Department of Psychology, Harvard University, Cambridge, Massachusetts; Center for Brain Science, Harvard University, Cambridge, Massachusetts.
Department of Psychology, Harvard University, Cambridge, Massachusetts.
Biol Psychiatry. 2019 Mar 1;85(5):425-433. doi: 10.1016/j.biopsych.2018.06.018. Epub 2018 Jul 2.
Human decision making exhibits a mixture of model-based and model-free control. Recent evidence indicates that arbitration between these two modes of control ("metacontrol") is based on their relative costs and benefits. While model-based control may increase accuracy, it requires greater computational resources, so people invoke model-based control only when potential rewards exceed those of model-free control. We used a sequential decision task, while concurrently manipulating performance incentives, to ask if symptoms and traits of psychopathology decrease or increase model-based control in response to incentives.
We recruited a nonpatient population of 839 online participants using Amazon Mechanical Turk who completed transdiagnostic self-report measures encompassing symptoms, traits, and factors. We fit a dual-controller reinforcement learning model and obtained a computational measure of model-based control separately for small incentives and large incentives.
None of the constructs were related to a failure of large incentives to boost model-based control. In fact, for the sensation seeking trait and anxious-depression factor, higher scores were associated with a larger incentive effect, whereby greater levels of these constructs were associated with larger increases in model-based control. Many constructs showed decreases in model-based control as a function of severity, but a social withdrawal factor was positively correlated; alcohol use and social anxiety were unrelated to model-based control.
Our results demonstrate that model-based control can reliably be improved independent of construct severity for most measures. This suggests that incentives may be a useful intervention for boosting model-based control across a range of symptom and trait severity.
人类决策表现出基于模型和无模型控制的混合。最近的证据表明，这两种控制模式（“元控制”）之间的仲裁是基于它们的相对成本和收益。虽然基于模型的控制可以提高准确性，但它需要更多的计算资源，因此只有当潜在奖励超过无模型控制时，人们才会调用基于模型的控制。我们使用了一个序列决策任务，同时操纵绩效激励，来询问精神病理学的症状和特征是否会随着激励的变化而减少或增加基于模型的控制。
我们使用亚马逊 Mechanical Turk 招募了 839 名在线参与者，他们完成了涵盖症状、特征和因素的跨诊断自我报告测量。我们拟合了一个双控制器强化学习模型，并分别获得了小激励和大激励下基于模型的控制的计算度量。
没有一个结构与大激励不能提高基于模型的控制的失败有关。事实上，对于感觉寻求特质和焦虑抑郁因素，得分越高与激励效应越大有关，即这些结构的水平越高，基于模型的控制的增加幅度越大。许多结构的基于模型的控制随着严重程度的增加而减少，但社会退缩因素呈正相关；酒精使用和社交焦虑与基于模型的控制无关。
我们的结果表明，对于大多数测量，基于模型的控制可以在不依赖于结构严重程度的情况下可靠地提高。这表明激励可能是一种有用的干预措施，可以在一定程度上提高基于模型的控制。
