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Related Experiment Videos

When a good fit can be bad.

Mark A. Pitt1, In Jae Myung

  • 1Dept of Psychology, Ohio State University, 1885 Neil Avenue, 43210-1222, Columbus, Ohio, USA

Trends in Cognitive Sciences
|November 5, 2002
PubMed
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Selecting computational cognitive models requires more than just data fit. New methods assess model generalizability, improving selection beyond standard goodness-of-fit measures for reliable cognitive process approximation.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • Evaluating computational models of cognition often relies on goodness-of-fit measures.
  • Standard metrics can be misleading due to issues like overfitting, where model fit doesn't reflect true cognitive process approximation.

Purpose of the Study:

  • To introduce and evaluate novel selection methods for computational models of cognition.
  • To address the limitations of traditional goodness-of-fit measures in model selection.

Main Methods:

  • Development of selection criteria that account for model properties beyond simple data fit.
  • Focus on assessing the generalizability of a model's ability to fit data.

Main Results:

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  • The proposed selection methods outperform standard goodness-of-fit measures.
  • Improved model selection based on enhanced generalizability assessment.

    • Conclusions:

    • Model selection in cognitive science should prioritize the generalizability of a model's fit.
    • Novel methods offer a more robust approach to selecting accurate computational models of cognition.