Institute of Clinical Medicine, University of Oslo, PO box 1171, Blindern, 0318 Oslo, Norway; Department of Psychology, University of Oslo, PO box 1094, Blindern, 0317 Oslo, Norway.
Department of Psychology, University of Oslo, PO box 1094, Blindern, 0317 Oslo, Norway; The Intervention Centre, Oslo University Hospital Rikshospitalet, PO box 4950, Nydalen, 0424 Oslo, Norway.
Neurosci Biobehav Rev. 2016 Dec;71:633-656. doi: 10.1016/j.neubiorev.2016.09.002. Epub 2016 Sep 5.
Attention deficit hyperactivity disorder (ADHD) is characterized by altered decision-making (DM) and reinforcement learning (RL), for which competing theories propose alternative explanations. Computational modelling contributes to understanding DM and RL by integrating behavioural and neurobiological findings, and could elucidate pathogenic mechanisms behind ADHD. This review of neurobiological theories of ADHD describes predictions for the effect of ADHD on DM and RL as described by the drift-diffusion model of DM (DDM) and a basic RL model. Empirical studies employing these models are also reviewed. While theories often agree on how ADHD should be reflected in model parameters, each theory implies a unique combination of predictions. Empirical studies agree with the theories' assumptions of a lowered DDM drift rate in ADHD, while findings are less conclusive for boundary separation. The few studies employing RL models support a lower choice sensitivity in ADHD, but not an altered learning rate. The discussion outlines research areas for further theoretical refinement in the ADHD field.
注意缺陷多动障碍（ADHD）的特征是决策（DM）和强化学习（RL）改变，对此有竞争理论提出了替代解释。计算建模通过整合行为和神经生物学发现，有助于理解 DM 和 RL，并阐明 ADHD 背后的发病机制。本文综述了 ADHD 的神经生物学理论，描述了 DM 的漂移-扩散模型（DDM）和基本 RL 模型所描述的 ADHD 对 DM 和 RL 的影响的预测。还回顾了使用这些模型的实证研究。虽然理论通常都同意 ADHD 应该如何反映在模型参数中，但每个理论都意味着一个独特的预测组合。实证研究与理论的假设一致，即 ADHD 中 DDM 漂移率降低，而边界分离的发现则不太明确。少数使用 RL 模型的研究支持 ADHD 中的选择敏感性降低，但 RL 学习率没有改变。讨论概述了 ADHD 领域进一步理论细化的研究领域。
