Bridging the Gap Between Self-Report and Behavioral Laboratory Measures: A Real-Time Driving Task With Inverse Reinforcement Learning
开发了一个实时驾驶任务（高速公路任务），发现自我报告的冲动性与该任务表现高度相关，而与传统行为任务不相关；结合逆强化学习推断主观奖励动态，揭示冲动个体对非理性情境赋予高主观奖励。
A major challenge in assessing psychological constructs such as impulsivity is the weak correlation between self-report and behavioral task measures that are supposed to assess the same construct. To address this issue, we developed a real-time driving task called the "highway task," in which participants often exhibit impulsive behaviors mirroring real-life impulsive traits captured by self-report questionnaires. Here, we show that a self-report measure of impulsivity is highly correlated with performance in the highway task but not with traditional behavioral task measures of impulsivity (47 adults aged 18-33 years). By integrating deep neural networks with an inverse reinforcement learning (IRL) algorithm, we inferred dynamic changes of subjective rewards during the highway task. The results indicated that impulsive participants attribute high subjective rewards to irrational or risky situations. Overall, our results suggest that using real-time tasks combined with IRL can help reconcile the discrepancy between self-report and behavioral task measures of psychological constructs.