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      Symptom Perception From a Predictive Processing Perspective

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          Abstract

          Background

          Bodily symptoms are highly prevalent in psychopathology, and in some specific disorders, such as somatic symptom disorder, they are a central feature. In general, the mechanisms underlying these symptoms are poorly understood. However, also in well-known physical diseases there seems to be a variable relationship between physiological dysfunction and self-reported symptoms challenging traditional assumptions of a biomedical disease model.

          Method

          Recently, a new, predictive processing conceptualization of how the brain works has been used to understand this variable relationship. According to this predictive processing view, the experience of a symptom results from an integration of both interoceptive sensations as well as from predictions about these sensations from the brain.

          Results

          In the present paper, we introduce the predictive processing perspective on perception (predictive coding) and action (active inference), and apply it to asthma in order to understand when and why asthma symptoms are sometimes strongly, moderately or weakly related to physiological disease parameters.

          Conclusion

          Our predictive processing view of symptom perception contributes to understanding under which conditions misperceptions and maladaptive action selection may arise.

          Abstract

          • There is a variable relationship between physiological dysfunction and self-reported symptoms.

          • We conceptualize symptom perception (and misperception) within a predictive processing perspective.

          • In this view, symptom perception integrates sensations and predictions about these sensations.

          • Failures of such integration can produce misperceptions and maladaptive action selection.

          • We use the perception (and misperception) of asthma symptoms as an example.

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          Most cited references30

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          The free-energy principle: a unified brain theory?

          A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.
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            Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

            We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
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              Whatever next? Predictive brains, situated agents, and the future of cognitive science.

              Andy Clark (2013)
              Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.
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                Author and article information

                Journal
                CPE
                Clin Psychol Eur
                Clinical Psychology in Europe
                Clin. Psychol. Eur.
                PsychOpen
                2625-3410
                17 December 2019
                2019
                : 1
                : 4
                : e35952
                Affiliations
                [a ]Institute of Cognitive Sciences and Technologies, National Research Council , Rome, Italy
                [b ]Institute for High Performance Computing and Networking, National Research Council , Naples, Italy
                [c ]Health Psychology, University of Leuven , Leuven, Belgium
                [4]Philipps-University of Marburg, Marburg, Germany
                Author notes
                [* ]Institute of Cognitive Sciences and Technologies, National Research Council, Via San Martino della Battaglia 44, 00185 Rome, Italy. giovanni.pezzulo@ 123456istc.cnr.it
                Article
                cpe.v1i4.2553
                10.32872/cpe.v1i4.35952
                6917af2a-af86-4ace-8477-a94c6148aa94
                Copyright @ 2019

                This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 06 May 2019
                : 13 August 2019
                Categories
                Scientific Update and Overview

                Psychology
                somatic symptom disorder,active inference,predictive coding,medically unexplained symptoms,symptom perception

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