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Record W2032978939 · doi:10.1167/14.10.806

Contextual disambiguation of rotating Necker cubes

2014· article· en· W2032978939 on OpenAlexaff
Marouane Ouhnana, F. A. A. Kingdom

Bibliographic record

VenueJournal of Vision · 2014
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsCube (algebra)Context (archaeology)Rotation (mathematics)Similarity (geometry)Motion (physics)MathematicsRotational speedObject (grammar)Artificial intelligenceGeometryComputer sciencePhysicsClassical mechanicsGeology

Abstract

fetched live from OpenAlex

Ambiguous figures perceptually alternate between different interpretations, and the particular interpretation is known to be affected by context. The aim of the study was to investigate the effect of an unambiguous rotating wire cube with various motion parameters on the perceived direction of an adjacent Necker cube continuously rotating at constant speed. The context figure parameters were rotation speed (same as, half, or twice the speed of the ambiguous figure) and rate of reversal (intervals between reversals 2s, 4s, and 8s). The two rotating figures were presented above and below fixation for 32s per trial. Observers indicated via key-press the direction of rotation of the ambiguous figure. Results show that the rate of ambiguous figure reversals was dependent on context, specifically reversal rates were correlated with those of the context figure. For some observers the correlation between reversal rates also depended on the similarity of speeds. These results suggest that changes in motion direction more than speed similarity binds ambiguous object motion to its unambiguous motion context. Meeting abstract presented at VSS 2014

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.363
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

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