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Model evaluation using grouped or individual data.

Andrew L Cohen¹, Adam N Sanborn, Richard M Shiffrin

¹Department of Psychology, University of Massachusetts, Amherst, Massachusetts 01003, USA. acohen@psych.umass.edu

Psychonomic Bulletin & Review

|September 17, 2008

Summary

This summary is machine-generated.

Group analysis can outperform individual data analysis when few trials per subject are available. This approach enhances model fitting and parameter estimation accuracy in cognitive modeling research.

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Area of Science:

Cognitive Science
Computational Neuroscience
Psychometrics

Background:

Individual data analysis offers advantages over group analysis by preserving unique patterns and parameters.
However, group analysis can be beneficial under specific circumstances, particularly when data is limited.

Purpose of the Study:

To investigate conditions where group analysis surpasses individual analysis in cognitive modeling.
To compare the efficacy of individual versus group analysis in model fitting and selection accuracy.

Main Methods:

A simulation technique generated data from known cognitive models with inter-individual parameter variation.
Individual and group analyses were applied to simulated data to assess model fitting and selection accuracy.
Systematic variation of trials per condition and individuals per experiment.

Main Results:

Group analysis outperformed individual analysis under conditions with limited trials per subject per condition.
This advantage was observed across three distinct pairs of cognitive models, including forgetting, categorization, and information integration models.
Group analysis mitigated distortions and biases introduced by small trial numbers in parameter estimation.

Conclusions:

Group analysis is a valuable approach when dealing with limited trials per subject, enhancing the reliability of cognitive model fitting and selection.
The choice between individual and group analysis depends on the experimental design, particularly the number of trials per condition.
Findings provide crucial insights for optimizing data analysis strategies in cognitive science research.