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An earned presence: studying the effect of multi-task improvisation systems on cognitive and learning capacity

2017· article· en· W6977289323 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2017
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsnot available
Fundersnot available
KeywordsEmbodied cognitionCognitionProcess (computing)ImprovisationSet (abstract data type)Test (biology)Experiential learningCognitive robotics

Abstract

fetched live from OpenAlex

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: <i>Active participation in performance generating systems has a positive effect on advanced student performers’ working memory capacity, executive functions, and learning</i>. 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.121
GPT teacher head0.369
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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