Self-control of augmented information: Evidence for a learning disadvantage?
Bibliographic record
Abstract
The learning advantages associated with a learner controlled practice context has extended our understanding of the cognitive processes underlying skill acquisition. Recently, learners controlling the availability of task-related information either before (proactive) or after (retroactive) a motor trial demonstrated equivalent learning (Patterson & Lee, 2010). Yet, recent motor learning theory suggests learning is expedited in practice contexts where the cognitive processes of the learner are continually challenged. Thus, we examined whether allowing learners the choice to either receive task information proactively or retroactively for a particular trial (termed the "hybrid" condition) would facilitate superior learning compared to those participants controlling task information only proactively or only retroactively during skill acquisition. Participants practiced 18 key-pressing sequences, ranging between 2 and 4 key presses in response to a motor prime. The dependent variable of interest was motor recall success (RS) of the key pressing patterns. The hybrid condition requested task information on 92% of the acquisition trials compared to the retroactive (67%) and proactive (66%) conditions. The results of the retention test showed the proactive (M=0.58) and retroactive (M=0.65) conditions demonstrated greater proportion RS compared to the hybrid condition (M=0.31). The RS of the hybrid condition in the retention test were opposite to our prediction, suggesting the amount of choice provided to these participants undermined the advantages of a learner defined practice context. The theoretical and practical implications of these findings in reference to motor skill acquisition will be discussed.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".