Measuring impulsivity using a real-time driving task and inverse reinforcement learning

Impulsivity has been extensively studied in relation to mental disorders and maladaptive behaviors using self-report questionnaires and behavioral tasks. A persistent issue is that self-report and behavioral measures show weak correlations between each other, although they are supposed to tap the same construct. To address this problem, we devised a real-time driving task called the “highway task” that allows participants to exhibit impulsive behaviors, such as reckless driving, which may mirror real-life impulsive traits assessed by self-report questionnaires. We hypothesized that the highway task would provide impulsivity measures that are strongly correlated with self-report measures of impulsivity. As hypothesized, statistical evidence supported the correlation between the performance in the highway task and a self-report measure of impulsivity (i.e., the Barratt impulsiveness scale, r=0.46). By contrast, measures of impulsivity from two traditional laboratory tasks (delay discounting and go/no-go tasks) did not correlate with BIS (r=0.01, 0.07, respectively). To infer subjective reward functions that underlie observed real-time behaviors in the highway task, we used an inverse reinforcement learning (IRL) algorithm combined with deep neural networks. The agents trained by IRL produced actions that resemble participants’ behaviors observed in the highway task. IRL inferred sensible reward functions from participants’ behaviors and revealed real-time changes in rewards around salient events (e.g., overtaking, a collision with a car ahead, etc.). The rewards inferred by IRL suggested that impulsive participants have high subjective reward values for irrational or risky behaviors. Overall, our results suggested that using real-time tasks with IRL may bridge the gap between self-report and behavioral measures of impulsivity, with IRL being a practical modeling framework for multidimensional data from real-time tasks.