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Record W2102383776 · doi:10.1167/10.11.24

The Magnocellular visual pathway and the flash-lag illusion

2010· article· en· W2102383776 on OpenAlexaff
Mark Chappell, Kathy T. Mullen

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsIllusionLagFlash (photography)Cognitive psychologyPsychologyNeuroscienceComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Determining how the visual system locates moving stimuli continues to be an experimental and theoretical challenge. By making a moving visual stimulus equiluminant with its background, and immersing it in luminance noise, the spatial lead it normal enjoys over a flashed stimulus (the flash-lag illusion) was completely eliminated (the illusion was actually reversed for 6 out of 11 participants). As this manipulation is typically used to reduce Magnocellular (M) visual pathway processing, this is strong evidence that processing in this pathway advances the moving stimulus' perceived position. However, when the flashed stimulus was also made equiluminant in luminance noise, the illusion reappeared, indicating that M pathway processing contributed to its perception too. The presence of the illusion when both stimuli were equiluminant in luminance noise indicates that the illusion can be generated in the absence of M cell activation. To explicate the result with moving stimuli, we displayed two adjacent moving stimuli, one luminance-modulated, and the other equiluminant in noise. The latter was perceived to significantly lag the former (an 'M-P-Hess' illusion), and 39% of the difference in flash-lag illusions, with comparable moving stimulus contrasts, could be accounted for by this illusion.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.261
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.309
Teacher spread0.290 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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