Evidence map›Paper›PMID 42706230›Full record

ArticleNature communications2026

Automatic value learning results in counterproductive human behavior.

Ido Ben-Artzi, Maayan Pereg, Roy Luria, Rani Moran, Nitzan Shahar

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Ido Ben-ArtziSchool of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel. idobenartzi@mail.tau.ac.il.ORCID http://orcid.org/0000-0001-5971-8613
Maayan PeregDepartment of Psychology, Achva Academic College, Yinon, Israel.ORCID http://orcid.org/0000-0003-2366-4953
Roy LuriaSchool of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.
Rani MoranDepartment of Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0002-7641-2402
Nitzan ShaharSchool of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.ORCID http://orcid.org/0000-0002-1364-6738

Funding

Israel Science Foundation (ISF) #2536/20Israel Science Foundation (ISF) #3564/25
6 · The paper itself

Abstract

Humans are adept at accurately estimating the value of available choices from accumulated experience. However, cognitive processing also incorporates irrelevant information during deliberation, undermining decision accuracy. Here, we show that credit assignment operates automatically, allowing irrelevant action features to acquire value and impair choice behavior. Across four preregistered experiments (N = 504, 158, 195, 319) and a re-analysis of previously published data (N = 50), we examine outcome-irrelevant learning to spatial-motor and visual action features under conditions of explicit instruction and extensive training. In all cases, automatic value updating persists even when participants clearly understood which features were non-predictive of reward, and reliance on these irrelevant values led to suboptimal choices. Moreover, working memory capacity, known as a key regulatory resource, predicts the magnitude of this automatic outcome-irrelevant learning. Collectively, our results broaden the concept of automatic cognitive processing beyond the selective-attention literature to encompass reinforcement learning mechanisms.

Indexed as

Choice BehaviorLearningAttentionCognitionDecision MakingFemaleHumansMaleMemory, Short-TermReinforcement Machine LearningReinforcement, PsychologyReward

Identifiers

PMID42706230
PMCPMC13550625

What OpenQuestion holds

Textmetadata
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.