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2 results

  • 6 - Dynamical Systems Approaches to Cognition

  • from Part II - Cognitive Modeling Paradigms
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book:
    The Cambridge Handbook of Computational Cognitive Sciences
    Published online:
    21 April 2023
    Print publication:
    11 May 2023, pp 210-241

  • Summary

    Dynamical systems thinking originated from the sensory-motor domain, but is hypothesized to reach all forms of cognition.Dynamic field theory (DFT) is a mathematically specific, neurally grounded formalization of dynamical systems thinking. Stable states of neural activation, realized as localized activation patterns in low-dimensional neural fields are the units of representation. Their dynamic instabilities lead to the emergence of events at discrete moments in time from continuous-time dynamics. These enable sequences of neural processing steps and flexible binding of multiple localist representations within neural dynamic architectures. Stability enables linking DFT accounts to sensory-motor systems and closed-loop behavior. Instabilities and coordinate transforms are key to reaching the flexibility and productivity of higher cognition. This chapter discusses the relationship between DFT and other approaches to cognition.

  • Chapter 17 - Situating Rationality

  • from Part III - Empirical Developments
  • Edited by Philip Robbins, Washington University, St Louis, Murat Aydede, University of Florida
  • Book:
    The Cambridge Handbook of Situated Cognition
    Published online:
    05 June 2012
    Print publication:
    03 November 2008, pp 322-346

  • Summary

    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.