Computational processes of simultaneous learning of stochasticity and volatility in humans

Abstract

Adapting to uncertain environments is crucial for survival. This study explores computational challenges in distinguishing two types of noise: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. For the learner, dissociating their effects on one’s observations is challenging because they both increase the variance of observations. Previous research examined these factors separately, but it remains unclear whether and how humans dissociate them. In two large-scale experiments, through novel behavioral tasks and computational modeling, we report compelling evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects, but not with a number of other models that fail to make this distinction. This research elucidates computational processes behind adaptive learning in uncertain environments.

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