Environmental dynamics shape human learning: change points versus random walks

Adaptive learning requires interpreting prediction errors in light of environmental dynamics. However, environments may not only differ in terms of when changes occur, but also how they occur: some nonstationary processes in nature exhibit slow drifts over time, while others show more abrupt changes. This raises the question as to whether humans can adapt their learning to reflect the generative structure of their environment. Here, we compared how humans learn in two canonical nonstationary environments: abrupt change points versus gradual random walks. Using a predictive inference task and a unified Bayesian framework, we show across two behavioral experiments that humans adapt their learning normatively between these two environments. Notably, identical prediction errors were interpreted differently across them. Large prediction errors triggered sharp increases in learning rate under change-point but not random-walk dynamics, where small and large errors were more equally weighted. This matched the predictions of a normative Bayesian model, which itself adopted the appropriate generative model for each environment. In addition, we found that humans could dissociate two latent variables that needed to be jointly inferred: the mean and variance (or stochasticity). This was demonstrated by explicit uncertainty reports that closely tracked the current variance, and only showed sustained changes following change points in variance but not mean. Together, these results show that humans adapt their learning strategy to both how and what aspects of the environment are changing. They establish a unified computational account of adaptive learning across different environmental dynamics.