"Inside out:" The action-specific effect of execution on imagination of continuous aiming movements
Bibliographic record
Abstract
The main principle of ideomotor theory is that neural codes for action are closely associated with neural codes for perception. These kinds of associations are built through experience with a given action-effect pairing. In action imagination, conceiving of an action's effects can activate the neural code for that action and facilitate internal motor simulation. Recent work has supported this notion; a stronger association between action and effect codes, acquired through experience, produces imagined movement times (MTs) that are more similar to actual execution MTs. The purpose of the current study was to determine if this effect of experience transfers to movement contexts that are similar to, but are not actually the movement contexts that were experienced. To this end, participants imagined themselves executing a continuous tapping task before and after execution of the task. Critically, the indexes of difficulty (IDs) experienced were manipulated such that participants were asked to imagine movements at levels of difficulty they did not experience. The critical finding was that execution experience at IDs above and below (ID = 2, 4) those that were non-executed (ID = 3) lead to a decrease in imagined MTs closer to actual MTs, whereas experience at lower IDs (ID = 2, 3, 4) did not lead to a decrease in imaged MT for IDs that were beyond those experienced (ID = 5-6). This result can be accounted for by differences in movement patterns between movements at lower and higher IDs, suggesting that experience-based action-effect binding is specific to a given action.Acknowledgments: This research was supported by grants from the Natural Sciences and Engineering Research Council and the Ontario Ministry of Research and Innovation.
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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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".