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Record W4401048263 · doi:10.31234/osf.io/x76fk

Sense of Agency during Group Control

2024· preprint· en· W4401048263 on OpenAlexaff
Carl Michael Galang, Emiel Cracco, Valerii Chirkov, Sukhvinder S. Obhi, Marcel Braß

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAgency (philosophy)Sense of agencySense (electronics)Social psychologyControl (management)PsychologyGroup (periodic table)Work (physics)SociologyComputer scienceArtificial intelligenceEngineeringSocial science

Abstract

fetched live from OpenAlex

Previous work has shown that the sense of agency increases when commanding other people. However, such work has primarily been limited to dyads, and little is known about how the sense of agency changes when the number of followers increases. Furthermore, it is unclear if commanding social agents vs. mere physical events changes one's sense of agency. Three experiments, involving making virtual agents clap their hands and/or streetlamps turning on, explore this topic. All three experiments reveal a robust linear increase in explicit agency judgments with follower count. Interestingly, experiments 2 and 3 show that this effect is amplified with human-like avatars (relative to streetlamps), suggesting that there may be something special about commanding a group of human-like social agents. This research provides further insight to our understanding of the sense of agency in group dynamics.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.248
Teacher spread0.224 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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