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Record W2118721090 · doi:10.1098/rspb.2013.2118

Vergence eye movements are not essential for stereoscopic depth

2013· article· en· W2118721090 on OpenAlexaff
Arthur J. Lugtigheid, Laurie M. Wilcox, Robert S. Allison, Ian P. Howard

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
Fundersnot available
KeywordsAfterimageVergence (optics)StereoscopyStereopsisPerceptionEye movementDepth perceptionDiplopiaComputer visionPsychologyArtificial intelligenceComputer scienceNeuroscienceOptometryMedicine

Abstract

fetched live from OpenAlex

The brain receives disparate retinal input owing to the separation of the eyes, yet we usually perceive a single fused world. This is because of complex interactions between sensory and oculomotor processes that quickly act to reduce excessive retinal disparity. This implies a strong link between depth perception and fusion, but it is well established that stereoscopic depth percepts are also obtained from stimuli that produce double images. Surprisingly, the nature of depth percepts from such diplopic stimuli remains poorly understood. Specifically, despite long-standing debate it is unclear whether depth under diplopia is owing to the retinal disparity (directly), or whether the brain interprets signals from fusional vergence responses to large disparities (indirectly). Here, we addressed this question using stereoscopic afterimages, for which fusional vergence cannot provide retinal feedback about depth. We showed that observers could reliably recover depth sign and magnitude from diplopic afterimages. In addition, measuring vergence responses to large disparity stimuli revealed that that the sign and magnitude of vergence responses are not systematically related to the target disparity, thus ruling out an indirect explanation of our results. Taken together, our research provides the first conclusive evidence that stereopsis is a direct process, even for diplopic targets.

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 score0.057
Threshold uncertainty score0.569

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.086
GPT teacher head0.329
Teacher spread0.242 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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