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Record W7000199264

Emotional information processing during alcohol hangover

2019· other· en· W7000199264 on OpenAlexaboutno aff

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStroop effectMoodVigilance (psychology)CognitionInformation processingChoice reaction timeAttentional biasPsychomotor learning
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Various studies have investigated the effects of alcohol hangover on cognitive functioning and mood. This research of independent assessments of mood and performance shows that both are negatively affected. The aim of this study was to examine their combined effect, employing the emotional Stroop test, to investigate alcohol hangover effects on emotional information processing.\nMethod: N=25 UK young adults participated in ta naturalistic study. On a hangover day and an alcohol-free control day, subjects completed a test battery consisting of the emotional Stroop test (social, physical, and no threat items), attentional blink test and Eriksen flanker task (selective attention), a choice serial reaction time test, and the psychomotor vigilance test (PVT). Reaction times and number of errors were variables of interest, and compared between the hangover day and control day.\nResults: On the emotional Stroop test, in the hangover condition responses to social threat, physical threat, and control items (no threat) were significantly slower than in the control condition. On the Eriksen flanker test and PVT significantly slower reaction times were found in the hangover condition. No effects on number of errors were observed, and the number of lapses in the PVT did not significantly differ between the hangover and control test day. On the choice serial reaction time test, no significant hangover effects were found.\nDiscussions and Conclusions: Emotional information processing was significantly impaired during alcohol hangover. The observed slower response times confirm the assumption that cognitive resources to perform tasks are suboptimal during the alcohol hangover.\nDisclosure of Interest Statement: This study was funded by Ulster University. Joris Verster has received grants/research support from the Dutch Ministry of Infrastructure and the Environment, Janssen, Nutricia, Red Bull, Sequential, and Takeda, and has acted as a consultant for Canadian Beverage Association, Centraal Bureau Drogisterijbedrijven, Clinilabs, Coleman Frost, Danone, Deenox, Eisai, Janssen, Jazz, More Labs, Purdue, Red Bull, Sanofi-Aventis, Sen-Jam Pharmaceutical, Sepracor, Takeda, Toast!, Transcept, Trimbos Institute, Vital Beverages, and ZBiotics. The other authors have no potential conflicts of interest to disclose.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0130.005
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.011

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 head0.026
GPT teacher head0.285
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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