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Dynamical models: an alternative or complement to mechanistic explanations?

David M Kaplan1, William Bechtel

  • 1Department of Anatomy and Neurobiology, Washington University School of Medicine, St. LouisDepartment of Philosophy, Center for Chronobiology, and Interdisciplinary Program in Cognitive Science, University of California, San Diego.

Topics in Cognitive Science
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Summary
This summary is machine-generated.

Dynamical models are crucial in cognitive science but not a replacement for mechanistic explanations. Integrating dynamical models with mechanisms offers a more complete understanding of cognitive processes.

Keywords:
Dynamical modelsExplanationHKB modelMechanistic explanationPredictivism

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Philosophy of Science

Background:

  • Dynamical models are increasingly used in cognitive science.
  • A debate exists on whether dynamical models can replace mechanistic explanations.
  • Concerns arise when dynamical models lack grounding in underlying mechanisms.

Purpose of the Study:

  • To evaluate the role of dynamical models as explanations in cognitive science.
  • To challenge the view that dynamical models are an alternative to mechanistic explanations.
  • To propose an integrated approach combining dynamical models and mechanisms.

Main Methods:

  • Review of existing literature on dynamical models and mechanistic explanations.
  • Analysis of problems faced by ungrounded dynamical models.
  • Examination of case studies (action potentials, circadian rhythms).

Main Results:

  • Dynamical models face limitations when not integrated with mechanisms.
  • The opposition between dynamical models and mechanisms is a false dichotomy.
  • Dynamical models that describe mechanism operations resolve explanatory issues.

Conclusions:

  • Dynamical models and mechanistic explanations are complementary, not opposing.
  • Integrating dynamical models with mechanistic details enhances explanatory power.
  • This integrated approach provides a more robust framework for cognitive science.