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The Importance of Complexity in Model Selection.

Myung1

  • 1Ohio State University

Journal of Mathematical Psychology
|March 29, 2000
PubMed
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Selecting a mathematical model requires balancing goodness-of-fit with model complexity. Overly complex models may fit data well but fail to represent the true cognitive process, leading to poor generalization.

Area of Science:

  • Cognitive Psychology
  • Mathematical Modeling
  • Computational Neuroscience

Background:

  • Model selection in cognitive psychology aims to identify the best representation of underlying mental processes.
  • Reliance solely on goodness-of-fit can lead to selecting overly complex models.
  • Complex models may overfit data, compromising their ability to generalize.

Purpose of the Study:

  • To highlight the necessity of considering model complexity alongside goodness-of-fit in model selection.
  • To explain why selecting models based only on data fit is insufficient.
  • To demonstrate how to offset the effects of overfitting in model selection.

Main Methods:

  • Theoretical analysis of model selection principles.
  • Examination of the relationship between model complexity, data fit, and generalization.

Related Experiment Videos

  • Application of selection methods to artificial data for illustration.

    • Main Results:

    • Model selection based solely on fit results in unnecessarily complex models that overfit data.
    • Overfitting leads to poor generalization, failing to capture the true underlying process.
    • Appropriate model selection methods must counteract the impact of overfitting.

    Conclusions:

    • Effective model selection in cognitive psychology necessitates a balance between fit and complexity.
    • Overfitting is a critical issue that must be addressed by selection methodologies.
    • The presented methods offer a way to select more parsimonious and generalizable models.