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)

Though researchers often refer to memory as if it were a unitary phenomenon, a natural kind, the apparent heterogeneity of the various "kinds" of memory casts doubt on this default view. This paper argues, first, that kinds of memory are individuated by memory systems. It argues, second, for a view of the nature of kinds of memory informed by the tri-level hypothesis. If this approach to kinds of memory is right, then memory is not in fact a natural kind.

Cognition is embodied when it is deeply dependent upon features of the physical body of an agent, that is, when aspects of the agent's body beyond the brain play a significant causal or physically constitutive role in cognitive processing. In general, dominant views in the philosophy of mind and cognitive science have considered the body as peripheral to understanding the nature of mind and cognition. Proponents of embodied cognitive science view this as a serious mistake. Sometimes the nature of the (...) dependence of cognition on the body is quite unexpected, and suggests new ways of conceptualizing and exploring the mechanics of cognitive processing. Embodied cognitive science encompasses a loose-knit family of research programs in the cognitive sciences that often share a commitment to critiquing and even replacing traditional approaches to cognition and cognitive processing. Empirical research on embodied cognition has exploded in the past 10 years. As the bibliography for this article attests, the various bodies of work that will be discussed represent a serious alternative to the investigation of cognitive phenomena. Relatively recent work on the embodiment of cognition provides much food for thought for empirically-informed philosophers of mind. This is in part because of the rich range of phenomena that embodied cognitive science has studied. But it is also in part because those phenomena are often thought to challenge dominant views of the mind, such as the computational and representational theories of mind, at the heart of traditional cognitive science. And they have sometimes been taken to undermine standard positions in the philosophy of mind, such as the idea that the mind is identical to, or even realized in, the brain. (shrink)

The philosophy of information (PI) is a new area of research with its own field of investigation and methodology. This article, based on the Herbert A. Simon Lecture of Computing and Philosophy I gave at Carnegie Mellon University in 2001, analyses the eighteen principal open problems in PI. Section 1 introduces the analysis by outlining Herbert Simon's approach to PI. Section 2 discusses some methodological considerations about what counts as a good philosophical problem. The discussion centers on Hilbert's famous analysis (...) of the central problems in mathematics. The rest of the article is devoted to the eighteen problems. These are organized into five sections: problems in the analysis of the concept of information, in semantics, in the study of intelligence, in the relation between information and nature, and in the investigation of values. (shrink)

Consciousness is a central theme of Susanne Langer's three-volume work Mind: An essay on human feeling. Langer proposes an evolutionary history of consciousness in order to establish a biological vocabulary for discussing the subject. This vocabulary is based on the qualities of organic processes rather than generic material objects. Her historical scenario and new terminology suggest that Langer views the “cash value” of consciousness in terms of symbolic thinking and aesthetics. This paper provides an overview of Langer's proposed evolutionary scenario (...) of consciousness, along with an examination of her process-oriented philosophy of mind. It is suggested that Langer's basic ideas are importantly similar to those present in dynamical systems theory. As research on consciousness in dynamical systems theory is still young, researchers in this field may find in Langer's work ideas for future exploration, particularly in its connection with aesthetics. (shrink)

Distributed Cognition and Integrational Linguistics have much in common. Both approaches see communicative activity and intelligent behaviour in general as strongly con- text-dependent and action-oriented, and brains as permeated by history. But there is some ten- sion between the two frameworks on three important issues. The majority of theorists of distributed cognition want to maintain some notions of mental representation and computa- tion, and to seek generalizations and patterns in the various ways in which creatures like us couple with technologies, (...) media, and other agents; many also want to offer explanations at subpersonal levels which may undercut the autonomy of personal-level accounts. In contrast, dominant views in integrational linguistics reject all invocation of representation, resist the explanatory search for similarity across contexts and moments, and see linguistics as a lay dis- cipline which should not offer explanations in terms alien to ordinary agents. On each of these issues, I argue that integrationists could move closer to the distributed cognition frame- work without losing the most important aspects of their view: integrationist criticisms of mainstream or classical theories can be respected while alliances with revised cognitivist views about representation, context, and explanation are developed. Ó 2004 Elsevier Ltd. All rights reserved. (shrink)

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

models of cognition are essentially incomplete because they fail to capture the temporal properties of mental processing. I present two possible interpretations of the dynamicists' argument from time and show that neither one is successful. The disagreement between dynamicists and symbolic theorists rests not on temporal considerations per se, but on differences over the multiple realizability of cognitive states and the proper explanatory goals of psychology. The negative arguments of dynamicists against symbolic models fail, and it is doubtful whether pursuing (...) dynamicists' explanatory goals will lead to a robust psychological theory. Introduction Elements of the symbolic theory Elements of dynamical systems theory The argument from time 4.1 First interpretation of the argument from time 4.2 Second interpretation of the argument from time Limits of dynamical systems theory. (shrink)

In recent years the cognitive science community has witnessed the rise of a new, dynamical approach to cognition. This approach entails a framework in which cognition and behavior are taken to result from complex dynamical interactions between brain, body, and environment. The advent of the dynamical approach is grounded in a dissatisfaction with the classical computational view of cognition. A particularly strong claim has been that cognitive systems do not rely on internal representations and computations. Focusing on this claim, we (...) take as a starting point a question recently raised by Cliff and Noble: " if evolution did produce a design that used internal representations, how would we recognize it?" (Knowledge-based vision and simple visual machines, Philosophical Transactions of the Royal Society: Biological Sciences, 352 , 1165-1175, 1997). We will argue that cognitive science lacks a proper operationalization of the notion of representation, and therefore is unable to fruitfully discuss whether a particular system has representations or not. A basic method to detect representations in a physical system, grounded in isomorphism, turns out to be quite unconstrained. We will look at a practical example of this problem by examining the debate on whether or not van Gelder's (What might cognition be, if not computation? Journal of Philosophy, 92 , 345-381, 1995) controversial example of the Watt Governor is representational. We will conclude that cognitive science, as of yet, has no empirically applicable means to answer Cliff and Noble's question unequivocally. This makes the recent representationalism vs. anti-representationalism debate a debate for the sake of appearance. (shrink)

The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device (...) interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence. (shrink)

Especialistas em desenvolvimento de Inteligência Artificial prevêem que o avanço no desenvolvimento de sistemas e agentes inteligentes irá remodelar áreas vitais em nossa sociedade. Contudo, se tal avanço não for realizado de maneira prudente e crítico-reflexiva, pode resultar em desfechos negativos para a humanidade. Por este motivo, diversos pesquisadores na área têm desenvolvido uma concepção de IA robusta, benéfica e segura para a preservação da humanidade e do meio-ambiente. Atualmente, diversos dos problemas em aberto no campo de pesquisa em IA (...) advêm da dificuldade de evitar comportamentos indesejados de agentes e sistemas inteligentes, e ao mesmo tempo especificar o que realmente queremos que tais sistemas façam, especialmente quando prospectamos a possibilidade de agentes inteligentes atuarem em vários domínios ao longo prazo. É de suma importância que agentes inteligentes artificiais tenham seus valores alinhados com os valores humanos, dado ao fato de que não podemos esperar que uma IA desenvolva valores morais humanos por conta de sua inteligência, conforme é discutido na Tese da Ortogonalidade. Talvez tal dificuldade venha da maneira que estamos abordando o problema de expressar objetivos, valores e metas, utilizando de métodos cognitivos representacionais. Uma solução para este problema seria a abordagem dinâmica proposta por Dreyfus, que com base na filosofia fenomenológica mostra que a experiência humana do ser-no-mundo em diversos aspectos não é bem representada pelo método cognitivo simbólico ou conexionista, especialmente na questão de aprendizagem de valores. Uma possível abordagem para esse problema seria a utilização de modelos téoricos como SED para abordar o porblema de aprendizagem de valores em IA. (shrink)

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme (...) dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation. (shrink)

Volume 33, Issue 8, November 2020, Page 1096-1120.

Robert Cummins [(1996) Representations, targets and attitudes, Cambridge, MA: Bradford/MIT, p. 1] has characterized the vexed problem of mental representation as "the topic in the philosophy of mind for some time now." This remark is something of an understatement. The same topic was central to the famous controversy between Nicolas Malebranche and Antoine Arnauld in the 17th century and remained central to the entire philosophical tradition of "ideas" in the writings of Locke, Berkeley, Hume, Reid and Kant. However, the scholarly, (...) exegetical literature has almost no overlap with that of contemporary cognitive science. I show that the recurrence of certain deep perplexities about the mind is a systematic and pervasive pattern arising not only throughout history, but also in a number of independent domains today such as debates over visual imagery, symbolic systems and others. Such historical and contemporary convergences suggest that the fundamental issues cannot arise essentially from the theoretical guise they take in any particular case. (shrink)

Questions concerning the nature of representation and what representations are about have been a staple of Western philosophy since Aristotle. Recently, these same questions have begun to concern neuroscientists, who have developed new techniques and theories for understanding how the locus of neurobiological representation, the brain, operates. My dissertation draws on philosophy and neuroscience to develop a novel theory of representational content.

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to (...) neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience. (shrink)

Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the (...) complexity of today cognitive theories suggest that there will hardly be only three levels of explanation. Instead, there will be many different degrees of commitments corresponding to the different granularities—from high-level (behavioural) models to low-level (neural and molecular) models of the cognitive research program. For instance, Bayesian approaches, that are usually advocated for formalizing Marr’s computational level and rational behaviour, have even been adopted to model synaptic plasticity and axon guidance by molecular gradients. As a result, we can consider the behavioural scientist as dealing with models at a multiplicity of levels. The purpose of this Research Topic in Frontiers in Theoretical and Philosophical Psychology is to promote an approach to the role of the levels and explanation and models which is of interest for cognitive scientists, neuroscientists, psychologists, behavioural scientists, and philosophers of science. (shrink)

The concepts and powerful mathematical tools of Dynamical Systems Theory (DST) yield illuminating methods of studying cognitive processes, and are even claimed by some to enable us to bridge the notorious explanatory gap separating mind and matter. This article includes an analysis of some of the conceptual and empirical progress Dynamical Systems Theory is claimed to accomodate. While sympathetic to the dynamicist program in principle, this article will attempt to formulate a series of problems the proponents of the approach in (...) question will need to face if they wish to prolong their optimism. The main points to be addressed involve the reductive tendencies inherent in Dynamical Systems Theory, its somewhat muddled position relative to connectionism, the metaphorical nature DST-C exhibits which hinders its explanatory potential, and DST-C's problematic account of causality. Brief discussions of the mathematical and philosophical backgrounds of DST, seminal experimental work and possible adaptations of the theory or alternative suggestions (dynamicist connectionism, neurophenomenology, R&D theory) are included. (shrink)

After more than twenty years of representational debate in the cognitive sciences, anti-representational dynamicism may be seen as offering a rival and radically new kind of explanation of systematicity phenomena. In this paper, I argue that, on the contrary, anti-representational dynamicism must face a version of the old systematicity challenge: either it does not explain systematicity, or else, it is just an implementation of representational theories. To show this, I present a purely behavioral and representation-free account of systematicity. I then (...) consider a case of insect sensorimotor systematic behavior: communicating behavior in honey bees. I conclude that anti-representational dynamicism fails to capture the fundamental trait of systematic behaviors qua systematic, i.e., their involving exercises of the same behavioral capacities. I suggest, finally, a collaborative strategy in pursuit of a rich and powerful account of this central phenomenon of high cognition at all levels of explanation, including the representational level. (shrink)

The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary dynamicist (...) research reveals the need for a more sophisticated account of mechanistic explanation. (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)

Although radical forms of relativism are perhaps beyond the epistemological pale, I argue here that a more moderate form may be plausible, and articulate the conditions under which moderate epistemic relativism could well serve our epistemic goals. In particular, as a result of our limitations as human cognizers, we find ourselves needing to investigate the dappled and difficult world by means of competing communities of highly specialized researchers. We would do well, I argue, to admit of the existence of unresolvable (...) disputes between such communities, but only so long as there is a sufficient amount of fruitful exchange between them as well. I close with some speculation about when it is or is not legitimate to make an “appeal to discipline”: responding to another’s argument by saying something like, “we should do it this way, because we are philosophers (/linguists/psychologists/…), and that’s just what we do”. (shrink)

I propose a semi-eliminative reduction of Fodors concept of module to the concept of attractor basin which is used in Cognitive Dynamic Systems Theory (DST). I show how attractor basins perform the same explanatory function as modules in several DST based research program. Attractor basins in some organic dynamic systems have even been able to perform cognitive functions which are equivalent to the If/Then/Else loop in the computer language LISP. I suggest directions for future research programs which could find similar (...) equivalencies between organic dynamic systems and other cognitive functions. This type of research could help us discover how (and/or if) it is possible to use Dynamic Systems Theory to more accurately model the cognitive functions that are now being modeled by subroutines in Symbolic AI computer models. If such a reduction of subroutines to basins of attraction is possible, it could free AI from the limitations that prompted Fodor to say that it was impossible to model certain higher level cognitive functions. (shrink)

Talk of “levels” can be found throughout the sciences, from “levels of abstraction”, to “levels of organization”, to “levels of analysis”. This has led to substantial disagreement regarding the ontology of levels, and whether the various senses of levels each have genuine value and utility to scientific practice. In this paper, I propose a unified framework for thinking about levels in science which ties together the various ways in which levels are invoked in science, and which can overcome the problems (...) that different senses of levels have faced. I argue that levels can best be understood as choices scientists make regarding what sort of information to foreground in their models and theories, and what sort of information to push into the background. To change levels is to change the foregrounding and backgrounding of information for different representational and pragmatic purposes. (shrink)

Review: Rabossi, E. (org). 2004. La Mente y sus Problemas – Temas actuales de filosofia de la psicología. Buenos Aires: Catálogos S. R. L.

The complex systems approach (CSA) to characterizing cognitive function is purported to underlie a conceptual and methodological revolution by its proponents. I examine one central claim from each of the contributed papers and argue that the provided examples do not justify calls for radical change in how we do cognitive science. Instead, I note how currently available approaches in ‘‘standard’’ cognitive science are adequate (or even more appropriate) for understanding the CSA provided examples.

This paper examines the dynamics of thought-language interactions within the organisational context of business. Based on an assessment of the cognition-voice debate within the cognitive sciences and related areas of philosophical enquiry, the paper proposes that thought and language are distinct systems. This notion of modularity is developed into a framework within which the two systems interact and, in doing so, influence and shape each other. These interactions form multiple thought and voiced drafts, reflecting the ‘multiple drafts’ model developed by (...) Daniel Dennett to examine the phenomenon of consciousness. The drafting and re-drafting of thought and language are analysed via critical consideration of two transcripts of interviews with owner-managers. The overall theoretical approach suggests that the dynamics of voice-cognition drafting offer insights into: the development of expert cognitive frameworks; patterns in group development — in particular the emergence of shared values and concepts within the business; and processes of experiential learning within organizations. (shrink)