Visuomotor associations facilitate movement preparation.
Bibliographic record
Abstract
Many motor skills require rapidly choosing a movement goal and preparing a movement to that goal, such as in sports where circumstances often change quickly and many actions are possible. Humans can benefit from learning the perceptual cues that predict the requirements of movement so that the choice of a movement goal and movement preparation can occur earlier. However, there remains uncertainty about how these perceptual cues are learned. Here we investigate the use and learning of these perceptual-motor associations. First, we ask if episodic memory for associations can support learning. In Experiment 1, participants first memorized associations between symbols and movement goals. When these symbols were subsequently presented as cues, reaching movements were prepared as efficiently as if the goals themselves were previewed, without the need for additional practice. Next, we ask whether statistical learning can be used to learn the associations. In Experiment 2, participants had to learn the associations during the movement task itself. This learning enabled efficient movement preparation, and the rate of improvement scaled with the number and complexity of associations. These findings suggest that movement preparation can be facilitated by perceptual cues via statistical learning and memory recall, highlighting a potential role for learning and memory systems not conventionally implicated in motor behavior. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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 teacher head, 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".