Can the output of human cognition be predicted from the assumption that it is an optimal response to the information-processing demands of the environment? A methodology called rational analysis is described for deriving predictions about cognitive phenomena using optimization assumptions. The predictions flow from the statistical structure of the environment and not the assumed structure of the mind. Bayesian inference is used, assuming that people start with a weak prior model of the world which they integrate with experience to develop (...) stronger models of specific aspects of the world. Cognitive performance maximizes the difference between the expected gain and cost of mental effort. Memory performance can be predicted on the assumption that retrieval seeks a maximal trade-off between the probability of finding the relevant memories and the effort required to do so; in categorization performance there is a similar trade-off between accuracy in predicting object features and the cost of hypothesis formation; in casual inference the trade-off is between accuracy in predicting future events and the cost of hypothesis formation; and in problem solving it is between the probability of achieving goals and the cost of both external and mental problem-solving search. The implemention of these rational prescriptions in neurally plausible architecture is also discussed. (shrink)

In traditional decision theory, utility is regarded as a mathematical representation of preferences to be inferred from agents hedonic experiences. Some go as far as to contend that utility is literally computed by specific neural areas and urge economists to complement or substitute their notion of utility with some neuro-psychological construct. In this paper, I distinguish three notions of utility that are frequently mentioned in debates about decision theory and examine some critical issues regarding their definition and measurability. Moreover, I (...) provide various empirical and conceptual reasons to doubt that economists should base decision theoretic analyses on some neuro-psychological notion of utility. (shrink)

Cognitive science is a child of the 1950s, the product of a time when psychology, anthropology and linguistics were redefining themselves and computer science and neuroscience as disciplines were coming into existence. Psychology could not participate in the cognitive revolution until it had freed itself from behaviorism, thus restoring cognition to scientific respectability. By then, it was becoming clear in several disciplines that the solution to some of their problems depended crucially on solving problems traditionally allocated to other disciplines. Collaboration (...) was called for: this is a personal account of how it came about. (shrink)

Everybody knows that humans are cultural animals. Although this fact is universally acknowledged, many opportunities to exploit it are overlooked. In this article, I propose shifting our attention from local examples of extended mind to the cultural-cognitive ecosystems within which human cognition is embedded. I conclude by offering a set of conjectures about the features of cultural-cognitive ecosystems.

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)

In the course of daily life we solve problems often enough that there is a special term to characterize the activity and the right to expect a scientific theory to explain its dynamics. The classical view in psychology is that to solve a problem a subject must frame it by creating an internal representation of the problem’s structure, usually called a problem space. This space is an internally generable representation that is mathematically identical to a graph structure with nodes and (...) links. The nodes can be annotated with useful information, and the whole representation can be distributed over internal and external structures such as symbolic notations on paper or diagrams. If the representation is distributed across internal and external structures the subject must be able to keep track of activity in the distributed structure. Problem solving proceeds as the subject works from an initial state in mentally supported space, actively constructing possible solution paths, evaluating them and heuristically choosing the best. Control of this exploratory process is not well understood, as it is not always systematic, but various heuristic search algorithms have been proposed and some experimental support has been provided for them. (shrink)

Behaviourism is the view that preferences, beliefs, and other mental states in social-scientific theories are nothing but constructs re-describing people's behaviour. Mentalism is the view that they capture real phenomena, on a par with the unobservables in science, such as electrons and electromagnetic fields. While behaviourism has gone out of fashion in psychology, it remains influential in economics, especially in ‘revealed preference’ theory. We defend mentalism in economics, construed as a positive science, and show that it fits best scientific practice. (...) We distinguish mentalism from, and reject, the radical neuroeconomic view that behaviour should be explained in terms of brain processes, as distinct from mental states. (shrink)

This paper lays out some of the empirical evidence for the importance of neural reuse—the reuse of existing (inherited and/or early-developing) neural circuitry for multiple behavioral purposes—in defining the overall functional structure of the brain. We then discuss in some detail one particular instance of such reuse: the involvement of a local neural circuit in finger awareness, number representation, and other diverse functions. Finally, we consider whether and how the notion of a developmental homology can help us understand the relationships (...) between the cognitive functions that develop out of shared neural supports. (shrink)

Our ability to acknowledge and recognise our own identity - our 'self' - is a characteristic doubtless unique to humans. Where does this feeling come from? How does the combination of neurophysiological processes coupled with our interaction with the outside world construct this coherent identity? We know that our social interactions contribute via the eyes, ears etc. However, our self is not only influenced by our senses. It is also influenced by the actions we perform and those we see others (...) perform. Our brain anticipates the effects of our own actions and simulates the actions of others. In this way, we become able to understand ourselves and to understand the actions and emotions of others. -/- This book is the first to describe the new field of 'Motor Cognition' - one to which the author's contribution has been seminal. Though motor actions have long been studied by neuroscientists and physiologists, it is only recently that scientists have considered the role of actions in building the self. How consciousness of action is part of self-consciousness, how one's own actions determine the sense of being an agent, how actions performed by others impact on ourselves for understanding others, differentiating ourselves from them and learning from them: these questions are raised and discussed throughout the book, drawing on experimental, clinical, and theoretical bases. -/- The advent of new neuroscience techniques, like neuroimaging and direct electrical brain stimulation, together with a renewal of behavioral methods in cognitive psychology, provide new insights into this area. Mental imagery of action, self-recognition, consciousness of actions, imitation can be objectively studied using these new tools. The results of these investigations shed light on clinical disorders in neurology, psychiatry and in neuro-development. -/- This is a major new work that will lay down the foundations for the field of motor cognition. (shrink)

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation (...) of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors. (shrink)

We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms , in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated (...) with multilevel mechanistic explanations. (shrink)

This paper discusses two perspectives, each of which recognises the importance of environmental resources in enhancing and amplifying our cognitive capacity. One is the Clark–Chalmers model, extended further by Clark and others. The other derives from niche construction models of evolution, models which emphasise the role of active agency in enhancing the adaptive fit between agent and world. In the human case, much niche construction is epistemic: making cognitive tools and assembling other informational resources that support and scaffold intelligent action. (...) I shall argue that extended mind cases are limiting cases of environmental scaffolding, and while the extended mind picture is not false, the niche construction model is a more helpful framework for understanding human action. (shrink)

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...) this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. (shrink)

This up-to-date introduction to decision theory offers comprehensive and accessible discussions of decision-making under ignorance and risk, the foundations of utility theory, the debate over subjective and objective probability, Bayesianism, causal decision theory, game theory, and social choice theory. No mathematical skills are assumed, and all concepts and results are explained in non-technical and intuitive as well as more formal ways. There are over 100 exercises with solutions, and a glossary of key terms and concepts. An emphasis on foundational aspects (...) of normative decision theory (rather than descriptive decision theory) makes the book particularly useful for philosophy students, but it will appeal to readers in a range of disciplines including economics, psychology, political science and computer science. • Has over 100 end of chapter review questions and exercises with solutions • Includes a chapter on how to draw a decision matrix • Explains the link between individual decision making, game theory and social choice theory Contents Preface; 1. Introduction; 2. The decision matrix; 3. Decisions under ignorance; 4. Decisions under risk; 5. Utility; 6. The mathematics of probability; 7. The philosophy of probability; 8. Why should we accept the preference axioms; 9. Causal vs. evidential decision theory; 10. Bayesian vs. non-Bayesian decision theory; 11. Game theory I: basic concepts and zero sum games; 12. Game theory II: nonzero sum and co-operative games; 13. Social choice theory; 14. Overview of descriptive decision theory; Appendix A. Glossary; Appendix B. Proof of the von Neumann-Morgenstern theorem; Further reading; Index. (shrink)

In the course of daily life we solve problems often enough that there is a special term to characterize the activity and the right to expect a scientific theory to explain its dynamics. The classical view in psychology is that to solve a problem a subject must frame it by creating an internal representation of the problem‘s structure, usually called a problem space. This space is an internally generable representation that is mathematically identical to a graph structure with nodes and (...) links. The nodes can be annotated with useful information, and the whole representation can be distributed over internal and external structures such as symbolic notations on paper or diagrams. If the representation is distributed across internal and external structures the subject must be able to keep track of activity in the distributed structure. Problem solving proceeds as the subject works from an initial state in this mentally supported space, actively construction possible solution paths, evaluating them and heuristically choosing the best. Control of this exploratory process is not well understood, as it is not always systematic, but various heuristic search algorithms have been proposed and some experimental support has been provided for them. (shrink)

A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...) is therefore not about discovering the unknown, but rather "a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education". (shrink)

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

In Cognitive Integration: Attacking The Bounds of Cognition Richard Menary argues that the real pay-off from extended-mind-style arguments is not a new form of externalism in the philosophy of mind, but a view in which the 'internal' and 'external' aspects of cognition are integrated into a whole. Menary argues that the manipulation of external vehicles constitutes cognitive processes and that cognition is hybrid: internal and external processes and vehicles complement one another in the completion of cognitive tasks. However, we cannot (...) make good on these claims without understanding the cognitive norms by which we manipulate bodily external vehicles of cognition. -/- Shaun Gallagher: “Menary sets out some extremely welcome clarifications that help to integrate the models of embodied and extended cognition. He not only provides convincing responses to all of the main objections that have been made against these approaches, he also puts flesh on the integrated model by incorporating concepts such as epistemic action, by expanding the discussion to include a Peircean view of representation, by demonstrating its evolutionary roots, and by exploring its implications for language and cognition. This is one of those books that takes us forward a number of giant steps. Menary makes it comprehensive and comprehensible.” . (shrink)

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

Thomas S. Kuhn's classic book is now available with a new index.

Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated action, and development. Particular attention is given to abstract concepts, language, and computational mechanisms.

Damasio's somatic marker hypothesis (SMH) is a prominent neuroscientific hypothesis about the mechanisms implementing decision-making. This paper argues that, since its inception, the SMH has not been clearly formulated. It is possible to identify at least two different hypotheses, which make different predictions: SMH-G, which claims that somatic states generally implement preferences and are needed to make a decision; and SMH-S, which specifically claims that somatic states assist decision-making by anticipating the long-term outcomes of available options. This paper also argues (...) that neither hypothesis is adequately supported empirically; the task originally proposed to test SMH is not a good test for SMH-S, and its results do not support SMH-G either. In addition, it is not clear how SMH-G could be empirically invalidated, given its general formulation. Suggestions are made that could help provide evidence for SMH-S, and make SMH-G more specific. 1 Introduction 2 Two Hypotheses: Somatic Markers as Embodied Preferences, and as a Source of Farsightedness 3 Lack of Evidence for Somatic Farsightedness 4 Does Making Decisions Require Somatic Markers, and can it be Shown in the Laboratory? 5 Conclusion CiteULike Connotea Del.icio.us What's this? (shrink)

Embodied Cognition is an approach to cognition that departs from traditional cognitive science in its reluctance to conceive of cognition as computational and in its emphasis on the significance of an organism's body in how and what the organism thinks. Three lines of embodied cognition research are described and some thoughts on the future of embodied cognition offered.

As yet, there is no enactive account of social cognition. This paper extends the enactive concept of sense-making into the social domain. It takes as its departure point the process of interaction between individuals in a social encounter. It is a well-established finding that individuals can and generally do coordinate their movements and utterances in such situations. We argue that the interaction process can take on a form of autonomy. This allows us to reframe the problem of social cognition as (...) that of how meaning is generated and transformed in the interplay between the unfolding interaction process and the individuals engaged in it. The notion of sense-making in this realm becomes participatory sense-making. The onus of social understanding thus moves away from strictly the individual only. (shrink)

This talk, delivered at De l''autopoièse à la neurophénoménologie: un hommage à Francisco Varela; from autopoiesis to neurophenomenology: a tribute to Francisco Varela, June 18–20, at the Sorbonne in Paris, explicates several links between Varela''s neurophenomenology and his biological concept of autopoiesis.

Often, sensory input underdetermines perception. One such example is the perception of illusory contours. In illusory contour perception, the content of the percept includes the presence of a contour that is absent from the informational content of the sensation. (By “sensation” I mean merely information-bearing events at the transducer level. I intend no further commitment such as the identification of sensations with qualia.) I call instances of perception underdetermined by sensation “underdetermined perception.” The perception of illusory contours is just one (...) kind of underdetermined perception. The focus of this chapter is another kind of underdetermined perception: what I shall call "active perception". Active perception occurs in cases in which the percept, while underdetermined by sensation, is determined by a combination of sensation and action. The phenomenon of active perception has been used by several to argue against the positing of representations in explanations of sensory experience, either by arguing that no representations need be posited or that far fewer than previously thought need be posited. Such views include, but are not limited to those of Gibson (1966, 1986), Churchland. (shrink)

The first full-scale history of cognitive science, this work addresses a central issue: What is the nature of knowledge?

This is a book about how we see: the environment around us (its surfaces, their layout, and their colors and textures); where we are in the environment; whether or not we are moving and, if we are, where we are going; what things are good for; how to do things (to thread a needle or drive an automobile); or why things look as they do.The basic assumption is that vision depends on the eye which is connected to the brain. The (...) author suggests that natural vision depends on the eyes in the head on a body supported by the ground, the brain being only the central organ of a complete visual system. When no constraints are put on the visual system, people look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision—and what this book is about. (shrink)

In treating cognition as problem solving, Andy Clark suggests, we may often abstract too far from the very body and world in which our brains evolved to guide...

Animate vision systems have gaze control mechanisms that can actively position the camera coordinate system in response to physical stimuli. Compared to passive systems, animate systems show that visual computation can be vastly less expensive when considered in the larger context of behavior. The most important visual behavior is the ability to control the direction of gaze. This allows the use of very low resolution imaging that has a high virtual resolution. Using such a system in a controlled way provides (...) additional constraints that dramatically simplify the computations of early vision. Another important behavior is the way the environment “behaves‘. Animate systems under real-time constraints can further reduce their computational burden by using environmental cues that are perspicuous in the local context. A third source of economy is introduced when behaviors are learned. Because errors are rarely fatal, systems using learning algorithms can amortize computational cost over extended periods. Further economies can be achieved when the learning system uses indexical reference, which is a form of dynamic variable binding. Animate vision is a natural way of implementing this dynamic binding. (shrink)

Second, this collection of cognitive programs evolved in the Pleistocene to solve the adaptive problems regularly faced by our hunter-gatherer ancestors-...

The idea that human thinking and machine computing are "radically the same" provides the central theme for this marvelously lucid and witty book on...

This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 The Psychological Reality (...) of Programs: Strong Equivalence 5 Constraining Functional Architecture 6 The Bridge from Physical to Symbolic: Transduction 7 Functional Architecture and Analogue Processes 8 Mental Imagery and Functional Architecture 9 Epilogue: What is Cognitive Science the Science of? (shrink)

Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...) producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. (shrink)

Connectionism and classicism, it generally appears, have at least this much in common: both place some notion of internal representation at the heart of a scientific study of mind. In recent years, however, a much more radical view has gained increasing popularity. This view calls into question the commitment to internal representation itself. More strikingly still, this new wave of anti-representationalism is rooted not in armchair theorizing but in practical attempts to model and understand intelligent, adaptive behavior. In this paper (...) we first present, and then critically assess, a variety of recent anti-representationalist treatments. We suggest that so far, at least, the sceptical rhetoric outpaces both evidence and argument. Some probable causes of this premature scepticism are isolated. Nonetheless, the anti-representationalist challenge is shown to be both important and progressive insofar as it forces us to see beyond the bare representational/non-representational dichotomy and to recognize instead a rich continuum of degrees and types of representationality. (shrink)

On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so (...) defined is internal to this concept, so it becomes a hypothesis that this notion of symbols includes the symbols that we humans use every day of our lives. In this paper we attempt systematically, but plainly, to lay out the nature of physical symbol systems. Such a review is in ways familiar, but not thereby useless. Restatement of fundamentals is an important exercise. (shrink)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be grounded (...) in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

The Embodied Mind provides a unique, sophisticated treatment of the spontaneous and reflective dimension of human experience.