MétaCan
Menu
Back to cohort
Record W2293886554 · doi:10.1167/16.3.24

Effect of motion discontinuities on discrimination of periodic trajectories

2016· article· en· W2293886554 on OpenAlexafffund
Hugh R. Wilson, Jeffrey Fung

Bibliographic record

VenueJournal of Vision · 2016
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClassification of discontinuitiesTrajectoryMotion (physics)PhysicsDiscontinuity (linguistics)RADIUSMathematical analysisMathematicsClassical mechanicsComputer science

Abstract

fetched live from OpenAlex

Many biologically important motions are described by periodic trajectories. Radial frequency (RF) trajectories are one example, in which the motion of a difference of Gaussians (DOG) target moves along a path described by a sinusoidal deviation of the radius from a perfect circle (Or, Thabet, Wilkinson, & Wilson, 2011). Here we explore the hypothesis that visual processing of RF trajectories involves global spatio-temporal processes that are disrupted by motion discontinuity. To test this hypothesis, RF trajectories were used that interspersed smooth, continuous motion with three or four discontinuous jumps to other portions of the trajectory. These jumps were arranged so that the entire trajectory was traversed in the same amount of time as in the continuous motion control condition. The motion discontinuities increased thresholds by a factor of approximately 2.1 relative to continuous motion. This result provides support for global spatio-temporal processing of RF motion trajectories. Comparison with previous results suggests that motion discontinuities erase memory for earlier parts of the trajectory, thereby causing thresholds to be based on only the final segment viewed. Finally, it is shown that RF trajectories obey the 1/3 power law characteristic of biological motion.

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)0.001
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 score0.117
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.028
GPT teacher head0.340
Teacher spread0.312 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of VisionSame topicVisual perception and processing mechanismsFrench-language works237,207