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Record W2114971731 · doi:10.1364/josaa.20.000401

Detection and discrimination of texture modulations defined by orientation, spatial frequency, and contrast

2003· article· en· W2114971731 on OpenAlexaff
Nicolaas Prins, Frederick A. A. Kingdom

Bibliographic record

VenueJournal of the Optical Society of America A · 2003
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsContrast (vision)Spatial frequencyTexture (cosmology)Orientation (vector space)OpticsMaterials scienceArtificial intelligencePattern recognition (psychology)Computer sciencePhysicsMathematicsGeometryImage (mathematics)

Abstract

fetched live from OpenAlex

We sought to determine whether the detection and the identification of texture modulations are mediated by a common mechanism. On each trial two textures were presented, one of which contained a modulation in orientation (OM), spatial frequency (FM), or contrast (CM). Observers were required to indicate whether the modulated texture was presented in the first or the second interval as well as the nature of the texture modulation. The results showed that for two of the three pairwise matchings (OM-FM and OM-CM) detection and identification performance were nearly identical, suggesting a common underlying mechanism. However, when FM and CM textures were paired, discrimination thresholds were significantly higher than detection thresholds. In the context of the filter-rectify-filter model of texture perception, our results suggest that the mechanisms underlying detection are labeled with respect to their first-order input; i.e., the identities of these mechanisms are available to higher levels of processing. Several possible explanations for the misidentification of FM and CM at detection threshold are considered.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.155

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 communication0.0000.000
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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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

Citations26
Published2003
Admission routes1
Has abstractyes

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