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Record W3196191772 · doi:10.1002/dev.22180

Longitudinal investigation of shyness and physiological vulnerability: Moderating influences of attention biases to threat and safety

2021· article· en· W3196191772 on OpenAlexafffund
Raha Hassan, Louis A. Schmidt

Bibliographic record

VenueDevelopmental Psychobiology · 2021
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsShynessPsychologyPsychopathologyVulnerability (computing)PsychosocialDevelopmental psychologyClinical psychologyAnxietyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Shyness has long been identified as a vulnerability factor to developing psychosocial problems, but there is heterogeneity in these observed outcomes. One potential factor underlying these relations is individual differences in threat sensitivity. Using a longitudinal design, we examined whether attentional biases toward social threat and safety measured during adulthood moderated the association between shyness measured in emerging adulthood ( N = 83, n female = 48; M age = 23.56 years, SD age = 1.09 years) and frontal electroencephalogram (EEG) asymmetry at rest, a physiological index of vulnerability to psychopathology, measured nearly a decade later in adulthood ( M age = 31.68 years, SD age = 2.27 years). We found that only biases to threat moderated the association between shyness and resting frontal EEG asymmetry longitudinally. In individuals who displayed relative vigilance to social threat, shyness was associated with greater relative right frontal EEG activity at rest (i.e., increased physiological vulnerability). These findings suggest that attentional biases to threat may play a role in understanding the relation between shyness and some known physiological vulnerabilities to psychopathology in adults.

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 score0.098
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.124
GPT teacher head0.372
Teacher spread0.247 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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