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Record W2005872264 · doi:10.1037/h0087329

Interactions between symmetry and elongation in determining reference frames for object perception.

2000· article· en· W2005872264 on OpenAlexaff
Allison B. Sekuler, Miranda B. Swimmer

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2000
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSalience (neuroscience)Symmetry (geometry)ElongationObject (grammar)Frame of referenceReference framePerceptionPsychologyBilateral symmetryComputer visionArtificial intelligenceFrame (networking)CommunicationComputer sciencePhysicsMathematicsGeometryClassical mechanicsEngineeringNeuroscienceStructural engineering

Abstract

fetched live from OpenAlex

Many theories of object recognition posit that objects are encoded with respect to a perceptual frame of reference. Such theories assume that factors such as symmetry and elongation are critical for the assignment of an object's primary axis, and consequently for the extraction of an object's reference frame. The present experiments directly examined the relative roles played by symmetry and elongation in the determination of an object's primary axis, and the extent to which symmetry and elongation interact with one another. We found that observers use both symmetry and elongation in extracting an object's primary axis, that the extent to which each cue dominates depends on its relative salience, and that symmetry and elongation are processed interactively, rather than in encapsulated modules.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.109
GPT teacher head0.371
Teacher spread0.262 · 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 designObservational
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

Citations40
Published2000
Admission routes1
Has abstractyes

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