Evidence map›Paper›PMID 40624399›Full record

ArticlePsychonomic bulletin & review2025

Super rapid learning of new attentional sets.

Seth A Marx, Sisi Wang, Geoffrey F Woodman

Abstract read
In one paragraph

Article in Psychonomic bulletin & review, 2025. 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.

Seth A MarxDepartment of Psychology, Vanderbilt Vision Research Center, Vanderbilt University, PMB 407817, 2301 Vanderbilt Place, Nashville, TN, USA.
Sisi WangInstitute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Geoffrey F WoodmanDepartment of Psychology, Vanderbilt Vision Research Center, Vanderbilt University, PMB 407817, 2301 Vanderbilt Place, Nashville, TN, USA. geoffrey.f.woodman@vanderbilt.edu.ORCID http://orcid.org/0000-0003-0946-9297

Funding

Shop Module CoreP30EY008126 · NEI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Kevin L Schey · 1989 to 2026
$19.6M
Training in Vision ResearchT32EY007135 · NEI · VANDERBILT UNIVERSITY · PI GEOFFREY F WOODMAN · 1994 to 2026
$5.8M
BioXFEL Science and Technology Center BCS-2147064NEI NIH HHS P30 EY008126NEI NIH HHS T32 EY007135
6 · The paper itself

Abstract

Humans guide attention to different targets as they navigate (e.g., going from a school zone to a construction zone while driving). To understand how observers shift focus between different sets of targets when the context shifts, we had observers perform a new visual search paradigm where target shapes are presented in multiple possible colors with distinct probabilities (e.g., 33%, 26%, 19%, 12%, and 10% baseline colors), such that some target colors are more task-relevant than others. Participants learned to prioritize the target colors that appeared with the target shape most often. We then scrambled the color-probability mapping to assess how quickly people could relearn a new set of target probabilities. Participants relearned these new colors significantly faster than their first set of colors, especially when there was less conflict between the new and old attentional sets. Our findings suggest that memory-guided attention is rapidly and continuously relearning to update task-relevant probabilities.

Indexed as

AttentionColor PerceptionLearningFemaleHumansMalePattern Recognition, VisualProbability LearningReaction TimeYoung AdultAttentional controlAttentional guidanceCognitive controlVisual long-term memory

Identifiers

PMID40624399
PMCPMC12627118

What OpenQuestion holds

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LicenceCC BY
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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.