An earned presence: studying the effect of multi-task improvisation systems on cognitive and learning capacity
Bibliographic record
Abstract
In this article, we articulate preliminary insights from two pilot studies. These studies contribute to an ongoing process of developing empirical, cross-disciplinary measures to understand the cognitive and learning effects of complex artistic practices – effects that we situate between theory of embodied concepts and conceptually calibrated physical attention and action. The stage of this process that we report on here was led by the cognitive performance studies scholar and dramaturge, Pil Hansen, and undertaken in collaboration with the experimental psychologist, Vina Goghari, and the behavioural economist, Robert Oxoby, assisted by four research assistants from Drama, Music, and Psychology at the University of Calgary. Our team set out to test the following hypothesis: Active participation in performance generating systems has a positive effect on advanced student performers’ working memory capacity, executive functions, and learning. Our results have implications, in particular, for understandings of embodied learning in the educational sector, however a perhaps more significant contribution is a better understanding of the measures and constructs needed to arrive at a more complex, yet operational concept of embodied learning and forward the experimental study of relationships between performing arts practices, cognition, and learning.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".