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Record W2905208754 · doi:10.1002/dev.21813

Valence matters: An electrophysiological study on how emotions influence cognitive performance in children

2018· article· en· W2905208754 on OpenAlexafffund
Aishah Abdul Rahman, Sandra A. Wiebe

Bibliographic record

VenueDevelopmental Psychobiology · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCognitionEvent-related potentialValence (chemistry)Negativity effectDevelopmental psychologyCognitive psychologyNegativity biasElectrophysiologyAudiologyNeuroscience

Abstract

fetched live from OpenAlex

Emotional stimuli have been found to influence cognitive performance in children, but it is not clear whether this effect varies with the cognitive demands of the task. In this study, we examined how emotional expressions influenced cognitive performance and event-related potentials (ERPs) in early and middle childhood under varying cognitive control demands. Two groups of children (4.5-6.0 and 7.0-8.5 years) completed a modified flanker task where the stimuli were faces displaying task-irrelevant emotional expressions. Emotional influence varied depending on emotional valence: Accuracy was greater for happy targets, while response time and N2 latency were longer for angry targets. In younger children only, angry targets elicited a larger late frontal negativity. Cognitive control demands did not modulate the effect of emotions on behavioral performance or ERPs, contrasting with findings in adults. Findings are discussed in relation to the dual competition model and previous work demonstrating a positivity bias in children.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.096
GPT teacher head0.364
Teacher spread0.269 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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