Smelling speech sounds: Association of odors with texture‐related ideophones
Bibliographic record
Abstract
Abstract Odors are often difficult to describe verbally, and little is known about the association of odors with the words that describe them. Following the literature on crossmodal correspondences between odors and sounds/haptics, this study aimed to reveal how odors are associated with the words describing textures and haptics in the Japanese language. Fifty participants smelled 17 food‐related odors (e.g., lemon, pepper) and matched the odors with words related to texture (e.g., sakusaku), haptics (e.g., soft, dry), and emotion (e.g., positive). The experiment was conducted with and without the verbal description of odor names. The results demonstrated that each odor was mainly categorized into words related to the concepts of (a) juicy/cool/jiggly/positive, (b) smooth/moist/soft, or (c) hard/rough/dry, regardless of whether participants smelled the odors with or without the verbal description. Our findings reveal novel odor‐sound/haptic associations and demonstrate how odors can be described verbally. Practical applications People find it difficult to verbalize or communicate various odors. This study contributes to the literature on odor‐sound/haptic correspondences by showing that the odors are associated with texture‐related ideophones and haptic words. Specifically, the results demonstrated that each odor was mainly categorized into words related to the concepts of (a) juicy/cool/jiggly/positive, (b) smooth/moist/soft, or (c) hard/rough/dry. These findings are relevant to marketing communications involving odors and emphasize the potential importance of the texture‐related ideophones and haptic words when marketers want to effectively communicate odors with consumers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".