Cognitive science has always included multiple methodologies and theoretical commitments. The philosophy of cognitive science should embrace, or at least acknowledge, this diversity. Bechtel’s (2009a) proposed philosophy of cognitive science, however, applies only to representationalist and mechanist cognitive science, ignoring the substantial minority of dynamically oriented cognitive scientists. As an example of nonrepresentational, dynamical cognitive science, we describe strong anticipation as a model for circadian systems (Stepp & Turvey, 2009). We then propose a philosophy of science appropriate to nonrepresentational, dynamical (...) cognitive science. (shrink)

Gestalt theory is discussed as one main precursor of synergetics, one of the most elaborated theories of self-organization. It is a precursor for two reasons: the Gestalt theoretical view of cognitive order-formation comes dose to the central ideas of self-organization. Furthermore both approaches have stressed the significance of non-linear perceptual processes (such as multistability) for the solution of the mind-brain problem. The question of whether Gestalt theory preferred a dualistic or a monistic view of the mind-body relation is answered in (...) that there was a preference for dualism in epistemological questions and for monism in the mind-brain relation. The latter was attained by the concept of psychophysical isomorphism. This concept, although widely misunderstood in many respects, was criticized on the basis of neurobiological findings. One main objection was the neglect of the importance of the elementary neurophysiological processes. A distinction between macroscopic and microscopic brain processes seemed to be required. This idea was taken up in synergetics which postulates a bottom-up and top-down interaction between these two levels. Macroscopic order emerges from elementary brain processes and, at the same time, has a backward slaving effect to the microscopic level In the light of such holistic emergentism, the question whether macroscopic order states might be attractors for psychological meanings is discussed. (shrink)

The authors ofImages of the Mindhave made a significant contribution to our understanding of the brain through imaging technology. The book is well written, timely, beautifully illustrated and conveys a sense of history. It will appeal to alay audience as well as a professional audience.

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

Cognitive science typically postulates unconscious mental phenomena, computational or otherwise, to explain cognitive capacities. The mental phenomena in question are supposed to be inaccessible in principle to consciousness. I try to show that this is a mistake, because all unconscious intentionality must be accessible in principle to consciousness; we have no notion of intrinsic intentionality except in terms of its accessibility to consciousness. I call this claim the The argument for it proceeds in six steps. The essential point is that (...) intrinsic intentionality has aspectual shape: Our mental representations represent the world under specific aspects, and these aspectual features are essential to a mental state's being the state that it is. (shrink)

There are methodological problems with the new techniques reviewed by Posner & Raichle. Some brain mechanisms are not detected by the temporal and spatial resolution. Questions are also raised by the stimulation paradigms.

This study addresses the dynamical nature of a “representation‐hungry” cognitive task involving an imagined action. In our experiment, participants were handed rods that systematically increased or decreased in length on subsequent trials. Participants were asked to judge whether or not they thought they could reach for a distant object with the hand‐held rod. The results are in agreement with a dynamical model, extended from Tuller, Case, Ding, and Kelso (1994). The dynamical effects observed in this study suggest that predictive judgments (...) regarding the possibility or impossibility of a certain action can be understood in terms of dynamically evolving basins of attraction instead of as depending on representational structures. (shrink)

Images of mindsuperbly summarizes work on cognitive neuroscienee using PET scanning. Some of the data emphasized suggests that parietal cortex is involved in the disengagement of attention. We have discovered neurons in awake monkeys which could perform this function. Another point of emphasis is the concept of neutral cues. Although an appealing concept, it is extremely difficult to define what is actually neutral.

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other (...) hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization. (shrink)

A map of the world that does not include Utopia is not worth glancing at, for it leaves out the country at which humanity is always landing.Oscar WildeThe further ahead one looks, the more the vision of the distant future resembles the golden age of the mythical past.John CohenBeing condemned (or chosen?) to be “the missing link” on its way to perfectibility (or redemption?) - half animal/half human - we always need in some way or another the transcendence of a (...) Utopian Vision. It therefore comes as no surprise that most of the essays in this issue, reflecting a vision of the world today, are openly or covertly concerned with Utopian expectations. Hence, it might be worthwhile to trace the origins and reflect in flashbacks - following the pioneering technique of the French film-maker Alain Resnais - a story of the Utopian Vision. (shrink)