Individual Differences in the Chemical Senses: Is There a Common Sensitivity?
Bibliographic record
Abstract
Taste, smell, and chemical irritation (so-called trigeminal sensation) combine in our daily experience to produce the supramodal sensation of flavor, are processed by partly overlapping neural mechanisms, and show functional interconnectivity in experiments. Given their collaboration in flavor formation and the well-established connections between these senses, it is plausible that polymodal detection mechanisms might contribute to individual differences in measured sensitivity. One would expect the existence of a general chemosensory sensitivity factor to result in associations among taste, smell, and trigeminal stimulation thresholds. Measures of 5 detection thresholds from all the chemical senses were assessed in the same group of young healthy subjects (n=57). An unbiased principal components analysis (PCA) yielded a 2-component solution. Component 1, on which taste thresholds loaded strongly, accounted for 29.4% of the total variance. Component 2, on which the odor and trigeminal lateralization thresholds loaded strongly, accounted for 26.9% of the total variance. A subsequent PCA restricted to a 3-component solution cleanly separated the 3 sensory modalities and accounted for 75% of the total variance. Thus, though there may be a common underlying factor that determines some individual differences in odor and trigeminal lateralization thresholds, a general chemical sensitivity that spans chemosensory modalities seems unlikely.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".