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Record W2036144318 · doi:10.1097/wnr.0b013e32832d0a67

Functional neuroimaging of mentalizing during the trust game in social anxiety disorder

2009· article· en· W2036144318 on OpenAlexaff
Chandra Sehkar Sripada, Mike Angstadt, Sarah Banks, Pradeep J. Nathan, Israel Liberzon, K. Luan Phan

Bibliographic record

VenueNeuroreport · 2009
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsMontreal Neurological Institute and Hospital
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsMentalizationPsychologySocial anxietyFunctional magnetic resonance imagingPrefrontal cortexAnxietyNeuroimagingSocial cognitionCognitionNeurosciencePerceptionSocial inhibitionCognitive psychologyPsychiatry

Abstract

fetched live from OpenAlex

Individuals with generalized social anxiety disorder tend to make overly negative and distorted predictions about social events, which enhance perceptions of threat and contribute to excessive anxiety in social situations. Here, we coupled functional magnetic resonance imaging and a multiround economic exchange game ('trust game') to probe mentalizing, the social-cognitive ability to attribute mental states to others. Relative to interactions with a computer, those with human partners ('mentalizing') elicited less activation of medial prefrontal cortex in generalized social anxiety patients compared with matched healthy control participants. Diminished medial prefrontal cortex function may play a role in the social-cognitive pathophysiology of social anxiety.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.032
GPT teacher head0.327
Teacher spread0.295 · 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

Citations124
Published2009
Admission routes1
Has abstractyes

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