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Record W2085917997 · doi:10.3200/gntp.167.4.443-462

Negative Affectivity Predicts Individual Differences in Decision Making for Preschoolers

2007· article· en· W2085917997 on OpenAlexaff
Nancy Garon, Chris Moore

Bibliographic record

VenueThe Journal of Genetic Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsNegative affectivityPsychologyTemperamentSadnessExtraversion and introversionAngerDevelopmental psychologyAssociation (psychology)Positive affectivitySocial psychologyPersonalityClinical psychologyBig Five personality traits

Abstract

fetched live from OpenAlex

The authors' goal in conducting this study was to explore the association between temperament and future-oriented decision making. Forty-three preschoolers (mean age = 51 months) were given a child variant of the Iowa Gambling Task (IGT) and asked to choose between a deck with higher immediate rewards and a deck with higher future rewards. Children who were higher on the Extraversion/Surgency factor of the Child Behavior Questionnaire chose more frequently from the higher immediate rewards deck early in the game. The externalizing dimension of Negative Affectivity (anger/frustration, soothability and discomfort) made the greatest contribution to prediction of performance in the last block of the game. Children who were more easily frustrated and had difficulty regulating negative emotions chose more from the deck with higher immediate rewards. There was a significant interaction between the externalizing dimension of Negative Affectivity, the internalizing dimension of Negative Affectivity (sadness and fear) and Extraversion/Surgency on the last block. These results suggest a complex association between IGT performance and temperament in preschoolers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.379

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 science0.0000.000
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 head0.042
GPT teacher head0.352
Teacher spread0.310 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

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