environmental, social, and cultural context

This point about the value of attending to neural context was somewhat eclipsed by my attention to the importance of local neural workings. Moreover, my near exclusive attention to neural facts appeared to Toskos Dils & Flusberg; Gomila & Calvo; Immordino-Yang, Chiao, & Fiske [Immordino-Yang et al.]; and Rozin to eclipse the value of attending to even broader contexts. I certainly endorse the general point that broader contexts – bodily, environmental, social, cultural – matter to our ascription of function. If we don’t understand what an organism is doing, we can hardly hope to understand what its brain is up to; and figuring out the best way to describe or characterize a behavior certainly involves detailed attention to context. On the other hand, this is where semantics – and, specifically, the imprecision of the term function – can sometimes get in the way of science. It is an obvious point, and barely worthy of scientific attention, that a single mechanism can be construed as serving multiple functions; the alarm system detects motion, and protects the house from intrusion. This is less an observation about the fundamental nature of alarm systems than about the potential variety of our epistemic interests when we ask “What is it doing?” The cases of scientific interest are those where a single mechanism is put to genuinely different uses, the way many people (myself included) use their e-mail inbox also as a “to-do” list. Note the implied converse, that I thereby put different mechanisms – my inbox, my task list—to the same use. So, is it the case, as Toskos Dils & Flusberg hypothesize, that the same cognitive behaviors can emerge from different, contextually variable sets of neural circuits? It is an interesting question and worth pursuing. But here is where attention to context, and its role in defining the conditions under which we will call one behavior the “same” as another, becomes crucial to the scientific enterprise. There can be no doubt that the same behaviors mechanically defined (writing on a piece of paper, say, or calculating exchange rates) can involve different neural circuits. But of course writing out a confession, or a love letter, or an essay are vastly different enterprises, and we should expect correspondingly different neural involvement. These would be cases where the neural context tracks the task context in ways that are interesting to discover, but also unsurprising. What would be somewhat surprising is the discovery of different unique sets of circuits for the very same function, where there is no discoverable contextual or other difference to explain the apparent redundancy. Here would be a failure of the neural context to track task context because of the surfeit of neural mechanisms for the task in question. The discovery of such an example would be very interesting and illuminating, although it would not be a specific counterexample to neural reuse. Nothing about the theory suggests that there is only a single neural token instantiating any given type of neural working, much less a single, unique high-level neural network for every identified use. Some redundancy, especially in workings, is to be expected; it will sometimes be the case, because energetic constraints favored the outcome, or perhaps just as the result of chance, that different neural circuits developed the same working or came to subserve a similar use. And the discovery that 296

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such redundancy was extensive at the use/network level, and, more importantly, that differences in which networks subserved specified uses did not track context in some identifiable way, would be very puzzling and would affect far more than the theory of neural reuse, including the dynamic models that Toskos Dils & Flusberg favor. Immordino-Yang et al. ask a similarly interesting and challenging series of questions. Can the same network of neural circuits in fact serve quite different uses, a difference that would only become apparent once cultural context was considered? Here again, it is not the least surprising (although not for that reason uninteresting) that cultural context affects which neural resources are brought to bear on cognitive tasks; for, after all, the context may well change (if only in subtle ways) the nature of the task. One would expect the neural context to track the environmental/cultural context in this way. What would be harder to assimilate is if it were often the case that the same network subserved different uses at the cultural level – genuinely different uses not arising from shift in epistemic perspective – without there being a detectable neural difference. It would be a bigger challenge because this would imply the existence of many cases where neural context does not track environmental context, and this would leave a large explanatory gap that behavioral science has not yet discovered a way to fill. Here again, this would not be a challenge specific to neural reuse; the discovery of radical context dependence in behavior would not undermine the discovery that neural resources are deployed in support of multiple uses across task categories and that differences in uses are better explained by patterns of inter-neural cooperation than by differences of activity in individual brain regions. But it certainly would suggest that this was only a part – perhaps a very small part – of the explanation of behavior. There are perhaps some hard-core neuroscientists who think that neural facts are the only relevant inputs to behavioral science, but I am not a member of that tribe, and the implications of neural reuse seem largely orthogonal to that debate. R2.3. Context and complexity

Still, there is an interesting quasi-dilemma that is illuminated by the advocates of context. Insofar as neural context tracks broader context, then although initial attention to context would be necessary to identify the nature of the cognitive task or behavior in question, the lab-minded neuroscientist could then (safely?) focus on the brain, for the broader context would be reflected therein. This somewhat blunts the force of the argument for the necessity of contextualization. On the other hand, although the discovery of cases where neural context did not track broader context would appear to strengthen the case for the necessity of contextualization in science, the attendant increase in the complexity of the problem space could encourage the community to simply ignore such cases as beyond the current reach of scientific method. If there is no neural difference despite relevant differences in context and behavior, to what degree are subjects aware of the difference, and controlling their behavior with respect to context? How do the non-neural aspects of intention and control manifest themselves? It is incumbent on advocates

Response/Anderson: Neural reuse: A fundamental organizational principle of the brain of context to go beyond gestures to dynamic modeling or genetic mechanisms; they must both identify examples of this sort and describe an approach to understanding them that promises more illumination than mystification (Chemero 2009). I should be clear that I am not faulting the commentators for not doing so here; this is an ongoing challenge for all of us in the neuro-geneticsocial-behavioral sciences. R2.4. Niche construction

For a final word on the topic of context, it is important to keep in mind the facts that context is itself malleable, and that we are among the most important agents of that change. Iriki makes the case not just for the importance of neural niche construction, but also for the possibility that the changing neural context influences our evolutionary pathway, by inducing stable cultural changes and thereby changing the environment within which selection operates. Both Rozin and Lindblom make similar points. Culture (and especially language) can speed up preadaptation, both by increasing the degree and frequency of innovation and by buffering group members against selection pressures that might otherwise tend to weed out initially maladaptive exaptations. There is an extremely interesting literature emerging at the intersection of brains, genes, and culture (Boyd & Richerson 2009; Hawks et al. 2009; Kitayama & Cohen 2007; Richerson et al. 2010; Suomi 2004), and I would be pleased if neural reuse turned out to be a more amenable perspective for coming to grips with these interdependencies than competing proposals on brain organization, such as modularity and localization (something Reimers suggests in noting the many parallels between neural reuse and molecular and epigenetic reuse). R3. Workings 9 to 85 In addition to worrying about my apparent lack of attention to context, Toskos Dils & Flusberg and Immordino-Yang et al. also question whether the notion of fixed local workings really gives an adequate picture of the functioning of the brain, since it appears to underplay the importance of development and plasticity, a sentiment echoed also by Aisenberg & Henik; Dekker & Karmiloff-Smith; and Katz. I certainly do not want to deny the importance of plasticity to the brain and its functions. But plasticity is a change in use as a result of a change in working. Neural reuse is the acquisition of a new use without a change in working. The target article reviews evidence for importance of the latter process in the functional organization of the brain; it is not an argument against the importance of the former. R3.1. Workings versus plasticity

Still, neural reuse does suggest that these two processes will be mutually constraining, not to say antagonistic, and that opens some very interesting avenues for further exploration. I think the matter should be framed in the following way. The regions of the developing brain are likely to (and the massive redeployment hypothesis positively predicts that they will) have some innate functional biases, the strength and specificity of which undoubtedly

vary from region to region. Where the nature of experiential input and the characteristics of the task demands being placed on the organism are consistent with these neural biases, then plasticity and reuse can act happily in concert. Neural plasticity generates local workings that reuse can arrange into various circuits that subserve the uses required for the organism’s flourishing. (Apropos of which, it should be noted contra Michaux, Pesenti, Badets, Di Luca, & Andres [Michaux et al.] that nothing in the theory of neural reuse requires denying the necessity of experience to shaping local circuitry; more on this issue in section R4, para. 3.) However, where the nature of the input or the characteristics of the task are inconsistent with existing cortical biases or established workings, then these processes can easily come into conflict. The experiments reported by Sur et al. (1988) and cited by many commentators here offer an excellent paradigm to further explore these sorts of conflicts. As is well known, Sur et al. (1988) rewired the ferret cortex by redirecting right visual field outputs to auditory rather than visual cortex. The result of this manipulation was the induction of neural circuitry in auditory cortex resembling that typically found in normally developing visual cortex – the classic “pinwheel” organization, for instance. The rewired cortex apparently instantiated workings typically associated with visual processing, such as orientation and direction selectivity, and subserved typical visual uses, such as orienting toward a visual stimulus (von Melchner et al. 2000). Plasticity is clearly a powerful force in the development of the brain. It is not, however, omnipotent; visual acuity in the right visual field was significantly lower than in the left visual field. This finding is consistent with the existence of congenital cortical biases potentially in conflict with the nature of sensory inputs, which had to be overcome to accommodate visual stimuli. From the perspective of neural reuse, it would be interesting to have a more complete behavioral inventory of these animals. Although in this particular case behavioral evidence would have to be treated with caution, given the multiple neural ablations these experiments involved, such information could nevertheless offer some clues as to what other uses the now missing auditory workings might have served. What performance impact did the induction of “visual” circuitry into auditory areas have on other functions relying on the same neural region? Were the neural complexes underlying these other behaviors systematically altered by the rewiring? If so, how? Certainly, this is a paradigm that could be used to systematically investigate such questions for various regions of the brain. Other opportunities for investigating the potential conflicts between plasticity and neural reuse come in the form of manipulations not of neural wiring, but of the task environment, and in particular manipulations of the order in which tasks are learned. Before local neural circuits have acquired very specific workings, and before these workings have been incorporated into multiple functional complexes subserving different uses, it may well be that the most efficient way to acquire novel capacities is inducing plasticity in local circuitry. But later in development such plasticity could prove costly, and learning may favor neural reuse as a strategy. If it is the case that different tasks induce different local workings when BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Anderson: Neural reuse: A fundamental organizational principle of the brain learned early, then it might be possible to systematically investigate the conflicts between plasticity and reuse by looking for specific order effects in learning. For instance, it might be easier to learn task A after task B than after task C, even when A is learned at the same stage of development in each case. (Naturally, choosing appropriate tasks would take some ingenuity; that it will be harder to learn calculus after learning Spanish than after learning algebra is obvious and uninteresting. That it might be easier to learn simple arithmetic after manipulating objects in gloves than after manipulating objects in mittens looks potentially more interesting.) Similarly, it may be that learning task D after A and B disrupts A, but does not do so when learned after tasks A and C, because in the former case the local workings needed to allow for neural reuse as a learning strategy have not developed, leaving plasticity as the only option. Reimers suggests some similar developmental studies, and I know that the entire community eagerly awaits the release of the first analyses from the various longitudinal fMRI studies currently underway (Paus 2010). R3.2. Evolution or development? Both!

In short, I think that the neural reuse model is much friendlier to the developmental perspective than it might have appeared in the target article (Dekker & Karmiloff-Smith and Moore & Moore rightly point out that development was under-discussed there) and that the two perspectives together suggest some novel avenues for further exploration. I think this account may also shed some light on the issue of how fixed I take neural workings to be (Aisenberg & Henik, Immordino-Yang et al., Toskos Dils & Flusberg) and how I take them to be fixed (Michaux et al.). While I think innate cortical biases are a likely feature of our neural organization, workings emerge over time, driven by experience and task demands. Although I think the brain eventually achieves a maturity that is marked in part by the existence of strong and stable workings, plasticity always remains a possibility, whether in response to extraordinary task demands or to physical injury. In this light, I think one can understand neural reuse as a learning strategy that greatly increases the flexibility of the brain while avoiding some of the potentially disruptive effects of local plasticity (especially plasticity that occurs late in development). This may make it sound like I am giving up on the phylogenetic claims of the massive redeployment hypothesis. I am not. Instead, I am suggesting that the evolutionary and developmental aspects of learning – especially when considered in the neural context – are typically complementary, mutually influencing, and extremely difficult to disentangle. Developmental trajectories, even those highly sensitive to local context, may nevertheless depend on specific evolutionary inheritances. Genetic effects can be influenced by environmental factors such as resource availability (Devlin et al. 2004), and even social factors such as parenting styles (Suomi 2004), and may themselves rely on typical developmental trajectories which, although not themselves hard-coded, have been driven long enough by stable environmental factors to have become established among the dependencies of the genetic pathway. 298

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R3.3. Workings versus polyfunctionality

This may assuage some of the concerns that my workings were too fixed to account for the dynamic nature of the brain, but several commentators question the very notion of local workings. Aisenberg & Henik; Brincker; Gomila & Calvo; Junge´ & Dennett; Katz; Petrov, Jilk, & O’Reilly [Petrov et al.]; and Toskos Dils & Flusberg all argue that local regions might well be natively polyfunctional, obviating the need for any explanation based on reuse. It is true that much of my imaging data is consistent with this possibility, as they show at most that neural regions subserve multiple uses, and multi-use could certainly result from the polyfunctionality of these regions. Moreover, as Junge´ & Dennett, Klein, and Ritchie & Carruthers point out, the imaging data are also consistent with there being multiple local workings in close proximity, such that the multiple uses only appear to use overlapping circuitry due to the poor spatial resolution of current functional imaging techniques. And, as I noted in the target article, these data are even consistent with there being no local functions at all. If brain functions are primarily established not by the structure of local circuitry but by the relative response patterns of neurons or neural assemblies (if, that is, functions are defined by the relational rather than the local properties of neural assemblies), then multi-use could result when these assemblies cooperate with different partners, thereby establishing different relational – and therefore different functional – properties. But imaging data demonstrating neural overlaps are not the only data I cited, and I think the broader picture sits uneasily with these possibilities. First, there are the data suggesting that more recently evolved uses are subserved by more broadly scattered neural circuits than are older uses. If we may presume an increase in the metabolic cost of establishing and maintaining more broadly scattered functional complexes, then, if polyfunctional local circuits were an option, one would expect uses to be consistently subserved by localized functional complexes. These data seem to favor the existence of local and relatively defined cortical biases. Second, there are the data on cognitive interference and cross-domain interactions. These data would appear to weigh against the possibility of multiple local workings, and favor actually shared neural components. Third – and most telling in my view – are the cases where there appears to be functional or semantic inheritance that results from the sharing of components. This suggests that the functional contributions of the shared local neural circuits are stable and detectible across multiple uses, a possibility apparently inconsistent with both relationally defined function and polyfunctionality. I recognize, of course, that these arguments are more suggestive than definitive, and will be more or less compelling depending on one’s other intellectual commitments. In the end, the only evidence that can truly establish the case is the consistent discovery of local workings that can explain the multiple uses to which the local circuit is put. I am laying an empirical bet that this will prove possible, but I must recognize that the evidence may not break my way. To counter the worries that apparent neural overlaps might be a side effect of the relatively poor spatial resolution of fMRI, Klein suggests that experiments leveraging

Response/Anderson: Neural reuse: A fundamental organizational principle of the brain neural adaptation effects might be in order. Functional magnetic resonance imaging adaptation, fMRIa (Krekelberg et al. 2006), exploits the fact that neurons exposed to the same stimulus attenuate their responses to that stimulus over time, resulting in a decreased BOLD signal. Klein’s idea is roughly this: Choose two tasks that appear to involve some overlapping neural circuits, attenuate neural responses by repeatedly engaging in one task, and then switch tasks. If the attenuation disappears in the overlapping region, this would be evidence that “fresh” neurons from a distinct neural subpopulation were responsible for the second task; whereas if the attenuation remained, this would be evidence that the very same neurons were responsible for processing in both tasks. Let me first of all heartily endorse any and all calls for converging evidence from multiple techniques, and for multiple alternate uses of MRI in particular, for example, Diffusion Tensor Imaging (DTI) (Hagmann et al. 2008); fMRI coherence analysis (Muller et al. 2001; 2003; Sun et al. 2004); Multi-Voxel Pattern Analysis (Periera et al. 2009), and so forth. Nevertheless, I do have some concerns about this particular suggestion. First, although there is good evidence for neural adaptation as the result of repeated stimuli, that is, as the result of being asked to represent the same thing, there is less evidence for task adaptation, that is, for the idea that there is a reduction in neural response as a result of being asked to do the same thing. This matters, because in most cases of neural reuse, the hypothesis is that the region of overlap is not being used to represent the same thing in both tasks, so any inherited neural response suppression between tasks would have to result from the region being asked to do the same thing, that is, express the same working in both tasks. Second, even if one were to observe neural response suppression as the result of repeating a task, it would remain difficult to interpret the outcome of any experimental manipulation. For, consider the case where the BOLD signal in a region continued to be attenuated during the second task. This could be because they use the same brain regions, and there is some inherited response suppression (apparent evidence for reuse); or it could be because practice at the first task makes the second task easier, or changes the strategy participants use to engage in it (evidence compatible with multiple views). Similarly, if the attenuation disappears, this could be because a distinct neural subpopulation in the same region was being recruited (apparent evidence against reuse); because in the two tasks the same neural populations are being asked to represent different things (compatible with reuse); or because the first task induced neural changes that interfere with performance of the second task (apparent evidence for reuse; see Glenberg et al. [2008a] for one such example, and Grill-Specter et al. [2006] for a discussion of various ways to interpret repetition suppression). For these reasons, I think that fMRIa might be of limited usefulness in teasing apart “real” neural reuse from the possibility that neighboring neural populations are responsible for the different uses to which individual regions are apparently put. As noted above, better techniques for this include cross-domain interference and use-induced plasticity paradigms (Glenberg & Kashak 2002; Glenberg et al. 2008a), and I certainly hope to see more such work in the near future.

Of course, the possibility that the limited spatial resolution of fMRI might be hiding some functional segregation isn’t Klein’s only worry about that data set. He also wonders whether fMRI activations are particularly good at identifying which brain regions are making genuinely functional contributions to a task in the first place. Rather, activation may spread around the brain network, leading to false positives: regions that are activated only as a side effect of their connectivity, and not because they are making a functional contribution to the task under investigation. He is right, of course; this is a possibility (although not one that cuts against neural reuse in particular). That is why it is crucial to have not just imaging data, but also behavioral data and, where possible, results from techniques like Transcranial Magnetic Stimulation (TMS). If applying TMS over a region thought to be functionally involved in two different tasks in fact disrupts both of those tasks, that is evidence that the activation there is not just a side effect of connectivity, but is making a genuine functional contribution. The target article purposely included data from all of these sources, but here again I would encourage and welcome more studies along these lines. R3.4. How do workings work?

Even those willing to entertain the possibility that the brain might actually have local workings had some questions about how best to understand what they are. Ritchie & Carruthers, for instance, ask whether they ought to be understood in computational or intentional terms, and express some skepticism that they could be both computational and multi-use, since it is hard to see how the same computations could realize distinct representational properties on different occasions of use. Rather than repeat or expand upon the arguments from the target article on this matter, I would like instead to refer the reader to the very interesting suggestions made by Bridgeman; Speed, Verzi, Wagner, & Warrender [Speed et al.]; and Donnarumma, Prevete, & Trautteur [Donnarumma et al.] Bridgeman quite succinctly describes the representational power and flexibility of even fairly simple computational elements, and Donnarumma et al. offer a specific proposal for how this representational flexibility might be harnessed for multiple uses via programmable neural networks. One especially noteworthy aspect of their proposal is a solution to one apparent problem for neural reuse, mentioned also by Klein and Ritchie & Carruthers, that reused brain components might send their outputs to all their consumer complexes all the time, which would presumably result in a great deal of noise and behavioral confusion. That this does not appear to be the outcome means either that there is little neural reuse, or that the brain has managed a solution to this problem. Donnarumma et al. offer one model for how selective routing of outputs could be achieved even in a network with fixed connections. Equally interesting is the proposal made by Speed et al. that reuse might be enabled by a mechanism similar to that employed in optical holography. Here, it is somewhat harder to understand what form local workings would take (as these commentators note, in optical holography every piece of the plate encodes the entire image, and nothing like this appears to obtain in the BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Anderson: Neural reuse: A fundamental organizational principle of the brain brain), but that massive reuse is possible on this model is quite clear; and the proposal is notable for detailing the high-level functional predictions that emerge from taking a holographic perspective. Whether these solutions resemble the ones implemented by the brain for managing reuse remains to be seen, of course, but knowing that there exist possible solutions is certainly a positive step forward. I look forward to seeing how these diverse research programs evolve. R4. Embodied cognition: Still standing Perhaps the most vehement commentators were those objecting to my criticism of embodied cognition, including Brincker, Michaux et al., and Vilarroya. Let me be clear: I was an early proponent of embodied cognition (O’Donovan-Anderson 1996; 1997), continue to be a staunch advocate (M. L. Anderson 2003; 2008b; 2009; M. L. Anderson & Rosenberg 2008), and do not think that any of the arguments made in the target article falsify any of the claims made on behalf of the embodied perspective. What I do think is that embodied cognition is only one form of a much larger phenomenon of borrowed cognition, driven largely by neural reuse. This most certainly does not mean that the kinds of semantic inheritance from sensorimotor to higher-order cognitive systems so important to embodied accounts can be explained away. Quite the contrary: they urgently need to be explained. The worst effect my arguments will have on advocates of embodied cognition (a limitation apparently lamented by Gomila & Calvo, who wish I had pressed the critique further) is to strip away the illusion that the semantic inheritance observed in so many domains was ever actually explained by the discovery of neural overlaps. But such disillusionment should be welcomed by any scientist, as it lays down the direction of future research. Therefore, although Vilarroya is right in his argument that we need better methods for attributing functions to brain regions, he is wrong to think that without the ability to specify local workings, it is not possible to establish the limitations of extant models of cognition such as concept empiricism and conceptual metaphor theory. First of all, I do not criticize these theories per se; I argue that not all of the evidence taken to support the theories in fact does so without further assumptions, including especially the assumption that neural overlaps imply semantic inheritance. My evidence shows that this assumption is unwarranted. For this argument, one does not need to know what the workings of any given region in fact are. Rather, one needs to know what some of the uses are. It is apparent that in some cases of overlap – as between spatial schemas and evaluative concepts – the working underlying the (presumably earlier) spatial use exerts a structural and semantic influence on the (presumably later) conceptual/linguistic use (hence “better” is also conceptually “higher” or “above”). But in other cases, there seems no obvious evidence for such inheritance. The borrowing of spatial resources for number storage revealed by the SNARC effect (Dehaene et al. 1993), the use of gesturing in learning (Goldin-Meadow 2003), and the use of a common brain region for both finger and magnitude representation (Penner-Wilger & Anderson 2008; submitted; Zago et al. 2001), can all be explained 300

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by positing that both later and earlier use share some functional requirements, such that one or more of the workings underlying the earlier use can also be of service in supporting the later use. In such cases, there need not be, and we in fact see no evidence in these particular cases for, any conceptual grounding or semantic inheritance between these domains as a result of these overlaps. Michaux et al. object to this line of reasoning in the specific case of the overlap between finger and number representations, but in fact all the evidence they cite is compatible with the functional inheritance account (see Penner-Wilger & Anderson, submitted, for a more detailed account). As noted already above, we argue that the observed overlap results from the fact that one of the functional needs in both domains is for a specific sort of ordered storage. If this is the case, any activity that tended to improve the functionality of the shared working would be expected to improve performance in both cognitive domains. Hence, one doesn’t need to posit semantic inheritance to explain the finding that finger differentiation exercises improve math performance (Gracia-Bafalluy & Noe¨l 2008). In fact, this same finding suggests that although sensorimotor experience can be crucial to the development of numerical cognition, insofar as it helps establish the functional structure of the brain regions used in both domains, the crucial experience needn’t be of using the fingers to do mathematics. Exercises that improve the sensory acuity of finger representations could be expected to improve performance on certain numerical tasks, without the further requirement that the actual fingers be used in a mathematical context. Similarly, whenever there is a shared resource, the overlapping uses would have the potential to interfere with one another. That there is indeed such interference between finger and number representations (e.g., Badets & Pesenti 2010) is therefore not surprising. More specifically, Penner-Wilger and Anderson (2008) predicted that there should be a set of self-interfering counting procedures, just in virtue of the fact that on some procedures the representations of which fingers had been touched or otherwise attended to would be incompatible with the representations of which number the fingers were standing in for (that is, the respective representation consumers would systematically misinterpret the content of the representations that resulted from the procedure). Once again, this explains the differences in performance (the number and kinds of mistakes, for example) observed when using nonstandard counting procedures (Di Luca et al. 2006) without needing to posit any semantic inheritance between the domains. Note that this at least begins to answer the question about encapsulation raised by Petrov et al. Neural reuse predicts that encapsulation will not be a prominent feature of functional complexes, precisely because in sharing parts each will have access to the information stored and manipulated by the others. Naturally, it is not the case that everything overlaps or communicates with everything else; there is definite and detectible structure to the functional arrangements. Hence, as Petrov et al. correctly describe, the degree of relative encapsulation between functional complexes will depend on the specifics of the physical overlaps and functional connections between them. Finally, I think the evidence from the cross-cultural imaging study (Tang et al. 2006) raised by Michaux

Response/Anderson: Neural reuse: A fundamental organizational principle of the brain et al. favors neither view. Anyone would predict differences in the relative contributions of some brain regions to otherwise similar cognitive tasks if the methods by which the tasks are taught, and the tools used to achieve them, were significantly different. The evidence simply does not bear on the question of the nature of the inheritance in this case. Nevertheless, I certainly agree with Michaux et al. that it remains to be explained how number concepts acquire their meanings. It may well be that the practice of finger counting can play a role in establishing number semantics, but it seems equally clear that there must be other avenues, because not all children who are proficient in math can or do use their fingers in this way. Much more research along these lines is needed. Brincker lays down a broader and more radical objection to my critique of embodied cognition. She questions whether the evidence that significant neural overlaps occur between regions that are not canonically associated with sensorimotor functions actually shows that neural reuse is a broader phenomenon than can be accounted for by embodied cognition. After all, if higher functions like language are grounded in and by sensorimotor engagement, then reuse of language areas is simply further evidence for the reuse of (fundamentally) sensorimotor circuitry. One problem with this objection is that it ignores the other sources of evidence in support of my claim. But the main trouble is that the critique comes dangerously close to the following argument: All observations of neural overlap – all repeated uses of neural circuitry – are compatible with embodied cognition, because all task domains are ultimately grounded in the sensorimotor system. That argument would indeed undermine my claim that neural reuse is a broader scope phenomenon that can be accounted for by embodied cognition, concept empiricism, and conceptual metaphor theory, but it equally undermines the claim that any specific observation of reuse is evidence for these theories. That this is not the way such discoveries have generally been interpreted suggests that this more radical view of the scope of the embodied cognition hypothesis is not widely shared. Moreover, the constraint that all task domains (and all aspects of all tasks) must be grounded in sensorimotor systems requires that we read prototypes of all the functional aspects of higher-order cognitive systems into the grounding system. The case in point here is language, which Brincker’s view requires that motor control systems have a meansend intentional structure, because language has that structure and language is built upon motor control. As it happens, I am a fan of this particular hypothesis in the case of language (M. L. Anderson 2007b), and so I look forward to the detailed exposition I expect will be offered by Brincker (forthcoming). But the requirement seems far too stringent to apply more generally. Must all the semantic and functional characteristics of recent systems be inherited directly from sensorimotor analogues? Can nothing novel emerge? I am doubtful that evolution has strictly observed this constraint. A somewhat more subtle challenge along similar lines is offered by Kiverstein. He suggests that although semantic inheritance may not be an outcome of every functional borrowing, the cases where there is such inheritance play a particularly crucial role in our intellectual evolution,

because only in these cases is there the possibility of bootstrapping from lower- to higher-order functions. The idea is that one hallmark of higher-order cognition is its use of symbols and abstraction, but when these representations are not grounded in sensorimotor systems, they remain semantically empty. Thus, bootstrapping useful higherorder systems out of older parts will require semantic inheritance. Kiverstein is right that the symbol grounding problem is a crucial one for cognitive science (Harnad 1990); that neural reuse does not by itself solve the problem; and that the embodied cognition movement offers some of the more promising approaches to it (M. L. Anderson 2003). But I think there are at least two things to explain in bootstrapping. One is indeed the origins of any contentful representations manipulated in these systems; but the other is the specific functional character of the system itself. Although I agree that neural reuse alone doesn’t address the content issue, I think it goes further toward addressing the functional one than does embodied cognition alone, because it leverages the full power of combinatorix (Lindblom) in undergirding new functional arrangements. Moreover, I think that the discovery of neural reuse shows that the embodied cognition movement actually hasn’t got as close to solving the content issue as has often been supposed, precisely because mere reuse doesn’t explain semantic inheritance. I see neural reuse as a strong ally to embodied cognition – and here I think Kiverstein agrees – but one that in the near term will be taking up the role of Socratic gadfly. R5. Reuse, reuse everywhere One of the more striking aspects of the set of commentaries was the number of additional examples of reuse they discuss. Katz cites evidence for the reuse of neural circuits across species; Immordino-Yang et al. offer discussion of the reuse of the somatosensory system in the processing of social emotions, numerical circuits in recognizing social hierarchy, and the oxytocin system in motherinfant bonding and parental pair bonding; Niven & Chittka review many examples of the redeployment of individual neurons for multiple uses in invertebrates; Bargh, Williams, Huang, Song, & Ackerman [Bargh et al.] discuss various physical-to-psychological effects that suggest reuse in the underlying control systems; Reimers reviews some striking analogies between neural reuse and reuse in genetics; Fishbein, Lau, DeJesu´s, & Alger [Fishbein et al.] suggest that the sleep cycle may have been redeployed for various purposes; Rozin notes that there can be reuse not just of actual circuits, but of the developmental processes or plan that generated them; Foglia & Grush note that physical objects like fingers, drawings, and chessboards are reused in multiple representational and task contexts; and Michaux et al. review further evidence for the overlaps between motor control and mathematical processing. This range of examples is evidence for the power of the concept of reuse as an organizational frame, but at the same time it greatly complicates the task of specifying a theory adequate to the variety. Perhaps, as Lindblom suggests, its reach must necessarily exceed its grasp, and there can be no universal theory of reuse, but only a group of BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Anderson: Neural reuse: A fundamental organizational principle of the brain explanations individually applying to sub-classes of a more general phenomenon. Although I agree that the theory is not fully adequate as it stands – and said as much in the target article – I am not quite ready to abandon the project of specifying a unified theory of neural reuse. And it is perhaps worth noting that Bargh et al. found the theory helpful in interpreting some of the fascinating findings coming out of their lab, demonstrating the influence on one’s social judgments of others of apparently unrelated physical stimuli such as the warmth of a coffee cup or the weight of a clipboard; Rabaglia & Marcus suggest it may help explain the positive manifold – inter-individual performance correlations observed across disparate cognitive tasks; and Kiverstein avers that it offers a useful frame for understanding the evolutionary mechanisms for bootstrapping. Although the theory is currently underspecified, it is nevertheless still useful. R5.1. Some additional classes of reuse

Foglia & Grush suggest that one way to further specify the theory is to distinguish between neurophysiological reuse – the physical use of neural circuits to support multiple tasks – and representational reuse – the reuse of physical and mental stand-ins for multiple purposes. They further divide the latter category into “domain” and “functional” reuse: the reuse of a model in one domain (space) to support tasks in another domain (time) versus the reuse of a model in a single domain for multiple purposes (visual representations for both on-line processing and imagining). From their perspective, what is most striking is the brain’s capacity to use and reuse models across diverse contexts. Foglia & Grush suggest that this may be the more general principle driving instances of neurophysiological reuse, and that the latter occurs when the models in question are neutrally instantiated. I think the distinction between neurophysiological and representational (model) reuse is a good one, and I agree that our ability to reuse models across contexts is a crucial cognitive ability (Landy et al., in press; Landy & Goldstone 2007a; 2007b). However, I don’t think it is right that the latter category falls under the former. Instead, I think these are largely but not entirely overlapping sets: There is model reuse without neural reuse (using a chessboard to play chess, and as a calendar); model reuse with neural reuse (using perceptual systems for imagination); and, I would argue, neural reuse without model reuse. For an example of the last category, consider again the case of using the fingers to represent numbers, raised by Foglia & Grush and Michaux et al. One way to use the fingers to support mathematical reasoning is to use them as models of numbers, and Butterworth (1999c) argues that this results in and explains the observed neural overlap between neural circuits involved in finger-representation and number-representation. But I think the evidence points to a different explanation for the observed overlap: infrastructural reuse without model reuse (Penner-Wilger & Anderson 2008; submitted; Penner-Wilger 2009). Here, the idea is that part of the finger representation circuit just happens to have a functional structure that lends itself to supporting certain aspects of number representation. It is not because the fingers are or can be used as models (although 302

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they certainly are and can), nor is the neural circuit being used as a model of anything; it is simply being used because it can serve the purpose. Although this example involved reuse of neural infrastructure, one imagines there could be important cases of non-neural infrastructural reuse – the use of the hands as an alternative representational resource that aids learning (GoldinMeadow 2003) may be one such case. Thus, it seems there are three large classes of cognitively relevant reuse: neural reuse, model reuse, and infrastructural reuse. None of these classes entirely reduces to the others. Foglia & Grush further divide model reuse into cross-domain and intra-domain reuse (I drop their term “functional” here, since all of this reuse seems functional to me), and, following Rozin and Fishbein et al., we can divide infrastructural reuse into structural token reuse, physiological process reuse, and developmental plan reuse. A cross-cutting category is reuse resulting in semantic inheritance, which Kiverstein has suggested has an especially important role in cognitive bootstrapping. Naturally, the target article was centrally concerned with neural reuse, and with understanding when such reuse is (merely) infrastructural, when it involves reuse of models, and when it results in semantic inheritance. But Foglia & Grush are quite right to draw our attention to the cognitive importance of non-neural reuse as well. R5.2. Does reuse rule out modularity?

One side-effect of the apparent underspecification of neural reuse theory is that it seemed to several commentators – including Toskos Dils & Flusberg, Junge´ & Dennett, and Ritchie & Carruthers – to leave neural reuse closer to modularity than my own rhetoric on the matter would indicate. For example, Junge´ & Dennett suggest that a software theory of modularity that posits a modular organization at the abstract level, with no commitment about neural implementation, could survive the critique offered by neural reuse. And Ritchie & Carruthers argue that their specific version of massive modularity is in fact compatible with neural reuse. Indeed, one might argue that no largely functionalist account of the mind, insofar as it leaves open the many possibilities for implementation, would have any intellectual friction with an account of how neural resources are deployed in the service of cognitive functions. Although I can see the attraction of this position, I think it doesn’t apply to the case of modularity. Any theory of modularity worthy of the name must have modules, of course, and these modules need to have some specific functional characteristics, such as relative encapsulation, or functional separability. These characteristics in fact place limits on the way such modules can be implemented and, in my view of how our brain is organized, this means they cannot be implemented there. Ritchie & Carruthers try two (apparently incompatible) tacks to avoid this conclusion: First, they suggest that it would be possible to functionally separate two part-sharing modules via double-dissociation, just so long as one did this by disrupting parts that were not shared between them; and, second, they suggest that maybe modules don’t share parts after all, since my imaging evidence is indeed compatible with there being distinct neural regions, smaller than the spatial granularity of

Response/Anderson: Neural reuse: A fundamental organizational principle of the brain fMRI, dedicated to individual modules. I have already discussed why I think this second possibility is not likely to obtain, but will note here that even if the brain were like that, this argument would fail to demonstrate the compatibility of massive modularity and neural reuse. For what it would show is not that these two theories were compatible, but that neural reuse was false. Unfortunately for one of our two theories, I think the first argument fares no better. It is certainly true (and not in conflict with neural reuse) that there will be pairs of functional complexes that are functionally dissociable because they share no parts. And it is also true that even functional complexes that share some parts can be distinguished by their reactions to disruptions of the parts not shared. But although the claim that for all modules A and B it will be possible to functionally dissociate them by disrupting any of their respective parts X and Y may be too strong for even the most dedicated modularist, surely a claim of the following logical form is too weak to distinguish between any competing theories of brain organization: that there exist some modules A and B that can be distinguished by disrupting some of their respective parts X and Y. Wouldn’t that be true on just about anyone’s theory of brain organization? So the fact that we both accept that statement doesn’t make for a particularly strong alliance. Yet, I don’t see that Ritchie & Carruthers have anything stronger to offer here. And if it is the case, as reuse predicts, that in disrupting region X one might not disrupt functional complex B, but would disrupt some complex C (and often many complexes C, D, . . .), then even though specific pairs of functional complexes will be functionally separable, it would appear that functional separability will not be a general characteristic of the brain. But this is exactly the core claim of massive modularity. I am forced to conclude once again that the two theories are incompatible and, as noted in the target article, in fact point cognitive science in very different empirical directions. This being said, it is important to note that the term module is used in many different ways in many different disciplines, and many of these senses of module are compatible with neural reuse. For instance, in graph theory the term module is often used to refer to a set of nodes that are highly connected to one another, and less connected with other parts of the graph. Note that this is a structural rather than a functional characterization. Modules are defined in terms of features of the abstract topology of the representational vehicle: the graph. Nevertheless, one of the reasons graphs have proven a useful representational format is that these abstract structures often identify functionally relevant features of the underlying system. In molecular and developmental biology, for instance, a “module” is a set of interacting elements – genes, gene networks, proteins, brain regions, and so forth – that make a specific, relatively context-insensitive contribution to some developmental process (Rives & Galitski 2003; Spirin & Mirny 2003; Tong et al. 2004; von Dassow & Munro 1999) wherever it is instantiated. This sense of module is roughly equivalent to what I have been calling a functional complex, and is perfectly compatible with the notion that the elements of the functional “module” cooperate with different sets of partners to support other outcomes in other circumstances. And, indeed, we know that developmental modules share parts and are often

nested as components of larger modules (Jablonka & Lamb 2006; Schlosser & Wagner 2004). This is a perfectly viable use of the term module, but note that these modules are individuated in ways quite distinct from the mental modules posited by evolutionary psychology (e.g., Pinker 1997). Mental modules are entities with abstract functional characteristics (encapsulation, domain specificity, functional separability, etc.) and flexible structural characteristics. In contrast, typical biological modules (as found in gene networks or neural co-activation graphs, for example) are entities with welldefined abstract structural characteristics but flexible functional characteristics. As tidy as it would be for neuroscience if the modules in neural co-activation graphs identified brain structures with the functional features of mental modules, that is not the way the brain is organized. Therefore, it is important in debates about brain organization and function to try to keep the different senses of “module” distinct; it is all too easy to let them blur into one another. R5.3. Reuse and evolutionary theory

One place where neural reuse theory is somewhat short on specifics involves its precise fit with existing evolutionary theory. The massive redeployment hypothesis, for instance, is based in part on an overly simplified, armchair-evolutionary story. I think we can and should do better than this. Thankfully, several of the commentators point the way to a better integration of reuse and evolution. Moore & Moore and Bergeron both suggest that the concept of homology can serve as an organizing framework for the further exploration of reuse in evolutionary and developmental context. Bergeron argues that we ought to search for cross-species cognitive homologies – workings with the same phylogenetic origins serving different uses in different animals – and he offers some evidence that the search will prove fruitful. Such discoveries would not only help further specify the evolutionary mechanisms behind neural reuse, but could also offer some unique insights into the cognitive relatedness of various species. Naturally, such a project would involve a great deal of comparative work. Katz and Niven & Chittka are rightly dismayed by the dearth of comparative data (in my defense, I plead lack of both expertise and space). These authors offer many examples of reuse in other species, and Katz in particular offers evidence for just the sorts of cognitive homologies that Bergeron suspects should exist. One interesting upshot from the commentaries of both Katz and Niven & Chittka is that invertebrates may prove the most promising class of animals for initial investigations. All I can say is I think that sounds like a great idea, and hope that someone – if not these authors, then some enterprising young graduate students – will successfully take up the challenge. Moore & Moore have a somewhat different take on the same general idea. They argue that the concept of homology can also be applied in developmental context, whenever two (or more) psychological traits or processes share a neural circuit that has been put to different uses. In this case, we may have identified a developmental homology, a shared ontogenetic “ancestor” circuit being used in BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Anderson: Neural reuse: A fundamental organizational principle of the brain different ways. I agree that this perspective offers the possibility of leveraging useful analogies from the evolutionary literature to form hypotheses about features of developmental change, and think that it can help continue the important process of integrating these two perspectives. There are some disanalogies to be careful of as well, however. Chief among these is the fact that traditional evolutionary and cognitive homologies in different species are far less functionally entangled than developmental homologies in a single animal. Whatever limitations imposed by the nature of the inheritance, the use one species subsequently makes of that inheritance does not affect the uses made by others. This is not the case with developmental homologies, where subsequent use can affect the functional properties of other uses, if only because of increased processing demand on the shared circuit. Thus, in cross-species homologies it is more possible to alter the properties of the underlying inheritance, whereas in a developmental homology changing the nature of the shared circuit could have deleterious consequences. Nevertheless, when properly noted, I think both the analogies and the disanalogies will prove a fruitful source of developmental hypotheses for future investigations. Exploring the parallels with reuse in genetics offers another very promising avenue both for hypothesis generation and for coming to better understand the mechanisms of neural reuse. As Reimers details, there are many examples of reuse in molecular evolution: Protein domains can be used for multiple purposes; novel metabolic pathways are often assembled by reusing and adapting parts from existing pathways; and signaling pathways are widely reused throughout development. That there is a such a pattern of structure-to-function mapping at the molecular level suggests, among other things, that the neural overlaps I uncovered by reviewing fMRI experiments are not going to go away with higher-resolution imaging techniques. There is too much to be gained functionally by taking advantage of reuse and recombination for this strategy, evident at the micro level, to be absent from the macro level. R5.4. A history of reuse

Bridgeman and Lia both do the community a service by placing the neural reuse hypothesis in historical context, pointing out some intellectual forebears of the idea in addition to those identified in the target article. Awareness of history is important to scientific progress, for while intellectual cycles are an inevitable by-product of the social structure of science, we can at least try to notice whether we are on a spiral staircase or a high-school track. Right now, the cognitive sciences feel to me more like the former than the latter – and neural reuse theory seems a genuine advance – but others should of course make their own judgments. R6. Where do we go from here? By this point, I hope it will be evident to the reader that, with a lot of help from the commentaries, neural reuse offers a useful, well-specified, and potentially researchguiding perspective in the cognitive sciences. Several 304

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commentaries offer specific techniques and other suggestions for future research. Many have been discussed already, but in this last section I would like to briefly acknowledge a few that have not yet been mentioned. As I noted in the target article, there is work here for many labs; I hope that at least a few of them are inspired to take some of it up. In the case of reuse that emerges in the course of development, Reimers and Gomila & Calvo suggest that developmental brain studies of various sorts would be very useful, perhaps especially those that focus on identifying the properties of the networks responsible for highlevel cognitive function. I couldn’t agree more. The next several years should see the release of data from longitudinal studies tracking both the change in structural connectivity (DTI) and functional connectivity over the course of human brain development (Paus 2010). The opportunity to see how these networks change over time and achieve their adult configuration will be an incredible boon not just for research on neural reuse, but across the whole spectrum of neuroscience. Possible guides for research in this area are suggested by Fishbein et al., Foglia & Grush, and Rozin. Perhaps one can use observations of the reuse of physiological processes, of models, and of developmental plans to guide the search for neural reuse. Clearly, not every instance of such reuse will involve the reuse of neural circuitry, but many probably will. And last, but certainly not least, Lia suggests that we should start thinking seriously about the potential clinical applications of both neural reuse and of the scientific techniques that, in light of widespread reuse, ought achieve greater prominence. Perhaps most important is the use of data-mining and meta-analysis of large imaging databases (Anderson et al. 2010; Fox et al. 1998). The amount of information we have about the functional organization of the brain is astounding but, as I have been arguing, we have too often been looking at that information through the wrong lens. I hope to have provided a better one, and with it – or, more likely, with a refined version of it – it should be possible for a dedicated group of researchers equipped with ample computational resources and expertise in data extraction (are you listening, Google?) to mine the many thousands of existing imaging studies to give an accurate model of the functional behavior of the brain under many different circumstances. Will the outcome of such a project be clinically relevant? It seems to me that such an exercise could begin to lay the foundations for baseline expectations of normal brain function across tasks – the identification of typical useselective networks – which can be as necessary a part of improving our understanding neurological disorders as the discovery of healthy cholesterol ratios was to improving our understanding of heart disease. Having a good measure of “normal” baseline function, one can begin to catalog the various deviations from these expectations, and their relations to psychiatric diagnoses. Of course, it may not prove possible to do so, but the payoff for success could be quite significant. The ability to define neural signatures for certain disorders can play a role in their diagnosis, of course, but it may also help with our ongoing attempts to categorize and understand them; the discovery that two distinct disorders appear to result from quantitatively similar deviations

References/Anderson: Neural reuse: A fundamental organizational principle of the brain from baseline expectations (e.g., increased coherence between regions generally only loosely coupled; or the substitution of one region for another in a known functional complex) might lead to a reassessment of the similarity of the disorders; likewise, the finding of two distinct signatures for a single identified disorder could be part of the argument for splitting the designation. As goes our understanding, so follows our clinical recommendations. In the ideal case, the features of the neural signatures could themselves suggest treatment options (e.g., can rTMS or deep brain stimulation be used to entrain brain regions to one another? Do neural overlaps suggest which indirect behavioral therapies might be effective?). But even without suggesting particular therapies, knowing a patient’s neural signature could sharpen the clinical picture, if one can discover a relation between features of that signature and the range of therapies to which a patient is likely to respond. Perhaps neural signatures will turn out to be as important a part of providing personalized medical care as gene sequencing (Ginsberg & McCarthy 2001; Westin & Hood 2004). Such possibilities are of course quite distant. But we have the technology and the ingenuity. Why not put them to work? NOTES 1. This attribution was refined after discovering multiple other uses for this circuit (see Penner-Wilger 2009; PennerWilger & Anderson, submitted). 2. Thanks to the Editor for this particular suggestion.

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BEHAVIORAL AND BRAIN SCIENCES (2010) 33, 315 –365 doi:10.1017/S0140525X10000907

Person as scientist, person as moralist Joshua Knobe Program in Cognitive Science and Department of Philosophy, Yale University, New Haven, CT 06520-8306 [email protected] http://pantheon.yale.edu/jk762/

Abstract: It has often been suggested that people’s ordinary capacities for understanding the world make use of much the same methods one might find in a formal scientific investigation. A series of recent experimental results offer a challenge to this widelyheld view, suggesting that people’s moral judgments can actually influence the intuitions they hold both in folk psychology and in causal cognition. The present target article distinguishes two basic approaches to explaining such effects. One approach would be to say that the relevant competencies are entirely non-moral but that some additional factor (conversational pragmatics, performance error, etc.) then interferes and allows people’s moral judgments to affect their intuitions. Another approach would be to say that moral considerations truly do figure in workings of the competencies themselves. I argue that the data available now favor the second of these approaches over the first. Keywords: Causal cognition; moral cognition; theory of mind

1. Introduction Consider the way research is conducted in a typical modern university. There are departments for theology, drama, philosophy . . . and then there are departments specifically devoted to the practice of science. Faculty members in these science departments generally have quite specific responsibilities. They are not supposed to make use of all the various methods and approaches one finds in other parts of the university. They are supposed to focus on observation, experimentation, the construction of explanatory theories. Now consider the way the human mind ordinarily makes sense of the world. One plausible view would be that the human mind works something like a modern university. There are psychological processes devoted to religion (the mind’s theology department), to aesthetics (the mind’s art department), to morality (the mind’s philosophy department) . . . and then there are processes specifically devoted to questions that have a roughly “scientific” character. These processes work quite differently from the ones we use in thinking about, say, moral or aesthetic questions. They proceed using more or less the same sorts of methods we find in university science departments. This metaphor is a powerful one, and it has shaped research programs in many different areas of cognitive science. Take the study of folk psychology. Ordinary people have a capacity to ascribe mental states (beliefs, desires, etc.), and researchers have sometimes suggested that people acquire this capacity in much the same way that scientists develop theoretical frameworks (e.g., Gopnik & Wellman 1992). Or take causal cognition. Ordinary people have an ability to determine whether one event caused another, and it has been suggested that they do so by looking at the same sorts of statistical information scientists normally consult (e.g., Kelley 1967). Numerous other fields have taken a similar path. In each case, the basic strategy is to look at the methods used by # Cambridge University Press 2010

0140-525X/10 $40.00

professional research scientists and then to hypothesize that people actually use similar methods in their ordinary understanding. This strategy has clearly led to many important advances. Yet, in recent years, a series of experimental results have begun pointing in a rather different direction. These results indicate that people’s ordinary understanding does not proceed using the same methods one finds in the sciences. Instead, it appears that people’s intuitions in both folk psychology and causal cognition can be affected by moral judgments. That is, people’s judgments about whether a given action truly is morally good or bad can actually affect their intuitions about what that action caused and what mental states the agent had. These results come as something of a surprise. They do not appear to fit comfortably with the view that certain aspects of people’s ordinary understanding work much like a scientific investigation, and a question therefore arises about how best to understand them. One approach would be to suggest that people truly are engaged in an effort to pursue something like a scientific investigation, but that they simply aren’t doing a very good job of it. Perhaps the competencies underlying people’s judgments actually are purely scientific in nature, but there are then various additional factors that get in the way of people’s ability to apply these competencies correctly. Such a view might allow us to explain the patterns observed in people’s intuitions while still holding onto the basic idea that people’s capacities for thinking about psychology, causation, and

JOSHUA KNOBE is Assistant Professor of Cognitive Science and Philosophy at Yale University. He is one of the founding members of the “experimental philosophy” movement.

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Knobe: Person as scientist, person as moralist the like, can be understood on the model of a scientific investigation. This approach has a strong intuitive appeal, and recent theoretical work has led to the development of specific hypotheses that spell it out with impressive clarity and precision. There is just one problem. The actual experimental results never seem to support these hypotheses. Indeed, the results point toward a far more radical view. They suggest that moral considerations actually figure in the competencies people use to make sense of human beings and their actions. 2. Introducing the person-as-scientist theory In the existing literature on causal cognition and theory-ofmind, it has often been suggested that people’s ordinary way of making sense of the world is in certain respects analogous to a scientific theory (Churchland 1981; Gopnik & Meltzoff 1997; Sloman 2005). This is an important and provocative suggestion, but if we are to grapple with it properly, we need to get a better understanding of precisely what it means and how experimental evidence might bear on it. 2.1. Ordinary understanding and scientific theory

To begin with, we will need to distinguish two different aspects of the claim that people’s ordinary understanding is analogous to a scientific theory. First, there is the claim that human thought might sometimes take the form of a theory. To assess this first claim, one would have to pick out the characteristics that distinguish theories from other sorts of knowledge structures and then ask whether these characteristics can be found in ordinary cognition. This is certainly a worthwhile endeavor, but it has already been pursued in a considerable body of recent research (e.g., Carey & Spelke 1996; Goldman 2006; Murphy & Medin 1985), and I will have nothing further to say about it here. Instead, the focus of this target article will be on a second claim, namely, the claim that certain facets of human cognition are properly understood as scientific. To begin with, it should be emphasized that this second claim is distinct from the first. If one looks to the usual sorts of criteria for characterizing a particular knowledge structure as a “theory” (e.g., Premack & Woodruff 1978), one sees immediately that these criteria could easily be satisfied by, for example, a religious doctrine. A religious doctrine could offer systematic principles; it could posit unobservable entities and processes; it could yield definite predictions. For all these reasons, it seems perfectly reasonable to say that a religious doctrine could give us a certain kind of “theory” about how the world works. Yet, although the doctrine might offer us a theory, it does not appear to offer us a specifically scientific theory. In particular, it seems that religious thinking often involves attending to different sorts of considerations from the ones we would expect to find in a properly scientific investigation. Our task here, then, is to figure out whether certain aspects of human cognition qualify as “scientific” in this distinctive sense. One common view is that certain aspects of human cognition do indeed make use of the very same sorts of considerations we find in the systematic sciences. So, for 316

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example, in work on causal cognition, researchers sometimes proceed by looking to the statistical methods that appear in systematic scientific research and then suggesting that those same methods are at work in people’s ordinary causal judgments (Gopnik et al. 2004; Kelley 1967; Woodward 2004). Different theories of this type appeal to quite different statistical methods, but these differences will not be relevant here. The thing to focus on is just the general idea that people’s ordinary causal cognition is in some way analogous to a scientific inquiry. And it is not only the study of causal cognition that proceeds in this way. A similar viewpoint can be found in the theory-of-mind literature (Gopnik & Meltzoff 1997), where it sometimes goes under the slogan “Child as Scientist.” There, a central claim is that children refine their understanding of the mind in much the same way that scientists refine their theories. Hence, it is suggested that we can look at the way Kepler developed his theory of the orbits of the planets and then suggest that children use the same basic approach as they are acquiring the concept of belief (Gopnik & Wellman 1992). Once again, the idea is that the cognitive processes people use in ordinary life show a deep similarity to the ones at work in systematic science. It is this idea that we will be taking up here. Genuinely scientific inquiry seems to be sensitive to a quite specific range of considerations and seems to take those considerations into account in a highly distinctive manner. What we want to know is whether certain aspects of ordinary cognition work in more or less this same way. 2.2. Refining the question

But now it might seem that the answer is obvious. For it has been known for decades that people’s ordinary intuitions show certain patterns that one would never expect to find in a systematic scientific investigation. People make wildly inappropriate inferences from contingency tables, show shocking failures to properly detect correlations, display a tendency to attribute causation to whichever factor is most perceptually salient (Chapman & Chapman 1967; McArthur & Post 1977; Smedslund 1963). How could one possibly reconcile these facts about people’s ordinary intuitions with a theory according to which people’s ordinary cognition is based on something like a scientific methodology? The answer, I think, is that we need to interpret that theory in a somewhat more nuanced fashion. The theory is not plausibly understood as an attempt to describe all of the factors that can influence people’s intuitions. Instead, it is best understood as an attempt to capture the “fundamental” or “underlying” nature of certain cognitive capacities. There might then be various factors that interfere with our ability to apply those capacities correctly, but the existence of these additional factors would in no way impugn the theory itself. To get a rough sense for the strategy here, it might be helpful to return to the comparison with religion. Faced with a discussion over religious doctrine, we might say: “This discussion isn’t best understood as a kind of scientific inquiry; it is something else entirely. So if we find that the participants in this discussion are diverging from proper scientific methods, the best interpretation is that they simply weren’t trying to use those methods in the first

Knobe: Person as scientist, person as moralist place.” This would certainly be a reasonable approach to the study of religious discourse, but the key claim of the person-as-scientist theory is that it would not be the right approach to understanding certain aspects of our ordinary cognition. Looking at these aspects of ordinary cognition, a defender of the person-as-scientist view would adopt a very different stance. For example, she might say: “Yes, it’s true that people sometimes diverge from proper scientific methods, but that is not because they are engaging in some fundamentally different sort of activity. Rather, their underlying capacities for causal cognition and theory-of-mind really are governed by scientific methods; it’s just that there are also various additional factors that get in the way and sometimes lead people into errors.” Of course, it can be difficult to make sense of this talk of certain capacities being “underlying” or “fundamental,” and different researchers might unpack these notions in different ways: 1. One view would be that people have a domainspecific capacity for making certain kinds of judgments but then various other factors intrude and allow these judgments to be affected by irrelevant considerations. 2. Another would be that people have a representation of the criteria governing certain concepts but that they are not always able to apply these representations correctly. 3. A third would be that the claim is best understood counterfactually, as a hypothesis about how people would respond if they only had sufficient cognitive resources and freedom from certain kinds of biases. I will not be concerned here with the particular differences between these different views. Instead, let us introduce a vocabulary that allows us to abstract away from these details and talk about this approach more generally. Regardless of the specifics, I will say that the approach is to posit an underlying competence and then to posit various additional factors that get in the way of people’s ability to apply that competence correctly. With this framework in place, we can now return to our investigation of the impact of moral considerations on people’s intuitions. How is this impact to be explained? One strategy would be to start out by finding some way to distinguish people’s underlying competencies from the various interfering factors. Then one could say that the competencies themselves are entirely scientific in nature, but that the interfering factors then prevent people from applying these competencies correctly and allow moral considerations to affect their intuitions. This strategy is certainly a promising one, and I shall discuss it in further detail later. But it is important to keep in mind that we also have open another, very different option. It could always turn out that there simply is no underlying level at which the relevant cognitive capacities are purely scientific, that the whole process is suffused through and through with moral considerations. 3. Intuitions and moral judgments Before we think any further about these two types of explanations, we will need to get a better grasp of the phenomena to be explained. Let us begin, then, just by considering a few cases in which moral considerations appear to be impacting people’s intuitions.

3.1. Intentional action

Perhaps the most highly studied of these effects is the impact of people’s moral judgments on their use of the concept of intentional action. This is the concept people use to distinguish between behaviors that are performed intentionally (e.g., hammering in a nail) and those that are performed unintentionally (e.g., accidentally bringing the hammer down on one’s own thumb). It might at first appear that people’s use of this distinction depends entirely on certain facts about the role of the agent’s mental states in his or her behavior, but experimental studies consistently indicate that something more complex is actually at work here. It seems that people’s moral judgments can somehow influence their intuitions about whether a behavior is intentional or unintentional. To demonstrate the existence of this effect, we can construct pairs of cases that are exactly the same in almost every respect but differ in their moral status.1 For a simple example, consider the following vignette: The vice-president of a company went to the chairman of the board and said, “We are thinking of starting a new program. It will help us increase profits, but it will also harm the environment.” The chairman of the board answered, “I don’t care at all about harming the environment. I just want to make as much profit as I can. Let’s start the new program.” They started the new program. Sure enough, the environment was harmed.

Faced with this vignette, most subjects say that the chairman intentionally harmed the environment. One might initially suppose that this intuition relies only on certain facts about the chairman’s own mental states (e.g., that he specifically knew his behavior would result in environmental harm). But the data suggest that something more is going on here. For people’s intuitions change radically when one alters the moral status of the chairman’s behavior by simply replacing the word “harm” with “help”: The vice-president of a company went to the chairman of the board and said, “We are thinking of starting a new program. It will help us increase profits, and it will also help the environment.” The chairman of the board answered, “I don’t care at all about helping the environment. I just want to make as much profit as I can. Let’s start the new program.” They started the new program. Sure enough, the environment was helped.

Faced with this second version of the story, most subjects actually say that the chairman unintentionally helped the environment. Yet it seems that the only major difference between the two vignettes lies in the moral status of the chairman’s behavior. So it appears that people’s moral judgments are somehow impacting their intuitions about intentional action. Of course, it would be unwise to draw any strong conclusions from the results of just one experiment, but this basic effect has been replicated and extended in numerous further studies. To begin with, subsequent experiments have further explored the harm and help cases to see what exactly about them leads to the difference in people’s intuitions. These experiments suggest that moral judgments truly are playing a key role, since participants who start out with different moral judgments about the act of harming the environment end up arriving at different intuitions about whether the chairman acted BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Knobe: Person as scientist, person as moralist intentionally (Tannenbaum et al. 2009). But the effect is not limited to vignettes involving environmental harm; it emerges when researchers use different cases (Cushman & Mele 2008; Knobe 2003a) and even when they turn to cases with quite different structures that do not involve side-effects in any way (Knobe 2003b; Nadelhoffer 2005). Nor does the effect appear to be limited to any one particular population: It emerges when the whole study is translated into Hindi and conducted on Hindispeakers (Knobe & Burra 2006) and even when it is simplified and given to 4-year-old children (Leslie et al. 2006a). At this point, there is really a great deal of evidence for the claim that people’s moral judgments are somehow impacting their intuitions about intentional action. Still, as long as all of the studies are concerned only with intuitions about intentional action specifically, it seems that our argument will suffer from a fatal weakness. For someone might say: “Surely, we have very strong reason to suppose that the concept of intentional action works in more or less the same way as the other concepts people normally use to understand human action. But we have good theories of many of these other concepts – the concepts of deciding, wanting, causing, and so forth – and these other theories do not assign any role to moral considerations. So the best bet is that moral considerations do not play any role in the concept of intentional action either.” In my view, this is actually quite a powerful argument. Even if we have strong evidence for a certain view about the concept of intentional action specifically, it might well make sense to abandon this view in light of theories we hold about various other, seemingly similar concepts. 3.2. Further mental states

As it happens, though, the impact of moral considerations does not appear to be limited to people’s use of the word “intentionally.” The very same effect also arises for numerous other expressions: “intention,” “deciding,” “desire,” “in favor of,” “advocating,” and so forth. To get a grip on this phenomenon, it may be helpful to look in more detail at the actual procedure involved in conducting these studies. In one common experimental design, subjects are randomly assigned to receive either the story about harming the environment or the story about helping the environment and then, depending on the case, are asked about the degree to which they agree or disagree with one of the following sentences: (1) a. The chairman of the board harmed the environment intentionally. W W W W W W W definitely disagree

unsure

definitely agree

b. The chairman of the board helped the environment intentionally. W W W W W W W definitely disagree

unsure

definitely agree

When the study is conducted in this way, one finds that subjects show moderate agreement with the claim that 318

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the chairman harmed intentionally and moderate disagreement with the claim that he helped intentionally (Knobe 2004a). The difference between the ratings in these two conditions provides evidence that people’s moral intuitions are affecting their intuitions about intentional action. It appears, however, that this effect is not limited to the concept of intentional action specifically. For example, suppose we eliminate the word “intentionally” and instead use the word “decided.” The two sentences then become: (2) a. The chairman decided to harm the environment. b. The chairman decided to help the environment.

Faced with these revised sentences, subjects show more or less the same pattern of intuitions. They tend to agree with the claim that the agent decided to harm, and they tend to disagree with the claim that the agent decided to help (Pettit & Knobe 2009). Now suppose we make the case a little bit more complex. Suppose we do not use the adverb “intentionally” but instead use the verb “intend.” So the sentences come out as: (3) a. The chairman intended to harm the environment. b. The chairman intended to help the environment.

One then finds a rather surprising result. People’s responses in both conditions are shifted over quite far toward the “disagree” side. In fact, people’s intuitions end up being shifted over so far that they do not, on the whole, agree in either of the two conditions (Shepard 2009; cf. Cushman 2010; Knobe 2004b; McCann 2005). Nonetheless, the basic pattern of the responses remains the same. Even though people’s responses don’t go all the way over to the “agree” side of the scale in either condition, they are still more inclined to agree in the harm case than they are in the help case. Once one conceptualizes the issue in this way, it becomes possible to find an impact of moral considerations in numerous other domains. Take people’s application of the concept in favor. Now consider a case in which an agent says: I know that this new procedure will [bring about some outcome]. But that is not what we should be concerned about. The new procedure will increase profits, and that should be our goal.

Will people say in such a case that the agent is “in favor” of bringing about the outcome? Here again, it seems that moral judgments play a role. People disagree with the claim that the agent is “in favor” when the outcome is morally good, whereas they stand at just about the midpoint between agreement and disagreement when the outcome is morally bad (Pettit & Knobe 2009). And similar effects have been observed for people’s use of many other concepts: desiring, intending, choosing, and so forth (Pettit & Knobe 2009; Tannenbaum et al. 2009). Overall, these results suggest that the effect obtained for intuitions about intentional action is just one example of a far broader phenomenon. The effect does not appear to be limited to the concept intentionally, nor even to closely related concepts such as intention and intending. Rather, it seems that we are tapping into a much more general tendency, whereby moral judgments impact the application of

Knobe: Person as scientist, person as moralist a whole range of different concepts used to pick out mental states and processes. 3.3. Action trees

But the scope of the effect does not stop there. It seems also to apply to intuitions about the relations that obtain among the various actions an agent performs. Philosophers and cognitive scientists have often suggested that such relations could be represented in terms of an action tree (Goldman 1970; Mikhail 2007). Hence, the various actions performed by our chairman in the help case might be represented with the tree in Figure 1. Needless to say, ordinary folks do not actually communicate with each other by writing out little diagrams like this one. Still, it seems that we can get a sense of how people are representing the action tree by looking at their use of various ordinary English expressions, for example, by looking at the way they use the expressions “in order to” and “by.” A number of complex issues arise here, but simplifying slightly, the key thing to keep in mind is that people only use “in order to” for relations that go upward in the tree, and they only use “by” for relations that go downward. Thus, people are willing to say that the chairman “implemented the program in order to increase profits” but not that he “increased profits in order to implement the program.” And, conversely, they are willing to say that he “increased profits by implementing the program” but not that he “implemented the program by increasing profits.” Looking at people’s intuitions about simple expressions like these, we can get a good sense of how they are representing the geometry of the action tree itself. But now comes the tricky part. Experimental results indicate that people’s intuitions about the proper use of these expressions can actually be influenced by their moral judgments (Knobe 2004b; forthcoming). Hence, people are willing to say: The chairman harmed the environment in order to increase profits.

but not: The chairman helped the environment in order to increase profits.

And, similarly, they are willing to say: The chairman increased profits by harming the environment.

but not: The chairman increased profits by helping the environment.

One natural way of explaining these asymmetries would be to suggest that people’s moral judgments are having an effect on their representations of the action tree itself. For example, suppose that when people make a judgment that

Figure 1. Action tree for the help case.

harming the environment is morally wrong, they thereby come to represent the corresponding node on the action tree as “collapsing” into a lower node (see Fig. 2). The asymmetries we find for “in order to” and “by” would then follow immediately, without the need for any controversial assumptions about the semantics of these specific expressions. Although the issue here is a complex one, recent research does seem to be supporting the claim that moral judgments are affecting action tree representations in this way (Knobe, forthcoming; Ulatowski 2009).

3.4. Causation

All of the phenomena we have been discussing thus far may appear to be quite tightly related, and one might therefore suspect that the effect of morality would disappear as soon as one turns to other, rather different cases. That, however, seems not to be the case. Indeed, the very same effect arises in people’s intuitions about causation (Alicke 2000; Cushman 2010; Hitchcock & Knobe 2009; Knobe, forthcoming; Knobe & Fraser 2008; Solan & Darley 2001). For a simple example here, consider the following vignette: The receptionist in the philosophy department keeps her desk stocked with pens. The administrative assistants are allowed to take pens, but faculty members are supposed to buy their own. The administrative assistants typically do take the pens. Unfortunately, so do the faculty members. The receptionist repeatedly e-mailed them reminders that only administrators are allowed to take the pens. On Monday morning, one of the administrative assistants encounters Professor Smith walking past the receptionist’s desk. Both take pens. Later that day, the receptionist needs to take an important message . . . but she has a problem. There are no pens left on her desk.

Faced with this vignette, most subjects say that the professor did cause the problem but that the administrative assistant did not cause the problem (Knobe & Fraser 2008). Yet, when we examine the case from a purely scientific standpoint, it seems that the professor’s action and the administrative assistant’s action bear precisely the same relation to the problem that eventually arose. The main difference between these two causal factors is just that the professor is doing something wrong (violating the departmental rule) while the administrative assistant is doing exactly what she is supposed to (acting in accordance with the rules of the department). So it appears that people’s judgment that the professor is doing something wrong is somehow affecting their intuitions about

Figure 2.

Action tree for the harm case.

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Knobe: Person as scientist, person as moralist whether or not the professor caused the events that followed. Now, looking just at this one case, one might be tempted to suppose that the effect is not at all a matter of moral judgment but simply reflects people’s intuitive sense that the professor’s action is more “unusual” or “strange” than the administrative assistant’s. But subsequent studies strongly suggest that there is something more afoot here. People continue to show the same basic effect even when they are informed that the administrative assistants never take pens whereas the professors always do (Roxborough & Cumby 2009), and there is a statistically significant effect whereby pro-life subjects are more inclined than pro-choice subjects to regard the act of seeking an abortion as a cause of subsequent outcomes (Cushman et al. 2008). All in all, the evidence seems strongly to suggest that people’s moral judgments are actually impacting their causal intuitions.

3.5. Doing and allowing

People ordinarily distinguish between actually breaking something and merely allowing it to break, between actually raising something and merely allowing it to rise, between actually killing someone and merely allowing someone to die. This distinction has come to be known as the distinction between doing and allowing. To explore the relationship between people’s intuitions about doing and allowing and their moral judgments, we used more or less the same methodology employed in these earlier studies (Cushman et al. 2008). Subjects were randomly assigned to receive different vignettes. Subjects in one condition received a vignette in which the agent performs an action that appears to be morally permissible: Dr. Bennett is an emergency-room physician. An unconscious homeless man is brought in, and his identity is unknown. His organ systems have shut down and a nurse has hooked him up to a respirator. Without the respirator he would die. With the respirator and some attention from Dr. Bennett he would live for a week or two, but he would never regain consciousness and could not live longer than two weeks. Dr. Bennett thinks to himself, “This poor man deserves to die with dignity. He shouldn’t spend his last days hooked up to such a horrible machine. The best thing to do would be to disconnect him from the machine.” For just that reason, Dr. Bennett disconnects the homeless man from the respirator, and the man quickly dies.

These subjects were then asked whether it would be more appropriate to say that the doctor ended the homeless man’s life or that he allowed the homeless man’s life to end. Meanwhile, subjects in the other condition were given a vignette that was almost exactly the same, except that the doctor’s internal monologue takes a somewhat different turn: . . . Dr. Bennett thinks to himself, “This bum deserves to die. He shouldn’t sit here soaking up my valuable time and resources. The best thing to do would be to disconnect him from the machine.”

These subjects were asked the same question: whether it would be more appropriate to say that the doctor ended the man’s life or allowed it to end. 320

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Notice that the doctor performs exactly the same behavior in these two vignettes, and in both vignettes, he performs this behavior in the hopes that it will bring about the man’s death. The only difference between the cases lies in the moral character of the doctor’s reasons for hoping that the man will die. Yet this moral difference led to a striking difference in people’s intuitions about doing versus allowing. Subjects who received the first vignette tended to say that the doctor “allowed” the man’s life to end, whereas subjects who received the second vignette tended to say that the doctor “ended” the man’s life. (Moreover, even within the first vignette, there was a correlation whereby subjects who thought that euthanasia was generally morally wrong were less inclined to classify the act as an “allowing.”) Overall, then, the results of the study suggest that people’s moral judgments are influencing their intuitions here as well. It would, of course, be foolhardy to draw any very general conclusions from this one study, but the very same effect has also been observed in other studies using quite different methodologies (Cushman et al. 2008), and there is now at least some good provisional evidence in support of the view that people’s intuitions about doing and allowing can actually be influenced by their moral judgments. 3.6. Additional effects

Here we have discussed just a smattering of different ways in which people’s moral judgments can impact their intuitions about apparently non-moral questions. But our review has been far from exhaustive: there are also studies showing that moral judgments can affect intuitions about knowledge (Beebe & Buckwalter, forthcoming), happiness (Nyholm 2009), valuing (Knobe & Roedder 2009), act individuation (Ulatowski 2009), freedom (Phillips & Knobe 2009), and naturalness (Martin 2009). Given that all of these studies were conducted just in the past few years, it seems highly probable that a number of additional effects along the same basic lines will emerge in the years to come. 4. Alternative explanations Thus far, we have seen that people’s ordinary application of a variety of different concepts can be influenced by moral considerations. The key question now is how to explain this effect. Here we face a choice between two basic approaches. One approach would be to suggest that moral considerations actually figure in the competencies people use to understand the world. The other would be to adopt what I will call an alternative explanation. That is, one could suggest that moral considerations play no role at all in the relevant competencies, but that certain additional factors are somehow “biasing” or “distorting” people’s cognitive processes and thereby allowing their intuitions to be affected by moral judgments. The first thing to notice about the debate between these two approaches is that we are unlikely to make much progress on it as long as the two positions are described only in these abstract, programmatic terms. Thus, suppose that we are discussing a new experimental result and someone says: “Well, it could always turn out that this

Knobe: Person as scientist, person as moralist effect is due to some kind of interfering factor.” How would we even begin to test such a conjecture? As long as the claim is just about the possibility of “some kind of interfering factor,” it is hard to know where one could go to look for confirming or disconfirming evidence. Fortunately, however, the defenders of alternative hypotheses have not simply put forward these sorts of abstract, programmatic conjectures. Instead, they have developed sophisticated models that make it possible to offer detailed explanations of the available experimental data. Such models start out with the idea that people’s actual competence includes no role for moral considerations, but they then posit various additional psychological factors that explain how people’s moral judgments might nonetheless influence their intuitions in specific cases. Each such alternative explanation then generates further predictions, which can in turn be subjected to experimental test. There has been a great deal of research in recent years devoted to testing these models, including some ingenious new experiments that enable one to get a better handle on the complex cognitive processes underlying people’s intuitions. At this point, then, the best approach is probably just to look in detail at some of the most prominent explanations that have actually been proposed and the various experiments that have been devised to test them. 4.1. The motivational bias hypothesis

Think of the way a District Attorney’s office might conduct its business. The DA decides to prosecute a suspect and hands the task over to a team of lawyers. These lawyers then begin looking at the case. Presumably, though, they do not examine the evidence with perfectly unbiased eyes. They have been hired to secure a conviction, and they are looking at the evidence with a view to achieving this goal (cf. Tetlock 2002). One might say that they are under the influence of a motivational bias. A number of researchers have suggested that a similar mechanism might be at the root of the effects we have been discussing here (Alicke 2008; Nadelhoffer 2006a). Perhaps people just read through the story and rapidly and automatically conclude that the agent is to blame. Then, after they have already reached this conclusion, they begin casting about for ways to justify it. They try to attribute anything they can – intention, causation, et cetera – that will help to justify the blame they have already assigned. In essence, the suggestion is that the phenomena under discussion here can be understood as the results of a motivational bias. This suggestion would involve a reversal of the usual view about the relationship between people’s blame judgments and their intuitions about intention, causation, and so forth. The usual view of this relationship looks something like what’s shown in Figure 3 Here, the idea is that people first determine that the agent fulfilled the usual criteria for moral responsibility (intention, cause, etc.) and then, on the basis of this initial judgment, go on to determine that the agent deserves blame. This sort of model has a strong intuitive appeal, but it does not seem capable of explaining the experimental data reviewed above. After all, if people determine whether or not the agent caused the outcome before they make any sort of moral judgment, how could

Figure 3. Traditional account of the process underlying blame ascription.

it be that their moral judgments affect their intuitions about causation? To resolve this question, one might develop a model that goes more like the one shown in Figure 4 In this revised model, there is a reciprocal relationship between people’s blame judgments and their intuitions about intention, causation, et cetera. As soon as people observe behavior of a certain type, they become motivated to find some way of blaming the agent. They then look to the evidence and try to find a plausible argument in favor of the view that the agent fulfills all of the usual criteria for responsibility. If they can construct a plausible argument there, they immediately blame the agent. Otherwise, they reluctantly determine that the agent was not actually blameworthy after all. In short, the hypothesis says that people’s intuitions about intention and causation affect their blame judgments but that the causal arrow can also go in the other direction, with people’s drive to blame the agent distorting their intuitions about intention and causation. One of the main sources of support for such a hypothesis is the well-established body of theoretical and experimental work within social psychology exploring similar effects in other domains. There is now overwhelming evidence that motivational biases can indeed lead people to interpret evidence in a biased manner (for a review, see Kunda 1990), and, within moral psychology specifically, there is a growing body of evidence suggesting that people often adopt certain views as part of a post hoc attempt to justify prior moral intuitions (Ditto et al. 2009; Haidt 2001). So the motivational bias hypothesis is perhaps best understood as the application to a new domain of a theoretical perspective that is already quite well supported elsewhere. More importantly, the hypothesis makes it possible to explain all of the existing results without supposing that moral considerations actually play any role at all in any of the relevant competencies. The thought is that people’s competencies are entirely non-moral but that a motivational bias then interferes with our ability to apply these concepts correctly. (An analogous case: If John sleeps with Bill’s girlfriend, Bill may end up concluding that John’s poetry was never really any good – but that does not mean that Bill’s criteria for poetry actually involve any reference to sexual behavior.) All in all, then, what we have here is an excellent hypothesis. It draws on well-established psychological theory, provides a clear explanation of existing results,

Figure 4.

Motivational bias account of blame ascription.

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Knobe: Person as scientist, person as moralist and offers a wealth of new empirically testable predictions. The one problem is that when researchers actually went out and tested those new predictions, none of them were empirically confirmed. Instead, the experimental results again and again seemed to go against what would have been predicted on the motivational bias view. At this point, the vast majority of researchers working on these questions have therefore concluded that the motivational bias hypothesis cannot explain the full range of experimental findings and that some other sort of psychological process must be at work here (Hindriks 2008; Machery 2008; McCann 2005; Nichols & Ulatowski 2007; Turner 2004; Wright & Bengson 2009; Young et al. 2006). 4.1.1. Neuropsychological studies. The usual way of

understanding the motivational bias hypothesis is that reading through certain kinds of vignettes triggers an immediate affective reaction, which then distorts people’s subsequent reasoning (Nadelhoffer 2006a). An obvious methodology for testing the hypothesis is therefore to find people who don’t have these immediate affective reactions and then check to see whether these people still show the usual effect. Young et al. (2006) did just that. They took the cases of the corporate executive who harms or helps the environment and gave these cases to subjects who had lesions in the ventromedial prefrontal cortex (VMPFC). Previous experiments had shown that such subjects have massive deficits in the ordinary capacity for affective response. They show little or no affective response in situations where normal subjects would respond strongly (Damasio et al. 1990), and when they are presented with moral dilemmas in which most people’s answers seem to be shaped by affective responses, they end up giving answers that are radically different from those given by normal subjects (e.g., Koenigs et al. 2007). The big question was whether they would also give unusual answers on the types of questions we have been examining here. The results showed that they did not (Young et al. 2006). Just like normal subjects, the VMPFC patients said that the chairman harmed the environment intentionally but helped the environment unintentionally. In fact, one hundred percent of patients in this study said that the environmental harm was intentional. On the basis of this experimental result, Young and colleagues concluded that the asymmetry observed in normal subjects was not, in fact, due to an affective reaction. But, of course, even if it turns out that affective reactions play no role in these effects, the motivational bias hypothesis would not necessarily be refuted (Alicke 2008). After all, it is important to distinguish carefully between affect and motivation, and we need to acknowledge the possibility that people are experiencing a motivational bias that does not involve any kind of affect at all. Perhaps people just calmly observe certain behaviors, rapidly arrive at certain moral appraisals, and then find themselves trying to justify a judgment of blame. This proposal is, I believe, an interesting and suggestive one. To address it properly, we will need to develop a more complex theoretical framework.

judgment of blame. This is the type of judgment we have been discussing thus far, and it certainly does play an important role in people’s psychology. But it is not the only type of moral judgment people make. They also make judgments about whether an agent did something morally wrong, about whether a behavior violated people’s moral rights, about whether its consequences were bad. A complete theory of moral cognition would have to distinguish carefully between these various types of moral judgments and explain how each relates to people’s intuitions about intention, causation, and the like. In any case, as soon as we distinguish these various types of moral judgment, we see that it would be possible for people’s intuitions to be influenced by their moral judgments even if these intuitions are not influenced by blame in particular. In fact, a growing body of experimental evidence suggests that the process actually proceeds in a quite different way (see Fig. 5). This model involves a quite radical rejection of the view that people’s intuitions about intention, causation, et cetera, are distorted by judgments of blame. Not only are these intuitions not distorted by blame, they are not even influenced by blame at all. Rather, people start out by making some other type of moral judgment, which then influences their intuitions about intention and causation, which in turn serves as input to the process of assessing blame. Though this model may at first seem counterintuitive, it has received support from experimental studies using a wide variety of methodologies. To take one example, Guglielmo and Malle (in press) gave subjects the vignette about the chairman and the environment and then used structural equation modeling to test various hypotheses about the relations among the observed variables. The results did not support a model in which blame judgments affected intuitions about intentional action. In fact, the analysis supported a causal model that went in precisely the opposite direction: it seems that people are first arriving at an intuition about intentional action, and that this intuition is then impacting their blame judgments. In short, whatever judgment it is that affects people’s intentional action intuitions, the statistical results suggest that it is not a judgment of blame per se. In a separate experiment, Guglielmo and Malle (2009) used reaction time measures to determine how long it took subjects to make a variety of different types of judgments. The results showed that people generally made judgments of intentional action before they made judgments of blame. (There was even a significant effect in this direction for some, though not all, of the specific cases we have been considering here.) But if the blame judgment does not even take place until after the intentional action judgment has been completed, it seems that people’s intentional action judgments cannot be distorted by feedback from blame. Finally, Keys and Pizarro (unpublished data) developed a method that allowed them to manipulate blame and then

4.1.2. Types of moral judgment. To begin with, we need

to distinguish between a variety of different types of moral judgment. One type of moral judgment is a 322

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Figure 5.

Distinct processes of moral judgment.

Knobe: Person as scientist, person as moralist look for an effect on intuitions about intentional action. Subjects were given the vignettes about the agent who either helps or harms the environment, but they were also randomly assigned to receive different kinds of information about the character of this agent. Some were given information that made agent look like a generally nice person; others were given information that made the agent look like a generally nasty person. The researchers could then examine the impact of this manipulation on intuitions about blame and about intentional action. Unsurprisingly, people’s intuitions about blame were affected by the information they received about the agent’s character, but – and this is the key result of the experiment – this information had no significant impact on people’s intuitions about intentional action. Instead, intuitions about intentional action were affected only by information about the actual behavior (helping vs. harming) the agent was said to have performed.2 In the face of these new results, friends of the motivational bias view might simply retreat to a weaker position. They might say: “Okay, so we initially suggested that people’s intuitions were distorted by an affective reaction associated with an impulse to blame, but we now see that the effect is not driven by affect and is not caused specifically by blame. Still, the basic idea behind the theory could nonetheless be on track. That is to say, it could still be that people’s intuitions are being distorted by an effort to justify some kind of moral judgment.” 4.1.3. Cause and blame. This approach certainly sounds

good in the abstract, but as one proceeds to look carefully at the patterns of intuition observed in specific cases, it starts to seem less and less plausible. The difficulty is that the actual patterns observed in these cases just don’t make any sense as an attempt to justify prior moral judgments. For a simple example, consider the case in which the receptionist runs out of pens and people conclude that the professor is the sole cause of the problem that results. In this case, it seems that some kind of moral judgment is influencing people’s intuitions about causation, but which moral judgment is doing the work here? One obvious hypothesis would be that people’s intuitions about causations are being influenced by a judgment that the agent deserves blame for the outcome. If this hypothesis were correct, it would make a lot of sense to suggest that people’s intuitions were being distorted by a motivational bias. The idea would be that people want to conclude that the professor is to blame for a particular outcome and, to justify this conclusion, they say that he is the sole cause of this outcome. The one problem is that the data don’t actually suggest that people’s causal intuitions are being influenced by a judgment that the agent is to blame for the outcome. Instead, the data appear to suggest that these intuitions are being influenced by a judgment that the agent’s action itself is bad. So, for example, in the case at hand, we can distinguish two different moral judgments that people might make: (a) The professor is to blame for the outcome (the receptionist’s lack of pens). (b) There is something bad about the professor’s action (taking a pen from the desk).

The key claim now is that it is the second of these judgments, rather than the first, that is influencing people’s intuition that the professor caused the outcome. To test this claim empirically, we need to come up with a case in which the agent is judged to have performed a bad action but in which the agent is nonetheless not judged to be blameworthy for the outcome that results. One way to construct such a case would be to modify our original story by switching the outcome over to something good. (For example: the receptionist was planning to stab the department chair’s eye out with a pen, but now that all of the pens have been taken, her plan is thwarted, and the department chair’s eyes are saved.) In such a case, the professor would still be performing a bad action, but there would not even be a question as to whether he was “to blame” for the outcome that resulted, since there would be no bad outcome for which anyone could deserve blame. Experiments using this basic structure have arrived at a surprising pattern of results (Hitchcock & Knobe 2009). Even when the outcome has been switched to something good, people continue to have the same causal intuitions. They still conclude that the agent who performed the bad action is more of a cause than the agent who performed the good action. Yet when the outcome is something good, it seems impossible to explain this pattern in terms of a motivational bias. After all, friends of the motivational bias hypothesis would then have to say that people are displeased with the agent who performs the bad action, that their intuitions thereby become distorted by moral judgment, and that they end up being motivated to conclude: “This bad guy must have been the sole cause of the wonderful outcome that resulted.” It seems quite difficult, however, to see how such a conclusion could possibly serve as a post hoc justification for some kind of negative moral judgment. 4.1.4. Conclusion. Of course, it might ultimately prove possible to wriggle out of all of these difficulties and show that the data reviewed here do not refute the motivational bias hypothesis. But even then, a larger problem would still remain. This problem is that no one ever seems to be able to produce any positive evidence in favor of the hypothesis. That is, no one seems to be able to provide evidence that motivational biases are at the root of the particular effects under discussion here. There is, of course, plenty of evidence that motivational biases do in general exist (e.g., Kunda 1990), and there are beautiful experimental results showing the influence of motivational biases in other aspects of moral cognition (Alicke 2000; Ditto et al. 2009; Haidt 2001), but when it comes to the specific effects under discussion here, there are no such experiments. Instead, the argument always proceeds by drawing on experimental studies in one domain to provide evidence about the psychological processes at work in another (see, e.g., Nadelhoffer 2006a). That is, the argument has roughly the form: “This explanation turned out to be true for so many other effects, so it is probably true for these ones, as well.” It now appears that this strategy may have been leading us astray. The basic concepts at work in the motivational bias explanation – affective reactions, post hoc rationalization, motivated reasoning – have proved extraordinarily helpful in understanding other aspects of moral cognition. But moral cognition is a heterogeneous phenomenon. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Knobe: Person as scientist, person as moralist What proves helpful in thinking about certain aspects of it may prove utterly irrelevant in thinking about others.

4.2. The conversational pragmatics hypothesis

Let us turn, then, to a second possible alternative hypothesis. When people are engaged in ordinary discussions, their use of words does not simply serve as a straightforward reflection of the way they apply the corresponding concepts. Instead, people strive to act as helpful conversation partners, following certain complex principles that enable them to provide useful information to their audience. The study of these principles falls under the heading of “conversational pragmatics,” and researchers engaged in this study have illuminated many puzzling aspects of the way people ordinarily use language in communication. A number of researchers have suggested that this approach might also serve to explain the phenomena we are trying to understand here (Adams & Steadman 2004a; 2004b; Driver 2008a; 2008b). To get a sense for this hypothesis, it might be helpful to start out by looking at a potentially analogous case in another domain. Imagine that you have a bathroom in your building but that this bathroom is completely nonfunctional and has been boarded up for the past three years. And now imagine that someone hands you a questionnaire that asks: Do you have a bathroom in your building? __Yes __No

It does seem that your actual concept bathroom might correctly apply to the room in your building, but when you receive this question, you immediately have an understanding of what the questioner really wants to know – namely, whether or not you have a bathroom that actually works – and you might therefore choose to check the box marked “No.” With these thoughts in mind, consider what might happen when subjects receive a questionnaire that asks whether they agree or disagree with the sentence:

W

W

definitely disagree

W

W

W

definitely agree

4.2.2. Cancelation. Of course, the study of linguistic deficits in people with Asperger’s brings up a host of complex

W

unsure

W

definitely agree

It might be thought that people’s concept of intentional action does not, in fact, apply to cases like this one; but that, as soon as they receive the questionnaire, they form an understanding of what the questioner really wants to know. The real question here, they might think, is whether the chairman deserves to be blamed for his behavior, and they might therefore check the circle marked “definitely agree.” Similar remarks might be applied to many of the other effects described above. Thus, suppose that subjects are asked whether they agree or disagree with the sentence: The administrative assistant caused the problem. W

W

definitely disagree

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W

W unsure

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4.2.1. Patient studies. One way to test the hypothesis

would be to identify subjects who show an inability to use conversational pragmatics in the normal way, and then to check to see whether these subjects still show the usual effect. Zalla, Machery, and Leboyer did exactly that in a recent study (Zalla et al. 2010). They took the story about the chairman who harms or helps the environment and presented it to subjects with Asperger’s syndrome, a developmental disorder characterized by difficulties in certain forms of communication and a striking inability to interact normally with others. Previous studies had shown that subjects with Asperger’s display remarkable deficits in the capacity to understand conversational pragmatics, tending instead to answer questions in the most literal possible way (e.g., De Villiers et al. 2006; Surian et al. 1996). If the original effect had been due entirely to pragmatic processes, one might therefore have expected subjects with Asperger’s to respond quite differently from neurotypical subjects. But that is not what Zalla and colleagues found. Instead, they found that subjects with Asperger’s showed exactly the same pattern of responses observed in previous studies. Just like neurotypical subjects, people with Asperger’s tended to say that the chairman harmed the environment intentionally but helped it unintentionally. This result suggests that the pattern displayed by subjects in earlier studies is not, in fact, a product of their mastery of complex pragmatic principles.

The chairman of the board harmed the environment intentionally. W

It might be thought that people’s concept of causation does apply in cases like this one, but it also seems that subjects might quite reasonably infer that the real point of the question is to figure out whether the administrative assistant deserves blame for this outcome and that they might therefore check the circle marked “definitely disagree.” Before going on any further, it might be helpful to take a moment to emphasize just how different this pragmatic hypothesis is from the motivational bias hypothesis we discussed above. The motivational bias hypothesis posits an error that affects people’s understanding of certain morally relevant events. By contrast, the pragmatic hypothesis does not involve any error or even any effect on people’s understanding of events. It simply suggests that people are applying certain kinds of conversational rules. The basic idea is that moral considerations aren’t actually affecting people’s understanding of the situation; it’s just that moral considerations do sometimes affect people’s view about which particular words would be best used to describe it. In any case, although the two hypotheses are very different in their theoretical approaches, they have proved remarkably similar in their ultimate fate. Like the motivational bias hypothesis, the pragmatic hypothesis initially looked very promising – a clear and plausible explanation, backed by a well-supported theoretical framework – but, as it happened, the actual empirical data just never came out the way the pragmatic hypothesis would predict. Indeed, the pragmatic hypothesis suffers from many of the same problems that plagued the motivational bias hypothesis, along with a few additional ones that are all its own.

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Knobe: Person as scientist, person as moralist issues, and this one experiment certainly should not be regarded as decisive. The thing to notice, though, is that results from a variety of other tests point toward the same basic conclusion, offering converging evidence for the claim that the effect here is not a purely pragmatic one (Adams & Steadman 2007; Knobe 2004b; Nichols & Ulatowski 2007; for a review, see Nadelhoffer 2006c). Indeed, one can obtain evidence for this claim using one of the oldest and most widely known tests in the pragmatics literature. Recall that we began our discussion of conversational pragmatics with a simple example. If a person says “There is a bathroom in the building,” it would be natural to infer that this bathroom is actually in working order. But now suppose that we make our example just a little bit more complex. Suppose that the person utters two sentences: “There is a bathroom in the building. However, it is not in working order.” Here it seems that the first sentence carries with it a certain sort of pragmatic significance but that the second sentence then eliminates the significance that this first sentence might otherwise have had. The usual way of describing this phenomenon is to say that the pragmatic “implicatures” of the first sentence have been cancelled by the second (Grice 1989). Using this device of cancellation, we could then construct a questionnaire that truly would accurately get at people’s actual concept of bathrooms. For example, subjects could be asked to select from among the options: – There is no bathroom in the building. – There is a bathroom in the building, and it is in working order. – There is a bathroom in the building, but it is not in working order. Subjects could then feel free to signify the presence of the bathroom by selecting the third option, secure in the knowledge that they would not thereby be misleadingly conveying an impression that the bathroom actually did work. In a recent experimental study, Nichols and Ulatowski (2007) used this same approach to get at the impact of pragmatic factors in intuitions about intentional action. Subjects were asked to select from among these options: – The chairman intentionally harmed the environment, and he is responsible for it. – The chairman didn’t intentionally harm the environment, but he is responsible for it. As it happened, Nichols and Ulatowski themselves believed that the original effect was entirely pragmatic, and they therefore predicted that subjects would indicate that the behavior was unintentional when they had the opportunity to do so without conveying the impression that the chairman was not to blame. But that is not at all how the data actually came out. Instead, subjects were just as inclined to say that the chairman acted intentionally in this new experiment as they were in the original version. In light of these results, Nichols and Ulatowski concluded that the effect was not due to pragmatics after all. 4.2.3. Other effects. Finally, there is the worry that, even

if conversational pragmatics might provide a somewhat plausible explanation of some of the effects described above, there are other effects that it cannot explain at all. Hence, the theory of conversational pragmatics would fail to explain the fact that moral considerations exert

such a pervasive effect on a wide range of different kinds of judgments. The pragmatic hypothesis was originally proposed as an explanation for people’s tendency to agree with sentences like: The chairman of the board harmed the environment intentionally.

And when the hypothesis is applied to cases like this one, it does look at least initially plausible. After all, it certainly does seem that a sentence like “He did not harm the environment intentionally” could be used to indicate that the agent was not, in fact, to blame for his behavior. But now suppose we take that very same hypothesis and apply it to sentences like: The chairman harmed the environment in order to increase profits.

Here the hypothesis does not even begin to get a grip. There simply isn’t any conversational rule according to which one can indicate that the chairman is not to blame by saying something like: “He didn’t do that in order to increase profits.” No one who heard a subject uttering such a sentence would ever leave with the impression that it was intended as a way of exculpating or excusing the chairman. Of course, one could simply say that the pragmatics hypothesis does explain the effect on “intentionally” but does not explain the corresponding effect on “in order to.” But such a response would take away much of the motivation for adopting the pragmatics hypothesis in the first place. The hypothesis was supposed to give us a way of explaining how moral considerations could impact people’s use of certain words without giving up on the idea that people’s actual concepts were entirely morally neutral. If we now accept a non-pragmatic explanation of the effect for “in order to, ” there is little reason not to accept a similar account for “intentionally” as well. 4.3. Summary

Looking through these various experiments, one gradually gets a general sense of what has been going wrong with the alternative explanations. At the core of these explanations is the idea that people start out with an entirely non-moral competence but that some additional factor then interferes and allows people’s actual intuitions to be influenced by moral considerations. Each alternative explanation posits a different interfering factor, and each explanation thereby predicts that the whole effect will go away if this factor is eliminated. So one alternative explanation might predict that the effect will go away when we eliminate a certain emotional response, another that it will go away when we eliminate certain pragmatic pressures, and so forth. The big problem is that these predictions never actually seem to be borne out. No one has yet found a way of eliminating the purported interfering factors and thereby making the effect go away. Instead, the effect seems always to stubbornly reemerge, coming back again and again despite all our best efforts to eliminate it. Now, one possible response to these difficulties would be to suggest that we just need to try harder. Perhaps the relevant interfering factor is an especially tricky or well-hidden one, or maybe there are a whole constellation BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Knobe: Person as scientist, person as moralist of different factors in place here, all working together to generate the effects observed in the experiments. When we finally succeed in identifying all of the relevant factors, we might be able to find a way of eliminating them all and thereby allowing people’s purely non-moral competence to shine through unhindered. Of course, it is at least possible that such a research program would eventually succeed, but I think the most promising approach at this point would be to try looking elsewhere. In my view, the best guess about why no one has been able to eliminate the interfering factors is that there just aren’t any such factors. It is simply a mistake to try to understand these experimental results in terms of a purely non-moral competence which then gets somehow derailed by various additional factors. Rather, the influence of moral considerations that comes out in the experimental results truly is showing us something about the nature of the basic competencies people use to understand their world. 5. Competence theories Let us now try to approach the problem from a different angle. Instead of focusing on the interfering factors, we will try looking at the competence itself. The aim will be to show that something about the very nature of this competence is allowing people’s moral judgments to influence their intuitions. 5.1. General approach

At the core of the approach is a simple and straightforward assumption that has already played an enormously important role in numerous fields of cognitive science. Specifically, I will be relying heavily on the claim that we make sense of the things that actually happen by considering other ways things might have been (Byrne 2005; Kahneman & Miller 1986; Roese 1997). A quick example will help to bring out the basic idea here. Suppose that we come upon a car that has a dent in it. We might immediately think about how the car would have looked if it did not have this dent. Thus, we come to understand the way the car actually is by considering another way that it could have been and comparing its actual status to this imagined alternative. An essential aspect of this process, of course, lies in our ability to select from among all the possible alternatives just the few that prove especially relevant. Hence, in the case at hand, we would immediately consider the possibility that the car could have been undented, and think: “Notice that this car is dented rather than undented.” But then there are all sorts of other alternatives that we would immediately reject as irrelevant or not worth thinking about. We would not take the time, for example, to consider the possibility that the car could have been levitating in the air, and then think: “Notice that this car is standing on the ground rather than levitating in the air.” Our ability to pick out just certain specific alternatives and ignore others is widely regarded as a deeply important aspect of human cognition, which shapes our whole way of understanding the objects we observe. It is, for example, a deeply important fact about our way of understanding the dented car that we compare it to an undented car. If we 326

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had instead compared it to a levitating car, we would end up thinking about it in a radically different way. A question now arises as to why people focus on particular alternative possibilities and ignore others. The answer, of course, is that all sorts of different factors can play a role here. People’s selection of specific alternative possibilities can be influenced by their judgments about controllability, about recency, about statistical frequency, about nonmoral forms of goodness and badness (for reviews, see Byrne 2005; Kahneman & Miller 1986; Roese 1997). But there is also another factor at work here that has not received quite as much discussion in the existing literature. A number of studies have shown that people’s selection of alternative possibilities can be influenced by their moral judgments (McCloy & Byrne 2000; N’gbala & Branscombe 1995). In other words, people’s intuition about which possibilities are relevant can be influenced by their judgments about which actions are morally right. For a simple illustration, take the case of the chairman who hears that he will be helping the environment, but reacts with complete indifference. As soon as one hears this case, one’s attention is drawn to a particular alternative possibility: (1) Notice that the chairman reacted in this way, rather than specifically preferring that the environment be helped.

This alternative possibility seems somehow to be especially relevant, more relevant at least than many other possibilities we could easily imagine. In particular, one would not think: (2) Notice that the chairman reacted in this way rather than specifically trying to avoid anything that would help the environment.

Of course, one could imagine the chairman having this latter sort of attitude. One could imagine him saying: “I don’t care at all whether we make profits. What I really want is just to make sure that the environment is harmed, and since this program will help the environment, I’m going to do everything I can to avoid implementing it.” Yet this possibility has a kind of peculiar status. It seems somehow preposterous, not even worth considering. But why? The suggestion now is that moral considerations are playing a role in people’s way of thinking about alternative possibilities. Very roughly, people regard certain possibilities as relevant because they take those possibilities to be especially good or right. With these thoughts in mind, we can now offer a new explanation for the impact of moral judgments on people’s intuitions. The basic idea is just that people’s intuitions in all of the domains we have been discussing – causation, doing/allowing, intentional action, and so on – rely on a comparison between the actual world and certain alternative possibilities. Because people’s moral judgments influence the selection of alternative possibilities, these moral judgments end up having a pervasive impact on the way people make sense of human beings and their actions.3 5.2. A case study

To truly spell out this explanation in detail, one would have to go through each of the different effects described above and show how each of these effects can be explained on a model in which moral considerations are impacting

Knobe: Person as scientist, person as moralist people’s way of thinking about alternative possibilities. This would be a very complex task, and I will not attempt it here. Let us proceed instead by picking just one concept whose use appears to be affected by moral considerations. We can then offer a model of the competence underlying that one concept and thereby illustrate the basic approach. For these illustrative purposes, let us focus on the concept in favor. We begin by introducing a fundamental assumption that will guide the discussion that follows. The assumption is that people’s representation of the agent’s attitude is best understood, not in terms of a simple dichotomy between “in favor” and “not in favor,” but rather, in terms of a whole continuum of different attitudes an agent might hold. So we will be assuming that people can represent the agent as strongly opposed, as strongly in favor, or as occupying any of the various positions in between. For simplicity, we can depict this continuum in terms of a scale running from con to pro. 4 (See Fig. 6.) Looking at this scale, it seems that an agent whose attitude falls way over on the con side will immediately be classified as “not in favor,” and that an agent whose attitude falls way over on the pro side will immediately be classified as “in favor.” But now, of course, we face a further question. How do people determine the threshold at which an agent’s attitude passes over from the category “not in favor” to the category “in favor”? To address this question, we will need to add an additional element to our conceptual framework. Let us say that people assess the various positions along the continuum by comparing each of these positions to a particular sort of alternative possibility. We can refer to this alternative possibility as the default. Then we can suggest that an agent will be counted as “in favor” when his or her attitude falls sufficiently far beyond this default point. (See Fig. 7.) The key thing to notice about this picture is that there needn’t be any single absolute position on the continuum that always serves as the threshold for counting an agent as “in favor.” Instead, the threshold might vary freely, depending on which point gets picked out as the default. To get a sense for the idea at work here, it may be helpful to consider a closely analogous problem. Think of the process a teacher might use in assigning grades to students. She starts out with a whole continuum of different percentage scores on a test, and now she needs to find a way to pick out a threshold beyond which a given score will count as an A. One way to do this would be to introduce a general rule, such as “a score always counts as an A when it is at least 20 points above the default.” Then she can pick out different scores as the default on different tests – treating 75% as default on easy tests, 65% as default on more difficult ones – and the threshold for counting as an A will vary accordingly. The suggestion now is that people’s way of thinking about attitudes uses this same sort of process. People always count an agent as “in favor” when the agent’s

Figure 6. Continuum of attitude ascription.

Figure 7. Criteria for ascription of “in favor.”

attitude falls sufficiently far beyond the default, but there is no single point along the continuum that is treated as default in all cases. Different attitudes can be treated as default in different cases, and the threshold for counting as “in favor” then shifts around from one case to the next. Now we arrive at the crux of the explanation. The central claim will be that people’s moral judgments affect their intuitions by shifting the position of the default. For morally good actions, the default is to have some sort of pro-attitude, whereas for morally bad actions, the default is to have some sort of con-attitude. The criteria for “in favor” then vary accordingly. Suppose we now apply this general framework to the specific vignettes used in the experimental studies. When it comes to helping the environment, it seems that the default attitude is a little bit toward the pro side. That is to say, the default in this case is to have at least a slightly positive attitude – not necessarily a deep or passionate attachment, but at least some minimal sense that helping the environment would be a nice thing to do. An attitude will then count as “in favor” to the extent that it goes sufficiently far beyond this default point. (See Fig. 8.) But look at the position of the agent’s actual attitude along this continuum. The agent is not even close to reaching up to the critical threshold here – he is only interested in helping the environment as a side-effect of some other policy, and people should therefore conclude that he does not count as “in favor” of helping. Now suppose we switch over to the harm case. There, we find that the agent’s actual attitude has remained constant, but the default has changed radically. When it comes to harming the environment, the default is to be at least slightly toward the con side – not necessarily showing any kind of vehement opposition, but at least having some recognition that harming the environment is a bad thing to do. An agent will then count as “in favor” to the extent that his or her attitude goes sufficiently far beyond this default (Fig. 9). In this new representation, the agent’s actual attitude remains at exactly the same point it was above (in Fig. 8), but its position relative to the default is now quite different. This time, the attitude falls just about at the critical threshold for counting as “in favor,” and people should therefore be just about at the midpoint in their intuitions as to whether the agent was in favor of harming – which, in fact, is exactly what the experimental results show.

Figure 8.

Representation of the continuum for the help case. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Knobe: Person as scientist, person as moralist 6. Conclusion

Figure 9.

Representation of the continuum for the harm case.

Notice how sharply this account differs from the alternative hypotheses discussed above. On those alternative hypotheses, people see that the agent harmed the environment, want to blame him for his behavior, and this interest in blame then shapes the way they conceptualize or describe various aspects of the case. The present account says nothing of the kind. Indeed, the account makes no mention at all of blame. Instead, it posits a role for an entirely different kind of moral judgment – a judgment that could be made even in the absence of any information about this specific agent or his behaviors. The claim is that before people even begin considering what actually happened in the case at hand, they can look at the act of harming the environment and make a judgment about what sort of attitude an agent could be expected to hold toward it. This judgment then serves as a standard they can use to make sense of the behavior they actually observe.

5.3. Extending the model

What we have here is a model of the competence underlying people’s use of one particular concept. The key question now is whether this same basic approach can be applied to the various other concepts discussed above. In a series of recent papers, I have argued that it can be used to explain the impact of moral judgment on people’s intuitions about freedom, knowledge, and causation5 (Hitchcock & Knobe 2009; Pettit & Knobe, 2009; Phillips & Knobe 2009). But new studies are coming out all the time, and we may soon be faced with experimental results that the model cannot explain. At any rate, one certainly should not expect that this model will turn out to be correct in every detail. Presumably, further work will show that it needs to be revised or expanded in various ways, and perhaps it will even have to be scrapped altogether. In the present context, however, our concern is not so much to explore the details of this one model as to use it as a way of illustrating a more general approach and the contrast between this approach and the one we saw in the alternative explanations described above. The alternative explanations start out with the idea that the relevant competencies are entirely non-moral, but that some additional factor then interferes and allows people’s intuitions to be influenced by moral considerations. These explanations therefore predict that it should be possible, at least in principle, to eliminate the interfering factors and examine the judgments people make in the absence of this influence. By contrast, in the approach under discussion here, moral considerations are not understood as some kind of extra factor that gets added in on top of everything else. Instead, the whole process is suffused with moral considerations from the very beginning. Hence, in this approach, no real sense can be attached to the idea of eliminating the role of morality and just watching the basic process unfold in its pure, non-moral form. 328

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This target article began with a metaphor. The suggestion was that people’s ordinary way of making sense of the world might be similar, at least in certain respects, to the way research is conducted in a typical modern university. Just as a university would have specific departments devoted especially to the sciences, our minds might include certain specific psychological processes devoted especially to constructing a roughly “scientific” kind of understanding. If one thinks of the matter in this way, one immediately arrives at a certain picture of the role of moral judgments in people’s understanding as a whole. In a university, there might be faculty members in the philosophy department who were hired specifically to work on moral questions, but researchers in the sciences typically leave such questions to one side. So maybe the mind works in much the same way. We might have certain psychological processes devoted to making moral judgments, but there would be other processes that focus on developing a purely “scientific” understanding of what is going on in a situation and remain neutral on all questions of morality. I have argued that this picture is deeply mistaken. The evidence simply does not suggest that there is a clear division whereby certain psychological processes are devoted to moral questions and others are devoted to purely scientific questions. Instead, it appears that everything is jumbled together. Even the processes that look most “scientific” actually take moral considerations into account. It seems that we are moralizing creatures through and through. ACKNOWLEDGMENTS For comments on earlier drafts, I am deeply grateful to John Doris, Shaun Nichols, Stephen Stich, and five anonymous reviewers.

NOTES 1. In each of the studies that follow, we found a statistically significant difference between intuitions about a morally good act and intuitions about a morally bad act, but one might well wonder how large each of those differences was. The answers are as follows. Intentional action: 33% vs. 82%. (All subsequent results are on a scale from 1 to 7.) Deciding: 2.7 vs. 4.6. In favor: 2.6 vs. 3.8. In order to: 3.0 vs. 4.6. By: 3.0 vs. 4.4. Causation: 2.8 vs. 6.2. Doing/allowing: 3.0 vs. 4.6. 2. Surprisingly, there was also a significant gender x character interaction, whereby women tended to regard the act as more intentional when the agent had a bad character, while men tended to regard the act as more intentional when the agent had a good character. I have no idea why this might be occurring, but it should be noted that this is just one of the many individual differences observed in these studies. Feltz and Cokely (2007) have shown that men show a greater moral asymmetry in intentional action intuitions when the vignettes are presented withinsubject, and Buckwalter (2010) has shown that women show a greater moral asymmetry when they are asked about the agent’s knowledge. Though not well-understood at the moment, these individual differences might hold the key to future insights into the moral asymmetries discussed here. (For further discussion, see Nichols & Ulatowski 2007.) 3. Strikingly, recent research has shown that people’s intuitions about intentional action can be affected by non-moral factors, such as judgments about the agent’s own interests (Machery 2008; Nanay 2010), knowledge of conventional rules (Knobe 2007), and implicit attitudes (Inbar et al. 2009). This recent discovery offers us an interesting opportunity to test the present account.

Commentary/Knobe: Person as scientist, person as moralist If we can come up with a general theory about how people’s evaluations impact their thinking about alternative possibilities – a theory that explains not only the impact of moral judgments but also the impact of other factors – we should be able to generate predictions about the precise ways in which each of these other factors will impact people’s intentional action intuitions. Such predictions can then be put to the test in subsequent experiments. 4. There may be certain general theoretical reasons for adopting the view that people’s representations of the agent’s attitude have this continuous character, but the principal evidence in favor of it comes from the actual pattern of the experimental data. For example, suppose that instead of saying that the agent does not care at all about the bad side-effect, we say that the agent deeply regrets the side-effect but decides to go ahead anyway so as to achieve the goal. Studies show that people then tend to say that the side-effect was brought about unintentionally (Phelan & Sarkissian 2008; Sverdlik 2004). It is hard to see how one could explain this result on a model in which people have a unified way of thinking about all attitudes that involve the two features (1) foreseeing that an outcome will arise but (2) not specifically wanting it to arise. However, the result becomes easy to explain if we assume that people represent the agent’s attitude, not in terms of sets of features (as I earlier believed; Knobe 2006), but in terms of a continuous dimension. We can then simply say that people take the regretful agent to be slightly more toward the con side of the continuum and are therefore less inclined to regard his or her behavior as intentional. 5. Very briefly, the suggestion is that intuitions in all three of these domains involve a capacity to compare reality to alternative possibilities. Thus, (a) intuitions about whether an agent acted freely depend on judgments about whether it was possible for her to choose otherwise, (b) intuitions about whether a person knows something depend on judgments about whether she has enough evidence to rule out relevant alternatives, and (c) intuitions about whether one event caused another depend on judgments about whether the second event would still have occurred if the first had not. Because moral judgments impact the way people decide which possibilities are relevant or irrelevant, moral judgments end up having an impact on people’s intuitions in all three of these domains.

Open Peer Commentary Competence: What’s in? What’s out? Who knows? doi:10.1017/S0140525X10001652 Joshua Alexander,a Ronald Mallon,b and Jonathan M. Weinbergc a Philosophy Department, Siena College, Loudonville, NY 12211; bDepartment of Philosophy, University of Utah, Salt Lake City, UT 84112; cDepartment of Philosophy, Indiana University, Bloomington, IN 47405-7005. [email protected] http://www.siena.edu/pages/1855.asp [email protected] http://www.philosophy.utah.edu/faculty/mallon/ [email protected] http://www.indiana.edu/phil/Faculty/Individual%20Pages/ Weinberg.html

Abstract: Knobe’s argument rests on a way of distinguishing performance errors from the competencies that delimit our cognitive

architecture. We argue that other sorts of evidence than those that he appeals to are needed to illuminate the boundaries of our folk capacities in ways that would support his conclusions.

Joshua Knobe argues that the various moral inflections of our folk psychology are part of “the competencies people use to understand the world” (target article, sect. 4, para. 1), a hypothesis that he contrasts with the claim that “certain additional factors are somehow ‘biasing’ or ‘distorting’ people’s cognitive processes and thereby allowing their intuitions to be affected by moral judgments” (sect. 4, para. 1). However, Knobe really never makes clear exactly what makes something “inside” or “outside” a competence. Clearly, both he and his past interlocutors have taken motivated cognition and pragmatic factors to count as “interfering,” rather than as part of the competence. But what can ground such judgments? We worry that any non-stipulative way of answering this question that plausibly excludes motivation and pragmatic considerations, can also be used to insist that moral considerations are “outside of” or “external to” the competence under consideration. We are not disputing the empirical facts that he does muster; rather, our concern is with a further theoretical interpretation he wants to place on those facts, which we argue is unwarranted. One natural way to circumscribe the boundaries of a competence is bottom-up, by appealing to a fairly literal, physical notion of containment provided by neuroanatomy. But neither Knobe nor his interlocutors muster any such neuroanatomical evidence, so this sort of approach is not a good contender. A more promising way of approaching questions of competence is to begin with a high-level characterization of the function that a cognitive process is supposed to compute, and on this basis attempt to specify an algorithm for computing that function and to address questions of actual physical implementation (see Mallon 2007). Once we are clear about what task a cognitive process is supposed to execute, constraints or problems in the execution of the task can be identified. According to this topdown approach to competence, then, what allows us to describe something as interfering with a cognitive process is a substantive account of the work the process is supposed to be doing. The problem with taking this kind of approach here is that there isn’t a settled account of what sort of job our folk psychological judgments are supposed to do. What we have are two different models, each of which stipulates what function is supposedly being calculated by our folk psychological judgments, and thus, what is and is not part of our competence with such judgments. On Knobe’s model, pragmatics and motivation may indeed properly lie outside the competence. But on his opponent’s model, the exact same line of reasoning would apply to the source of the morality effects that Knobe is appealing to. There is no “supposed to” to be found within those sorts of findings, and so where to draw an inside/outside line is, thus far, an empirical free move – stipulated, not discovered. One can contrast the situation regarding our folk-psychological capacities with the comparatively much better established taxonomy of competences in both language and vision. For example, linguists are used to separating out the semantic, syntactic, phonological, and pragmatic components of our overall linguistic capacities. This division has proved empirically fruitful, and it is grounded in bottom-up considerations as well (such as deficit patterns due to various lesions). In debates about language, then, it makes sense that showing a phenomenon to be a proper part of one member of that partition is thereby a good reason to reject it as part of some other member. Interestingly, we see exactly this dynamic in earlier stages of the debate about the side-effect effect, which for several years was explicitly framed in terms of whether there was a moral dimension to the semantic component of our “intentionally” discourse. And so it made sense, in the context of the debate so construed, to take a pragmatic explanation of the side-effect effect to preempt an explanation of it in semantic terms. The existing framework legitimated ruling pragmatics to be “outside” of semantics. But in this BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Knobe: Person as scientist, person as moralist target article, Knobe switches from a debate about semantic competence to a debate about competence in some abstract sense. And we think Knobe has made the right decision to move away from the semantic debate, in part because of some Quinean pessimism about certain forms of conceptual analysis (see Alexander et al. 2010). But more importantly, Knobe’s own favored account (sect. 5) locates the source of this moral inflection in a mechanism for the allocation of cognitive resources, selecting different alternatives for cognitive attention. He writes, “The basic idea is just that people’s intuitions . . . rely on a comparison between the actual world and certain alternative possibilities. Because people’s moral judgments influence the selection of alternative possibilities, these moral judgments end up having a pervasive impact” (sect. 5.1, para. 8). But this influence on cognitive attention would count as part of general cognition, but not part of semantics, in the traditional linguistic taxonomy, and thus would have counted as an “external” causal factor in the old debate. Given his current choice of hypothesis, this shift away from a specifically semantic framing of the issues to a more generic one makes sense. Unfortunately, in doing so, he has abandoned one set of resources for underwriting an inside/outside distinction without replacing them with something else. The challenge he faces is how to substantiate such a distinction in a way that both (1) isn’t merely stipulative and (2) puts pragmatics and motivation on the “outside” and cognitive attention on the “inside.” We think that, in order to do so, other sorts of evidence will be needed than those that Knobe appeals to in his article. Perhaps an evolutionary or teleological argument could ground a topdown approach here; or perhaps neuroanatomical evidence could ground a bottom-up approach; or perhaps – and where we would place our bets – further re-refinement of the basic question is still in order.

Culpable control or moral concepts? doi:10.1017/S0140525X10001664 Mark Alickea and David Roseb a

Department of Psychology, Ohio University, Athens, OH 45701; bDepartment of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15235. [email protected] [email protected]

Abstract: Knobe argues in his target article that asymmetries in intentionality judgments can be explained by the view that concepts such as intentionality are suffused with moral considerations. We believe that the “culpable control” model of blame can account both for Knobe’s side effect findings and for findings that do not involve side effects.

Virtually everyone, including both professional and lay evaluators of human behavior, agrees that to praise or blame an agent, the agent must have acted intentionally, with foresight of the consequences, and must have caused the outcome. In a perfect evaluative world, assessments of intentionality, foresight, and causation would be made independently of the judge’s expectations, affective and attitudinal reactions, and moral beliefs and predilections. By now, however, there is abundant evidence that such factors have a powerful and pervasive influence on intentionality, foresight, and causation judgments. In his target article, Knobe offers an alternative to the view that such influences are motivated by the desire to justify praising or blaming an agent who evokes positive or negative evaluative reactions. Knobe has argued that the reason that concepts such as intentionality, causation, and foresight are influenced by moral considerations is because these concepts are suffused with moral considerations (i.e., moral considerations figure into the underlying competencies). To illustrate his position, he uses his welltraveled example of an executive who knows that, as a side effect of initiating a certain program, the environment will be

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helped or harmed, but whose only concern is to increase profits. Knobe assumes that in the “help” scenario, the normative (or “default”) expectation would be to have at least a moderately pro-attitude toward helping the environment. The default expectation in the “harm” scenario would be for a moderately anti-attitude (or in Knobe’s term, con-attitude) toward harming environment. In the “help” case, the executive doesn’t meet the threshold required for having a pro-attitude; whereas in the “harm” case, the executive’s failure to endorse a con-attitude places him in the range of plausibly having a pro-attitude. Thus, in the latter case but not the former, the executive is thought to have acted intentionally because he apparently has a pro-attitude toward the harmful outcome. Ascriptions of intentionality are thus dependent on the sort of attitude an evaluator thinks an agent should have about a particular outcome. Intentionality (and presumably other concepts such as causation and foresight) is applied when the agent’s presumed attitude crosses the evaluator’s normative threshold. Our alternative to Knobe’s position – the culpable control model of blame (Alicke 1992; 2000; Alicke & Zell 2009; Alicke et al. 2010) – can explain the environmental harming/helping findings without positing that concepts such as intentionality are inherently moral. Further, it can explain cases other than the specialized side-effect scenarios that are the focus of Knobe’s theory. The culpable control model assumes that positive and negative evaluative reactions – which are judgments of right or wrong, good or bad, or approval or disapproval – to the people involved in an event, their actions, and the outcomes that ensue can induce social perceivers to process information in a “blame validation” mode. Blame validation involves interpreting the evidence regarding intentionality, causation, and foresight in a way that justifies praising an agent who elicits positive evaluations or blaming one who arouses negative evaluations. The culpable control model explains Knobe’s findings by assuming that social perceivers view the environment-harming executive as a major jerk (i.e., one who arouses strong negative evaluations), but view the environment-helping executive as only a minor one. Imputing intentionality to the environmentharming executive, therefore, validates social perceivers’ negative evaluative reactions, and in turn, supports a blame attribution. The culpable control model, therefore, does not require the assumption that concepts such as intentionality, causation, and foresight are suffused with moral considerations. Rather, the influences of these evaluations can be explained in terms of the desire to blame an agent whose actions arouse strong disapproval. It is important to note that most of Knobe’s examples apply to cases where foreseen but unintended side-effects occur, which narrows the application of the theory. Furthermore, Knobe’s example is not an optimal one for considering the relative merits of his position and the culpable control model because his default assumption regarding an agent’s attitude is confounded with positive and negative evaluations of the agent’s goals and actions and the outcomes that occur. For example, in the “harm” scenario, the agent’s indifference toward harming the environment diverges from the attitude we would expect most agents to have in this situation; but this indifference, as well as both the agent’s decision to let the environment be harmed and the fact that the environment ultimately is harmed, also provides a basis for negative evaluative reactions. So, we need some other case to differentiate these two views. In an early set of studies (Alicke 1992, Study 1), participants learned that a young man was speeding home to hide either an anniversary present or a vial of cocaine from his parents before they arrived home. A car accident occurred under somewhat ambiguous circumstances: It could have been due in part to his speeding, but also to environmental impediments such as a partly obscured stop sign. The study’s results were clear: When the driver’s motive was undesirable (i.e., to hide cocaine), his driving was cited as far more causal than the environmental obstacles. However, precisely the opposite was true when his

Commentary/Knobe: Person as scientist, person as moralist motive was to hide an anniversary present. This case would be very difficult for Knobe’s theory to explain: Why would anyone assume that the driver who was speeding home to hide cocaine had a pro-attitude towards causing car accidents, and that the driver who was speeding to hide an anniversary present did not? The more plausible alternative, based on the culpable control model, is that negative evaluations of the driver whose motive was to hide cocaine induced participants to skew the evidential criteria for causation to support their desire to blame him. In sum: Knobe’s position is plausible in cases involving foreseen but unintended side-effects, but it has some trouble explaining cases outside of this narrow scope. The culpable control model can explain many cases involving side-effects, as well as most cases that do not involve side-effects, and can do so without claiming that concepts such as intentionality, causation, and foresight are suffused with moral considerations.

Person as moralist and scientist doi:10.1017/S0140525X10001676 Marcus Vinı´cius C. Baldoa and Anouk Barberousseb a “Roberto Vieira” Laboratory of Sensory Physiology, Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of Sa˜o Paulo, SP 05508-900, Sa˜o Paulo, Brazil; bInstitut d’Histoire et de Philosophie des Sciences et des Techniques (IHPST), UMR 8590, CNRS, Universite´ Paris 1, ENS, 75006 Paris, France. [email protected] http://www.fisio.icb.usp.br/vinicius/[email protected] http://www-ihpst.univ-paris1.fr/en/4,anouk_barberousse.html

Abstract: Scientific inquiry possibly shares with people’s ordinary understanding the same evolutionary determinants, and affect-laden intuitions that shape moral judgments also play a decisive role in decision-making, planning, and scientific reasoning. Therefore, if ordinary understanding does differ from scientific inquiry, the reason does not reside in the fact that the former (but not the latter) is endowed with moral considerations.

According to Knobe’s central thesis, we are “moralizing creatures,” with moral judgments lying at the core of the competencies we use to make sense of our actions and ourselves. By “ordinary understanding,” Knobe means the way people make sense of the world without having any scientific education. He argues that human cognition, in general, is intrinsically and inescapably moral, in the sense that people just do not make sense of certain situations without performing proto-moral judgments at the very time they perceive them. However, the target article’s argument is not as generally applicable as Knobe claims: the article does not address people’s cognition in general, but only how we, as human beings, perceive and interpret human interactions, as is made clear in the reported experiments. While this topic in fact belongs to the study of causal cognition, it is far from exhausting it. Also, the way Knobe contrasts his main thesis with other claims that have been made before seems to be misleading, promoting an erroneous interpretation of his own thesis. He opposes his view about cognition to the idea that the functioning of the human mind mirrors the functioning of scientists (Gopnik 1996; Gopnik & Schulz 2004; Gopnik & Tenenbaum 2007). Despite Knobe’s insistence on criticizing this view, it is the wrong opponent to his own thesis. What he is actually attacking is a view according to which humans’ perception of humans’ interactions is objective, in the sense of being devoid of any moral commitment. Gopnik’s view, for instance, is about the development of cognition as much as about cognition in adults, independently of their being impregnated or not with moral considerations. Actually, if we adopt the thesis according to which scientific inquiry is only a very refined manifestation of our effort to make

sense of the world, sharing with people’s ordinary understanding the same evolutionary roots, then Knobe’s claim that ordinary understanding does not build itself in the same manner as scientific understanding does, loses much of its strength. The justification of this thesis will follow in two steps. An increasing body of evidence, from fields such as psychology, anthropology, and neuroscience, lends support to the idea that quick and automatic affect-laden intuitions indeed shape higher levels of human reasoning, which belong to a slower and phylogenetically newer set of cognitive resources (Eslinger & Damasio 1985; Fiske 1992; Greene & Haidt 2002; Haidt 2001; 2007; Moll et al. 2005; Prinz 2006). Thus, the first step is to consider emotional and affective factors as the driving forces behind moral judgments. This view is by no means new; it goes back at least to David Hume’s proposal that moral reasoning is driven by moral emotions and intuitions: “Reason is, and ought only to be the slave of the passions” (Hume 1739/2000). The second step is to dissolve the concept of “moral judgment” by no longer envisaging it as a single entity, but rather as a compound cognitive act. Based on neuroscientific evidence, it is becoming increasingly clear that there are no specific regions of the brain responsible for moral judgments, which would be the combined result of basic processes involving abstract reasoning, its emotional content, and possibly other cognitive factors (Greene & Haidt 2002). Lending support to this view, the “affect as information” hypothesis, from social psychology, emphasizes the importance of people’s mood and feelings when making decisions and judgments (Haidt 2001). In the same vein, Damasio’s “somatic marker” hypothesis points to the role of emotional experiences in guiding decision-making by ascribing affective valence to behavioral options, and this has been substantiated by empirical data originating from several clinical and neuroimaging studies (Eslinger & Damasio 1985; Bechara et al. 2000). In conclusion, Knobe’s article has the merit of reinforcing, with both experimental facts and theoretical considerations, the not often recognized importance of moral values in assembling apparently neutral and objective evaluations. However, we believe that such an idea must be brought to a wider scenario, in which we could integrate basic neurophysiological mechanisms underlying emotional states and decisional processes into a framework able to account for more elaborate cognitive tasks, such as planning, moral judgments, and scientific reasoning. Finally, granting to Knobe that we inescapably moralize our perception of human interactions, it would be interesting to say a word about the relationships between this intuitive grasping and the scientific grasping that psychologists and sociologists try to achieve. What does happen in a psychologist’s mind when she studies someone looking at two people interacting? Such a question is likely to allow one to go deeper into the implications of Knobe’s proposition. ACKNOWLEDGMENT This work was partially supported by Brazilian funding agencies (FAPESP/ CNPq).

Reasoning asymmetries do not invalidate theory-theory doi:10.1017/S0140525X10001688 Karen Bartsch and Tess N. Young Psychology Department 3415, University of Wyoming, Laramie, WY 820713415. [email protected] [email protected] http://uwadmnweb.uwyo.edu/psychology/ displayfaculty.asp?facultyid¼1285

Abstract: In this commentary we suggest that asymmetries in reasoning associated with moral judgment do not necessarily invalidate a theory-

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Commentary/Knobe: Person as scientist, person as moralist theory account of naı¨ve psychological reasoning. The asymmetries may reflect a core knowledge assumption that human nature is prosocial, an assumption that heightens vigilance for antisocial dispositions, which in turn leads to differing assumptions about what is the presumed topic of conversation.

We question Knobe’s thesis that in acknowledging the asymmetries observed in reasoning associated with moral judgment we must perforce abandon a “theory-theory” characterization of naı¨ve psychological reasoning. Certainly there are pervasive asymmetries, at least when the situation affords opportunity for moral evaluation, but such asymmetries may not, in themselves, invalidate a theory-theory perspective. We suspect that underlying the asymmetries is a tendency to view prosocial dispositions as the norm, a tendency that makes us especially vigilant about antisocial dispositions. In other words, we firmly expect others to have prosocial intentions and behavior. When asked about actions that involve harm-infliction or rule breaking, we assume these anomalies are the events that require explanation and attention. This vigilance, which recognizes moral culpability as a conspicuous and interesting phenomenon, can account for the empirical findings in question without compelling rejection of a theory-theory characterization of naı¨ve psychological reasoning. That is, just because we bring to our reasoning something like a base-rate assumption, or perhaps even a “core knowledge” assumption (a` la Spelke & Kinzler 2007) concerning prosocial intentions and behavior, it does not follow that we are essentially unscientific or irrational in the reasoning that ensues. Even if we begin with a default assumption about its nature, we may nevertheless view human behavior through a theorylike framework that can be rationally revised through experience (e.g., Gopnik & Wellman 1992). In essence, we suggest that, despite the arguments raised by Knobe against motivational and conversational pragmatics hypotheses, there is some such alternative view that can account for the empirical findings without invalidating a theory-theory characterization of naı¨ve psychological reasoning. Knobe’s point regarding the pervasive nature of observed asymmetries in psychological reasoning, at least in reasoning that invites moral judgment, is well supported. But the asymmetries may simply reveal that people are poised to seize on moral failings and treat them as focal. They are less inclined to view prosocial activities as warranting explanation and evaluation. In keeping with the widely popular notion that we have a deepseated intuitive sensitivity regarding harm (e.g., Haidt 2001; Hoffman 2000; Turiel 2006), perhaps we exhibit what a theorytheorist might consider a core knowledge bias when reasoning about others. Specifically, we view good intentions and behavior as the norm and are consequently hypersensitive to deviations from it. When asked about people who act badly, we assume the deviation is what we should attend to and explain (in a Gricean fashion), and respond accordingly. On this view, when asked whether the chairman of the board has intentionally harmed the environment by instituting a new program that he knows would create harm in addition to increasing profits, people respond affirmatively. Such a response, we think, reflects vigilant attention to someone knowingly acting in a way that causes harm. The same interpretation applies to the findings reflecting variations on the terms (“deciding,” “desire,” “advocating,” etc.). In these cases, the unacceptable, yet fully cognizant, behavior is simply assumed to be the topic of conversation. But when asked whether the chairman intentionally helped, rather than harmed, the environment, people respond differently – because helping is not a weird and worrying thing that commands vigilant attention. A question about whether helping was intended is interpreted as a query about whether the chairman had that outcome as a goal. In a world where prosocial aims are assumed to be the norm, we are not inclined to give the chairman moral credit for the outcome because the original goal, according to the narrative, was to increase profit. These

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asymmetrical responses do not reflect irrational reasoning; they reflect different assessments of what is the focal issue, or curiosity, to be addressed and explained. It may be that such asymmetries are universal because there is a core knowledge presupposition about the prosocial nature of people, possibly from very early in development (see Hamlin et al. 2007). This assumption, in itself, does not mean the reasoning is illogical or unscientific, but it does influence the topic presumed to warrant explanation and discussion. How does this interpretation account for people’s judgments of causation? When both an administrative assistant and a faculty member are said to have removed pens from the receptionist’s desk, the former with permission and the latter illicitly, people indicate that the professor caused the problem. This response may also reflect vigilant attention to the anomalous bad behavior, the event that requires explanation, and a corresponding shift in conversational focus. Knobe astutely notes that it is not just that the behavior is strange or unusual (see, e.g., Roxborough & Cumby 2009) but specifically that the behavior is morally bereft. So it seems it is moral culpability in general that is regarded as anomalous, not the specific actions of a certain person or even a group of people. Moreover, it does not matter whether in fact the final outcome is actually good (Hitchcock & Knobe 2009, cited in the target article); the focus continues to be on the “bad” behavior. So – and here we think we agree with Knobe – it is not accurate to characterize what we bring to these reasoning tasks as purely a base-rate assumption regarding any specific action; rather, we simply assume that people will usually have benign intentions and act in a prosocial fashion. In one respect, our interpretation resembles Knobe’s. We agree that moral considerations are being taken into account in the reasoning reported in the literature. And it may be objected that our interpretation is simply another version of the motivational and conversational pragmatics hypotheses refuted by Knobe. But we think it is different to view people as bringing to their reasoning core content assumptions about human nature, assumptions that influence the presumed focus of conversations. The fact that people have expectations about others’ prosocial and antisocial proclivities does not in itself mean that reasoning about causality in the social realm is irrational, impervious to experience, or otherwise at odds with scientific theorizing.

“Stupid people deserve what they get”: The effects of personality assessment on judgments of intentional action doi:10.1017/S0140525X1000169X Berit Brogaard Department of Philosophy and Department of Psychology, University of Missouri –St. Louis, St. Louis, MO 63121-4400. [email protected] http://sites.google.com/site/brogaardb/

Abstract: Knobe argues that people’s judgments of the moral status of a side-effect of action influence their assessment of whether the side-effect is intentional. I tested this hypothesis using vignettes akin to Knobe’s but involving economically or eudaimonistically (wellness-related) negative side-effects. My results show that it is people’s sense of what agents deserve, and not the moral status of side-effects, that drives intuition.

In line with his empirically grounded theory that interpretations of other people’s minds do not follow scientific principles, Knobe hypothesizes that our judgments of the intentional nature of sideeffects depend on the side-effect’s assumed moral status. I conducted a study involving 150 participants which challenges this hypothesis (Brogaard 2010b). The participants were divided into four groups of 25, and the subjects in each group were randomly assigned a vignette featuring either an economically or

Commentary/Knobe: Person as scientist, person as moralist eudaimonistically negative side-effect or a positive side-effect1; plus I had two of these groups of 25 test the last vignette of the target article (hence totaling 150 subjects). Each of the first four groups received one of the following different vignettes: (1A) The famous stand-up comedian Rob’s personal assistant went to Rob and said, “We are thinking of changing your medication. It will help your popularity immensely by completely treating your stage fright, but it will also hurt you by causing morning headaches.” Rob answered, “I don’t care at all about having morning headaches. I just want to be as popular as possible. Let’s switch to the new medication.” Sure enough, Rob suffered from morning headaches. (1B) [. . .] “It will help your popularity immensely by completely treating your stage fright, and it will also help you by curing your morning headaches.” Rob was cured of his morning headaches. (2A) The famous stand-up comedian Rob’s personal assistant went to Rob and said, “We are thinking of hiring a new PR assistant. It will help your popularity immensely, but it will also harm your financial situation.” Rob answered, “I don’t care at all about my financial situation. I just want to be as popular as possible. Let’s hire the new PR assistant.” Sure enough, Rob’s financial situation was harmed. (2B) [. . .] “It will help your popularity immensely, and it will also help your financial situation.” Rob’s financial situation was helped.

Of the participants in the group that received vignette (1A), 84% judged that Rob intentionally harmed himself; 76% in the group receiving (1B) judged that Rob didn’t intentionally help himself; 88% of the group that got (2A) judged that Rob intentionally hurt his financial situation; and 76% of the group that got (2B) judged that Rob didn’t intentionally help his financial situation. In these vignettes, the side-effects have no direct bearing on morality. But the vignettes are akin to Knobe’s in describing an agent with undesirable personality traits. The agent is either greedy and self-centered (the chairman), or superficial (Rob). I found that these personality traits figured in participants’ answers to follow-up questions. When asked to “describe Rob’s personality traits,” 88% replied with one of the following words: “shallow,” “superficial,” “stupid,” “flaky,” “irresponsible,” or “careless.” Participants given the vignettes (1A) and (1B) were also asked whether Rob deserved to suffer from headaches or economically, given the decision he made. Here, 98% checked the options “yes” or “leaning towards ‘yes’.” When asked to justify their answers (“Rob deserves/does not deserve to suffer from headaches/economically because:”), 72% of the participants who replied “yes/ leaning towards ‘yes’ ” used descriptive terms such as “superficial,” “stupid,” and “irresponsible.” The results indicate that the driving force behind rendering the negative side-effects in (1A) and (2A) as intentional is a feeling that Rob deserves to suffer because of his undesirable personality traits. I hypothesize that whether a (moral or non-moral) negative outcome is considered intentional depends on whether the agent is believed to deserve the outcome or (moral or nonmoral) blame associated with it. If the agent is greedy, selfish, or superficial, he is thought to deserve the bad outcome or the blame. Consequently, the outcome is considered intentional. This hypothesis explains why the chairman in Knobe’s original cases is judged to have intentionally harmed the environment but not to have intentionally helped it. Because the chairman is considered greedy and selfish, he is thought to deserve potential blame associated with harming the environment. Accordingly, the outcome is considered intentional. To further test this hypothesis, I arbitrarily assigned one of two other vignettes, similar to Knobe’s in the target article, to 50 participants: (3A) The vice-president of a company went to the chairman of the board and said, “We are thinking of starting a new program. It will help the environment, and it will also hurt our profits.” The chairman of the board answered, “I don’t care at all about profits. I just want us to help the environment. Let’s start the new program.” They started the new program. Sure enough, the company’s profits decreased. (3B) [. . .] “It will help the environment, and it will help us increase profits.” . . . Sure enough, the company’s profits increased.

Here, 92% said the chairman in (3A) did not intentionally hurt the company, and 72% said the chairman in (3B) did not intentionally help the company. The first result confirms our hypothesis. The chairman in (3A) has desirable personality traits: He cares about the environment, not profits. So, the subjects infer that he does not deserve the potential blame associated with having intentionally brought about a decrease in profits, and hence, that he did not intentionally bring about the side-effect. The results in (3B) indicate that for an agent to intentionally bring about a positive side-effect, he or she must not only deserve the outcome or the potential praise associated with it, he or she must also aim at bringing it about. In conclusion: The results of my study are in agreement with Knobe’s suggestion that people’s judgments of side-effects do not rely on scientific methods, but the results disagree with Knobe concerning the underlying principles driving these judgments. Knobe (2006) proposes a model for how moral assessments affect judgments of intentional action. In his original cases, we are confronted with the side-effect, harmed environment. We determine that the side-effect is morally bad and that the chairman showed foresight. We then employ the principle “If the side-effect is morally bad, and the agent showed either trying or foresight, then the side-effect is intentional” and infer that the chairman intentionally harmed the environment and is to blame for his behavior (see my Fig. 1). My study suggests a different model for the attribution of intentionality. When we are confronted with a side-effect (e.g., harmed environment, harmed self, or harmed financial situation), we determine whether the side-effect is negative. We then assess the agent’s personality in order to determine whether he or she deserves the bad outcome or the potential blame associated with it. Finally, we employ the principle “If the side-effect is negative, and the agent showed trying or foresight, and he or she deserves the side-effect or the potential blame associated with it, then the side-effect is intentional” and infer that the agent intentionally harmed the environment, him/herself, or his/her financial situation and therefore is to blame for his or her behavior (see my Fig. 2). In a second IRB-approved2 project involving 1,500 participants, currently in progress (Brogaard 2010a), we seek to determine the correlation among positive side-effects, undesirable personality traits/good fortune, and intentionality. Initial results indicate that an agent’s bad personality traits, a history of undeserved success, or good fortune leads us to judge that the agent did not intentionally bring about the positive side-effect and hence does not deserve praise. In a pilot study preceding this larger project, participants were presented with vignettes featuring positive side-effects but differing in terms of whether the agent had good or bad personality traits or had a history of undeserved success or failure. Agents with bad personality traits or a history of undeserved success were judged not to have intentionally brought about the positive sideeffect, whereas the opposite was true for agents with good personality traits or a history of undeserved failure.

Figure 1 (Brogaard). Knobe’s model of the mechanisms of the side-effect asymmetry. The identification of a morally bad sideeffect triggers a selective search for features that are sufficient to judge the side-effect as being brought about intentionally. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Figure 2 (Brogaard). New model of the mechanisms of the side-effect asymmetry. The identification of a morally bad sideeffect triggers an assessment of personality traits, and the identification of undesirable personality traits triggers a search for features that are sufficient to judge the side-effect as being brought about intentionally.

NOTES 1. Overlapping material has been omitted. 2. This project was approved by the University of Missouri–St. Louis Institutional Review Board for the Protection of Human Subjects in Research on May 21, 2010.

The social origin and moral nature of human thinking doi:10.1017/S0140525X10001706 Jeremy I. M. Carpendale,a Stuart I. Hammond,a and Charlie Lewisb a

Department of Psychology, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada; bDepartment of Psychology, Fylde College, Lancaster University, Bailrigg, Lancaster LA1 4YF, United Kingdom. [email protected] [email protected] [email protected] http://www.psyc.sfu.ca/people/faculty.php?topic¼finf&id¼67 http://www.psych.lancs.ac.uk/people/CharlieLewis.html

Abstract: Knobe’s laudable conclusion that we make sense of our social world based on moral considerations requires a development account of human thought and a theoretical framework. We outline a view that such a moral framework must be rooted in social interaction.

According to Knobe, moral considerations are an integral part of the way we make sense of and reason about our social world. The problem is that Knobe requires an account of the nature of human thought explaining its moral nature, which, we argue, must be developmental (Carpendale & Lewis 2004). We take over where Knobe stops short of such a proposal, and sketch in an account of the development of thinking, showing how this is rooted in social interaction, which is moral in nature. We propose a socially based view of the evolution and development of thinking. From this perspective, human cognition involves “moral considerations” because it originates as a social process that is gradually mastered by individuals. This social process has moral preconditions. We make sense of our social world in moral terms because this is a fundamental aspect of our human form of life, involving coordinating our actions and interests with others. Knobe’s comparison of the person as scientist versus moralist constructs a straw man (Gellatly 1997), which does not explain how moral thinking is possible, let alone how “scientific” thinking and moral thinking fit together. For us, the problem is how the person as moralist could come into being. We draw on Mead’s (1934) account of the social origin of thinking and mind, according to which meaning arises interpersonally as persons come to

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realize the significance of their actions for others. Thus, meaning is necessarily social because it requires experiencing others’ attitudes to one’s actions. The view that reflective thought originates as a social process has many implications for the role of morality in thought. At the most basic level, social interaction involves moral preconditions of responsiveness, of give-and-take, and of turn-taking (Turnbull 2003). Although we often think of morality at a grand scale, in terms of life-and-death issues, morality is also embedded in various aspects of everyday interaction with others and the way we treat one another. There is a level of morality even at the level of interpersonal interaction; it is built into the foundations of what makes our interaction possible. This social process is based on responding to one another and it is therefore moral in its roots, because we treat each other as persons, not things. We respond to one another; not to do so is to be morally accountable. At another level, human forms of communication function through assuming cooperation because we infer meaning from what others say based on the assumption that they are cooperating with us and they want us to understand them (Grice 1975a). Another aspect of the moral preconditions of social interaction is Winch’s (1972) point that, “the social conditions of language and rationality must also carry with them certain fundamental moral conceptions,” and “a norm of truth-telling is a moral condition of language” (Winch 1972, pp. 60 –63, emphasis in original). Holiday (1988) also argued that the fabric of human communication is built on the assumption that we tell the truth. Of course, lying is possible, but it is only possible because truth-telling is the default expected pattern. We develop communication in parent-child interaction in relationships of trust. It is within such relationships that communication arises and this is mastered as a form of thought. Piaget’s seminal work focused on the origins of morality in children’s practical interaction with each other, and how young children instantiate reciprocity in their play (Piaget 1932/1965). Children then gradually become aware of this level of morality on which their interaction is based, and this becomes available for reflective thought; but, for Piaget, this moral understanding is rooted in earlier, practical understanding developed with social interaction. We have outlined how the social process, which is the cradle for human forms of reflective thought, has moral foundations. Moral considerations are part of the way we make sense of our social world because thinking is rooted in, and built on, the social process, which has moral preconditions. Knobe disregards a whole tradition according to which thinking is rooted in a system of socially embedded processes of which morality is an integral part. Drawing on this tradition would enable Knobe to dispense with the view of the person as a scientist and instead consider thinking as embedded in and emerging from social interaction, which has moral preconditions at a number of levels.

Moral evaluation shapes linguistic reports of others’ psychological states, not theory-ofmind judgments doi:10.1017/S0140525X10001718 Florian Cova,a Emmanuel Dupoux,b and Pierre Jacoba a

Institut Jean Nicod, Ecole Normale Supe´rieure, 75005 Paris, France; Laboratoire de Sciences Cognitives et Psycholinguistique, Ecole Normale Supe´rieure, 75005 Paris, France. [email protected] [email protected] [email protected] [email protected]

b

Commentary/Knobe: Person as scientist, person as moralist Abstract: We use psychological concepts (e.g., intention and desire) when we ascribe psychological states to others for purposes of describing, explaining, and predicting their actions. Does the evidence reported by Knobe show, as he thinks, that moral evaluation shapes our mastery of psychological concepts? We argue that the evidence so far shows instead that moral evaluation shapes the way we report, not the way we think about, others’ psychological states.

Knobe has shown that people are far more likely to judge that an agent intentionally caused (or had the desire to cause) a negative side-effect than a positive one (e.g., harming vs. helping the environment). In his target article, he argues that such asymmetries are good evidence for a “moralist” (as opposed to a “scientific”) picture, according to which the naı¨ve human capacity to ascribe psychological states to others for the purpose of describing, explaining, and predicting their actions presupposes the moral cognitive capacity to evaluate and judge others. He also offers an interesting semantics of psychological predicates such as intention, deciding, desiring. We think that neither Knobe’s evidence nor his semantic analysis supports the moralist picture. Knobe’s semantics for psychological predicates can be seen as an extension of the semantics of gradable predicates such as cold. Following Pettit and Knobe (2009), suppose a beer and a coffee are both at the temperature of 208C. Application of “cold” might plausibly yield a true statement in the coffee case and a false statement in the beer case. People rate each liquid relative to a default value that specifies what it is supposed to be like for it to be cold. In other words, the concepts respectively expressed by the words “coffee” and “beer” generate different standards of comparison for the application of the predicate “cold.” Similarly, the concepts expressed, respectively, by “harm” and “help” generate different comparison classes for the application of psychological predicates (e.g., “desire,” “intention”). The threshold generated by the concept harm is significantly lower than the threshold generated by the concept help. As a result, people are more likely to judge, for example, that the chairman had the desire to harm than the desire to help the environment. Now, the fact that the semantics of gradable predicates can be extended to psychological predicates is not convincing evidence for the moralist picture of naı¨ve psychology. Does the fact that the concepts harm and help generate different moral standards for the application of psychological predicates show that our understanding of psychological states itself is driven by moral evaluations? Consider the standards involved in the application of the quantifier many. Suppose that five children died in a fire and five children survived. The concepts expressed by “die” and “survive” generate different standards for the application of one and the same quantifier “many.” When asked, most people were inclined to accept that many children died, but to deny that many survived. But it would be odd to conclude, on this basis, that our mastery of numerical concepts expressed, for example, by the quantifier “many” (our numerical cognition) is shaped by moral evaluation. In fact, Knobe’s own semantics for psychological predicates is not consistent with the assumption that normative standards and moral evaluation directly shape our mastery of the relevant concepts of psychological states. On his account, a speaker’s assumption about an agent’s “pro-attitude” towards either a negative or a positive outcome will change the speaker’s willingness to apply a psychological predicate (e.g., "intentional") to the agent’s action. But if so, then the psychological concept of an agent’s pro-attitude must be retrieved and used by the speaker before moral considerations can come into play. We have started to address the empirical question whether moral evaluation shapes, not just our application of psychological predicates, but our very understanding of mental states themselves. Instead of testing the distinct conditions in which participants are willing to apply the verb “desire” for evaluating the chairman’s action, we asked them to use their psychological concept in order to predict the chairman’s decision. We designed such an experiment and ran the following study on 40

participants. After receiving either the HARM or HELP scenario, participants were then given the following text: Imagine that, before the program is started, the VP comes back to the chairman and tells him: “It appears that we have to choose between three programs. All three will generate the same amount of benefits: hundreds of millions of dollars. The difference is that program A will have no impact on the environment, while program B will harm the environment and program C will help the environment. Anyway, it will be impossible to prove our responsibility in anything that would happen to the environment. So, if we harm the environment, no one will know of our responsibility. But, if we help the environment, that won’t benefit our image. Starting program B or C will cost 10 dollars more than starting program A.” If the chairman had to make this choice, what program would he choose? A, B, or C?

In this case, if participants think that the chairman has the desire to harm the environment, then they should select answer B. If they think that he has the desire to help the environment, then they should select C. If they take the chairman to be indifferent to the environment, then they should select A. Now, if we assume that their inclination to apply the verb “desire” is a reliable guide to their prediction of the chairman’s choice, then we should make the following prediction: Reading the HARM scenario should cause participants to select B more than reading the HELP case should cause them to select C. Among the participants who received the HARM case, 90% answered A, 0% answered B, and 10% answered C. Among those who received the HELP case, 80% answered A, 10% answered B, and 10% answered C. Clearly, the participants’ predictions show that they do not think that the chairman’s desire to harm the environment, in the HARM case, is stronger than the chairman’s desire to help the environment, in the HELP case. Arguably, the moral standards triggered, respectively, by the concepts expressed by “harm” and “help” generate different comparison classes for the application of the verb “desire,” which might enable people to convey to others their moral opinion of the chairman. But even so, these moral standards did not affect participants’ use of the psychological concept desire in the prediction of the chairman’s choice.

Qualitative judgments, quantitative judgments, and norm-sensitivity doi:10.1017/S0140525X1000172X Paul Egre´ Ecole Normale Supe´rieure, De´partement d’Etudes Cognitives, Institut JeanNicod (ENS-EHESS-CNRS), 75005 Paris, France. [email protected] http://paulegre.free.fr

Abstract: Moral considerations and our normative expectations influence not only our judgments about intentional action or causation but also our judgments about exact probabilities and quantities. Whereas those cases support the competence theory proposed by Knobe in his paper, they remain compatible with a modular conception of the interaction between moral and nonmoral cognitive faculties in each of those domains.

Joshua Knobe makes three main claims in his paper. The first is that the influence of moral considerations on our judgments does not appear to be limited to the concept intentionally, nor even to closely related concepts such as intention and intending (sect. 3.2). Thus, it appears to affect our judgments about causation, knowledge, desire, and a number of other attitudes or processes. Knobe’s second main claim is that the asymmetry found by Knobe and colleagues in people’s judgments for such cases depends essentially on our normative evaluation with regard to counterfactual actions or situations; namely, on what should or could have been the case. Knobe’s third claim and fairly radical conclusion, finally, is that we cannot make “a clear division BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Knobe: Person as scientist, person as moralist whereby certain psychological processes are devoted to moral questions and others are devoted to purely scientific questions” (sect. 6, last para.). In this commentary I would like to add further evidence in support of Knobe’s first two claims, but express why I think we should be skeptical of the main conclusion he draws from them. In agreement with Knobe’s first claim, it may be pointed out that moral considerations influence at least two other general competences that would appear prima facie to be non-moral and that are not mentioned in Knobe’s paper; namely, our qualitative evaluation of precise numerical probabilities and our qualitative evaluation of precise quantities (Egre´ 2010). The evaluation of identical numerical probabilities is known to be subjectively influenced by how detrimental the outcome is perceived. The effect has been called the severity bias in the psychological literature (see Bonnefon & Villejoubert 2006; Pighin et al. 2009; Weber & Hilton 1990). For example, Pighin et al. (2009) ran an experiment comparing the evaluations made by four groups of pregnant women of a scenario in which a gynecologist tells Elisa, a 30-year-old pregnant woman, that “there is a risk of [1 in 28; 1 in 307] that your child will be affected by [Down’s syndrome; insomnia].” Subjects in each group were asked to rank the probability communicated for each disease on a 7-point scale ranging from “extremely low” to “extremely high.” What Pighin et al.’s study found was that when the numerical risk for the two conditions was made the same, the women still ranked the probability of the child getting Down’s syndrome as significantly higher than for insomnia. Even the probability of 1/307 for the child getting Down’s syndrome was ranked higher than the probability of 1/28 for insomnia. Moreover, subjects were asked to rank each disease according to how severe they judged it to be. Their assessments of probabilities were found to correlate with those severity judgments. Cova and Egre´ (2010) looked for the same effect regarding people’s qualitative evaluation of identical quantities in terms of the word many. Subjects were given a scenario reporting that a fire had broken out in a school in which there were 10 children, 5 of whom died in the fire and 5 managed to escape. Each subject had to judge true or false the two sentences: “Many children perished in the fire. Many children survived from the fire.” Irrespective of the order in which the sentences were presented, the vast majority of subjects agreed that many children had perished; but they did not agree that many children had survived, despite the identical quantities and ratios involved. Such cases comport with Knobe’s model and main explanatory hypothesis in his paper (see also Pettit & Knobe 2009); namely, they suggest that our subjective evaluation of probabilities or quantities, just like our evaluation of causation or intentional action, is sensitive not only to extensive magnitudes or processes, but also to normative expectations that are highly context-dependent and that vary with the kind of outcome under consideration. For example, it is known from the semantics literature that our judgments concerning whether many As are Bs are not purely extensional (see Fara 2000; Lappin 2000; Sapir 1944). That is, as the data with Cova confirm, those judgments do not merely depend on the cardinality of As and Bs and on the ratio of As to Bs; they intensionally depend on the kind of entities referred to by A and B, and on what is taken to be either normal or more desirable relative to context. In agreement with Knobe’s remark about the importance of counterfactual evaluations, presumably we judge that many children died because we reason that in a better and alternative course of events, fewer children would have died (and as a result, that more would have survived). Similarly, how high a probability value is considered for an outcome may depend on how much more probable or less probable we consider that outcome could be or should have been. It would be quite doubtful, however, to infer from those considerations that we cannot distinguish between the moral

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processes that influence our qualitative evaluation of quantities or probabilities and the non-moral processes that underlie our scientific judgments based on numerical quantities or probabilities. Indeed, when it comes to having a scientific attitude towards relative or absolute quantities, our evaluation can safely rely on our nonmoral capacity to compare extensional magnitudes. (Contrast “Did many children die?,” which calls on our subjective and moral evaluation, with “How many children died?,” which can be given an exact and objective answer.) More generally, I wish to make the qualification that whereas sensitivity to normative expectations is most likely directly encoded in the lexical semantics of most of our qualitative vocabulary (see Egre´ 2010; Kennedy 2007), including for vague concepts such as knowing, desiring, causing, and so on, this remains compatible with the hypothesis Knobe appears to reject in his article. That is, it is compatible with the view that our cognitive competence in each of those domains works in a modular way, based on the interaction of non-moral evaluative faculties and moral evaluative faculties. From Knobe’s interesting data and examples, it would be safer to conclude that our folk concepts of causation, knowledge, and desire are irreducibly norm-sensitive, without that impugning the division between moral and non-moral cognition. ACKNOWLEDGMENT Research supported by the Agence Nationale de la Recherche (grant ANR-07-JCJC-0070). Thanks to F. Cova, S. Pighin, D. Ripley, and P. Schlenker for exchanges related to this commentary.

Modalities of word usage in intentionality and causality doi:10.1017/S0140525X10001731 Herbert Gintis Santa Fe Institute and Central European University, Northampton, MA 01060. [email protected] http://people.umass.edu/gintis

Abstract: Moral judgments often affect scientific judgments in real-world contexts, but Knobe’s examples in the target article do not capture this phenomenon.

Moral considerations often affect reasoning about facts in the real world, clouding the judgments of both scientists and non-scientists. The elementary psychological processes that underlie this phenomenon are important to uncover. The experimental evidence presented in Knobe’s target article, however, does not illuminate these underlying judgments. Consider first the scenario in which a profit-maximizing individual A chooses an action that harms versus helps the environment, and a majority of subjects say the harm was intentional but the help was unintentional. Is there a disagreement concerning the facts among decision-makers? Almost certainly not. For instance, all subjects might agree with the assertion that A foresaw the effect of his decision on the environment and did not factor in this effect in deciding upon his action. All subjects must agree with this, in fact, because the description of the situation says precisely this. It follows that attributing intentionality in one case and not the other is not a judgment of fact, but rather a moral judgment. The experiment then shows that moral judgments affect other moral judgments, which is not a contested assertion. One might object that attribution of intentionality is a factual statement concerning an individual’s mental state, and sometimes indeed this is the case. For instance, we might conclude that after copulation, an insect may “intentionally” feed himself to his mate, or that the prey may “intentionally” reveal his

Commentary/Knobe: Person as scientist, person as moralist awareness of the predator to the predator. In such cases, we are saying that it is a normal part of the behavioral repertoire of the organism to engage in this act even when the organism has the capacity and the information to behave otherwise. But intentionality has a distinct second meaning that lies clearly in the moral realm. We say a undesirable result of an individual decision is “intentional” if the individual foresaw the result and could have prevented the result and achieved all other effects of the decision, except that doing so would have incurred additional personal cost. Intentionality has yet a distinct third meaning, also in the moral realm. We say a welcome result of an individual decision is “intentional” if the individual foresaw the result and acted to bring about the result at personal cost. When a subject says that the harm was “intentional,” it is most plausibly the second meaning that is being invoked. When a subject says that the help was “unintentional,” it is most plausibly the third meaning that is being invoked. We rule out the first meaning of “intentional” in these cases – because this meaning is strictly factual, whereas the context of the situation calls for a moral evaluation. The interpretation of this evidence is complicated by the fact that there are several other commonly used meanings of intentionally, one being “foresaw the result and acted in order to achieve this result.” In this sense, profit-maximizer A did not intentionally harm in the first scenario and did not intentionally help in the second. Very likely, many subjects chose to use this definition, despite the fact that it renders the choice completely trivial, as the statement of the problem includes non-intentionality overtly in the description of the situation. The Gricean analysis of meaningful communication is relevant here. According to Grice (1975b), in normal conversation, a listener assumes that when a speaker solicits information, the speaker expects the information to be useful to the speaker. Thus, if someone asks, “Is there a washroom on this floor?” acceptable answers include “Yes, down the hall on the right,” or “Yes, but it is out of order; there is a working washroom the next flight up,” or “You’ll have to go across the street.” A simple yes or no would be considered a somewhat bizarre answer. In the current case, some of the common usages of the word “intentionally” are explicitly assumed in the statement of the situation, so a Gricean subject can supply useful information only by referring to those usages of the terms that require some sort of substantive inference. These usages are the second and third ones defined above. Related problems of the multiple meaning of words beset Knobe’s causation analysis. Consider the scenario of the philosophy department receptionist and the taking of pens. The question as to whether the professor, the administrator, or both caused the problem is not a matter of fact. The facts are laid out quite clearly in the statement of the scenario, and would be agreed upon by all. The notion of “cause” in question is not that of Newtonian mechanics, but rather systems theory or product design. To see this, let us change the scenario a bit, to a machine that needs a certain level of motor oil to prevent seizure: Half the oil is devoted to a mechanism that burns 10% of its oil allotment each day, the lost oil being replenished at the start of each day. The other half of the oil is devoted to a mechanism designed to burn no oil at all. At the end of one day, the machine seizes up and it is determined that the first mechanism consumed its allotted 10% of oil, but the second mechanism consumed an additional 10% through a malfunction. If asked whether the first mechanism, the second mechanism, or both “caused” the failure, the correct answer is the second.

There is here, of course, no factual dispute and the inspectors are making no moral judgments in placing blame on the second mechanism. In general, when a complex mechanism fails, blame is placed on elements that failed their designed tasks, even if in some sense their behavior according to Newton’s laws was exactly the same as other elements that performed as designed.

ACKNOWLEDGMENT I would like to thank the European Science Foundation for financial support.

Morals, beliefs, and counterfactuals doi:10.1017/S0140525X10001743 Vittorio Girotto,a Luca Surian,b and Michael Siegalc a

Department of Arts and Design, University IUAV of Venice, 30123 Venice, Italy, and Laboratory of Cognitive Psychology, CNRS and University of Provence, 13003 Marseilles, France; bDepartment of Cognitive Sciences and Education, Center for Mind/Brain Sciences. University of Trento, 38068 Rovereto (TN), Italy; cDepartment of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, United Kingdom. [email protected] [email protected] [email protected] http://www.iuav.it/Ricerca1/Dipartimen/dADI/Docenti/girotto-vi/ index.htm http://portale.unitn.it/cimec/persone/luca.surian http://alacode.psico.units.it/index.html

Abstract: We have found that moral considerations interact with belief ascription in determining intentionality judgment. We attribute this finding to a differential availability of plausible counterfactual alternatives that undo the negative side-effect of an action. We conclude that Knobe’s thesis does not account for processes by which counterfactuals are generated and how these processes affect moral evaluations.

Ever since Aristotle’s Nicomachean Ethics, there has been debate over the extent to which there is separation between morality and cognition. We applaud Knobe’s modern effort to integrate the investigation of these areas. There are three main reasons, however, to doubt his thesis according to which moral evaluations affect the ordinary understanding of social and psychological phenomena “from the very beginning” (target article, sect. 5.3, para. 3). First, moral and non-moral evaluations of the social world do not always work together. In particular, in support of the position that Theory of Mind reasoning is not theory-like and does not proceed in terms of a process that can be characterized in terms of “child-as-scientist” (Leslie et al. 2004), preverbal infants appear to possess basic mind-reading skills (e.g., Surian et al. 2007). No evidence suggests that such acquisition depends on input from moral competencies. Moreover, children with selective impairments of mind-reading skills appear to have an intact ability to make some basic moral judgments (Blair 1996; Leslie et al. 2006b). Second, even when moral evaluations appear to shape ordinary intuitions about the social world, non-moral considerations are a necessary input to the shaping of these intuitions. We have found that both adults (Pellizzoni et al. 2010) and preschoolers (Pellizzoni et al. 2009) attribute intentionality to a negative side-effect produced by an agent who was not aware of it. By contrast, participants did not do this when the agent was described as having a false belief about the negative side-effect. When the side-effect was positive, participants judged that it had been produced unintentionally, regardless of whether the agent believed that it could occur or not. Thus, evaluative considerations interact with belief ascription in determining intentionality judgment. Third, counterfactual thinking affects moral evaluations, rather than vice versa. We have attributed the above-described results to a differential availability of plausible counterfactual alternatives that undo the negative side-effect. When individuals read about an agent who did not know that his action could produce a negative side-effect, they could easily think, “Had he made an inquiry, he would have discovered the side-effect and made a different choice.” Indeed, when readers undo the negative outcome of a story, they alter the protagonist’s choices (Girotto BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Knobe: Person as scientist, person as moralist et al. 1991). But when a misinformed agent had no reason to anticipate a negative side-effect, individuals could not easily imagine a plausible alternative (e.g., ”Had he imagined that he was misinformed, he would have made a different choice”). Counterfactual thinking seems to play an important role in Knobe’s thesis, too: Individuals attribute intentionality to a negative but not to a positive side-effect because they tend to construct alternatives that are morally right (“If the agent had chosen differently, he might have produced a positive sideeffect”) rather than morally wrong (“If the agent had chosen differently, he might have produced a negative side-effect”). The problem with Knobe’s interpretation that moral evaluations determine the selection of counterfactuals is that it does not explain how counterfactuals are generated or how counterfactuals affect other mental activities, including moral judgment. His interpretation neglects the finding that individuals do construct morally dubious alternatives. For example, they imagine breaking the constitutive rules of a game in order to undo a failure (e.g., Girotto et al. 2007). When applied to intentionality attribution, Knobe’s interpretation appears to confuse morality with normality. According to Knobe, individuals who read the positive side-effect story do not imagine the chairman damaging the environment because this possibility is morally wrong. We would say that they don’t do so simply because this possibility alters normal events, that is, the normal tendency of chairmen to seek to make profits (Kahneman & Miller 1986; Uttich & Lombrozo 2010). With regard to the effects of counterfactual thinking, Knobe’s interpretation neglects the finding that moral evaluations often depend on the availability of counterfactual alternatives. For example, individuals attribute more compensation to the victim of an accident (and more responsibility to the perpetrator) when it is preceded by exceptional rather than by normal circumstances (Macrae 1992). Finally, Knobe’s interpretation cannot easily explain our results: The possible alternatives evoked by the non-informed agent version (e.g., ”Had he made an inquiry. . .”) were not morally different from those evoked by the misinformed agent version (e.g., ”Had he imagined that he was misinformed. . .”). Yet, only in the first case did individuals attribute intentionality to the negative sideeffect (Pellizzoni et al. 2010). To investigate the relations between the moral and non-moral facets of naı¨ve psychology remains a high priority for future research. However, in this connection, it is not necessary to postulate that moral evaluations play a pervasive role in the ordinary understanding of intentional actions.

Questioning the influence of moral judgment doi:10.1017/S0140525X10001755 Steve Guglielmo Department of Psychology, Brown University, Providence, RI 02912. [email protected] http://research.clps.brown.edu/mbq/guglielmo/

Abstract: Moral judgment – even the type discussed by Knobe – necessarily relies on substantial information about an agent’s mental states, especially regarding beliefs and attitudes. Moreover, the effects described by Knobe can be attributed to norm violations in general, rather than moral concerns in particular. Consequently, Knobe’s account overstates the influence of moral judgment on assessments of mental states and causality.

Knobe’s “person as moralist” account provides a novel contribution to the study of human morality. Whereas most research in this domain has examined the features of behavior that guide moral judgment (Cushman 2008; Guglielmo et al. 2009; Shaver 1985) or the processes that underlie moral judgment (Greene 2008; Haidt 2001), Knobe’s target article extends the

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literature by probing the influence of morality on other psychological judgments. Despite its promise, however, Knobe’s account has several limitations. Knobe neither measures nor defines moral judgment, leaving it unclear precisely what the account posits and how it may be falsified. Nonetheless, any conceptualization of moral judgment consistent with Knobe’s account necessarily relies on substantial information about an agent’s mental states. Moreover, the results described by Knobe are likewise obtained by instances of entirely non-moral norm violations. Finally, Knobe should clarify why it would be the case that people’s moral judgments of badness and blame share no direct relationship. The crux of Knobe’s argument is that moral judgments of badness (hereafter “MJ1”) impact judgments about an agent’s mental states and causal role, which thereby impact moral judgments of blame (hereafter “MJ2”). Although MJ2 are often measured, studies of Knobe’s account rarely (if ever) measure MJ1. It is therefore critical to know the conditions under which such judgments arise. To this end, Knobe claims to examine “judgment[s] that the agent’s action itself is bad.” But this definition does not provide much clarity – if MJ1 are not simply judgments about bad outcomes (sect. 4.1.3, para. 4), are they judgments that an agent caused /knew about/ intended something bad? Absent either a measurement or definition of MJ1, it is unclear precisely what is alleged to influence mental state and causality assessments, and how one could attempt to falsify the account. In any case, Knobe’s account would be most compelling if MJ1 arise in the absence of any considerations of the agent’s mental states (which, after all, are proposed to be influenced by MJ1). However, this is clearly not the case. First, the agent’s knowledge is relevant to these moral judgments. For example, the harming chairman’s action is bad in part because he knew that harm would occur. When agents lack knowledge of the harmful consequences of their action, people no longer view the consequences as intentional (Nadelhoffer 2006b; Pellizzoni et al. 2010). According to Knobe’s account, therefore, such actions must not be bad. But if this is true, then MJ1 require consideration of an agent’s knowledge. An agent’s attitude is likewise relevant to moral judgment. The harming chairman’s action is bad in part because he displayed absolutely no concern for the environment. When an agent regrets or feels bad about a negative outcome, people are markedly less likely to say the action was intentional (Cushman & Mele 2008; Guglielmo & Malle, in press; Phelan & Sarkissian 2008). On Knobe’s account, therefore, such actions also must not be bad, suggesting that MJ1 require consideration of an agent’s attitude. Accordingly, MJ1 substantially depend on mental state information, particularly regarding beliefs (that the agent know about a negative outcome) and attitudes (that the agent not care about the outcome). These two elements are widely recognized as essential inputs to moral judgment (Cushman 2008; Darley & Shultz 1990; Guglielmo et al. 2009; Young & Saxe 2009). Even if one grants that morality impacts mental state judgments, this effect appears to be a special case of norm violation more generally. In fact, the same empirical patterns on which Knobe’s account is based are also found for cases of norm violations that have nothing whatsoever to do with morality (Machery 2008). For example, people judged an agent’s making of black toys to be more intentional when doing so violated, rather than conformed to, the conventional color designation (Uttich & Lombrozo 2010). People also judged it more intentional to violate a dress code than to conform to one (Guglielmo & Malle, in press). This is because norm violations – whether moral or not – provide diagnostic information about a person’s disposition, motives, intentions, and so on (Reeder & Brewer 1979; Skowronski & Carlston 1989). Interestingly, Knobe’s recent work adopts precisely this explanation, highlighting the impact of non-moral norms on causality judgments (Hitchcock & Knobe 2009). But this perspective suggests that people are not

Commentary/Knobe: Person as scientist, person as moralist “moralists” at all; rather, their judgments are sensitive to norms, just as those of a “scientist” would be. Setting aside the criticisms raised here, Knobe should clarify a puzzling aspect of his proposed account. Knobe distinguishes between early MJ1 (badness judgments) and later MJ2 (e.g., blame judgments). One might expect these judgments to be tightly linked, as they both assess the morality of a given action. However, the connection between them is argued to be fully mediated by non-moral assessments (e.g., regarding mental states and causality, see Figure 5 of the target article). Knobe’s account would benefit from a psychological explanation for the existence of such a circuitous path between the conceptually similar MJ1 and MJ2. Why might it be that two moral judgments have no direct relationship to each other? One possible answer to the puzzle is that MJ1 are not actually moral judgments, but simply judgments about whether an action violated an expectation. Such expectations are sometimes a function of valence – people expect others to bring about positive events and avoid negative ones (Pizarro et al. 2003). Perceivers may adopt different thresholds for what constitutes a relevant mental state or causal role, depending on the extent to which the action violates expectations. This possibility is largely consistent with Knobe’s discussion of default attitude positions (Figs. 8 and 9 of the target article), except that Knobe maintains the threshold is set by moral judgments in particular. Given the discussion here, it is not clear how this can be true. Although expectations (including, but not limited to, those concerning valence) may impact the evidential threshold set by perceivers, moral judgments depend on assessments of an agent’s mental states. Accordingly, the claim that such assessments are “suffused with moral considerations” (sect. 5.3, para. 3) is greatly overstated.

Person as lawyer: How having a guilty mind explains attributions of intentional agency doi:10.1017/S0140525X10001767 Frank Hindriks Faculty of Philosophy, University of Groningen, 9712 GL Groningen, The Netherlands. [email protected] http://www.rug.nl/staff/f.a.hindriks/index

Abstract: In criminal law, foresight betrays a guilty mind as much as intent does: both reveal that the agent is not properly motivated to avoid an illegal state of affairs. This commonality warrants our judgment that the state is brought about intentionally, even when unintended. In contrast to Knobe, I thus retain the idea that acting intentionally is acting with a certain frame of mind.

The experimental findings Knobe discusses suggest that normative considerations influence our judgments about non-normative issues. The core finding is this: When an individual brings about a harmful side-effect, foresees that he does so, but does not care about it, people nevertheless tend to judge that he does so intentionally. The key question is whether these judgments are correct, calling for a revision of prevailing analyses of intentional action, or whether no such revision is needed since the judgments are simply incorrect. The controversy surrounding this finding is marked by a conspicuous absence of the legal perspective (Malle & Nelson 2003 and Nadelhoffer 2006a are exceptions). In particular, it has gone unnoticed that the way intent and foresight are interpreted in law provides support for taking the attributions of intentional agency at face value. Intention and foresight in criminal law. In criminal law, it is common practice to classify cases of foresight as intent, even though the agent did not strictly intend to bring about the

relevant effect. Courts are “entitled to infer” intent from foresight (Ashworth 2006, p. 178). The underlying idea is that foresight betrays a guilty mind (mens rea) as much as intent does. This insight is often expressed by saying that the agent “obliquely” intends the effect. An agent obliquely intends an effect when she anticipates it as a consequence of her action, even though it does not contribute to the successful performance of that action (Duff 1996, p. 17). In practice, this notion of an oblique intention is used only in relation to harmful consequences. So an agent who is said to obliquely intend a consequence will be someone who lacks the proper motivation to avoid a harmful or illegal consequence of her action. My hypothesis is that this misalignment between what actually motivated her and what (she realized) should have motivated her warrants our judgment that she brought about the effect intentionally (see Hindriks 2008). Many foreseen consequences do not really concern us. I might realize that I increase the humidity in my bathroom when I take a shower, but normally I do not really care about this and I have no reason to do so. It would be odd to say that I increase the humidity “intentionally.” When a consequence of my action is harmful, however, I should be concerned about it. When a foreseen consequence should concern us, it makes much more sense to attribute intentional agency. Intentional action would then be broader than intent, but narrower than foresight. Duff argues that “the wider legal definitions of ‘intention’ try to capture this broader notion” (1990, p. 37). This suggests that legal practice supports the folk attributions of intentional agency. The mens rea explanation. On my hypothesis, intentional action is of special interest to lawyers or prosecutors. A misalignment between what actually motivated a defendant and what (he realized) should have motivated him bears directly on whether he acted intentionally, and thereby on whether he satisfies the mens rea requirement of the relevant criminal offense. In light of this, I call my account of the attributions of intentional agency that Knobe has investigated “the mens rea explanation.” This explanation shares with Knobe’s account the idea that moral considerations figure in the competences of people who attribute intentional agency. The way in which Kobe’s account differs from mine can be illuminated in terms of the distinction between conduct and fault, between actus reus and mens rea (interpreted broadly to cover both illegal and immoral acts). Knobe argues that the moral character or badness of the sideeffect influences judgments of intentional action. This is a matter of actus reus rather than mens rea. Knobe’s actus reus explanation has an important drawback. The moral character of a consequence that constitutes the actus reus is not something mental and does not concern the motivation of the agent. Thus, the actus reus explanation severs or significantly weakens the tie between intentional action and motivation. In particular, Knobe has to abandon the idea that acting intentionally is a matter of acting with a certain frame of mind. This is a core commitment in our understanding of intentional action (Bratman 1987; Setiya 2003; Velleman 1989). The mens rea explanation places the agent’s failure to be motivated appropriately at the center of the relevant attributions of intentional agency; it focuses on the agent’s ignoring of a normative reason that counts against his intended action. It thereby preserves the idea that acting intentionally is a matter of acting in a certain frame of mind. The mens rea explanation has at least two other virtues. Its second virtue is that it reveals why the notion of intentional action is so useful as input for judgments about criminal and moral responsibility: Culpability and blame require both (illegal or immoral) conduct and fault (mens rea), and the notion of intentional action serves to provide (defeasible) evidence for fault (there is no use for a notion broader than intent for beneficial consequences, because, in contrast to blame, praise requires intent; Stocker 1973, p. 60). Many have argued that BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Knobe: Person as scientist, person as moralist Knobe’s core finding undermines this traditional conception of intentional action and responsibility. The mens rea explanation shows that it actually supports it. The third virtue concerns the shifting standard that Knobe postulates with respect to which issues such as acting intentionally, deciding, and favoring are judged. By changing the focus from what is good or bad to what the agent has reason to do, the mens rea account makes better sense of why the default does not apply when legal or moral issues are concerned: People are held to a different standard with respect to what motivates them because (and in particular when they realize that) they have reason to behave differently. Standards shift when legal prosecution or moral criticism becomes pertinent.

Person as moral scientist

True enough, ordinary people as scientists are not equally attentive to all kinds of prior information. And when it comes to predicting the behaviour of others, there is no question that morally relevant information takes pride of place. In particular, as Cosmides and Tooby have shown, people tend to be on the alert for any evidence that another person has deliberately broken a social contract. Moreover, if and when people suspect this, they begin to think all the more rationally (see, e.g., Cosmides et al. 2010). Now, the evolved “cheater-detection mechanism,” which Cosmides and Tooby have identified, would certainly be activated by news about the chairman who does not pull his weight in protecting the environment. We might, therefore, expect subjects in the experiment to be thinking particularly clearly about intentionality, causation, and so on. No doubt the cheater-detection module plays a key role too when scientists review each other’s scientific work – which is why we all do it so well. (What’s that motto at Yale, where Knobe comes from? Lux et Veritas – “Light and Truth.”)

doi:10.1017/S0140525X10001779 Nicholas Humphrey London School of Economics (Emeritus Professor). Home address: 18 Bateman Street, Cambridge CB2 1NB, United Kingdom. [email protected] www.humphrey.org.uk

Abstract: Scientists are generally more moral, and moralists more scientific, than Knobe suggests. His own experiments show that people, rather than making unscientific judgements about the moral intentions of others, are behaving as good Bayesians who take account of prior knowledge.

Knobe’s home university must be a remarkable place if, as he suggests, scientists there “typically leave [moral] questions to one side” (sect. 6, para. 2). In the wider world, science is nothing if not a moral enterprise. At the very least, scientists make a public commitment to tell the truth, to respect the rules of argument, to make their arguments open to refutation, not to cheat, and so on. Think of a scientist who is engaged in peerreviewing a colleague’s work: He or she is probably using the “ethical circuits” in his or her brain in similar ways to a judge at a criminal trial. Contrary to the picture Knobe paints, I would say science is an approach to the world that could only have been developed by humans who were already constantly aware of right and wrong. Persons as scientists ought to be moral. But persons as moralists ought to be scientific, too. Knobe claims that his experimental studies show that when people are morally engaged, they begin to think “unscientifically.” Yet it can be argued, on the evidence of his own experiments, that the opposite is true. Let’s consider the chairman study. Subjects are asked to judge what the chairman’s intentions were. But, it is important to note that, since subjects have only limited access to the facts, the best they can do is to make an informed guess. What Knobe then finds is that they guess, on one hand, the chairman intended to harm the environment, but on the other, he did not intend to help it. So, either way, they guess the chairman’s intentions were reprehensible. But isn’t this exactly what we might expect if the subjects are rational guessers who have, as it happens, been given prior reason to believe that the chairman is a bad man? Knobe himself comes close to saying as much in the last paragraph of section 5.2 when he says that “before people even begin considering what actually happened [. . .] they make a judgement about what sort of attitude an agent could be expected to hold.” However, what he does not seem to realise is that this is a thoroughly scientific approach. Philosophers of science widely agree that the best procedure under conditions of uncertainty is to adopt a Bayesian algorithm and calculate the probabilities of a particular outcome based on prior knowledge (see the discussion in Pugliucci 2010).

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The cultural capital of the moralist and the scientist doi:10.1017/S0140525X10001780 Min Ju Kanga and Michael Glassmanb a

Department of Child and Family Studies, Yonsei University, Seoul 120-749, Korea; bDepartment of Human Development and Family Science, The Ohio State University, Columbus, OH 43210. [email protected] [email protected]

Abstract: In this commentary we explore Knobe’s ideas of moral judgments leading to moral intuitions in the context of the moral thought and moral action debate. We suggest that Knobe’s primary moral judgment and the setting of a continuum with a default point is in essence a form of cultural capital, different from moral action, which is more akin to social capital.

The idea that there is a difference between moral thought and moral action has bedeviled the study of moral development and decision making for years (Blasi 1980). At the core of the debate is the idea that individuals make very different decisions, and oftentimes show very different sensibilities, when they are judging others who have engaged in some type of transgression versus when they themselves are actively involved in an ambiguous social problem. Are these observed differences representative of some qualitative difference between moral thought and moral action, or is the difference the result of the same basic decision-making process adjusting to two very different situations and perspectives? (For example, it is a common theme in ethnography that actually being in the situation changes your perspective of the situation; Malinowski 1922.) In his target article, Knobe never really addresses a possible division between moral thought and moral action in any overt way, relying primarily on judgment/decision-making scenarios describing the actions of a social agent to make his case that generalized judgments precede and serve as context for moral intuitions. What Knobe adds to the equation in his complex analysis of moral competencies is the idea that our intuitions concerning the intentions of an agent (and therefore the possible moral culpability of the agent) are deeply affected by primary, dynamic judgments of the generalized situation/dilemma faced by the agent – what Knobe refers to as “moral considerations.” We feel Knobe’s thesis makes sense but leaves open two critical questions related to the moral thought/moral action dilemma. The first is: Where do these initial moral judgments that serve as context for intuition and further decision-making come from? Establishing an initial, complex moral judgment as the originating point of moral intuitions and decision-making in a sense

Commentary/Knobe: Person as scientist, person as moralist begs the question of what is driving moral decision processes. The second question we are left with is: What, if any, role would this continuum play in the moral actions of the individual? Do we use the same type of default system when we are making socially ambiguous decisions that might directly affect us and/or those around us? (For example, are there pre-judged lines that we will not cross?) We have made the argument (Kang & Glassman 2010) that moral thought, including the type of moral judgments Knobe describes, is actually a form of what Bourdieu (1986) refers to as cultural capital, while moral action is a form of social capital (Portes 1998). The motivation and goal (Glassman 1996) of cultural capital is to signal to those around you that you are a member in good standing of the social group. It is a short-hand for the types of social interactions that allow individuals to establish affiliation through community standards. Moral judgments are one of the easiest forms of cultural capital to use to establish group membership, whether it is gossip around a community pool or the establishment of a common enemy, villain, and/or scapegoat. We suggest that the primary moral judgment that Knobe describes is made in the service of cultural capital, and that it is more about signaling and establishing membership in a given community than “about controllability, about recency, about statistical frequency” (sect. 5.1, para. 5). The default position of what is acceptable for the businessman in Knobe’s scenario example would change dramatically based on whether you were trying to signal membership and affiliation in the Chamber of Commerce or in the Sierra Club. The setting of a continuum and establishing of a default is a form of cultural capital, and if we are on target in our thesis (Kang & Glassman 2010), it would all but disappear when individuals are engaged in collective moral action. In moral action, individuals are less concerned with establishing a signal/symbol system for long-term group maintenance and belonging than in coming together as a group to solve a critical problem. In moral action, the focus is almost completely on the problem at hand, rather than on who should be included (and excluded) from the working group. The action is integrated with the specifics of the problem to be solved, and as the common problem dissipates, so, too, does the motivation behind the group (Putnam 2001). We see two reasons why there is little to be gained by using the primary moral judgments of generalized situations in moral action. The first is that group membership is malleable in problem solving, and placement in the group is dependent on abilities. The second reason is that problems are dynamic and shifting, and individuals who are taking action might have to continuously abandon or change their default point based on circumstances. To take a crude example, a person with a specific default position on sharing of community resources might take a very different view if he or she is placed in charge of such resources. (For example, how would the individuals in Knobe’s academic example change if they found themselves being denied access to pens when they needed them? Or if their salary were dependent on maintaining a supply of pens?) We take the real-world example of the recent British Petroleum (BP) oil spill to illustrate our point, similar to Knobe’s businessman who does not care about risks to the environment. Suppose, before the spill occurred, people were asked about the intentions of the president of BP if he said the company could engage in deep water drilling without harming the environment: There would be a wide array of responses, directly based on the primary judgments Knobe discussed, but judgments used to signal community belonging. If you asked an officer in an environmental group, he or she might have set the default point for acceptable action so that the greater part of the continuum led to intuitions of morally bad intentions (e.g., being willing to drill at all, or not actively investing in sustainable energy). If you asked a politician from the Gulf Region, he or she might have set a default position with far more of the continuum

devoted to a neutral position (e.g., drilling could occur as long as there were minimal safety precautions) in order to signal kinship with the oil-dependent community. After the spill, many members of the two groups have acted together in attempting to stop the spill and reclaim the Gulf. Intuitions about good or bad intentions and the moral judgments that led to them have become secondary or even irrelevant for many working in this group, and it is considered bad form to bring them up. Ties have been established based on the need to solve the immediate problem. Once the problem has diminished, or retreats into the background, the social group will dissipate and moral judgments as cultural capital will move to the fore again. It represents a cycle of moral thought as cultural capital and moral action as social capital.

Are mental states assessed relative to what most people “should” or “would” think? Prescriptive and descriptive components of expected attitudes doi:10.1017/S0140525X10001792 Tamar A. Kreps and Benoıˆt Monin Graduate School of Business, Stanford University, Stanford, CA 94305. [email protected] [email protected] http://www.stanford.edu/people/monin

Abstract: For Knobe, observers evaluate mental states by comparing agents’ statements with “defaults,” the attitudes they are expected to hold. In our analysis, Knobe’s model relies primarily on what agents should think, and little on expectancies of what they would think. We show the importance and complexity of including descriptive and prescriptive norms if one is to take expectancies seriously.

If you claimed at a dinner party to have no opinion about child abuse, you would get funny looks. In Knobe’s analysis, because you should strongly oppose abuse, neutrality is tantamount to support. Similarly, expressing neutrality about women’s suffrage, which our society supports, would appear sexist. Thus, observers do not take agents’ claims at face value, but instead assess them relative to what Knobe calls a “default.” Observers essentially convert an agent’s claim to their own metric, much like converting Celsius to Fahrenheit, based on the object of judgment (e.g., helping vs. hurting the environment) and the associated “default” attitude (see our Fig. 1, Panel A). This “default,” defined in the target article as “what sort of attitude an agent could be expected to hold toward” an object (sect. 5.2, last para.), and elsewhere (Pettit & Knobe 2009) as what any reasonable person “would” (p. 597) or “should” (p. 598) think, is thus a central part of Knobe’s model. In this commentary, we aim to analyze and clarify this concept, which we believe is more complex than Knobe lets on. There is much to be gained from such analysis, especially from distinguishing the should and would aspects of default expectations. What influences people’s expectations about how others behave and think? Certainly, one factor, as Knobe points out, is personal moral judgment: we expect people to behave in (what we ourselves believe is) a moral fashion. However, two other social factors seem at least as important as personal moral judgment in determining defaults: prescriptive norms (how we think the group believes people should act) and descriptive norms (how we think group members actually act, regardless of how they should). Personal moral judgments do not always correspond to group prescriptive norms, and the default expectation often depends on the latter, as when an agnostic, hearing an American presidential candidate publicly espousing agnosticism, sees this as a forceful anti-religion stance given American norms, even if it accords with his own views. Similarly, a BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Figure 1 (Kreps & Monin). Converting expressed attitudes into inferred attitudes by reference to a default (Panel A) or to known pressure (Panel B). default based on descriptive norms explains why, even if I know that I (personal moral judgment) and my colleagues (prescriptive norm) believe it is better to take public transportation than to drive to work, my assessment of a colleague who drives (and whether that means she “supports” public transportation) still depends on whether I know my colleagues generally drive or not. These examples illustrate that we evaluate other people’s choices not just relative to the default of our own personal values (how they should act), but also relative to what we can reasonably expect from others given our knowledge of the world (how they would act). Knobe privileges the should aspect: For example, one version of the pen/professor study (sect. 3.4) pits moral judgment against descriptive norms, and the relative importance of moral judgment is taken to support the model. Although Knobe’s issues (the environment; reasonable rules about pens) are fairly prescriptively consensual, perceived prescriptive norms could be divorced from personal moral judgment, in which case Knobe would still favor the latter. Imagine I feel strongly that eating meat is immoral, while realizing my view is the minority one. Knobe would say that I think others who express indifference are really in favor, because my should default is strong opposition, even though I would not reasonably expect a random stranger to share my view (would default).

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While Knobe may be right that should factors matter in many circumstances, other evidence suggests the importance of would factors in evaluating attitudes. For example, people use prescriptive norms to infer situational pressure and correct expectations accordingly. Observers assuming strong pressures against expressing support for harming the environment can sensibly infer a suppressed pro-harm attitude behind an expression of indifference (Fig. 1, panel B). Similarly, a speaker advocating immediate action by a corporation to reduce pollution is perceived as more anti-environment when speaking to a pro-environment audience, where such a message is expected, than to a pro-business audience (Eagly et al. 1978). Here, assumed audience pressure changes the default, although participants’ personal moral judgment presumably remains constant. Ironically, this is exactly the “augmentation” process described in Kelley’s (1971) attribution theory, which Knobe dismisses as a wrongheaded “person-asscientist” theory. Another example where would matters is the impact of intergroup perceptions. A devout Catholic claiming no particular opinion on Roe v. Wade might seem more in favor than a staunch feminist making the same claim. Biernat (2005) showed that expectations associated with different groups lead to such contrast effects. (Intriguingly, Biernat’s research also suggests an assimilation effect with more objective measures – the

Commentary/Knobe: Person as scientist, person as moralist Catholic would still seem less likely to get an abortion – suggesting that Knobe’s might have found a different pattern using objective outcome measures.) Thus, group-specific descriptive norms evoked by agents’ identities influence the default. The value of distinguishing should and would influences on defaults is further suggested by research indicating possible interactions between them. For example, personal moral judgments affect perceived norms: Research on naı¨ve realism and social projection (e.g., Ross & Ward 1996) shows that individuals generally believe their own judgments are rational, objective, and ethically appropriate, and therefore overestimate the similarity of others’ attitudes. Also, norms can influence personal judgment: People’s desire to fit in can lead them to change their own judgment to conform to perceived norms (e.g., Asch 1956). Further, descriptive norms are sometimes inferred from prescriptive norms, and vice versa (Prentice & Miller 1996). In summary, we believe Knobe’s model makes a valuable addition to our understanding of defaults and social judgments, but it seems to be unreasonably limited to factors based on “should”; for a fuller understanding of what determines people’s default expectations, the model could be enriched by including other factors based on “would,” such as group descriptive and prescriptive norms. Including these factors – which often have little to do with morality – might dilute the model’s focus on how moral considerations suffuse social judgment, but such a change seems warranted given the important role of non-moral factors in determining default expectations. We hope future research will extend Knobe’s model to include such factors.

Understanding the adult moralist requires first understanding the child scientist doi:10.1017/10.1017/S0140525X10002037 Tamar Kushnir and Nadia Chernyak Department of Human Development, Cornell University, Ithaca, NY 14853. [email protected] [email protected] http://www.human.cornell.edu/che/bio.cfm?netid¼tk397

Abstract: Children learn from people and about people simultaneously; that is, children consider evidentiary qualities of human actions which cross traditional domain boundaries. We propose that Knobe’s moral asymmetries are a natural consequence of this learning process: the way “child scientists” gather evidence for causation, intention, and morality through early social experiences.

Knobe’s “person as moralist” view contests two related claims about human cognition: that it is clustered by discipline, much as university departments are, and that cognition in two “scientific” disciplines – folk psychology and causal inference – is analogous to scientific inquiry. Knobe then presents evidence that the psychology of intention and causation are “suffused with moral considerations” (sect. 5.3, para. 3), by which he means to show that there is neither a separation between disciplines, nor can reasoning about scientific topics be considered “scientific.” We suggest another perspective on these moral asymmetries: that they are, at least in part, the consequences of early links between causal learning and social learning. Specifically, they are the result of how, as children, we gather evidence for such learning by observing and interacting with people. The adult moralist recruits knowledge gained from years of social evidence gathering – years spent learning from people and about people simultaneously. Therefore, to understand the adult moralist we must first understand her predecessor – the child scientist. For a long time, developmental psychologists studied children’s knowledge separately, according to domain. Some research examined early causal reasoning – intuitions about spatio-temporal relations (Leslie & Keeble 1987; Oakes &

Cohen 1990), causal mechanisms (Bullock et al. 1982; Schulz 1982), and the use of statistical cues in causal judgments (Gopnik et al. 2001; Sobel & Kirkham 2006). Other research focused on children’s “mind-reading” abilities – what they knew about the intentions, desires, beliefs, and knowledge states underlying human actions (e.g., Lutz & Keil 2002; Repacholi & Gopnik 1997; Wellman 1990; Woodward 1998). Others sought to understand children’s knowledge of social categories (Bigler & Liben 2007; Heyman & Gelman 2000), and still others focused on developing moral and conventional knowledge (e.g., Turiel 1983). The picture that emerged from these separate subfields is a lot like the mental university described by Knobe – separate departments for separate knowledge structures. The domain-specific approach has lead to important discoveries about the content of early physical, biological, psychological, and social and moral knowledge. However, trying to apply this approach wholesale to learning processes has been less fruitful. Take causal learning: spatio-temporal cues and mechanism knowledge are useful, but are often unavailable. Statistical cues are also useful, but cannot help distinguish between causes and spurious correlations. Most often, ordinary causal learning depends on social interaction; evidence comes from doing things and watching others do things. Human actions are a child scientist’s natural causal experiments (Gopnik et al. 2004; Schulz et al. 2007). Importantly, along with physical evidence (e.g., toys making noise, milk spilling, sticks breaking), causal actions contain valuable social evidence (a knowing glance at the right button, a cry of “oops!”, a desire for two short sticks). To evaluate the quality of causal evidence, children take knowledge, ability, and intention into account. For example, infants and preschoolers distinguish intentional actions from accidental ones, and this leads them to make different causal inferences (Carpenter et al. 1998; Meltzoff 1995). Preschoolers prefer to learn new causal relations from knowledgeable rather than ignorant causal agents (Kushnir et al. 2007). Children also treat causal evidence differently when a demonstrator is explicitly teaching them (Bonawitz et al. 2009; Rhodes et al., in press). This evidentiary link is not limited to passive observations – it influences and interacts with the evidence children generate themselves through play. Thus, when children get ambiguous evidence from another person, they privilege evidence from their own past actions (Kushnir et al. 2009), or are motivated to explore further to generate new evidence (Schulz & Bonawitz 2007). Other research suggests that children break traditional domain boundaries when learning about people, as well. For example, infants use contingency detection (Shimizu & Johnson 2004) or violations of contiguity (Saxe et al. 2007; Spelke et al. 1995) to infer the presence of a psychological agent when other cues to agency are absent. Toddlers and preschoolers infer other people’s preferences based on violations of random sampling, not merely positive regard and enthusiasm (Kushnir et al. 2010). Children may use statistical cues to track other individual regularities, such as personality traits (Siever et al, under review). They also readily track social regularities, such as norms and group characteristics (Kalish 2002; Rhodes & Gelman 2008). From her earliest social experiences, the child scientist is engaged in a dynamic process of hypotheses formation, evidence-gathering, and theory change. The adult moralist, on the other hand, is asked to reason about a single instance of human behavior. The adult must therefore rely on her existing knowledge – knowledge acquired through this early learning process. We now have a better sense of where this knowledge begins; recent studies show early understandings of empathy, fairness, help, harm, and a host of moral precursors (e.g., Hamlin et al. 2007). Knobe’s analysis encourages us not to stop with domainspecific characterizations of knowledge. Instead, we should broaden how we view evidence from human actions to include their moral and normative dimensions, and investigate how these early evidential links give rise to later moral asymmetries BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Knobe: Person as scientist, person as moralist in reasoning. This approach leads to interesting questions for research with adults, so long as we carefully distinguish between reasoning based on existing knowledge and the process of learning something new. When adults learn, for example, how do moral asymmetries change in response to further evidence? Is the evidence itself evaluated asymmetrically? To conclude, while it may be wise at times to abandon the separation of disciplines, it seems premature to draw conclusions from Knobe’s experimental data about the process by which they are integrated. To better understand this process, we need to look at learning at all ages, and continue research connecting moral development to both causal learning and social cognition.

Scientists and the folk have the same concepts doi:10.1017/S0140525X10001809 Neil Levy Florey Neuroscience Institutes, Carlton South 3053, Australia; and Oxford Centre for Neuroethics, Oxford, OX1 1PT, United Kingdom. [email protected]

Abstract: If Knobe is right that ordinary judgments are normatively suffused, how do scientists free themselves from these influences? I suggest that because science is distributed and externalized, its claims can be manipulated in ways that allow normative influences to be hived off. This allows scientists to deploy concepts which are not normatively suffused. I suggest that there are good reasons to identify these normatively neutral concepts with the folk concepts.

Joshua Knobe has added considerably to our knowledge of the ways in which ordinary people attribute intentions and make judgments regarding causation. In this commentary, I do not want to criticize his claim that the competencies agents deploy in making these judgments are deeply suffused with normative influences. However, I will suggest that there are nevertheless grounds for regarding these competencies as distorting influences on our concepts. Our perfectly ordinary concept of causation (for instance), I suggest, is not normatively suffused. This is best brought out by thinking about science; I therefore begin with Knobe’s claim that we ought not to understand folk judgments on the analogy of scientific hypothesis testing. Knobe’s claim that folk judgments are made in ways very unlike scientific hypothesis testing leaves us with a puzzle: Given that scientists are ordinary people too, how do they manage to engage in scientific research? If the relevant competencies are suffused with normative influences, how do scientists manage to free themselves of these influences (sufficiently well that they can identify them in the first place)? This question is important for several reasons, including, that if we can identify the means whereby scientists succeed in separating normative influences from the relevant judgments, we might all be in a position to make better normative judgments. At least on standard normative theories, our normative claims ought to follow from, rather than themselves cause, judgments of causation and intention; hence, separating out the normative from the non-normative might be a precondition of justified normative judgment. So how do scientists manage to transcend the normative influences Knobe identifies? The answer is multifaceted, but an important part of it refers to the structure of the scientific enterprise. Science is an essentially distributed enterprise. The structure of a scientific community enables its members to compensate for the limitations and biases of individuals (Kitcher 1993). Individual biases can thereby be cancelled out; one scientist’s bias toward a hypothesis will be cancelled out by another’s against it. Of course, this cancellation process is powerless against the kind of normative influences Knobe identifies, as they are universal.

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But the structure of science has a second property: it externalizes scientific knowledge. Since science, by virtue of its essentially distributed nature, requires that data and theories be available to a multiplicity of researchers, they must be presented in a format that makes this possible, and that requires externalization. Once theories and data are externalized in this way, they become available for manipulation using formal techniques, and these techniques are designed to be impervious to the normative influences Knobe identifies. They can also be manipulated through the use of methods such as double blinding, which can also serve to filter out normative influences. One implication of the forgoing is that the finding that ordinary people are not best understood on the model of scientists is unsurprising: no one is a scientist alone. An agent can be a scientist only as part of a community of researchers engaged in systematic inquiry. The contrast between scientific judgments and folk judgments is therefore misplaced: The contrast is not between different modes of thinking so much as between different ways of manipulating mental representations; one individualistic and the other deeply social. An important implication is that there are grounds for seeing the competencies that agents utilize in making judgments as distortions of their concepts. We do not wish to say that scientists are mistaken in making causal judgments that are not normatively suffused. We therefore should not see the concept of causation as constituted by the structure of the competencies Knobe has elegantly uncovered. Scientists are members of the folk, and their onboard competencies are identical to everyone else’s, yet they understand their causal judgments, qua scientists, as deploying the ordinary concept of causation, not a theoretical innovation. I suspect that given the choice between the concept of causation used in science and one that is explicitly normative, ordinary people would also choose the former, providing further evidence that scientists use the ordinary concept. In saying this, I take issue neither with Knobe’s arguments in favor of the view that our competencies are themselves normatively suffused, nor with his correlative claim that the rival view (according to which moral judgments bias our application of our concepts) is false. I am accepting that normative influences figure into the relevant competencies, but I am claiming that nevertheless we need to distinguish between these competences and the relevant concepts, even though we probably derive the concept from the competency (via some process of idealization). The concept of causation is normatively neutral, even though ordinary people deploy the concept using competencies that are normatively suffused. It may be that we can dissociate the normatively neutral concept from the normatively suffused competencies only by externalizing and distributing our application of our concepts. We can hope to deploy our concepts better by becoming more like scientists. Doing so does not involve changing our onboard competencies – that may be a task that is beyond us – but instead requires that we alter the context in which we deploy them. By dividing and distributing cognitive labor, and by designing institutions that filter out the normative influences, we may become better reasoners, both in the normative and the nonnormativerealms.

Putting normativity in its proper place doi:10.1017/S0140525X10001810 Tania Lombrozo and Kevin Uttich Department of Psychology, University of California –Berkeley, Berkeley, CA 94720. [email protected] [email protected] http://cognition.berkeley.edu/

Commentary/Knobe: Person as scientist, person as moralist Abstract: Knobe considers two explanations for the influence of moral considerations on “non-moral” cognitive systems: the “person as moralist” position, and the “person as [biased] scientist” position. We suggest that this dichotomy conflates questions at computational and algorithmic levels, and suggest that distinguishing the issues at these levels reveals a third, viable option, which we call the “rational scientist” position.

In this elegant and provocative article, Knobe summarizes a growing body of work suggesting that moral considerations influence a range of “non-moral” judgments, from mental state ascriptions to causal ratings. Knobe offers two interpretations for these data: (1) his preferred view of people as “moralists,” and (2) the traditional position of people as intuitive “scientists,” albeit poor ones subject to moral biases. We unpack these options using Marr’s levels of analysis, and suggest at least one viable alternative, which we call the “rational scientist” position. In Knobe’s “person as moralist” position, “moral considerations actually figure in the competencies people use to make sense of human beings and their actions” (sect. 1, para. 7, emphasis added). In contrast, the “person as scientist” position claims that the “fundamental” capacities underlying these judgments are analogous to processes in scientific inquiry (sect. 2.2, para. 2). Both positions, as laid out by Knobe, involve a distinction between the “fundamental” or “primary” aspects of a cognitive system and those that are “secondary.” Knobe suggests that to account for the data, the scientist approach must claim that moral considerations play a secondary role, biasing judgments that are fundamentally scientific. Examining these positions in terms of Marr’s levels of analysis (Marr 1982) reveals two different questions at play: one at the computational level, about the function of the cognitive system in question, and one at the algorithmic level, about the representations and processes that carry out that computation. For an advocate of the moralist position, the computational-level description of a cognitive system appeals to a “moralizing” function (perhaps evaluating people and their actions), and the algorithmic level is merely doing its job. For an advocate of the “biased” scientist position that Knobe considers, the computational-level description appeals to a scientific function (perhaps predicting and explaining people’s actions), but the algorithmic level is buggy, with moral considerations biasing judgments. This leaves two additional options (see Table 1). First is the “biased moralist” position, with a “moralizing” function at the computational level, but a buggy algorithm. Without a fuller computational-level analysis that provides a normative account of the

Table 1 (Lombrozo & Uttich). Four possible positions to account for the data Knobe cites demonstrating an influence of moral considerations on non-moral judgments, such as mental state ascriptions and causal ratings. The positions are expressed in terms of Marr’s levels of analysis, with one of two computational level functions, and algorithms that generate the judgments they do either as a result of their computational level functions (nonbuggy) or because they are biased by other (e.g., moral) considerations (buggy). Four positions to account for the data Knobe cites Algorithm

Buggy Non-buggy

Computational Level Function Scientific Moralizing Biased Biased Scientist Moralist Rational Moralist Scientist

judgments the algorithmic level should generate, this position is hard to distinguish from the “non-biased” moralist. Second is the “rational scientist” position, which we advocate for some cognitive systems (Uttich & Lombrozo 2010). According to this position, a given cognitive system has a scientific function at the computational level, and the algorithm is just doing its job. To account for the slew of data Knobe cites, an advocate for this position must explain how moral considerations can influence judgments without threatening claims about the system’s function (at the computational level) or the efficacy of the processes that carry out that function (at the algorithmic level). In a recent paper (Uttich & Lombrozo 2010), we attempt precisely this for ascriptions of intentional action. The cognitive system in question, broadly speaking, is theory of mind: the capacity to ascribe mental states to others. Traditionally, this capacity has been conceptualized as analogous to a scientific theory, with the function of predicting, explaining, and controlling behavior. At the computational level, this puts the traditional picture in the “scientific” camp. But what are the implications for the role of moral considerations in carrying out this function? Knobe seems to assume that moral considerations have no legitimate role in this picture. But we argue the reverse: that accurately inferring mental states can in fact require sensitivity to moral considerations, particularly whether a behavior conforms to or violates moral norms. Here, in brief, is our argument. Norms – moral or conventional – provide reasons to act in accordance with those norms. For example, a norm to tip cab drivers provides a reason to do so. Observing someone conform to this norm is relatively uninformative: We can typically infer knowledge of the norm, but not necessarily a personal desire to provide additional payment. In contrast, norm-violating behavior can be quite informative, particularly when other mental-state information is lacking. If we believe a person knows the norm, then observing that person fail to tip a driver suggests an underlying preference, desire, or constraint that is strong enough to outweigh the reason to conform. This same logic applies to Knobe’s chairman vignettes (sect. 3.1). When the side effect of the chairman’s actions helps the environment, he is conforming to a norm, and the action is relatively uninformative about his underlying mental states. When he proceeds with a plan that causes environmental harm, the action is norm violating, and allows us to infer underlying mental states that support an ascription of intentional action. Our aim here is not to elaborate and marshal evidence for this position; we direct interested readers to Uttich and Lombrozo (2010). Rather, we hope to populate the space of possible positions and call attention to what seem to be distinct computational- and algorithmic-level assumptions lurking in the background of Knobe’s target article. Knobe argues against various versions of the “biased scientist” position, but does not consider the “rational scientist” position. Like the two “moralist” positions, the biased and the rational scientist positions can be difficult to distinguish, and require a more fully specified computational-level description with a corresponding normative theory to identify which judgments stem from buggy versus non-buggy algorithms. Knobe infuses normativity into folk considerations, painting a picture of people as moralists. But distinguishing the four positions we identify (Table 1) may actually require appeals to normativity in the generation and evaluation of empirically testable theoretical claims. In other words, we must appeal to normativity as theorists, regardless of whether or how we do so as folk. We suspect that Knobe avoids this framing as a side effect of other commitments and a preference for process-level theorizing. Whether or not it was intentional, we think it is a mistake to collapse computational and algorithmic questions. We hope future debate can restore normative questions to their proper place in scientific theorizing, whether the folk are ultimately judged scientists or moralists. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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doi:10.1017/S0140525X10001822

multiple studies attempting to measure possible non-moral effects on judgments of intention. In one such study, we used the following vignettes1:

Eric Mandelbauma and David Ripleyb

Normal case:

Expectations and morality: A dilemma

a

Faculty of Philosophy, The Future of Humanity Institute, University of Oxford, Oxford, OX1 1PT, United Kingdom; bInstitut Jean Nicod, DEC-ENS, 75005 Paris, France. [email protected] [email protected] http://www.fhi.ox.ac.uk/our_staff/research/eric_mandelbaum http://sites.google.com/site/davewripley

Abstract: We propose Knobe’s explanation of his cases encounters a dilemma: Either his explanation works and, counterintuitively, morality is not at the heart of these effects; or morality is at the heart of the effects and Knobe’s explanation does not succeed. This dilemma is then used to temper the use of the Knobe paradigm for discovering moral norms.

Knobe presents two kinds of theories that compete with his own: motivational bias theories and conversational pragmatic theories. He presents his own theory as a competence account. While we agree with his criticisms of the other accounts, we think his taxonomy is incomplete. We would like to suggest a different form of competence account, one that does not take morality as such to play a crucial role in these effects. (In this regard, we agree with Phelan and Sarkissian [2008], Machery [2008], and indeed even Knobe and Mendlow [2004].) On our account, the effects of morality are a piece of a larger puzzle: Morality affects judgments of intentionality and related concepts only in virtue of its effects on expectations. Consequently, we think that anything affecting expectations will produce effects similar to those produced by moral norms. In fact, Knobe’s own account points to a similar conclusion, although he doesn’t acknowledge this. According to Knobe’s competence theory, people’s moral norms influence their default expectations of others’ intentions, beliefs, values, causal roles, and so on, and these default expectations in turn affect participants’ judgments. Thus, he concludes, morality plays a deep role in explaining judgments in these various domains. But in this explanation, expectations are doing all the work; moral expectations have their effects only because they are expectations, not because they are moral. Thus, if Knobe’s theory is right, we should find effects similar to the effects cited here in cases that have nothing to do with morality, but instead involve participants’ non-moral expectations in parallel ways. And if this is right, it suggests that there is nothing specifically moral going on in the cases Knobe cites. These effects are, rather, effects of expectation, and expectations can be affected by both moral and non-moral factors (e.g., we expect people to have con-attitudes towards losing a game, although losing a game is not, normally at least, moral in any way). Consequently, we think Knobe encounters a dilemma: Either his explanation of the effects he cites is correct, and then there is nothing especially moral at play here, but only an effect of expectations in general; or else his explanation of the effects is incorrect (in which case there may still be room for morality to play a distinctive role). Either way, Knobe finds himself in an awkward position; it doesn’t seem that his explanation of morality’s effects is compatible with the conclusion that moral considerations as such figure in our folk-psychological competence. But we do not merely mean to present the dilemma. We take sides. We think Knobe’s explanation is substantially correct, and that the effects Knobe finds would follow from any expectations participants hold firmly enough, whether or not those expectations have a moral character. To see whether this is indeed the case, it is not enough to look at cases that involve moral factors. Similar cases involving non-moral norms must be constructed and tested. As a step in this direction, we have conducted some preliminary studies involving variations on the CEO cases that involve non-moral norms. These studies were conducted using participants on Amazon.com’s Mechanical Turk website. We ran

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Two people are playing chess. One of them considers moving her queen to square A6. She thinks, “If I move my queen to square A6, I will capture my opponent’s rook. But I don’t care at all if I capture my opponent’s rook; I think moving my queen to square A6 will allow me to checkmate in three moves.” She moves her queen to square A6. Sure enough, she captures her opponent’s rook.

Did she “intentionally” capture her opponent’s rook? Abnormal case: The abnormal case was identical, except that the player allowed her own rook to be captured, instead of capturing her opponent’s rook. We expected that participants reading the abnormal case would be more likely to judge that the sideeffect of the player’s move was intentional, when compared to participants reading the normal case. After all, it is normal to want to capture an opponent’s rook, and normal to want one’s own rook to remain uncaptured. Although our results almost invariably trend in the expected direction, none actually reaches significance. (The closest result to significance arose from the vignettes given above; here, x2(1, N ¼ 124) ¼ 3.03, p ¼ .08.) For comparison, we also reproduced the original CEO cases using Mechanical Turk participants. Here, the results were highly significant: x2(1, N ¼ 33) ¼ 14.73, p , .001. One possibility is that moral norms have a stronger effect on participants’ expectations than do non-moral norms (or at least the non-moral norms we tested). Another possibility is that Knobe’s explanation, which depends entirely on expectations, needs revision. Of course, either way, more systematic research is needed. Our main point: One cannot only examine moral norms when judging whether Knobe’s data show an effect of morality. We must look at non-moral norms as well, to find just how broad the phenomenon is. In fact, Knobe has, in the past, thought similar things. In Knobe and Mendlow (2004), the authors propose that the kind of badness that affects intentional action judgments extends beyond just moral badness. They propose this in light of studies that seem to show similar effects involving clearly non-moral factors. These theoretical possibilities matter for further work involving this effect. If indeed the effects Knobe finds are not specific to moral norms, then we must be careful not to interpret the effects as telling us about participants’ moral norms. For example, Inbar et al. (2009) use participants’ judgments of intentionality as a way to measure implicit moral norms. This is risky; although judgments of intentionality might tell us something about participants’ expectations in general, they cannot tell us which of those expectations are particularly moral and which are not. Use of intentionality judgments to measure implicit moral norms thus runs the risk of seeing moral norms where there are none. NOTE 1. We thank Jesse Prinz, whose suggestion inspired these cases.

Norms, causes, and alternative possibilities doi:10.1017/S0140525X10001834 Peter Menzies Department of Philosophy, Faculty of Arts, Macquarie University, North Ryde, NSW 2109, Australia. [email protected] http://www.phil.mq.edu.au/staff/menzies.htm

Commentary/Knobe: Person as scientist, person as moralist Abstract: I agree with Knobe’s claim in his “Person as Scientist, Person as Moralist” article that moral considerations are integral to the workings of people’s competence in making causal judgments. However, I disagree with the particular explanation he gives of the way in which moral considerations influence causal judgments. I critically scrutinize his explanation and outline a better one.

Knobe’s general explanation of the way in which moral considerations influence intuitive judgments goes like this: In judging causation, doing/allowing, intentional action, and so on, people select alternative possibilities to compare with what actually happens and their selection of these possibilities is influenced by their moral judgments. How does this idea explain the data about people’s causal judgments? Unfortunately, Knobe offers only the briefest hint in his Note 5, which suggests that moral considerations affect people’s causal judgments by influencing which counterfactuals of the form “If event c had not occurred, event e would not have occurred” they regard as true. This suggested explanation doesn’t work, however, for his own example in which Professor Smith’s action rather than the administrative assistant’s is regarded as the cause of a problem. This difference is not reflected in any difference in the counterfactuals people regard as true, since it is true that there wouldn’t have been a problem if either Professor Smith or the administrative assistant hadn’t taken a pen. Luckily, Hitchcock and Knobe (2009) provide the missing elements of the explanation. Hitchcock and Knobe appeal to the finding in the literature on counterfactual availability that people are very inclined to entertain counterfactual hypotheses about what would have happened if a normal event had occurred instead of an abnormal one; and, by contrast, they are much less inclined to entertain counterfactual hypotheses in which normal events are replaced by abnormal ones. So people are willing to entertain the counterfactual about what would have happened if Professor Smith hadn’t taken a pen because it “mutates” an abnormal event into a normal event. By contrast, people are less willing to entertain the corresponding counterfactual about the administrative assistant’s action because it does not involve the privileged kind of “mutation.” Finally, by positing that people’s willingness to make a causal judgment “c caused e” goes hand-in-hand with their willingness to entertain the counterfactual “If c had not occurred, e would not have occurred,” they explain why people are more inclined to regard Professor Smith as the cause of the problem. I suspect this explanation cannot be right for two reasons. The first is that the explanation involves an uneconomical hypothesis about the capacities involved in causal cognition. The explanation implies that people have an underlying competence for understanding counterfactuals that is linked to their understanding the objective core of the causal concept (the “causal structure” in Hitchcock & Knobe 2009). This competence is exercised when people understand counterfactuals of all kinds, including the counterfactuals about Professor Smith and the administrative assistant. Sitting alongside this competence, the explanation implies, is a psychological tendency to entertain some counterfactuals as “available,” a tendency aligned to people’s propensity to select certain events as salient causes. This hypothesis strikes me as implausible because of its doubling up of capacities involved in causal cognition. My second reason for suspecting that this explanation can’t be correct is that empirical evidence casts doubt on the assumption that people’s causal judgments depend on their counterfactual judgments. Mandel and Lehman (1996), Mandel (2003), and Byrne (2005) cite experimental data that show that people’s causal judgments “c caused e” are dissociated from their counterfactual judgments “If c had not occurred, e would not have occurred”: the former go with judgments about sufficient conditions and productive mechanisms, whereas the latter go with judgments about enabling conditions and preventative mechanisms. There is another way of developing Knobe’s general idea that moral considerations influence people’s causal judgments by way

of their selection of alternative possibilities. In their classic work, Hart and Honore´ (1985) argue that the concept of actual causation originates in the situation in which a human action intervenes in the normal course of events and makes a difference in the way these develop. “The notion, that a cause is essentially something which interferes with or intervenes in the course of events which would normally take place, is central to the common-sense concept of a cause” (Hart & Honore´ 1985, p. 29). They argue that our judgments about what constitutes the normal course of events are guided context-sensitively – sometimes by what usually happens, and sometimes by social, moral, and legal norms. Their account readily explains why we regard Professor Smith’s action rather than the administrative assistant’s as the cause of the problem: for his action makes a difference to what happens normally – that is, in conformity with the prevailing norms – in a way that the administrative assistant’s does not. Hart and Honore´’s account of the way our causal judgments are shaped by moral considerations is better than Hitchcock and Knobe’s for several reasons: (1) Hart and Honore´’s account captures in a seamless fashion the idea that causes are difference-makers for their effects. In contrast, it isn’t clear how Hitchcock and Knobe’s account captures this idea. Is it through the link with counterfactuals or through the rules about counterfactual availability? (2) Hart and Honore´’s account doesn’t tie people’s causal judgments so closely with their counterfactual judgments, which is a virtue given the empirical evidence dissociating them. If it makes a link with counterfactuals, it is with counterfactuals that are based not on the actual world but on “normalised” worlds that abstract from the abnormal features of the actual world (Menzies 2007). (3) Hart and Honore´’s account provides a more uniform account of the contrastive structure of actual causation. Many philosophers have observed that causal judgments have an implicit contrastive structure: the causal judgment “c caused e” has the implicit contrastive structure “c rather than c  caused e rather than e  .” People typically select as the contrast elements c  and e  events that would normally have occurred if the abnormal actual events c and e had not occurred (Menzies 2009). This follows straightforwardly from Hart and Honore´’s account, which incorporates the contrastive structure into the semantic content of causal judgments. If Hitchcock and Knobe’s account is to explain the contrastive character of causal judgments, it must do so through appealing to pragmatic or non-semantic rules about counterfactual availability.

Neither moralists, nor scientists: We are counterfactually reasoning animals doi:10.1017/S0140525X10001846 Bence Nanay Department of Philosophy, University of Antwerp, 2000 Antwerp, Belgium; and University of Cambridge, Cambridge CB2 1RD, United Kingdom. [email protected] http://webh01.ua.ac.be/bence.nanay [email protected]

Abstract: We are neither scientists nor moralists. Our mental capacities (such as attributing intentionality) are neither akin to the scientist’s exact reasoning, nor are they “suffused through and through with moral considerations” (Knobe’s target article, sect. 2.2, last para.). They are more similar to all those simple capacities that humans and animals are equally capable of, but with enhanced sensitivity to counterfactual situations: of what could have been.

Knobe presents us with a false dilemma on the level of the metaphors he uses: maybe we are neither scientists nor moralists. But he also presents us with a false dilemma when it comes to the two explanatory schemes he considers: The first one is that the BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Knobe: Person as scientist, person as moralist competences that underlie our mental capacities (to attribute intentionality or to spot causal relevance) are influenced by moral considerations. The second is that these competences are themselves non-moral, but there is some additional factor that makes it the case that our attribution of intentionality is influenced by moral considerations. I will focus on the attribution of intentionality that Knobe considers to be the strongest case in favor of his claims. The two options Knobe offers are not exhaustive. In fact, they share a premise that we have good reasons to doubt: that is, the premise that the attribution of intentionality is influenced by moral considerations. Knobe’s reason for holding this claim is that in two very similar scenarios, the “harm” and the “help” scenarios (Knobe 2003a; see also sect. 3.1 of the target article) that differ only in their moral overtones, our attribution of intentionality also differs. As he says, “the only major difference between the two vignettes lies in the moral status of the chairman’s behavior” (sect. 3.1, para. 2). But that is definitely not the only major difference (see Nanay [2010] for an overview). One striking feature of the experiments Knobe and his collaborators conducted on this topic is that they all share the same structure. To put it very simply, in one scenario, the agent has two reasons for performing a certain action and ignores one of these; in the other, the agent has a reason for and a reason against performing an action and ignores the reason against. Thus, in Knobe’s most famous helping/harming experiment (Knobe 2003a; see also target article, sect. 3.1), we have the following two scenarios: (a) In the harm case, the chairman has a reason (R1) for introducing the plan (i.e., to increase profit) and a reason (R2) against (i.e., to avoid harming the environment). (b) In the help case, in contrast, the chairman has two different reasons to introduce the plan: he had a reason to increase the company’s profit (R1) and he also had a reason to help the environment (R3).

In short, the difference between scenario (a) and scenario (b) is that in (a) the chairman has R1 for and R2 against introducing the plan, whereas in (b) he has R1 and R3 both in favor of performing this action. Importantly, the chairman chooses to ignore the environmental considerations: R2 and R3, respectively. This leaves R1 in both scenarios, which is a reason for introducing the plan. There is no difference between (a) and (b) in the actual reason the chairman is acting on. But there is a modal difference between (a) and (b): a difference in what would happen if the chairman did not ignore R2 and R3, respectively. Contrast the original scenarios (a) and (b) with another pair of cases where the chairman chooses not to ignore the environmental considerations: (a ) The chairman chooses not to ignore R2 (i.e., a reason against introducing the plan). Then his action would, or at least it could, be different, as now he has a reason for (R1) and a reason against (R2) introducing the plan. (b ) The chairman chooses not to ignore R3 (i.e., a reason for introducing the plan). His action would still be the same, as now he has two reasons (R1 and R3) in favor of introducing the plan.

So an important difference between case (a) and case (b) is a modal one: The outcome would be different if the chairman didn’t ignore the environmental considerations. In (b), ignoring that the plan helps the environment would make no difference, as there are two independent reasons in favor of introducing the plan: The chairman’s actions in (b) and in (b ) will be the same. In (a), on the other hand, ignoring that the plan harms the environment would make (or at least it could make) a difference: The chairman’s actions in (a) and in (a ) will be (or at least can be) different. Thus, what this experiment shows is that in (b), introducing the new scheme does not depend counterfactually on ignoring the environmental considerations, whereas in (a), there is counterfactual dependence between ignoring the environmental considerations and introducing the new scheme. This counterfactual dependence in (a) is not very strong, as not

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ignoring will not guarantee that the chairman’s action will be different, but it is an instance of counterfactual dependence nonetheless. In (b), we have no counterfactual dependence, weak or strong. What I have said so far shows that the experimental data Knobe uses can be explained with the help of an alternative hypothesis, where the attribution of intentionality does not depend on our moral judgments. In other words, we have two ways of explaining Knobe’s original experiments: one appeals to moral judgments, the other one does not. The fact that my explanatory scheme is consistent with Knobe’s findings in itself casts doubt on his conclusion. But we can say something even stronger. My explanatory scheme is in fact preferable to Knobe’s for two reasons. First, my explanatory scheme is more robust than Knobe’s: it can explain cases of the attribution of intentionality that Knobe’s cannot. There are several scenarios where we get differences in the attribution of intentionality without any moral difference (Machery 2008; Mallon 2008; Nanay 2010; Nichols & Ulatowski 2007; maybe even Knobe 2007). As these cases all follow the modal asymmetry I identified, I can account for them (Nanay 2010). Knobe cannot. Second, those of us with naturalist leanings prefer to explain our complex mental capacities in simple terms. When explaining the mental capacity of attributing intentionality to others, the (broadly) naturalistic way to proceed would be to account for this mental capacity with reference to simple mental processes. This is exactly my strategy: If we can explain the attribution of intentionality with reference to mental capacities that nonhuman animals also possess, plus some further ability to be sensitive to counterfactual situations (which at least some nonhuman primates may also possess; see Suddendorf & Whiten 2001), we should not rely on any further, uniquely human higher-order phenomena, such as morality.

Ambiguity of “intention” doi:10.1017/S0140525X10001858 Thomas M. Scanlon Department of Philosophy, Harvard University, Cambridge, MA 02138. [email protected]

Abstract: Knobe reports that subjects’ judgments of whether an agent did something intentionally vary depending on whether the outcome in question was seen by them as good or as bad. He concludes that subjects’ moral views affect their judgments about intentional action. This conclusion appears to follow only if different meanings of “intention” are overlooked.

Knobe describes a number of studies in which, he claims, subjects’ moral judgments influence their views about whether the actions of others were intentional, about whether an agent did something or merely allowed it to happen, and about whether an agent caused an undesirable consequence. He concludes that the exercise of competencies that humans use in making what might seem to be purely factual judgments about the world – such as judgments about causes and judgments about other agents’ mental states – is “suffused with moral considerations from the very beginning” (sect. 5.3, para. 3). Knobe suggests, very plausibly, that people’s judgments about “the cause” of an event depend on a selection of relevant alternatives. His experimental evidence supports the conclusion that in some cases moral considerations partly determine this selection, although it remains an open question how wide this range of cases is. The same may well be true of judgments distinguishing between “doing” and “allowing.” In this comment, however, I will focus on Knobe’s claims regarding judgments about intentional action. Here his

Commentary/Knobe: Person as scientist, person as moralist conclusions seem to me not to be supported by the evidence he describes, because there is an alternative interpretation of his experimental results that is more plausible than the one he proposes. The use of intentional and its cognates involves a well-known ambiguity (see Anscombe 1958, p. 9; Scanlon 2008, p. 10). One sense of “intentional” is the one opposed to “unintentional.” An agent does something “intentionally” in this sense if he or she realizes that this is what he or she is doing – call an action that is intentional in this sense belief-intentional. An agent’s intention in the other sense is what he or she aims at in so acting. What an agent does “intentionally” in this other sense is opposed to what he or she sees as a mere side-effect of so acting – call what is intentional in this sense aim-intentional. The effects on the environment of the policies adopted by the chairmen in the two experiments Knobe describes are beliefintentional: the description of the cases makes clear that they are aware that the policies they choose will have these effects. But these effects are not aim-intentional: the descriptions make clear that the chairmen are indifferent to these effects, and are concerned only with profits. Given that these facts are made clear in the presentation of the scenarios, it is reasonable to believe that the subjects in each case have the same beliefs about the chairman’s mental state: that the bringing about of these effects is belief-intentional but not aim-intentional. The differing answers that the subjects give to the question of whether the chairmen harmed or helped the environment “intentionally” is indeed due to moral considerations, but not in the way that Knobe suggests. The important moral fact here is that agents are commonly open to moral criticism for bringing about bad effects when they know that these effects will occur even if they do not aim at these effects – that is to say, when they do so belief-intentionally, even if not aim-intentionally. But agents are generally held to merit moral praise or credit for bringing about good consequences only if they do so aim-intentionally. Given that the subjects see harm to the environment as a bad thing, when they are asked whether the chairman in the first scenario harmed the environment intentionally, what they are likely to ask themselves is whether the chairman’s action was intentional in the sense relevant to moral criticism for bringing about such an effect (that is to say, whether it was belief-intentional). In the other case, since the subjects are likely to view helping the environment as a good thing, when they are asked whether the chairman helped the environment intentionally, what they are likely to ask themselves is whether what the chairman did was intentional in the sense relevant to moral praise or credit (that is to say, whether it was aim-intentional). What the shift from harming to helping does is not to change the subjects’ interpretation of the chairman’s mental states in the respective scenarios, but rather, to change the question about those mental states to one that seems to the subjects to be relevant. This interpretation of the subjects’ responses seems to me extremely plausible. It is also supported by some of the further details that Knobe mentions. For example, he reports that when subjects are asked whether “the chairman intended to harm the environment,” answers are moved strongly in a negative direction (sect. 3.2, para. 4). This is to be expected on the interpretation I propose, because the verb intend suggests (aim) intention more strongly than does the adverb intentionally. My interpretation also explains why subjects disagree with the claim that an agent was “in favor of” a morally good outcome but are neutral on the question of whether the agent was “in favor of” a morally bad outcome (sect. 3.2, para. 5). This is because the agent fails to favor the morally good outcome in the way relevant to moral praise or credit; but, even if he or she does not actively favor the morally bad outcome, an agent who is perfectly willing to bring about that outcome for some other reason is more favorably disposed toward it than he or she should be, and therefore open to some criticism on this score.

Alternatives and defaults: Knobe’s two explanations of how moral judgments influence intuitions about intentionality and causation doi:10.1017/S0140525X1000186X Walter Sinnott-Armstrong Philosophy Department and Kenan Institute for Ethics, Duke University, Durham, NC 27708. [email protected] http://kenan.ethics.duke.edu/people/faculty/walter-sinnottarmstrong/

Abstract: Knobe cites both relevant alternatives and defaults on a continuum to explain how moral judgments influence intuitions about certain apparently non-moral notions. I ask (1) how these two accounts are related, (2) whether they exclude or supplement supposedly competing theories, and (3) how to get positive evidence that people consider relevant alternatives when applying such notions.

Joshua Knobe’s novel theory of how moral judgments influence people’s intuitions about certain apparently non-moral notions, including intentionality and causation, is a version of contrastivism (see Sinnott-Armstrong 2008). As with many other topics (including knowledge, free will, explanation, and morality), it is illuminating to consider the range of possible contrasts or alternatives and ask when and why people limit their attention to a smaller contrast class. So I am very sympathetic. I would, however, like to press Knobe to develop three aspects of his theory. First, Knobe formulates his “general approach” in his section 5.1 in terms of relevant alternatives. Moral judgments are said to affect which counterfactual alternatives are seen or treated as relevant. Next, Knobe discusses his “case study” in his section 5.2 in terms of defaults on a continuum. Moral judgments are said to affect the position of the default. These views are not equivalent, because alternatives need not always fall on a continuum, and relevant alternatives might fall on either side of a default. Knobe describes the default as “a particular sort of alternative possibility,” but it does not seem to be the only relevant alternative, so comparing a default and comparing a range of alternative possibilities seem quite different. My question for Knobe is then: What exactly is the relation between these two theories? Second, it is also not clear what the relation is between either of Knobe’s suggestions and the views against which he has argued in the earlier parts of his article. Motivation, blame, emotion, and pragmatic context would seem to be promising candidates for explaining why we treat certain alternatives rather than others as relevant, or why we place the default at one point instead of another on a continuum. If Knobe agrees, then his own theory, though a crucial part of the story, would need to be supplemented by central aspects of the views he criticizes. His theory then works together with his supposed opponents, rather than supplanting them. But if Knobe denies that these features explain why we adopt certain relevant alternatives and defaults, then we need an alternative explanation of relevance and default. It is not enough to refer to alternatives and defaults without explaining how the alternatives and defaults get set. Addressing this issue, Knobe says, “all sorts of different factors can play a role here” (sect. 5.1, para. 5). This is surely right, and he cites supporting literature. However, it leaves open the possibility that motivation, blame, emotions, and pragmatic context do sometimes play roles in determining which alternatives we see as relevant and where we place the default. No theory that focuses on one single factor can or should be expected to cover all examples, even if each factor does explain some variance in some areas. Hence, I also want to ask Knobe whether his arguments are supposed to show that motivation, blame, emotions,

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Commentary/Knobe: Person as scientist, person as moralist and pragmatic context do not always play a role or do not ever play a role in determining relevant alternatives and defaults. Third, after he criticizes his opponents for failing “to produce any positive evidence in favor of the hypothesis” (sect. 4.1.4, para. 1), I would like to see Knobe’s positive evidence in favor of his own hypotheses. Consider first his claim about relevant alternatives, and focus on his example of what caused the dent in the car. He suggests that, when we ask whether a certain person, object, or event caused a certain effect, we “think about,” “consider,” “compare,” and “pick out just certain specific alternatives” (sect. 5.1, para. 4). As Knobe says, we do not “consider the possibility that the car could have been levitating in the air.” But what is the positive evidence that we do consider other alternatives? I do not deny that we treat a range of alternatives as relevant. However, it is not clear whether we actually represent these alternatives, even unconsciously. Another possibility is that we have a disposition to dismiss certain alternatives as irrelevant, if raised, and to accept other alternatives as relevant, if raised; but we never explicitly “think about” or “consider” the relevant alternatives any more than the irrelevant alternatives unless prompted. How can we decide between these views? Perhaps we could get evidence that subjects consider or think about certain alternatives by asking the subjects, but self-report would not be reliable. Another method would be to measure subjects’ memory errors, word completion patterns, or reaction times when asked whether certain words were in the scenarios. If subjects really do consider an alternative that would naturally be formulated in certain terms that were not actually in the scenario, then we would expect them to be more likely to misremember those terms as being in the scenario, to complete letter strings so as to form those terms, and to unscramble the letters from those terms more quickly than if they never considered that alternative. However, before we can apply these techniques, we would need to formulate specific hypotheses about which alternatives are and are not thought about or considered in which scenarios. It is not enough simply to say that moral judgments affect the range of alternatives that are taken to be relevant. We need to know which alternatives are supposed to be seen as relevant. Only then can we test whether those alternatives are actually considered or thought about, as Knobe claims. The same basic issue arises for Knobe’s theory that moral judgments affect which point on a continuum is seen as the default. Certain hypotheses might seem plausible and might have explanatory power, but it is not easy to figure out how to gather positive evidence for the hypothesis that people actually set different defaults depending on their moral judgments. So my last question for Knobe is: How will you get positive evidence for your claims that moral judgments affect relevant alternatives and defaults?

“Very like a whale”: Analogies about the mind need salient similarity to convey information doi:10.1017/S0140525X10001871 David Spurrett and Jeffrey Martin School of Philosophy and Ethics, University of KwaZulu-Natal, Durban 4041, South Africa. [email protected] [email protected] http://ukzn.academia.edu/DavidSpurrett http://ukzn.academia.edu/JeffMartin

Abstract: Knobe relies on unhelpful analogies in stating his main thesis about the mind. It isn’t clear what saying the mind works, or doesn’t work, “like a modern university” or “a scientific investigation” means. We suggest he should say that some think that human cognition respects a ban on fallacies of relevance, where considerations actually irrelevant to truth are taken as evidence. His research shows that no such ban is respected.

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We have no dispute with Knobe’s description of the ways in which various accounts – including motivational bias and conversational pragmatics – of the experimental results he describes don’t succeed, and how the competency approach he favours currently does better. This said, it remains unclear precisely what Knobe’s position is, because his exposition depends on analogies that are both underdeveloped and problematic. Consequently, the answers to two questions are not sufficiently clear. The first question is: What specific commitment regarding human cognition is being rejected? The second question is: What specific claim about human cognition is being defended? Analogical reasoning transfers information from one object to another, non-identical one. For this to work well, the two objects need to have enough salient features or relationships in common. Disanalogies between paradigmatic features of the two objects impede the transfer. Niels Bohr, for example, explained his rejection of previous models of the atom partly by drawing an analogy with the solar system. Despite important differences between atoms and planetary systems, this was a good way of getting at a few key and, at the time, radical ideas: Atoms are mostly empty; very small parts of them are in approximately orbital activity around other central ones. Knobe offers two analogies for the view he is ostensibly rejecting. According to the first, the human mind works “something like a modern university” (sect. 1, para. 2). According to the second, which is an analogy within the first, some mental processes use the “same sorts of methods we find in university science departments” (sect. 1, para. 2). Except for relatively cryptic remarks on the ways disciplines are supposedly separated in universities, and some (also brief) remarks on science, Knobe develops neither analogy in significant detail. What might it mean for the mind to be like a university? Knobe suggests that the organisation of a university corresponds to a set of distinctions between types of questions, so that the mind has something analogous to theology, art, philosophy, and some scientific departments. He goes on to argue that the mind is not like this. But the administrative organisation of universities into departments exists along with a patchwork of overlapping techniques, theories, problems, and collaborative research programmes cutting across departmental divisions. The analogy also doesn’t do the work Knobe requires because some departments, such as those of history and politics, consider both factual and moral questions, just as art departments consider factual and aesthetic ones (not merely “is this painting good?” but also “is it genuine?”). Philosophy departments notoriously consider almost anything – these days they even do experiments. The fact that the overall organisation of universities is not consistently or strictly modular need not be a big problem, since most of the heavy lifting is done by the second analogy, suggesting a view (the one to be rejected) where some mental processes use the same methods as scientists do. Unfortunately, though, there are no agreed upon set of criteria that separate science from non-science, partly because there is no clear division between the methods of “science” and those of other enterprises. Philosophers of science have argued for generations without converging on consensus about what, if anything, demarcates science from pseudo-science and non-science. That this is so is reason to recognise that “like science” is not a promising explanatory analogy. We suggest that neither analogy need be repaired, or even replaced. Instead, the claim at issue can be stated directly. Knobe gives us a clue when he says that “Genuinely scientific inquiry seems to be sensitive to a quite specific range of considerations and seems to take those considerations into account in a highly distinctive manner” (sect. 2.1, para. 5). We think it makes most sense to read this as saying that the “specific range of considerations” are epistemic considerations, which is to say ones strictly relevant to whether or not some claim is true. It is a good normative rule for truth seekers to avoid fallacies of

Commentary/Knobe: Person as scientist, person as moralist relevance. One example of such a fallacy is an appeal to consequences. Saying that evolution by natural selection should be rejected because believing it (supposedly) leads to selfishness, appeals to considerations which have no evidential value. Likewise, that an experimenter is very nice, or nasty, or eccentric, has no epistemic value as far as the empirical test of a hypothesis itself is concerned. The phenomena to which Knobe draws our attention, and which his own empirical work has done a great deal to document, are all examples of fallacies of relevance, mostly in the attribution of credit for intention and causation. Whether someone caused something, intended it, or is responsible for it, depends on what they did and how that influenced the world. It does not depend on whether what happened is the sort of thing we would regard as morally objectionable. The fact that considerations relating to the moral value of the outcomes appear to affect judgements regarding what was intended, or caused, suggests that some of our mental processes are routinely prone to what, by responsible epistemic lights, are fallacies of relevance. The general claim about human cognition that Knobe is rejecting, we therefore suggest, is one to the effect that the organisation of (human) cognition respects this normative standard, and that it does so by not allowing strictly irrelevant considerations to interact during processing. We already have ample evidence that the general claim is false, from, among other things, a long history of social psychology and behavioural economic experiments. Thorndike (1920), for example, showed that in assessments of other people, perceptions of some traits were more correlated with perceptions of other traits than should be the case if traits (such as attractiveness and competence) varied independently. What is exciting and surprising about the work Knobe reviews (and has been conducting himself) is that, from this point of view, it shows the persistent influence of moral reactions in judgements about matters where those reactions are irrelevant to truth. It would be interesting, not to mention extremely important, to see whether the effects are reduced when people deliberate about causation and responsibility in organised groups charged with an epistemic task – for example, juries.

Are we really moralizing creatures through and through? doi:10.1017/S0140525X10001883 Stephen Sticha and Tomasz Wysockib a Department of Philosophy, Rutgers University, New Brunswick, NJ 089011107; bInstitute of Philosophy, University of Wroclaw, ul. Koszarowa 3, 51-149 Wrocł aw, Poland. [email protected] http://www.rci.rutgers.edu/stich/ [email protected] http://www.xphi-europe.org/

Abstract: Knobe contends that in making judgments about a wide range of matters, moral considerations and scientific considerations are “jumbled together” and thus that “we are moralizing creatures through and through.” We argue that his own account of the mechanism underlying these judgments does not support this radical conclusion.

In his conclusion, Knobe reminds us that the target article began with a metaphor (well, a simile, actually) comparing the organization of the mind to the organization of a modern university: “Just as a university would have specific departments devoted especially to the sciences, our minds might include certain specific psychological processes devoted especially to constructing a roughly ‘scientific’ kind of understanding” (sect. 6, para. 1) This suggests a view on which moral judgments play a quite limited role in cognition. In a university, there might be faculty members in the philosophy department who were hired specifically to work on moral questions,

but researchers in the sciences typically leave such questions to one side. So maybe the mind works in much the same way. We might have certain psychological processes devoted to making moral judgments, but there would be other processes that focus on developing a purely ‘scientific’ understanding of what is going on in a situation and remain neutral on all questions of morality (sect. 6, para. 2). Knobe maintains that this picture is “deeply mistaken”: [There is no] clear division whereby certain psychological processes are devoted to moral questions and others are devoted to purely scientific questions. Instead, it appears that everything is jumbled together. Even the processes that look most “scientific” actually take moral considerations into account. It seems that we are moralizing creatures through and through. (sect. 6, para. 3, emphasis added)

This is a bold and radical view. And while we share Knobe’s fondness for views that fly in the face of conventional wisdom, we are not persuaded that he has made a convincing case. Indeed, we think that Knobe’s own explanation for the sorts of phenomena he so clearly and carefully documents flies in the face of these audacious and dramatic claims. To explain our skepticism, we will focus on the target article’s Figures 6–8. These are aimed at explaining how people make judgments about whether an agent is in favor of an outcome. Knobe begins his explanation with the “fundamental assumption” that people’s representation of the agent’s attitude is best understood not in terms of a simple dichotomy between “in favor” and “not in favor,” but rather, in terms of a whole continuum of different attitudes an agent might hold. . .. For simplicity, we can depict this continuum in terms of a scale running from con to pro. (Fig. 6) (sect. 5.2, para. 2)

An agent whose attitude falls way over on the con side, Knobe tells us, will be classified as “not in favor” and an agent whose attitude falls way over on the pro side will be classified as “in favor.” But that does not tell us “how . . . people determine the threshold at which an agent’s attitude passes over from the category ‘not in favor’ to the category ‘in favor’” (sect. 5.2, para. 2). To explain this, Knobe posits “an additional element” that includes a variable default position whose location along the continuum is determined, in part, by people’s moral judgments. Knobe proceeds to tell us, in some detail, how this default-setting system works, and how it plays a role in determining whether we judge that an agent is in favor of the outcome in question. There is, however, one central and important part of the system about which Knobe tells us nothing at all. The lacunae emerges very clearly when we compare Figure 7 to Figure 8. One difference between these two figures, the one that Knobe focuses on, is that the Default position, and thus the part of the continuum that supports a judgment that the agent is “IN FAVOR,” has been shifted to the right. But there is another difference. In Figure 8, the position of the Agent on the continuum has been marked. And that position is, of course, crucial to the account. In Figure 8, the Agent is located to the left of the Default, leading to a judgment that the Agent is not in favor of the outcome in question. Had the Agent been located significantly further to the right, the system would produce the judgment that the Agent is in favor of the outcome. But how does the psychological mechanism that Knobe posits succeed in locating the Agent along the continuum? As far as we can see, Knobe tells us nothing about this, and there is certainly no hint that the psychological processes responsible for locating the Agent along the continuum are sensitive to any moral or evaluative judgment made elsewhere in the system. Rather, it seems, this crucial determination is made in a value-free way. To revert to Knobe’s recurrent metaphor, it is made by one of the mind’s “science departments” that focuses on “developing a purely ‘scientific’ understanding of what is going on in a situation and remain[s] neutral on all questions of morality” (sect. 6, para. 2). The point is underscored by Knobe’s analogy with the process that a teacher might use in assigning grades. The teacher starts out with a “continuum of different percentage scores on a test” (sect. 5.2, para. 4) and must then decide on a threshold beyond which a score will count as an A. Her process for setting the BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Commentary/Knobe: Person as scientist, person as moralist threshold involves a variable default determined by the teacher’s assessment of the difficulty of the test. This is analogous to the variable, morally influenced default depicted in Figures 7 and 8. However, presumably the test scores themselves are not influenced by the teacher’s assessment of the difficulty of the test. They, like the position of the Agent in Figure 8, are determined by a “purely scientific” component in the assessment process. The bottom line is that on Knobe’s own account of how we decide whether an agent is in favor of an outcome, there is a clear division between psychological processes that involve moral considerations and those that do not. It is not the case that “everything is jumbled together,” nor is it the case that “we are moralizing creatures through and through.”

Depression affecting moral judgment doi:10.1017/S0140525X10001895 Luisa Terronia and Renerio Fraguasb a Liaison Psychiatry Group, Department of Psychiatry, Institute of Psychiatry, Clinics Hospital, Medical Faculty, University of Sa˜o Paulo, Sa˜o Paulo, Brazil; b Liaison Psychiatry Group, Laboratory of Psychiatric Neuroimaging (LIM-21), Department of Psychiatry, Institute of Psychiatry, Clinics Hospital, Medical Faculty, University of Sa˜o Paulo, Sa˜o Paulo, Brazil. [email protected] http://www.fm.usp.br [email protected] http://www.fm.usp.br

Abstract: Depressive mood can be involved in the moral judgments made by people with depression. Here, we focus on the negative judgments depressed patients have of themselves and the world. Possibly, the alterations in moral judgment in subjects with depression can be understood by taking into account the neural basis of depression.

In his article, Knobe discusses the role of moral judgments in people’s understanding. The author focuses his study on the moral influence present in the process of cognition. In discussing the motivational bias hypothesis (sect. 4.1), Knobe mentions studies in patients who had a lesion in the ventromedial prefrontal cortex. These studies tried to demonstrate the non-involvement of affective reaction in the process of moral judgment. In this commentary, we consider moral judgment in depressed subjects. We feel that depressive mood is particularly relevant to the negative moral judgments often made by patients with depression. In the psychopathology of depression, patients’ actions and thoughts can be affected by the depressive mood, which in turn tends to affect their moral judgments. This psychopathological process is characterized by ruminations of negative thoughts. Patients with major depression understand the world and themselves in the same way, that is, in a negative way. Here, these disturbed thoughts can be understood as a distortion of moral judgments influenced by the presence of depressive mood. In the moral judgments, patients often evaluate themselves or their acts as something “bad” or “wrong.” This process supposes that patients are evaluating themselves axiologically. During this process, depressed patients consider themselves to “blame.” Such depressed patients with these negative moral judgments and thoughts can be an example for the line of study developed by Knobe. Patients’ negative moral judgment and other psychopathological alterations return to normal with the remission of depression (Benedetti et al. 2007). This process of continuous and rigid negative moral judgments may have a biological explanation. What kind of process can be underlying this alteration on negative moral judgment in depression? In normal human subjects, studies with functional brain imaging have found increased activity in brain areas in a resting state and reduced activity when there is a proposed goal for brain function . This organized mode of brain function identified in specific brain regions constitutes the default system (Drevets et al. 2008). A disturbance in

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this network can explain depression symptomatology. Studies developed by Sheline et al. (2009) found that depressed subjects showed less decrease in activity than control subjects in areas of the default system, or default mode network (DMN), during performance of emotional tasks. These findings have supported the view that alterations in areas of the DMN may constitute a basis for the disordered self-referential thoughts of depression. In Knobe’s article, there is a mention about a study investigating cognition in people who don’t have immediate affective reactions as a result of lesions in the ventromedial prefrontal cortex. He uses this study to show that the results of his analyses about moral judgment in normal subjects are not due to an affective reaction. However, in people with distortion of judgment caused by depressive mood, the neural dysfunction in specific brain areas found in depression investigations can explain the moral judgment disturbance, and supports the interference of the depressive mood on moral judgments. This construction supports our view that the neural basis of depression may explain depressive mood and, consequently, moral judgment; albeit it does not exclude Knobe’s point that moral judgment may occur independently of affective reaction.

Fixing the default position in Knobe’s competence model doi:10.1017/S0140525X10001901 Joseph Ulatowskia and Justus Johnsonb a Department of Philosophy, University of Nevada – Las Vegas, Las Vegas, NV 89154-5028; bDepartment of Philosophy, University of Wyoming, Laramie, WY 82071. [email protected] [email protected] http://web.mac.com/oohlah

Abstract: Although we agree with the spirit of Knobe’s competence model, our aim in this commentary is to argue that the default position should be made more precise. Our quibble with Knobe’s model is that we find it hard to ascribe a coherent view to some experimental subjects if the default position is not clearly defined.

In the target article “Person as Scientist, Person as Moralist,” Joshua Knobe has devised an innovative model where moral appraisals play a fundamental role in how people make sense of agents and their actions. According to Knobe, people’s intuitions depend on a comparison between the action under consideration and an alternative possibility, which he calls the “default position.” The default position falls somewhere along a continuum, but experimenters fail to designate its exact location. In this commentary, we contend that the default position must be fixed and clearly articulated. Otherwise, some of the subjects’ intuitions seem incoherent. We agree with Knobe that there seems to be a default position against which people judge whether or not some action under consideration is favored. But we believe that his approach may be made more precise than it is by specifying clearly what the default position is. According to Knobe’s competence model, moral considerations figure into how subjects make a comparison between the action under consideration and certain alternative possibilities. It seems people who view an action as morally bad uphold an attitude at least slightly toward the con side, and people who view an action as morally good tend to have an attitude at least slightly toward the pro side. An action is favored when “the agent’s attitude falls sufficiently far beyond the default” (sect. 5.2, para. 5). The core of Knobe’s explanation has it that “moral judgments affect [people’s] intuitions by shifting the position of the default” (sect. 5.2, para. 6, emphasis Knobe’s). Knobe’s competence model has done a nice job of explaining why a majority of subjects answered the harm and help scenarios

Response/Knobe: Person as scientist, person as moralist as they did (sect. 3.1). When a majority of subjects (82% according to Knobe 2003a) compare the chairman’s attitude to the default position that harming the environment is morally bad, they favor the response that the chairman intentionally harmed the environment. Likewise, when a majority of subjects (77% according to Knobe 2003a) compare the chairman’s attitude to the default position that helping the environment is morally good, they do not favor the response that the chairman intentionally helped the environment. Although Knobe’s competence model has succeeded in explaining the majority’s intuitions, his model may not succeed in explaining the intuitions of subjects who gave the minority view. Some subjects responded that the chairman did not intentionally harm the environment (18%) or that the chairman did intentionally help the environment (23%) (Knobe 2003a). These represent a minority response in the harm case and help case, respectively. If Knobe’s competence model is correct, then the minority’s default position for the harm scenario is that harming the environment is a morally good thing. The data also suggest that the minority’s default position in the help case is that helping the environment is a morally bad thing. These views are unusual and the result of applying Knobe’s competence model. Knobe may object to this assessment. Subjects receiving the harm scenario may hold that harming the environment is morally bad but the chairman’s indifference does not constitute that he intentionally harmed the environment. These subjects may refrain from saying that the chairman intentionally harmed the environment because the chairman did not want to harm the environment. Subjects who responded that the chairman intentionally helped the environment may uphold the default position that helping the environment is morally good. Since the chairman knew that the program would help the environment, subjects chose the response that he intentionally helped the environment. If this is correct, Knobe is able to show why the competence model explains the minority’s intuitions. The problem with this response is that one of us (Ulatowski) collected data where two-thirds of subjects given both the harm and the help case chose minority responses (Nichols & Ulatowski 2007). Of the two-thirds, half responded that: (1) The chairman intentionally helped the environment and the chairman intentionally harmed the environment. or that: (2) The chairman did not intentionally harm the environment and the chairman did not intentionally help the environment.

On response (1), if the competence model is correct, then respondents judged that not only is harming the environment morally bad but helping the environment is morally bad, too. On response (2), if the competence model is correct, the default position for subjects is not only that helping the environment is morally good but that harming the environment is morally good, too. Subjects’ default positions seem to be inconsistent and, therefore, incoherent. We suggest that the default position be clearly defined to avoid incoherence of subjects’ intuitions. In a series of experiments testing whether the distinction between doing and allowing depends on moral appraisals, we specified an alternative possibility against which subjects should compare the agent’s action (Ulatowski & Johnson 2010): Five people are in imminent danger of death, and you are a part of a team that is taking a special train to rescue the five people. Every second counts. You have just taken over from the driver, who has gone to the back of the train to check on something. Since the train is on automatic control, you don’t need to do anything to keep it going. But you can stop it by putting on the brakes. You suddenly see someone trapped ahead on the track. If you don’t do anything, he will be killed (though the train will be able to continue on its way). But if you do stop, and then free the man, the rescue mission will be aborted. So you let the train continue.

We asked subjects, “Since you could have stopped the train, did you kill the man on the track?” We stipulated the default position: to stop the train. We believe that by specifying the default position, it may prevent an incoherent interpretation of people’s intuitions. Our aim in this commentary has been to expose the incoherence in subjects’ responses when an experiment fails to stipulate the default position. Ultimately, we cannot assume that we know what the subjects’ default position is. ACKNOWLEDGEMENTS We would like to thank Dave Beisecker for helpful conversation leading to a draft of this commentary, and Elijah Millgram for comments on an earlier draft.

Author’s Response The person as moralist account and its alternatives doi:10.1017/S0140525X1000230X Joshua Knobe Program in Cognitive Science and Department of Philosophy, Yale University, New Haven, CT 06520-8306. [email protected] http://pantheon.yale.edu/jk762/

Abstract: The commentators offer helpful suggestions at three levels: (1) explanations for the particular effects discussed in the target article; (2) implications of those effects for our understanding of the role of moral judgment in human cognition; and (3) more theoretical questions about the overall relationship between ordinary cognition and systematic science. The present response takes up these three issues in turn.

The commentators have offered helpful suggestions and criticisms at all levels, from the nitty-gritty of the individual experiments to the broadest sorts of theoretical and philosophical issues. Clearly, the questions at these different levels are intimately connected, but since one has to begin somewhere, perhaps it is best to start by focusing in on the trees and then move gradually toward thinking about the overall shape of the forest. In other words, we can start with specific questions about the explanations for particular effects and then move to implications for broader theoretical and philosophical issues. R1. Alternative hypotheses Recent studies indicate that people’s moral judgments can impact their application of a surprising range of different concepts. Moral judgments appear to be impacting people’s application of the concepts of intentional action, causation, freedom, knowledge, doing and allowing, desire, and many other concepts besides. The primary aim of the target article was to provide an explanation for this pervasive impact of moral judgment. To explain these phenomena, I offered a specific hypothesis. The suggestion was that people come to an understanding of the actual world by comparing it with certain alternative possibilities (counterfactuals). People’s BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Knobe: Person as scientist, person as moralist moral judgments impact their selection of alternatives and thereby influence their application of a wide range of different concepts. A number of commentators responded by developing competing hypotheses. These hypotheses explain the impact of moral considerations in terms of quite different sorts of cognitive processes.

R1.1. A case study

One worry about many of these hypotheses is that they proceed by picking out just one concept whose application is affected by moral judgment and examining this one concept in isolation from all the others. Hence, these hypotheses offer explanations for one of the effects of moral judgment but say nothing about other effects that seem, at least initially, to be closely related. Of course, the fact that a hypothesis is framed entirely in terms of one of these effects does not mean that this hypothesis has to be incorrect. Future research might show that the hypothesis can be extended in fairly natural ways to handle other related phenomena, or perhaps it will be shown that the phenomena that initially seem so closely related are, in fact, fundamentally different. The problem, then, is not that these hypotheses are necessarily wrong but just that they have not yet been developed to the point where they can be properly evaluated. Thus, to take one example, Scanlon suggests that we might be able to explain the apparent asymmetries in people’s intuitions about intentional action by looking more closely at the meaning of the word intentionally. Specifically, suppose we assume that an expression like “John brought about the outcome intentionally” actually has two distinct meanings: (a) John knew that he was bringing about the outcome. (b) John aimed at bringing about the outcome.

People’s moral judgments might then impact their intuitions simply by affecting their sense of which of these two meanings is the relevant one in the context at hand. This hypothesis does seem to do a nice job of accounting for the asymmetries observed in people’s intuitions about intentional action, but the first thing to notice here is that the very same effect can be observed for numerous other concepts. When people determine that a foreseen sideeffect is morally bad, they are not only more inclined to say that the agent brought it about intentionally; they are also more willing to say that she was in favor of it, that she decided to bring it about, even that she advocated it. Presumably, it is not merely a coincidence that we find this exact same effect arising in the application of so many different concepts. So what we really need here is an explanation for the pattern as a whole. One option would be to extend Scanlon’s hypothesis by claiming that the ambiguity posited for the word intentionally can also be found in numerous other expressions. For example, one might say that an expression of the form “John advocated the outcome” also has two distinct meanings. Roughly: (a) John called on people to adopt a policy with the aim of bringing about the outcome. (b) John called on people to adopt a policy that he knew would bring about the outcome.

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But we would then be offering a hypothesis of a very different type. We would no longer just be pointing to some idiosyncratic feature of the word intentionally. Instead, we would be positing a general feature of language that led to a systematic ambiguity within a whole class of expressions. And, of course, the methods used for testing the hypothesis would then have to be correspondingly different. We couldn’t proceed just by looking at patterns in people’s intuitions about intentional action. We would have to introduce a more general claim about word meanings and then evaluate this claim both by gathering data involving people’s use of numerous different expressions and by thinking about the ways in which it fit into larger theories about lexical semantics, polysemy, and so forth. R1.2. Application to further examples

This very same worry also arises, albeit in somewhat different forms, for a number of the other alternative hypotheses. For example: Nanay points out that people’s judgments about the two intentional action cases differ not only from a moral perspective, but also from a modal perspective. Specifically, he claims that people who are given the harm case make the judgment: If the chairman had not ignored the environmental considerations, he would not have harmed the environment.

but that people who are given the help case do not make the judgment: If the chairman had not ignored the environmental considerations, he would not have helped the environment.

Nanay then suggests that this difference in people’s modal judgments can lead to a difference in people’s intuitions about intentional action. Hence, it might be possible to explain the effect without introducing moral considerations in any way. Menzies argues that the asymmetries observed for people’s causal judgments can be explained if we adopt a theory of causal cognition that emphasizes the role of normality. Suppose we assume that people only regard an event as a cause to the extent that this event “intervenes in the normal course of events and makes a difference in the way these develop” (para. 5). Now suppose we further assume that people’s ordinary notion of normality is not simply a matter of statistical frequency but also takes into account social, legal, and moral norms. Starting from these two assumptions, we arrive at an interesting and surprising conclusion: If both the behavior of the administrative assistant (a perfectly normal behavior) and the behavior of the professor (a violation of social and moral norms) were necessary for the problem to arise, people will tend to pick out the behavior of the professor and regard it, in particular, as the cause of the problem. Hindriks suggests that we can come to a better understanding of the intentional action effect by applying the legal distinction between actus reus (guilty act) and mens rea (guilty mind). He then notes that most research in this domain has focused on the impact of people’s judgments of the moral status of the agent’s action, with the assumption being that these judgments are somehow influencing people’s intuitions about intentional action. By contrast, he suggests that people’s intuitions might

Response/Knobe: Person as scientist, person as moralist actually be affected by a judgment of mens rea, that is, a judgment about the status of the agent’s mental states. In earlier work, Hindriks has spelled out this claim in terms of the hypothesis that people tend to think that an agent S intentionally performed an action A to the extent that “An agent S fs intentionally if S intends to c, fs by cing, expects to f by cing, and cs in spite of the fact that he believes his expected fing constitutes a normative reason against cing” (Hindriks 2008, p. 635). Humphrey argues that the intentional action effects can be given a straightforward Bayesian interpretation. All one needs to consider is the conditional probabilities people assign in the relevant cases. Thus, suppose we compare (a) the conditional probability that the agent harmed the environment intentionally, given that he implemented the program, and (b) the conditional probability that the agent helped the environment intentionally, given that he implemented the program. If one assigns priors in such a way that (a) is greater than (b), it will follow straightforwardly that people should be more inclined to guess that the agent harmed intentionally than they are to say that the agent helped intentionally. Brogaard agrees that people’s intuitions about intentional action are not purely scientific in nature, but she argues that it would also be a mistake to understand them in terms of the judgments people make about whether actions are morally right or wrong. Instead, she claims, we should understand these intuitions in terms of judgments of desert. People make judgments about whether the agent deserves a side-effect, or the blame for it, and these judgments of desert end up influencing their intuitions about whether or not the behavior was performed intentionally. Lombrozo & Uttich note that people ascribe different attitudes in cases of norm violation from the attitudes they ascribe in cases of more ordinary behavior. If we see that a person has chosen to implement a program that has some entirely innocuous effect, we might assume that this person did not actually care very much about the program either way – maybe he just decided to adopt it without much thought. But now suppose, instead, that we saw a person choosing to implement a program that he knew would harm the environment. Since harming the environment is a norm violation, we might immediately conclude that he must have had some strong interest in adopting this program, and we would therefore be more inclined to attribute to him the kind of pro-attitude that would lead us to say that he acted intentionally. Each of these proposals offers interesting suggestions about a particular concept – and many of these proposals will no doubt lead to important new insights – but all of them seem to leave us with a mystery as to why the impact of moral judgment is so pervasive. For a particularly promising example, consider the hypothesis that Menzies offers about people’s causal intuitions. Menzies suggests that causal intuitions can be affected in a complex way by judgments of what might be called “normality.” Now, it is an interesting question whether this hypothesis is right or wrong. (As it happens, I think that it is completely correct; Hitchcock & Knobe 2009.) However, the key point is that this hypothesis does not explain why the effect we find for the concept of causation can also be found for so many other concepts. Indeed, there is an important sense in which it does not

really explain the effect for causal intuitions at all. It simply describes a certain pattern in people’s application of this concept, without telling us why the concept works like this and not some other way. So this sort of hypothesis gives us a tantalizing glimpse into the phenomenon at work here, but it seems that we will not really have an adequate account until we can offer a more general theory. If I may be permitted to speculate, it seems to me that contemporary work on these problems is suffering from the legacy of a certain tradition of conceptual analysis. In early work in that tradition, it was thought that we should proceed by developing for each concept a list of necessary and sufficient conditions. The aim was to provide a separate list of conditions for each concept – one list for the concept of intentional action, one for the concept of causation, and so forth. This tradition has now been widely repudiated. None of the commentators on the present target article attempted to provide lists of necessary and sufficient conditions, and I am sure that most of them would agree that such an approach is unlikely to prove fruitful. Yet, though researchers today are anxious to distance themselves from this program of listmaking, I suspect that a certain remnant of that earlier tradition still remains. There are still attempts to go through people’s various concepts and provide something like an “analysis” for each of them; it’s just that these analyses no longer take the form of necessary and sufficient conditions. In my view, we should make an even more radical break with the tradition. There is simply no use in developing something like an “analysis of the concept of intentional action” and then, separately, an “analysis of the concept of causation.” Instead, we should recognize that people’s intuitions about each of these concepts are shaped by a number of distinct psychological processes, and that each of these processes in turn influences intuitions about a number of different concepts. So what we really need is not a separate theory for each of the separate concepts but rather unifying theories of the underlying processes. Such theories might not offer us a comprehensive picture of any one concept, but they will allow us to generate specific testable predictions regarding a whole range of different concepts. R1.3. Motivation to blame

The contribution from Alicke & Rose pursues precisely this strategy. They suggest that the phenomena might be explained in terms of a single underlying psychological process that can affect people’s intuitions across a wide variety of different domains. Specifically, they suggest that people sometimes experience a motivation to justify attributions of blame and that this motivation can affect their views about intention, causation, and numerous other issues. In the target article, I had argued that this sort of process could not explain the effects under discussion here. Alicke & Rose reply by reviewing some very impressive data from Alicke’s earlier work (Alicke 1992), which they take to provide conclusive evidence that people’s judgments actually can be distorted by a motivation to blame. This commentary definitely raises a number of important issues, but I worry that I was not sufficiently clear in BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Knobe: Person as scientist, person as moralist articulating the nature of the disagreement in the target article itself. The thing to keep in mind is that no one is actually trying to refute the key claim made in Alicke’s earlier work. In that earlier work, Alicke provides excellent evidence for the claim that people’s intuitions can be distorted by a motivation to blame, and none of the people writing on these issues more recently have been trying to call that claim into question. Rather, the issue is just about whether the theory developed in Alicke’s earlier work provides the best explanation for a specific class of effects that have been uncovered in more recent work. Some researchers have argued that it can (Alicke 2008; Nadelhoffer 2006a); others have argued that it cannot (Nichols & Ulatowski 2007; Wright & Bengson 2009; Young et al. 2006). At this point, I think that Alicke’s basic theoretical claims about the importance of a motivation to blame have been established beyond reasonable doubt, and there is no need to provide any further evidence for them. The thing to focus on now is just the detailed structure of these particular effects and whether a motivational explanation can account for them. In the target article, I reviewed some of the experimental evidence for the view that it cannot. R1.4. Sources of evidence

Sinnott-Armstrong raises more or less this same issue about my own preferred account. The account suggests that people’s moral judgments affect their counterfactual reasoning, which in turn plays a role in their application of numerous different concepts. But, Sinnott-Armstrong asks, how is such an account to be assessed? Given that we can’t actually see directly which counterfactuals people regard as relevant, how can we know whether the account is true or false? This is exactly the right question to be asking, and I am sure that future research will offer us certain new techniques for answering it. At present, though, we have two major methods at our disposal. First, the account predicts a particular pattern of intuitions across a broad range of different concepts. At the very heart of the approach is the idea that we should, as far as possible, avoid introducing ad hoc hypotheses just to explain the impact of moral judgment on one or another particular concept. Instead, we start out with perfectly general principles about the impact of moral judgment on counterfactual thinking. Then we introduce independently testable claims about the role of counterfactual thinking in the application of certain individual concepts. Together, these two types of claims generate specific testable predictions. The thing to notice about this strategy is that it allows us to make predictions about the impact of moral considerations on the application of numerous concepts that have not yet been empirically investigated. Thus, to take one example, Jonathan Phillips (personal communication) points out that counterfactual reasoning seems to play a role in people’s ordinary notion of choosing. (An agent cannot be said to have “chosen” one specific option unless other options were also available.) Hence, we should immediately predict an impact of moral judgment on people’s intuitions about whether or not an agent can truly be said to have “chosen” a particular option. Or, to 356

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take a different case, it seems that counterfactual reasoning plays a role in people’s intuitions about whether a given trait is innate. Accordingly, one might predict an impact of moral judgments on intuitions about innateness, and Richard Samuels and I are testing that prediction in a series of studies under development now. In essence, then, the first answer to Sinnott-Armstrong’s question is that we can test the theory by using it to generate new predictions about the application of various concepts and checking to see whether those predictions are borne out. But there is also a second way in which the theory can be put to the test. We can use various methods to look more directly at people’s judgments about the relevance of counterfactuals. For example, numerous studies have proceeded by presenting participants with questions of the form: “If only ___, this outcome would not have arisen.” Participants can fill in the blank with whichever possibility they prefer, and researchers then infer that the possibilities chosen most often are regarded as most relevant. Studies using this methodology consistently show that moral judgments do have an impact on intuitions about counterfactual relevance (McCloy & Byrne 2000; N’gbala & Branscombe 1995). In conclusion, then, our research can proceed by looking at the relationships among a complex constellation of different kinds of data. We start out with certain principles about the role of moral judgment in counterfactual thinking and certain hypotheses about the role of counterfactual thinking in the application of particular concepts. Then we check the theory against evidence regarding both counterfactual thinking and the application of concepts, testing to see whether all of these data conform to the theoretical predictions.1 Presumably, they will not, and the theory will have to be revised in important respects. However, my hope is that we will at least be looking in roughly the right neighborhood and thereby moving toward a better understanding of these phenomena. R2. The role of moral judgment Suppose now that we focus, if only for the sake of argument, on the particular account advanced in the target article. The most important and controversial aspect of this account is the role it assigns to moral judgment. Yet, it can prove surprisingly difficult even to say what that role is and why it should be controversial, much less to determine whether the account is right or wrong. R2.1. Investigating the judgments themselves

To begin with, there is the question as to what we even mean by the phrase “moral judgment.” When one first hears this phrase, one is naturally drawn to think of a specific sort of conscious event. One thinks, for example, of cases in which we focus in on a particular behavior, bring to bear a variety of different considerations, and then determine that an agent deserves moral blame or praise. Now, conscious episodes like this certainly do take place, but it sounds a bit implausible to suppose that such episodes could somehow be exerting a pervasive impact on people’s whole way of understanding the

Response/Knobe: Person as scientist, person as moralist world. We quite often wonder whether, for example, a person has a particular intention, and it seems absurd to suppose that whenever we want to answer such a question, we have to start out by making a full-blown moral judgment. There is, however, a way of interpreting the hypothesis on which this sense of absurdity dissolves. To get a feeling for the issue, consider the way we might proceed if someone suggested that people’s whole way of understanding the world was shaped by statistical reasoning. Clearly, when one first turns to the topic of statistical reasoning, one imagines a particular sort of conscious episode. (One thinks, perhaps, of a person moving stepby-step through the computations involved in a formal analysis of variance.) But surely the claim is not that this sort of cognition is shaping our whole understanding of the world! Rather, the idea is that people go through a kind of immediate, automatic, non-conscious process and that this process is analogous in certain important respects to what people do when they are consciously conducting statistical analyses. The claim under discussion here should be understood in more or less this same way. We are certainly not suggesting that people’s conscious moral beliefs can somehow shape their whole understanding of the world (see Knobe 2007). Rather, the claim is that people make certain immediate, automatic, non-conscious moral appraisals and that these automatic appraisals then exert a surprising influence on the rest of their cognition. With this basic framework in mind, we can now turn to a series of interesting suggestions from the commentators. R2.1.1. Theory-of-mind and counterfactuals. The com-

mentaries from Guglielmo and Girotto, Surian, & Siegal (Girotto et al.) point to two important characteristics of people’s moral judgments: 1. Guglielmo notes that conscious moral judgments are based in part on reasoning about the agent’s mental states. 2. Girotto et al. note that conscious moral judgments are based in part on counterfactual reasoning. These two points appear to spell trouble for the theory presented in the target article. After all, the claim was that people make a moral judgment which then influences their reasoning about mental states and counterfactuals. But if people have to think about mental states and counterfactuals before they can even make this moral judgment, how could the process ever get off the ground? My answer is that the initial judgment that influences people’s subsequent reasoning is deeply different from the conscious judgment that this reasoning can ultimately inform. People’s conscious moral judgments can take into account information about numerous different considerations, including mental states, counterfactuals, and a great deal else besides. But their initial, purely non-conscious judgments do not work like that. These initial judgments are instead the product of an extremely rapid and far less complex process. To see the basic idea here, imagine what might go through your mind if you were actually in the room as the vignette about the professor and the pens unfolded. There you are, watching as the professor moves toward the desk and starts reaching for one of the pens. Ultimately, you might end up making a conscious moral judgment about this behavior. You might decide that the

professor deserves blame for the problem that results, or that his act was morally wrong, or something of the kind. But before you can even begin any of this sophisticated reasoning, you might go through a more automatic, entirely non-conscious process of moral appraisal. As you see the professor reaching for the pens, you recognize that he is suppose to refrain from taking them, and you therefore conceptualize his action by comparing it to the behavior he was supposed to perform, namely, refraining from taking pens. The key claims now are that (a) your tendency to focus on this specific comparison involves a kind of very simple moral cognition and (b) this simple form of moral cognition does not itself depend on your subsequent reasoning about mental states or counterfactuals. R2.1.2. Origins of moral judgment. A question now arises about how exactly people make these rapid and automatic moral judgments. Here a number of commentators have provided helpful suggestions. Kang & Glassman propose that moral judgments are shaped by the aim of acquiring cultural capital. People seek to signal their membership in particular communities and end up arriving at moral judgments accordingly. (Just as one might wear skinny jeans to signal one’s membership in the community of Brooklyn hipsters, one might condemn abortion to signal one’s membership in the community of Southern evangelicals.) Terroni & Fraguas suggest that people’s moral judgments can be impacted by their emotional states. They then hypothesize that people might make substantially different moral judgments when their emotional states were altered by clinical depression. So a person might arrive at different judgments about the very same case depending on whether that person happened to be depressed or not. Carpendale, Hammond, & Lewis (Carpendale et al.) argue that people’s capacity for moral judgment develops in the context of social interaction. Children learn to treat others as human beings (as opposed to mere physical objects), and they thereby acquire an understanding of moral norms. Each of these hypotheses seems plausible and promising, but it would be especially exciting if we could use these approaches to drive a wedge between people’s conscious moral judgments and their more automatic moral appraisals. Thus, suppose that an individual is trying to gain cultural capital by signaling membership in the community of liberal intellectuals. She might thereby end up arriving at the obvious sorts of conscious moral judgments: opposition to sexism and homophobia, support for disadvantaged groups, and so forth. But would her non-conscious appraisals go in this same way? Perhaps not. It might be that her conscious moral judgments would be shaped by the aim of gaining cultural capital, whereas her intuitions about intentional action, causation, and the like would continue to reveal a very different system of values at work (see, e.g., Inbar et al. 2009). Or consider the case of depression. Even when a person is clinically depressed, she may be able to exert enough cognitive control to continue making exactly the same sorts of conscious judgments that she would have otherwise. But perhaps her depression would nonetheless impact her non-conscious appraisals, and we might be able to pick up this impact just by asking questions about intention or causation. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Knobe: Person as scientist, person as moralist R2.2. Impact of non-moral considerations

The commentaries from Girotto et al. and Guglielmo point out that people’s intuitions about intentional action can be influenced, not only by moral considerations, but also by information about the agent’s mental states. Thus, people are reluctant to say that an agent brought about an outcome intentionally when the agent shows regret (Guglielmo & Malle, in press; Phelan & Sarkissian 2008; Sverdlik 2004) or when the agent falsely believed that she would not be bringing the outcome about (Pellizzoni et al. 2010). These are good points, and any correct theory of intentional action ascription will have to accommodate them. The theory presented in the target article does so by suggesting that moral considerations are used to set a kind of threshold, while information about the agent’s mental states is used to determine whether the agent falls above or below that threshold. Hence, the position of the agent relative to the threshold ends up depending on a complex combination of moral considerations and mental state information. R2.3. Moral concepts

What we have here, then, is a concept whose application can be influenced both by moral considerations and by mental state information. How should such a concept be understood? Gintis suggests that the best interpretation might be that people are simply using the concept of intentional action as a moral concept. The whole effect would then be rather unsurprising and unimportant. All it would show is that moral considerations can impact the application of moral concepts. At least initially, this does seem like an appealing strategy. One starts out with a distinction between “moral” concepts and “non-moral” concepts, such that any concept whose application is impacted by moral considerations is supposed to fall in the former category. If one then finds an impact of moral considerations on a concept that had previously been classified as non-moral, one should not conclude that the whole framework is thereby called into question. All one needs to do is just reclassify that one concept. Still, it does seem that there is a certain point at which this sort of strategy begins to look unworkable. If we find an impact of moral considerations on just one concept, we can always proceed by reclassifying it. But that is not the situation in which we actually find ourselves. These effects are arising not only for the concept of intentional action, but also for the concepts of causation and knowledge, even for the concept of advocating. At some point, I think, one has to conclude that it is becoming unhelpful to divide off a special sphere of “moral concepts” and claim that the impact of moral considerations arises only for them. R2.4. Morality and normality

Kreps & Monin and Mandelbaum & Ripley take things even further in this direction. They suggest that the representation that is influencing people’s intuitions in these cases is not actually specific to morality in particular. Rather, it is a representation of something like “normality” 358

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or “expectation.” Such a representation would then unite moral considerations with considerations of a more purely statistical variety. Continuing with this general approach, Ulatowski & Johnson propose that one can impact the relevant representation simply by creating stimulus materials that present a given outcome as a “default.” Even if this outcome is not described as in any way morally good or right, the claim is that it will nonetheless be seen as having a particular sort of status that will prove relevant in people’s subsequent cognition. I think that the commentators are exactly right on this score, and I look forward to further research expanding on this theme. My only disagreement, if it can be considered a disagreement at all, is on the level of rhetoric. The commentators see themselves as deflating the claims made in the target article, showing that moral considerations are actually less central than I had originally suggested. By contrast, I would describe them as radicalizing the target article’s original thesis. What they are showing is that it is not even possible to isolate a particular point in the process where the moral judgments come in. Instead, moral and statistical considerations appear to be welded seamlessly together from the very beginning. R2.5. Morality and language

However, a number of commentators actually suggested moving in the opposite direction. They proposed theories according to which moral considerations are confined to a single, highly delimited role, while the remainder of the process has nothing to do with morality and proceeds more or less like a scientific investigation. In particular, Egre´ and Cova, Dupoux, & Jacob (Cova et al.) suggest that the role of moral considerations might be confined entirely to language. The basic idea here is a simple and powerful one. Suppose that people’s actual capacity for theory-of-mind works by classifying attitudes along a continuous scale. Still, it might be that our language cannot describe attitudes in these purely continuous terms. If we want to capture an agent’s attitude in language, we need to impose some kind of threshold and then say that a particular term or phrase applies whenever the attitude goes beyond this threshold. So perhaps it is there that morality enters into the picture. In other words, it might be that the underlying scale is entirely non-moral, but that morality plays a role in the process we use to determine the position of the threshold for particular linguistic expressions. One way of spelling out this sort of account would be to represent the underlying scale using numbers. We could say that the number 0 stands for absolute indifference, the positive numbers stand for pro-attitudes, and the negative numbers for con-attitudes. A particular agent’s attitude could be represented using the diagram shown in Figure R1: Yet, although people would have some representation of the agent’s attitude along this scale, the actual expressions of natural language would not correspond to points on the scale in any absolute sense. So there would not be any expressions in English that could describe an agent as having an attitude of, say, “þ2 or higher.” Instead, all of the expressions of natural language would stand in a

Response/Knobe: Person as scientist, person as moralist

Figure R1. Representation of an agent’s attitude on an absolute scale.

more complex relationship to the scale. They would characterize the agent’s attitude relative to a (partially moral) default. Thus, if it turned out in the case at hand that the default was to an attitude of -1, the expressions of our language would describe the agent’s attitude only relative to this default position, characterizing it as “3 points past the default.” There is, however, another possible way in which this system could work. It could be that human beings do not make use of any purely absolute representations at any stage of processing. Instead, the attitude would be represented from the very beginning in terms of its position relative to the default. We would start by labeling the 0 point as default and then represent the agent’s attitude like this (Fig. R2): On this latter view, the comparison with the default is already available in people’s underlying, nonlinguistic representation of the attitude. The expressions of natural language can then correspond in a straightforward way to these nonlinguistic representations. The primary difference between these two hypotheses is that the first posits an entirely non-moral representation, which is then obscured in certain ways by complex linguistic rules, whereas the second does not posit any purely non-moral representation at any level. The key to adjudicating between these hypotheses, then, is to come up with a richer account of what the non-moral representation is supposed to be doing. Given that it is not necessary as an explanation for the way people use expressions in natural language, what exactly is it used for? If we had a better account of what the non-moral representation was supposed to be doing, we would be better able to decide whether it is actually there. R2.6. Characterizing the effect

The target article claims that moral considerations play a surprisingly important role in people’s cognition. In trying to characterize this role, I adopted a number of different formulations. Sometimes I said that moral considerations figure in people’s competence, sometimes that moral considerations suffuse the process through and through. The commentators suggest that both of those formulations are misleading and unhelpful. Alexander, Mallon, & Weinberg (Alexander et al.) point out that no clear criteria are ever given for picking out a “competence” and distinguishing it from the

Figure R2. Representation of an agent’s attitude relative to a default.

various other factors that impact people’s intuitions. They therefore suggest that we dispense with this distinction between competence and other factors and simply focus on exploring the various different processes that impact people’s intuitions. Stich & Wysocki note that there is a perfectly clear sense in which my own account does not have moral considerations influencing the process “through and through.” On the contrary, the account says that moral considerations play a role in one specific part of the process but do not exert any influence on certain other parts. These are both excellent points, and I agree that the formulations adopted in the target article may indeed be unhelpful in certain respects. So instead of defending what I wrote there, let me simply accept these criticisms and try now to formulate the point more accurately. My aim in the target article was to argue against a particular vision. This vision distinguishes two aspects of the processes that generate people’s intuitions: 1. A kind of “underlying” or “fundamental” capacity 2. Various additional factors that in some way “bias” or “distort” people’s intuitions The claim, then, is that the fundamental capacity is entirely non-moral and that the impact of moral considerations only arises because of the presence of these distorting factors. Now, the distinction between these two aspects might be spelled out in various different ways, and different researchers would presumably adopt quite different accounts of the distinction. What unites all of these various accounts, however, is the claim that we can carve off a distinct capacity that is entirely non-moral and that is capable, all by itself, of generating an answer to the issue in question. Hence, faced with a person’s intuition about intentional action, we might say: “This person’s fundamental capacity for theory-of-mind would normally have classified this behavior as unintentional. However, her moral judgments got in the way and led her to regard it as intentional.” My aim was to show that this sort of strategy cannot be made to work. On the view I develop, there simply is no distinct capacity that is entirely non-moral and that is capable, all by itself, of determining whether a behavior is intentional or unintentional. Thus, on the model provided in the target article, there would be no sense in asking a question like: “Suppose we got rid of all the moral considerations and just allowed people’s fundamental capacity for theory-of-mind to proceed undisturbed. Which conclusion would they then draw about whether this behavior was intentional?” The trouble here is that the model does not involve any kind of distinct nonmoral capacity which could answer the question in the absence of all moral considerations. Note that this argument does not require me to say anything positive about the distinction between competence and performance. Nor does it require me to claim that there is no stage anywhere in the process that is untouched by moral considerations. All it requires is a kind of negative claim. Specifically: that it not be possible to isolate a distinct capacity that has a particular sort of non-moral character. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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Response/Knobe: Person as scientist, person as moralist R3. Ordinary cognition and science In thinking about people’s ordinary ways of making sense of the world, it sometimes proves helpful to draw analogies with more systematic and explicit systems of thought. So one might say that people’s ordinary understanding is similar in certain respects to Aristotelian metaphysics, or to legal theory, or to certain religious doctrines. These analogies can then help to illuminate aspects of this ordinary understanding that might otherwise have remained obscure. One particularly common analogy here has been between people’s ordinary understanding and systematic science. This analogy calls up a specific picture of what the human mind is like. A scientific researcher might have two different kinds of beliefs in a particular domain – a system of scientific beliefs and then, quite separately, a system of moral beliefs. Such a researcher might then find that her collaborators strongly disagree with her moral beliefs but that they are nonetheless in complete agreement with her scientific beliefs. In the target article, I argued that this analogy was misleading. People’s ordinary cognition does not appear to involve a clear distinction between purely “scientific” beliefs and moral beliefs. It might be helpful, therefore, to reject the analogy with science and to look instead at analogies between ordinary cognition and forms of systematic thought in which moral and non-moral considerations are more explicitly mixed. R3.1. The relevance of moral considerations

Spurrett & Martin argue that there is little to be gained by discussing the respects in which ordinary cognition might or might not resemble science. Instead, they suggest that we simply focus directly on the ways in which people apply specific considerations to address particular questions. Adopting this latter approach, they claim that the effects described in the target article are best characterized as “fallacies of relevance.” That is, these effects are best understood as cases in which people apply moral considerations to questions in which only non-moral considerations would be relevant. Spurrett & Martin may turn out in the end to be right on this score, but it is important to emphasize that the claim they are making is precisely the claim that is up for debate here. The central thesis of the target article was that people’s ordinary cognition is radically different from scientific inquiry and that, in particular, ordinary questions like “Who caused the problem?” are not best understood on the model of scientific questions about causal relations. So, on the view defended in the target article, moral considerations actually are relevant to the ordinary questions people ask about whether one thing caused another, and there is no fallacy involved in applying such considerations to questions like these.

beliefs held by trained scientists, but the process people go through as children to acquire those beliefs might turn out to show many of the stages characteristic of scientific inquiry: looking for evidence, checking its fit to existing views, modifying these views when they do not fit the evidence, and so forth. Kushnir & Chernyak’s reference to the developmental literature here is a very helpful one, and future research could examine these developmental issues more directly. But it seems important at the outset to emphasize the very distinctive claim one makes when one says that ordinary human cognition resembles science. Such a claim presumably is not merely saying that ordinary human cognition involves taking in evidence and using it to assess prior views (a claim which is obviously true and needs no further defense). Instead, the claim seems to be an interesting and controversial one, which says something in particular about the precise way in which human beings use evidence to update their beliefs. To see why, consider the way we might apply a similar approach in another domain. Suppose that someone says, “Human visual cognition uses Fourier transforms.” The claim here is presumably not just that human visual cognition uses some kind of computation. Rather, what is being claimed is that visual cognition makes use of one specific kind of computation – a kind of computation that was first formalized by modern mathematicians and is now known as a Fourier transform. This is an interesting hypothesis, which can be put to the test in further experimental studies. Now suppose that someone says: “Human cognitive development uses the methods of science.” In just the same way, this claim cannot simply mean that cognitive development involves taking in evidence and using it to adjust our beliefs. (After all, that basic approach long predates the development of systematic science and can be found in an enormous variety of quite different modes of thought.) Rather, the claim has to be understood as saying that cognitive development makes use of the sorts of methods, first made explicit in the “scientific revolution” of the sixteenth and seventeenth centuries, that are now regarded as the distinctive methods of science. This is certainly an interesting hypothesis, which we can set about testing in experimental studies. The thesis of the target article, however, was that existing experiments do not suggest that this hypothesis is correct. If we look to the distinctive characteristics of science – the characteristics that distinguish science from other systematic modes of thought – we find that people’s ordinary non-conscious cognition does not tend to show these characteristics. For that reason, it might be helpful to understand ordinary cognition, not by looking to an analogy with contemporary science, but by looking to an analogy with the earlier modes of thought that the scientific revolution displaced.

R3.3. The function of theory-of-mind R3.2. Science and development

Kushnir & Chernyak suggest that the analogy to science might apply not so much to the beliefs people have at any given time but rather to the development of these beliefs in the first place. Hence, the beliefs people hold as adults might be radically different in various respects from the 360

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Yet, even if the methods people use in ordinary theory-ofmind turn out to be radically different from the one we find at work in science, the function of theory-of-mind might be exactly the same as the function of scientific psychology. Thus, it might be that people’s ordinary theoryof-mind makes use of moral considerations, but that

Response/Knobe: Person as scientist, person as moralist there is some sense in which its aim is simply to generate accurate predictions and explanations of behavior. Exploring this possibility, Bartsch & Young suggest that the impact of moral considerations might be understood in terms of information about frequency or probability. Suppose people generally assume that morally bad behaviors are infrequent or improbable. The judgment that a behavior was morally bad would then impact their statistical understanding, which could in turn influence their intuitions about intention, causation, and the like. A number of other commentators take up related themes. Baldo & Barberousse propose that affective reactions can themselves serve as information and that this information may influence people’s intuitions. And Lombrozo & Uttich point out that, even if moral considerations are entering into people’s judgments at the algorithmic level, the best description at the computational level might still be in terms of an attempt to predict and explain behavior. Now, it certainly does seem to be the case that people can sometimes use moral judgments to arrive at statistical truths, and these proposals therefore merit closer analysis. We should distinguish, however, between two possible ways in which the proposals might be interpreted. One possible claim would be about the actual cognitive process people go through on-line. It might be claimed, for example, that people make a moral judgment, then use this judgment to make an inference about the frequency of the relevant behaviors, which in turn influences their intuitions about causation. If the proposal is understood in this way, I think that it is probably false. The problem is that when researchers independently vary information about frequency and moral status, they continue to find that moral status is playing an independent role (Roxborough & Cumby 2009). But perhaps there is another, very different way of understanding the proposal. One might say that facts about frequencies are playing a role, not at the level of people’s on-line cognition, but rather at the level of an “ultimate” or “evolutionary” explanation. Thus, suppose that theory-of-mind evolved as a mechanism for predicting and explaining behavior. Then, if violations of moral norms were generally infrequent, knowing that a behavior violated a norm might be a good cue for making certain statistical judgments about it, and our capacity for theoryof-mind might therefore have evolved to take moral considerations into account. In other words, the actual sequence of cognitive processes taking place in people’s minds might involve all sorts of irreducibly moral appraisals, but the best evolutionary explanation of this process might be that it generally serves to enable accurate prediction. (For an especially clear defense of this approach, see the commentary by Lombrozo & Uttich.) What we have here is a quite interesting hypothesis, but it is hard to know exactly how one might put it to the test empirically. In essence, we are dealing with a conflict between two very different visions. One vision focuses specifically on the nature of people’s capacity for theoryof-mind. It says that this capacity has a particular “purpose” or “function” – for example, to accurately predict and explain behavior – and the patterns of intuition under discussion here can be explained in terms of their tendency to fulfill that function. By contrast, the vision I develop in the target article emphasizes certain general principles governing human cognition as a

whole. The claim, then, is that the patterns we find in people’s theory-of-mind judgments are not best understood as fulfilling any kind of purpose that is specific to theory-of-mind. Rather, these patterns simply reflect certain perfectly general principles about the impact of moral judgment on human cognition. Clearly, the debate between these two visions is not the sort of thing that could be settled by a single critical experiment. Nonetheless, it does seem that further studies can offer us some illumination here. The key thing to notice is that the theory advanced in the target article predicts that the effects found in theory-of-mind should also be found in other domains that have nothing to do with theory-ofmind or even with prediction and explanation. So we can test the theory by looking to these other domains and checking to see whether similar effects are found there. An initial step in that direction can be found in the commentaries from Egre´ and from Cova et al., both of which show an impact of moral judgment on the use of quantifiers like many. If we continue to find effects of that basic type, we will gradually acquire stronger and stronger reasons to conclude that the effects under discussion here are best explained in terms of very general facts about the structure of human cognition. R3.4. The cognitive basis of science

Suppose, then, that people’s ordinary way of making sense of the world really is deeply different from what one finds in systematic science. A question now arises about how the emergence of systematic science could even have been possible. Given that science is itself a human invention, how could the methods of science have ended up diverging so substantially from the methods characteristic of ordinary human cognition? Levy offers a fascinating answer to this question. He suggests that the solution lies in the social character of science. In other words, the solution is not that each individual scientist can somehow enter a kind of special psychological state that allows her to transcend the limitations of ordinary human cognition and put all of her moral views to one side. Rather, the key point is that scientific inquiry is pursued by a whole community of different individuals, each of whom holds a slightly different set of moral views, and that this community as a whole is able to engage in a kind of inquiry that no single person could follow through on her own. This suggestion strikes me as a deeply intriguing and promising one, and it would be wonderful to put it to the test in further experimental studies. Ideally, one would want to bring scientists into the lab and look systematically at the factors that influence their judgments. Assuming that scientists show many of the same effects found in lay people (e.g., Mercier & Sperber, forthcoming), there is good reason to expect that the presence of a broader community would have a substantial impact on their ability to call into question their own initial intuitions. R4. Conclusion Replies like this one are governed by some peculiar expectations. The author is supposed to fend off all the commentators’ objections and show that his or her original article was actually completely correct all along. But, of course, BEHAVIORAL AND BRAIN SCIENCES (2010) 33:4

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References/Knobe: Person as scientist, person as moralist I don’t actually believe anything like that. A number of the hypotheses I defended in the past were subsequently refuted by other researchers, and I am sure that many of the hypotheses I have defended here will meet with a similar fate. Accordingly, it might be best to conclude, not by summarizing the views I hold right now, but rather by saying a few words about where things might move in the future. When I first started investigating the impact of moral judgments on intuitions about intentional action, I assumed that most of people’s cognition was entirely non-moral, and I therefore introduced a series of ad hoc maneuvers to explain the new experimental results. That strategy turned out to be completely misguided. As researchers began uncovering more and more cases in which morality influenced people’s intuitions, it became ever more clear that we needed a theory that offered a more abstract characterization of the impact of morality on people’s cognition as a whole. I suspect that we will actually have to move even farther in that direction. As a number of the commentators noted, it might be a mistake to look for some special place where moral considerations enter the picture. Instead, we might need to develop a view on which the mind makes little distinction between moral and non-moral factors, so that the very same theory that explains the impact of moral considerations also explains our ability to make apparently “scientific” use of purely statistical or descriptive information. NOTE 1. A quick note about the relevance of these data: The claim under discussion here is that judgments of counterfactual relevance play a role in intuitions about, e.g., causation. Hence, this claim yields the prediction that any factor that impacts judgments of counterfactual relevance should also impact intuitions about causation. In other words, if we uncover five different factors that influence judgments of counterfactual relevance, we should predict that all five of these factors influence causal intuitions, as well. However, the claim does not also go the other way. We are not claiming that counterfactual thinking is the only thing that ever affects causal intuitions, so we are not claiming that every factor that influences causal intuitions must also influence counterfactual reasoning. On the contrary, as Menzies helpfully notes, a whole series of excellent studies have shown that people’s causal intuitions can be influenced by factors that seem not to play a role in counterfactual thinking.

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Tables and figures (i.e., photographs, graphs, charts, or other artwork) should be numbered consecutively, and should appear in its appropriate location. Every table should have a title; every figure, a caption. Endnotes and appendices should be grouped together at the end of the paper and should ideally be locally linked to in the text to facilitate the reader (and of course the referee’s task). Acknowledgements should be placed at the end of the paper. The short abstract will appear by way of an advertisement, one issue in advance of the publication issue. The long abstract will be circulated to referees and then potential commentators should the paper be accepted, and will appear with the printed article. BBS’s rigorous timetable constraints (requiring the coordination of target articles, commentaries and author’s responses within the publishing queue) make it extremely difficult for us to process follow-up drafts of your submission. Please make sure that the paper you submit is the carefully checked final draft to which you wish the referees to address. Please also ensure that your submission has been proof-read by a native English speaker before submission. This, of course, greatly improves its chances at the refereeing stage. References: Bibliographic citations in the text must include the author’s last name and the date of publication and may include page references. Complete bibliographic information for each citation should be included in the list of references. Please also include and link to the WWW URL for any paper for which it exists. Examples of correct styles are: Brown (1973); (Brown 1973); Brown 1973; 1978); (Brown 1973; Jones 1976); (Brown & Jones 1978); (Brown et al. 1978). References should be in alphabetical order in the style of the following examples. Do not abbreviate journal titles: Freeman, W. J. (1958) Distribution in time and space of prepyriform electrical activity. 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://journals.cambridge.org/ BBSJournal/Inst 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.

Neural reuse: A fundamental organizational principle of the brain Michael L. Anderson

Person as scientist, person as moralist Joshua Knobe

To appear in upcoming issues

Human kinship, from conceptual structure to grammar

Behavioral and Brain Sciences

In this issue

Doug Jones, University of Utah

With commentary from C Behme; G Bennardo; T Biró; M Bloch; F El Guindi; D Gerkey & L Cronk; L Hogeweg, G Legendre & P Smolensky; R Hudson; FM Jordan & M Dunn; P Kay; C Knight; DB Kronenfeld; MJ Leaf; SC Levinson; C Liu, Y Ge, X Mai & Y-J Luo; SM Lyon; P McConvell; P Miers; S Musgrave & DL Dowe; A Nevins; DW Read; C Salazar; RM Seyfarth & DL Cheney; A Wierzbicka

The Simulation of Smiles (SIMS) model: Embodied simulation and the meaning of facial expression

Theories of embodied cognition have motivated an explosion of research in psychology and the neurosciences on the processing of facial expression of emotion. Still unclear is how the component processes of emotion and their neural mechanisms support embodied simulation, and what triggers its occurrence. We examine the roles of the brain’s reward systems, amygdala, somatosensory cortices, and motor centers in recognizing and interpreting smiles, and we integrate these with research on facial mimicry and eye gaze. A model of the processing of the smile is presented as a means to advance the application of theories of embodied cognition in the study of facial expression of emotion more generally.

With commentary from MW Alibali & AB Hostetter; MS Bartlett; F Basso & O Ouillier; P Bouissac; P Briñol, KG DeMarree & KR Smith; R Caldara; S Centorrino, E Djemai, A Hopfensitz, M Milinski & P Seabright; B Chakrabarti; B Chang & N Vermeulen; C Chantelle, S Laureys, S Majerus & C Schnakers; L Conty, J Grèzes & D Sander; CM Covas-Smith, J Fine, AM Glenberg, E Keylor, YJ Li, E Marsh, E Osborne, T Soliman & C Yee; K Evers, I Noens, J Steyaert & J Wagemans; J-M Fernández-Dols & P Carrera; C Hamon-Hill & J Barresi; L Huang & AD Galinsky; J Kiverstein & E Zamuner; D Lakens & KI Ruys; C Liu, Y Ge, W-B Luo & Y-J Luo; JS Lobmaier & MH Fischer; TA Mann & Y Choe; M Mehu K N'Diaye; E Morsella, C Montemayor, J Hubbard & P Zarolia; JJ Ohala; DA Sauter & SC Levinson; A Senju & MH Johnson; E Simpson & D Fragaszy; JE Swain & S Ho; JM Vigil & P Coulombe; P Winkielman

Volume 33, Number 4

Paula M. Niedenthal, Martial Mermillod, Centre national de la recherche scientifique and the University of Clermont-Ferrand, France, Marcus Maringer, University of Amsterdam, and Ursula Hess, Humboldt-Universität Berlin

August 2010

Research in anthropology has shown that kin terminologies have a complex combinatorial structure and vary systematically across cultures. This article argues that universals and variation in kin terminology result from the interaction of (1) an innate conceptual structure of kinship and (2) principles of optimal, “grammatical” communication active in language in general. The conceptual structure of kinship seems to borrow its organization from the conceptual structure of space, while being specialized for representing genealogy. The grammar of kinship looks like the product of an evolved grammar faculty, opportunistically active across traditional domains of semantics, syntax, and phonology.

Among the articles to appear in forthcoming issues of BBS:

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

Pages 245–366

D. Jones, “Human kinship, from conceptual structure to grammar” P. M. Niedenthal, M. Mermillod, M. Maringer & U. Hess, “The Simulation of Smiles (SIMS) model: Embodied simulation and the meaning of facial expression” S. Carey, “Précis of The Origin of Concepts” W. von Hippel & R. Trivers, “The evolution and psychology of self-deception” H. Mercier & D. Sperber, “Why do humans reason? Arguments for an argumentative theory”

An International journal of current research and theory with open peer commentary Volume 33 | Issue 4 | August 2010 | ISSN: 0140-525X