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Ten simple rules for the computational modeling of behavioral data.

Robert C Wilson1,2, Anne Ge Collins3,4

  • 1Department of Psychology, University of Arizona, Tucson, United States.

Elife
|November 27, 2019
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Summary

This study provides ten simple rules for computational modeling in psychology and neuroscience. Following these guidelines helps researchers use computational models effectively to gain meaningful insights from experimental data.

Keywords:
computational modelingmodel fittingneurosciencereproducibilityvalidation

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Area of Science:

  • Psychology
  • Neuroscience
  • Computational Neuroscience
  • Behavioral Science

Background:

  • Computational modeling is a powerful tool in psychology and neuroscience.
  • It aids in understanding behavioral algorithms, neural correlates, and intervention effects.
  • However, responsible and careful application is crucial for meaningful insights.

Purpose of the Study:

  • To provide ten practical rules for the responsible use of computational modeling.
  • To offer a beginner-friendly guide on relating computational models to experimental data.
  • To help researchers avoid common pitfalls in computational modeling.

Main Methods:

  • The study outlines ten simple rules for computational modeling.
  • It focuses on beginner-friendly techniques for relating models to data.
  • Examples and online code are provided for illustration.

Main Results:

  • The proposed rules ensure computational modeling is used with care.
  • Researchers can gain meaningful insights by applying these guidelines.
  • The rules are applicable to both simple and advanced modeling techniques.

Conclusions:

  • Adhering to these ten rules will enhance the rigor and impact of computational modeling.
  • This guide empowers researchers to effectively apply computational modeling to their data.
  • It aims to foster a more responsible and insightful use of computational tools in behavioral sciences.