Learning structures: Predictive representations, replay, and generalization

Momennejad, Ida

doi:10.1016/j.cobeha.2020.02.017

Cited by 159Memory and Neural MechanismsReinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Learning structures: Predictive representations, replay, and generalization

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Momennejad, Ida·2020·Current Opinion in Behavioral Sciences

DOI 10.1016/j.cobeha.2020.02.017PMC PMC9004662

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Abstract

Memory and planning rely on learning the structure of relationships among experiences. Compact representations of these structures guide flexible behavior in humans and animals. A century after 'latent learning' experiments summarized by Tolman, the larger puzzle of cognitive maps remains elusive: how does the brain learn and generalize relational structures? This review focuses on a reinforcement learning (RL) approach to learning compact representations of the structure of states. We review evidence showing that capturing structures as predictive representations updated via replay offers a neurally plausible account of human behavior and the neural representations of predictive cognitive maps. We highlight multi-scale successor representations, prioritized replay, and policy-dependence. These advances call for new directions in studying the entanglement of learning and memory with prediction and planning.

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Several previous surveys on predictive representations have scratched the surface of these connections.
“Several previous surveys on predictive representations have scratched the surface of these connections (Gershman, 2018; Momennejad, 2020).”
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Authors: Momennejad, I.
Affiliations: Columbia University (US)
Corresponding author: Momennejad, Ida
ORCID: Momennejad, Ida
Venue: Current Opinion in Behavioral Sciences
Publisher: Elsevier BV
Published: 2020-04
Volume: 32
Pages: 155-166
ISSN: 2352-1546 (ISSN-L)
DOI: 10.1016/j.cobeha.2020.02.017
PMC: PMC9004662
OpenAlex: W3022287533
Cited by: 159
References: 99
Citations by year: 2026: 20 · 2025: 28 · 2024: 21 · 2023: 29 · 2022: 26 · 2021: 21 · 2020: 14
FWCI: 3.04
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Current Opinion in Behavioral Sciences (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Mental Health
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords: predictive representations, cognitive maps, latent learning, successor representation, policy dependence, reinforcement learning, generalization

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Learning structures: Predictive representations, replay, and generalization

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