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Record W2020355018 · doi:10.1037/0096-1523.31.2.235

Multiple-Object Tracking Is Based on Scene, Not Retinal, Coordinates.

2005· article· en· W2020355018 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2005
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer visionArtificial intelligenceComputer scienceTracking (education)ZoomReference frameTranslation (biology)Object (grammar)Rotation (mathematics)Video trackingCoherence (philosophical gambling strategy)Frame (networking)MathematicsPhysicsPsychology

Abstract

fetched live from OpenAlex

This study tested whether multiple-object tracking-the ability to visually index objects on the basis of their spatiotemporal history-is scene based or image based. Initial experiments showed equivalent tracking accuracy for objects in 2-D and 3-D motion. Subsequent experiments manipulated the speeds of objects independent of the speed of the scene as a whole. Results showed that tracking accuracy was influenced by object speed but not by scene speed. This held true whether the scene underwent translation, zoom, rotation, or even combinations of all 3 motions. A final series of experiments interfered with observers' ability to see a coherent scene by moving objects at different speeds from one another and by distorting the perception of 3-D space. These reductions in scene coherence led to reduced tracking accuracy, confirming that tracking is accomplished using a scene-based, or allocentric, frame of reference.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.100
GPT teacher head0.403
Teacher spread0.303 · 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