Increased Odor Detection Speed in Highly Anxious Healthy Adults
Bibliographic record
Abstract
Anxiety can either impair or enhance performance depending on the context. Increased sensitivity to threat seems to be an important feature of sensory processing in anxiety since anxious individuals tend to be more attentive to threatening visual stimuli. Evidence of anxiety effects in olfaction is rare; though alterations of olfactory performance in psychiatric patients and some effects of trait and state anxiety on olfactory performance have been reported. Our main objective was thus to investigate whether olfactory processing speed varies as a function of trait anxiety levels. We additionally investigated a possible preferential bias for unpleasant odors in highly anxious participants. Thirty-eight healthy adults participated in a simple odor detection task, where response times (RTs) and anxiety levels were measured. We compared RTs to a pleasant and an unpleasant food odor between high- and low-trait anxiety participants. We found that high-trait anxiety participants detected both odors faster than low-trait anxiety participants, independently of odor pleasantness. Moreover, trait anxiety levels significantly correlated with reaction times to both odors, indicating that trait anxiety but not odor pleasantness influences olfactory detection speed. These findings provide new insights into olfactory processing in healthy adults showing how various levels of trait anxiety affect the olfactory modality.
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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 | 0.001 | 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".