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Record W4399324219 · doi:10.1523/eneuro.0204-24.2024

Shared Mechanisms Drive Ocular Following and Motion Perception

2024· article· en· W4399324219 on OpenAlexafffund
Philipp Kreyenmeier, Romesh D. Kumbhani, J. Anthony Movshon, Miriam Spering

Bibliographic record

VenueeNeuro · 2024
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
FundersNational Eye InstituteNatural Sciences and Engineering Research Council of CanadaRobert E. Leet and Clara Guthrie Patterson Trust
KeywordsContrast (vision)PerceptionEye movementMotion perceptionMotion (physics)GratingNeurosciencePsychologyVisual perceptionComputer visionHigh contrastCommunicationArtificial intelligenceComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

How features of complex visual patterns are combined to drive perception and eye movements is not well understood. Here we simultaneously assessed human observers' perceptual direction estimates and ocular following responses (OFR) evoked by moving plaids made from two summed gratings with varying contrast ratios. When the gratings were of equal contrast, observers' eye movements and perceptual reports followed the motion of the plaid pattern. However, when the contrasts were unequal, eye movements and reports during early phases of the OFR were biased toward the direction of the high-contrast grating component; during later phases, both responses followed the plaid pattern direction. The shift from component- to pattern-driven behavior resembles the shift in tuning seen under similar conditions in neuronal responses recorded from monkey MT. Moreover, for some conditions, pattern tracking and perceptual reports were correlated on a trial-by-trial basis. The OFR may therefore provide a precise behavioral readout of the dynamics of neural motion integration for complex visual patterns.

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 score0.171
Threshold uncertainty score0.488

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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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