Working memory as a representational template for reinforcement learning.

Kengo Shibata, Verena Klar, Sean J Fallon, Masud Husain, Sanjay G Manohar

Abstract read

In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Kengo ShibataNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU, UK. kengo.shibata@ndcn.ox.ac.uk.

Verena KlarDepartment of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK.

Sean J FallonSchool of Psychology, University of Plymouth, Plymouth, PL4 8AA, UK.

Masud HusainNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU, UK.

Sanjay G ManoharNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU, UK.

Funding

Wellcome Trust

6 · The paper itself

Abstract

Working memory (WM) and reinforcement learning (RL) both influence decision-making, but how they interact to affect behaviour remains unclear. We assessed whether RL is influenced by the format of visual stimuli held in WM, either feature-based or unified, object-based representations. In a pre-registered paradigm, participants learned stimulus-action combinations that provided reward through 80% probabilistic feedback. In parallel, participants retained the RL stimulus in WM and were asked to recall this stimulus after each RL choice. Crucially, the format of representation probed in WM was manipulated, with blocks encouraging either separate features or bound objects to be remembered. Incentivising a feature-based WM representation facilitated feature-based learning, shown by an improved choice strategy. This reveals a role of WM in providing sustained internal representations that are harnessed by RL, providing a framework by which these two cognitive processes cooperate.

Indexed as

Memory, Short-TermReinforcement, PsychologyAdultDecision MakingFemaleHumansLearningMaleRewardYoung AdultFeature-based representationsObject-based representationsReinforcement learningRepresentational templatesWorking memory

Identifiers

PMID39532969

DOI10.1038/s41598-024-79119-2

PMCPMC11557606

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.