Now You Like Me, Now You Don't: Impact of Labels on Odor Perception
Bibliographic record
Abstract
Past research has shown that odor perception can be affected by how we label odors. The aim of this study was to expand on previous work by investigating the impact of labels on edibility, pleasantness, and intensity ratings as well as on reaction times when detecting labeled odors. We tested 50 subjects. Five odorants were administered, each with a positive and a negative label. Participants had to detect odors as fast as possible and then rate their edibility, pleasantness, and intensity. Because of a lack of fit, only 4 of the initial 5 odorants were analyzed. All odorants presented with positive labels were rated as being more edible than when they were presented with negative labels. Specifically, the effect was also seen for the 2 nonfood odorants suggesting an unbiased effect. All odorants presented with positive labels were rated as being more pleasant than when they were presented with negative labels. Labels also modulated intensity ratings and reaction times for some odors. In summary, odor labels affect pleasantness ratings and edibility perception. Although labels appear to also influence intensity ratings and reaction times, this seems to be a more complex relationship that could be modulated by additional factors such as odor valence, label fit, and possibly the edibility attributed to an odor or a label.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".