Observing or simulating? It depends on prior exposure
Bibliographic record
Abstract
We have shown that observers learn to move in novel, adapted, visuomotor environment after watching a learning or expert model. Although this is an effective practice technique, in none of our experiments (and no observers) have we shown after-effects following observation (that is, negative carry-over effects from being in a perturbed environment when knowingly transferring back to normal conditions). This is despite the fact that actors consistently show strong effects. These findings suggest that observational practice does not engage the motor system or a simulation-type network and that there has been no updating of an internal model of their visual-motor environment. Arguably, for this to occur, the motor capabilities would already need to be part of the observer's motor repertoire. To test this we trained a group of observers (n=6) to move in a 30 degree rotated environment in an early adaptation phase. We then tested for after-effects and washed out any carry-over effects. Participants then watched 150 trials of an actor performing in the same environment and immediately following we again tested for after-effects. All observers showed some evidence of after-effects, although the two initial participants had issues with washout and the instructions (and only weak effects were seen in 2). Although further testing is needed, including control conditions where the observation period is removed, these data support the idea that observation is affected by prior experience and that after-effects can be evidenced in observers, but only if they have had prior motor experience with the task environment.Acknowledgments: This research is supported by NSERC (Hodges)
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".