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 highfunctioning 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 University of Leuven, Leuven, Belgium 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.

On the Structure and Source of Individual Differences in Toddlers' Comprehension of Transitive Sentences

“…Future studies should aim to understand how individual differences in the comprehension of children map onto IMPL tasks. A promising tool for doing so is the combination of cognitive process models and psychometric models, so-called cognitive psychometrics (Voorspoels et al, 2018 ). Such studies may prove a necessary pre-requisite for testing the more precise predictions of individual differences discussed in this report.…”

The Value of Bayesian Methods for Accurate and Efficient Neuropsychological Assessment

“…That is, through modelling performance on a list learning task (Alexander et al, 2016;Lee et al, 2016;Pooley et al, 2011), researchers were able to differentiate between patients with different levels of subjective memory complaints who could not be differentiated using traditional measures of performance (Shankle et al, 2013). Custom cognitive models can also be designed for other neuropsychological tasks, such as for the Iowa gambling task (Haines et al, 2018), but also for experimental paradigms used in the context of autism research (Voorspoels et al, 2018) and to separate out several attention processes (Habekost, 2015).…”

Prototype-based category learning in autism: A review

“…Our search revealed no publication in which the prototype and exemplar models were directly compared against each other in individuals with ASD within the context of category learning. Table 1B summarizes the two articles in which a formal prototype model was compared between individuals with and without ASD (Church et al, 2010;Voorspoels et al, 2018), both based on the same data set. In the experiment from which the data set originated (Church et al, 2010), children (aged 7 to 12 years) with (n = 20) and without ASD (n = 20; TD) performed a prototype-distortion task with abstract shapes created from dot patterns.…”

“…To further investigate the occurrence of potential ASD subgroups in sensitivity, a hierarchical latent mixture model was applied (Voorspoels et al, 2018; Analysis 2), allowing for discrete ingroup differences (see also Bartlema et al, 2014). This approach incorporated a binary latent group indicator variable, which assigned participants either to a prototype or a simple guessing model.…”