The Meaning of Food Preferences in the Human Behaviour and Personalities.
Bibliographic record
Abstract
Learned predisposition to choice in a favorable or unfavorable manner with respect to a given food is a complex and plastic trait resulting from interactions between both nutritional properties of food and individual chemosensory perception, this last modulated by personality properties and interoceptive awareness (alexithymia). We report data obtained by a total of ~700 individuals (aged 18-76) coming from 6 different small villages in the Friuli Venezia Giulia region in northern Italy. Participants completed a food preferences questionnaire on 66 different foods, rating their liking of each item on a 9 point scale ranging from “extremely like” to “extremely dislike”. Standardized questionnaires were also administered to characterize subjects on selected personality traits (Temperament and Character Inventory – TCI) and alexithymia (Toronto Alexithymia Scale – TAS-20). We conducted a linear regression between the mean of all food preferences and neuropsychological traits, using sex and age as covariates. The mean of food preferences was associated negatively with TAS total score (p=7.5e-05) and externally oriented cognitive style (subitem 3 of TAS-20) (p=2.0e-07), and was positively associated with self-transcendence total score (ST) (p=5.9e-04) and spiritual acceptance (ST3) (p=6.5e-04) among TCI dimensions. We conclude that food preferences are modulated by the enduring tendency to transcend contingent sensorimotor representations (ST) on the one hand, by difficulty distinguishing between feelings and the bodily sensations of emotional arousal, and concrete thinking, often with the exclusion of emotional responses to stimuli of the other. These results can provide insight to a clearer understanding of the motivations of consumers and their effects to choice diets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".