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Record W2795183827 · doi:10.1111/psyp.13081

Alcohol hangover impacts learning and reward processing within the medial‐frontal cortex

2018· article· en· W2795183827 on OpenAlexafffund
Ashley D. Howse, Cameron D. Hassall, Chad C. Williams, Greg Hajcak, Olave E. Krigolson

Bibliographic record

VenuePsychophysiology · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaNova Scotia Health Research Foundation
KeywordsPsychologyTask (project management)CognitionAlcohol intoxicationAlcoholCognitive psychologyDevelopmental psychologyNeurosciencePoison controlInjury preventionMedicine

Abstract

fetched live from OpenAlex

It is common knowledge that alcohol intoxication impairs motor coordination, judgment, and decision making. Indeed, an abundance of literature links intoxication to impaired cognitive control that leads to accidents and injury. A broadening body of research, however, suggests that the impact of alcohol may continue beyond the point of intoxication and into the period of alcohol hangover. Here, we examined differences in the amplitude of reward positivity-a component of the human ERP associated with learning-between control and hangover participants. During performance of a learnable gambling task, we found a reduction in the reward positivity during alcohol hangover. Additionally, participants experiencing alcohol hangover demonstrated reduced performance in the experimental task in comparison to their nonhangover counterparts. Our results suggest that the neural systems that underlie performance monitoring and reward-based learning are impaired during alcohol hangover.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.086
GPT teacher head0.388
Teacher spread0.302 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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