Sensitivity to the prototype in children with high-functioning autism spectrum disorder: An example of Bayesian cognitive psychometrics

Abstract

We present a case study of hierarchical Bayesian explanatory cognitive psychometrics, examining information processing characteristics of individuals with high-functioning autism spectrum disorder (HFASD). On the basis of previously published data, we compare the classification behavior of a group of children with HFASD with that of typically developing (TD) controls using a computational model of categorization. The parameters in the model reflect characteristics of information processing that are theoretically related to HFASD. Because we expect individual differences in the model’s parameters, as well as differences between HFASD and TD children, we use a hierarchical explanatory approach. A first analysis suggests that children with HFASD are less sensitive to the prototype. A second analysis, involving a mixture component, reveals that the computational model is not appropriate for a subgroup of participants, which implies parameter estimates are not informative for these children. Focusing only on the children for whom the prototype model is appropriate, no clear difference in sensitivity between HFASD and TD children is inferred.