ArticleDecision (Washington, D.C.)2024
Cognitive Graphs: Representational Substrates for Planning.
Article in Decision (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed.
- Sparsity and memory constraints interact with training sequence to bias learning of associative maps.Neuropsychologia · 2026Article
- Multistep inference across the human lifespan can be improved with individualized memory interventions.The journals of gerontology. Series B, Psychological sciences and social sciences · 2026Article
- Building Goal-Directed Cognitive Graphs.bioRxiv : the preprint server for biology · 2026Article
- Trial-by-trial learning of successor representations in human behavior.bioRxiv : the preprint server for biology · 2025Article
- Trial-by-trial learning of successor representations in human behavior.PLoS computational biology · 2025Article
- Novelty as a drive of human exploration in complex stochastic environments.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Causal and Chronological Relationships Predict Memory Organization for Nonlinear Narratives.Journal of cognitive neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Making plans for upcoming actions is a computationally demanding process. To mitigate these demands, individuals can build extensive internal models of their environment-states, actions, and their sequential relationships-that allow for plans to be developed with minimal computational costs. Initially, these models reflect elaborate networks of learned associative relationships, which can be used to generate plans for reward through more iterative computations such as trajectory sampling. After sufficient experience, compressed forms of these models can efficiently capture long-range sequential structure, allowing them to be used for rapid planning even in pursuit of novel or changing rewards. Here, we review recent work on the multitude of representations that can support different forms of planning. We discuss how
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Registered trials
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.