Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I (...) argue that they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations. (shrink)

I argue that cultural conventions such as social etiquette facilitate a specific (non‐Lewisian) kind of action coordination—role–interaction coordination—that is required for division of labour. Playing one's roles and coordinating them with those of others is a form of multitasking. Such multitasking is made possible on a large scale because we can offload cognition aimed at coordination onto a stable infrastructure of cultural conventions. Our natural tendency to prefer multitasking in instances where one task requires low cognitive control can thus (...) explain our preference for and expectation of familiar cultural conventions—that is, their perceived normativity. (shrink)

Skinner has assigned the persistence of cognitive explanations to the literature of freedom and dignity. This view is challenged especially as it applies to behavioral scientists. It is argued that cognitive explanations persist because current behaviorism does not challenge cognitive epistomology; because behavior analysts have failed to provide research evidence at the level of human behavior, and finally because a science of behavior based solely on operant principles is necessarily incomplete. The implications of these problems for behavior (...) analysis are addressed. (shrink)

Computational properties, it is standardly assumed, are to be sharply distinguished from semantic properties. Specifically, while it is standardly assumed that the semantic properties of a cognitive system are externally or non-individualistically individuated, computational properties are supposed to be individualistic and internal. Yet some philosophers (e.g., Tyler Burge) argue that content impacts computation, and further, that environmental factors impact computation. Oron Shagrir has recently argued for these theses in a novel way, and gave them novel interpretations. In this paper (...) I present a conception of computation in cognitive science that takes Shagrir's conception as its starting point, but further develops it in various directions and strengthens it. I argue that the explanatory role of computational properties emerges from the idea that syntactical properties and the relevant external factors presented by cognitive systems compose wide computational properties. I also elaborate upon the notion of content that is in play, and argue that it is contents of the kind that are ascribed by transparent interpretations of content ascriptions that impact computation. This fact enables the thesis that external factors impact computation to rebuff the challenge which concerns the claim that psychology must be individualistic. (shrink)

Bilişsel Din Bilimi (CSR), dini inanç ve uygulamaların nedensel açıklamalarını sağlamayı amaçlayan din araştırmalarına bilimsel bir yaklaşımdır. CSR savunucuları, insan zihninin doğal özelliklerini ve nasıl işlediğini açıklayarak dini inançların oluşumu, kabulü, aktarımı ve yaygınlığı sürecini açıklamaya çalışırlar. Tüm insan kültürlerinde var olan ve son on yılda birçok CSR akademisyeninin dikkatini çeken dini inançlardan biri de öbür dünyaya olan inançtır. CSR araştırmacılarına göre, bu inanç, insan zihninin doğal yapılarına dayanmaktadır. Ölümden sonraki hayata olan inancı, zihinsel araçların işleyişinden kaynaklanan, yansıtıcı olmayan veya (...) sezgisel bir inanç olarak görürler. Ölümden sonra hayata inancın oluşumunu, gelişimini, yayılmasını ve aktarılmasını açıklamak için çeşitli teoriler önerdiler. Ancak bu teoriler arasında, iki teori daha geniş kabul görmüştür, sezgisel düalizm teorisi ve simülasyon kısıtlama teorisi. Sezgisel düalizm teorisi, tüm insanların iki zihinsel araca sahip olduğunu söyler: Sezgisel Biyoloji ve Sezgisel Psikoloji. Sezgisel Biyoloji, ölü bir kişinin karşısına geçip hareket edemediği için artık hayatta olmadığına inanmamızı sağlar. Sezgisel Psikoloji, ölü kişiye otomatik olarak görünmez özellikleri (arzular, düşünceler, inançlar ve duygular gibi) atfetmeye devam eder. Yukarıdaki iki zihinsel aracın aynı anda işleyişi, insan zihnini ölü kişinin bir kısmının önemsiz olduğuna ve fiziksel ölümden sonra kaldığına inandırır. Simülasyon kısıtlama teorisi, tüm insanların çevreden gelen bilgileri işlemek ve dini inançlar edinmek için zihinsel araçlara sahip olduğunu söyler. Zihinsel araçların hiçbiri, kişinin arzularının, düşüncelerinin, inançlarının ve duygularının var olmadığını hayal edemez veya simüle edemez. Bu nedenle ölü kişinin karşısındaki insan zihni, fiziksel ölümünü kolayca hayal etmesine rağmen, kişinin başka bir bölümünün (düşünceler, arzular vb.) Varlığına inanmaya devam eder. Bu teorilerin her ikisi de, öbür dünyaya inancın oluşumunu açıklamada zorluklar ve sınırlamalarla karşı karşıya görünüyor. Bunlar arasında nedensel açıklama yapamama, ölümden sonraki hayata olan inancın doğal ve rasyonel temelleri arasındaki ayrım eksikliği ve öbür dünya inancının doğaüstü temellerini göz ardı etme yer alır. İki teoriden hiçbiri öbür dünyaya inancın oluşumu için yeterli nedensel açıklama sağlamıyor gibi görünmektedir. Her iki teori de zihinsel araçların nasıl işlediğine bağlı olarak öbür dünya inançlarının oluşumu hakkında olası bir hikaye sunmaya çalışır. Ölümden sonraki hayata inanmaya yol açan sürecin yalnızca makul bir hikayesini sunarlar. Bu iki teorinin sunduğu şey, insan zihninin ve araçlarının ve bunların nasıl işlediğinin bir açıklamasıdır (nedensel açıklama değil). Bu, kendi başına, bu aletlerin öbür dünyaya bir inanç ürettiğini açıklamaz. Dolayısıyla ahirete olan inancın zihinsel araçların işleyişinin bir sonucu olduğu iddiası, zihinsel araçlar ile bu inanç arasında nedensel bir ilişki gerektirir. Bu iki teoriden hiçbiri bu nedensel ilişkiyi açıklayamaz ve yalnızca aralarındaki ilişkinin makul bir hikayesini açıklarlar. Dahası, dini inancın rasyonel temelleri ile dini inancın doğal temelleri arasındaki ayrım, ahirete inanmak veya onunla ilişkili bilişsel mekanizmaları tanımlamak için doğal bir köken bulmanın hiçbir şekilde bu inancı reddetmek veya itibarını sarsmak anlamına gelmediğini göstermektedir. Ahirete olan inancın doğal kökenleri hakkındaki bilişsel teoriler bize bu inancın rasyonel mi irrasyonel mi olduğunu gösteremez. Bu açıklamalar ancak (eğer yapabilirlerse) bize bu inancın oluşumunun ve yaygınlığının doğal köklerini gösterebilir. Ayrıca Dinsel inanç karmaşık bir kavramdır. Birincisi, doğal bir kavramdır, bu anlamda insan doğasına dayanır ve insanın bilişsel sistemleri ve zihinsel araçlarıyla ilgilidir. İkincisi, kültürel ve sosyal bir kavramdır, bu anlamda hem kültürel ve sosyal değişimden etkilenir hem de onu etkiler. Üçüncüsü, doğaüstü bir kavramdır, bu anlamda hem vahiy hem de kehanet ve insanın maddi olmayan yönüyle derinden bağlantılıdır. Öbür dünyaya inanç, her üç düzeyde açıklamayı gerektiriyor gibi görünüyor. (shrink)

In absence of effective pharmaceutical treatments, the individual's compliance with a series of behavioral recommendations provided by the public health authorities play a critical role in the control and prevention of SARS-CoV2 infection. However, we still do not know much about the rate and determinants of adoption of the recommended health behaviors. This paper examines the compliance with the main behavioral recommendations, and compares sociocultural, psychosocial, and social cognitive explanations for its variation in the French population. Based on the (...) current literature, these 3 categories of factors were identified as potential determinants of individual differences in the health preventive behaviors. The data used for these analyses are drawn from 2 cross-sectional studies conducted after the lockdown and before the peak of the COVID-19 epidemic in France. The participants were drawn from a larger internet consumer panel where recruitment was stratified to generate a socio-demographically representative sample of the French adult population. Overall, the results show a very high rate of compliance with the behavioral recommendations among the participants. A hierarchical regression analysis was then performed to assess the potential explanatory power of these approaches in complying with these recommendations by successively entering sociocultural factors, psychosocial factors, social cognitive factors in the model. Only the inclusion of the cognitive variables substantially increased the explained variance of the self-reported adoption of preventive behaviors, providing better support for the social cognitive than the sociocultural and psychosocial explanations. (shrink)

A violation of procedure invariance in preference measurement is that the predominant or prominent attribute looms larger in choice than in a matching task. In Experiment 1, this so-called prominence effect was demonstrated for choices between pairs of options, choices to accept single options, and preference ratings of single options. That is, in all these response modes the prominent attribute loomed larger than in matching. The results were replicated in Experiment 2, in which subjects chose between or rated their preference (...) for pairs of options which were matched to be equally attractive either in the same session or 1 week earlier. On the basis of these and previous results, it is argued that the prominence effect is a reliable phenomenon. However, none of several cognitive explanations which have been offered appears to be completely viable. (shrink)

According to cognitive averaging theory, preferences for attractive faces result from their similarity to facial prototypes, the categorical central tendencies of a population of faces. Prototypical faces are processed more fluently, resulting in increased positive affect in the viewer.

In 1987, psychologist Alan Leslie published the essay “Pretense and Representation: The Origins of ‘Theory of Mind.’”1 Even after more than twenty years, this remains a benchmark essay, having been cited over seven hundred times in the PsychINFO database as of summer 2011. “Theory of mind” is the cognitive-psychological term for the human ability to attribute mental states—intentions, desires, emotions—to others. Our social being depends on this ability, which humans demonstrate from infancy, though, of course, it develops as the (...) child grows. This ability depends on a certain kind of thought process that Leslie calls “metarepresentation.” Leslie’s essay offers an explanation of how metarepresentational thinking .. (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)

In this paper, I outline two strands of evidence for the conclusion that the dynamical approach to cognitive science both seeks and provides covering law explanations. Two of the most successful dynamical models—Kelso’s model of rhythmic finger movement and Thelen et al.’s model of infant perseverative reaching—can be seen to provide explanations which conform to the famous explanatory scheme first put forward by Hempel and Oppenheim. In addition, many prominent advocates of the dynamical approach also express the provision of (...) this kind of explanation as a goal of dynamical cognitive science. I conclude by briefly outlining two consequences. First, dynamical cognitive science’s explanatory style may strengthen its links to the so-called “situated” approach to cognition, but, secondly, it may also undermine the widespread intuition that dynamics is related to emergentism in the philosophy of mind. (shrink)

The topic of this dissertation is what thought must be like in order for the laws and generalizations of psychology to be true. I address a number of contemporary problems in the philosophy of mind concerning the nature and structure of concepts and the ontological status of mental content. Drawing on empirical work in psychology, I develop a number of new conceptual tools for theorizing about concepts, including a counterpart model of concepts' role in linguistic communication, and a deflationary theory (...) of concepts' formal features. I also suggest some new answers to old problems, arguing, for example, that content realism is not hostage to a naturalized semantics. This dissertation can, as a whole, be read as a sympathetic re-evaluation of the language of thought hypothesis (LOT). LOT claims that thoughts are sentences in a mental language of computation, and are composed of meaningful, atomic symbols—concepts—which are individuated entirely by their formal features. Each chapter either defends various components of LOT from recent criticism, or fills in gaps in the theory. I begin the dissertation by introducing the language of thought project, and motivating and explaining each of its central components. In chapter 2, I discuss a recent competitor to atomism—neo-empiricism—and argue that it fails to meet several key desiderata on theories of concepts, and defend atomism from similar charges. In chapter 3, I argue against a view common to both philosophy and psychology: that concepts must be shareable. If true, atomism is in jeopardy. I find shareability to be unmotivated, and suggest an alternative counterpart model of concepts that does a better job of explaining the things shareability was supposed to explain. Chapter 4 takes up the question of what formal features are and how mental symbols are individuated. I develop a reductive account, arguing that formal features are certain sorts of physical properties and symbol types are sets of these properties. I turn to the topic of content in chapter 5, and defend a very strong version of content realism against recent criticism. (shrink)

With some regularity, cognitive scientists seem to introduce cognitive values into their explanations. After identifying examples of this practice, I sketch an account of psychological explanation that, under certain conditions, legitimizes value-laden cognitive explanations in which evaluative claims appear in the explanandum. I then present and discuss two applications of the proposed account in order to show its viability and explore its consequences.

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.

Cognitive science views thought as computation; and computation, by its very nature, can be understood in both rational and mechanistic terms. In rational terms, a computation solves some information processing problem (e.g., mapping sensory information into a description of the external world; parsing a sentence; selecting among a set of possible actions). In mechanistic terms, a computation corresponds to causal chain of events in a physical device (in engineering context, a silicon chip; in biological context, the nervous system). The (...) discipline is thus at the interface between two very different styles of explanation—as the papers in the current special issue well illustrate, it explores the interplay of rational and mechanistic forces. (shrink)

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)

My purpose in this essay is to clarify the notion of explanation by computer simulation in artificial intelligence and cognitive science. My contention is that computer simulation may be understood as providing two different kinds of explanation, which makes the notion of explanation by computer simulation ambiguous. In order to show this, I shall draw a distinction between two possible ways of understanding the notion of simulation, depending on how one views the relation in which a (...) computing system that performs a cognitive task stands to the program that the system runs while performing that task. Next, I shall suggest that the kind of explanation that results from simulation is radically different in each case. In order to illustrate the difference, I will point out some prima facie methodological difficulties that need to be addressed in order to ensure that simulation plays a legitimate explanatory role in cognitive science, and I shall emphasize how those difficulties are very different depending on the notion of explanation involved. (shrink)

Pseudo‐mechanistic explanations in psychology and cognitive neuroscienceThis paper focuses on the level of systems/cognitive neuroscience. It argues that the great majority of explanations in psychology and cognitive neuroscience is “pseudo‐mechanistic.” On the basis of various case studies, Hommel argues that cognitive neuroscience should move beyond what he calls an “Aristotelian phase” to become a mature “Galilean” science seeking to discover actual mechanisms of cognitive phenomena.

The traditional approach to explanation in cognitive neuroscience is realist about psychological constructs, and treats them as explanatory. On the “standard framework,” cognitive neuroscientists explain behavior as the result of the instantiation of psychological functions in brain activity. This strategy is questioned by results suggesting the distribution of function in the brain, the multifunctionality of individual parts of the brain, and the overlap in neural realization of purportedly distinct psychological constructs. One response to this in the field (...) has been to employ the tools of databasing and machine learning to attempt to find and quantify specific correlations between psychological kinds such as ‘memory’ or ‘attention’ (or sub-kinds thereof) and patterns of activity in the brain. I assess the status and prospects of these projects. I argue that current proponents of the project are vague about their aims, vis-à-vis the standard framework, sometimes suggesting substantiation of the framework, sometimes suggesting retaining the framework but revising the ontology of mental constructs, and sometimes suggesting abandonment of the framework. I argue that extant results from within the projects fail to substantiate the standard framework, and propose an alternative. On my view, psychological constructs should not be viewed as explanantia, but instead as heuristic concepts that help us uncover ways that behaviors can vary and the ways that the brain implements those distinctions. I then discuss the normative upshot of these views for databasing and brain mapping projects. (shrink)

I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by (...) grounded representational content, that is, content evaluated by appeal to its truth, falsity, accuracy, inaccuracy and other relational properties. (shrink)

What sorts of systematic explanations should we and can we seek in cognitive science for perception, language comprehension, rational action and other forms of cognition? In broad outline I think the answer is reasonably clear: We are looking for causal explanations, and our subject matter is certain functions of a biological organ, the human and animal brain.

These essays draw on work in the history and philosophy of science, the philosophy of mind and language, the development of concepts in children, conceptual..

I discuss three arguments that have been advanced in support of the epistemic mentalist view, i.e., the view that infants' social cognitive abilities manifest a capacity to attribute beliefs. The argument from implicitness holds that SCAs already reflect the possession of an “implicit” and “rudimentary” capacity to attribute representational states. Against it, I note that SCAs are significantly limited, and have likely evolved to respond to contextual information in situated interaction with others. I challenge the argument from parsimony by (...) claiming that parsimony per se favors neither a mentalist nor a behavior-reading account. Finally, I argue that early SCAs do not develop continuously into four-year-olds' belief attribution abilities. Accordingly, the argument from developmental continuity is empirically inadequate. Careful analysis of both the empirical data and the theoretical assumptions leading to the epistemic mentalist view is needed in order to improve our understanding of SCAs in earl.. (shrink)

This chapter examines the core explanatory strategies of cognitive science and their application to the study of psychopathology. In addition to providing a taxonomy of different strategies, we illustrate their application, with special attention to Autism Spectrum Disorder and Major Depressive Disorder. We conclude by considering two challenges to the prospects of a developed cognitive science of psychopathology.

How do cognitive neuroscientists explain phenomena like memory or language processing? This book examines the different kinds of experiments and manipulative research strategies involved in understanding and eventually explaining such phenomena. Against this background, it evaluates contemporary accounts of scientific explanation, specifically the mechanistic and interventionist accounts, and finds them to be crucially incomplete. Besides, mechanisms and interventions cannot actually be combined in the way usually done in the literature. This book offers solutions to both these problems based (...) on insights from experimental practice. It defends a new reading of the interventionist account, highlights the importance of non-interventionist studies for scientific inquiry, and supplies a taxonomy of experiments that makes it easy to see how the gaps in contemporary accounts of scientific explanation can be filled. The book concludes that a truly empirically adequate philosophy of science must take into account a much wider range of experimental research than has been done to date. With the taxonomy provided, this book serves a stepping-stone leading into a new era of philosophy of science—for cognitive neuroscience and beyond. (shrink)

The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of (...) processes at the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level. (shrink)

The aim of this paper is to refer basic philosophical approaches to the problem of musical meaning and, on the other hand, to describe some examples of the research on musical meaning found in the field of cognitive neuroscience. By looking at those two approaches together it can be seen that there is still no agreement on how musical meaning should be understood, often due to several methodological problems of which the most important seem to be the possibility of (...) inter-theoretical reduction and application of an accurate theory of explanation. I am suggesting that the application of some form of the mechanistic model of explanation might be found useful for clarifying reductionism-antireductionism dispute concerning musical meaning, and more importantly, for providing some answers for the debate in music-as-language controversy. (shrink)

This paper advances the view that some explanations in cognitive science are extra-mathematical explanations. Demonstrating the plausibility of this interpretation centers around certain efficient coding cases which ineliminably enlist information theoretic laws, facts and theorems to identify in-principle, mathematical constraints on neuronal information processing capacities. The explanatory structure in these cases is shown to parallel other putative instances of mathematical explanation. The upshot for cognitive mathematical explanations is thus two-fold: first, the view capably rebuts standard mechanistic objections (...) to non-mechanistic explanation; second, this view serves to importantly widen the possibility space for non-mechanistic forms of explanation in cognitive science. (shrink)

Recent work has established a framework for explaining the origin of cognitive novelties—qualitatively distinct cognitive traits—in human beings. This niche construction approach argues that humans engineer epistemic environments in ways that facilitate the ontogenetic and phylogenetic development of such novelties. I here argue that attention to the organized relations between content-carrying informational vehicles, or informational form, is key to a valuable explanatory strategy within this project, what I call structural-causal explanations. Drawing on recent work from Cecilia Heyes, and (...) developing a case study around a novel mathematical capacity, I demonstrate how structural-causal explanations can contribute to the niche construction approach by underwriting the application of explanatory tools and generating new empirical targets. (shrink)

The aim of this paper is to examine the usefulness of the Machamer, Darden, and Craver (2000) mechanism approach to gaining an understanding of explanation in cognitive neuroscience. We argue that although the mechanism approach can capture many aspects of explanation in cognitive neuroscience, it cannot capture everything. In particular, it cannot completely capture all aspects of the content and significance of mental representations or the evaluative features constitutive of psychopathology.

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)

It is widely believed that mathematics carries a substantial part of the explanatory burden in science. However, mathematics can also play important heuristic roles of a different kind, being a source of new ideas and approaches, allowing us to build toy models, enhancing expressive power and providing fruitful conceptualizations. In this paper, we focus on the application of dynamical systems theory (DST) within the extended cognition (EC) field of cognitive science, considering this case study to be a good illustration (...) of a general phenomenon. In the paper, we justify both a negative and a positive claim. The negative claim is that dynamical systems theory hardly plays any explanatory role in EC research. We justify our claim by analyzing several accounts of the explanatory role of mathematics and stressing the way mathematical arguments are used in explanations. Our positive claim is that even though, for now, DST has no explanatory power in many of the EC approaches, it still plays an important heuristic role there. In particular, using mathematical notions improves the expressive power of the language and gives a sense of understanding of the phenomena under investigation. The case study of EC allows us to identify and analyze this important role of mathematics, which seems to be neglected in contemporary discussions. (shrink)

Jonathan Walkan challenges cognitive science's dominant model of mental representation and proposes a novel, well-devised alternative. The traditional view in the cognitive sciences uses a linguistic model of mental representation. That logic-based model of cognition informs and constrains both the classical tradition of artificial intelligence and modeling in the connectionist tradition. It falls short, however, when confronted by the frame problem---the lack of a principled way to determine which features of a representation must be updated when new information (...) becomes available. So far, proposed alternatives, including the imagistic model, have not resolved the problem. Waskan proposes the Intrinsic Cognitive Models hypothesis, according to which representational states can be concpetualized as the cognitive equivalent of scale models.Waskan argues further that the proposal that humans harbor and manipulate cognitive counterparts to scale models offers the only viable explanation for what most clearly differentiates humans from other creatures: the capacity to engage in truth-preserving manipulation of representations. The ICM hypothesis, he claims, can be distinguished from sentence-based accounts of truth preservation in a way that is fully compatible with what is known about the brain. (shrink)

Some controversies in cognitive science, such as arguments about whether classical or distributed connectionist architectures best model the human cognitive system, reenact long‐standing debates in the philosophy of science. For millennia, philosophers have pondered whether mentality can submit to scientific explanation generally and to physical explanation particularly. Recently, positive answers have gained popularity. The question remains, though, as to the analytical level at which mentality is best explained. Is there a level of analysis that is peculiarly (...) appropriate to the explanation of either consciousness or mental contents? Are human consciousness, cognition, and conduct best understood in terms of talk about neurons and networks or schemas and scripts or intentions and inferences? If our best accounts make no appeal to our hopes or beliefs or desires, how do we square those views with our conception of ourselves as rational beings? Moreover, can models of physical processes explain our mental lives? Does mentality require a special level of rational or cognitive explanation, or is it best understood in terms of overall brain functioning or neuronal or molecular or even quantum activities – or any of a dozen levels of physical explanation in between? Also, regardless of how they compare with explanations cast at physical levels, what is the status of psychological explanations that appeal fundamentally to mental contents? (shrink)

In the debate about values in science, it is a time-honored tradition to distinguish between the normative question of whether non-cognitive values should play a role in science and the descriptive question of whether they in fact do so or not.1 Among philosophers of science, it is now an accepted view that the descriptive question has been settled. That is, it is no longer disputed that non-cognitive values play a role in science. Hence, all that is left to (...) do on the descriptive front is to describe these values and their roles in more detail. In the words of Longino: “We should stop asking whether social values play a role in science and ask which values and whose values play a role and how” (2004, p. .. (shrink)

In this paper I consider Stich's principle of autonomy argument (From Folk Psychology To Cognitive Science) as an argument that computationalism is an incorrect approach to explanation and prediction in cognitive science. After considering the principle of autonomy argument in light of several computational systems and psychological examples, I conclude that the argument is unsound. I formulate my reasons for rejecting Stich's argument as unsound into the conjunction argument. Finally, I argue that the conjunction argument is sound, (...) and that its soundness adds additional plausibility to computationalism as an explanatory schema in cognitive science. (shrink)

Cognitive science began as a multidisciplinary endeavor to understand how the mind works. Since the beginning, cognitive scientists have been asking questions about the right methodologies and levels of explanation to pursue this goal, and make cognitive science a coherent science of the mind. Key questions include: Is there a privileged level of explanation in cognitive science? How do different levels of explanation fit together, or relate to one another? How should explanations at (...) one level inform or constrain explanations at some other level? Can the different approaches to the mind, brain, and culture be unified? The aim of this issue of topiCS is to provide a platform for discussing different answers to such questions and to facilitate a better understanding between the different strands of thinking about the right levels of explanation in cognitive science. (shrink)

In this chapter, I argue that some aspects of cognitive phenomena cannot be explained computationally. In the first part, I sketch a mechanistic account of computational explanation that spans multiple levels of organization of cognitive systems. In the second part, I turn my attention to what cannot be explained about cognitive systems in this way. I argue that information-processing mechanisms are indispensable in explanations of cognitive phenomena, and this vindicates the computational explanation of cognition. (...) At the same time, it has to be supplemented with other explanations to make the mechanistic explanation complete, and that naturally leads to explanatory pluralism in cognitive science. The price to pay for pluralism, however, is the abandonment of the traditional autonomy thesis asserting that cognition is independent of implementation details. (shrink)

Active materials are self-propelled non-living entities which, in some circumstances, exhibit a number of cognitively interesting behaviors such as gradient-following, avoiding obstacles, signaling and group coordination. This has led to scientific and philosophical discussion of whether this may make them useful as minimal models of cognition (Hanczyc, 2014; McGivern, 2019). Batterman and Rice (2014) have argued that what makes a minimal model explanatory is that the model is ultimately in the same universality class as the target system, which underpins why (...) it exhibits the same macrobehavior. We appeal to recent research in basal cognition (Lyon et al., 2021) to establish appropriate target systems and essential features of cognition as a target of modeling. Looking at self-propelled oil droplets, a type of active material, we do not find that organization alone indicates that these systems exhibit the essential features of cognition. We then examine the specific behaviors of oil droplets but also fail to find that these demonstrate the essential features of cognition. Without a universality class, Batterman & Rice’s account of the explanatory power of minimal models simply does not apply to cognition. However, we also want to stress that it is not intended to; cognition is not the same type of behavioral phenomena as those found in physics. We then look to the minimal cognition methodology of Beer (1996, 2020a, b) to show how active materials can be explanatorily valuable regardless of their cognitive status because they engage in specific behaviors that have traditionally been expected to involve internal representational dynamics, revealing misconceptions about the cognitive underpinnings of certain, specific behaviors in target systems where such behaviors are cognitive. Further, Beer’s models can also be genuinely explanatory by providing dynamical explanations. (shrink)