Changes in the perceived size of the body following exposure to distorted self-body images
Bibliographic record
Abstract
Inaccurate perceptions, such as under- or over-estimation of body size are often found in clinical eating disorder populations but have recently been shown also in healthy people. However, it is not yet clear how body size perception may be affected when the internal body representation is manipulated. In this study, visual adaptation was used to investigate whether exposure to distorted visual feedback alters the representation of body size and how long any such effects might last. Participants were exposed for five minutes to a distorted life-size image of themselves that was either 20% wider or 20% narrower than their normal size. Accuracy was measured using our novel psychophysical method that taps into the implicit body representation. The accuracy of the representation was assessed at 6, 12 and 18 min following exposure to adaptation. Altered visual feedback caused changes in participants' judgements of their body size: adapting to a wider body resulted in size overestimation whereas underestimations occurred after adapting to a narrower body. These distortions lasted throughout testing and did not fully return back to normal within 18 min. The results are discussed in terms of the emerging literature indicating that the internal representation of the body is dynamic and flexible.
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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 | 0.003 |
| 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) | 0.003 | 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".