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Record W2750585205 · doi:10.1167/17.10.440

A Comparison of Electrophysiological and Behavioral Measures of Visual Acuity

2017· article· en· W2750585205 on OpenAlexaff
Nakita Ryan, Gabrielle Hodder, Lauren King, James R. Drover

Bibliographic record

VenueJournal of Vision · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVisual acuityPsychologyRepeatabilityOptometryAudiologyElectrophysiologyOphthalmologyMedicineMathematicsNeuroscience

Abstract

fetched live from OpenAlex

Unlike behavioral techniques, the measurement of visual evoked potentials (VEPs) provides an objective electrophysiological measure of vision directly from the visual cortex. The purpose of the present study is to provide the first comparison of visual acuity scores obtained using a new VEP system and those obtained using behavioral tests. Grating acuity was estimated in 27 participants (M = 21.2±1.5 years) using the VeriSci Neucodia VEP system following the sweep VEP (sVEP) procedure. During each 10 second sweep, participants were presented with 8 horizontal square wave gratings ranging from 5.3 to 35.6 cpd (0.75 to -0.07 logMAR). Each participant completed 8 sweeps. Grating acuity was also measured using the Teller Acuity Cards II (TAC) and optotype acuity was estimated using the Early Treatment Diabetic Retinopathy Study (ETDRS) visual acuity test. Scores from the three tests were compared. In addition, coefficients of repeatability (COR) were determined for all possible test pairs in order to determine level of agreement. Friedman analyses revealed a significant test effect (p< 0.0001). Specifically, ETDRS scores were significantly finer than scores obtained with sVEP (-0.04 v. 0.12 logMAR, p< 0.0001) and the TAC (-0.04 v. 0.11 logMAR, p< 0.0001). TAC and sVEP scores did not differ (TAC=0.11 logMAR; VEP=0.12 logMAR; p=0.52). COR analyses indicated that the level of agreement between all test pairs was poor and virtually identical (TAC and sVEP COR=0.30 logMAR; sVEP and ETDRS COR=0.30 logMAR; TAC and ETDRS COR=0.31 logMAR). The poor agreement between tests is not surprising given that they assess different visual abilities (i.e., TAC and sVEP: resolution acuity; ETDRS: recognition acuity). Furthermore, the tests likely tap different underlying neural mechanisms as VEPs are recorded directly from the visual cortex, whereas the TAC and the ETDRS require a behavioural response and therefore, likely tap mechanisms further upstream within the visual system and beyond. Meeting abstract presented at VSS 2017

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.217
GPT teacher head0.487
Teacher spread0.270 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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