"I Can Only Imagine": Effect of Task-Specific Execution on Accuracy of Imagined Aiming Movements
Bibliographic record
Abstract
According to ideomotor theory, the codes that represent action and the perceptual consequences of those actions are tightly bound in a common code. It is thought that these common codes are not only used for efficient action selection and execution, but also during the imagination and perception of action. For action imagination, bound action and perceptual codes are thought to be internally activated at a sub-threshold level. In support of this hypothesis, recent research has shown that the accuracy of action imagination increased following experience executing the task. Specifically, Wong et al. (2013) observed that movement times (MTs) in imagined reciprocal aiming movements were closer to actual execution MTs after the participants gained experience completing the aiming movements. This increased accuracy was suggested to occur because the binding and refining of the common codes occurs through training and/or experience with an action and its perceptual consequences. The current study was conducted to examine the task-specific nature of the effects of experience on imagination (i.e., if improvements in accuracy of action imagination occur only with experience of the reciprocal aiming task or with any aiming task). To this end, participants were divided into two groups. One group executed a reciprocal pointing task while the second group executed a discrete aiming task with comparable accuracy requirements. Influence of task specificity on imagination performance was assessed by evaluating the changes in imagination MTs pre- and post-execution. Consistent with earlier findings, there was an overall change in imagined MTs following task execution. Of greater theoretical relevance, there were no reliable between-group differences in the pre/post-execution changes in MT. Therefore, it appears that the imagination of aiming movements is affected by experience with speed-accuracy demands regardless of the specific context of that experience. Meeting abstract presented at VSS 2014
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".