ArticleNature communications2021
Linear reinforcement learning in planning, grid fields, and cognitive control.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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.
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
43 citing papers in PubMed.
- Dynamic Hippocampal-Striatal Information Flow Accompanies Behavioral Strategy Transitions During Sequential Learning in Pigeons: A Preliminary Study.Animals : an open access journal from MDPI · 2026Article
- Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty.Psychological review · 2026Article
- Dissociating volatility and stochasticity reveals transdiagnostic computational signatures of psychopathology.bioRxiv : the preprint server for biology · 2026Article
- Planning in the Brain: It's Not What You Think It Is.Annual review of neuroscience · 2026Review
- Flexible route planning and rapid structure learning by mice in complex environments.bioRxiv : the preprint server for biology · 2026Article
- Contextual inference through flexible integration of environmental features and behavioural outcomes.PLoS computational biology · 2026Article
- Addressing low statistical power in computational modelling studies in psychology and neuroscience.Nature human behaviour · 2026Article
- Article
- Composing egocentric and allocentric maps for flexible navigation.PLoS computational biology · 2026Article
- Between planning and map building: Prioritizing replay when future goals are uncertain.Neuron · 2025Review
- Separable cognitive and motor decline tracked across adult life-span for goal-directed navigation.iScience · 2025Article
- Dissociable habits of response preparation versus response initiation.Nature human behaviour · 2025Article
- Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive map.Nature communications · 2025Article
- Prioritizing replay when future goals are unknown.bioRxiv : the preprint server for biology · 2025Article
- Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation.PLoS computational biology · 2025Article
- Constructing future behavior in the hippocampal formation through composition and replay.Nature neuroscience · 2025Article
- A unified neural representation model for spatial and conceptual computations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Humans rationally balance detailed and temporally abstract world models.Communications psychology · 2025Article
- Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments.Advances in neural information processing systems · 2025Article
- Computational processes of simultaneous learning of stochasticity and volatility in humans.Nature communications · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is thought that the brain's judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of specific, quantifiable biases. It replaces the classic nonlinear, model-based optimization with a linear approximation that softly maximizes around (and is weakly biased toward) a default policy. This solution demonstrates connections between seemingly disparate phenomena across behavioral neuroscience, notably flexible replanning with biases and cognitive control. It also provides insight into how the brain can represent maps of long-distance contingencies stably and componentially, as in entorhinal response fields, and exploit them to guide choice even under changing goals.
Indexed as
Identifiers
What OpenQuestion holds
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.