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Record W649579624 · doi:10.1167/15.5.2

Dichoptic color saturation mixture: Binocular luminance contrast promotes perceptual averaging

2015· article· en· W649579624 on OpenAlexafffund
Frederick A. A. Kingdom, Lauren Libenson

Bibliographic record

VenueJournal of Vision · 2015
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsLuminanceHueContrast (vision)Chromatic scaleSaturation (graph theory)OpticsBrightnessPerceptionMathematicsBinocular visionColor visionHigh contrastPsychologyComputer visionCommunicationArtificial intelligenceComputer sciencePhysicsNeuroscience

Abstract

fetched live from OpenAlex

We demonstrate a new type of interaction between suprathreshold color (chromatic) and luminance contrast in the context of binocular vision. When two isoluminant colored disks of identical hue but different saturations are presented to different eyes, the apparent saturation of the resulting "dichoptic" mix is close to that of the more saturated patch if presented binocularly. This result is commensurate with previous findings using luminance contrast and is close to the scenario termed "winner-take-all." However, when binocularly matched luminance contrast is added to the dichoptic saturation mixture, the apparent saturation of the mixture shifts away from winner-take-all towards the average of the two dichoptic saturations. The likely cause of this effect is that the matched luminance contrasts reduce the interocular suppression between the unmatched color saturations. We suggest that the presence of binocularly matched luminance contrast promotes the interpretation that the dichoptic color saturations, even though unmatched, nevertheless originate from the same object. We term this idea the "object commonality" hypothesis.

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)0.001
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 score0.107
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.046
GPT teacher head0.326
Teacher spread0.279 · 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

Citations36
Published2015
Admission routes2
Has abstractyes

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