This chapter explores a way in which visual processing may involve innate constraints and attempts to show how such processing overcomes one enduring challenge to nativism. In particular, many challenges to nativist theories in other areas of cognitive psychology have focused on the later development of such abilities, and have argued that such development is in conflict with innate origins. Innateness, in these contexts, is seen as antidevelopmental, associated instead with static processes and principles. In contrast, certain perceptual models demonstrate (...) how the very same mental processes can both be innately specified and yet develop richly in response to experience with the environment. This process is entirely unmysterious, as shown in certain formal theories of visual perception, including those that appeal to spontaneous endogenous stimulation and those based on Bayesian inference. (shrink)

This is the first volume of a projected three-volume set on the subject of innateness. The extent to which the mind is innate is one of the central questions in the human sciences, with important implications for many surrounding debates. By bringing together the top nativist scholars in philosophy, psychology, and allied disciplines these volumes provide a comprehensive assessment of nativist thought and a definitive reference point for future nativist inquiry. The Innate Mind: Structure and Content, concerns the fundamental architecture (...) of the mind, addressing such question as: What capacities, processes, representations, biases, and connections are innate? How do these innate elements feed into a story about the development of our mature cognitive capacities, and which of them are shared with other members of the animal kingdom? The editors have provided an introduction giving some of the background to debates about innateness and introducing each of the subsequent essays, as well as a consolidated bibliography that will be a valuable reference resource for all those interested in this area. The volume will be of great importance to all researchers and students interested in the fundamental nature and powers of the human mind. Together, the three volumes in the series will provide the most intensive and richly cross-disciplinary investigation of nativism ever undertaken. They point the way toward a synthesis of nativist work that promises to provide a new understanding of our minds and their place in the natural order. (shrink)

This book is the second of a three-volume set on the subject of innateness. The book is highly interdisciplinary, and addresses such question as: to what extent are mature cognitive capacities a reflection of particular cultures and to what extent are they a product of innate elements? How do innate elements interact with culture to achieve mature cognitive capacities? How do minds generate and shape cultures? How are cultures processed by minds?Concerned with the fundamental architecture of the mind, this text (...) addresses questions about the existence & extent of human innate abilities, how these inate abilities affect the development of the mature mind, & which of them is shared with other species. (shrink)

This is the first of three volumes on the subject of innateness. The extent to which the mind is innate is one of the central questions in the human sciences, with important implications for many surrounding debates. This book along with the following two volumes provide assess of nativist thought and a definitive reference point for future nativist inquiry. This book is concerned with the fundamental architecture of the mind, addressing such question as: what capacities, processes, representations, biases, and connections (...) are innate? How do these innate elements feed into a story about the development of our mature cognitive capacities, and which of them are shared with other members of the animal kingdom? The book includes an introduction giving some of the background to debates about innateness and introducing each of the subsequent chapters, as well as a consolidated bibliography. (shrink)

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to (...) adaptive success. This target article critically examines this approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency. (shrink)

Enquiries into the possible nature and scope of innate knowledge never proceed in an empirical vaccuum. Instead, such conjectures are informed by a theory concerning probable representational form. Classical approaches to the nativism debate often assume a quasi-linguistic form of knowledge representation and deliniate a space of options accordingly. Recent connectionist theorizing posits a different kind of represenational form, and thus determines a different picture of the space of possible nativisms.

Though nativist hypotheses have played a pivotal role in the development of cognitive science, it remains exceedingly obscure how they—and the debates in which they figure—ought to be understood. The central aim of this paper is to provide an account which addresses this concern and in so doing: a) makes sense of the roles that nativist theorizing plays in cognitive science and, moreover, b), explains why it really matters to the contemporary study of cognition. I conclude by outlining a range (...) of further implications of this account for current debate in cognitive science. (shrink)

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)

Controversies over the innateness of cognitive processes, mechanisms, and structures play a persistent role in driving research in philosophy as well as the cognitive sciences, but the appropriate way to understand the category of the innate remains subject to dispute. One venerable approach in philosophy and cognitive science merely contrasts innate features with those that are learned. In fact, Jerry Fodor has recently suggested that this remains our best handle on innateness.

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

Along with the increasing popularity of connectionist language models has come a number of provocative suggestions about the challenge these models present to Chomsky's arguments for nativism. The aim of this paper is to assess these claims. We begin by reconstructing Chomsky's argument from the poverty of the stimulus and arguing that it is best understood as three related arguments, with increasingly strong conclusions. Next, we provide a brief introduction to connectionism and give a quick survey of recent efforts to (...) develop networks that model various aspects of human linguistic behavior. Finally, we explore the implications of this research for Chomsky's arguments. Our claim is that the relation between connectionism and Chomsky's views on innate knowledge is more complicated than many have assumed, and that even if these models enjoy considerable success the threat they pose for linguistic nativism is small. (shrink)

This article explores some of the philosophical implications of the Bayesian modeling paradigm. In particular, it focuses on the ramifications of the fact that Bayesian models pre‐specify an inbuilt hypothesis space. To what extent does this pre‐specification correspond to simply ‘‘building the solution in''? I argue that any learner must have a built‐in hypothesis space in precisely the same sense that Bayesian models have one. This has implications for the nature of learning, Fodor's puzzle of concept acquisition, and the role (...) of modeling in cognitive science. (shrink)

This paper takes a fresh look at the nativism–empiricism debate, presenting and defending a nativist perspective on the mind. Empiricism is often taken to be the default view both in philosophy and in cognitive science. This paper argues, on the contrary, that there should be no presumption in favor of empiricism (or nativism), but that the existing evidence suggests that nativism is the most promising framework for the scientific study of the mind. Our case on behalf of nativism has four (...) parts. (1) We characterize nativism’s core commitments relative to the contemporary debate between empiricists and nativists, (2) we present the positive case for nativism in terms of two central nativist arguments (the poverty of the stimulus argument and the argument from animals), (3) we respond to a number of influential objections to nativist theories, and (4) we explain the nativist approach to the conceptual system. (shrink)

The concept of innateness is a part of folk wisdom but is also used by biologists and cognitive scientists. This concept has a legitimate role to play in science only if the colloquial usage relates to a coherent body of evidence. We examine many different candidates for the post of scientific successor of the folk concept of innateness. We argue that none of these candidates is entirely satisfactory. Some of the candidates are more interesting and useful than others, but the (...) interesting candidates are not equivalent to each other and the empirical and evidential relations between them are far from clear. Researchers have treated the various scientific notions that capture some aspect of the folk concept of innateness as equivalent to each other or at least as tracking properties that are strongly correlated with each other. But whether these correlations exist is an empirical issue. This empirical issue has not been thoroughly investigated because in the attempt to create a bridge between the folk view and their theories, researchers have often assumed that the properties must somehow cluster. Rather than making further attempts to import the folk concept of innateness into the sciences, efforts should now be made to focus on the empirical questions raised by the debates and pave the way to a better way of studying the development of living organisms. Such empirical questions must be answered before it can be decided whether a good scientific successor – in the form of a concept that refers to a collection of biologically significant properties that tend to co-occur – can be identified or whether the concept of innateness deserves no place in science. (shrink)

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One (...) of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights. (shrink)

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...) explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)

One of the most notable aspects of Fodor's work is that it articulates features not only of speculative cognitive architectures but also of current research in ...

This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...) e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation. (shrink)

The idea that perceptual and cognitive systems must incorporate knowledge about the structure of the environment has become a central dogma of cognitive theory. In a Bayesian context, this idea is often realized in terms of “tuning the prior”—widely assumed to mean adjusting prior probabilities so that they match the frequencies of events in the world. This kind of “ecological” tuning has often been held up as an ideal of inference, in fact defining an “ideal observer.” But widespread as this (...) viewpoint is, it directly contradicts Bayesian philosophy of probability, which views probabilities as degrees of belief rather than relative frequencies, and explicitly denies that they are objective characteristics of the world. Moreover, tuning the prior to observed environmental frequencies is subject to overfitting, meaning in this context overtuning to the environment, which leads (ironically) to poor performance in future encounters with the same environment. Whenever there is uncertainty about the environment—which there almost always is—an agent's prior should be biased away from ecological relative frequencies and toward simpler and more entropic priors. (shrink)

A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...) explained within the Bayesian framework. As a forewarning, progress in cognitive science may be hindered if too many scientists continue to focus their efforts on Bayesian modelling, which risks to monopolize scientific resources that may be better allocated to alternative approaches. (shrink)

Enquiries into the possible nature and scope of innate knowledge never proceed in an empirical vaccuum. Instead, such conjectures are informed by a theory (perhaps only tacitly endorsed) concerning probable representational form. Classical approaches to the nativism debate often assume a quasi-linguistic form of knowledge representation and deliniate a space of options (concerning the nature and extent of innate knowledge) accordingly. Recent connectionist theorizing posits a different kind of represenational form, and thus determines a different picture of the space of (...) possible nativisms. (shrink)

Enquiries into the possible nature and scope of innate knowledge never proceed in an empirical vaccuum. Instead, such conjectures are informed by a theory concerning probable representational form. Classical approaches to the nativism debate often assume a quasi-linguistic form of knowledge representation and deliniate a space of options accordingly. Recent connectionist theorizing posits a different kind of represenational form, and thus determines a different picture of the space of possible nativisms.

As Ruben notes, the macrostrategy can allow that the distinction may also be drawn at some micro level, but it insists that descent to the micro level is ...

The book from which these sections are excerpted is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits the growth of rich and highly articulated structures along (...) an intrinsically determined course under the triggering and partially shaping effect of experience, which fixes parameters in an intricate system of predetermined form. It is argued that the mind is modular in character, with a diversity of cognitive structures, each with its specific properties arid principles. Knowledge of language, of the behavior of objects, and much else crucially involves these mental structures, and is thus not characterizable in terms of capacities, dispositions, or practical abilities, nor is it necessarily grounded in experience in the standard sense of this term. (shrink)

The book from which these sections are excerpted is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as “mental organs.” These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits the growth of rich and highly articulated (...) structures along an intrinsically determined course under the triggering and partially shaping effect of experience, which fixes parameters in an intricate system of predetermined form. It is argued that the mind is modular in character, with a diversity of cognitive structures, each with its specific properties arid principles. Knowledge of language, of the behavior of objects, and much else crucially involves these mental structures, and is thus not characterizable in terms of capacities, dispositions, or practical abilities, nor is it necessarily grounded in experience in the standard sense of this term.Various types of knowledge and modes of knowledge acquisition are discussed in these terms. Some of the properties of the language faculty are investigated. The basic cognitive relation is “knowing a grammar”; knowledge of language is derivative and, correspondingly, raises further problems. Language as commonly understood is not a unitary phenomenon but involves a number of interacting systems: the “computational” system of grammar, which provides the representations of sound and meaning that permit a rich range of expressive potential, is distinct from a conceptual system with its own properties; knowledge of language must be distinguished from knowledge of how to use a language; and the various systems that enter into the knowledge and use of language must be further analyzed into their specific subcomponents. (shrink)

Lorenz proposed in his (1935) articulation of a theory of behavioral instincts that the objective of ethology is to distinguish behaviors that are “innate” from behaviors that are “learned” (or “acquired”). Lorenz’s motive was to open the investigation of certain “adaptive” behaviors to evolutionary theorizing. Accordingly, since innate behaviors are “genetic”, they are open to such investigation. By Lorenz’s light an innate/acquired or learned dichotomy rested on a familiar Darwinian distinction between genes and environments. Ever since Lorenz, ascriptions of innateness (...) have become widespread in the cognitive, behavioral, and biological sciences. The trend continues despite decades of strong arguments that show, in particular, the dichotomy that Lorenz invoked in his theory of behavioral instincts is literally false: no biological trait is the product of genes alone. Some critics suggest that the failure of Lorenz’s account shows that innateness is not well-defined in biology and the practice of ascribing innateness to various biological traits should be dropped from respectable science. Elsewhere (Ariew 1996) I argued that despite the arguments of critics, there really is a biological phenomenon underlying the concept of innateness. On my view, innateness is best understood in terms of C.H. Waddington’s concept of “canalization”, i.e. the degree to which a trait is innate is the degree to which its developmental outcome is canalized. The degree to which a developmental outcome is canalized is the degree to which the developmental process is bound to produce a particular endstate despite environmental fluctuations both in the development’s initial state and during the course of development. The canalization account differs in many ways to the traditional ways that ethologists such as Konrad Lorenz originally understood the concept of innateness. Most importantly, on the canalization account the distinction between innate and acquired is not a dichotomy, as Konrad Lorenz had it, but rather a matter of degree difference that lies along a spectrum with highly canalized development outcomes on the one end and highly environmentally sensitive development outcomes on the other end. Nevertheless, I justified the canalization account on the basis of a set of desiderata or criteria that I suggested falls-out of what seemed uncontroversial about Lorenz’s account of innateness (briefly): innateness is a property of a developing individual, innateness denotes environmental stability, and innate-ascriptions are useful in certain natural selection explanations (more below). From that same set of desiderata I argued (in my 1996) that neither the concept of heritability nor of norms of reactions—two concepts from population genetics—suffice to ground innateness. In this essay, I wish to provide further support of the canalization account in two ways. First, I wish to better motivate the desiderata by revisiting a debate between Konrad Lorenz and Daniel Lehrman over the meaning and explanatory usefulness of innate ascriptions in ethology. Second, I wish to compare my canalization account of innateness with accounts proposed by contemporary philosophers, one by Stephen Stich (1975), another by Elliott Sober (forthcoming), and a third by William Wimsatt (1986). (shrink)

How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics.In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to (...) be prediction machines - devices that have evolved to anticipate the incoming streams of sensory stimulation before they arrive. These predictions then initiate actions that structure our worlds and alter the very things we need to engage and predict. Clark takes us on a journey in discovering the circular causal flows and the self-structuring of the environment that define "the predictive brain." What emerges is a bold, new, cutting-edge vision that reveals the brain as our driving force in the daily surf through the waves of sensory stimulation. (shrink)

This interdisciplinary new work explores one of the central theoretical problems in linguistics: learnability. The authors, from different backgrounds---linguistics, philosophy, computer science, psychology and cognitive science-explore the idea that language acquisition proceeds through general purpose learning mechanisms, an approach that is broadly empiricist both methodologically and psychologically. Written by four researchers in the full range of relevant fields: linguistics, psychology, computer science, and cognitive science, the book sheds light on the central problems of learnability and language, and traces their implications (...) for key questions of theoretical linguistics and the study of language acquisition. (shrink)

A new theory is taking hold in neuroscience. It is the theory that the brain is essentially a hypothesis-testing mechanism, one that attempts to minimise the error of its predictions about the sensory input it receives from the world. It is an attractive theory because powerful theoretical arguments support it, and yet it is at heart stunningly simple. Jakob Hohwy explains and explores this theory from the perspective of cognitive science and philosophy. The key argument throughout The Predictive Mind is (...) that the mechanism explains the rich, deep, and multifaceted character of our conscious perception. It also gives a unified account of how perception is sculpted by attention, and how it depends on action. The mind is revealed as having a fragile and indirect relation to the world. Though we are deeply in tune with the world we are also strangely distanced from it. (shrink)

This study synthesizes current information from the various fields of cognitive science in support of a new and exciting theory of mind. Most psychologists study horizontal processes like memory and information flow; Fodor postulates a vertical and modular psychological organization underlying biologically coherent behaviors. This view of mental architecture is consistent with the historical tradition of faculty psychology while integrating a computational approach to mental processes. One of the most notable aspects of Fodor's work is that it articulates features not (...) only of speculative cognitive architectures but also of current research in artificial intelligence. (shrink)

This powerfully iconoclastic book reconsiders the influential nativist position toward the mind. Nativists assert that some concepts, beliefs, or capacities are innate or inborn: "native" to the mind rather than acquired. Fiona Cowie argues that this view is mistaken, demonstrating that nativism is an unstable amalgam of two quite different--and probably inconsistent--theses about the mind. Unlike empiricists, who postulate domain-neutral learning strategies, nativists insist that some learning tasks require special kinds of skills, and that these skills are hard-wired into our (...) brains at birth. This "faculties hypothesis" finds its modern expression in the views of Noam Chomsky. Cowie, marshaling recent empirical evidence from developmental psychology, psycholinguistics, computer science, and linguistics, provides a crisp and timely critique of Chomsky's nativism and defends in its place a moderately nativist approach to language acquisition. Also in contrast to empiricists, who view the mind as simply another natural phenomenon susceptible of scientific explanation, nativists suspect that the mental is inelectably mysterious. Cowie addresses this second strand in nativist thought, taking on the view articulated by Jerry Fodor and other nativists that learning, particularly concept acquisition, is a fundamentally inexplicable process. Cowie challenges this explanatory pessimism, and argues convincingly that concept acquisition is psychologically explicable. What's Within? is a clear and provocative achievement in the study of the human mind. (shrink)