Early Influences on Development of Sensory Perception and Eating Habits
Bibliographic record
Abstract
Infancy and early childhood are important periods for the development of food choices and eating preferences that are tracked into adult life, influencing weight gain, body composition, and metabolism and ultimately affecting the balance between health and disease. In this narrative review, we discuss studies focused on the effects of fetal programming and early food experiences, highlighting recent advances in the discovery of factors that contribute to the development of food preferences and eating behavior. Food preference can be influenced by early direct contact with flavors, textures, and aromas, as well as by environmental adversities during early development. Evidence suggests that exposure to intrauterine growth restriction is associated with increased preferences for highly palatable foods, such as those rich in carbohydrates and fats, over the life course. Early flavor experiences, whether from amniotic fluid or human milk, may also shape the development of food preferences. Finally, children are more likely to accept textures that they are able to manipulate, and early exposure to a range of textures facilitates the acceptance of foods of various textures later on. Improving dietary habits during gestation (fetal) and postnatal periods is of critical importance for the establishment of positive eating habits and healthy growth in infants and should be an important focus of primary prevention efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".