Understanding Bipolar Disorder: A Developmental Psychopathology Perspective
Bibliographic record
Abstract
Research has shown that body size judgements are frequently biased, or inaccurate. Critically, judgement biases are further exaggerated for individuals with eating disorders, a finding that has been attributed to difficulties integrating body features into a perceptual whole. However, current understanding of <i>which</i> body features are integrated when judging body size is lacking. In this study, we examine whether individuals integrate three-dimensional (3D) cues to body volume when making body size judgements. Computer-generated body stimuli were presented in a 3D Virtual Reality environment. Participants (<i>N</i> = 412) were randomly assigned to one of the two conditions: in one condition, the to-be-judged body was displayed binocularly (containing 3D cues to body volume); in the other, bodies were presented monocularly (two-dimensional [2D] cues only). Across 150 trials, participants were required to make a body size judgement of a target female body from a third-person point of view using an unmarked visual analogue scale (VAS). It was found that 3D cues significantly influenced body size judgements. Namely, thin 3D bodies were judged <i>smaller</i>, and overweight 3D bodies were judged <i>larger</i>, than their 2D counterpart. Furthermore, to reconcile these effects, we present evidence that the two perceptual biases, regression to the mean and serial dependence, were reduced by the additional 3D feature information. Our findings increase our understanding of how body size is perceptually encoded and creates testable predictions for clinical populations exhibiting integration difficulties.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".