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How to fit a response time distribution.

¹Johns Hopkins University, Baltimore, Maryland, USA.

Psychonomic Bulletin & Review

|November 18, 2000

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Statistical fitting of mathematical models to response time data is crucial in behavioral science. Our study reveals that least squares fits to density estimates are unreliable, while other methods offer better parameter recovery.

Area of Science:

Cognitive Science
Behavioral Science
Computational Neuroscience

Background:

Mathematical models are essential for understanding cognitive processes.
Response time distributions are key data in behavioral science.
Current fitting techniques vary, with unknown efficacy.

Purpose of the Study:

To assess the performance of different statistical fitting techniques for cognitive models.
To identify reliable methods for parameter estimation in response time data analysis.

Main Methods:

Simulated response time data from six established cognitive models.
Applied various fitting techniques, including maximum likelihood and least squares.
Compared parameter recovery accuracy and variability across methods.

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Main Results:

Empirical density estimates showed bias, even with large sample sizes.
Certain fitting techniques provided more accurate and stable parameter estimates.
Least squares fits to density estimates performed poorly.

Conclusions:

The choice of fitting technique significantly impacts the reliability of cognitive model parameter estimates.
Researchers should exercise caution with least squares methods applied to density estimates.
Further research is needed to establish best practices for fitting cognitive models to response time data.