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Record W4387032945 · doi:10.1167/jov.23.10.17

Integration and suppression interact in binocular vision

2023· article· en· W4387032945 on OpenAlexaff
Rong Jiang, Ming Meng

Bibliographic record

VenueJournal of Vision · 2023
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsCanadian Nutrition Society
FundersChinesisch-Deutsche Zentrum für WissenschaftsförderungNational Natural Science Foundation of China
KeywordsBinocular rivalryBinocular visionOcular dominanceStereopsisBinocular disparityPsychologyPerceptionRivalryOptometryVisual cortexComputer scienceComputer visionNeuroscienceVisual perceptionMedicine

Abstract

fetched live from OpenAlex

Contingent on stereo compatibility, two images presented dichoptically can lead to either binocular integration, thus generating stable stereopsis, or interocular suppression that induces binocular rivalry with bistable perception that alternates between the two images. The relationship between binocular integration and interocular suppression concerns how our brain processes binocular inputs to form unified visual awareness but remains unclear. Here, a series of psychophysical experiments were conducted to address this question, revealing that these collaborative and competitive binocular interactions are interconnected and would mediate one another according to their strength. Specifically, Experiments 1a and 1b showed that the presence of binocular rivalry inhibited peripheral stereopsis, significantly elevating the stereo threshold, with higher elevation resulting from increasing rivalry contrast. Experiments 2a and 2b showed that existing stereopsis with increasing binocular disparity balanced the dynamics of peripheral binocular rivalry, rendering more equivalent eye dominance. Based on these interactions, we suggest that binocular integration and interocular suppression may mediate one another through an overlapping mechanism for regulating eye dominance, with strong stereo percepts tending to reduce eye dominance and strong rivalry tending to increase eye dominance.

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)0.000
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 score0.142
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.391
Teacher spread0.342 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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