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Record W1984755273 · doi:10.1097/opx.0b013e31815b9e25

Normal Aging and the Perception of Curvature Shapes

2007· article· en· W1984755273 on OpenAlexafffund
Isabelle Legault, Rémy Allard, Jocelyn Faubert

Bibliographic record

VenueOptometry and Vision Science · 2007
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsCurvaturePerceptionStimulus (psychology)MathematicsQuadratic equationPsychologyQuadratic functionGeometryCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

PURPOSE: The present study assessed whether different curve geometries involve different perceptual processing levels and whether these perceptual requirements can interact with the normal aging process. METHODS: Amplitude thresholds for three different curve types were assessed for young and older observers using 2AFC psychophysical methods. The stimuli were individually adjusted for visibility. The three stimulus types evaluated represented a bell shape, a quadratic, and a compressed arc function. RESULTS: As predicted, the geometry influenced the perception of curvature where the compressed arc was most difficult to perceive followed by the quadratic and bell-shaped curves. Moreover, older observers showed relatively higher thresholds for the quadratic and compressed arc shapes, while they had similar thresholds to the younger observers for the bell-shaped function. CONCLUSIONS: In general, the data support the notion that aging affects the processing of curvature requiring the integration of oriented receptors. This is in accordance with studies that have found reduced orientation selectivity of cortical neurons in senescent animals. Our findings suggest that older observers would have more difficulties with form discrimination tasks where curvature is an inherent component of the image and also predict age-related differences in perceiving ophthalmic lens-induced distortions.

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)0.003
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 score0.429
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.435
Teacher spread0.405 · 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

Citations5
Published2007
Admission routes2
Has abstractyes

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