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)

Though nativist hypotheses have played a pivotal role in the development of cognitive science, it remains exceedingly obscure how they—and the debates in which they figure—ought to be understood. The central aim of this paper is to provide an account which addresses this concern and in so doing: a) makes sense of the roles that nativist theorizing plays in cognitive science and, moreover, b), explains why it really matters to the contemporary study of cognition. I conclude (...) by outlining a range of further implications of this account for current debate in cognitive science. (shrink)

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

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

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)

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

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.

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

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

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

Vādanyāya is one of the representative works of Dharmakīrti. It is concerned with debate logic and deals with win-or-lose reasoning rules in the broad sense of logic. In this paper, we will concentrate our discussion on Dharmakīrti’s theory of nigrahasthāna (fault) in his debate logic, a key issue in Vādanyāya. First, we point out that the justification of three logical reasons as proof conditions of debate constitutes the rational point of departure for Dharmakīrti’s debate logic. Second, we analyze the differences (...) between Dharmakīrti’s and the Naiyāyika’s notion of debate and point out that Dharmakīrti’s ‘debate’ involves two parties (the disputant and the opponent) attempting to rationally refute the other’s opinion to persuade the other to change their wrong ideas. Last, based on the discussion of Dharmakīrti’s concept of debate logic, we identify seven types of nigrahasthāna in Vādanyāya with regard to the two parties of a debate (the disputant and the opponent). (shrink)

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

The 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)

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

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

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

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

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)

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

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)

We present a general classification of the conditions under which cognitive science, concerned, e.g. with decision making, requires the use of quantum theoretical notions. The analysis is done in the frame of the mathematical approach based on the theory of quantum measurements. We stress that quantum effects in cognition can arise only when decisions are made under uncertainty. Conditions for the appearance of quantum interference in cognitive sciences and the conditions when interference cannot arise are formulated.

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

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

Freedom of will is fundamental to morality, intuition of self, and normal functioning of society. However, science does not provide a clear logical foundation for this idea. This paper considers the fundamental argument against free will, so called reductionism, and why the choice for dualism against monism, follows logically. Then, the paper summarizes unexpected conclusions from recent discoveries in cognitive science. Classical logic turns out not to be a fundamental mechanism of the mind. It is replaced by (...) dynamic logic. Mathematical and experimental evidence are considered conceptually. Dynamic logic counters logical arguments for reductionism. Contemporary science of mind is not reducible; free will can be scientifically accepted along with scientific monism. (shrink)

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)

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

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)

We introduce a new metric for interdisciplinarity, based on co-author publication history. A published article that has co-authors with quite different publication histories can be deemed relatively “interdisciplinary,” in that the article reflects a convergence of previous research in distinct sets of publication outlets. In recent work, we have shown that this interdisciplinarity metric can predict citations. Here, we show that the journal Cognitive Science tends to contain collaborations that are relatively high on this interdisciplinarity metric, at about (...) the 80th percentile of all journals across both social and natural sciences. Following on Goldstone and Leydesdorff, we describe how scientometric tools provide a valuable means of assessing the role of cognitive science in broader scientific work, and also as a tool to investigate teamwork and distributed cognition. We describe how data-driven metrics of this kind may facilitate this exploration without relying upon rapidly changing discipline and topic keywords associated with publications. (shrink)

This paper addresses, fundamentally, a single issue: assessing whether the currently very influent approach in cognitive sciences, i.e., Radical Embodied Cognitive Science, is committed to some version of emergentism. The structure of the paper is the following. In the first section I introduce the leading ideas of RECS. In the second section I compare certain standard formulations of emergentism with the main claims of RECS, trying to assess whether the latter involve some emergentist tenets. Some conclusions, in (...) the third section, follow. My conclusion will be that, on the one hand, there are some substantive epistemological analogies between RECS and emergentism, but, on the other hand, the metaphysics of RECS is not of an emergentist kind, in spite of some shallow similarities. Therefore, depending on one’s taking emergentism as an epistemological rather than a metaphysical thesis, RECS will be considered as being committed to emergentism or not. (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)

The paper is an examination of the ways and extent to which neuroscience places constraints on cognitive science. In Part I, I clarify the issue, as well as the notion of levels in cognitive inquiry. I then present and address, in Part II, two arguments designed to show that facts from neuroscience are at a level too low to constrain cognitive theory in any important sense. I argue, to the contrary, that there are several respects in (...) which facts from neurophysiology will constrain cognitive theory. Part III then turns to an examination of Connectionism and Classical Cognitivism to determine which, if either, is in a better position to accomodate neural constraints in the ways suggested in Part II. (shrink)

This commentary gives a personal perspective on modeling and modeling developments in cognitive science, starting in the 1950s, but focusing on the author’s personal views of modeling since training in the late 1960s, and particularly focusing on advances since the official founding of the Cognitive Science Society. The range and variety of modeling approaches in use today are remarkable, and for many, bewildering. Yet to come to anything approaching adequate insights into the infinitely complex fields of (...) mind, brain, and intelligent systems, an extremely wide array of modeling approaches is vital and necessary. (shrink)

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

Drawn sequences of images, like those in comics and picture stories, are a pervasive and fundamental way that humans have communicated for millennia. Yet, the study of visual narratives has only recently gained traction in Cognitive Science. Here we explore what has held back the study of the cognition of visual narratives, and why researchers should join in scholarship of this ubiquitous aspect of expression.

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

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 is indeed an auspicious time for Cognitive Science. I stand here before you this evening as the first Chair to give a presidential address to this austere body, to place on record before you what you are to accept as the Society's official view on the new science of the mind.

The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and we discuss implications for research in cognitive science. In particular, we show how various strands of research in cognitive science can be interpreted (...) in light of the bias–variance tradeoff, giving insight into individual differences in learning, the nature of cognitive processes, and debates in cognitive science research. (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)

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)

In this paper we study the problem of a possibility to use quantum observables to describe a possible combination of the order effect with sequential reproducibility for quantum measurements. By the order effect we mean a dependence of probability distributions on the order of measurements. We consider two types of the sequential reproducibility: adjacent reproducibility ) and separated reproducibility). The first one is reproducibility with probability 1 of a result of measurement of some observable A measured twice, one A measurement (...) after the other. The second one, \, is reproducibility with probability 1 of a result of A measurement when another quantum observable B is measured between two A’s. Heuristically, it is clear that the second type of reproducibility is complementary to the order effect. We show that, surprisingly, this may not be the case. The order effect can coexist with a separated reproducibility as well as adjacent reproducibility for both observables A and B. However, the additional constraint in the form of separated reproducibility of the \ type makes this coexistence impossible. The problem under consideration was motivated by attempts to apply the quantum formalism outside of physics, especially, in cognitive psychology and psychophysics. However, it is also important for foundations of quantum physics as a part of the problem about the structure of sequential quantum measurements. (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)

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)

Cognitive Science, Volume 46, Issue 8, August 2022.

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