Abstract
As individuals learn through trial and error, some are more influenced by good outcomes, while others weight bad outcomes more heavily. Such valence biases may also influence memory for past experiences. Here, we examined whether valence asymmetries in reinforcement learning change across adolescence, and whether individual learning asymmetries bias the content of subsequent memory. Participants ages 8-27 learned the values of 'point machines,' after which their memory for trial-unique images presented with choice outcomes was assessed. Relative to children and adults, adolescents overweighted worse-than-expected outcomes during learning. Individuals' valence biases modulated incidental memory, such that those who prioritized worse- (or better-) than-expected outcomes during learning were also more likely to remember images paired with these outcomes, an effect reproduced in an independent dataset. Collectively, these results highlight age-related changes in the computation of subjective value and demonstrate that a valence-asymmetric valuation process influences how information is prioritized in episodic memory.
Highlights
Throughout our lives, we encounter many new or uncertain situations in which we must learn, through trial and error, which actions are beneficial and which are best avoided
We examined the relation between age and self-reported risk taking to the Domain-Specific Risk Taking (DOSPERT) scale (Blais and Weber, 2006)
We examined whether asymmetry in learning from good versus bad choice outcomes changed across adolescence, and whether valence biases in Reinforcement learning (RL) influenced episodic memory encoding
Summary
Throughout our lives, we encounter many new or uncertain situations in which we must learn, through trial and error, which actions are beneficial and which are best avoided. In Experiment 1 of the present study, we assessed whether valence asymmetries in RL varied from childhood to adulthood, using a risk-sensitive RL task (Niv et al, 2012) in which probabilistic and deterministic choice options have equal EV, making no particular learning asymmetry optimal This parameterization allows any biases in the weighting of positive versus negative prediction errors to be revealed through subjects’ systematic risk-averse or risk-seeking choice behavior. We tested whether a valence-d ependent effect of PE on memory might be evident after accounting for idiosyncratic valence biases in learning
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