Motor adaptation to environment changes predicting object behaviour can be flexible and implicit
Bibliographic record
Abstract
Abstract Human motor behaviour can adapt in response to perturbations in the environment, either through updating existing motor control models or by creating context-specific motor memories or strategies. Context informed motor adaptation can allow for flexible motor behaviour in changing environments, albeit with costs associated with action selection. While our dynamic natural environments necessitates flexible motor behaviour, many studies of motor control and motor learning limit their focus to model-based motor adaptation. In this study, we investigate if motor adaptation is flexible when a perturbation is applied to either the acceleration of a rolling ball, or to the throw direction at release during a virtual throw-to-target task. We also determine if the tendency for model updating is influenced by immersive and informative visual cues indicating the presence of a perturbation, such as the slant of a surface on which thrown objects travel. Despite the visual slant allowing for more rapid performance change when adapting to both perturbation scenarios, our findings reveal that perturbations resembling accelerations enabled flexible motor adaptation regardless of the presence of the slant cue. Perturbations in the throw direction conversely predominantly led to internal model updating. Additionally, informative visual slant properties of the task surface elicited implicit, slant-specific changes in performance. Our findings underscore the role of visual properties of both perturbations and environments in flexible motor learning.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".