Observational learning of a mixed model is subject to contextual interference effects
Bibliographic record
Abstract
The evidence that a portion of the neurophysiological processes that are involved in performing an action are also involved during observation of that action (Higuchi et al., 2012) supports the idea that skill learning is enhanced by skill observation (Hayes et al., 2010). Recent research on this phenomenon indicates that skill learning through observation is optimized when the observation includes a combination of expert and novice models (Rohbanford and Proteau, 2011). The experts demonstrate blueprints of the task, while novices demonstrate the mistakes that may occur. In this study we explored whether mixed-model observational learning is subject to contextual interference effects. Contextual interference refers to manipulations of cognitive events during learning that facilitate skill retention (Lee and Magill, 1983). Three groups of participants engaged in an observational learning study of a precision bi-manual endoscopic task, which involved picking up a bean with a grasper implement, transferring it to another, and placing it into a small pot all within the confines of simulated minimal access surgical environment. Skill practice involved sets of physical practice (3 blocks; 6 trials/block) that were interspersed with sets of observational practice (4 blocks; 10 trials/block). The first group’s (Blocked; n=15) observational sets were organized such that the first 2 blocks consisted of expert content followed by 2 blocks of novice content. The observational sets for the second group (Semi-Interleaved; n=15) were organized to include interleaving expert and novice content that alternated every 5 attempts, within each set. The third group’s (Interleaved; n=15) observational set consisted of expert and novice content alternating after each attempt. All three groups werer counterbalanced. All participants performed post-practice, retention, and transfer tests. Preliminary analyses indicate that organizing mixed-model observational practice in an interleaved order elicits increased transfer of skill learning (F1,18= 4.34, p=0.052). Acknowledgments: SIM-one, Ontario Simulation Network
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".