Powell Georgie, Jones Catherine R G, Hedge Craig, Charman Tony, Happé Francesca, Simonoff Emily, Sumner Petroc
School of Psychology.
Department of Psychology, Institute of Psychiatry Psychology and Neuroscience, King's College London.
Neuropsychology. 2019 May;33(4):445-461. doi: 10.1037/neu0000524. Epub 2019 Feb 25.
Research using cognitive or perceptual tasks in autism spectrum disorder (ASD) often relies on mean reaction time (RT) and accuracy derived from alternative-forced choice paradigms. However, these measures can confound differences in task-related processing efficiency with caution (i.e., preference for speed or accuracy). We examined whether computational models of decision-making allow these components to be isolated.
Using data from two face-processing tasks (face recognition and egocentric eye-gaze discrimination), we explored whether adolescents with ASD and wide-ranging intellectual ability differed from an age and IQ matched comparison group on model parameters that are thought to represent processing efficiency, caution, and perceptual encoding/motor output speed.
We found evidence that autistic adolescents had lower processing efficiency and caution but did not differ from nonautistic adolescents in the time devoted to perceptual encoding/motor output. These results were more consistent across tasks when we only analyzed participants with IQ above 85. Cross-task correlations suggested that processing efficiency and caution parameters were relatively stable across individuals and tasks. Furthermore, logistic classification with model parameters improved discrimination between individuals with and without ASD relative to classification using mean RT and accuracy. Finally, previous research has found that ADHD symptoms are associated with lower processing efficiency, and we observed a similar relationship in our sample, but only for autistic adolescents.
Together, these results suggest that models of decision-making could provide both better discriminability between autistic and nonautistic individuals on cognitive tasks and also a more specific understanding of the underlying mechanisms driving these differences. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
在自闭症谱系障碍(ASD)中使用认知或感知任务的研究通常依赖于平均反应时间(RT)以及源自二选一强制选择范式的准确率。然而,这些测量可能会将与任务相关的处理效率差异与谨慎程度(即对速度或准确性的偏好)相混淆。我们研究了决策计算模型是否能将这些成分分离出来。
利用两项面部处理任务(人脸识别和自我中心注视辨别)的数据,我们探究了智力水平各异的ASD青少年与年龄及智商匹配的对照组在被认为代表处理效率、谨慎程度以及感知编码/运动输出速度的模型参数上是否存在差异。
我们发现有证据表明,自闭症青少年的处理效率和谨慎程度较低,但在用于感知编码/运动输出的时间上与非自闭症青少年并无差异。当我们仅分析智商高于85的参与者时,这些结果在各项任务中更为一致。跨任务相关性表明,处理效率和谨慎程度参数在个体和任务之间相对稳定。此外,相对于使用平均反应时间和准确率进行分类,使用模型参数进行逻辑分类提高了对有无ASD个体的区分能力。最后,先前的研究发现注意缺陷多动障碍(ADHD)症状与较低的处理效率相关,我们在样本中也观察到了类似的关系,但仅适用于自闭症青少年。
总之,这些结果表明,决策模型既能在认知任务中更好地区分自闭症和非自闭症个体,也能更具体地理解驱动这些差异的潜在机制。(《心理学文摘数据库记录》(c)2019美国心理学会,保留所有权利)