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

Overview of attention for article published in Psychonomic Bulletin & Review, March 2017
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Title
Sensitivity to the prototype in children with high-functioning autism spectrum disorder: An example of Bayesian cognitive psychometrics
Published in
Psychonomic Bulletin & Review, March 2017
DOI 10.3758/s13423-017-1245-4
Pubmed ID
Authors

Wouter Voorspoels, Isa Rutten, Annelies Bartlema, Francis Tuerlinckx, Wolf Vanpaemel

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.

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Geographical breakdown

Country Count As %
Unknown 98 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 22%
Researcher 12 12%
Student > Master 12 12%
Student > Bachelor 11 11%
Student > Doctoral Student 7 7%
Other 15 15%
Unknown 19 19%
Readers by discipline Count As %
Psychology 53 54%
Social Sciences 4 4%
Agricultural and Biological Sciences 4 4%
Linguistics 3 3%
Neuroscience 3 3%
Other 6 6%
Unknown 25 26%