Cargando…

Explaining distortions in metacognition with an attractor network model of decision uncertainty

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as d...

Descripción completa

Detalles Bibliográficos
Autores principales: Atiya, Nadim A. A., Huys, Quentin J. M., Dolan, Raymond J., Fleming, Stephen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341696/
https://www.ncbi.nlm.nih.gov/pubmed/34310613
http://dx.doi.org/10.1371/journal.pcbi.1009201
_version_ 1783733962310418432
author Atiya, Nadim A. A.
Huys, Quentin J. M.
Dolan, Raymond J.
Fleming, Stephen M.
author_facet Atiya, Nadim A. A.
Huys, Quentin J. M.
Dolan, Raymond J.
Fleming, Stephen M.
author_sort Atiya, Nadim A. A.
collection PubMed
description Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.
format Online
Article
Text
id pubmed-8341696
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-83416962021-08-06 Explaining distortions in metacognition with an attractor network model of decision uncertainty Atiya, Nadim A. A. Huys, Quentin J. M. Dolan, Raymond J. Fleming, Stephen M. PLoS Comput Biol Research Article Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible. Public Library of Science 2021-07-26 /pmc/articles/PMC8341696/ /pubmed/34310613 http://dx.doi.org/10.1371/journal.pcbi.1009201 Text en © 2021 Atiya et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Atiya, Nadim A. A.
Huys, Quentin J. M.
Dolan, Raymond J.
Fleming, Stephen M.
Explaining distortions in metacognition with an attractor network model of decision uncertainty
title Explaining distortions in metacognition with an attractor network model of decision uncertainty
title_full Explaining distortions in metacognition with an attractor network model of decision uncertainty
title_fullStr Explaining distortions in metacognition with an attractor network model of decision uncertainty
title_full_unstemmed Explaining distortions in metacognition with an attractor network model of decision uncertainty
title_short Explaining distortions in metacognition with an attractor network model of decision uncertainty
title_sort explaining distortions in metacognition with an attractor network model of decision uncertainty
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341696/
https://www.ncbi.nlm.nih.gov/pubmed/34310613
http://dx.doi.org/10.1371/journal.pcbi.1009201
work_keys_str_mv AT atiyanadimaa explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty
AT huysquentinjm explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty
AT dolanraymondj explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty
AT flemingstephenm explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty