Odorant-specific Patterns of Sniffing during Imagery Distinguish ‘Bad’ and ‘Good’ Olfactory Imagers
Bibliographic record
Abstract
There are large individual differences in the self-reported ability to form vivid olfactory mental imagery. Based on such self-reports, subjects have been classified as 'bad' or 'good' imagers. The present study examined whether a differential strategy in re-enacting the olfactomotor response during imagery may explain the dissociation between 'bad' and 'good' olfactory imagers. As previously reported, odor imagery was accompanied by sniffing. Although 'bad' and 'good' olfactory imagers did not differ in their overall sniffing volume, they used different strategies when re-enacting the motor component of olfaction during imagery. Particularly, as in real perception, 'good' but not 'bad' imagers generated bigger sniffs when imagining a pleasant smell compared with an unpleasant smell (P<0.02). Furthermore, preventing sniffing significantly hampered mental imagery of pleasant odors in 'good' but not 'bad' imagers (P<0.03). Taken together, these results suggest (i) the validity of the dissociation between 'bad' and 'good' olfactory imagers as revealed by self-report; (ii) that sniffing may be a causal factor in the creation of olfactory imagery; and (iii) that sniff measurements may serve as a reliable non-verbal tool in exploring individual differences in odor imagery.
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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".