On the Relationship Between Execution, Perception, and Imagination of Action.
Bibliographic record
Abstract
Humans are able to perceive, imagine, and execute movement. Studies investigating these abilities typically employ variants of the Fitts aiming task because Fitts’ equation captures the way in which movement time (MT) is adapted to maintain accuracy as movement amplitude and target width change. In separate studies, it has been demonstrated that the Fitts relationship is present in movement execution (Fitts, 1954), perception (Grosjean et al., 2007), and imagination (Decety & Jeannerod, 1996). The consistent emergence of Fitt’s relationship in all three tasks has lead the researchers to suggest that the core process underlying imagination and perception is a motor simulation process where the response codes used in execution are activated offline. The present study was designed to test this hypothesis by being the first study to assess the characteristics of the Fitts relationship for movement execution, perception, and imagination within the same group of individuals. Participants were asked to imagine and perceive reciprocal aiming movements at varying Indices of Difficulty (Fitts, 1954) before and after performing the movements. If response code-based motor simulation is the core process of action imagination and perception, then: 1) the characteristics of the regression lines should be similar across different tasks; and 2) the performance of the movement task should increase the accuracy of action perception and imagination. Consistent with these predictions, the analyses revealed that the Fitts’ relationship held across all conditions and that there were no differences in slopes across conditions. Importantly, the y-intercept of the lines for imagined MTs was significantly closer to the y-intercept for the execution MTs in the Post-Execution condition. A non-significant trend toward a Post-Execution improvement in action perception was also noted. Overall, the results support the notion that action perception, and imagination have an underlying common response code-based motor simulation process. Meeting abstract presented at VSS 2012
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".