Abstract

Paranoia is increasingly recognized as a multidimensional psychological phenomenon influenced by trauma-related distress, shame, maladaptive interpersonal experiences, and emotional functioning. Although these factors have been extensively associated with paranoia, their relationships with neurocognitive functioning, social cognition, and global functioning remain less well understood. The present study examined these relationships using traditional statistical analyses, network analysis, and machine-learning approaches in a non-clinical sample of 42 adults.

Participants completed self-report measures assessing trauma-related distress, childhood interpersonal experiences, shame, paranoia, depressive symptoms, fear of negative evaluation, self-esteem, and hostile attribution bias, in addition to a comprehensive neurocognitive battery, an emotion recognition task, and clinician-rated measures of global role and social functioning. Pearson correlations, hierarchical multiple regression analyses, network analysis, and supervised machine-learning models with Minimum Redundancy Maximum Relevance (MRMR) feature selection were conducted.

Results indicated that trauma-related distress, maladaptive interpersonal schemas, and paranoia demonstrated limited associations with objective neurocognitive functioning but were significantly associated with social cognition and global functioning. Emotional variables, particularly shame, depressive symptoms, and maladaptive interpersonal schemas,

demonstrated stronger relationships with functional outcomes than with broad neurocognitive performance. Network analysis identified a highly interconnected psychological network linking emotional, cognitive, and functional domains, whereas machine-learning analyses showed that integrated models combining emotional, cognitive, and interpersonal variables provided the most accurate prediction of global role and social functioning.

Overall, the findings support a multidimensional conceptualization of paranoia and psychosocial functioning in which emotional, cognitive, and interpersonal processes interact within an integrated psychological system. The study further demonstrates the value of combining traditional statistical analyses, network analysis, and machine-learning techniques to improve understanding and prediction of psychological functioning across the psychosis continuum.

Date of publication

Summer 2026

Document Type

Dissertation (Local Only Access)

Language

english

Persistent identifier

http://hdl.handle.net/10950/5138

Committee members

Dr. Dennis Combs; Dr. Premananda Indic; Dr. Sarah Sass; Dr. Eric Stocks

Degree

Clinical Psychology, Ph.D.

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