A Bayesian Model of Rule Induction in Raven's Progressive Matrices

Abstract

Raven's Progressive Matrices is one of the most prevalent assays of fluid intelligence; however, most theoretical accounts of Raven's focus on producing models which can generate the correct answer but do not fit human performance data. We provide a computational-level theory which interprets rule induction in Raven's as Bayesian inference. The model computes the posterior probability of each rule in the set of possible rule hypotheses based on whether those rules could have generated the features of the objects in the matrix and the prior probability of each rule. Based on fits to both correct and incorrect response options across both the Standard and Advanced Progressive Matrices, we propose several novel mechanisms that may drive responding to Raven's items.


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