Individuals Higher in Eating Restraint Show Heightened Physiological Arousal to Food Images
Bibliographic record
Abstract
Individuals higher in eating restraint report feeling ambivalent (i.e., both positive and negative) about food, regardless of whether it is perceived to be unhealthy or healthy (Norris, Do, Close & Deswert, 2019). Given that ambivalence is thought to be a highly unpleasant, unstable, and arousing state, we sought in the current study to examine whether individuals higher in eating restraint show enhanced physiological arousal toward food (but not nonfood) images. Replicating our earlier findings (Norris et al., 2019), individuals higher in eating restraint exhibited more ambivalence towards both unhealthy and healthy food (but not nonfood) images than did those lower in eating restraint. Importantly, skin conductance reactivity (SCR) toward both unhealthy and healthy food images was greater for individuals higher in eating restraint than those lower in eating restraint; there were no group differences for nonfood images. Furthermore, eating restraint scores were positively correlated with SCR toward both unhealthy and healthy food images, suggesting that more extreme restraint is associated with stronger physiological arousal. Together, our results suggest that individuals higher in eating restraint experience more ambivalence and enhanced physiological arousal toward food images regardless of their perceived health value. Implications for treating individuals with eating disorders are discussed.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".