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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Combining meta-learned models with process models of cognition.

Adam N Sanborn1, Haijiang Yan1, Christian Tsvetkov1

  • 1Department of Psychology, University of Warwick, Coventry, UK a.n.sanborn@warwick.ac.uk haijiang.yan@warwick.ac.uk chris.tsvetkov@warwick.ac.ukhttps://go.warwick.ac.uk/adamsanborn.

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Summary

Meta-learned models accurately predict cognition, but studying biases is complex. Combining them with intuitive cognitive process models offers a practical solution for understanding judgment biases.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Meta-learned models offer optimal predictions for cognitive tasks.
  • Investigating judgment biases using constrained neural networks is computationally challenging.

Purpose of the Study:

  • To propose a more tractable approach for studying judgment biases.
  • To integrate meta-learned models with cognitive process models.

Main Methods:

  • Combining meta-learned models with cognitive process models.
  • Utilizing rational process models that sample from posterior distributions of meta-learned models.

Main Results:

  • This integration provides a more intuitive framework for understanding biases.
  • Rational process models are identified as a suitable method for this integration.

Conclusions:

  • Combining meta-learned models with cognitive process models simplifies bias investigation.
  • This approach enhances the interpretability of cognitive models.