Evidence map›Paper›PMID 40540594›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Model-based algorithms shape automatic evaluative processing.

David E Melnikoff, Benedek Kurdi

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. 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. Model-based algorithms shape automatic evaluative processing.Proceedings of the National Academy of Sciences of the United States of America · 2025
    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

2 authors.

David E MelnikoffGraduate School of Business, Stanford University, Stanford, CA 94305.ORCID 0000-0002-7090-9995
Benedek KurdiDepartment of Psychology, University of Illinois Urbana-Champaign, Champaign, IL 61820.ORCID 0000-0001-5000-0584

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational theories of reinforcement learning suggest that two families of algorithm-model-based and model-free-tightly map onto the classic distinction between automatic and deliberate systems of control: Deliberate evaluative responses are thought to reflect model-based algorithms, which are accurate but computationally expensive, whereas automatic evaluative responses are thought to reflect model-free algorithms, which are error-prone but computationally cheap. This framework has animated research on psychological phenomena ranging from habit formation to social learning, moral decision-making, and cognitive development. Here, we propose that model-based and model-free algorithms may not be as aligned with deliberate and automatic evaluative processing as prevailing theories suggest. Across three preregistered behavioral experiments involving adult human participants (total

Indexed as

AlgorithmsModels, PsychologicalReinforcement, PsychologyAdultDecision MakingFemaleHumansMaleYoung Adultautomaticityevaluationmodel-based controlmodel-free controlreinforcement learning

Identifiers

PMID40540594
PMCPMC12207468

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.