Retrospective Inference as a Form of Bounded Rationality, and Its Beneficial Influence on Learning.

ABSTRACT: Probabilistic models of cognition typically assume that agents make inferences about current states by combining new sensory information with fixed beliefs about the past, an approach known as Bayesian filtering. This is computationally parsimonious, but, in general, leads to suboptimal beliefs about past states, since it ignores the fact that new observations typically contain information about the past as well as the present. This is disadvantageous both because knowledge of past states may be intrinsically valuable, and because it impairs learning about fixed or slowly changing parameters of the environment. For these reasons, in offline data analysis it is usual to infer on every set of states using the entire time series of observations, an approach known as (fixed-interval) Bayesian

SUBMITTER: FitzGerald THB 

PROVIDER: S-EPMC7861256 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature