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      Alien limb syndrome: A Bayesian account of unwanted actions

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          Abstract

          An alien limb is a debilitating disorder of volitional control. The core feature of alien limb is the performance of simple or complex semi-purposeful movements which the patient reports to be unintentional or unwanted, or occasionally in opposition to their intentions. Theories of the mechanism of alien limb phenomena have emphasised the role of disinhibition in the brain, and exaggerated action ‘affordances’. However, despite advances in cognitive neuroscience research and a large public and media interest, there has been no unifying computational and anatomical account of the cause of alien limb movements. Here, we extend Bayesian brain principles to propose that alien limb is a disorder of ‘predictive processing’ in hierarchical sensorimotor brain networks. Specifically, we suggest that alien limb results from predictions about action outcomes that are afforded unduly high precision. The principal mechanism for this abnormally high precision is an impairment in the relay of input from medial regions, predominantly the supplementary motor area (SMA), which modulate the precision of lateral brain regions encoding the predicted action outcomes. We discuss potential implications of this model for future research and treatment of alien limb.

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          How to grow a mind: statistics, structure, and abstraction.

          In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?
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            Principles of sensorimotor learning.

            The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.
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              Active Inference: A Process Theory.

              This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.
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                Author and article information

                Contributors
                Journal
                Cortex
                Cortex
                Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
                Masson
                0010-9452
                1973-8102
                1 June 2020
                June 2020
                : 127
                : 29-41
                Affiliations
                [a ]Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
                [b ]MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
                Author notes
                [] Corresponding author. Department of Clinical Neurosciences, University of Cambridge, Herchel Smith Building, Cambridge CB2 0SZ, UK. nw305@ 123456medschl.cam.ac.uk
                Article
                S0010-9452(20)30053-8
                10.1016/j.cortex.2020.02.002
                7212084
                32155475
                8bb8dcf9-becb-41c4-b4e1-6ace49df8052
                © 2020 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 June 2019
                : 6 December 2019
                : 4 February 2020
                Categories
                Article

                Neurology
                alien limb,predictive processing,supplementary motor area,dual premotor,action affordance

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