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...

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Autores principales:	Nadim A A Atiya, Quentin J M Huys, Raymond J Dolan, Stephen M Fleming
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d79a5db3a51d4baa8b6bdca3a166783b
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spelling	oai:doaj.org-article:d79a5db3a51d4baa8b6bdca3a166783b2021-12-02T19:57:22ZExplaining distortions in metacognition with an attractor network model of decision uncertainty.1553-734X1553-735810.1371/journal.pcbi.1009201https://doaj.org/article/d79a5db3a51d4baa8b6bdca3a166783b2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009201https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Metacognition 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.Nadim A A AtiyaQuentin J M HuysRaymond J DolanStephen M FlemingPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009201 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Nadim A A Atiya Quentin J M Huys Raymond J Dolan Stephen M Fleming Explaining distortions in metacognition with an attractor network model of decision uncertainty.
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	article
author	Nadim A A Atiya Quentin J M Huys Raymond J Dolan Stephen M Fleming
author_facet	Nadim A A Atiya Quentin J M Huys Raymond J Dolan Stephen M Fleming
author_sort	Nadim A A Atiya
title	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_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_sort	explaining distortions in metacognition with an attractor network model of decision uncertainty.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d79a5db3a51d4baa8b6bdca3a166783b
work_keys_str_mv	AT nadimaaatiya explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty AT quentinjmhuys explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty AT raymondjdolan explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty AT stephenmfleming explainingdistortionsinmetacognitionwithanattractornetworkmodelofdecisionuncertainty
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Explaining distortions in metacognition with an attractor network model of decision uncertainty.

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