Rapid motor adaptation to bounce perturbations in online Pong game is independent from the visual tilt of the bouncing surface
Bibliographic record
Abstract
Abstract Motor adaptation describes the ability of the motor system to counteract repeated perturbations in order to reduce movement errors. Most research in the field investigated adaptation in response to perturbations affecting the moving hand. Fewer studies looked at the effect of a perturbation applied to the movement target, however they used simplistic visual stimuli. In this study, we examined motor adaptation to perturbations affecting the motion of dynamic targets. In addition, we asked whether external visual cues in the environment could facilitate this process. To do so, participants were asked to play an online version of the Pong game in which they intercepted a ball bouncing off a wall using a paddle. A perturbation was applied to alter the post-bounce trajectory of the ball and the wall orientation was manipulated to be consistent or not with the ball trajectory. The “trained tilt” group (n = 34) adapted to the consistent condition and the “trained horizontal” group (n = 36) adapted to the inconsistent condition. In case participants optimally integrate external visual cues, the “trained tilt” group is expected to exhibit faster and/or more complete adaptation than the “trained horizontal” group. We found that the perturbation reduced interception accuracy. Participants showed large interception errors when the perturbation was introduced, followed by rapid error decrease and aftereffects (errors in the opposite direction) once the perturbation was removed. Although both experimental groups showed these typical markers of motor adaptation, we did not find differences in interception success rates or errors between the “trained tilt” and “trained horizontal” groups. Our results demonstrate that participants quickly adapted to the dynamics of the pong ball. However, the visual tilt of the bouncing surface did not enhance their performance. The present study highlights the ability of the motor system to adapt to external perturbations applied to a moving target in a more dynamical environment and in online settings. These findings underline the prospects of further research on sensorimotor adaptation to unexpected changes in the environment using more naturalistic and complex real-world or virtual reality tasks as well as gamified paradigms.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".