Task-relevant and task-irrelevant choices differentially impact error estimation and motor learning
Bibliographic record
Abstract
Learning is enhanced when learners exercise choice over task-relevant features (e.g., feedback schedule) compared to when the opportunity for choice is denied. Lewthwaite et al. (2015) showed that learning is also enhanced with choice over aspects irrelevant to the to-be-learned motor task (e.g., ball colour [Exp 1] and artwork selection [Exp 2]). Lewthwaite and colleagues argued that such choices could not be used in a way to benefit task-related processes and therefore, the learning benefits from choice must be motivational in nature. However, other researchers have provided evidence that task-related processes such as error estimation play a role in the learning benefits associated with choice; which makes one question the extent to which choice over task-relevant features compares to choice over irrelevant features. These results extend from Carter et al. (2016) by investigating how different levels of choice affected error estimation and motor learning. Participants practiced a waveform matching task in one of three choice groups: Task-Relevant (feedback schedule), Task-Irrelevant (colour of arm wrap and game to play once the experiment was over), and No-Choice. The Task-Irrelevant and No-Choice groups were matched to the feedback schedule of a participant in the Task-Relevant group. Results showed that the Task-Relevant group demonstrated superior retention and transfer performance, as well as superior error estimation abilities in transfer (P's < .05). Contrary to the motivation-based conclusions of Lewthwaite and colleagues, our findings suggest that task-relevant choices are more effective for learning than task-irrelevant choices, presumably due to the acquisition of accurate error estimation abilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".