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Record W2022372769 · doi:10.1167/9.8.1022

Off-kilter: Orientation discrimination during childhood

2010· article· en· W2022372769 on OpenAlexaff
Terri L. Lewis, S. Chong, Daphne Maurer

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOrientation (vector space)Contrast (vision)Tilt (camera)AudiologyPsychologyOpticsMathematicsPhysicsMedicineGeometry

Abstract

fetched live from OpenAlex

In the only study measuring sensitivity to orientation during childhood, we showed that 5-year-olds are four times worse than adults when tested with high contrast gratings (Lewis, et al., 2007). Here, we tested older children to chart the development of sensitivity to orientation between 5 years and adulthood. Methods. We measured orientation discrimination in 20 7-year-olds (+/− 3 months) and 20 9-year-olds (+/− 3 months) using methods identical to those used previously with 5-year-olds and adults (Lewis, et al., 2007). The stimuli consisted of 1 cpd black-and-white high contrast sine-wave gratings within a 10° circular aperture. The task on each trial was to indicate whether the top of the stripes was tilted to the left or right of vertical. Tilt was varied over trials according to a ML-PEST staircase procedure (Harvey, 1986) to measure the minimum tilt discriminable from vertical. Results. Minimum discriminable tilt improved with age (p ps ps [[gt]] 0.30). The data were best fit by an exponential function (r2= 0.35, p Conclusions. The pattern of development for sensitivity to orientation (this study) resembles those for the development of sensitivity to spatial frequency (Patel, et al., 2009) and contrast (Ellemberg et al, 1999). These similar patterns are consistent with theories of common underlying mechanisms (Vincent & Regan, 1995; Shapely et al., 2003). The immaturities at 5 years of age may be caused by higher internal noise.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.291

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 integrity0.0000.001
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 head0.013
GPT teacher head0.393
Teacher spread0.380 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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