Perception and Misperception of Bodily Symptoms From an Active Inference Perspective: Modelling the Case of Panic Disorder.

We advance a novel computational model that characterizes formally the ways we perceive or misperceive bodily symptoms, in the context of panic attacks. The computational model is grounded within the formal framework of Active Inference, which considers top-down prediction and attention dynamics as...

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Publicado en:Psychological Review Vol. 128; no. 4; pp. 690 - 711
Autores principales: Maisto, Domenico, Barca, Laura, Van den Bergh, Omer, Pezzulo, Giovanni
Formato: Artículo
Publicado: American Psychological Association Jul2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jul2021
      vid: 128
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      pub: American Psychological Association
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        10.1037/rev0000290
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        atl: Perception and Misperception of Bodily Symptoms From an Active Inference Perspective: Modelling the Case of Panic Disorder.
      aug:
        au:
          Maisto, Domenico
          Barca, Laura
          Van den Bergh, Omer
          Pezzulo, Giovanni
        affil:
          Institute for High Performance Computing and Networking, National Research Council, Naples, Italy
          Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
          Health Psychology, University of Leuven
      su:
        Decision making
        Phenomenology
        Panic disorders
        Panic attacks
        Symptoms
      sug:
        subj:
          Decision making
          Phenomenology
          Panic disorders
          Panic attacks
          Symptoms
      keyword:
        active inference
        computational psychiatry
        maladaptive inference
        panic disorder
        predictive coding
        active inference
        computational psychiatry
        maladaptive inference
        panic disorder
        predictive coding
      ab: We advance a novel computational model that characterizes formally the ways we perceive or misperceive bodily symptoms, in the context of panic attacks. The computational model is grounded within the formal framework of Active Inference, which considers top-down prediction and attention dynamics as key to perceptual inference and action selection. In a series of simulations, we use the computational model to reproduce key facets of adaptive and maladaptive symptom perception: the ways we infer our bodily state by integrating prior information and somatic afferents; the ways we decide whether or not to attend to somatic channels; the wayswe use the symptominference to make decisions about taking or not taking a medicine; and the ways all the above processes can go awry, determining symptom misperception and ensuing maladaptive behaviors, such as hypervigilance or excessive medicine use. While recent existing theoretical treatments of psychopathological conditions focus on prediction-based perception (predictive coding), our computational model goes beyond them, in at least two ways. First, it includes action and attention selection dynamics that are disregarded in previous conceptualizations but are crucial to fully understand the phenomenology of bodily symptom perception and misperception. Second, it is a fully implemented model that generates specific (and personalized) quantitative predictions, thus going beyond previous qualitative frameworks.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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