MétaCan
Menu
Back to cohort
Record W2034119223 · doi:10.1080/02699930541000110

Enhanced accuracy of mental state decoding in dysphoric college students

2005· article· en· W2034119223 on OpenAlexaff
Kate L. Harkness, Mark A. Sabbagh, Jill A. Jacobson, Neeta Chowdrey, Tina Chen

Bibliographic record

VenueCognition & Emotion · 2005
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyDysphoriaValence (chemistry)Theory of mindSocial anxietyCognitionAnxietySocial cognitionCognitive psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

A significant clinical feature of depression involves difficulties in social functioning. At the foundation of these difficulties may lie alterations in “theory of mind” reasoning—the ability to decode others' mental states. Participants included 124 undergraduates who participated in a theory of mind task that involved attributing emotion states (e.g., happy, embarrassed) to photographs of eyes. Across two studies, dysphoria was significantly positively associated with greater accuracy on this task, suggesting an increased sensitivity to the subtle social cues required to make theory of mind judgements. This association held regardless of the emotional valence of the judgement. Furthermore, this finding was robust after controlling for reaction time and level of anxiety. These findings are discussed in terms of their implications for developing a model of social cognition in depression.

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)0.007
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.371
Teacher spread0.336 · 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

Citations245
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCognition & EmotionSame topicAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesFrench-language works237,207