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Record W2790151715 · doi:10.21037/aes.2018.ab074

AB074. Link between interocular correlation sensitivity and stereoscopic vision

2018· article· en· W2790151715 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Eye Science · 2018
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsStereoscopyComputer visionArtificial intelligenceStereopsisComputer scienceBinocular visionBinocular disparityOptometryFeature (linguistics)Medicine

Abstract

fetched live from OpenAlex

Background: Stereoscopic Vision uses the disparity between the two images received by the two eyes in order to create a tridimensional representation. With this study, we aimed at providing an estimate of binocular vision at a level prior to disparity processing. In particular, we wanted to assess the spatial properties of the visual system for detecting interocular correlations (IOC). Methods: We developed dichoptic stimuli, made of textures which IOC is sinusoidally modulated at various correlation spatial frequencies. Then, we compared the sensitivity to these stimuli to the sensitivity to analogous stimuli with disparity modulation. Results: We observed that IOC sensitivity presents a low-pass/band-pass profile and increases as a function of presentation duration and contrast, in a similar way as disparity sensitivity. Conclusions: IOC sensitivity is weakly—though significantly—correlated with disparity sensitivity in the general population, which suggests that it could provide a marker for binocular vision, prior to disparity processing.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.422
Teacher spread0.298 · 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