Evidence map›Paper›PMID 39752178›Full record

ArticleJournal of vision2025

Effect of sign language learning on temporal resolution of visual attention.

Serpil Karabüklü, Sandra Wood, Chuck Bradley, Ronnie B Wilbur, Evie A Malaia

Abstract read
In one paragraph

Article in Journal of vision, 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. 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.

Serpil KarabüklüDepartment of Linguistics, University of Chicago, Chicago, IL USA.
Sandra WoodDepartment of Linguistics, University of Southern Maine, Portland, ME, USA.
Chuck BradleyDepartment of Linguistics, Purdue University, West Lafayette, IN, USA.
Ronnie B WilburDepartment of Linguistics, Purdue University, West Lafayette, IN, USA.
Evie A MalaiaDepartment of Communicative Disorders, University of Alabama, Tuscaloosa, AL, USA.

Funding

Modeling the Nonmanuals in American Sign LanguageR01DC005241 · NIDCD · PURDUE UNIVERSITY WEST LAFAYETTE · PI WILBUR, RONNIE B · 2004 to 2008
$2.4M
NIDCD NIH HHS R01 DC005241NIH HHS R01 108306US National Science Foundation 1734938US National Science Foundation 1932547
6 · The paper itself

Abstract

The visual environment of sign language users is markedly distinct in its spatiotemporal parameters compared to that of non-signers. Although the importance of temporal and spectral resolution in the auditory modality for language development is well established, the spectrotemporal parameters of visual attention necessary for sign language comprehension remain less understood. This study investigates visual temporal resolution in learners of American Sign Language (ASL) at various stages of acquisition to determine how experience with sign language affects perceptual sampling. Using a flicker paradigm, we assessed the accuracy of identifying out-of-phase visual flicker objects at frequencies up to 60 Hz. Our findings reveal that third-semester ASL learners show increased accuracy in detecting high-frequency flicker, indicating enhanced temporal resolution. Interestingly, as learners achieve higher proficiency in ASL, their perceptual sampling reverts to typical levels, likely because of a shift toward predictive processing mechanisms in sign language comprehension. These results suggest that the temporal resolution of visual attention is malleable and can be influenced by the process of learning a visual language.

Indexed as

AttentionLearningSign LanguageAdolescentAdultFemaleHumansMalePhotic StimulationVisual PerceptionYoung Adult

Identifiers

PMID39752178
PMCPMC11706239

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.