Thermal tasting: methodological considerations and implications for alcohol behaviour.
Bibliographic record
Abstract
Thermal tasting is a phenomenon whereby some individuals perceive thermally-induced taste sensations when their tongue is warmed or cooled. These individuals, known as thermal tasters (TT), report a variety of thermally-induced tastes and the tastes reported can vary with temperature regime used and location on the tongue tested. TT are typically compared to thermal non-tasters (TnT), individuals who do not experience thermally-induced sensations. The literature suggests that TT give higher intensity ratings to orosensory stimuli than TnT; however, small sample sizes and differences in classification schemes between studies confound our understanding of TTS (thermal taste status). It is unknown whether the increased orosensory responsiveness of TT is universal or whether it is driven by a subgroup of TT. Furthermore, up to 50% of individuals are non-classifiable (NC). The largest database of individuals who have undergone TTS screening was compiled to address the literature gaps. Findings indicate that TT are more responsive than TnT to orosensory stimuli, regardless of the classification scheme used. The orosensory responsiveness of NC is not homogeneous, suggesting that NC are not a separate group but rather misclassified TT and TnT. Sweet TT are more likely than non-sweet TT to experience thermally-induced sensations during lingual warming. Similarly, sour TT are more likely than non-sour TT to report thermally-induced tastes during cooling. However, no differences in orosensory responsiveness based on these or other subgroups are identified, suggesting that the heightened orosensory responsiveness of TT is universal across this phenotype. The final study sought to characterize the binary interactions between ethanol and four orosensory stimuli (fructose, quinine, tartaric acid and alum sulphate) both overall and by comparing TT and TnT. In general, TT are more responsive than TnT to all stimuli in the study. Few interactions between TTS and stimulus intensity exist suggesting that TT and TnT perceive the sensations elicited by alcoholic beverages similarly, albeit at different intensities. Together, the thesis helps inform best practices for TTS screening and classification, provides insights into TTS mechanisms and furthers our understanding of alcoholic beverage perception.
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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 | 0.119 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".