Making Sense of Scents: The Colour and Texture of Odours
Bibliographic record
Abstract
The purpose of this study was to document colour and texture associations to odours using a variety of odours including both pleasant and unpleasant odours, some of which were likely to be unfamiliar. We asked non-synaesthetic adults (n = 78) to make colour and shape/texture associations to 22 odours. A subset of the participants (n = 41) smelled the odours a second time in order to identify them. Each odour stimulus was associated consistently to one or more specific colours and/or textures (all p's < 0.01 by binomial probability statistics). Associations to the four odours that were identified accurately (cinnamon, lemon, peppermint and licorice) seemed to be based on learning/memory (e.g. lemon = yellow). The associations to the 18 odours that were not identified accurately are less likely to be based on learning/memory (e.g. ginger = black, rough, sharp; lavender = green, white, liquid, sticky). We speculate that sensory associations to odours, like those to pitch and letters (e.g. Mondloch and Maurer, 2004; Spector and Maurer, 2008), may result from the joint influence of learning and natural biases linking dimensions across sensory systems. Such links may reflect inherent neural organization that is modifiable with learning and that can manifest as cross-modal associations or synaesthetic percepts.
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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 | 0.002 |
| 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 | 0.001 | 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".