Since Darwin, the idea of psychological continuity between humans and other animals has dominated theory and research in investigating the minds of other species. Indeed, the field of comparative psychology was founded on two assumptions. First, it was assumed that introspection could provide humans with reliable knowledge about the causal connection between specific mental states and specific behaviors. Second, it was assumed that in those cases in which other species exhibited behaviors similar to our own, similar psychological causes were at (...) work. In this paper. we show how this argument by analogy is flowed with respect to the case of second-order mental states. As a test case, we focus on the question of how other species conceive of visual attention, and in particular whether chimpanzees interpret seeing as a mentalistic event involving internal states of perception, attention, and belief. We conclude that chimpanzees do not reason about seeing in this manner, and indeed, there is considerable reason to suppose that they do not harbor representations of mental states in general. We propose a reinterpretation model in which the majority of the rich social behaviors that humans and other primates share in common emerged long before the human lineage evolved the psychological means of interpreting those behaviors in mentalistic terms. Although humans, chimpanzees, and most other species may be said to possess mental states, humans alone may have evolved a cognitive specialization for reasoning about such states. (shrink)

How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

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)

This chapter subsumes David Marr’s levels of analysis account of explanation in cognitive science under the framework of mechanistic explanation: Answering the questions that define each one of Marr’s three levels is tantamount to describing the component parts and operations of mechanisms, as well as their organization, behavior, and environmental context. By explicating these questions and showing how they are answered in several different cognitive science research programs, this chapter resolves some of the ambiguities that remain (...) in Marr’s account, and shows that many different areas and traditions of cognitive scientific research can be unified under the mechanistic framework. (shrink)

Cognitive science was established as an interdisciplinary domain of research in the 1970s. Since then, the domain has flourished, despite disputes concerning its interdisciplinarity. Multiple methods exist for the assessment of interdisciplinary research. The present study proposes a methodology for quantifying interdisciplinary aspects of research in cognitive science. We propose models for text similarity analysis that provide helpful information about the relationship between publications and their specific research fields, showing potential as a robust measure of interdisciplinarity. (...) We designed and developed models utilizing the Doc2Vec method for analyzing cognitive science and related fields. Our findings reveal that cognitive science collaborates closely with most constituent disciplines. For instance, we found a balanced engagement between several constituent fields—including psychology, philosophy, linguistics, and computer science—that contribute significantly to cognitive science. On the other hand, anthropology and neuroscience have made limited contributions. In our analysis, we find that the scholarly domain of cognitive science has been exhibiting overt interdisciplinary for the past several decades. (shrink)

It is sometimes suggested that the history of computation in cognitive science is one in which the formal apparatus of Turing-equivalent computation, or effective computability, was exported from mathematical logic to ever wider areas of cognitive science and its environs. This paper, however, indicates some respects in which this suggestion is inaccurate. Computability theory has not been focused exclusively on Turing-equivalent computation. Many essential features of Turing-equivalent computation are not captured in definitions of computation as symbol (...) manipulation. Turing-equivalent computation did not play the role in McCulloch and Pitts’s early cybernetic work that is sometimes attributed to it. Finally, various segments of the neuroscientific community invoke a notion of computation that differs from the Turing-equivalent notion.Keywords: Circular causality; Computation; Cortical maps; Neural networks; Symbol manipulation; Turing-equivalent computation. (shrink)

In their constructive reviews, Frances Egan, Randy Gallistel and Steven Gross have raised some important problems for the account of content advanced by Nicholas Shea in Representation in Cognitive Science. Here the author addresses their main challenges. Egan argues that the account includes an unrecognised pragmatic element; and that it makes contents explanatorily otiose. Gallistel raises questions about homomorphism and correlational information. Gross puts the account to work to resolve a dispute about probabilistic contents in perception, but argues (...) that a question remains about whether probabilities are found in the content or instead in the manner of representation. (shrink)

This paper considers the past and future of Psychology within Cognitive Science. In the history section, I focus on three questions: (a) how has the position of Psychology evolved within Cognitive Science, relative to the other disciplines that make up Cognitive Science; (b) how have particular Cognitive Science areas within Psychology waxed or waned; and (c) what have we gained and lost. After discussing what’s happened since the late 1970s, when the Society (...) and the journal began, I speculate about where the field is going. (shrink)

has a more specific role to play in the development of Of course, the conclusion to draw is not that innateness innate cognitive structure. In particular, a common claim claims are trivially false or that they cannot be character-.

This paper reviews the uneven history of the relationship between Anthropology and Cognitive Science over the past 30 years, from its promising beginnings, followed by a period of disaffection, on up to the current context, which may lay the groundwork for reconsidering what Anthropology and (the rest of) Cognitive Science have to offer each other. We think that this history has important lessons to teach and has implications for contemporary efforts to restore Anthropology to its proper (...) place within Cognitive Science. The recent upsurge of interest in the ways that thought may shape and be shaped by action, gesture, cultural experience, and language sets the stage for, but so far has not fully accomplished, the inclusion of Anthropology as an equal partner. (shrink)

The study of human intelligence was once dominated by symbolic approaches, but over the last 30 years an alternative approach has arisen. Symbols and processes that operate on them are often seen today as approximate characterizations of the emergent consequences of sub- or nonsymbolic processes, and a wide range of constructs in cognitive science can be understood as emergents. These include representational constructs (units, structures, rules), architectural constructs (central executive, declarative memory), and developmental processes and outcomes (stages, sensitive (...) periods, neurocognitive modules, developmental disorders). The greatest achievements of human cognition may be largely emergent phenomena. It remains a challenge for the future to learn more about how these greatest achievements arise and to emulate them in artificial systems. (shrink)

The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese’s special issue. (...) In the introductory paper, we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell’s “unified theories of cognition”, or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue. (shrink)

Machine generated contents note: Part 1 - The Constituent Disciplines of Cognitive Science -- Philosophical Epistemology -- Glossary -- 1.0 What is Philosophical Epistemology? -- 1.1 The reduced history of Philosophy Part I - The Classical Age -- 1.2 Mind and World - The problem of objectivity -- 1.3 The reduced history of Philosophy Part II - The twentieth century -- 1.4 The philosophy of Cognitive Science -- 1.5 Mind in Philosophy: summary -- 1.6 The Nolanian (...) Framework (so far) -- Psychology -- 2.0 Why is Psychology so difficult? -- 2.1 A brief history of Experimental Psychology -- 2.2 Methodologies in Psychology -- 2.3 Perception -- 2.4 Memory -- 2.5 Mind in Psychology -- Linguistics -- 3.0 Introduction -- 3.1 Why Linguistics? -- 3.2 Computation and Linguistics -- 3.3 The main grammatical theories -- 3.4 Language development and linguistics -- 3.5 Toward a definition of context -- 3.6 The multifarious uses of Language -- 3.7 Linguistics and Computational Linguistics -- 3.8 Language and other symbol systems -- 3.9 On the notion of context -- 3.10 Mind in Linguistics: summary -- Neuroscience -- 4.0 The constituent disciplines of Neuroscience -- 4.1 The methodology of Neuroscience -- 4.2 Gross Neuroanatomy -- 4.3 Some relevant findings -- 4.4 Connectionism (PDP) -- 4.5 The victory of Neuroscience? -- 4.6 Mind in Neuroscience: summary -- Artificial Intelligence -- 5.0 Introduction -- 5.1 Al and Cognitive Science -- 5.2 Skeptics and their techniques -- 5.3 Al as Computer Science -- 5.4 Al as software -- 5.5 The current methodological debate -- 5.6 Context, syntax and semantics -- 5.7 Mind in Al -- 5.8 Texts on Al -- Etholoqy and Ethnoscience -- 6.1 Etology -- 6.2 Ethnoscience -- 6.3 Mind in Ethology arid Ethnoscience -- Part II - A New Foundation for Cognitive Science -- - Symbol Systems -- 7.1 Characteristics of symbol systems -- 7.2 Context and the layers of symbol systems -- 7.3 Mind and symbol systems -- Consciousness and Selfhood -- 8.0 Introduction -- 8.1 Cognitive views -- 8.2 What is at stake? -- 8.3 Consciousness as treated in Philosophy -- 8.4 The Development of Selfhood -- 8.5 The minimal requirements for this theory -- 8.6 Self as a filter -- 8.7 Self and motivation -- 8.8 Conclusions -- 8.9 Recent developments -- Cognitive Science and the Search for Mind -- 9.1 Introduction -- 9.2 Review -- 9.3 A Theory of Mind anyone? -- 9.4 Foundational considerations -- 9.5 Coda: the Nolanian Framework. (shrink)

Cognitive science currently offers models of cognition that depart substantively from those of information processing models and classical artificial intelligence, while it embraces methods of inquiry that include case-based, ethnographic, and philosophical methods. To illustrate, five overlapping approaches that constitute departures from classical representational cognitive science are briefly discussed in this paper: dynamical cognition, situated cognition, embodied cognition, extended mind theory, and integrative cognition. Critical responses to these efforts from members of the self-proclaimed cognitive (...) class='Hi'>science orthodoxy are also summarized. The paper then discusses ethical and epistemological implications arising from the “new” cognitive science and from critical responses to it and considers the broader importance of this literature for theoretical and philosophical psychology. 2012 APA, all rights reserved). (shrink)

Colour fascinates all of us, and scientists and philosophers have sought to understand the true nature of colour vision for many years. In recent times, investigations into colour vision have been one of the main success stories of cognitive science, for each discipline within the field - neuroscience, psychology, linguistics, computer science and artificial intelligence, and philosophy - has contributed significantly to our understanding of colour. Evan Thompson's book is a major contribution to this interdisciplinary project. Colour (...) Vision provides an accessible review of the current scientific and philosophical discussions of colour vision. Thompson steers a course between the subjective and objective positions on colour, arguing for a relational account. This account develops a novel `ecological' approach to colour vision in cognitive science and the philosophy of perception. It is vital reading for all cognitive scientists and philosophers whose interests touch upon this central area. (shrink)

According to a brief and very general definition Cognitive Science is an interdisciplinary scientific study of how information is represented and transformed in a human nervous system. “Information”, “representation” and “transformation” are keywords here. Many disciplines bring considerable contribution to Cognitive Science. Logic is one of them. Logic investigates these rules which allow us to recognize valid reasonings and distinguish them from those that fail to fulfill the condition of valid- ity. Thus logic investigates some representation (...) (or representations) of reasoning. Significant part of information transformed in nervous system is related to reasoning and inference. This fact opens special perspectives on applying Logic in Cognitive Science both in representing as well as in transforming information. Any formal logical system constitutes a kind of representation of a class of propositions considered as sentence content. In this way each logical system provides a representation of a broad class of belief states. At the same time any inference relation, related to a given logical system, represents a transformation of some type of information. As a consequence it would be hard to find logical investigations which could not be applied in Cognitive Science. Such an idea guided us while we were preparing the present volume. (shrink)

Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility (...) of models to non-programming researchers is essentially non-existent, and even for other modelers, the profusion of source code in a multitude of programming languages, written without programming guidelines, makes it almost impossible to access, check, explore, re-use, or continue to develop. It is high time to change this situation, especially since the tools are now readily available to do so. We propose that the modeling community adopt three simple guidelines that would ensure that computational models would be accessible to the broad range of researchers in cognitive science. We further emphasize the pivotal role that journal editors must play in making computational models accessible to readers of their journals. (shrink)

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive (...) systems may well be dynamical systems, and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 634-645, July 2022.

The Linguistic Review 24, 347-401 (2007).

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead (...) to unification are analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models. (shrink)

A tension exists between those who do—e.g. Meyer (The British Journal for the Philosophy of Science 71:959–985, 2020 ) and Chemero ( 2011 )—and those who do not—e.g. Kaplan and Craver (Philosophy of Science 78:601–627, 2011 ) Piccinini and Craver (Synthese 183:283–311, 2011 )—afford nonmechanistic explanations a role in cognitive science. Here, I argue that one’s perspective on this matter will cohere with one’s interpretation of the tasks of cognitive science; that is, of the (...) actions for which cognitive scientists are specially fitted. To make this point concrete, I consider two tasks of cognitive science—establishing causal claims and unifying—and show how these tasks are interpreted differently by mechanists and nonmechanists respectively. I then argue that, as a result of these different interpretations, we cannot expect to resolve the tension between mechanists and nonmechanists by appeal to cognitive scientific practice. I conclude that we should give up on the debate between mechanists and nonmechanists, and search for progress elsewhere. (shrink)

A recent article (Núñez et al., 2019) claims that cognitive science, while starting off as a multidisciplinary enterprise, has “failed to transition to a mature inter‐disciplinary coherent field.” Two indicators reported in support of this claim target one of the two journals of the Cognitive Science Society, Cognitive Science, depicting cognitive science as an increasingly monodisciplinary subfield which is dominated by psychology. With a focus on the society's other journal, Topics in (...) class='Hi'>Cognitive Science, the present commentary reveals a greater degree of interdisciplinarity and discusses the relative values of diversity and integration for the field. (shrink)

This paper surveys applications of logical methods in the cognitive sciences. Special attention is paid to non-monotonic logics and complexity theory. We argue that these particular tools have been useful in clarifying the debate between symbolic and connectionist models of cognition.

What role does mathematics play in cognitive science today, what role should mathematics play in cognitive science tomorrow? The cautious short answers are: to the factual question, a rather modest role, except in peripheral areas; to the normative question, a far greater role, as the periphery’s place is reevaluated and as both cognitive science and mathematics grow. This paper aims at providing more detailed, perhaps more contentious answers.

The term phenomenology can be used in a generic sense to cover a variety of areas related to the problem of consciousness. In this sense it is a title that ranges over issues pertaining to first-person or subjective experience, qualia, and what has become known as "the hard problem" (Chalmers 1995). The term is sometimes used even more generally to signify a variety of approaches to studying such issues, including contemplative, meditative, and mystical studies, and transpersonal psychology.(1) Within the disciplines (...) of philosophy and psychology, however, phenomenology has a more specialized meaning. In this case it refers to the methodology and philosophy initiated by the philosopher Edmund Husserl at the beginning of the twentieth-century and developed in various ways by theorists such as Heidegger, Sartre, Merleau-Ponty, and Schutz. I restrict the scope of this review to phenomenology in this more specialized sense. In the light of several recent publications, the general questions I address are: What is and what ought to be the relationship between phenomenology and cognitive science? and What, if any, recent contributions has phenomenology made to the science of consciousness? (shrink)

This brief commentary has three goals. The first is to argue that ‘‘framework debate’’ in cognitive science is unresolvable. The idea that one theory or framework can singly account for the vast complexity and variety of cognitive processes seems unlikely if not impossible. The second goal is a consequence of this: We should consider how the various theories on offer work together in diverse contexts of investigation. A final goal is to supply a brief review for readers (...) who are compelled by these points to explore existing literature on the topic. Despite this literature, pluralism has garnered very little attention from broader cognitive science. We end by briefly considering what it might mean for theoretical cognitive science. (shrink)

Over the last decades, Interdisciplinarity (ID) has become one of the leading research practices. Traditionally, cognitive science is considered one of the most prominent examples of ID research by including disciplines such as philosophy, psychology, artificial intelligence (AI), neuroscience, anthropology and linguistics. Recently, however the ID character of cognitive science has become under pressure. According to a study by Leydesdorff and Goldstone (2013), research in this domain gets more and more absorbed by cognitive psychology and (...) the interdisciplinary character of cognitive science is steadily fading away. In this paper, we will examine this claim and argue that its conclusion is premature. We will show that there are reasons to think that the interdisciplinary character of cognitive science is more robust and that the configuration of ID relations may be more dynamic than portrayed by ID skeptics. The reason, or so we will argue, is that ID research is a consequence of the theoretical framework(s) in place, i.e. it is in the nature of ID that fluctuations occur depending on what is held to be the nature of cognition. Our findings are twofold. On the one hand, we will show that the reintegration of cognitive science into cognitive psychology – and with it an approximation towards biology and neuroscience – is, as a matter of fact, the fruit of past ID research. On the other hand, we will demonstrate that novel conceptual frameworks open the possibility for restoring ID relations and foster new ID research. (shrink)

It is a rare privilege to have such eminent and insightful reviewers. Their kind words about the book are much appreciated – perhaps more than they realise. And I'm grateful to all three for having read the book so constructively. Each has given me several things to think about. In the space available here I will focus on the objections that seem most critical. Robert Rupert argues that I rely on an overly narrow understanding of what the cognitive sciences (...) explain (x1). Elisabeth Camp presses me on what precisely it takes to qualify as a structural representation and raises questions about holism (x2). John Krakauer makes a fundamental objection to positing representations when they seem not to be needed to explain behaviour (x3). (shrink)

Fifty years before the present volume appeared, artificial intelligence (AI) and cognitive science (Cogsci) emerged from a couple of small-scale academic encounters on the East Coast of the United States. Wedded together like Siamese twins, these nascent research programs appeared to rest on some general assumptions regarding the human mind, and closely connected methodological principles, which set them at such a distance from phenomenology that no contact between the two approaches seemed conceivable. Soon however contact was made, in (...) the form of a head-on critique of the AI/Cogsci project mostly inspired by arguments from phenomenology. For a while, it seemed like nothing would come of it: AI/Cogsci bloomed while the small troop of critical phenomenologists kept objecting. Then AI and Cogsci went their separate ways. AI underwent a deep transformation and all but surrendered to the phenomenological critique. Cogsci meanwhile pursued the initial program with a far richer collection of problems, concepts and methods, and was for a long time quite unconcerned by suggestions and objections from phenomenology. The last decade and half has seen a remarkable reversal: on the one hand, a few cognitive scientists have been actively pursuing the goal of reconciliating Cogsci, whether empirically or foundationally, with some of the insights procured by phenomenology; on the other, many cognitive scientists and philosophers of mind who think of themselves as, respectively, mainstream and analytic, and have no or little acquaintance with, and often little sympathy for, phenomenology, have been actively pursuing research programs geared toward some of the key issues identified by phenomenological critics of early AI/Cogsci. It might seem then as if those critics were now vindicated. But while these new directions are undoubtedly promising, it is not yet clear that phenomenology and Cogsci can be truly reconciled. Some suspect that Cogsci must distort beyond recognition those phenomenological themes it means to weave into its fabric, while phenomenology may be losing touch with its roots by tuning onto the logic of Cogsci which is, after all, an empirical science. To the present writer, it is far too early for anything like a verdict, as the task of clarifying the issues and gaining a much deeper understanding of the issues on both sides has barely begun. But whatever emerges from this exploration will probably have deep consequences for both Cogsci and philosophy.. (shrink)

In recent decades, a new scientific approach to understand, explain, and predict many features of religion has emerged. The cognitive science of religion has amassed research on the forces that shape the tendency for humans to be religious and on what forms belief takes. It suggests that religion, like language or music, naturally emerges in humans with tractable similarities. This new approach has profound implications for how we understand religion, including why it appears so easily, and why people (...) are willing to fight-and die for it. Yet it is not without its critics, and some fear that scholars are explaining the ineffable mystery of religion away, or by showing that religion is natural proves or disproves the existence of God. An Introduction to the Cognitive Science of Religion offers students and general readers an accessible introduction to the approach, providing an overview of key findings and the debates that shape it. The volume includes a glossary of key terms, and each chapter includes suggestions for further thought and further reading as well as chapter summaries highlighting key points. This book is an indispensable resource for introductory courses on religion and a much-needed option for advanced courses. (shrink)

Attempts to apply the mathematical tools of dynamical systems theory to cognition in a systematic way has been well under way since the early 90s and has been recognised as a “third contender” to computationalist and connectionist approaches :441–463, 1996). Nevertheless, it was also realised that such an application will not lead to a solid paradigm as straightforwardly as was initially hoped. In this paper I explicate a method for assessing such proposals by drawing upon Lakatos’s Criticism and the growth (...) of knowledge, Cambridge University Press, London, pp 91–195, 1970) methodology of scientific research programs. MSRP focuses on the heuristics of a particular field and gauges the model/theory building stratagems by reference to theoretical and empirical progress, on the one hand, and the continuity and the autonomy of the way the field’s heuristic generates its series of models/theories, on the other. The requirement of continuity and autonomy afford distinct senses of ad hoc-ness, which serve as an effective tool to detect various subtleties which may otherwise be missed: the present approach identifies shortcomings missed by Chemero’s radical embodied cognitive science and falsifies Chemero’s claim that the methodological powers of his model-based account is on a par with computationalism. In general, I claim that MSRP is relevant to current methodological issues in cognitive science and can supplement debates regarding “local” assessments of methodologies, such as that between mechanical versus covering-law explanations. MSRP must at least be viewed as a necessary constraint for any methodological considerations in cognitive science. (shrink)

In this paper, we defend a novel, multidimensional account of representational unification, which we distinguish from integration. The dimensions of unity are simplicity, generality and scope, non-monstrosity, and systematization. In our account, unification is a graded property. The account is used to investigate the issue of how research traditions contribute to representational unification, focusing on embodied cognition in cognitive science. Embodied cognition contributes to unification even if it fails to offer a grand unification of cognitive science. (...) The study of this failure shows that unification, contrary to what defenders of mechanistic explanation claim, is an important mechanistic virtue of research traditions. (shrink)

A concern for cultural specificity, the staple of traditional anthropological research, survives the transition to domain-specific accounts of cognitive structuring such as Atran's, and is arguably better off for having made the transition. The identification of domain-specific processes provide us with criteria for sorting cultural differences and integrating cultural concerns within cognitive science.

A central component of the cognitive revolution is a commitment to explaining behaviour by reference to internal representations of the world. This core aspect.

Dispositionalism about belief has had a recent resurgence. In this paper we critically evaluate a popular dispositionalist program pursued by Eric Schwitzgebel. Then we present an alternative: a psychofunctional, representational theory of belief. This theory of belief has two main pillars: that beliefs are relations to structured mental representations, and that the relations are determined by the generalizations under which beliefs are acquired, stored, and changed. We end by describing some of the generalizations regarding belief acquisition, storage, and change.

This book is a major contribution to the interdisciplinary project of investigating the true nature of color vision.

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that (...) simplification has no downsides; it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

This volume introduces central issues in cognitive science by means of debates on key questions.

Dynamical systems theory (DST) is a branch of mathematics that assesses abstract or physical systems that change over time. It has a quantitative part (mathematical equations) and a related qualitative part (plotting equations in a state space). Nonlinear dynamical systems theory applies the same tools in research involving phenomena such as chaos and hysteresis. These approaches have provided different ways of investigating and understanding cognitive systems in cognitive science and neuroscience. The ‘dynamical hypothesis’ claims that cognition is (...) and can be understood as dynamical systems. Common consequences for such an approach include rejecting understanding cognition as information processing in nature, including eschewing explanatory roles for computation or representation. Contemporary applications of DST include mouse‐tracking studies in cognitive science and nonrepresentational perspectives on motor control in neuroscience. Such work has philosophical implications concerning the boundaries of cognition, explanation, and representations. DST offers powerful methodology and theories that raise many topics of philosophical significance. (shrink)

Much of cognitive science is committed to the modular approach to the study of cognition. The core of this approach consists of a pair of assumptions - the anatomical and the functional modularity assumptions - which motivate two kinds of inference: the anatomical and the functional modularity inferences. The legitimacy of both of these inferences has been strongly challenged, a situation that has had surprisingly little impact on most theorizing in the field. Following the introduction of an important, (...) yet rarely made, distinction between two functional concepts - the distinction between cognitive working and cognitive role - this paper analyses these kinds of inference, and refocuses the attention on new aspects of their main limitations. It is argued that both the anatomical and functional modularity inferences can, and do, operate in three distinct modes in contemporary cognitive science, and that seeing this is essential to understanding both the power and the limitations of these methodological tools. (shrink)

When the various disciplines participating in cognitive science are listed, philosophy almost always gets a guernsey. Yet, a couple of years ago at the conference of the Cognitive Science Society in Boulder (USA), there was no philosophy or philosopher with any prominence on the program. When queried on this point, the organizer (one of the "superstars" of the field) claimed it was partly an accident, but partly also due to an impression among members of the committee (...) that philosophy is basically a waste of time. Philosophy, they thought, is mostly obscure bullshit that does little to help, and much to hinder, real progress in cognitive science. (shrink)

This special issue on what some regard as a crisis of replicability in cognitive science (i.e. the observation that a worryingly large proportion of experimental results across a number of areas cannot be reliably replicated) is informed by three recent developments. -/- First, philosophers of mind and cognitive science rely increasingly on empirical research, mainly in the psychological sciences, to back up their claims. This trend has been noticeable since the 1960s (see Knobe, 2015). This development (...) has allowed philosophers to draw on a wider range of relevant resources, but it also makes them vulnerable to relying on claims that may not survive further scrutiny. If we have reasons to believe that a large proportion of findings in the psychological sciences cannot be reliably replicated, this would be a problem for philosophers who use such findings in their work. Second, philosophers are increasingly designing and carrying out their own experiments to back up claims, or to test claims earlier made from the armchair, for example, on the perceived permissibility of diverting trolleys or on the nature of free will. This growing field of experimental philosophy has diversified the intellectual field in philosophy, but may also be vulnerable to issues of replicability that philosophers did not face before. -/- Third, the recent evidence of apparently widespread non-replicability in the social sciences (and other fields) has forced philosophers of science to grapple with long standing questions from their field from a new perspective. To what extent does replicability matter for theory construction? How do the notions of replicability and scientific progress interact? How can normative insights from philosophy of science be used in order to improve scientific practice? -/- It is with these three developments in mind—the increasing importance of empirically-informed philosophy, of experimental philosophy, and of philosophy of science around replicability—that this special issue has been conceived. (shrink)

underpinning of the cognitive sciences. I argue, however, that it often fails to provide adequate explanations, in particular in conjunction with competence theories. This failure originates in the idealizations in competence descriptions, which either ?block? the cascade, or produce a successful cascade which fails to explain cognition.

When cognitive scientists are looking for the neural basis of consciousness or the computational processes underlying vision, what are they looking to find? I argue for a new account of this explanatory project in cognitive science (and the special sciences more generally) on which it is best understood on close analogy with causal explanation in the special sciences. Causal explanations cite causal difference-makers: they explain how certain events causally depend on other events. Generative explanations cite generative difference-makers: (...) they explain how certain events obtain in virtue of (and so are generated by) other events. I show how that this generative difference-making account (GDM) extends well regarded developments in philosophical work on causal explanation, helps to usefully clarify and expands the hypothesis space of a key project in cognitive science – that of understanding how underlying nonmental processes give rise to the mental, including consciousness. In section 2, I develop GDM by close analogy with causal explanation, and locate both of them as species of a more general project in the special sciences: that of discovering what makes a difference to what. In section 3, I motivate GDM by contrasting it with other rival accounts, including correlationist, identity, and grounding accounts, as well as Carl Craver’s constitutive relevance account. In section 4, I tie all of the strands of the preceding discussion together to show how GDM provides a new and compelling response to the so-called hard problem of consciousness. (shrink)