Dataset Information

Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory.

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.

SUBMITTER: Rosenbaum GM

PROVIDER: S-EPMC8786311 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory.

Rosenbaum Gail M GM Grassie Hannah L HL Hartley Catherine A CA

eLife 20220124

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 pr ...[more]

PMID: 35072624

Similar Datasets

Project description:Understanding how learning changes during human development has been one of the long-standing objectives of developmental science. Recently, advances in computational biology have demonstrated that humans display a bias when learning to navigate novel environments through rewards and punishments: they learn more from outcomes that confirm their expectations than from outcomes that disconfirm them. Here, we ask whether confirmatory learning is stable across development, or whether it might be attenuated in developmental stages in which exploration is beneficial, such as in adolescence. In a reinforcement learning (RL) task, 77 participants aged 11-32 years (four men, mean age = 16.26) attempted to maximize monetary rewards by repeatedly sampling different pairs of novel options, which varied in their reward/punishment probabilities. Mixed-effect models showed an age-related increase in accuracy as long as learning contingencies remained stable across trials, but less so when they reversed halfway through the trials. Age was also associated with a greater tendency to stay with an option that had just delivered a reward, more than to switch away from an option that had just delivered a punishment. At the computational level, a confirmation model provided increasingly better fit with age. This model showed that age differences are captured by decreases in noise or exploration, rather than in the magnitude of the confirmation bias. These findings provide new insights into how learning changes during development and could help better tailor learning environments to people of different ages. RESEARCH HIGHLIGHTS: Reinforcement learning shows age-related improvement during adolescence, but more in stable learning environments compared with volatile learning environments. People tend to stay with an option after a win more than they shift from an option after a loss, and this asymmetry increases with age during adolescence. Computationally, these changes are captured by a developing confirmatory learning style, in which people learn more from outcomes that confirm rather than disconfirm their choices. Age-related differences in confirmatory learning are explained by decreases in stochasticity, rather than changes in the magnitude of the confirmation bias.

Dataset Information

Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory.

Publications

Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets