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)

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.

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)

Cognitive science, at least as done by many philosophers, seeks to develop what one might call a content-based theory of cognition. These theorists generally seek to predict/explain cognition by employing generalizations between contentful states like beliefs and desires. In his book, From Folk Psychology To Cognitive Science, Stephen Stich argues that cognitive science should not attempt to employ content-based theories in its explanations of human (and other) behavior. For the most part Stich directs his (...) arguments towards belief/desire psychology. Some of his arguments, however, are directed at content-based theories in general. In this paper, I will be concerned with one of these more general arguments—the principle of autonomy argument.The conclusion of the principle of autonomy argument is that the predictions and explanations generated by content-based theories, in so far as they are possible, do not fall within the domain of prediction and explanation in cognitive science. (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)

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)

Upshot: The computational theory of mind has been elaborated in many different ways throughout the last decades. In Explaining the Computational Mind, Milkowski defends his view that the mind can be explained as computational through his defense of mechanistic explanation. At no point in this book is there explicit mention of constructivist approaches to this topic. We will, nevertheless, argue that it is interesting for constructivist readers.

Applying the concepts of dynamical systems theory to explain cognitive phenomena is still a fairly recent trend in cognitive science and its potential and consequences are not nearly mapped out. A decade ago, dynamical approaches were introduced as a paradigm shift in cognitive science and in this paper I concentrate on how to substantiate this claim. After having considered and rejected the possibility that continuous time is the crucial factor, I present Kelso's model of a (...) near-cognitive phenomenon which invokes self-organization as the guiding principle. Then, the explanatory strategy implicit in this approach is explicated and its underlying assumption presented. Finally, I discuss how we should characterize this explanatory framework using the notion of emergence. (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)

In this paper, I argue that explaining cognitive behavior can be achieved through what I call hybrid explanatory inferences: inferences that posit mechanisms, but also draw on observed regularities. Moreover, these inferences can be used to achieve unification, in the sense developed by Allen Newel in his work on cognitive architectures. Thus, it seems that explanatory pluralism and unification do not rule out each other in cognitive science, but rather that the former represents a way to (...) achieve the latter. (shrink)

Computational physical systems may exhibit indeterminacy of computation (IC). Their identified physical dynamics may not suffice to select a unique computational profile. We consider this phenomenon from the point of view of cognitive science and examine how computational profiles of cognitive systems are identified and justified in practice, in the light of IC. To that end, we look at the literature on the underdetermination of theory by evidence and argue that the same devices that can be successfully (...) employed to confirm physical hypotheses can also be used to rationally single out computational profiles, notwithstanding IC. (shrink)

Most of this work is concerned with two theories that underlie cognitive science; theories which I call "the representational theory of intentionality" and "the computational theory of cognition" . While the representational theory of intentionality asserts that mental states are about the world in virtue of a representation relation between the world and the state, the computational theory of cognition asserts that humans and others perform cognitive tasks by computing functions on these representations. CTC draws upon a (...) rich analogy between the mind and digital computers, portraying cognizers as receiving input and generating outputs . ;Since researchers within cognitive science--especially within cognitive psychology, computer science, and philosophy of mind--share a common theoretic background in their adherence to the combination of RTI and CTC, what I call "computationalism" most of the work done in philosophy of mind has been aimed at either refuting or further articulating these two theories. This work is strongly within that tradition. ;RTI and CTC combine to create an explanatory framework for explanation in cognitive science. In this work, I seek to delineate the exact structure of this framework, to determine its components and their overall relationships. In my mind, this involves making implicit aspects of computational explanation explicit, providing an overall rationale for the various components and their relationships, and defending that rationale against criticism. ;In particular, the project of this work is to argue that computationalist explanations of cognition within cognitive science need to make appeals to both representation and knowledge; to distinguish the respective roles of knowledge and of representation in an overall picture of computational explanations, and to develop and argue for definitions of representation and knowledge that would allow those concepts to play their respective roles within computational explanations in cognitive science. ;The first half of this work , therefore, is devoted to theories of representation, especially to the need for, and role of representation in computational explanations. The first half of the work culminates in chapter three with a definition of representation and a picture of the structure of computational explanations. The second half of the project then becomes to generate a definition of knowledge that defines knowledge in non-epistemic language amenable to computationalism and that carries the epistemic burdens placed upon knowledge by its use in computational explanations, as well as to offer a limited defence of that definition within the framework of traditional epistemology. (shrink)

Applying the concepts of dynamical systems theory to explain cognitive phenomena is still a fairly recent trend in cognitive science and its potential and consequences are not nearly mapped out. A decade ago, dynamical approaches were introduced as a paradigm shift in cognitive science and in this paper I concentrate on how to substantiate this claim. After having considered and rejected the possibility that continuous time is the crucial factor, I present Kelso’s model of a (...) near-cognitive phenomenon which invokes self-organization as the guiding principle. Then, the explanatory strategy implicit in this approach is explicated and its underlying assumption presented. Finally, I discuss how we should characterize this explanatory framework using the notion of emergence. (shrink)

This paper considers the question of whether the influential distinction between levels of explanation introduced by David Marr can be used as a general framework for contemplating levels of explanation in cognitive sciences. Marr introduced three levels at which we can explain cognitive processes: the computational, algorithmic, and implementational levels. Some argue that Marr’s levels of explanation can only be applied to modular cognitive systems. However, since many psychological processes are non-modular, it seems that (...) Marr’s levels of explanation cannot explain such psychological processes. To show that the latter claim is not convincing, the paper draws upon an influential paradigm from cognitive sciences that is based on the principle of free energy. Based on this paradigm, the paper argues that even non-modular psychological processes can be computationally analyzed and algorithmically implemented. The conclusion of the paper is that, at least under the assumption that the func- tion of the mind is to minimize free energy, Marr’s levels of explanation can be successfully used as a general framework for understanding psychological processes at different levels of description. (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 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)

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system (...) in terms of predictive coding. (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)

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)

Over the past century or more, cognitive neuropsychologists have discussed many of the issues raised in this volume. On the basis of this literature, we argue that autism is not a single homogeneous condition, and so can have no single cause. Instead, each of its symptoms has a cause, and the proper study of autism is the separate study of each of these symptoms and its cause. We also offer evidence to support the radical view advanced by Stoljar and (...) Gold in this volume, namely, that in explaining such syndromes the cognitive level of explanation has priority over the neuroscientific. (shrink)

Over the past century or more, cognitive neuropsychologists have discussed many of the issues raised in this volume. On the basis of this literature, we argue that autism is not a single homogeneous condition, and so can have no single cause. Instead, each of its symptoms has a cause, and the proper study of autism is the separate study of each of these symptoms and its cause. We also offer evidence to support the radical view advanced by Stoljar and (...) Gold in this volume, namely, that in explaining such syndromes the cognitive level of explanation has priority over the neuroscientific. (shrink)

I begin by tracing some of the confusions regarding levels and reduction to a failure to distinguish two different principles according to which theories can be viewed as hierarchically arranged — epistemic authority and ontological constitution. I then argue that the notion of levels relevant to the debate between symbolic and connectionist paradigms of mental activity answers to neither of these models, but is rather correlative to the hierarchy of functional decompositions of cognitive tasks characteristic of homuncular functionalism. Finally, (...) I suggest that the incommensurability of the intentional and extensional vocabularies constitutes a strongprima facie reason to conclude that there is little likelihood of filling in the story of Bechtel''s missing level in such a way as to bridge the gap between such homuncular functionalism and his own model of mechanistic explanation. (shrink)

In the article author analyses the extent to which it is possible to regard the Aristotelian conception of the soul as actually necessary and applicable for modern neuroscience. The framework in which this objective is going to be accomplished is provided by the idea of the coexistence of the “manifest” and “scientific” images of the world and persons, as introduced by Wilfrid Sellars. In subsequent sections, author initially formulates an answer to the questions of what it is that Aristotle sought (...) to explain with his conception of soul as formal cause, and how this notion could be portrayed in terms drawn from contemporary neurological science. It is author’s intention to show that no other concepts—be they the Cartesian “mind” or a Chalmers-style “self”—come anywhere close to matching the breadth of scope of Aristotelian “soul”. At the same time, though, analysis carried out here do not intend to undertake any kind of defence of the soul as a spiritual entity of the sort popular where religious faith is concerned, namely as an entity separable from the body, immortal, and inimitably tied to God. Then, in the second major part of the article, author analyses the extent to which the Aristotelian conception of the soul can be thought of as involving criteria of the sort that we encounter in the contemporary theory of strong emergence. According to the author the theory of strong emergence has, as a heuristic theory, quite general predictive and explanatory power as regards the field of human thought and behaviour. (shrink)

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)

ID: 89 / Parallel 4k: 2 Single paper Topics: Philosophy of mind, Philosophy of science Keywords: Cognitive Science, Cognitive Neuroscience, Mechanistic explanations, Reductionism, Normativity, Generality, Emerging School of Philosophers of Science. The role of philosophy in cognitive science: mechanistic explanations, normativity, generality Mohammadreza Haghighi Fard Leiden University, Netherlands, The; [email protected] Introduction -/- Cognitive science, as an interdisciplinary research endeavour, seeks to explain mental activities such as reasoning, remembering, language use, and problem (...) solving, and the explanations it advances commonly involve descriptions of the mechanisms responsible for these activities. Cognitive mechanisms are distinguished from the mechanisms invoked in other domains of biology by involving the processing of information. Many of the philosophical issues discussed in the context of cognitive science involve the nature of information processing. For philosophy of science, a central question is what counts as a scientific explanation. But what is a mechanistic explanation and how does it work, how can philosophy of science use it as a solution for the problem of integration in cognitive science? By answering these questions and merging my answers with discussion of concepts of philosophy, normativity and generality, I will investigate the following claim. -/- I claim that philosophy by using strength concepts such as normativity, generality, and a mechanistic philosophy of explanations, can be a most important contributor to cognitive science. I also investigate how philosophy of science could be (can be) a bridge between psychology and neuroscience. We need a distinction between philosophy of cognitive science and philosophy in cognitive science; I am talking about the latter. -/- This claim is very important for the integration and the future of the interdisciplinary field known as cognitive science. -/- Philosophy as a true cognitive science -/- When the Cognitive Science Society was founded, in the late 1970s, philosophy, neuroscience, and anthropology were playing smaller roles. The three disciplines that formed the core group were artificial intelligence, psychology, and linguistics. The curious thing is that George Miller, a psychologist and an important founder of cognitive sciences in a hexagon diagram that he presented, put philosophy at the top of the diagram and neuroscience at the very bottom. There is enough agreement now that neuroscience is the most important contributor to cognitive science and there are fair connections between philosophy and neuroscience. In that diagram there was almost no connection between philosophy and neuroscience. -/- The developments and rise of cognitive science in the last half-century has been accompanied by considerable amount of philosophical activity. Perhaps no other area within analytic philosophy in the second half of that period has attracted more attention or produced more publications. (Bechtel and Graham, 1998. Rumelhart and Bly 1999. Bechtel, Mandik, Mundale 2001. Thagard, 2007. Bennett and Dennett et al, 2007. Bennett and Hacker, 2008. Andler, 2009. Frankish and Ramsey, 2012.) -/- Many philosophers of science offer conclusions that have a direct bearing on cognitive science and its practitioners can profit from closer engagement with the rest of cognitive science. For example, William Bechtel has discussed three projects, two in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years, that he contends, can make theoretical and methodological contributions to cognitive science (Bechtel, 2009). Paul Thagard is another example of the mentioned emerging school of philosophers of science that define cognitive science as the interdisciplinary investigation of mind and intelligence (Thagard, 2006). Thagard by presenting some general but important philosophical questions such as, “What is the nature of the explanations and theories developed in cognitive science?”, and by providing answers to these central questions has showed how philosophy of science can help cognitive science by the advantage of its generality. Andrew Brook, however, believes that philosophical approaches have never had a settled place in cognitive science but he is listed in a group of the philosophers of science that they are contributing very closely the cognitive science (Brook, 2009). Daniel Dennett , as well as being a member the mentioned naturalistic philosophers of science, believes that there is much good work for philosophers to do in cognitive science if they adopt the constructive attitude that prevails in science. -/- What are mechanisms? Let us begin abstractly before considering an example. Mechanisms are collections of entities and activities organized together to do something (cf. Machamer, Darden, & Craver, 2000; Craver & Darden, 2001; Bechtel &Richardson, 1993; Glennan, 1996). These explanations are known as ‘mechanistic explanations’. By using and developing these mechanistic explanations of philosophy of science one can draw normative consequences for cognitive science. Paul Thagard (Thagard, 2006 and 2009), William Bechtel (Bechtel, 2008 and 2009), Andrew Brook (Brook, 2008) investigated and promoted using the ‘normativity’ in philosophy to show a better and crucial role for philosophy of science in an interdisciplinary known as cognitive science. Some philosophers have thought that, in order to pursue this normative function, philosophy must distance itself from empirical matters, but there are examples where the investigations of descriptive and normative issues go hand in hand. ( Thagard, 2009). -/- I will investigate how we can reduce a higher-level science such as psychology to neuroscience without the problems of reductionism but via mechanistic explanations. By problem I mean psychology does not lose its autonomy. -/- Conclusion -/- If cognitive science is all about understanding the human mind, or if cognitive science is the interdisciplinary investigation of mind and intelligence, since the whole life of philosophy was involving with the ways of knowing (epistemology) and conceptions of reality (metaphysics), also philosophy has considered the so-called mind-body problem ( identity theory, functionalism, and heuristic identity theory) , then philosophy could be the most deserved discipline to be a most contributor in cognitive science. I tried to discuss this by using the three advantages in philosophy, normativity, and generality and by introducing an emerging school of mechanistic (not mechanical) philosophers. One thing left, as cognitive science is a two-way street, philosophers need also to stop in a station of cognitive science and learn from the most important advances in brain and neuroscience. Key references : Cognitive Science, Cognitive Neuroscience, Mechanistic explanations, Reductionism, Normativity, Generality, Emerging School of Philosophers of Science. (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.

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)

On observe une nouvelle tendance dans la philosophie des sciences cognitives, manifeste dans les écrits de Betchel et al. qui met en avant l’importance des explications mécanistes au détriment du rôle explicatif des principes. Cet article est un plaidoyer pour rétablir l’équilibre. Il met l’accent sur l’effort d’explication des effets du niveau de base, l’une des plus importantes découvertes empiriques dans l’histoire de la recherche en catégorisation. L’analyse de trois différentes périodes de cette histoire révèle que le recours aux principes (...) y a joué un rôle crucial. Cependant, afin de reconnaître pleinement le rôle explicatif des principes, nous devrions nous préparer à admettre que les explications déductives-nomologiques ne sont pas les seuls types d’explications basées sur des principes. (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)

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)

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.

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)

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)

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)

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)

Nicholas Shea offers Varitel Semantics as a naturalistic account of mental content. I argue that the account secures determinate content only by appeal to pragmatic considerations, and so it fails to respect naturalism. But that is fine, because representational content is not, strictly speaking, necessary for explanation in cognitive science. Even in Shea’s own account, content serves only a variety of heuristic functions.

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)

On observe une nouvelle tendance dans la philosophie des sciences cognitives, manifeste dans les écrits de Betchel et al. qui met en avant l’importance des explications mécanistes au détriment du rôle explicatif des principes. Cet article est un plaidoyer pour rétablir l’équilibre. Il met l’accent sur l’effort d’explication des effets du niveau de base, l’une des plus importantes découvertes empiriques dans l’histoire de la recherche en catégorisation. L’analyse de trois différentes périodes de cette histoire révèle que le recours aux principes (...) y a joué un rôle crucial. Cependant, afin de reconnaître pleinement le rôle explicatif des principes, nous devrions nous préparer à admettre que les explications déductives-nomologiques ne sont pas les seuls types d’explications basées sur des principes. (shrink)

I discuss whether there are some lessons for philosophical inquiry over the nature of simulation to be learnt from the practical methodology of reengineering. I will argue that reengineering serves a similar purpose as simulations in theoretical science such as computational neuroscience or neurorobotics, and that the procedures and heuristics of reengineering help to develop solutions to outstanding problems of simulation.

Research in cognitive science has often assumed the existence of a privileged level that unifies theoretical explanations arising from different disciplines. Philosophical accounts differ about the locus of those intertheory relations. In this paper, four different views are analyzed: classical, connectionist, pragmatist, and reductionist, as exemplified in the works of von Eckardt, Horgan and Tienson, Hardcastle, and Bickle, respectively. Their divergences are characterized in terms of the possibility of such a privileged level. The classical view favors a privileged (...) computational level. The connectionist revision tries to draw biological intuitions into computational models to provide a link between neurons and computations. The pragmatist approach rejects the idea of a privileged level and offers a more eclectic view of cognitive theory building. The reductionist account pursues theoretical unification by means of the reduction of higher level theories to basic level ones. /// La investigatión en ciencia cognitiva suele asumir la existencia de un nivel privilegiado que unifica las explicaciones teóricas provenientes de distintas disciplinas. Las interpretaciones filósoficas difieren respecto al locus de tales relaciones interteóricas. En este artículo se analizan cuatro concepciones: clásica, conexionista, pragmática y reduccionista, ejemplificadas respectivamente en las obras de von Eckardt, Horgan y Tienson, Hardcastle y Bickle. Sus divergencias se caracterizan en términos de la posible existencia del nivel privilegiado. El enfoque clásico propugna un nivel privilegiado computacional. La revisión conexionista importa intuiciones de la biología a los modelos computacionales, con la intentión de vincular lo neuronal con lo computacional. El enfoque pragmático rechaza la idea de un nivel privilegiado y ofrece una visión más ecléctica de la teorización cognitiva. La conceptión reduccionista promueve la unificatión teórica mediante la reductión de teorías de alto nivel a teorías de nivel básico. (shrink)

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

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

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)

Some philosophers argue that we should eschew cross-explanatory integrations of mechanistic, dynamicist, and psychological explanations in cognitive science, because, unlike integrations of mechanistic explanations, they do not deliver genuine, cognitive scientific explanations. Here I challenge this claim by comparing the theoretical virtues of both kinds of explanatory integrations. I first identify two theoretical virtues of integrations of mechanistic explanations—unification and greater qualitative parsimony—and argue that no cross-explanatory integration could have such virtues. However, I go on to argue (...) that this is only a problem for those who think that cognitive science aims to specify one fundamental structure responsible for cognition. For those who do not, cross-explanatory integration will have at least two theoretical virtues to a greater extent than integrations of mechanistic explanations: explanatory depth and applicability. I conclude that one’s views about explanatory integration in cognitive science cannot be segregated from one’s views about the explanatory task of cognitive science. (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.

Cognitive science aims to provide scientific explanations of various mental phenomena. Attempts to study the mind, however, go back thousands of years, and what is distinctive about cognitive science is not its aim but the use of computations and representations in psychological explanations. We shall discuss whether the computational approach comes under challenge from dynamics, and look at some of the main themes in recent developments in cognitive science. In the final part of this (...) paper we shall look at two areas where cognitive science might provide significant benefits to the contemporary world. (shrink)

In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed (...) by proponents of mechanistic explanations that such models do not really explain cognitive phenomena . I will argue against this view. Although I agree that the three arguments usually offered to vindicate the explanatory power of non-mechanistic dynamical models are not enough, I consider a fourth argument, namely that such models provide understanding. The Voss strong anticipation model is used to illustrate this. (shrink)

The concept of emergence is widely used in both the philosophy of mind and in cognitive science. In the philosophy of mind it serves to refer to seemingly irreducible phenomena, in cognitive science it is often used to refer to phenomena not explicitly programmed. There is no unique concept of emergence available that serves both purposes.