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Record W2024135865 · doi:10.1037/a0026989

Eccentricity dependence of the curveball illusion.

2012· article· en· W2024135865 on OpenAlexafffund
Rick Gurnsey, Mathieu Biard

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIllusionScalingStimulus (psychology)Visual fieldOptical illusionPeripheral visionOpticsArtificial intelligenceMathematicsComputer visionPhysicsComputer sciencePsychologyGeometryCognitive psychology

Abstract

fetched live from OpenAlex

When the Gaussian envelope of a Gabor patch moves straight down while its sine wave component drifts to the left, the Gabor is seen to move down to the left when viewed with peripheral vision (exhibiting the so-called curveball illusion) but straight down when viewed foveally. We used a nulling procedure to quantify the strength of the curveball illusion at a range of stimulus sizes and eccentricities to determine if the illusion is size dependent and whether size scaling can equate the strength of the illusion across the visual field. We find that the illusion is size dependent (illusion strength decreases with increasing stimulus size) but that size scaling is not sufficient to elicit an illusion at fixation that matches the strength of the illusion at 10°. These results suggest that certain changes in motion processing across the visual field differ in more than just the local scale of the mechanisms involved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.094
GPT teacher head0.348
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2012
Admission routes2
Has abstractyes

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