Feedback Effects on Learning a Novel Bimanual Coordination Pattern: Support for the Guidance Hypothesis
Bibliographic record
Abstract
The authors tested specificity and the guidance hypothesis by examining the effects of continuous or discrete concurrent feedback during acquisition, retention, and transfer of a 90 degrees phase offset bimanual coordination pattern. The authors tested both groups immediately following acquisition, 1 week later under retention conditions (i.e., identical feedback as acquisition) and under transfer conditions with a change in feedback (i.e., discrete to continuous or vice versa) and with no feedback. Acquisition results revealed superior performance by the continuous feedback group. However, during immediate transfer conditions, the continuous group showed decreased pattern accuracy and stability, whereas the discrete group improved its performance. These results support the guidance hypothesis, as the participants with a high amount of feedback in acquisition became reliant on the feedback and could not adapt their learning to a new situation. These results also show partial support for the specificity of practice hypothesis as the continuous group was only able to mimic their acquisition performance under identical conditions as practice. Practice specificity effects were not found for the discrete group, as performance was not negatively affected with a change or removal of afferent information.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".