From Groups to Grits: Social Identity Shapes Evaluations of Food Pleasantness
Bibliographic record
Abstract
Throughout human history, food consumption has been deeply tied to cultural groups. Past models of food preference have assumed that social concerns are dissociated from basic appetitive qualities—such as tastiness—in food choice. Providing a counter to this notion, we tested and found support for the novel idea that social identities can shape the evaluation of food pleasantness. Specifically, individual differences in social identification (Study 1) as well as experimentally manipulated identity salience (Study 2) were associated with the anticipated tastiness of identity-relevant foods. We also found that identity salience influenced perceived food pleasantness during consumption (Study 3). These results suggest social identity may shape evaluations of food pleasantness, both through long-term motivational components of identification as well as short-term identity salience. Thus, the influence of social identity on cognition appears to extend beyond social evaluation, to hedonic experience. We discuss implications for theories of identity, decision-making, and food consumption.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".