Cognitive science is, more than anything else, a pursuit of cognitive mechanisms. To make headway towards a mechanistic account of any particular cognitive phenomenon, a researcher must choose among the many architectures available to guide and constrain the account. It is thus fitting that this volume on contemporary debates in cognitive science includes two issues of architecture, each articulated in the 1980s but still unresolved:

• Just how modular is the mind? (section 1) – a debate initially pitting encapsulated (...)
mechanisms (Fodorian modules that feed their ultimate outputs to a nonmodular central
cognition) against highly interactive ones (e.g., connectionist networks that continuously
feed streams of output to one another).
• Does the mind process language-like representations according to formal rules? (this
section) – a debate initially pitting symbolic architectures (such as Chomsky’s generative
grammar or Fodor’s language of thought) against less language-like architectures (such
as connectionist or dynamical ones).

Our project here is to consider the second issue within the broader context of where cognitive science has been and where it is headed. The notion that cognition in general—not just language processing—involves rules operating on language-like representations actually predates cognitive science. In traditional philosophy of mind, mental life is construed as involving propositional attitudes—that is, such attitudes towards propositions as believing, fearing, and desiring that they be true—and logical inferences from them. On this view, if a person desires that a proposition be true and believes that if she performs a certain action it will become true, she will make the inference and (absent any overriding consideration) perform the action. (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)

Much of cognitive neuroscience as well as traditional cognitive science is engaged in a quest for mechanisms through a project of decomposition and localization of cognitive functions. Some advocates of the emerging dynamical systems approach to cognition construe it as in opposition to the attempt to decompose and localize functions. I argue that this case is not established and rather explore how dynamical systems tools can be used to analyze and model cognitive functions without abandoning the use of decomposition and (...) localization to understand mechanisms of cognition. (shrink)

The dynamical patterns in mental phenomena have a characteristic suppleness&emdash;a looseness or softness that persistently resists precise formulation&emdash;which apparently underlies the frame problem of artificial intelligence. This suppleness also undermines contemporary philosophical functionalist attempts to define mental capacities. Living systems display an analogous form of supple dynamics. However, the supple dynamics of living systems have been captured in recent artificial life models, due to the emergent architecture of those models. This suggests that analogous emergent models might be able to explain (...) supple dynamics of mental phenomena. These emergent models of the supple mind, if successful, would refashion the nature of contemporary functionalism in the philosophy of mind. (shrink)

Arguments in favor of anti-representationalism in cognitive science often suffer from a lack of attention to detail. The purpose of this paper is to fill in the gaps in these arguments, and in so doing show that at least one form of anti- representationalism is potentially viable. After giving a teleological definition of representation and applying it to a few models that have inspired anti- representationalist claims, I argue that anti-representationalism must be divided into two distinct theses, one ontological, one (...) epistemological. Given the assumptions that define the debate, I give reason to think that the ontological thesis is false. I then argue that the epistemological thesis might, in the end, turn out to be true, despite a potentially serious difficulty. Along the way, there will be a brief detour to discuss a controversy from early twentieth century physics. (shrink)

Scientific and Philosophical Studies of Mind Franklin and Marshall College Lancaster, PA 17604-3003 USA .

Markman and Dietrich¹ recently recommended extending our understanding of representation to incorporate insights from some “alternative” theories of cognition: perceptual symbol systems, situated action, embodied cognition, and dynamical systems. In particular, they suggest that allowances be made for new types of representation which had been previously under-emphasized in cognitive science. The amendments they recommend are based upon the assumption that the alternative positions each agree with the classical view that cognition requires representations, internal mediating states that bear information.² In the (...) case of one of the alternatives, dynamical systems³, this is simply false: many dynamically-oriented cognitive scientists are anti-representationalists.^4,5,6. (shrink)

1. Throughout the paper, and especially in the section called "LISP vs. DST", I worried that there was not enough focus on EXPLANATION. For the real question, it seems to me, is not whether some dynamical system can implement human cognition, but whether the dynamical description of the system is more explanatorily potent than a computational/representational one. Thus we know, for example, that a purely physical specification can fix a system capable of computing any LISP function. But from this it (...) doesn't follow that the physical description is the one we need to understand the power of the system considered as an information processing device. In the same way, I don't think your demonstration that bifurcating attractor sets can yield the same behavior as a LISP program goes any way towards showing that we should not PREFER the LISP description. To reduce symbolic stories to a subset of DST (as hinted in that section) requires MORE than showing this kind of equivalence: it requires showing that there is explanatory gain, or at the very least, no explanatory loss, at that level. I append an extract from a recent paper of mine that touches on these issues, in case it helps clarify what I am after here. (shrink)

Mind, it has recently been argued1, is a thoroughly temporal phenomenon: so temporal, indeed, as to defy description and analysis using the traditional computational tools of cognitive scientific understanding. The proper explanatory tools, so the suggestion goes, are instead the geometric constructs and differential equations of Dynamical Systems Theory. I consider various aspects of the putative temporal challenge to computational understanding, and show that the root problem turns on the presence of a certain kind of causal web: a web that (...) involves multiple components (both inner and outer) linked by chains of continuous and reciprocal causal influence. There is, however, no compelling route from such facts about causal and temporal complexity to the radical anti- computationalist conclusion. This is because, interactive complexities notwithstanding, the computational approach provides a kind of explanatory understanding that cannot (I suggest) be recreated using the alternative resources of pure Dynamical Systems Theory. In particular, it provides a means of mapping information flow onto causal structure -- a mapping that is crucial to understanding the distinctive kinds of flexibility and control characteristic of truly mindful engagements with the world. Where we confront especially complex interactive causal webs, however, it does indeed become harder to isolate the syntactic vehicles required by the computational approach. Dynamical Systems Theory, I conclude, may play a vital role in recovering such vehicles from the burgeoning mass of real-time interactive complexity. (shrink)

I find myself ambivalent with respect to the line of argument that Schonbein offers. I certainly want to acknowledge and emphasize at the outset that Schonbein’s discussion has brought to the fore a number of central, compelling and intriguing issues regarding the nature of the dynamical approach to cognition. Though there is much that seems right in this essay, perhaps my view is that the paper invites more questions than it answers. My remarks here then are in the spirit of (...) scouting some of the surrounding terrain in order to see just what Schonbein’s claim is and what arguments or options may be open to the dynamicist. (shrink)

I argue that dynamicism does not provide a convincing alternative to currently available cognitive theories. First, I show that the attractor dynamics of dynamicist models are inadequate for accounting for high-level cognition. Second, I argue that dynamicist arguments for the rejection of computation and representation are unsound in light of recent empirical findings. This new evidence provides a basis for questioning the importance of continuity to cognitive function, challenging a central commitment of dynamicism. Coupled with a defense of current connectionist (...) theory, these two critiques lead to the conclusion that dynamicists have failed to achieve their goal of providing a new paradigm for understanding cognition. (shrink)

Van Gelder has presented a position which he ties closely to a broad class of models known as dynamical models. While supporting many of his broader claims about the importance of this class (as has been argued by connectionists for quite some time), I note that there are a number of unique characteristics of his brand of dynamicism. I suggest that these characteristics engender difficulties for his view.

Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt governor is to be the theoretical exemplar (...) of the class of systems necessary for cognition and MOT is an empirical instantiation of that class. However, I shall argue that neither the Watt governor nor MOT successfully fulfill these prescribed roles. This failure, along with van Gelder's peculiar use of the concept of computation and his struggle with representationalism, prevent him from providing a convincing alternative to current cognitive theories. (shrink)

In a recent series of publications, dynamicist researchers have proposed a new conception of cognitive functioning. This conception is intended to replace the currently dominant theories of connectionism and symbolicism. The dynamicist approach to cognitive modeling employs concepts developed in the mathematical field of dynamical systems theory. They claim that cognitive models should be embedded, low-dimensional, complex, described by coupled differential equations, and non-representational. In this paper I begin with a short description of the dynamicist project and its role as (...) a cognitive theory. Subsequently, I determine the theoretical commitments of dynamicists, critically examine those commitments and discuss current examples of dynamicist models. In conclusion, I determine dynamicism's relation to symbolicism and connectionism and find that the dynamicist goal to establish a new paradigm has yet to be realized. (shrink)

Given that the mind is the brain, as materialists insist, those who would understand the mind must understand the brain. Assuming that arrays of neural firing frequencies are highly salient aspects of brain information processing (the vector functional account), four hurdles to an understanding of the brain are identified and inspected: indeterminacy, micro-specificity, chaos, and openness.

What new implications does the dynamical hypothesis have for cognitive science? The short answer is: None. The _Behavior and Brain Sciences _target article, “The dynamical hypothesis in cognitive science” by Tim Van Gelder is basically an attack on traditional symbolic AI and differs very little from prior connectionist criticisms of it. For the past ten years, the connectionist community has been well aware of the necessity of using (and understanding) dynamically evolving, recurrent network models of cognition.

The purpose of this paper is to explore the merits of the idea that dynamical systems theory (also known as chaos theory) provides a model of the mind that can vindicate the language of thought (LOT). I investigate the nature of emergent structure in dynamical systems to assess its compatibility with causally efficacious syntactic structure in the brain. I will argue that anyone who is committed to the idea that the brain's functioning depends on emergent features of dynamical systems should (...) have serious reservations about the LOT. First, dynamical systems theory casts doubt on one of the strongest motives for believing in the LOT: principle P, the doctrine that structure found in an effect must also be found in its cause. Second, chaotic emergence is a double-edged sword. Its tendency to cleave the psychological from the neurological undermines foundations for belief in the existence of causally efficacious representations. Overall, a dynamic conception of the brain sways us away from realist conclusions about the causal powers of representations with constituent structure. (shrink)

David Marr provided a useful framework for theorizing about cognition within classical, AI-style cognitive science, in terms of three levels of description: the levels of (i) cognitive function, (ii) algorithm and (iii) physical implementation. We generalize this framework: (i) cognitive state transitions, (ii) mathematical/functional design and (iii) physical implementation or realization. Specifying the middle, design level to be the theory of dynamical systems yields a nonclassical, alternative framework that suits (but is not committed to) connectionism. We consider how a brain's (...) (or a network's) being a dynamical system might be the key both to its realizing various essential features of cognition — productivity, systematicity, structure-sensitive processing, syntax — and also to a non-classical solution of (frame-type) problems plaguing classical cognitive science. (shrink)

For Merleau-Ponty,consciousness in skillful coping is a matter of prereflective ‘I can’and not explicit ‘I think that.’The body unifies many domain-specific capacities. There exists a direct link between the perceived possibilities for action in the situation (‘affordances’) and the organism’s capacities. From Merleau-Ponty’s descriptions it is clear that in a flow of skillful actions, the leading ‘I can’ may change from moment to moment without explicit deliberation. How these transitions occur, however, is less clear. Given that Merleau-Ponty suggested that a (...) better understanding of the self-organization of brain and behavior is important, I will re-read his descriptions of skillful coping in the light of recent ideas on neurodynamics. Affective processes play a crucial role in evaluating the motivational signif- icance of objects and contribute to the individual’s prereflective responsive- ness to relevant affordances. (shrink)

Clark ends his appendix with a description of what he calls "dynamic computationalism", which he describes as an interesting hybrid between DST and GOFAI. My 'horseLISP" example could be described as an example of dynamic computationalism. It is clearly not as eliminativist as Van Gelder's computational governor example, for I am trying to come up with something like identities between computational entities and dynamic ones. Thus unlike other dynamicists, I am not doing what Clark calls "embracing a different vocabulary for (...) the understanding and analysis of brain events". I think we probably can keep much of the computational vocabulary, although the meanings of many of its terms will probably shift as much as the meaning of 'atom' has shifted since Dalton's time. The label of "dynamic computationalism" is perhaps as good a description of my position as any, but I think I would mean something slightly different by it than Clark would. (For the following, please insert the mantra "of course, this is an empirical question" (OCTEQ) every paragraph or so.). (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)

To some degree, Fodor's claim that Cognitive science divides the mind into modules tells us more about the minds doing the studying than the mind being studied. The knowledge game is played by analyzing an object of study into parts, and then figuring out how those parts are related to each other. This is the method regardless of whether the object being studied is a mind or a solar system. If a module is just another name for a part, then (...) to say that the mind consists of modules is simply to say that it is comprehensible. Fodor comes close to acknowledging this in the following passage. (shrink)

Traditional approaches to modeling cognitive systems are computational, based on utilizing the standard tools and concepts of the theory of computation. More recently, a number of philosophers have argued that cognition is too subtle or complex for these tools to handle. These philosophers propose an alternative based on dynamical systems theory. Proponents of this view characterize dynamical systems as (i) utilizing continuous rather than discrete mathematics, and, as a result, (ii) being computationally more powerful than traditional computational automata. Indeed, the (...) logical possibility of such super-powerful systems has been demonstrated in the form of analog artificial neural networks. In this paper I consider three arguments against the nomological possibility of these automata. While the first two arguments fail, the third succeeds. In particular, the presence of noise reduces the computational power of analog networks to that of traditional computational automata, and noise is a pervasive feature of information processing in biological systems. Consequently, as an empirical thesis, the proposed dynamical alternative is under-motivated: What is required is an account of how continuously valued systems could be realized in physical systems despite the ubiquity of noise. (shrink)

This paper challenges arguments that systematic patterns of intelligent behavior license the claim that representations must play a role in the cognitive system analogous to that played by syntactical structures in a computer program. In place of traditional computational models, I argue that research inspired by Dynamical Systems theory can support an alternative view of representations. My suggestion is that we treat linguistic and representational structures as providing complex multi-dimensional targets for the development of individual brains. This approach acknowledges the (...) indispensability of the intentional or representational idiom in psychological explanation without locating representations in the brains of intelligent agents. (shrink)

In cognitive science, the dynamical systems theory (DST) has recently been advocated as an approach to cognitive modeling that is better suited to the dynamics of cognitive processes than the symbolic/computational approaches are. Often, the differences between DST and the symbolic/computational approach are emphasized. However, alternatively their commonalities can be analyzed and a unifying framework can be sought. In this paper, the possibility of such a unifying perspective on dynamics is analyzed. The analysis covers dynamics in cognitive disciplines, as well (...) as in physics, mathematics and computer science. The unifying perspective warrants the development of integrated approaches covering both DST aspects and symbolic/computational aspects. The concept of a state-determined system, which is based on the assumption that properties of a given state fully determine the properties of future states, lies at the heart of DST. Taking this assumption as a premise, the explanatory problem of dynamics is analyzed in more detail. The analysis of four cases within different disciplines (cognitive science, physics, mathematics, computer science) shows how in history this perspective led to numerous often used concepts within them. In cognitive science, the concepts desire and intention were introduced, and in classical mechanics the concepts momentum, energy and force. Similarly, in mathematics a number of concepts have been developed to formalize the state-determined system assumption [e.g. derivatives (of different orders) of a function, Taylor approximations]. Furthermore, transition systems - a currently popular format for specification of dynamical systems within computer science - can also be interpreted from this perspective. One of the main contributions of the paper is that the case studies provide a unified view on the explanation of dynamics across the chosen disciplines. All approaches to dynamics analyzed in this paper share the state-determined system assumption and the (explicit or implicit) use of anticipatory state properties. Within cognitive science, realism is one of the problems identified for the symbolic/computational approach - i.e. how do internal states described by symbols relate to the real world in a natural manner. As DST is proposed as an alternative to the symbolic/computational approach, a natural question is whether, for DST, realism of the states can be better guaranteed. As a second main contribution, the paper provides an evaluation of DST compared to the symbolic/computational approach, which shows that, in this respect (i.e. for the realism problem), DST does not provide a better solution than the other approaches. This shows that DST and the symbolic/computational approach not only have the state-determined system assumption and the use of anticipatory state properties in common, but also the realism problem. (shrink)

Cognitive science has always been dominated by the idea that cognition is _computational _in a rather strong and clear sense. Within the mainstream approach, cognitive agents are taken to be what are variously known as _physical symbol_ _systems, digital computers_, _syntactic engines_, or_ symbol manipulators_. Cognitive operations are taken to consist in the shuffling of symbol tokens according to strict rules (programs). Models of cognition are themselves digital computers, implemented on general purpose electronic machines. The basic mathematical framework for understanding (...) cognition is the theory of discrete computation, and the core theoretical tools for developing and understanding models of cognition are those of computer science. (shrink)

“There is a familiar trio of reactions by scientists to a purportedly radical hypothesis: (a) “You must be our of your mind!”, (b) “What else is new? Everybody knows _that_!”, and, later—if the hypothesis is still standing—(c) “Hmm. You _might _be on to something!” ((Dennett, 1995) p. 283).

The nature of the dynamical hypothesis in cognitive science (the DH) is further clarified in responding to various criticisms and objections raised in commentaries. Major topics addressed include the definitions of “dynamical system” and “digital computer;” the DH as Law of Qualitative Structure; the DH as an ontological claim; the multiple-realizability of dynamical models; the level at which the DH is pitched; the nature of dynamics; the role of representations in dynamical cognitive science; the falsifiability of the DH; the extent (...) to which the DH is open; the role of temporal and implementation considerations; and the novelty or importance of the DH. The basic formulation and defense of the DH in the target article survives intact, though some refinements are recommended. (shrink)

The dynamical hypothesis is the claim that cognitive agents are dynamical systems. It stands opposed to the dominant computational hypothesis, the claim that cognitive agents are digital computers. This target article articulates the dynamical hypothesis and defends it as an open empirical alternative to the computational hypothesis. Carrying out these objectives requires extensive clarification of the conceptual terrain, with particular focus on the relation of dynamical systems to computers.

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)

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)

The paper argues that cognitive states of biological systems are inherently temporal. Three adequacy conditions for neuronal models of representation are vindicated: the compositionality of meaning, the compositionality of content, and the co-variation with content. Classicist and connectionist approaches are discussed and rejected. Based on recent neurobiological data, oscillatory networks are introduced as a third alternative. A mathematical description in a Hilbert space framework is developed. The states of this structure can be regarded as conceptual representations satisfying the three conditions.

I develop a “two-systems” interpretation of Husserl’s theory of belief. On this interpretation, Husserl accounts for our sense of the world in terms of (1) a system of embodied horizon meanings and passive synthesis, which is involved in any experience of an object, and (2) a system of active synthesis and sedimentation, which comes on line when we attend to an object’s properties. I use this account to defend Husserl against several forms of Heideggerean critique. One line of critique, recently (...) elaborated by Taylor Carman, says that Husserl wrongly loads everyday perception with explicit beliefs about things. A second, earlier line of critique, due to Hubert Dreyfus, charges Husserl with thinking of belief on a problematic Artificial Intelligence (AI) model which involves explicit rules applied to discrete symbol structures. I argue that these criticisms are based on a conflation of Husserl’s two systems of belief. The conception of Husserlian phenomenology which emerges is compatible with Heideggerean phenomenology and associated approaches to cognitive science (in particular, dynamical systems theory). (shrink)

Husserl is well known for his critique of the “mathematizing tendencies” of modern science, and is particularly emphatic that mathematics and phenomenology are distinct and in some sense incompatible. But Husserl himself uses mathematical methods in phenomenology. In the first half of the paper I give a detailed analysis of this tension, showing how those Husserlian doctrines which seem to speak against application of mathematics to phenomenology do not in fact do so. In the second half of the paper I (...) focus on a particular example of Husserl’s “mathematized phenomenology”: his use of concepts from what is today called dynamical systems theory. (shrink)