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This study explores how computer science algorithms can explain human cognitive processes. Monte Carlo methods, specifically particle filters, effectively model how people approximate optimal solutions in categorization tasks.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Rational models of cognition assume optimal problem-solving, contrasting with formal models focusing on psychological processes.
  • A key challenge is explaining how psychological processes approximate optimal solutions.
  • Computer science and statistics offer approximation algorithms that can bridge this gap.

Purpose of the Study:

  • To propose a strategy for developing rational process models of cognition.
  • To investigate the psychological plausibility of approximation algorithms from computer science.
  • To apply these methods to Anderson's rational model of categorization (RMC).

Main Methods:

  • Exploring Monte Carlo methods as a source for rational process models.
  • Connecting the RMC to nonparametric Bayesian statistics.
  • Proposing Gibbs sampling and particle filters as algorithms for approximate inference in the RMC.

Main Results:

  • Monte Carlo methods, particularly particle filters, can link optimal solutions to psychological processes.
  • A particle filter with a single particle accurately describes human inferences in categorization tasks.
  • The proposed algorithms provide a computational framework for understanding approximate inference in cognition.

Conclusions:

  • Approximation algorithms from computer science offer a viable path for developing psychologically plausible rational models of cognition.
  • Particle filters provide a promising computational mechanism for human approximate inference.
  • This approach advances our understanding of the interplay between optimal computation and cognitive processes.