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Record W4381163954 · doi:10.1167/jov.23.6.10

Detection of vertical interocular phase disparities using luster as cue

2023· article· en· W4381163954 on OpenAlexafffund
Frederick A. A. Kingdom, Keyvan Mohammad-Ali, Camille Breuil, Deuscies Chang-Ou, Artur Irgaliyev

Bibliographic record

VenueJournal of Vision · 2023
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill UniversityMontreal General Hospital
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsContrast (vision)Spatial frequencyStandard deviationPsychophysicsBinocular disparityMathematicsOpticsArtificial intelligenceBinocular visionPsychologyPattern recognition (psychology)Computer scienceStatisticsPhysicsPerceptionNeuroscience

Abstract

fetched live from OpenAlex

Interocular disparities in contrast generate an impression of binocular luster, providing a cue for their detection. Disparities in the carrier spatial phase of horizontally oriented Gabor patches also generate an impression of luster, so the question arises as to whether it is the disparities in local contrast that accompany the phase disparities that give rise to the luster. We examined this idea by comparing the detection of interocular spatial phase disparities with that of interocular contrast disparities in Gabor patches, in the latter case that differed in overall contrast rather than phase between the eyes. When bandwidth was held constant and Gabor spatial frequency was varied, the detection of phase and contrast disparities followed a similar pattern. However, when spatial frequency was fixed and Gabor envelope standard deviation (and hence number of modulation cycles) was varied, thresholds for detecting phase disparities followed a U-shaped function of Gabor standard deviation, whereas thresholds for contrast disparities, following an initial decline, were more-or-less constant as a function of Gabor standard deviation. After reviewing a number of possible explanations for the U-shape found with phase disparities, we suggest that the likely cause is binocular sensory fusion, the strength of which increases with the number of modulation cycles. Binocular sensory fusion would operate to reduce phase but not contrast disparities, thus selectively elevating phase disparity thresholds.

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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.044
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.083
GPT teacher head0.412
Teacher spread0.329 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2023
Admission routes2
Has abstractyes

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