Characteristic impairments of goal-directed and habitual control in bulimia nervosa
Bibliographic record
Abstract
Abstract The relationship between clinical eating disorder symptom severity and balance of model-based (MB) and model-free (MF) control is unclear, and these traits’ predictive capacity is untested in this population. In 25 healthy controls (HCs), 25 subjects with binge eating disorder (BED), and 25 subjects with bulimia nervosa (BN), we show an inverse relationship between symptom severity and MB (though not MF) control. However, trial-by-trial behavioural data discriminated BN from other groups (area under receiver operating characteristic curve of 0.78; 95% CI=0.64–0.92) based primarily on impaired MF control. Our data—including analyses of reaction time and theory-driven computational modeling—support the hypothesis that among pathological binge eating groups, BN may be characterized by impaired value function learning. Our results suggest that trial-by-trial analysis of behavioural data may provide unique insights into the BN phenotype, which may thus be computationally distinct from the related disorder of BED and the HC state.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".