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Record W2220898940 · doi:10.3389/fpsyg.2015.01375

Cognitive synergy in groups and group-to-individual transfer of decision-making competencies

2015· article· en· W2220898940 on OpenAlexaff
Petru Lucian Curșeu, Nicoleta Meslec, Helen Pluut, Gerardus J. M. Lucas

Bibliographic record

VenueFrontiers in Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsSimon Fraser University
FundersConsiliul National al Cercetarii StiintificeUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiAutoritatea Natională pentru Cercetare Stiintifică
KeywordsPsychologyGroup (periodic table)Group decision-makingCognitionCognitive psychologySocial psychologyChemistryNeuroscience

Abstract

fetched live from OpenAlex

In a field study (148 participants organized in 38 groups) we tested the effect of group synergy and one's position in relation to the collaborative zone of proximal development (CZPD) on the change of individual decision-making competencies. We used two parallel sets of decision tasks reported in previous research to test rationality and we evaluated individual decision-making competencies in the pre-group and post-group conditions as well as group rationality (as an emergent group level phenomenon). We used multilevel modeling to analyze the data and the results showed that members of synergetic groups had a higher cognitive gain as compared to members of non-synergetic groups, while highly rational members (members above the CZPD) had lower cognitive gains compared to less rational group members (members situated below the CZPD). These insights extend the literature on group-to-individual transfer of learning and have important practical implications as they show that group dynamics influence the development of individual decision-making competencies.

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)0.005
metaresearch head score (Gemma)0.027
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.027
GPT teacher head0.329
Teacher spread0.301 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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