Uncertainty-driven regulation of learning and exploration in adolescents: A computational account

• Published in: PLoS computational biology 2020-09, Vol.16 (9), p.e1008276-e1008276
• Main Authors: Jepma, Marieke, Schaaf, Jessica V, Visser, Ingmar, Huizenga, Hilde M
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptation; Adaptation, Psychological - physiology; Adolescent; Adolescent psychology; Adolescents; Adult; Adults; Age; Algorithms; Analysis; Bayes Theorem; Bayesian analysis; Behavior; Biology and Life Sciences; Choice learning; Computational Biology; Computer applications; Control; Decision making; Environmental changes; Expected values; Exploration; Exploratory Behavior - physiology; Female; Humans; Influence; Kalman filters; Learning - physiology; Learning strategies; Machine learning; Male; Mathematical models; Methods; Models, Psychological; Noise; Parameter estimation; People and Places; Physical Sciences; Research and Analysis Methods; Social Sciences; Task Performance and Analysis; Teenagers; Uncertainty; Uncertainty analysis; Volatility; Young Adult
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008276

Healthy adults flexibly adapt their learning strategies to ongoing changes in uncertainty, a key feature of adaptive behaviour. However, the developmental trajectory of this ability is yet unknown, as developmental studies have not incorporated trial-to-trial variation in uncertainty in their analyses or models. To address this issue, we compared adolescents' and adults' trial-to-trial dynamics of uncertainty, learning rate, and exploration in two tasks that assess learning in noisy but otherwise stable environments. In an estimation task-which provides direct indices of trial-specific learning rate-both age groups reduced their learning rate over time, as self-reported uncertainty decreased. Accordingly, the estimation data in both groups was better explained by a Bayesian model with dynamic learning rate (Kalman filter) than by conventional reinforcement-learning models. Furthermore, adolescents' learning rates asymptoted at a higher level, reflecting an over-weighting of the most recent outcome, and the estimated Kalman-filter parameters suggested that this was due to an overestimation of environmental volatility. In a choice task, both age groups became more likely to choose the higher-valued option over time, but this increase in choice accuracy was smaller in the adolescents. In contrast to the estimation task, we found no evidence for a Bayesian expectation-updating process in the choice task, suggesting that estimation and choice tasks engage different learning processes. However, our modeling results of the choice task suggested that both age groups reduced their degree of exploration over time, and that the adolescents explored overall more than the adults. Finally, age-related differences in exploration parameters from fits to the choice data were mediated by participants' volatility parameter from fits to the estimation data. Together, these results suggest that adolescents overestimate the rate of environmental change, resulting in elevated learning rates and increased exploration, which may help understand developmental changes in learning and decision-making.