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Record W2137035371 · doi:10.1364/josaa.19.000158

Structural modeling of contrast sensitivity in adulthood

2002· article· en· W2137035371 on OpenAlexafffund
Charles T. Scialfa, Donald Kline, Philip K. Wood

Bibliographic record

VenueJournal of the Optical Society of America A · 2002
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContrast (vision)Sensitivity (control systems)Filter (signal processing)Spatial frequencyVisual acuityMathematicsAudiologyStatisticsPsychologyOpticsArtificial intelligenceComputer sciencePhysicsMedicineComputer vision

Abstract

fetched live from OpenAlex

Structural equation modeling was used to assess the utility of the sensorineural model of contrast sensitivity proposed by Sekuler et al. [Vision Res. 24, 689 (1984)] to account for spatial vision in adulthood. In Study 1, visual acuity and contrast sensitivity (1.5-18 c/deg) were measured in 84 people between the ages of 19 and 81 yr. No three-filter model fitted the data well. Although a two-filter model was associated with good fit indices, parameter estimates for both filters were inconsistent with physiological and behavioral data. In Study 2, acuity and contrast sensitivity (1.5-18 c/deg) were assessed in 95 observers between the ages of 23 and 73 yr. All measures were gathered once per month over a three-month period. The Sekuler et al. three-filter model did not fit the data from any time of measure, but a two-filter, bandpass model provided a consistent and excellent fit for all three waves. The model suggests that age-related change in the neural mechanisms underlying contrast sensitivity is minimal once acuity is controlled. Discrepancies between this conclusion and that reported by Sekuler et al. may be related to test type, psychophysical method, reliability, and sample selection.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.199

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 head0.050
GPT teacher head0.295
Teacher spread0.245 · 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

Citations16
Published2002
Admission routes2
Has abstractyes

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