Motor imagery of joint action is shaped by assumed partner abilities and challenged by cognitive demands
Bibliographic record
Abstract
Motor imagery (MI) is a motor-cognitive process involving the mental rehearsal of movement without actual physical execution. When imagining joint actions, individuals must not only imagine their own movements but also integrate those with the imagined movements of a partner. Although previous research has shown that MI of joint action is possible, the field remains underexplored. The overarching purpose of this dissertation was to explore the social and cognitive dynamics of MI in joint action contexts. Specifically, this dissertation describes five experiments where participants performed and imagined performing a serial disc transfer task alone and with imagined partners of varying abilities. Imagined movement time (MT) was used to examine whether participants considered the assumed abilities of a partner when imagining performing the task, and to assess the cognitive demands involved in imagining the joint task. Five main conclusions were derived from this research: 1) individuals adjust their imagination of a partner’s movements based on the assumed motor abilities of the partner; 2) adjustments to an imagined partner’s movements influence the imagination of the imagers’ own movements; 3) most individuals are aware that their own imagined movements are affected by a partner’s perceived abilities, although these adjustments occur unintentionally; 4) individuals have difficulty controlling their own imagined movements, particularly when paired with a high-performing partner; and 5) imagining a serial joint action task is more cognitively demanding than imagining the same task performed alone. Overall, this research demonstrates that MI of a serial joint action task is cognitively demanding and can be adjusted to account for the assumed abilities of a partner, which in turn affects one’s ability to control their own imagined movements. These findings offer a novel contribution to the study of MI and joint action.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".