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Related Concept Videos

Personality Disorders: Paranoid and Schizoid01:22

Personality Disorders: Paranoid and Schizoid

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Personality disorders represent enduring cognition, affect, and behavior patterns that significantly deviate from societal norms. These maladaptive traits often lead to difficulties in various domains, including interpersonal relationships, occupational settings, and overall psychological well-being. Paranoid personality disorder and schizoid personality disorder are two distinct conditions marked by odd or eccentric behavior.
Paranoid Personality Disorder
Paranoid personality disorder is...
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A Temporal Network Approach to Paranoia: A Pilot Study.

Alba Contreras1, Carmen Valiente1, Alexandre Heeren2,3

  • 1Department of Personality, Assessment and Clinical Psychology, Complutense University of Madrid, Madrid, Spain.

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Understanding paranoia dynamics is key. Feeling close to others can reduce paranoia, while sadness and low self-esteem may increase it, highlighting targets for mental health interventions.

Keywords:
experience sampling methodologyintensive time-seriesparanoiasadnessself-esteemtemporal network analysis

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Area of Science:

  • Psychology
  • Psychopathology
  • Network Science

Background:

  • Paranoid beliefs are central to schizophrenia and other mental disorders.
  • Dynamic, temporal aspects of paranoia mechanisms are crucial for understanding severity.
  • Previous research often focused on static levels rather than temporal interactions.

Purpose of the Study:

  • To conceptualize paranoia as a system of interacting psychological elements.
  • To investigate the temporal dynamics between core paranoia-related mechanisms.
  • To explore potential targets for paranoia prevention and treatment.

Main Methods:

  • Utilized temporal network analysis and a multilevel vector autoregressive (mlVAR) model.
  • Collected time-series data from 23 participants via experience sampling methodology (ESM).
  • Computed contemporaneous, temporal, and between-subject network models to analyze paranoia mechanisms.

Main Results:

  • Paranoia and sadness co-occurred within the same time frame.
  • Sadness was linked to low self-esteem and lack of social closeness.
  • Increased feelings of closeness to others predicted lower paranoia in subsequent time frames.

Conclusions:

  • Network analysis provides a data-driven method to study paranoia's temporal fluctuations.
  • Understanding these dynamic interactions can help determine paranoia severity.
  • This approach offers novel insights for developing interventions for paranoia-related issues.