MétaCan
Menu
Back to cohort
Record W4401812656 · doi:10.1167/jov.24.8.13

Contribution of low-level motion to position shifts

2024· article· en· W4401812656 on OpenAlexafffund
Donald I. A. MacLeod, Patrick Cavanagh, Stuart Anstis

Bibliographic record

VenueJournal of Vision · 2024
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of California, San DiegoDartmouth College
KeywordsPosition (finance)Motion (physics)GeodesyComputer scienceGeologyArtificial intelligenceEconomics

Abstract

fetched live from OpenAlex

Motion can produce large changes in the apparent locations of briefly flashed tests presented on or near the motion. These motion-induced position shifts may have a variety of sources. They may be due to a frame effect where the moving pattern provides a frame of reference for the locations of events within it. The motion of the background may act through high-level mechanisms that track its explicit contours or the motion may act on position through the signals from low-level motion detectors. Here we isolate the contribution of low-level motion by eliminating explicit contours and trackable features. In this case, motion still supports a robust shift in probe locations with the shift being in the direction of the motion that follows the probe. Although robust, the magnitude of the shift in our first experiment is about 20% of the shift seen in a previous study with explicit frames and, in the second, about 45% of that found with explicit frames. Clearly, low-level motion alone can produce position shifts although the magnitude is much reduced compared to that seen when high-level mechanisms can contribute.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.198

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 communication0.0000.001
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 head0.014
GPT teacher head0.322
Teacher spread0.308 · 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 designOther design
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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of VisionSame topicAdvanced Vision and ImagingFrench-language works237,207