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Record W2734405941 · doi:10.1038/s41598-017-06151-w

Interocular contrast difference drives illusory 3D percept

2017· article· en· W2734405941 on OpenAlexafffund
Alexandre Reynaud, Robert F. Hess

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University Health CentreMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPerceptContrast (vision)LuminanceComputer visionComputer scienceDepth perceptionStereopsisMotion (physics)Artificial intelligenceOptical illusionPerceptionPhysicsOpticsPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Any processing delay between the two eyes can result in illusory 3D percepts for moving objects because of either changes in the pure disparities over time for disparity sensors or by changes to sensors that encode motion/disparity conjointly. This is demonstrated by viewing a fronto-parallel pendulum through a neutral density (ND) filter placed over one eye, resulting in the illusory 3D percept of the pendulum following an elliptical orbit in depth, the so-called Pulfrich phenomenon. Here we use a paradigm where a cylinder rotating in depth, defined by moving Gabor patches is presented at different interocular phases, generating strong to ambiguous depth percepts. This paradigm allows one to manipulate independently the contrast and the luminance of the patches to determine their influence on perceived motion-in-depth. Thus we show psychophysically that an interocular contrast difference can itself result in a similar illusory 3D percept of motion-in-depth. We argue that contrast, like luminance (ND filter) can modify the dynamics of visual neurons resulting in an interocular processing delay or an interocular velocity difference.

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 categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
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 score0.065
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.323
Teacher spread0.265 · 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.

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

Citations33
Published2017
Admission routes2
Has abstractyes

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