Modulation of auditory deviance detection by acute nicotine is baseline and deviant dependent in healthy nonsmokers: a mismatch negativity study
Bibliographic record
Abstract
OBJECTIVE: Cognitive enhancement resulting from nicotinic acetylcholine receptor stimulation may be evidenced by increased efficiency of the auditory-frontal cortex network of auditory discrimination, which is impaired in schizophrenia, a cognitive disorder associated with excessive tobacco use. Investigating automatic (preattentive) detection of acoustic change with the mismatch negativity (MMN) brain event-related potential in response to nicotine in individuals with varying baseline levels of auditory discrimination may provide useful insight into the cholinergic regulation of this neural network and its potential amelioration with novel nicotinic agents. METHODS: Sixty healthy, non-smoking male volunteers were presented with an 'optimal' multi-feature MMN paradigm in a randomized, placebo controlled double-blind design with 6 mg of nicotine gum. RESULTS: Participants with low, medium, and high baseline amplitudes responded differently to nicotine (vs. placebo), and nicotine response was feature specific. Whereas MMN in individuals with high amplitudes was diminished by nicotine, MMN increased in those with low amplitudes. Nicotine effects were not shown in medium amplitude participants. CONCLUSIONS: These findings provide preliminary support for the role of nicotinic neurotransmission in sensory memory processing of auditory change and suggest that nicotinic receptor modulation can both enhance and diminish change detection, depending on baseline MMN and its eliciting stimulus feature.
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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) | 0.003 | 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".