Huang He, Harlé Katia, Movellan Javier, Paulus Martin
Department of Cognitive Science, UC San Diego, La Jolla, California.
Machine Perception Lab, UC San Diego, La Jolla, California.
PLoS One. 2016 Dec 14;11(12):e0167960. doi: 10.1371/journal.pone.0167960. eCollection 2016.
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.
区分行动能力和行动动机对一般精神疾病尤其是抑郁症至关重要。然而，很难开发出定量方法来将抑郁症与目标导向任务中的运动表现不佳联系起来。在此，我们使用逆最优控制方法来提供一个计算框架，该框架可用于推断并将表现缺陷分解为三个组成部分：感觉运动速度、目标设定和动机。通过一项新颖的计算机模拟驾驶实验，我们发现：（1）抑郁症严重程度与感觉运动速度和动机功能的改变均相关；（2）中度至重度抑郁症患者与停车标志的距离增加，表明厌恶学习影响目标设定功能。总之，逆最优控制框架可以在个体层面上区分抑郁症中感觉运动功能障碍和动机功能障碍，这可能有助于开发更具针对性的干预措施。
