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Risks of drawing inferences about cognitive processes from model fits to individual versus average performance.
1Department of Psychology, Indiana University, Bloomington, Indiana 47405, USA. wkestes@indiana.edu
Psychonomic Bulletin & Review
|October 21, 2005
Summary
Estimating cognitive model parameters requires careful consideration of data fitting methods. Fits to individual performance data, particularly with complex models, offer more accurate parameter recovery than averaged data fits.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Psychology
Background:
- Theoretical models are crucial for understanding cognitive processes.
- Estimating model parameters from cognitive data is challenging.
- Model fitting to individual versus averaged data presents distinct challenges.
Purpose of the Study:
- To compare the risks and benefits of fitting theoretical models to individual performance data versus averaged data.
- To investigate the impact of model complexity and number of subjects on parameter estimation accuracy.
- To provide guidelines for improving parameter recovery in cognitive modeling.
Main Methods:
- Applied several theoretical models to recognition and categorization experimental data.
- Utilized artificial, computer-generated data for controlled comparisons.
- Analyzed parameter estimation accuracy based on fits to individual and averaged data.
Main Results:
- Model fitting outcomes are significantly influenced by model complexity and the number of subjects.
- Accurate parameter estimation was primarily achieved through fits to individual performance data.
- Parameter recovery was successful for some parameters of complex models when using individual data.
Conclusions:
- Fitting models to individual cognitive performance data is generally superior for parameter estimation.
- Model complexity and sample size are critical factors influencing the reliability of parameter estimates.
- Guidelines are proposed to overcome common obstacles in estimating cognitive model parameters.
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