Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

Theoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degre...

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Main Authors: Heiner Stuke, Hannes Stuke, Veith Andreas Weilnhammer, Katharina Schmack
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC5249047?pdf=render
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spelling doaj-2fecadb7dee1494ba08a436dafe8af392020-11-25T01:46:02ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-01-01131e100532810.1371/journal.pcbi.1005328Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.Heiner StukeHannes StukeVeith Andreas WeilnhammerKatharina SchmackTheoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors). Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.http://europepmc.org/articles/PMC5249047?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Heiner Stuke
Hannes Stuke
Veith Andreas Weilnhammer
Katharina Schmack
spellingShingle Heiner Stuke
Hannes Stuke
Veith Andreas Weilnhammer
Katharina Schmack
Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
PLoS Computational Biology
author_facet Heiner Stuke
Hannes Stuke
Veith Andreas Weilnhammer
Katharina Schmack
author_sort Heiner Stuke
title Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
title_short Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
title_full Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
title_fullStr Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
title_full_unstemmed Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
title_sort psychotic experiences and overhasty inferences are related to maladaptive learning.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2017-01-01
description Theoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors). Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.
url http://europepmc.org/articles/PMC5249047?pdf=render
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