Bayesian modeling of temporal expectations in the human brain.
TLDR
A Bayesian computational approach with brain imaging was combined to map updating of temporal expectations in the human brain and showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON).About:
This article is published in NeuroImage.The article was published on 2019-11-15 and is currently open access. It has received 28 citations till now. The article focuses on the topics: Surprise.read more
Citations
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From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of neuroimaging studies.
TL;DR: The GradiATOM theory (Gradient Theory of Magnitude) is re-named, proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space- and time-related neural populations to interact with each other over minimal distances.
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Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network.
Linda Ficco,Lorenzo Mancuso,Jordi Manuello,Alessia Teneggi,Donato Liloia,Sergio Duca,Tommaso Costa,Gyula Kovács,Franco Cauda +8 more
TL;DR: P predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time, and there is no evidence, at the network level, for a distinction between error and prediction processing.
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The Quest for Hemispheric Asymmetries Supporting and Predicting Executive Functioning.
TL;DR: This narrative review addresses the neural bases of two executive functions: criterion setting, the capacity to flexibly set up and select task rules and associations between stimuli, responses, and nonresponses, and monitoring, the process of continuously evaluating whether task rules are being applied optimally.
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Neural surprise in somatosensory Bayesian learning
TL;DR: The cortical dynamics of the somatosensory learning system is described to investigate both the form of the generative model as well as its neural surprise signatures, and to provide a dissociation of the neural correlates of belief inadequacy and belief updating.
Journal ArticleDOI
Neural surprise in somatosensory Bayesian learning.
Sam Gijsen,Sam Gijsen,Miro Grundei,Miro Grundei,Robert Tjarko Lange,Dirk Ostwald,Felix Blankenburg +6 more
TL;DR: In this paper, the authors describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures.
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