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Oaksford & Chater (O&C) argue that a rational analysis is required to explain why a functional process model is successful, and that, when a rational analysis is intractable, the prospects for understanding cognition from a functional perspective are gloomy. We discuss how functional explanations can be arrived at without seeking the optimal response function demanded by a rational analysis, and argue that explaining function does not require optimality.
This chapter discusses the importance of dynamics to understanding cognition. The author turns to the issue of how dynamics have been integrated into various theories of cognition. The author describes strengths and weaknesses of three main contenders in cognitive science, in relation to their incorporation of time into their methods of model construction. The neural engineering framework (NEF) is a general theory of neurobiological systems. Neural dynamics are characterized by considering neural representations as control theoretic state variables. Thus, the dynamics of neurobiological systems can be analyzed using control theory. The model employs biologically realistic neurons to learn the relevant structural transformations appropriate for a given context, and it generalizes such transformations to novel contents with the same syntactic structure. The intent of the NEF is to provide a suggestion as to how we might take seriously many of the important insights generated from cognitive science.
One way of dealing with the proliferation of conjectures that accompany the diverse study of the evolution of language is to develop precise and testable models which reveal otherwise latent implications. We suggest how verbal theories of the role of individual development in language evolution can benefit from formal modeling, and vice versa.
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