Effects of acute nicotine on event-related potential and performance indices of auditory distraction in nonsmokers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".