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Record W2561500337 · doi:10.3389/fpsyg.2016.02039

Joint Action: Mental Representations, Shared Information and General Mechanisms for Coordinating with Others

2017· review· en· W2561500337 on OpenAlexaff
Cordula Vesper, Ekaterina Abramova, Judith Bütepage, Francesca Ciardo, Benjamin Philip Crossey, Alfred O. Effenberg, Dayana Hristova, April Karlinsky, Luke Sebanz McEllin, Sari R. R. Nijssen, Laura Schmitz, Basil Wahn

Bibliographic record

VenueFrontiers in Psychology · 2017
Typereview
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsAction (physics)Task (project management)Context (archaeology)PsychologyJoint (building)Mental representationPhenomenonCognitive psychologyCognitive scienceHuman–computer interactionComputer scienceCognitionEpistemologyNeuroscience

Abstract

fetched live from OpenAlex

In joint action, multiple people coordinate their actions to perform a task together. This often requires precise temporal and spatial coordination. How do co-actors achieve this? How do they coordinate their actions toward a shared task goal? Here, we provide an overview of the mental representations involved in joint action, discuss how co-actors share sensorimotor information and what general mechanisms support coordination with others. By deliberately extending the review to aspects such as the cultural context in which a joint action takes place, we pay tribute to the complex and variable nature of this social phenomenon.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.003
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.153
GPT teacher head0.439
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations194
Published2017
Admission routes1
Has abstractyes

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