Book contents
- The Cambridge Handbook of Computational Cognitive Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Computational Cognitive Sciences
- Copyright page
- Contents
- Preface
- Contributors
- Part I Introduction
- Part II Cognitive Modeling Paradigms
- 2 Connectionist Models of Cognition
- 3 Bayesian Models of Cognition
- 4 Symbolic and Hybrid Models of Cognition
- 5 Logic-Based Modeling of Cognition
- 6 Dynamical Systems Approaches to Cognition
- 7 Quantum Models of Cognition
- 8 Constraints in Cognitive Architectures
- 9 Deep Learning
- 10 Reinforcement Learning
- Part III Computational Modeling of Basic Cognitive Functionalities
- Part IV Computational Modeling in Various Cognitive Fields
- Part V General Discussion
- Index
- References
6 - Dynamical Systems Approaches to Cognition
from Part II - Cognitive Modeling Paradigms
Published online by Cambridge University Press: 21 April 2023
- The Cambridge Handbook of Computational Cognitive Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Computational Cognitive Sciences
- Copyright page
- Contents
- Preface
- Contributors
- Part I Introduction
- Part II Cognitive Modeling Paradigms
- 2 Connectionist Models of Cognition
- 3 Bayesian Models of Cognition
- 4 Symbolic and Hybrid Models of Cognition
- 5 Logic-Based Modeling of Cognition
- 6 Dynamical Systems Approaches to Cognition
- 7 Quantum Models of Cognition
- 8 Constraints in Cognitive Architectures
- 9 Deep Learning
- 10 Reinforcement Learning
- Part III Computational Modeling of Basic Cognitive Functionalities
- Part IV Computational Modeling in Various Cognitive Fields
- Part V General Discussion
- Index
- References
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.
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- Information
- The Cambridge Handbook of Computational Cognitive Sciences , pp. 210 - 241Publisher: Cambridge University PressPrint publication year: 2023