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Related Experiment Videos

Model complexity: the fit to random data reconsidered.

J C Dunn1

  • 1Department of Psychiatry and Behavioural Science, University of Western Australia, Australia. jdunn@cyllene.uwa.edu.au

Psychological Research
|August 18, 2000
PubMed
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This study introduces a new complexity index, the mean minimum distance, to evaluate depth perception models. Findings indicate the Linear Integration and Fuzzy Logic of Perception models have equal complexity.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • Model testing in depth perception faces challenges related to model complexity.
  • Model complexity is defined as the model's propensity to fit arbitrary datasets.
  • Existing methods for assessing model complexity in depth perception are insufficient.

Purpose of the Study:

  • Introduce a novel index for quantifying model complexity in depth perception.
  • Apply this index to resolve the complexity debate between two prominent depth perception models.
  • Provide a standardized method for comparing model complexity in cognitive science.

Main Methods:

  • Define and introduce the mean minimum distance (MMD) as a complexity index.
  • Express MMD as a dimensionless quantity: scaled mean minimum distance (SMMD).

Related Experiment Videos

  • Derive theoretical SMMD values and variances for linear models and compare with empirical fits to random data.

    • Main Results:

    • The mean minimum distance (MMD) effectively quantifies model complexity.
    • Theoretical and empirical analyses provide comparable complexity estimates.
    • The Linear Integration model and the Fuzzy Logic of Perception model exhibit equivalent complexity.

    Conclusions:

    • The proposed mean minimum distance index offers a robust measure of model complexity.
    • The Linear Integration and Fuzzy Logic of Perception models are equally complex.
    • This complexity index can aid in resolving controversies in computational modeling of perception.