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On Testing and Developing Cognitive Models.

Thomas J Palmeri1

  • 1Vanderbilt University.

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Summary
This summary is machine-generated.

This commentary discusses best practices for computational modeling in cognitive science, focusing on preregistration and postregistration to enhance transparency and trust in scientific research.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • A "crisis of confidence" is impacting scientific disciplines like psychology and neuroscience.
  • There is a growing need for robust and transparent computational modeling practices.

Purpose of the Study:

  • To comment on recommended practices for computational modeling.
  • To focus on preregistration and postregistration as key strategies for robust modeling.

Main Methods:

  • Review of recommended practices in computational modeling.
  • Reflection on personal experiences with developing new models and modeling approaches.
  • Analysis of preregistration and postregistration methods.

Main Results:

  • Many recommended practices for robust modeling are uncontroversial.
  • Preregistration and postregistration are highlighted as crucial for enhancing trust in models.

Conclusions:

  • Implementing preregistration and postregistration can improve the reliability of computational models.
  • These practices contribute to making scientific modeling more transparent and trusted.