Event-Related Brain Potentials Associated With the Olfactory-Visual Stroop Effect and Its Modulation by Olfactory-Induced Emotional States
Bibliographic record
Abstract
This study investigated the event-related brain potentials associated with the olfactory-visual cross-modal Stroop effect and its modulation by olfactory-induced and self-reported affective states. Eighteen healthy participants were presented with an olfactory stimulus and the image of a plant, and they had to categorize the olfactory attribute of the image as "aromatic" or "pungent" by pressing the relevant button as quickly as possible. The type of olfactory-visual stimuli (congruent or incongruent) and the valence of the olfactory-induced emotional states (positive or negative) were manipulated following a 2 × 2 factorial design. Interference effects were observed at the behavioral and the electrophysiological levels: response times recorded in the incongruent condition were higher than those observed in the congruent condition; an incongruent minus congruent negative difference component was discovered between 350 and 550 ms after stimulus onset in the negative-but not in the positive-olfactory-induced emotional state condition. This ND350-550 component was interpreted as reflecting the amount of selective attention involved in the olfactory-visual cross-modal Stroop effect. These results are also consistent with a facilitatory effect of positive emotional state on selective attention which could reduce brain potentials associated with the cross-modal interference effect.
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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 | 0.002 |
| 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) | 0.002 | 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".