Bridging the gap between self-report and behavioral laboratory measures: A real-time driving task with inverse reinforcement learning

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”, where participants often exhibit impulsive behaviors, such as reckless driving, thereby mirroring real-life impulsive traits captured by self-report surveys. Here, we first 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. By integrating deep neural networks with an inverse reinforcement learning (IRL) algorithm, we inferred dynamic changes of subjective rewards during the highway task. The IRL results indicated that impulsive participants attribute high subjective rewards to irrational or risky driving behaviors and 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 including impulsivity, with IRL being a practical modeling framework for multidimensional data from real-time tasks.