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Record W2152720338 · doi:10.1080/00222895.2015.1089833

Continuous Cognitive Tasks Improve Postural Control Compared to Discrete Cognitive Tasks

2015· article· en· W2152720338 on OpenAlexaff
Yves Lajoie, Natalie Richer, Deborah A. Jehu, Ylan Tran

Bibliographic record

VenueJournal of Motor Behavior · 2015
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCenter of pressure (fluid mechanics)CognitionPhysical medicine and rehabilitationPsychologyControl (management)Elementary cognitive taskForce platformTask (project management)Cognitive psychologyComputer scienceMedicineArtificial intelligenceNeuroscienceEngineering

Abstract

fetched live from OpenAlex

Research suggests that postural control synergies are sensitive to cognitive manipulations; however, the impact of different types of cognitive tasks on postural control remains inconclusive. The authors examined the effect of discrete and continuous tasks on postural control. Sixteen healthy young adults (M age = 22.7 ± 2.2 years) stood with feet together on a force platform while performing randomly assigned discrete and continuous cognitive tasks. Results demonstrated marked improvements in the area of 95% confidence ellipse and the standard deviation of the center of pressure in the anterior-posterior and medial-lateral directions for continuous compared to discrete tasks. This reinforces the notion that continuous tasks are sufficient in providing less opportunity to consciously attend to postural control, thereby facilitating automatic postural control.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.041
GPT teacher head0.387
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2015
Admission routes1
Has abstractyes

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