The role of associative learning in healthy and sustainable food evaluations: An event-related potential study
Bibliographic record
Abstract
Individuals in industrialized societies frequently include processed foods in their diet. However, overconsumption of heavily processed foods leads to imbalanced calorie intakes as well as negative health consequences and environmental impacts. In the present study, normal-weight healthy individuals were recruited in order to test whether associative learning (Evaluative Conditioning, EC) could strengthen the association between food-types (minimally processed and heavily processed foods) and concepts (e.g., healthiness), and whether these changes would be reflected at the implicit associations, at the explicit ratings and in behavioral choices. A Semantic Congruency task (SC) during electroencephalography recordings was used to examine the neural signature of newly acquired associations between foods and concepts. The accuracy after EC towards minimally processed food (MP-food) in the SC task significantly increased, indicating strengthened associations between MP-food and the concept of healthiness through EC. At the neural level, a more negative amplitude of the N400 waveform, which reflects semantic incongruency, was shown in response to MP-foods paired with the concept of unhealthiness in proximity of the dorsal lateral prefrontal cortex (DLPFC). This implied the possible role of the left DLPFC in changing food representations by integrating stimuli's features with existing food-relevant information. Finally, the N400 effect was modulated by individuals' attentional impulsivity as well as restrained eating behavior.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".