Evidence for dual mechanisms of action prediction dependent on acquired visual-motor experiences.
Bibliographic record
Abstract
To test mechanisms underpinning action prediction, we directly controlled experience in a dart-throwing training study. A motor-visual group physically practiced throwing darts and a perceptual training group learned to associate dart throw actions (occluded video clips) with landing outcomes. A final control group did not practice. Accuracy was assessed on related prediction tests before and after practice (involving temporally occluded video clips). These tests were performed while additionally performing simple, action-incongruent secondary motor tasks with either the right (observed throwing arm) or left effector, in addition to an attention control task. Motor proficiency tests were also performed. Although both trained groups improved their prediction accuracy after training, only the motor-visual group showed interference associated with the right-arm secondary motor task after practice. No interference was shown for the left-arm motor task. These effects were evidenced regardless of whether predictions were made in response to video stimuli or static clips. Moreover, improvements on the motor proficiency test were only shown for the motor-visual group. These results show evidence in support of motor simulation processes during action prediction among observers with motor experience. Prediction accuracy can be achieved via nonmotor processes (for the perceptual group), but there was no evidence that physically experienced performers could effectively switch processes to maintain prediction accuracy. (PsycINFO Database Record
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".