MétaCan
Menu
← Back to cohort
Record W3097082624 · doi:10.1167/jov.20.11.1634

Why is stereoacuity poor in amblyopia? Evidence from a disparity noise-masking paradigm

2020· article· en· W3097082624 on OpenAlexaff
Sara Alarcon Carrillo, Alex S. Baldwin, Mao Yu, Jiawei Zhou, Robert F. Hess

Bibliographic record

VenueJournal of Vision · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsStereoscopic acuityStereopsisNoise (video)AudiologyMasking (illustration)Contrast (vision)OptometryMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

People with amblyopia generally have a reduced ability to use stereopsis to make depth judgements. Our understanding of this reduced ability is limited by the insensitivity of standard clinical stereo tests. These tests fail to detect residual stereo function in some amblyopic subjects. The current study employs a test designed for individuals with poor stereoacuity. The subject identifies the location of a 3D target in a random-dot display. The target is presented in stereoscopic depth using 3D shutter-glasses. We applied the equivalent noise method to determine the role of equivalent internal noise (signal to noise ratio of disparity signals) and processing efficiency (how efficiently the system processes noisy input) in amblyopic stereopsis. We tested 30 amblyopic (7 strabismic, amblyopic eye visual acuity above 20/200) and 17 control (visual acuity above 20/20) adults. Our test detected stereoacuity in 50% of amblyopic participants. Amblyopic stereoacuity thresholds (m =118 arsec) were significantly higher than those from controls (m = 57 arcsec) (t = 2.8, p < 0.05). From a linear amplifier model fit, we found higher mean equivalent internal noise in amblyopic subjects (239 arsec) compared to controls (134 arcsec) (t = 3.45, p < 0.05). The two groups did not significantly differ in the processing efficiency for the task. A multiple linear regression was performed to determine the contribution of the two factors (equivalent internal noise and efficiency) to the individual differences in amblyopic stereoacuity. The two factors accounted for 66% of the variance in stereoacuity, with differences in equivalent internal noise as the strongest predictor. This study introduced a more sensitive assessment of residual stereopsis in amblyopia and evaluated how two factors contribute to individual performance. Overall, we find that reduced amblyopic stereoability is explained by poorer input quality to the stereoscopic disparity processing mechanism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.121
GPT teacher head0.373
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Vision→Same topicVisual perception and processing mechanisms→French-language works237,207→