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Record W2032269556 · doi:10.3200/jmbr.38.6.439-450

Integration of Intermittent Visual Samples Over Time and Between the Eyes

2006· article· en· W2032269556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Motor Behavior · 2006
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMonocularMonocular visionBinocular visionComputer visionPerceptionInterval (graph theory)Artificial intelligencePsychologyDepth perceptionComputer scienceMathematicsOptometryMedicineNeuroscienceCombinatorics

Abstract

fetched live from OpenAlex

The authors investigated the integration of alternate disparate monocular inputs for binocular perception in 1-handed catching experiments (N = 14, 32, 22, and 15 participants, respectively in Experiments 1-4). They varied the no-vision interval between alternate monocular samples to measure catching performance, and they compared the alternating monocular conditions with binocular and monocular conditions with equal no-vision intervals. They found no evidence of a binocular advantage for one-handed catching in the alternating monocular conditions. Performance in monocular and alternating monocular conditions did not differ across no-vision intervals ranging from 0-80 ms and was particularly worse than performance in binocular viewing conditions when the no-vision interval was 40 ms or more. The authors argue that the dissimilarity between disparate monocular inputs created by the approaching object limited the integration of those inputs and subsequent binocular perception.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.190

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 head0.059
GPT teacher head0.347
Teacher spread0.289 · 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