Memory retrieval explains dynamic effects of expectations on perceptual decisions

Expectations--prior knowledge of environmental statistics--support adaptive behavior by reducing uncertainty during inference. However, the questions of where expectations come from and how that ought to impact their integration has remained largely unexplored. Here, we present a novel model formalizing the role of memory retrieval in the process of integrating expectations with uncertain sensory information. To do this, we first articulate how uncertainty about expectations--omitted from existing normative models--ought to induce memory retrieval processes. We then model expectations as a dynamic source of internal evidence that observers sample from in parallel to vision, and that are weighed according to a time-evolving estimate of their reliability relative to sensory evidence. Finally, we show that this single, principled model can reproduce several previously disparate observations of time-varying effects of expectations in neural and behavioral data, each of which was previously explained using different neural and computational mechanisms. These findings suggest that our model identifies a basic mechanism governing the integration of expectations with sensory evidence on assumptions that more closely approximate the dynamic, heterogeneous, and uncertain nature of real-world decision settings.