Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training

TY - JOUR

T1 - Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training

AU - Hauke, D J

AU - Roth, V

AU - Karvelis, P

AU - Adams, R A

AU - Moritz, S

AU - Borgwardt, S

AU - Diaconescu, A O

AU - Andreou, C

N1 - © The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

PY - 2022/6/21

Y1 - 2022/6/21

N2 - BACKGROUND AND HYPOTHESIS: In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions.STUDY DESIGN: We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task-the fish task-with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual's behavior, could predict treatment response to Metacognitive Training using machine learning.STUDY RESULTS: We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level.CONCLUSIONS: Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders.

AB - BACKGROUND AND HYPOTHESIS: In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions.STUDY DESIGN: We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task-the fish task-with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual's behavior, could predict treatment response to Metacognitive Training using machine learning.STUDY RESULTS: We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level.CONCLUSIONS: Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders.

KW - Delusions/psychology

KW - Humans

KW - Metacognition

KW - Problem Solving

KW - Psychotic Disorders/complications

U2 - 10.1093/schbul/sbac029

DO - 10.1093/schbul/sbac029

M3 - SCORING: Journal article

C2 - 35639557

VL - 48

SP - 826

EP - 838

JO - SCHIZOPHRENIA BULL

JF - SCHIZOPHRENIA BULL

SN - 0586-7614

IS - 4

ER -