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Record W2951086238

Is between limb response planning similar to planning an action between people

2012· article· en· W2951086238 on OpenAlexaffabout
Matthew Ray, Maxie Richman, Timothy N. Welsh

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2012
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTask (project management)PsychologyMotor planningAction (physics)Object (grammar)Plan (archaeology)Representation (politics)Social psychologyCognitive psychologyCommunicationComputer scienceArtificial intelligenceEngineeringGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Research on response selection during sequential joint action tasks has shown that individuals plan their actions to aid their co-actors portion of the task. Action co-representation is thought to be the underlying cognitive process that allows co-actors to facilitate each others actions. In essence, it is thought that, by representing each others actions, people plan joint tasks as if they were performing the task themselves. To test this hypothesis, the present study was designed to determine if people adopt similar response planning strategies when they pass objects to co-actors and when they pass objects between their own limbs. Participants performed the following three tasks: 1) pick up a jug of water and pour a glass of water; 2) pick up a jug with one hand and pass it to the other hand and then pour a glass of water; 3) pick up a jug with one hand and pass it to a confederate, who then poured the glass of water. If individuals plan joint actions through co-representation and coding the co-actor as an extension of themselves, then individuals should pass objects in a similar way between their own limbs and between other individuals. In contrast to this prediction, the results indicate that individuals adopted different strategies when passing to their own limb and when passing to the co-actor. It appears that object properties, such as jug diameter, may have influenced response planning strategies to a greater degree in this task.Acknowledgments: NSERC, Ontario Ministry of Research and Innovation

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.062
GPT teacher head0.340
Teacher spread0.278 · 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.

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

Citations0
Published2012
Admission routes2
Has abstractyes

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