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Record W2966642003 · doi:10.1177/0165025419865620

Age-group differences in beginning-state comfort reveal an increase in motor planning capabilities

2019· article· en· W2966642003 on OpenAlexafffund
Sara M. Scharoun Benson, Pamela J. Bryden, Éric Roy

Bibliographic record

VenueInternational Journal of Behavioral Development · 2019
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsWilfrid Laurier UniversityUniversity of WindsorUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyAffordanceObject (grammar)Action (physics)GRASPCognitive psychologyMotor planningHammerDevelopmental psychologySocial psychologyCommunicationArtificial intelligenceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Objects can be grasped in different ways to ensure a movement plan is aligned with the intended action. The current study assessed grasp posture in joint action object manipulation in children (ages 6–11, n = 68), young adults ( n = 21), and older adults ( n = 23). Participants performed two actions (pickup and pass; pickup and pass for use) within two movement contexts (using a dowel as if it were the actual object; actual object use), using two objects (glass and hammer) that differed in use-dependent experience. Beginning-state comfort (prioritizing a comfortable initial hand posture for an object recipient) was assessed. Taken together, findings support the notion that the ability to anticipate the intended action, and thus consider an action partner in one’s action plan, increases with age. With age and use-dependent experience, it can be argued that there is a shift from stimulus-driven, familiar responses, to considering affordances and task demands. Together, findings add to our understanding of changes in motor planning capabilities across the life span.

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)0.000
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 score0.016
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.356
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

Citations2
Published2019
Admission routes2
Has abstractyes

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