Self-control of KR during acquisition does not always influence performance in retention, dual task, and transfer tests
Bibliographic record
Abstract
This study examined the influence of participants' control over their own knowledge of results (KR) schedule during acquisition on tests of learning. Acquisition consisted of 90 trials performing a 5-key sequence with a goal time of 2550ms. Participants in the self-control group chose whether or not to receive KR (time in ms) following each trial. In the other group, each participant was yoked to a self-control participant, and followed the KR schedule chosen by that counterpart. Three tests of learning were administered 10m and again 24h post-practice: a retention test (the task performed in acquisition), a dual-task test (the task performed in acquisition while concurrently performing a tone recognition and counting task), and a transfer test (a key-pressing sequence with a novel timing goal). No KR was provided for any of the learning tests. Those in the self-control group were predicted to 1) perform better on the transfer tests, indicative of a better-developed schema and 2) perform worse on the dual-task tests, indicative of greater use of working memory during acquisition than the yoked group. Performance on block nine was improved significantly from block one of acquisition. Test effects were present in tests of learning with participants producing significantly less error in retention than dual-task and transfer tests. No group differences were seen in acquisition or in tests of learning. These results suggest that neither practice condition created a more favourable training environment for developing a schema from which to extrapolate novel performances. The results suggest that both groups likely learned under explicit processes as both groups decreased in performance from retention to dual-task tests. The lack of group differences, replicating previous work may be due to differences in chosen KR schedules or the inclusion of the dual-task test.Acknowledgments: This study was supported by NSERC
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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) | 0.000 | 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) | 0.002 | 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".