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Record W4297200332 · doi:10.1016/j.dib.2022.108630

Dataset of physiological, behavioral, and self-report measures from a group decision-making lab study

2022· article· en· W4297200332 on OpenAlexaff
Alon Burns, Sebastian Wallot, Yair Berson, Ilanit Gordon

Bibliographic record

VenueData in Brief · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsMcMaster University
FundersDeutsche Forschungsgemeinschaft
KeywordsTask (project management)PsychologyData collectionApplied psychologyAnxietyTraitSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This paper presents data from a study conducted in twenty groups of three participants each. Data were collected from sixty participants during a lab visit which was video recorded from several angles. Upon arrival to the lab and following informed consent, participants were told that they would be a part of a group decision-making task and were given instructions for a procedure titled "the desert survival task" Lafferty and Pond (1974). Participants were then connected to several electrodes on their upper body and palm for the collection of their electrocardiogram, respiration and electrodermal activity throughout the group task. Participants then performed the task together. The collection of physiological data from all group members was conducted simultaneously and in synchrony with the video recording. The video recordings of the group interactions were later coded by trained psychology students for positive affective behaviors made by participants (smiling and laughing) throughout the group task. Self-report measures (trait anxiety and social phobia) were collected prior to the group task from all participants. This multimodal dataset thus integrates behavioral, self-report, and physiological measures from group members, which are important for understanding group dynamics. These data will allow verification, replications, and additional analyses of the data from new perspectives.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.222
GPT teacher head0.499
Teacher spread0.277 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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