The influence of mood on the jumping to conclusions bias in individuals with schizotypal traits: an experience sampling paradigm
Bibliographic record
Abstract
The jumping to conclusions bias (JTC) refers to making a decision before collecting a sufficient amount of information to warrant doing so. Very little research has been conducted on the ways in which mood influences JTC in schizophrenia and healthy individuals along the continuum of risk for psychosis. It was hypothesized that elevations in schizotypal traits will be associated with greater JTC, and that negative affect will moderate the relationship between schizotypal traits and JTC. 100 undergraduate students enrolled at the University of Toronto Scarborough (UTSC) were recruited for this study. The study employed an experience-sampling approach. Positive affect demonstrated a small positive relationship to JTC, meaning that as an individual's positive affect increased so too did their JTC tendency, regardless of their elevations on schizotypal traits. While a significant negative relationship was found between schizotypal traits and JTC, the effect size was negligible, which may highlight the need for effort testing in undergraduate populations and evaluating the sensitivity of experimental tasks to increase data quality. Overall, identifying the influence of mood on metacognition is critical in determining how JTC functions within the illness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".