Gender-specific effects of self-objectification on visuomotor adaptation and learning
Bibliographic record
Abstract
Self-objectification can influence cognitive and motor task performance by causing resources to be reallocated towards monitoring the body. The present study investigated effects of recalling positive or negative body-related experiences on visuomotor adaptation in women and men. Moderating effects of positive and negative affect were also explored. Participants (100 women, 47 men) were randomly assigned to complete a narrative writing task focused on body-related pride or embarrassment before performing a visuomotor adaptation (cursor rotation) task. A retention test of the visuomotor task was completed after 24 h. Men in the embarrassment group were more impacted by the initial cursor rotation (in movement time and accuracy) than the pride group and showed poorer retention of movement time. Women in the embarrassment group were less accurate than the pride group following initial rotation. In women only, affect modulated the effects of the negative recalled scenario. Further analysis indicated that the differences between embarrassment and pride groups remained in a subset of participants (34 women, 28 men) who explicitly referred to their own movement within their recalled scenarios. These results demonstrate that recalling body-related self-conscious emotions can impact visuomotor adaptation and learning in both women and men, but effects may differ between genders.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".