Contextual inference in learning and memory

James B. Heald

doi:10.1016/j.tics.2022.10.004

Contextual inference in learning and memory

2023-01-01

22

OA

AI

DOI：10.1016/j.tics.2022.10.004

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Abstract

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Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.

Keywords:

EVENT SEGMENTATION

INTERNAL-MODELS

MOTOR MEMORIES

EXTINCTION

REINFORCEMENT

BOUNDARIES

REPRESENTATION

ADAPTATION

REINSTATEMENT

HIPPOCAMPUS

Contextual inference in learning and memory

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