A Theoretical Proposal for a Perceptually Driven, Food-Based Disgust that Can Influence Food Acceptance During Early Childhood
Bibliographic record
Abstract
Disgust, the “revulsion at the prospect of (oral) incorporation of an offensive substance”, is not thought to influence the acceptability of food during infancy and early childhood. This is because the feelings of disgust require a person to have developed an understanding of contagion and to be aware of the nature and origin of a given disgust stimulus, which does not occur until around seven years of age. Despite this need for higher cognitive functioning, studies have demonstrated the potential for disgust in children as young as two years of age. Furthermore, it seems that young children can demonstrate aspects of disgust without having the cognitive understanding of contagion. This review is the first paper to demonstrate how core disgust may influence the acceptability of foods from late infancy. Firstly, food neophobia may act as a catalyst for disgust. Secondly, that disgust in young children can result from the visual perceptual features of food (as opposed to a cognitive response based on non-food disgust stimuli). Thirdly, that some disliked foods have contaminating properties, much like non-food, adult disgust stimuli (e.g. insects). Fourthly, that the response reduces as the child ages and learns more about food and its variability between presentations. Finally, individual differences exist to explain why an individual child may be more or less likely to respond to a given food with a disgust response. This proposal adds to the current debate relating to the motivations of ‘picky’ eating during early childhood and introduces an alternative to the proposal that these behaviours are the result of a child’s desire for autonomy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".