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Record W1969968601 · doi:10.1167/9.9.4

Scale dependence and channel switching in letter identification

2009· article· en· W1969968601 on OpenAlexafffund
İpek Oruç, Michael S. Landy

Bibliographic record

VenueJournal of Vision · 2009
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersNational Eye InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsChannel (broadcasting)Computer scienceContrast (vision)Speech recognitionAcousticsPhysicsArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Letters are broadband visual stimuli with information useful for discrimination over a wide range of spatial frequencies. Yet, recent evidence suggests that observers use only a single, fixed spatial-frequency channel to identify letters and that the scale of that channel, in units of letter size, is determined by the size of the letter (scale dependence). We report two letter-identification experiments using critical-band masking. With sufficiently high-amplitude, low- or high-pass masking noise, observers switched to a different range of spatial frequencies for the task. Thus, letter channels are not fixed for a given letter size. When an additional white-noise masker was added to the stimulus to flatten the contrast-sensitivity function, the letter channel used by the observer still depended on letter size, further supporting the hypothesis that letter identification is scale dependent.

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)0.003
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.037
GPT teacher head0.344
Teacher spread0.308 · 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

Citations25
Published2009
Admission routes2
Has abstractyes

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