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Related Experiment Videos

On some two-way barriers between models and mechanisms.

W R Uttal1

  • 1Department of Psychology, Arizona State University, Tempe 85287.

Perception & Psychophysics
|August 1, 1990
PubMed
Summary
This summary is machine-generated.

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Computational and neural net models offer valuable insights into cognitive processes but face inherent limitations. These models may describe perception but cannot serve as reductive explanations or predict behavior from neural primitives.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Philosophy of Mind

Background:

  • Recent and classic theories suggest limitations in computational, mathematical, and neural net models.
  • These limitations impact the explanatory power for visual and cognitive processes.

Purpose of the Study:

  • To clarify the appropriate meanings and scope of formal models in cognitive science.
  • To discuss the inherent constraints on the explanatory role of these models.

Main Methods:

  • Synthesizing ideas from mathematics, automata theory, chaos theory, thermodynamics, neurophysiology, and psychology.
  • Analyzing the verifiability and testability of neural or cognitive mechanisms underlying formal models.

Main Results:

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  • Formal models, while descriptive, may not provide reductive explanations for cognitive processes.
  • The neural or cognitive mechanisms underpinning many formal models are potentially untestable and unverifiable.
  • Predicting molar-level behavior from neural primitives using these models is not feasible in principle.

Conclusions:

  • Models can enhance our appreciation of cognitive processes but have inherent limitations in explanatory depth.
  • Understanding these constraints is crucial for appropriate interpretation and application of cognitive models.
  • The modeling endeavor continues to progress, deepening our understanding of plausible cognitive mechanisms.