Differences in Swallowing between High and Low Concentration Taste Stimuli
Bibliographic record
Abstract
Taste is a property that is thought to potentially modulate swallowing behavior. Whether such effects depend on taste, intensity remains unclear. This study explored differences in the amplitudes of tongue-palate pressures in swallowing as a function of taste stimulus concentration. Tongue-palate pressures were collected in 80 healthy women, in two age groups (under 40, over 60), stratified by genetic taste status (nontasters, supertasters). Liquids with different taste qualities (sweet, sour, salty, and bitter) were presented in high and low concentrations. General labeled magnitude scale ratings captured perceived taste intensity and liking/disliking of the test liquids. Path analysis explored whether factors of taste, concentration, age group, and/or genetic taste status impacted: (1) perceived intensity; (2) palatability; and (3) swallowing pressures. Higher ratings of perceived intensity were found in supertasters and with higher concentrations, which were more liked/disliked than lower concentrations. Sweet stimuli were more palatable than sour, salty, or bitter stimuli. Higher concentrations elicited stronger tongue-palate pressures independently and in association with intensity ratings. The perceived intensity of a taste stimulus varies as a function of stimulus concentration, taste quality, participant age, and genetic taste status and influences swallowing pressure amplitudes. High-concentration salty and sour stimuli elicit the greatest tongue-palate pressures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".