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Record W1976290672 · doi:10.15288/jsad.2007.68.493

Alcohol Does Not Affect Dark Adaptation or Luminance Increment Thresholds

2007· article· en· W1976290672 on OpenAlexaff
Sarah Khan, Brian Timney

Bibliographic record

VenueJournal of Studies on Alcohol and Drugs · 2007
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsWestern University
Fundersnot available
KeywordsFovealLuminanceAffect (linguistics)Adaptation (eye)AlcoholExposure durationRetinaMesopic visionAudiologyOpticsRetinalOphthalmologyPsychologyMedicinePhysicsBiologyCommunication

Abstract

fetched live from OpenAlex

OBJECTIVE: It has been proposed that alcohol might induce within the retina a state akin to dark adaptation. However, the evidence to support this proposal is quite indirect. Another possibility is that alcohol might affect retinal gain control rather than sensitivity. To investigate these proposals psychophysically, we measured dark adaptation functions and increment thresholds with the increment threshold procedure in individuals with moderate blood alcohol concentrations (BACs). METHOD: Individuals were tested under both alcohol and no-alcohol conditions (BAC approximately .08%). In Experiment 1, thresholds for the detection of a parafoveal target were measured over a 25-minute period following a 3-minute bleach in six males. In Experiment 2, the cone dark adaptation function of four males was examined in more detail for a foveal target following bleaching at three different levels. In Experiment 3, we measured the thresholds of nine men for a small target superimposed on a background field that varied over 4 log units in luminance. RESULTS: We found no effects of alcohol on either the rod or the cone portion of the dark adaptation curve or on increment thresholds. CONCLUSIONS: Together, these data indicate that moderate alcohol ingestion does not affect the recovery of visual sensitivity in the dark nor does it affect gain control at the retinal level.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.149
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.134
GPT teacher head0.393
Teacher spread0.259 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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