Can losses help attenuate learning traps?

Recent work has demonstrated robust learning traps during learning from experience – decision-making biases that persist due to the choice-contingent nature of outcome feedback. In two experiments, we investigate the effect of outcome valence on learning trap development. Participants chose to approach or avoid category exemplars associated with rewards or losses, and, to maximize reward, must learn a categorization rule based on two stimulus dimensions. We replicate previous findings showing that when outcome feedback was contingent upon approaching exemplars, people frequently fell into the trap of using an incomplete categorization rule based on only a single dimension, which was suboptimal for long-term reward. Notably, learning trap development was attenuated in an environment with frequent loss outcomes, even when participants received explicit information about the base rates of gains and losses. The implications of these findings for theoretical models and future research are discussed.