A Rose by Any Other Name: Would it Smell as Sweet?
Bibliographic record
Abstract
We examined whether presenting an odor with a positive, neutral, or negative name would influence how people perceive it. In experiment 1, 40 participants rated 15 odors for their pleasantness, intensity, and arousal. In experiment 2, 30 participants passively smelled 10 odors while their skin conductance (SC), heart rate (HR), and sniffing were recorded. We found significant overall effects of odor names on perceived pleasantness, intensity, and arousal. Pleasantness showed the most robust effect of odor names: the same odors were perceived as more pleasant when presented with positive than with neutral and negative names and when presented with neutral than with negative names. In addition, odorants were rated as more intense when presented with negative than with neutral and positive names and as more arousing when presented with positive than with neutral names. Furthermore, SC and sniff volumes, but not HR, were modified by odor names, and the SC changes could not be accounted for by sniffing changes. Importantly, odor names presented with odorless water did not produce any effect on skin conductance and sniff volumes, ruling out the possibility that the naming-related findings were triggered by an emotional reaction to odor names. Taken together, these experiments show that there is a lot to a name, at least when it comes to olfactory perception.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 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".