Evidence map›Paper›PMID 36435674›Full record

ReviewTrends in cognitive sciences2023

Contextual inference in learning and memory.

James B Heald, Máté Lengyel, Daniel M Wolpert

Abstract readReview
In one paragraph

Review in Trends in cognitive sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed.

  1. Article
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  4. Feasible cognition requires latent state.Biological cybernetics · 2026
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  10. Cerebellar contributions to action and cognition: Prediction, timescale, and continuity.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  11. Article
  12. Article
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  14. Review
  15. Article
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  17. Attention Defines the Context for Implicit Sensorimotor Adaptation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

James B HealdDepartment of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA. Electronic address: jamesbheald@gmail.com.
Máté LengyelComputational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary. Electronic address: m.lengyel@eng.cam.ac.uk.
Daniel M WolpertDepartment of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK. Electronic address: wolpert@columbia.edu.

Funding

Modeling multi-area dynamics during motor controlU19NS104649 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI JESSELL, THOMAS M. · 2017 to 2021
$15.3M
Computations in Human Motor LearningR01NS117699 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WOLPERT, DANIEL · 2021 to 2025
$2.5M
NINDS NIH HHS R01 NS117699NINDS NIH HHS U19 NS104649
6 · The paper itself

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.

Indexed as

LearningMemoryBayes TheoremHippocampusHumansBayesian inferencecontext-dependent learninglearningmemory

Identifiers

PMID36435674
PMCPMC9789331

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.