Evidence map›Paper›PMID 32072934›Full record

ArticleDevelopmental cognitive neuroscience2020

Top-down knowledge rapidly acquired through abstract rule learning biases subsequent visual attention in 9-month-old infants.

D M Werchan, D Amso

Open access · goldAbstract read
In one paragraph

Article in Developmental cognitive neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
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

15 citing papers in PubMed, 30 citations in OpenAlex.

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  9. Eight-Month-Old Infants Meta-Learn by Downweighting Irrelevant Evidence.Open mind : discoveries in cognitive science · 2023
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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 at 1 institution in 1 country.

D M WerchanDepartment of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, 02912 United States. Electronic address: Denise_Werchan@Brown.edu.
D AmsoDepartment of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, 02912 United States.
Brown University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Visual attention is an information-gathering mechanism that supports the emergence of complex perceptual and cognitive capacities. Yet, little is known about how the infant brain learns to direct attention to information that is most relevant for learning and behavior. Here we address this gap by examining whether learning a hierarchical rule structure, where there is a higher-order feature that organizes visual inputs into predictable sequences, subsequently biases 9-month-old infants' visual attention to the higher-order visual feature. In Experiment 1, we found that individual differences in infants' ability to structure simple visual inputs into generalizable rules was related to the change in infants' attention biases towards higher-order features. In Experiment 2, we found that increased functional connectivity between the PFC and visual cortex was related to the efficacy of rule learning. Moreover, Granger causality analyses provided exploratory evidence that increased functional connectivity reflected PFC influence over visual cortex. These findings provide new insights into how the infant brain learns to flexibly select features from the cluttered visual world that were previously relevant for learning and behavior.

Indexed as

KnowledgeChild DevelopmentFemaleHumansInfantLearningMaleFeature-Based attentionInfantsLearningPFCRule learningVisual attention

Identifiers

PMID32072934
PMCPMC7242661
OpenAlexW3002320921

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.