MétaCan
Menu
Back to cohort
Record W2799319595 · doi:10.3389/fnhum.2018.00174

Can Tai Chi and Qigong Postures Shape Our Mood? Toward an Embodied Cognition Framework for Mind-Body Research

2018· article· en· W2799319595 on OpenAlexaff
Kamila Osypiuk, Evan Thompson, Peter M. Wayne

Bibliographic record

VenueFrontiers in Human Neuroscience · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of British Columbia
FundersNational Center for Complementary and Integrative HealthNational Institutes of HealthOsher Center for Integrative Medicine
KeywordsEmbodied cognitionCognitionMoodPsychologyCognitive psychologyObservational studyAnxietyMind–body problemMental healthCognitive sciencePsychotherapistSocial psychologyMedicineNeuroscienceComputer sciencePsychiatryEpistemologyArtificial intelligence

Abstract

fetched live from OpenAlex

Dynamic and static body postures are a defining characteristic of mind-body practices such as Tai Chi and Qigong (TCQ). A growing body of evidence supports the hypothesis that TCQ may be beneficial for psychological health, including management and prevention of depression and anxiety. Although a variety of causal factors have been identified as potential mediators of such health benefits, physical posture, despite its visible prominence, has been largely overlooked. We hypothesize that body posture while standing and/or moving may be a key therapeutic element mediating the influence of TCQ on psychological health. In the present paper, we summarize existing experimental and observational evidence that suggests a bi-directional relationship between body posture and mental states. Drawing from embodied cognitive science, we provide a theoretical framework for further investigation into this interrelationship. We discuss the challenges involved in such an investigation and propose suggestions for future studies. Despite theoretical and practical challenges, we propose that the role of posture in mind-body exercises such as TCQ should be considered in future research.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.220
GPT teacher head0.513
Teacher spread0.294 · 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 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

Citations59
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Human NeuroscienceSame topicMental Health Research TopicsFrench-language works237,207