Implicit and explicit safety evaluation of foods: The importance of food processing
Bibliographic record
Abstract
Identifying beneficial foods in the environment, while avoiding ingesting something toxic is a crucial task humans face on a daily basis. Here we directly examined adults’ implicit and explicit safety evaluations of the same foods presented with different degrees of processing, ranging from unprocessed (raw) to processed (cut or cooked). Moreover, we investigated whether individual characteristics (e.g., Body Mass Index, food neophobia and hunger) modulated their evaluations. We hypothesized that adults would associate the processed form of a food with safety more than its unprocessed form since processing techniques, which are ubiquitously applied in different cultures, often reduce the toxicity of foods, and signal previous human intervention and intended consumption. Adults (N = 109, 43 females) performed an implicit Go/No-Go association task (GNAT) online, assessing the association between safety attributes and food images differing on their degree of processing; both unfamiliar and familiar foods were used. Then, each food was explicitly evaluated. Results revealed that individual characteristics affected both implicit and explicit evaluations. Individuals with overweight and obesity had a strong and positive implicit association between processed foods and safety attributes, but explicitly rated cooked foods as the least safe overall, this latter result was found in highly neophobic individuals as well. Yet, at the explicit level, when looking at unfamiliar foods only, processed foods were rated safer than unprocessed foods by all participants. Our results are the first evidence that directly highlights the relevance of the degree of processing in food safety evaluation and suggest that thinking of the important tasks humans face regarding food selection enriches our understanding of food behaviors.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".