MétaCan
Menu
Back to cohort
Record W3089329285 · doi:10.1177/2041669520939107

Jumpy and Jerky: When Peripheral Vision Faces Reverse-Phi

2020· article· en· W3089329285 on OpenAlexafffund
Jean Lorenceau, Patrick Cavanagh

Bibliographic record

Venuei-Perception · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
FundersCentre National de la Recherche ScientifiqueCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPeripheral visionFovealIllusionFlickerMotion (physics)PeripheralStimulus (psychology)Eccentricity (behavior)Annulus (botany)Motion perceptionJumpComputer visionArtificial intelligencePsychologyCommunicationComputer sciencePhysicsNeuroscienceCognitive psychologyChemistryBiology

Abstract

fetched live from OpenAlex

When an annulus in fast apparent motion reverses its contrast over time, the foveal and peripheral percepts are strikingly different. In central vision, the annulus appears to follow the same path as an annulus without flicker, whereas in the periphery, the stimulus seems to randomly jump across the screen. The illusion strength depends on motion speed and reversal rate. Our observations suggest that it results from a balance between conflicting phi and reverse-phi motion, positional uncertainty, and attention. In addition to illustrating the differences between central and peripheral motion processing, this illusion shows that both discrete positional sampling and motion energy combine to generate motion percepts, although with eccentricity dependent weights that are themselves affected by attention.

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)0.003
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
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.070
GPT teacher head0.325
Teacher spread0.255 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuei-PerceptionSame topicVisual perception and processing mechanismsFrench-language works237,207