Evidence against the automaticity of motor simulation in action prediction: Separately acquired visual-motor and visual representations can be used flexibly to aid in prediction accuracy
Bibliographic record
Abstract
We have shown that individuals with visual-motor experience exhibit interference from secondary motor tasks when making perceptual decisions. This is consistent with the motor simulation hypothesis of action prediction. Because of the interference, despite concurrent accrual of visual and motor experiences, this simulation appears to be automatic. However, it has been suggested that we can acquire separate (visual)motor and purely visual representations that allow for accuracy in the presence of motor interference. Therefore, we trained people (N=16 to date) to develop separate (visual)motor and visual representations. "Motor" practice involved learning to throw darts at 3 sections of a dartboard. "Visual" practice involved matching occluded throws with landing outcomes, on video. One group (Motor>Vis) underwent motor practice on day1 and visual on day2. The Vis>Motor group did the reverse. Prediction tasks (judging outcomes from occluded clips) were performed by both groups before and after practice each day, and some trials involved secondary motor tasks (pushing on a force gauge). Consistent with previous work, the Motor>Vis group improved prediction accuracy on day1, except when performing the secondary task. After visual practice on day2, prediction accuracy was maintained, but now there was no interference. The Vis>Motor group, after day1 visual training, showed a similar improvement in prediction accuracy, with no decrement under secondary task conditions. After motor training on day2, accuracy was maintained, without interference. These data suggest that motor simulation is not automatic, and that separately-acquired motor and visual experiences allow flexibility in adopting prediction strategies most robust to external task demands.Acknowledgments: The last author would like to acknowledge NSERC for financial support of this research
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".