A Psychophysiological Investigation of Mechanisms Underlying “Feeling Fat” in Women With and Without Binge Eating
Bibliographic record
Abstract
OBJECTIVE: "Feeling fat", the somatic experience of being overweight not fully explained by objective body weight, is considered to be an eating pathology maintenance factor. The traditional clinical understanding of "feeling fat" is based on the body displacement hypothesis, which suggests that negative emotions are projected onto the body and experienced as "feeling fat" in lieu of adaptive emotion regulation. A more recent theory suggests that "feeling fat" occurs in response to thought-shape fusion (TSF), a cognitive distortion in response to the imagined consumption of perceived fattening food. The present experimental study compared the roles of these two proposed mechanisms of "feeling fat" using self-report and psychophysiological measures. METHOD: Eighty-two women (41 with binge eating, 41 control participants) self-reported "feeling fat" and had their heart rate variability (HRV), a physiological index of emotion regulation, measured before and after imagined inductions. Participants imagined either a personalized negative affective experience or consuming a preferred, so-called 'fattening' food. RESULTS: The TSF induction increased self-reports of "feeling fat" among participants with binge eating but not among control women. The negative affect induction did not increase self-reported "feeling fat" in either group. HRV did not significantly change in response to either induction for either group. DISCUSSION: TSF may be a more potent precursor to "feeling fat" than negative affect for individuals with binge eating. This may suggest new treatment directions, such as cognitive defusion from TSF when patients experience "feeling fat." The utility of HRV in monitoring "feeling fat" is questionable.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".