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Record W1997525945 · doi:10.1111/1467-9280.00382

Generalized Common Fate: Grouping by Common Luminance Changes

2001· article· en· W1997525945 on OpenAlexaff
Allison B. Sekuler, Patrick Bennett

Bibliographic record

VenuePsychological Science · 2001
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLuminanceSegmentationObject (grammar)Artificial intelligencePsychologyOrientation (vector space)Computer visionCommunicationComputer scienceMathematicsGeometry

Abstract

fetched live from OpenAlex

A critical step toward object recognition is the segmentation of a scene into relevant regions. One of the most important cues for segmentation is that of common fate: Elements that move together are grouped together Here we describe a new instantiation of common fate, in which elements move together not through physical space, but through luminance space. Experiment I shows that when elements of a scene become brighter together or darker together, observers group those elements together Experiments 2 and 3 show that this effect is not due to the availability of fixed luminance differences between target and background regions, but requires common changes within each region in the direction of luminance over time. The effect is differentiated from the recently discovered grouping cue of temporal synchrony, and is considered instead to be an extension of Wertheimer's original grouping factor of common fate. Common fate for luminance, or generalized common fate, is an extremely strong cue for the segmentation of a scene, yielding a tremendous advantage over grouping by fixed luminance cues.

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)0.000
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 score0.157
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.136
GPT teacher head0.418
Teacher spread0.282 · 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

Citations56
Published2001
Admission routes1
Has abstractyes

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