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The overconstraint of response time models: rethinking the scaling problem
Chris Donkin1, Scott D Brown, Andrew Heathcote
1School of Psychology, University of Newcastle, Callaghan, NSW 2308 Australia. chris.donkin@newcastle.edu.au
Psychonomic Bulletin & Review
|December 8, 2009
Summary
Evidence accumulation models explain decision-making but have a scaling property requiring parameter fixing. New methods loosen this constraint, improving model accuracy and revealing unexamined psychological assumptions in choice response time research.
Area of Science:
- Cognitive Psychology
- Decision Science
- Computational Neuroscience
Background:
- Theories of choice response time (RT) are crucial for understanding decision-making processes.
- Evidence accumulation (or sequential sampling) models are prominent in explaining choice RT.
- These models share a 'scaling' property, necessitating parameter fixing for estimation.
Purpose of the Study:
- To investigate the implications of the 'scaling' property in evidence accumulation models.
- To demonstrate how traditional parameter fixing overconstrains these models.
- To propose and evaluate minimally constrained model versions for improved data fitting.
Main Methods:
- Analysis of the 'scaling' property inherent in evidence accumulation models.
- Development of minimally constrained versions of these models.
- Comparison of the explanatory power of overconstrained versus minimally constrained models using empirical data.
Main Results:
- The traditional method of fixing a parameter due to the scaling property overconstrains the models.
- This overconstraint limits the models' ability to accurately account for choice RT data.
- Minimally constrained model versions provide a superior fit to the data compared to their overconstrained counterparts.
Conclusions:
- Addressing the scaling problem with minimal constraints enhances the descriptive accuracy of evidence accumulation models.
- Overconstrained models may embed unexamined psychological assumptions.
- Revised modeling approaches offer a more flexible and accurate account of decision-making processes.
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