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Record W2743686749 · doi:10.1123/mc.2016-0064

End-State Comfort Across the Lifespan: A Cross-Sectional Investigation of How Movement Context Influences Motor Planning in an Overturned Glass Task

2017· article· en· W2743686749 on OpenAlexaff
Sara M. Scharoun Benson, David A. Gonzalez, Éric Roy, Pamela J. Bryden

Bibliographic record

VenueMotor Control · 2017
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsWilfrid Laurier UniversityUniversity of WaterlooUniversity of Windsor
Fundersnot available
KeywordsContext (archaeology)Movement (music)Task (project management)Motor planningPhysical medicine and rehabilitationPsychologyCognitive psychologyMedicineEngineeringGeographyPhysics

Abstract

fetched live from OpenAlex

Young adults plan actions in advance to minimize the cost of movement. This is exemplified by the end-state comfort (ESC) effect. A pattern of improvement in ESC in children is linked to the development of cognitive control processes, and decline in older adults is attributed to cognitive decline. This study used a cross-sectional design to examine how movement context (pantomime, demonstration with image/glass as a guide, actual grasping) influences between-hand differences in ESC planning. Children (5- to 12-year-olds), young adults, and two groups of older adults (aged 60-70, and aged 71 and older) were assessed. Findings provide evidence for adult-like patterns of ESC in 8-year-olds. Results are attributed to improvements in proprioceptive acuity and proficiency in generating and implementing internal representations of action. For older adults early in the aging process, sensitivity to ESC did not differ from young adults. However, with increasing age, differences reflect challenges in motor planning with increases in cognitive demand, similar to previous work. Findings have implications for understanding lifespan motor behavior.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.050
GPT teacher head0.312
Teacher spread0.262 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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