Explanation In Cognitive Science Research Articles

The study of movement skills in sports: toward an integrative approach.

The article commences with a fundamental objective: to comprehend movement skills in sports in a manner that can bridge the dualist gap between experiential qualities observed in practice and theoretical and mechanistic explanations. Drawing inspiration from Kuhn's concept of scientific paradigms, practical examples from skiing research, and innovative insights into the integration of phenomenology and mechanistic explanation in cognitive science, we have outlined a three-step integrative approach. The first step entails the development of phenomenological descriptions of the primary experiential qualities inherent in the execution of the skills being investigated. In the second step, phenomenological descriptions play a pivotal role by setting constraints and delineating a space for the elaboration of multilevel mechanistic analyses. These analyses draw upon insights from various fields, encompassing biomechanics, motor control approaches, expertise studies, and cognitive science. The third step involves the systematization of findings and the formulation of sport-specific movement skills theories. We contend that such theories hold substantial significance as they serve as valuable supplements to skill studies conducted within rigid, nomological frameworks. Sport-specific theories include descriptions of first-person experiential qualities and can contribute to bridging the theory-practice gap effectively.

Computational indeterminacy and explanations in cognitive science

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.

Information in Explaining Cognition: How to Evaluate It?

The claims that “The brain processes information” or “Cognition is information processing” are accepted as truisms in cognitive science. However, it is unclear how to evaluate such claims absent a specification of “information” as it is used by neurocognitive theories. The aim of this article is, thus, to identify the key features of information that information-based neurocognitive theories posit. A systematic identification of these features can reveal the explanatory role that information plays in specific neurocognitive theories, and can, therefore, be both theoretically and practically important. These features can be used, in turn, as desiderata against which candidate theories of information may be evaluated. After discussing some characteristics of explanation in cognitive science and their implications for “information”, three notions are briefly introduced: natural, sensory, and endogenous information. Subsequently, six desiderata are identified and defended based on cognitive scientific practices. The global workspace theory of consciousness is then used as a specific case study that arguably posits either five or six corresponding features of information.

Causation and cognition: an epistemic approach

Kaplan and Craver (Philos Sci 78(4):601–627, 2011) and Piccinini and Craver (Synthese, 183(3):283–311, 2011) argue that only mechanistic explanations of cognition are genuine causal explanations, because only evidence of mechanisms reveals the causal structure of cognition. I first argue that this claim is grounded in a commitment to the mechanistic account of causality, which cannot be endorsed by a defender of causal-nonmechanistic explanations. Then, I defend the epistemic theory of causality, which holds that causal explanations are not genuine to the extent that they reveal mechanistic causal structure, but, rather, to the extent that they have evidential support and yield successful prediction, explanation, and control inferences. Finally, I enact an epistemic unification of causal explanation in cognitive science, according to which both mechanistic and nonmechanistic explanations of cognition can be genuine causal explanations.

Introduction to Volume 12, Issue 4 of topiCS

Introduction to Volume 12, Issue 4 of <i>topiCS</i>

Explanations in cognitive science: unification versus pluralism

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.

Editors' Review and Introduction: Levels of Explanation in Cognitive Science: From Molecules to Culture.

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.

Content is pragmatic: Comments on Nicholas Shea'sRepresentation in cognitive science

Nicholas Shea offers what he takes to be a naturalistic account of representational content in cognitive science. 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.

The Multiplicity of Explanation in Cognitive Science

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.

The exploratory status of postconnectionist models

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism - from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply on connectionism in general. In this paper we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology - exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified.

Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem.

Patterns in Cognitive Phenomena and Pluralism of Explanatory Styles.

Debate about cognitive science explanations has been formulated in terms of identifying the proper level(s) of explanation. Views range from reductionist, favoring only neuroscience explanations, to mechanist, favoring the integration of multiple levels, to pluralist, favoring the preservation of even the most general, high-level explanations, such as those provided by embodied or dynamical approaches. In this paper, we challenge this framing. We suggest that these are not different levels of explanation at all but, rather, different styles of explanation that capture different, cross-cutting patterns in cognitive phenomena. Which pattern is explanatory depends on both the cognitive phenomenon under investigation and the research interests occasioning the explanation. This reframing changes how we should answer the basic questions of which cognitive science approaches explain and how these explanations relate to one another. On this view, we should expect different approaches to offer independent explanations in terms of their different focal patterns and the value of those explanations to partly derive from the broad patterns they feature.

Two kinds of explanatory integration in cognitive science

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 (cf. Kaplan and Craver in Philos Sci 78:601–627, 2011; Milkowski in Stud Log 48:13–33, 2016; Piccinini and Craver in Synthese 183:283–311, 2011). 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.

A tale of two explanatory styles in cognitive psychology

The development of a theory of mechanistic explanation has been a welcome advance over previous theories of explanation, such as deductive nomological explanation. However, despite the claims of some supporters of mechanistic explanation, not all explanation in cognitive science is or should be mechanistic. I defend the claim that functional analysis remains a distinct and legitimate form of explanation within cognitive science.

Cognitive and Computational Complexity: Considerations from Mathematical Problem Solving

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general.

Review of HUTTO, Daniel and MYIN, Erik, Evolving Enactivism - Basic minds meet content (2017, MIT Press, xxvi + 328 p.)

ABSTRACT The following review presents some of the themes developed in Evolving Enactivism - basic minds meet content. Hutto and Myin's new book on the Radical Enactive approach to Cognition (REC) aims to provide a thoroughy naturalistic explanation for cognitive phenomena. The main themes investigated here concern Hutto and Myin's criticisms of the nature and role that the notion of content traditionally plays in mainstream cognitive science explanations of cognition and their attempt to provide an account for a variety of cognitive phenomena in which the questionable notion of content is not necessary. It is argued that REC is a promising research framework for cognitive phenomena and deserves further investigation.

Is statistical learning a mechanism?

Philosophers of science have offered several definitions of mechanism, most of which have biological or neuroscientific roots. In this paper, I consider whether these definitions apply equally well to cognitive science. I examine this question by looking at the case of statistical learning, which has been called a domain-general learning mechanism in the cognitive scientific literature. I argue that statistical learning does not constitute a mechanism in the philosophical sense of the term. This conclusion points to significant limitations in the scope of the mechanist philosophy when it comes to accounting for explanation in cognitive science.

Bayesian Cognitive Science, Unification, and Explanation

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.

Semantic Externalism and Knowing Our Own Minds: Ignoring Twin‐Earth and Doing Naturalistic Philosophy

AbstractIn this article I offer a naturalistic defence of semantic externalism. I argue against the following: (1) arguments for externalism rest mainly on conceptual analysis; (2) the community conceptual norms relevant to individuation of propositional attitudes are quasi‐analytic; (3) externalism raises serious questions about knowledge of propositional attitudes; and (4) externalism might be OK for “folk psychology” but not for cognitive science. The naturalist alternatives are as follows. (1) Community norms are not anything like a priori; sometimes they are incoherent. (2) Often propositional attitudes lack determinate content: we do not know the content of thoughts or sentences because there is no fully definite content to be known. (3) Often achieving determinate content is a major socially mediated cognitive achievement that depends on just the factors of social and environmental embedding posited as individuative by externalists, so (4) externalism explains how people can, sometimes, come to have, and to know, determinate attitude contents. (5) Reference and content, for both thought and language, are determined by complex and messy dialectical relations involving many such environmental and social factors; consequently, determinate reference, truth‐conditions, etc., are somewhat uncommon outcomes. (6) The basic semantic relation is (typically imperfect) socially mediated accommodation between perceptual, cognitive, linguistic, classificatory and inferential dispositions and relevant causal structures in the environment. (7) This accommodation explains how concepts, language, taxonomies, etc., contribute to individuals' rational inductive, explanatory and practical achievements. (8) So externally individuated propositional attitudes are required for cognitive science explanations of individual human rationality and its inductive and explanatory achievements. “Individual rationality ain't (entirely) in the individual head.”

Functions, levels, and mechanisms: Explanation in cognitive science and its problems

In the first part of the paper we describe the philosophical debate on the expansions of cognitive science into the brain and into the environment, take sides against the “revolutionary” positions on them and in favor of a “reformist” approach, and conclude that the most appropriate model for cognitive sciences is pluralistic. This is meant in a twofold sense. On the one hand, mental phenomena require a variety of explanatory levels, whose inter-relations are of two kinds: decomposition and contextualization. On the other hand, the arguably quasi-holistic character of some cognitive tasks suggests that the mechanistic style of explanation has to be integrated in these cases with a dynamical explanatory style. This theoretical picture, however, raises two classes of problems: (a) the compatibility between the mechanistic-computationalist explanation and the dynamical one and (b) the nature of theoretical entities and relations postulated at the different levels of a pluralistic model involving computational explanations. Each point will be discussed in the second part of the paper.