Neural correlates of body size estimation: A systematic review and narrative synthesis
Bibliographic record
Abstract
Inaccurate body size estimation (BSE), the discrepancy between an individual's actual and perceived body size and shape, is observed not only in clinical conditions like eating disorders and body dysmorphic disorder but also in healthy individuals. Understanding the neural mechanisms that support BSE is timely, given growing interest in perceptual biases and their potential relevance for identifying mechanisms that may be disrupted in clinical populations. However, the field has an incomplete understanding of brain systems functionally involved in BSE ability. To address this, we performed a systematic review, accompanied by a narrative synthesis, to identify brain regions associated with BSE across studies of healthy individuals. Studies using functional neuroimaging were selected if they elicited BSE with a task, contrasted BSE with a control task, and used whole-brain analyses (rather than being restricted to a priori regions of interest). This yielded a set of nine functional magnetic resonance imaging papers. There is relatively consistent involvement of ventral (fusiform/occipitotemporal regions) and dorsal (intraparietal areas) visual pathways, and discrete regions of the prefrontal cortex, suggesting recurring engagement of perceptual and higher-order cognitive systems during BSE. However, current knowledge is limited by the small number and heterogeneity of available studies. We identify both consistent and variable neural correlates of BSE, offering refined targets for future investigations of BSE in clinical populations. Based on these findings, we additionally provide specific suggestions for improving neuroimaging task design for future studies.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".