Computation noise in human learning and decision-making: origin, impact, function

Abstract

Making sense of uncertain and volatile environments, a cognitive process modeled across domains as statistical inference, constitutes a difficult yet ubiquitous challenge for human intelligence. Beside sensory errors and exploratory choices, recent research has identified the limited computational precision of cognitive inference as a surprisingly large contributor to the variability and suboptimality of perceptual and reward-guided decisions made under uncertainty. This focused review discusses the theoretical and experimental evidence scattered across psychology and neuroscience which, taken together, provides key insights into the origin, impact and function of this ‘computation noise’ for learning and decision-making. Moving beyond the classical description of internal noise as performance-limiting constraint on neural function and cognition, we outline the possible emergent benefits of computation noise for adaptive behavior in adverse conditions and highlight open questions for future research.

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