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Record W2027265976 · doi:10.1093/ntr/ntp044

Effects of acute nicotine on event-related potential and performance indices of auditory distraction in nonsmokers

2009· article· en· W2027265976 on OpenAlexaff
Verner Knott, Kiley Bolton, Adam Heenan, Dhrasti Shah, Derek J. Fisher, Crystal M. Villeneuve

Bibliographic record

VenueNicotine & Tobacco Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Ottawa
Fundersnot available
KeywordsDistractionNicotineAudiologyPsychologyMedicineSmoking cessationPsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Although nicotine has been purported to enhance attentional processes, this has been evidenced mostly in tasks of sustained attention, and its effects on selective attention and attentional control under conditions of distraction are less convincing. METHODS: This study investigated the effects of nicotine on distractibility in 21 (11 males) nonsmokers with event-related potentials (ERPs) and behavioral performance measures extracted from an auditory discrimination task requiring a choice reaction time response to short- and long-duration tones, with and without imbedded deviants. Administered in a randomized, double-blind, placebo-controlled crossover design, nicotine gum (6 mg) failed to counter deviant-elicited behavioral distraction characterized by longer reaction times and increased response errors. RESULTS: Of the deviant-elicited ERP components, nicotine did not alter the P3a-indexed attentional switching to the deviant, but in females, it tended to diminish the automatic processing of the deviant as shown by a smaller mismatch negativity component, and it attenuated attentional reorienting following deviant-elicited distraction, as reflected by a reduced reorienting negativity ERP component. DISCUSSION: Results are discussed in relation to attentional models of nicotine and with respect to future research directions.

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)0.000
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 score0.263
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.316
Teacher spread0.309 · 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

Citations39
Published2009
Admission routes1
Has abstractyes

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