Evidence map›Paper›PMID 41814465›Full record

ArticleDevelopmental science2026

The Development of Selective Attention in Children's Emotion Reasoning.

Andrea G Stein, Seth D Pollak

Abstract read
In one paragraph

Article in Developmental science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

2 authors.

Andrea G SteinDepartment of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID https://orcid.org/0000-0002-1792-6606
Seth D PollakDepartment of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID https://orcid.org/0000-0001-5184-9846

Funding

Emotion Processing:Risk for Psychopathology in ChildrenR01MH061285 · NIMH · UNIVERSITY OF WISCONSIN-MADISON · PI SETH D POLLAK · 2001 to 2026
$12.4M
TRAINING PROGRAM IN EMOTION RESEARCHT32MH018931 · NIMH · UNIVERSITY OF WISCONSIN-MADISON · PI Richard J Davidson · 1989 to 2026
$11.6M
NIMH NIH HHS R01MH061285NIMH NIH HHS T32MH018931
6 · The paper itself

Abstract

We investigated developmental differences in the attention strategies children use in their emotion reasoning across middle childhood. Specifically, we examined whether, with age, children become less likely to distribute their attention broadly when reasoning about emotions, and more likely to come to attend selectively to the most reliable cues. To test this hypothesis, N = 180 five- through ten-year-old children learned to classify an alien's emotional displays into two categories. Categories could be learned via two different strategies: distributing attention across all possible emotion-related cues or selectively attending to the most predictive cue. Consistent with our hypothesis, Bayesian zero-one inflated beta regression revealed that from five through approximately eight years old, with age, participants were increasingly likely to use a selective attention strategy. Selective attention use increased by an average of 5 percentage points per year across this age range (95% CI [2%pt, 7%pt]). This result was also characterized by decreasing strategic variability across development. For every year older a participant was, they were also 1.77 times more likely to use a single consistent attention strategy, rather than a hybrid of distributed and selective attention (95% CI [1.43, 2.22]). These results reflect a potential trade-off across development between flexibility and efficiency in emotion reasoning. SUMMARY: There are many different cues we can use to reason about others' emotions; the ways we attend to such cues may vary across development We investigated children's attention strategy use during emotion reasoning in the context of a novel emotion category learning paradigm From ages five through eight, with age, children were increasingly likely to attend selectively to the most reliable emotion cue, rather than distribute their attention This shift may give rise to a changing trade off across middle childhood between flexibility and efficiency in emotion reasoning.

Indexed as

AttentionChild DevelopmentEmotionsBayes TheoremChildChild, PreschoolCuesFemaleHumansLearningMaledistributed attentionemotion learningemotion reasoningmiddle childhoodselective attentionstrategic variability

Identifiers

PMID41814465
PMCPMC12979691

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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.