Impulsivity Relates to Multi-Trial Choice Strategy in Probabilistic Reversal Learning

Amy R Zou; Daniela E Muñoz Lopez; Sheri L Johnson; Anne G E Collins

doi:10.3389/fpsyt.2022.800290

Impulsivity Relates to Multi-Trial Choice Strategy in Probabilistic Reversal Learning

Front Psychiatry. 2022 Mar 14:13:800290. doi: 10.3389/fpsyt.2022.800290. eCollection 2022.

Authors

Amy R Zou¹, Daniela E Muñoz Lopez^{1

2}, Sheri L Johnson¹, Anne G E Collins^{1

3}

Affiliations

¹ Department of Psychology, University of California, Berkeley, Berkeley, CA, United States.
² Institute of Human Development, University of California, Berkeley, Berkeley, CA, United States.
³ Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States.

Abstract

Impulsivity is defined as a trait-like tendency to engage in rash actions that are poorly thought out or expressed in an untimely manner. Previous research has found that impulsivity relates to deficits in decision making, in particular when it necessitates executive control or reward outcomes. Reinforcement learning (RL) relies on the ability to integrate reward or punishment outcomes to make good decisions, and has recently been shown to often recruit executive function; as such, it is unsurprising that impulsivity has been studied in the context of RL. However, how impulsivity relates to the mechanisms of RL remains unclear. We aimed to investigate the relationship between impulsivity and learning in a reward-driven learning task with probabilistic feedback and reversal known to recruit executive function. Based on prior literature in clinical populations, we predicted that higher impulsivity would be associated with poorer performance on the task, driven by more frequent switching following unrewarded outcomes. Our results did not support this prediction, but more advanced, trial-history dependent analyses revealed specific effects of impulsivity on switching behavior following consecutive unrewarded trials. Computational modeling captured group-level behavior, but not impulsivity results. Our results support previous findings highlighting the importance of sensitivity to negative outcomes in understanding how impulsivity relates to learning, but indicate that this may stem from more complex strategies than usually considered in computational models of learning. This should be an important target for future research.

Keywords: computational modeling; impulsivity; reinforcement learning; reversal learning; working memory.