Evidence map›Paper›PMID 42371972›Full record

ArticlePLoS computational biology2026

Delayed reward information is underweighted in reinforcement learning with dispersed feedback.

Miruna Cotet, David Poensgen, Ian Krajbich

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. 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

3 authors.

Miruna CotetDepartment of Psychology, The Ohio State University, Columbus, Ohio, United States of America.ORCID https://orcid.org/0000-0003-4301-4755
David PoensgenFaculty of Economics and Business, Goethe University Frankfurt, Frankfurt, Germany.ORCID https://orcid.org/0000-0003-2098-2120
Ian KrajbichDepartment of Psychology, The Ohio State University, Columbus, Ohio, United States of America.ORCID https://orcid.org/0000-0001-6618-5675

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Learning is fundamental to adaptive behavior. In the typical learning task, each action is associated with only one outcome, which could be immediate or delayed. However, actions often have multiple consequences that unfold over time. Here, we used behavioral and eye-tracking experiments to study how people learn when their choices yield both immediate and delayed reward information. Importantly, the rewards themselves were all delivered at the end of the study so there was no reason to weight immediate and delayed reward information differently. Instead, we found that our subjects overweighted immediate reward information. Moreover, this bias increased over the course of the experiment and was still present when learning from others' choices. The gaze data reveal mixed evidence that subjects looked more at immediate vs. delayed feedback, and across subjects, the relative dwell proportion did not predict the behavioral bias. Our results indicate that people prioritize not just immediate rewards, but immediate reward information. Unlike temporal discounting, this form of impatience is a clear mistake and leads to objectively worse outcomes.

Indexed as

LearningReinforcement, PsychologyRewardAdultChoice BehaviorComputational BiologyEye-Tracking TechnologyFemaleHumansMaleReinforcement Machine LearningYoung Adult

Identifiers

PMID42371972
PMCPMC13349302

What OpenQuestion holds

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LicenceCC BY
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.