Computational Perspectives on Cognition in Anorexia Nervosa: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anorexia nervosa (AN) is a severe eating disorder, marked by persistent changes in behaviour, cognition and neural activity that result in insufficient body weight. Recently, there has been a growing interest in using computational approaches to understand the cognitive mechanisms that underlie AN symptoms, such as persistent weight loss behaviours, rigid rules around food and preoccupation with body size. Our aim was to systematically review progress in this emerging field. Based on articles selected using systematic and reproducible criteria, we identified five current themes in the computational study of AN: 1) reinforcement learning; 2) value-based decision-making; 3) goal-directed and habitual control over behaviour; 4) cognitive flexibility; and 5) theory-based accounts. In addition to describing and appraising the insights from each of these areas, we highlight methodological considerations for the field and outline promising future directions to establish the clinical relevance of (neuro)computational changes in AN.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it