Evidence map›Paper›PMID 34630011›Full record

ArticleFrontiers in neuroscience2021

Transfer of Perceptual Learning From Local Stereopsis to Global Stereopsis in Adults With Amblyopia: A Preliminary Study.

Adrien Chopin, Michael A Silver, Yasha Sheynin, Jian Ding, Dennis Michael Levi

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

5 authors.

Adrien ChopinSchool of Optometry, University of California, Berkeley, Berkeley, CA, United States.
Michael A SilverSchool of Optometry, University of California, Berkeley, Berkeley, CA, United States.
Yasha SheyninSchool of Optometry, University of California, Berkeley, Berkeley, CA, United States.
Jian DingSchool of Optometry, University of California, Berkeley, Berkeley, CA, United States.
Dennis Michael LeviSchool of Optometry, University of California, Berkeley, Berkeley, CA, United States.

Funding

A New Approach to Restoring Visual Acuity and Stereopsis in Adults with AmblyopiaR01EY020976 · NEI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BANKS, MARTIN S, BAVELIER, DAPHNE I · 2010 to 2020
$4.7M
Depth perception in normal and abnormal visionR01EY030544 · NEI · UNIVERSITY OF CALIFORNIA BERKELEY · PI DING, JIAN · 2020 to 2023
$1.4M
NEI NIH HHS R01 EY020976NEI NIH HHS R01 EY030544
6 · The paper itself

Abstract

It has long been debated whether the analysis of global and local stereoscopic depth is performed by a single system or by separate systems. Global stereopsis requires the visual system to solve a complex binocular matching problem to obtain a coherent percept of depth. In contrast, local stereopsis requires only a simple matching of similar image features. In this preliminary study, we recruited five adults with amblyopia who lacked global stereopsis and trained them on a computerized local stereopsis depth task for an average of 12 h. Three out of five (60%) participants recovered fine global stereoscopic vision through training. Those who recovered global stereopsis reached a learning plateau more quickly on the local stereopsis task, and they tended to start the training with better initial local stereopsis performance, to improve more on local stereopsis with training, and to have less severe amblyopia. The transfer of learning from local stereopsis to global stereopsis is compatible with an interacting two-stage model.

Indexed as

amblyopiaamblyopia treatment in adultsdepth perceptionglobal stereopsislocal stereopsisperceptual learningstereoblindnessstereopsis

Identifiers

PMID34630011
PMCPMC8498040

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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.