Smelling speech sounds: Association of odors with texture‐related ideophones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it