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Record W2023540851 · doi:10.1364/josaa.17.001516

Contour interaction in fovea and periphery

2000· article· en· W2023540851 on OpenAlexaff
Robert F. Hess, Steven C. Dakin, Neil Kapoor, Marc A. Tewfik

Bibliographic record

VenueJournal of the Optical Society of America A · 2000
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersMedical Research Council
KeywordsFovealSpatial frequencyFovea centralisPeripheral visionComputer scienceStimulus (psychology)CrowdingArtificial intelligenceOpticsComputer visionPhysicsNeurosciencePsychologyCognitive psychologyOphthalmology

Abstract

fetched live from OpenAlex

It has been known for some time that both foveal and peripheral visual acuity are higher for single letters than for letters in a row. Early work showed that this was due to the destructive interaction of adjacent contours (termed contour interaction). It has been assumed to have a neural basis, and a number of competing explanations have been advanced that implicate either high-level or low-level stages of visual processing. Our previous results for foveal vision suggested a much simpler explanation, one determined primarily by the physics of the stimulus rather than the physiology of the visual system. We show that, under conditions of contour interaction or crowding, the most relevant physical spatial-frequency band of the letter is displaced to higher spatial frequencies and that foveal vision tracks this change in spatial scale. In the periphery, however, beyond 5 degrees, the physical explanation is not sufficient. Here we show that there are genuine physiological lateral spatial interactions, which are due to changes in the spatial scale of analysis.

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 score0.222
Threshold uncertainty score0.439

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 head0.032
GPT teacher head0.314
Teacher spread0.282 · 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

Citations45
Published2000
Admission routes1
Has abstractyes

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