Modeling the development of decision making in volatile environments using strategies, reinforcement learning, and Bayesian inference

Abstract

Continuously adjusting behavior in changing environments is a crucial skill for intelligent creatures, but we know little about how this ability develops in humans. Here, we investigate this question in a large sample using behavioral analyses and computational modeling. We assessed over 200 participants (ages 8-30) on a probabilistic, volatile reinforcement learning task, and measured pubertal development status and salivary testosterone. We used three classes of models to analyze behavior on the task: fixed strategies, incremental reinforcement learning, and Bayesian inference. All model classes provided converging evidence for a decrease in decision noise or exploration with age. Individual models also provided insight into unique aspects of decision making, such as changes in estimated reward probabilities, and sed-specific changes in the sensitivity to positive versus negative outcomes. Our results show that the combination of models can provide detailed insight into the development of decision making, and into complex cognition more generally.