MétaCan
Menu
Back to cohort
Record W3176499316 · doi:10.1038/s42003-021-02319-3

Joint control of visually guided actions involves concordant increases in behavioural and neural coupling

2021· article· en· W3176499316 on OpenAlexaff
David R. Painter, Jeffrey J. Kim, Angela Renton, Jason B. Mattingley

Bibliographic record

VenueCommunications Biology · 2021
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsCanadian Institute for Advanced Research
FundersAustralian Research Council
KeywordsJoint attentionPsychologyCognitionCognitive psychologyJoint (building)Interpersonal communicationControl (management)Motor coordinationNeuroscienceComputer scienceCommunicationArtificial intelligenceDevelopmental psychologyAutismEngineering

Abstract

fetched live from OpenAlex

It is often necessary for individuals to coordinate their actions with others. In the real world, joint actions rely on the direct observation of co-actors and rhythmic cues. But how are joint actions coordinated when such cues are unavailable? To address this question, we recorded brain activity while pairs of participants guided a cursor to a target either individually (solo control) or together with a partner (joint control) from whom they were physically and visibly separated. Behavioural patterns revealed that joint action involved real-time coordination between co-actors and improved accuracy for the lower performing co-actor. Concurrent neural recordings and eye tracking revealed that joint control affected cognitive processing across multiple stages. Joint control involved increases in both behavioural and neural coupling - both quantified as interpersonal correlations - peaking at action completion. Correspondingly, a neural offset response acted as a mechanism for and marker of interpersonal neural coupling, underpinning successful joint actions.

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)0.000
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 score0.071
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.219
GPT teacher head0.410
Teacher spread0.192 · 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

Citations21
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCommunications BiologySame topicAction Observation and SynchronizationFrench-language works237,207