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Paranoia and Bias in Social Belief Updating in Clinical High Risk for Psychosis

精神病临床高危人群的偏执和社会信仰偏见更新

基本信息

批准号：
10750091
负责人：
Trevor Ford Williams
金额：
$ 6.87万
依托单位：
NORTHWESTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10750091
关键词：
Accounting Affect Attenuated Automobile Driving Bayesian Modeling Behavioral Belief Biological Assay Brain Clinical Computer Models Control Groups Cues Data Data Analyses Delusions Development Disease Disease Progression Early identification Educational workshop Environment Feedback Food Funding Future Hypersensitivity Impairment Individual Intervention Interviewer Judgment Knowledge Learning Linear Models Link Literature Longitudinal Studies Measures Modeling National Institute of Mental Health Occupational Outcome Paranoia Participant Pathologic Processes Performance Persons Population Positioning Attribute Predictive Value Probability Probability Learning Process Psychological reinforcement Psychopathology Psychoses Psychotic Disorders Punishment Research Reversal Learning Rewards Risk Role Sampling Schizophrenia Sensory Social Change Social Environment Social Functioning Social Processes Social Reinforcement Specificity Symptoms Testing Time Training Uncertainty Universities Update Variant Volatilization Work career clinical high risk for psychosis clinical predictors common symptom disabling symptom experience functional outcomes information processing meetings preventive intervention programs psychosis risk psychotic symptoms skills social social bias social cognition social learning social relationships stem symposium theories

项目摘要

Project Summary Individuals at clinical high risk for psychosis (CHR) experience attenuated positive symptoms (e.g., delusions) and may later develop psychosis spectrum disorders (e.g., schizophrenia).1 Paranoia is a common and impairing psychotic symptom, also present in CHR individuals.2–5 Emerging mechanistic research using reinforcement learning task paradigms suggest that individuals with paranoia hold rigid prior beliefs about their environments being highly unpredictable and others being untrustworthy, which is in turn linked to reduced learning from actual environmental feedback that may challenge these beliefs.6–8 While these explanations hold promise for explaining a complicated phenomenon, they have not been tested in CHR individuals and a number of questions remain. For instance, there is debate within the literature as to whether general vs. social learning processes are most relevant to paranoia; however, existing tasks poorly examine this issue.9,10 Furthermore, previous research has focused mainly on one reinforcement learning paradigm (e.g., learning volatile probabilities) and paradigms measuring other learning processes relevant to delusions (e.g., inappropriate learning about blocked cues) may also be related to paranoia.11 Finally, the connection between reinforcement learning processes and symptom progression and functional outcomes (e.g., worsening) has not been examined. The proposed study will examine these gaps in the literature using samples of CHR individuals (N = 50), clinical controls (N = 50), and healthy controls (N = 50), with half being followed for one year. Using this data, the study aims to develop and validate two distinct socially-salient reinforcement learning tasks (Aim 1), compare these two distinct learning paradigms in terms of relevance to paranoia (Aim 2), examine the value of these two social learning tasks for predicting symptom progression (e.g., worsening paranoia) and social functioning decline, above and beyond non-social versions of these tasks (Aim 3). These aims and this project will make use of a larger, ongoing NIMH-funded project,12 which will provide data on 50 CHR individuals, as well as 50 clinical controls and 50 healthy controls. Training Plan: to complete this project and build an independent research career the applicant will (a) pursue in-depth knowledge of psychosis risk, development, delusion formation, social cognition, and learning theory, (b) learn to develop and validate dynamic behavioral tasks, and (c) develop longitudinal data analysis skills. This training will be accomplished through regular meetings with a team of expert scholars, coursework, workshops, and attending conferences. Training Environment: The training will take place at Northwestern University, which offers an excellent and collaborative training environment, which includes both the sponsor and the project’s data analysis consultant. The environment, alongside the proposed study and training plan, will position the applicant to launch a unique program of research and advance our understanding of social processes in psychopathology.

项目总结