Data from: Using optimal control to disambiguate the effect of depression on sensorimotor, motivational and goal-setting functions

Differentiating the ability from the motivation to act is of central importance to psychiatric disorders in general and depression in particular. However, it has been difficult to develop quantitative approaches to relate depression to poor motor performance in goal-directed tasks. Here, we use an inverse optimal control approach to provide a computational framework that can be used to infer and factorize performance deficits into three components: sensorimotor speed, goal setting and motivation. Using a novel computer-simulated driving experiment, we found that (1) severity of depression is associated with both altered sensorimotor speed and motivational function; (2) moderately to severely depressed individuals show an increased distance from the stop sign indicating aversive learning affecting goal setting functions. Taken together, the inverse optimal control framework can disambiguate on an individual basis the sensorimotor from the motivational dysfunctions in depression, which may help to develop more precisely targeted interventions.

区分行动能力与行动动机，对于所有精神障碍，尤其是抑郁症而言，均具有核心重要性。然而，此前始终难以构建量化方法，将抑郁症与目标导向任务中的运动表现缺陷关联起来。本研究采用逆最优控制（Inverse Optimal Control）方法，构建了一套计算框架，可将表现缺陷推断并分解为三个维度：感觉运动速度、目标设定与动机。借助一项全新的计算机模拟驾驶实验，我们发现：（1）抑郁严重程度与感觉运动速度异常及动机功能损伤均存在关联；（2）中重度抑郁个体距停车标志的距离更远，这表明厌恶性学习会对目标设定功能造成影响。综上，逆最优控制框架可在个体层面区分抑郁症患者的感觉运动功能障碍与动机功能障碍，这有助于开发更为精准的靶向干预方案。