Examining Body Surveillance and Motor Adaptation through Biometric Sensors
Bibliographic record
Abstract
Body surveillance negatively influences motor and cognitive performance outcomes that are integral to sport contexts, however, the mechanisms that explain these associations are not well-elucidated. Given the theoretical implications of arousal and emotion as factors affected by body surveillance (a manifestation of self-objectification and a habitual self-monitoring of the body) which has been found to influence motor performance and adaptation, the current quasi-experimental study tested the impact of a body surveillance manipulation on motor performance (Aim #1) and arousal and expressed emotion (Aim #2). Also, the combined impact of body surveillance on arousal and emotion was examined as impacting motor performance and adaptation (Aim #3), controlling for trait body surveillance. Participants (n = 73; Mage = 22.3 years) completed pre-questionnaires on measures of trait body surveillance and completed a visuo-motor performance and adaptation task. Body-focused measurements were taken as the body surveillance manipulation during the visuo-motor task, and galvanic skin response (GSR; biosensor indicator of arousal) and facial expression analysis (FEA; biosensor indicator of expressed emotion) were measured throughout the experiment. Findings demonstrated significant impacts of body surveillance manipulation on movement time and radial error, arousal, various expressed emotions [joy, sadness, disgust, confusion, sentimentality, engagement, attention], and negative and positive expressed emotional valence [valence describes the overall emotional tone in response to stimuli, and ranges from high (positive emotions) to low (negative emotions)]. Generally, the findings indicated heightened arousal and emotional distress during the manipulation. When controlling for trait body surveillance, participants with lower negative affect/arousal had significantly less radial error, although no difference was observed in movement time. Overall, the current study highlights the complex dynamics between body surveillance, motor performance and adaptation, arousal, and emotional responses. Further research is needed to understand the ecological replication and validity of these findings in contexts such as sport and exercise.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".