Imagine all the people! Can we account for the assumed motor abilities of other people when imagining performing joint actions?
Bibliographic record
Abstract
Imagining performing joint actions requires one to integrate one’s own imagined actions with the imagined actions of another person. In two experiments, we investigated motor imagery of joint action to determine if and how imagery processes are adapted based on the assumed motor capabilities of the partner. Participants imagined performing a joint serial transfer task (moving 4 discs to a peg) quickly and accurately with an imagined partner. Participants imagined transferring the first 2 discs in the sequence onto the target peg themselves and then imagined the partner transferring the last 2 discs. The description of the imagined partner (high vs low performer) was manipulated to determine if participants adapt their imagination based on the partner’s characteristics. Results revealed imagined MTs for the whole sequence were shorter when the description of the partner gave the impression of a ‘high’ performer compared to when the description was of a ‘low’ performer or when no description of the partner was provided. Results further revealed participants not only adjusted the imagined MTs of the partner’s portion of the task, but of their own portion of the task as well. That is, imagined MTs of the first 2 disc transfers were shorter when imagining performing the task with a high performer than with a low performer or when no description of the partner was provided. These findings suggest participants are able to adapt their imagination to the assumed capabilities of their partner, but that these adjustments also affected the imagination of their own movements.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".