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Record W2096011954 · doi:10.1167/10.5.7

Resolution of blur in the older eye: Neural compensation in addition to optics?

2010· article· en· W2096011954 on OpenAlexaff
Gwan Ho Jung, Donald Kline

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReading (process)MiosisPupilPupil sizeCompensation (psychology)GLAREPsychologyOptometryVisual acuityAudiologyArtificial intelligenceOpticsComputer scienceMedicinePhysicsLinguisticsNeuroscienceSocial psychology

Abstract

fetched live from OpenAlex

This study examined the roles of pupillary miosis and experience-mediated compensation in older observers' superior ability to read optically blurred text. The size thresholds of younger and older adult observers for reading common words and identifying line drawings of everyday objects were compared with natural and artificial pupils as a function of systematically varied far letter acuity: best corrected, 20/30, and 20/40. With best corrected letter acuity, younger observers' size thresholds for reading words were lower than those of older observers with either natural or artificial pupils. In the 20/40 condition, however, older observers' reading size thresholds with natural pupils were significantly lower than those of the young. No significant age differences were seen at 20/30 or 20/40 on word reading with artificial pupils or on line drawing identification size thresholds with either pupil type. Prior blur experience, as estimated from observers' presenting and best optical corrections, was inversely associated with older adults' word acuity. Pupillary miosis in conjunction with neural compensation appears to account for older observers' greater ability to read blurred text.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.394
Teacher spread0.366 · 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 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

Citations18
Published2010
Admission routes1
Has abstractyes

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