MétaCan
Menu
Back to cohort
Record W4386496733 · doi:10.1080/23268263.2023.2250242

A Focus on the Voice: Attention as a Unifying Mechanism Underlying Vocal Training and Mindfulness

2023· article· en· W4386496733 on OpenAlexaff
Shannon Blanchet, Alice Elizabeth Atkin

Bibliographic record

VenueVoice and Speech Review · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsMindfulnessMeditationPsychologyMechanism (biology)Empirical researchCognitive psychologyPsychotherapistEpistemologyHistory

Abstract

fetched live from OpenAlex

Recent decades have generated a proliferation of research into the phenomenon known as mindfulness. This literature gives voice and speech researchers an opportunity to reconceptualize and assess their practices. Voice and Speech Training (VST) for actors shares certain techniques and attitudes in common with mindfulness. Thus, it is plausible that these two practices rely on similar psychological and neurological mechanisms and may produce similar benefits. We provide an interdisciplinary review of the contemporary literature regarding vocal production, VST practices, mindfulness, and meditation, highlighting conceptual and practical connections between them. We propose that attention is a common mechanism that unifies VST practices and mindfulness. We provide an overview of how attention and mindfulness meditation have been conceptualized and summarize recent empirical studies on their possible relationship. Finally, we suggest future directions for how interdisciplinary research teams might investigate the relationship between VST, mindfulness, and attention, and bring empirical methodologies to bear on questions of artistic significance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.172
GPT teacher head0.356
Teacher spread0.184 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueVoice and Speech ReviewSame topicNeuroscience and Music PerceptionFrench-language works237,207