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Contextual inference in learning and memory
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
Bayes Theorem
Hippocampus
Humans
Learning
Memory
Hippocampus
Humans
Learning
Memory
URI
https://hdl.handle.net/20.500.14018/26809
Abstract
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2023-01-01
Language
ISBN
Identifiers
10.1016/j.tics.2022.10.004