A reinforcement-learning approach to understanding loss-chasing behavior in rats

Abstract

Risky decisions are inherently characterized by the potential to receive gains and losses from these choices, and gains and losses have distinct effects on global risky choice behavior and the likelihoods of making risky choices depending on the outcome of the previous choice. One translationally-relevant phenomenon of risky choice is loss-chasing, in which individuals make risky choices following losses. However, the mechanisms of loss-chasing are poorly understood. The goal of two experiments was to illuminate the mechanisms governing individual differences in loss-chasing and risky choice behaviors. In two experiments, rats chose between a certain outcome that always delivered reward and a risky outcome that probabilistically delivered reward. In Experiment 1, loss processing and loss-chasing behavior were assessed in the context of losses-disguised-as-wins (LDWs), or loss outcomes presented along with gain-related stimuli. The rats presented with LDWs were riskier and less sensitive to differential losses. In Experiment 2, these behaviors were assessed relative to the number of risky losses that could be experienced. Here, the addition of reward omission or a small non-zero loss to the possible risky outcomes elicited substantial individual differences in risky choice, with some rats increasing, decreasing, or maintaining their previous risky choice preferences. Several reinforcement learning (RL) models were fit to individual rats’ data to elucidate the possible psychological mechanisms that best accounted for individual differences in risky choice and loss-chasing behaviors. The RL analyses indicated that the critical predictors of risky choice and loss-chasing behavior were the different rates that individuals updated value estimates with newly experienced gains and losses. Thus, learning deficits may predict individual differences in maladaptive risky decision making. Accordingly, targeted interventions to alleviate learning deficits may ultimately increase the likelihood of making more optimal and informed choices.