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)

Further tests of Amit's model are indicated. One strategy is to use the apparent coding sparseness of the model to make predictions about coding sparseness in Miyashita's network. A second approach is to use memory overload to induce false positive responses in modules and biological systems. In closing, the importance of temporal coding and timing requirements in developing biologically plausible attractor networks is mentioned.

The concept of an attractor in a mathematical dynamical system is reviewed. Emphasis is placed on the distinction between a cell assembly, the corresponding attractor, and the attractor dynamics. The biological significance of these entities is discussed, especially the question of whether the representation of the stimulus requires the full attractor dynamics, or merely the cell assembly as a set of reverberating neurons. Comparison is made to Freeman's study of dynamic patterns in olfaction.

The authors ofImages of mindhave been highly successful in unravelling the neural basis of complex brain functions. Their emphasis on top-down processingin experimental neuroscience is especially important and, it is hoped, influential. Tracking brain activation accurately botli in space and in time would benefit from studiesofindividual subjects without relying on grand average data.

Although this is an impressive piece of modeling work, I worry that the two models that Wright & Liley have created do not yet provide us with useful empirical information regarding brain processing.

Top-down dynamical models of cognitive processes, such as the one presented by Thelen et al., are important pieces in understanding the development of cognitive abilities in humans and biological organisms. Unlike standard symbolic computational approaches to cognition, such dynamical models offer the hope that they can be connected with more bottom-up, neurologically inspired dynamical models to provide a complete view of cognition at all levels. We raise some questions about the details of their simulation and about potential limitations of top-down (...) dynamical models. (shrink)

PET detects changes in metabolism between task periods and is thus insensitive to areas that are activated during all or most of cognition. Depth-recorded, evokedpotentials indicate that many multimodal and limbic cortical areas may be activated during most cognitive tasks. Thus, PET may be insensitive to some core processes of awareness that are difficult to eliminate from the control periods.

On the basis of neuroiinaging studies, Posner & Raichle summarily report that the prefrontal cortex is involved in executive functioning and attention. In contrast to that superficial view, we briefly describe a testable model of the kinds of representations that are stored in prefrontal cortex, which, when activated, are expressed via plans, actions, thematic knowledge, and schemas.

Research reported by Posner & Raichle may be usefully applied to the rehabilitation of persons with brain damage. Their findings are related to the “dual premotorsystems hypothesis” that reciprocally interactive medial and lateral brain systems are involved in attention and learning. Recent studies show that “brain healing” occurs through dynamic reorganization involving attentional networks postulated by Posner & Raichle.

Images of mindis an exciting book, well-written and wellorganized, but many of the connections the authors draw between PET scan results and more general psychological issues are somewhat strained.

The fan effect says that “activation” spreading from a concept is divided among the concepts it spreads to. Because this activation is not a physical entity, but an abstraction of unknown lower‐level processes, the spreading activation model has predictive but not explanatory power. We provide one explanation of the fan effect by showing that distributed neuronal memory networks (specifically, Hopfield networks) reproduce four qualitative aspects of the fan effect: faster recognition of sentences containing lower‐fan words, faster recognition of sentences when (...) more cues are provided, faster acceptance of studied sentences than rejection of probes, and faster recognition of sentences studied more frequently. These are all a natural result of the dynamics of distributed associative memory. (shrink)

A possible relation between Derrida's deconstruction of metaphysics and connectionism is explored by considering diffeacuterance in neural nets terms. First diffeacuterance, as the crossing of Saussurian difference and Freudian deferral, is modeled and then the fuller 'sheaf of diffeacuterance is taken up. The metaphysically conceived brain has two versions: in the traditional computational version the brain processes information like a computer and in the connectionist version the brain computes input vector to output vector transformations non-symbolically. The 'deconstructed brain' neither processes (...) information nor computes functions but is spontaneously economical. (shrink)

This paper explores a line of argument against the classical paradigm in cognitive science that is based upon properties of non-linear dynamical systems, especially in their chaotic and near-chaotic behavior. Systems of this kind are capable of generating information-rich macro behavior that could be useful to cognition. I argue that a brain operating at the edge of chaos could generate high-complexity cognition in this way. If this hypothesis is correct, then the symbolic processing methodology in cognitive science faces serious obstacles. (...) A symbolic description of the mind will be extremely difficult, and even if it is achieved to some approximation, there will still be reasons for rejecting the hypothesis that the brain is in fact a symbolic processor. (shrink)

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)

This paper explores the possibility that chaos theory might be helpful in explaining free will. I will argue that chaos has little to offer if we construe its role as to resolve the apparent conflict between determinism and freedom. However, I contend that the fundamental problem of freedom is to find a way to preserve intuitions about rational action in a physical brain. New work on dynamic computation provides a framework for viewing free choice as a process that is sensitive (...) and unpredictable, while at the same time organized and intelligent. I conclude that this vision of a chaotic brain may make a modest contribution to an intuitively acceptable physicalist account of free will. (shrink)

This target article draws together two groups of experimental studies on the control of human movement through peripheral feedback and centrally generated signals of motor commands. First, during natural movement, feedback from muscle, joint, and cutaneous afferents changes; in human subjects these changes have reflex and kinesthetic consequences. Recent psychophysical and microneurographic evidence suggests that joint and even cutaneous afferents may have a proprioceptive role. Second, the role of centrally generated motor commands in the control of normal movements and movements (...) following acute and chronic deafferentation is reviewed. There is increasing evidence that subjects can perceive their motor commands under various conditions, but that this is inadequate for normal movement; deficits in motor performance arise when the reliance on proprioceptive feedback is abolished either experimentally or because of pathology. During natural movement, the CNS appears to have access to functionally useful input from a range of peripheral receptors as well as from internally generated command signals. The unanswered questions that remain suggest a number of avenues for further research. (shrink)