Department of Psychology, The Ohio State University.
Department of Psychiatry and Behavioral Health, The Ohio State University.
Psychol Bull. 2021 Feb;147(2):169-231. doi: 10.1037/bul0000319. Epub 2020 Dec 28.
Computational models, in conjunction with (neuro)cognitive tests, are increasingly used to understand the cognitive characteristics of participants with attention-deficit/hyperactivity disorder (ADHD). We reviewed 50 studies from a broad range of cognitive tests for ADHD to synthesize findings and to summarize the new insights provided by three commonly applied computational models (i.e., diffusion decision models, absolute accumulator models, ex-Gaussian distribution models). Four areas are discussed to improve the utility of (neuro)cognitive testing for ADHD: (a) the requirements for appropriate application of the computational models; (b) the consideration of sample characteristics and neurophysiological measures; (c) the integration of findings from cognitive psychology into the literature of cognitive testing to reconcile mixed evidence; and (d) future directions for the study of ADHD endophenotypes. We illustrate how computational models refine our understanding of cognitive concepts (slow processing speed, inhibition failures) presumed to characterize ADHD. We also show that considering sample characteristics and integrating findings from computational models and neurophysiological measures provide evidence for ADHD endophenotype-specific cognitive characteristics. However, studying the cognitive characteristics of ADHD endophenotypes often lies beyond the scope of existing research for three reasons: some cognitive tests lack sensitivity to detect clinical characteristics; analysis methods do not allow the study of subtle cognitive differences; and the precategorization of participants restricts the study of symptom severity on a continuous spectrum. We provide recommendations for cognitive testing, computational modeling, and integrating electrophysiological measures to produce more valuable tools in research and clinical practice (above and beyond the research domain of ADHD). (PsycInfo Database Record (c) 2021 APA, all rights reserved).
计算模型与（神经）认知测试一起，被越来越多地用于理解注意力缺陷/多动障碍（ADHD）患者的认知特征。我们综述了来自广泛认知测试的 50 项研究，以综合发现，并总结三种常用计算模型（即扩散决策模型、绝对累加器模型、外伽马分布模型）提供的新见解。为了提高（神经）认知测试对 ADHD 的效用，我们讨论了四个方面：（a）适当应用计算模型的要求；（b）考虑样本特征和神经生理测量；（c）将认知心理学的发现纳入认知测试文献，以调和混合证据；（d）ADHD 内表型研究的未来方向。我们举例说明了计算模型如何细化我们对假定为 ADHD 特征的认知概念（处理速度慢、抑制失败）的理解。我们还表明，考虑样本特征和整合来自计算模型和神经生理测量的发现，为 ADHD 内表型特异性认知特征提供了证据。然而，出于三个原因，研究 ADHD 内表型的认知特征往往超出了现有研究的范围：一些认知测试缺乏检测临床特征的敏感性；分析方法不允许研究微妙的认知差异；参与者的预先分类限制了对连续谱上症状严重程度的研究。我们为认知测试、计算建模和整合电生理测量提供了建议，以在研究和临床实践中产生更有价值的工具（超越 ADHD 的研究领域）。（PsycInfo 数据库记录（c）2021 APA，保留所有权利）。
