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Record W3136133012 · doi:10.1016/j.actpsy.2021.103291

Coordination effort in joint action is reflected in pupil size

2021· article· en· W3136133012 on OpenAlexafffund
Basil Wahn, Veera Ruuskanen, Alan Kingstone, Sebastiaan Mathôt

Bibliographic record

VenueActa Psychologica · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsTask (project management)Pupil sizePupilDivision of labourControl (management)Action (physics)Joint (building)PsychologyObject (grammar)Computer scienceCognitive psychologyPupil diameterDivision (mathematics)Eye trackingHuman–computer interactionArtificial intelligenceEngineeringMathematics

Abstract

fetched live from OpenAlex

Humans often perform visual tasks together, and when doing so, they tend to devise division of labor strategies to share the load. Implementing such strategies, however, is effortful as co-actors need to coordinate their actions. We tested if pupil size - a physiological correlate of mental effort - can detect such a coordination effort in a multiple object tracking task (MOT). Participants performed the MOT task jointly with a computer partner and either devised a division of labor strategy (main experiment) or the labor division was already pre-determined (control experiment). We observed that pupil sizes increase relative to performing the MOT task alone in the main experiment while this is not the case in the control experiment. These findings suggest that pupil size can detect a rise in coordination effort, extending the view that pupil size indexes mental effort across a wide range of cognitively demanding tasks.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.289
GPT teacher head0.438
Teacher spread0.150 · 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 designBench or experimental
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

Citations9
Published2021
Admission routes2
Has abstractyes

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