University of Leuven, Leuven, Belgium.
Faculty of Psychology and Educational Sciences, Tiensestraat 102, bus 3713, 3000, Leuven, Belgium.
Psychon Bull Rev. 2018 Feb;25(1):271-285. doi: 10.3758/s13423-017-1245-4.
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.
我们呈现了一个分层贝叶斯解释性认知心理计量学的案例研究，研究了高功能自闭症谱系障碍（HFASD）个体的信息处理特征。基于先前发表的数据，我们使用分类的计算模型比较了一组 HFASD 儿童和典型发育（TD）对照者的分类行为。模型中的参数反映了与 HFASD 理论上相关的信息处理特征。因为我们期望模型参数存在个体差异，以及 HFASD 和 TD 儿童之间存在差异，所以我们使用分层解释方法。第一项分析表明，HFASD 儿童对原型的敏感性较低。第二项涉及混合成分的分析表明，计算模型不适合一部分参与者，这意味着这些儿童的参数估计没有信息量。仅关注适用于原型模型的儿童，没有推断出 HFASD 和 TD 儿童在敏感性方面存在明显差异。
