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)

A discussion is going on in cognitive science about the use of representations to explain how intelligent behavior is generated. In the traditional view, an organism is thought to incorporate representations. These provide an internal model that is used by the organism to instruct the motor apparatus so that the adaptive and anticipatory characteristics of behavior come about. So-called interactionists claim that this representational specification of behavior raises more problems than it solves. In their view, the notion of internal representational (...) models is to be dispensed with. Instead, behavior is to be explained as the intricate interaction between an embodied organism and the specific make up of an environment. The problem with a non-representational interactive account is that it has severe difficulties with anticipatory, future oriented behavior. The present paper extends the interactionist conceptual framework by drawing on ideas derived from the study of morphogenesis. This extended interactionist framework is based on an analysis of anticipatory behavior as a process which involves multiple spatio-temporal scales of neural, bodily and environmental dynamics. This extended conceptual framework provides the outlines for an explanation of anticipatory behavior without involving a representational specification of future goal states. (shrink)

O'Brien & Opie do not address the question of the psychotherapeutic role of unconscious representational states such as beliefs. A dilemma is proposed: if they accept the legitimacy of such states then they should modify what they say about dissociation, and if they do not, they owe us an account of why.

This paper outlines an original interactivist-constructivist approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process (...) modulation which allows a system to "steer" itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified learning is an important component of intelligence. A model of self-directed anticipative learning is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism paradigm, enriched by Montague and Sejnowski's dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches. (shrink)