Acute nicotine fails to alter event‐related potential or behavioral performance indices of auditory distraction in cigarette smokers
Bibliographic record
Abstract
Behavioral studies have shown that nicotine enhances performance in sustained attention tasks, but they have not shown convincing support for the effects of nicotine on tasks requiring selective attention or attentional control under conditions of distraction. We investigated distractibility in 14 smokers (7 females) with event-related brain potentials (ERPs) and behavioral performance measures extracted from an auditory discrimination task requiring a choice reaction time response to short- and long-duration tones, both with and without embedded deviants. Nicotine gum (4 mg), administered in a randomized, double-blind, placebo-controlled crossover design, failed to counter deviant-elicited behavioral distraction (i.e., slower reaction times and increased response errors), and it did not influence the distracter-elicited mismatch negativity, the P300a, or the reorienting negativity ERP components reflecting acoustic change detection, involuntary attentional switching, and attentional reorienting, respectively. Results are discussed in relation to a stimulus-filter model of smoking and in relation 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 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 | 0.001 |
| 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".