Modeling individual differences in cognition |
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Authors: | Lee Michael D Webb Michael R |
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Institution: | Department of Psychology, University of Adelaide, SA 5005, Australia. michael.lee@psychology.adelaide.edu.au |
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Abstract: | Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects,
and so fail to consider the possibility of important individual differences between subjects. Other evaluations are done at
the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially
provides. To overcome these weaknesses, we have developed a general approach to modeling individual differences using families
of cognitive models in which different groups of subjects are identified as having different psychological behavior. Separate
models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine
the appropriate number of groups. We evaluate this individual differences approach in a simulation study and show that it
is superior in terms of the key modeling goals of prediction and understanding. We also provide two practical demonstrations
of the approach, one using the ALCOVE model of category learning with data from four previously analyzed category learning
experiments, the other using multidimensional scaling representational models with previously analyzed similarity data for
colors. In both demonstrations, meaningful individual differences are found and the psychological models are able to account
for this variation through interpretable differences in parameterization. The results highlight the potential of extending
cognitive models to consider individual differences. |
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