According to models of objectification, viewing someone as a body induces de-mentalization, stripping away their psychological traits. Here evidence is presented for an alternative account, where a body focus does not diminish the attribution of all mental capacities but, instead, leads perceivers to infer a different kind of mind. Drawing on the distinction in mind perception between agency and experience, it is found that focusing on someone's body reduces perceptions of agency but increases perceptions of experience. These effects were found (...) when comparing targets represented by both revealing versus nonrevealing pictures or by simply directing attention toward physical characteristics. The effect of a body focus on mind perception also influenced moral intuitions, with those represented as a body seen to be less morally responsible but more sensitive to harm. These effects suggest that a body focus does not cause objectification per se but, instead, leads to a redistribution of perceived mind. (shrink)

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (shrink)

Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational (...) limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the modal response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate. Key Words: biases; descriptive models; heuristics; individual differences; normative models; rationality; reasoning. (shrink)

Many weaknesses of game theory are cured by new models that embody simple cognitive principles, while maintaining the formalism and generality that makes game theory useful. Social preference models can generate team reasoning by combining reciprocation and correlated equilibrium. Models of limited iterated thinking explain data better than equilibrium models do; and they self-repair problems of implausibility and multiplicity of equilibria.

In recent years evolutionary psychologists have developed and defended the Massive Modularity Hypothesis, which maintains that our cognitive architecture—including the part that subserves ‘central processing’ —is largely or perhaps even entirely composed of innate, domain-specific computational mechanisms or ‘modules’. In this paper I argue for two claims. First, I show that the two main arguments that evolutionary psychologists have offered for this general architectural thesis fail to provide us with any reason to prefer it to a competing picture of the (...) mind which I call the Library Model of Cognition. Second, I argue that this alternative model is compatible with the central theoretical and methodological commitments of evolutionary psychology. Thus I argue that, at present, the endorsement of the Massive Modularity Hypothesis by evolutionary psychologists is both unwarranted and unmotivated. (shrink)

Psychologists and philosophers have recently been exploring whether the mechanisms which underlie the acquisition of ‘theory of mind’ (ToM) are best charac- terized as cognitive modules or as developing theories. In this paper, we attempt to clarify what a modular account of ToM entails, and why it is an attractive type of explanation. Intuitions and arguments in this debate often turn on the role of develop- ment: traditional research on ToM focuses on various developmental sequences, whereas cognitive modules are thought (...) to be static and ‘anti-developmental’. We suggest that this mistaken view relies on an overly limited notion of modularity, and we explore how ToM might be grounded in a cognitive module and yet still afford development. Modules must ‘come on-line’, and even fully developed modules may still develop internally, based on their constrained input. We make these points con- crete by focusing on a recent proposal to capture the development of ToM in a module via parameterization. (shrink)

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...) the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?". (shrink)

Although the study of visual perception has made more progress in the past 40 years than any other area of cognitive science, there remain major disagreements as to how closely vision is tied to general cognition. This paper sets out some of the arguments for both sides and defends the position that an important part of visual perception, which may be called early vision or just vision, is prohibited from accessing relevant expectations, knowledge and utilities - in other words it (...) is cognitively impenetrable. That part of vision is complex and articulated and provides a representation of the 3-D surfaces of objects sufficient to serve as an index into memory, with somewhat different outputs being made available to other systems such as those dealing with motor control. The paper also addresses certain conceptual and methodological issues, including the use of signal detection theory and event-related potentials to assess cognitive penetration of vision. A distinction is made among several stages in visual processing. These include, in addition to the inflexible early-vision stage, a pre-perceptual attention allocation stage and a post-perceptual evaluation, memory-accessing, and inference stage which provide several different highly constrained ways in which cognition can affect the outcome of visual perception. The paper discusses arguments that have been presented in both computer vision and psychology showing that vision is "intelligent" and involves elements of problem solving". It is suggested that these cases do not show cognitive penetration, but rather they show that certain natural constraints on interpretation, concerned primarily with optical and geometrical properties of the world, have been compiled into the visual system. The paper also examines a number of examples where instructions and "hints" are alleged to affect. (shrink)

Experiments with young infants provide evidence for early-developing capacities to represent physical objects and to reason about object motion. Early physical reasoning accords with 2 constraints at the center of mature physical conceptions: continuity and solidity. It fails to accord with 2 constraints that may be peripheral to mature conceptions: gravity and inertia. These experiments suggest that cognition develops concurrently with perception and action and that development leads to the enrichment of conceptions around an unchanging core. The experiments challenge claims (...) that cognition develops on a foundation of perceptual or motor experience, that initial conceptions are inappropriate to the world, and that initial conceptions are abandoned or radically.. (shrink)

Evolutionary psychologists claim that the mind contains “hundreds or thousands” of “genetically specified” modules, which are evolutionary adaptations for their cognitive functions. We argue that, while the adult human mind/brain typically contains a degree of modularization, its “modules” are neither genetically specified nor evolutionary adaptations. Rather, they result from the brain’s developmental plasticity, which allows environmental task demands a large role in shaping the brain’s information-processing structures. The brain’s developmental plasticity is our fundamental psychological adaptation, and the “modules” that result (...) from it are adaptive responses to local conditions, not past evolutionary environments. If different individuals share common environ- ments, however, they may develop similar “modules,” and this process can mimic the development of genetically specified modules in the evolutionary psychologist’s sense. (shrink)

As the dust jacket proclaims, “this is surely Fodor’s most irritating book in years …. It should exasperate philosophers, linguists, cognitive psychologists, and cognitive neuroscientists alike.” Yes, Fodor is an equal-opportunity annoyer. He sees no job for conceptual analysts, no hope for lexical semanticists, and no need for prototype theorists. When it comes to shedding light on concepts, these luminaries have delivered nothing but moonshine. Fodor aims to remedy things, and not just with snake oil. He serves up plenty of (...) clever barbs, potshots, and one-liners, not to mention arguments, to promote his “informational atomism.” It states that there is a large class of concepts, namely lexical concepts, that are ontologically and semantically primitive: each such concept has no internal structure and has its content not in virtue of any relation it bears to other concepts but in virtue of a nomic relation it bears to some property. The property it is thus “locked to” is its content. (shrink)

What counts as human rationality: reasoning processes that embody content-independent formal theories, such as propositional logic, or reasoning processes that are well designed for solving important adaptive problems? Most theories of human reasoning have been based on content-independent formal rationality, whereas adaptive reasoning, ecological or evolutionary, has been little explored. We elaborate and test an evolutionary approach, Cosmides' social contract theory, using the Wason selection task. In the first part, we disentangle the theoretical concept of a “social contract” from that (...) of a “cheater-detection algorithm”. We demonstrate that the fact that a rule is perceived as a social contract — or a conditional permission or obligation, as Cheng and Holyoak proposed — is not sufficient to elicit Cosmides' striking results, which we replicated. The crucial issue is not semantic, but pragmatic: whether a person is cued into the perspective of a party who can be cheated. In the second part, we distinguish between social contracts with bilateral and unilateral cheating options. Perspective change in contracts with bilateral cheating options turns P & not-Q responses into not-P & Q responses. The results strongly support social contract theory, contradict availability theory, and cannot be accounted for by pragmatic reasoning schema theory, which lacks the pragmatic concepts of perspectives and cheating detection. (shrink)

When cognitive neuropsychologists make inferences about the functional architecture of the normal mind from selective cognitive impairments they generally assume that the effects of brain damage are local, that is, that the nondamaged components of the architecture continue to function as they did before the damage. This assumption follows from the view that the components of the functional architecture are modular, in the sense of being informationally encapsulated. In this target article it is argued that this “locality” assumption is probably (...) not correct in general. Inferences about the functional architecture can nevertheless be made from neuropsychological data with an alternative set of assumptions, according to which human information processing is graded, distributed, and interactive. These claims are supported by three examples of neuropsychological dissociations and a comparison of the inferences obtained from these impairments with and without the locality assumption. The three dissociations are: selective impairments in knowledge of living things, disengagment of visual attention, and overt face recognition. In all three cases, the neuropsychological phenomena lead to more plausible inferences about the normal functional architecture when the locality assumption is abandoned. Also discussed are the relations between the locality assumption in neuropsychology and broader issues, including Fodor's modularity hypothesis and the choice between top-down and bottom-up research approaches. (shrink)

Fiona Cowie’s What’s Within consists of three parts. In the first, she examines the early modern rationalist-empiricist debate over nativism, isolating what she considers the two substantive “strands” that truly separated them: whether there exist domain-specific learning mechanisms, and whether concept acquisition is amenable to naturalistic explanation. She then turns, in the book’s succeeding parts, to where things stand today with these issues. The second part argues that Jerry Fodor’s view of concepts is continuous with traditional nativism in that it (...) precludes a naturalistic story of concept acquisition. Cowie objects, however, to Fodor’s path to this conclusion and thus sees no reason to endorse it. The third part assesses Chomskyan nativism as a contemporary instance of positing domain-specific learning mechanisms. Though she is highly critical of how “poverty of the stimulus” arguments and the like have been used to lend credence to stronger conclusions, she holds that such arguments do indeed support the nativist’s domain-specificity claim. Cowie’s reconsideration of nativism thus limits itself to concepts and language. The terrain she does cover, however, is vast; and Cowie’s illuminating discussions will stimulate anyone interested in the area. As I focus on a few large-scale qualms in what follows, let me mention in particular that much of what is of interest in Cowie’s book is to be found in her detailed consideration of specific arguments. (shrink)

Currently, there is widespread skepticism that higher cognitive processes, given their apparent flexibility and globality, could be carried out by specialized computational devices, or modules. This skepticism is largely due to Fodor’s influential definition of modularity. From the rather flexible catalogue of possible modular features that Fodor originally proposed has emerged a widely held notion of modules as rigid, informationally encapsulated devices that accept highly local inputs and whose opera- tions are insensitive to context. It is a mistake, however, to (...) equate such features with computational devices in general and therefore to assume, as Fodor does, that higher cognitive processes must be non-computational. Of the many possible non-Fodorean architectures, one is explored here that offers possible solutions to computational problems faced by conventional modular systems: an ‘enzymatic’ architecture. Enzymes are computational devices that use lock-and-key template matching to iden- tify relevant information (substrates), which is then operated upon and returned to a common pool for possible processing by other devices. Highly specialized enzymes can operate together in a common pool of information that is not pre-sorted by information type. Moreover, enzymes can use molecular ‘tags’ to regulate the operations of other devices and to change how particular substrates are construed and operated upon, allowing for highly interactive, context-specific processing. This model shows how specialized, modular processing can occur in an open system, and suggests that skepti- cism about modularity may largely be due to failure to consider alternatives to the standard model. (shrink)

How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with recent neurobiological evidence (...) that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. (shrink)

The claim that the human cognitive system tends to allocate resources to the processing of available inputs according to their expected relevance is at the basis of relevance theory. The main thesis of this chapter is that this allocation can be achieved without computing expected relevance. When an input meets the input condition of a given modular procedure, it gives this procedure some initial level of activation. Input-activated procedures are in competition for the energy resources that would allow them to (...) follow their full course. What determines which of the procedures in competition get sufficient resources to trigger their full operation is the dynamics of their activation. This dynamics depend both on the prior degree of mobilisation of a modular procedure and on the activation that propagates from other active modules. It is quite conceivable also that the mobilisation of some procedures has inhibitory effects on some others. The relevance-theoretic claim is that, at every instant, this dynamics of activation provides rough physiological indicators of expected relevance. The flow of energy in the system is locally regulated by these indicators. As a result, those input-procedure combinations that have the greatest expected relevance are the more likely ones to receive sufficient energy to follow their course. (shrink)

In my book How the Mind Works, I defended the theory that the human mind is a naturally selected system of organs of computation. Jerry Fodor claims that 'the mind doesn't work that way'(in a book with that title) because (1) Turing Machines cannot duplicate humans' ability to perform abduction (inference to the best explanation); (2) though a massively modular system could succeed at abduction, such a system is implausible on other grounds; and (3) evolution adds nothing to our understanding (...) of the mind. In this review I show that these arguments are flawed. First, my claim that the mind is a computational system is different from the claim Fodor attacks (that the mind has the architecture of a Turing Machine); therefore the practical limitations of Turing Machines are irrelevant. Second, Fodor identifies abduction with the cumulative accomplishments of the scientific community over millennia. This is very different from the accomplishments of human common sense, so the supposed gap between human cognition and computational models may be illusory. Third, my claim about biological specialization, as seen in organ systems, is distinct from Fodor's own notion of encapsulated modules, so the limitations of the latter are irrelevant. Fourth, Fodor's arguments dismissing of the relevance of evolution to psychology are unsound. (shrink)

This chapter addresses the flexibility problem for massive modularity. It argues that massively modular architectures exhibit flexibility largely as a result of context-sensitive competition between modules for the allocation of cognitive resources. Thus, it is the cognitive system as a whole that exhibits flexibility, rather than any particular subsystem within it.

This chapter examines the scope and limits of the tractability argument. It argues for two claims. First, that when explored with appropriate care and attention, it becomes clear that the argument provides no good reason to prefer massive modularity to the more traditional rationalist alternative. Second, while it is denied that tractability considerations support massive modularity per se, this does not mean that they show nothing whatsoever. Careful analysis of tractability considerations suggests a range of characteristics that any plausible version (...) of psychological rationalism is likely to possess. The chapter proceeds as follows: Section 1 outlines and clarifies the general form of the tractability argument. Section 2 explains how massive modularity is supposed to resolve intractability worries. Sections 3 to 7 highlight the deficiencies of the main extant arguments for claiming that nonmodular mechanisms are intractable. Section 8 concludes by sketching some of the general characteristics that a plausible rationalist alternative to massive modularity — one capable of subserving tractable cognitive processes — is likely to possess. (shrink)

This chapter addresses the question of why many cognitive and behavioural scientists still fail to recognize the truth of nativism despite the power of poverty of stimulus arguments. It argues that this is primarily because the domains in which these arguments have hitherto been applied, for example, language or naive physics, are all ones in which the knowledge that children acquire is objectively present in their environment. Thus, the possibility always remains open that children could somehow be acquiring this knowledge (...) from the environment through general learning. In the case of motivation, however, this last bastion of resistance is unavailable, since desires do not serve to represent information that is already present in the environment. The closest thing to a knockdown argument for nativism can therefore be developed in respect of innate motivational systems. (shrink)

The question posed by Dunn and Kirsner (D&K) is an instance of a more general one: What can we infer from data? One answer, if we are talking about logically valid deductive inference, is that we cannot infer theories from data. A theory is supposed to explain the data and so cannot be a mere summary of the data to be explained. The truth of an explanatory theory goes beyond the data and so is never logically guaranteed by the data. (...) This is not just a point about cognitive neuropsychology, or even about psychology in general. It is a familiar point about all science. (shrink)

Most biologists and some cognitive scientists have independently reached the conclusion that there is no such thing as learning in the traditional “instructive‘ sense. This is, admittedly, a somewhat extreme thesis, but I defend it herein the light of data and theories jointly extracted from biology, especially from evolutionary theory and immunology, and from modern generative grammar. I also point out that the general demise of learning is uncontroversial in the biological sciences, while a similar consensus has not yet been (...) reached in psychology and in linguistics at large. Since many arguments presently offered in defense of learning and in defense of “general intelligence‘ are often based on a distorted picture of human biological evolution, I devote some sections of this paper to a critique of “adaptationism,‘ providing also a sketch of a better evolutionary theory. Moreover, since certain standard arguments presented today as “knock-down‘ in psychology, in linguistics and in artificial intelligence are a perfect replica of those once voiced by biologists in favor of instruction and against selection, I capitalize on these errors of the past to draw some lessons for the present and for the future. (shrink)