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Record W4224251963 · doi:10.1098/rsos.210722

Changes in the perceived size of the body following exposure to distorted self-body images

2022· article· en· W4224251963 on OpenAlexaff
Sarah D’Amour, Deborah Alexe, Laurence R. Harris

Bibliographic record

VenueRoyal Society Open Science · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsYork University
Fundersnot available
KeywordsRepresentation (politics)PerceptionAdaptation (eye)PsychologyCognitive psychologyStatisticsAudiologyMathematicsMedicineNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.321
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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