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Record W2127342574 · doi:10.1093/alcalc/agg041

ALCOHOL INTOXICATION REDUCES IMPULSIVITY IN THE DELAY-DISCOUNTING PARADIGM

2003· article· en· W2127342574 on OpenAlexaff
Catherine N. M. Ortner

Bibliographic record

VenueAlcohol and Alcoholism · 2003
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImpulsivityDelay discountingPsychologyDiscountingCognitionPreferenceAlcohol intoxicationTask (project management)AlcoholPlaceboPoison controlDevelopmental psychologyClinical psychologyInjury preventionPsychiatryMedicine

Abstract

fetched live from OpenAlex

AIMS: To examine the moderating effects of alcohol myopia on cognitive impulsivity in humans using the delay-discounting paradigm. METHODS: Seventy-six male undergraduate students were randomly assigned to sober, placebo or alcohol conditions. In the delay-discounting task, participants made a series of hypothetical choices between a small, immediate reward and a large, delayed reward. To test the predictions of alcohol myopia theory, participants completed a standard version of the task or one containing cues which impelled the impulsive choice (i.e. preference for the small, immediate reward). Participants also completed a personality measure of impulsivity and the go/no-go task, which assesses motor impulsivity. RESULTS: Intoxicated participants tended to discount delayed rewards at lower rates than sober participants, and blood alcohol level was inversely correlated with delay discounting. The impelling cues did not moderate the effects of alcohol on delay discounting. CONCLUSIONS: Alcohol intoxication does not always increase cognitive impulsivity and may lead to more cautious decision-making under certain conditions.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.041
GPT teacher head0.317
Teacher spread0.276 · 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".

Quick stats

Citations134
Published2003
Admission routes1
Has abstractyes

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