Computational Models of Learning Mechanisms in Psychosis
Computational Models of Learning Mechanisms in Psychosis
Psychotic experiences may best be described as an alteration in the self-ascription of thoughts and actions, which is associated with a profoundly altered experience of oneself and the surrounding world. Computational models of key symptoms of psychiatric disorders are discussed with respect to the attribution of salience and self-relatedness to otherwise irrelevant stimuli and the role of top-down modelling in the generation of delusions. Top-down and bottom-up approaches in understanding mental disorders and their computational models are compared and critically reflected.
Keywords: Psychotic experiences, Self-ascription, Computational models, Psychiatric disorders, Stimuli, Top-down modelling
MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
Please, subscribe or login to access full text content.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.