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A generative framework for the study of delusions.

Tore Erdmann1, Christoph Mathys2

  • 1Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy.

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|March 2, 2021
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Summary
This summary is machine-generated.

This study introduces a computational model for understanding delusional thinking. The framework generates belief updates similar to delusions by creating overly precise, compartmentalized explanations, even when faced with ambiguity.

Keywords:
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Area of Science:

  • Computational psychiatry
  • Cognitive neuroscience
  • Artificial intelligence

Background:

  • Delusional information processing is common in mental health conditions and daily life.
  • Formal characterizations of these inferential processes are currently lacking.

Purpose of the Study:

  • To propose a generative computational framework for modeling belief updates in delusions.
  • To characterize the aberrant inferential processes underlying delusional ideation.

Main Methods:

  • Developed a generative framework using a Dirichlet process mixture model.
  • Implemented a sampling-based Bayesian inference algorithm in a virtual agent.
  • Simulated the generation of belief updates under varying parameter settings.

Main Results:

  • The model preferentially generates highly precise, compartmentalized explanations, mimicking delusional inferences.
  • Ambiguous situations can trigger the formation of these explanations, seeding delusional ideation.
  • Overly precise explanations hinder the revision of established beliefs when integrating new information.

Conclusions:

  • The proposed model provides a computational basis for studying delusions.
  • The framework aligns with Bayesian inference and hierarchical predictive coding principles.
  • This work advances the characterization of aberrant inferential processes in psychopathology.