# Inferring priors in compositional cognitive models

- Eric Bigelow,
*University of Rochester, Rochester, New York, United States*
- Steven Piantadosi,
*University of Rochester, Rochester, New York, United States*

## Abstract

We apply Bayesian data analysis to a structured cognitive model in
order to determine the priors that support human generalizations in a simple
concept learning task. We modeled 250,000 ratings in a "number game" experiment
where subjects took examples of a numbers produced by a program (e.g. 4, 16, 32)
and rated how likely other numbers (e.g. 8 vs. 9) would be to be generated. This
paper develops a data analysis technique for a family of compositional "Language
of Thought" (LOT) models which permits discovery of subjects' prior probability
of mental operations (e.g. addition, multiplication, etc.) in this domain. Our
results reveal high correlations between model mean predictions and subject
generalizations, but with some qualitative mismatch for a strongly compositional
prior.

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