S75. PERFORMANCE IN WORKING MEMORY AND EXECUTIVE FUNCTIONS AS IT RELATES TO THEORY OF MIND ABILITIES IN A NONCLINICAL SAMPLE OF INDIVIDUALS WITH SCHIZOTYPAL TRAITS
Bibliographic record
Abstract
Abstract Background In recent decades, research in the treatment of schizophrenia has shifted to early detection and intervention. Unfortunately, the development of psychosis is still poorly understood, making such an endeavour more challenging. Cognitive models of psychosis suggest that neurocognitive deficits place an individual at greater risk of developing metacognitive deficits. Such deficits in metacognition have been shown to contribute to the development of positive symptomatology. A large body of literature supports that patients with schizophrenia exhibit impairments across nearly all domains of neurocognition, as well as metacognition. Theory of mind (ToM) is one of the most widely studied components of metacognition, which includes both cognitive (i.e., understanding what another person is thinking) and affective (i.e., understanding what another person is feeling) processes. Research indicates patients with schizophrenia demonstrate deficits in cognitive and affective ToM, and these deficits are associated with delusional symptomatology. If ToM is involved in the development of positive symptoms, it is expected that this deficit would be present prior to the onset of a first episode psychosis. It is unclear from current research findings if this is the case, however. Additionally, research examining the role of neurocognition as it relates to ToM is lacking. While some research has examined these variables in clinically-high-risk (CHR) groups, little research has examined nonclinical samples at risk for psychosis. Thus, this study sought to examine the relationship between ToM and neurocognition in a nonclinical sample with schizotypal traits, as research suggests these individuals may be at risk of developing a psychotic illness. It was hypothesized that lower performance in working memory and executive functioning would be related to poorer performance in cognitive and affective ToM, which would subsequently be associated with subsyndromal delusions. It was further predicted that schizotypal traits would moderate the relationship between neurocognitive performance and ToM abilities. Methods Undergraduate students (N = 99) completed self-report measures of personality and psychosocial functioning, including the Schizotypal Personality Questionnaire, Beck Depression Inventory-II, Launay-Slade Hallucination Scale-Revised, and 21-Item Peters Delusions Inventory. Participants also completed the Neuropsychological Assessment Battery Screening Module, which is a screening measure for neurocognitive dysfunction. Finally, they completed the Recognition of Faux Pas Test, a task-based measure that evaluates cognitive and affective ToM. Results Data collection is complete, and the data will be analysed using partial least squares structural equation modeling. This is a regression-based path analysis designed for exploratory models. This statistical method is better able to handle non-normally distributed data and smaller sample sizes when compared to covariance-based structural equation modeling. Discussion Study findings will be discussed in the context of cognitive models for the development of psychosis. The ways in which these findings, and cognitive models more broadly, can facilitate early detection of schizophrenia will be discussed, along with how such models can be used to inform psychosocial interventions for the illness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".