4.5 Article

What Can Performance in the IEDS Task Tell Us About Attention Shifting in Clinical Groups?

期刊

AUTISM RESEARCH
卷 14, 期 6, 页码 1237-1251

出版社

WILEY
DOI: 10.1002/aur.2484

关键词

ASD; Schizophrenia; extra‐ dimensional shift

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The study found that the Intra-Extra-Dimensional Set shift task (IEDS) is not an ideal measure to differentiate reduced attention shifting from other causes of impaired performance, but is more likely influenced by differences in learning rate.
The Intra-Extra-dimensional set shift task (IEDS) is a widely used test of learning and attention, believed to be sensitive to aspects of executive function. The task proceeds through a number of stages, and it is generally claimed that patterns of errors across stages can be used to discriminate between reduced attention switching and more general reductions in rates of learning. A number of papers have used the IEDS task to argue for specific attention shifting difficulties in Autism Spectrum Disorder (ASD) and Schizophrenia, however, it remains unclear how well the IEDS really differentiates between reduced attention shifting and other causes of impaired performance. To address this issue, we introduce a simple computational model of performance in the IEDS task, designed to separate the competing effects of attention shifting and general learning rate. We fit the model to data from ASD and comparison individuals matched on age and IQ, as well as to data from four previous studies which used the IEDS task. Model fits do not show consistent evidence for reductions in attention shifting rates in ASD and Schizophrenia. Instead, we find performance is better explained by differences in learning rate, particularly from punishment, which we show correlates with IQ. We, therefore, argue that the IEDS task is not a good measure of attention shifting in clinical groups. Lay Summary The Intra-Extra-Dimensional Set shift task (IEDS) is often given to autistic individuals, who tend to make more errors relative to comparison groups. This higher error rate is taken to mean that autistic individuals struggle with attention control. Our computational model of the IEDS shows that the performance of ASD and some other clinical groups can be explained instead by differences in learning rate, rather than differences in attention control.

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