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Record W2987001868 · doi:10.1037/abn0000490

Jumping to social conclusions?: The implications of early and uninformed social judgements in first episode psychosis.

2019· article· en· W2987001868 on OpenAlexaff
Michael Grossman, Christopher R. Bowie

Bibliographic record

VenueJournal of Abnormal Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologySocial cognitionPsychosisPsycINFOCognitionSocial perceptionCognitive biasSocial skillsDevelopmental psychologyInterpersonal communicationSocial cognitive theoryCognitive psychologyPerceptionClinical psychologySocial psychologyPsychiatryMEDLINENeuroscience

Abstract

fetched live from OpenAlex

= 35) were presented with a modified version of the Interpersonal Perception Task in which video clips of naturalistic social scenarios were paused at 3 predetermined time points. All participants were prompted to answer a series of questions during these time points to examine the processes by which individuals arrive at social judgments. A JTC response pattern was defined as endorsing overconfident responses and a low need for additional social information at the beginning time points of the video clips when limited social cues were available. Compared with controls, a greater proportion of patients exhibited a response pattern suggestive of JTC, which was also strongly associated with poorer overall task accuracy, regardless of group status. Results from this study provide evidence that overconfidence in premature and uninformed social judgments has direct consequences for the accurate processing of social information. Furthermore, this response pattern, which was more characteristic of early psychosis patients, may represent JTC in real-world social contexts, and could be an important therapeutic target for social cognition in the early stages of illness. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.001
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 score0.094
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.460
Teacher spread0.386 · 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
Published2019
Admission routes1
Has abstractyes

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