Perception and misperception of bodily symptoms from an active inference perspective: Modelling the case of panic disorder.

Abstract

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 ways we use the symptom inference to make decisions about taking or not taking a medicine; and the ways all the above processes can go awry, determining symptoms 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 symptoms perception and misperception. Second, it is a fully implemented model that generates specific (and personalized) quantitative predictions, thus going beyond previous qualitative frameworks.

Narrative fallacy and other limitations of psychodynamic case formulation.

“…From the standpoint of active inference, beliefs, inference, and causality are entirely appropriate modeling tools. One of the possible active-inference-based understandings of the perception-action cycle is that an organism infers about a cause that corresponds to some inner state/sensation (Maisto et al, 2021; Parr et al, 2022).…”

Breathing in Waves: Understanding Respiratory-Brain Coupling as a Gradient of Predictive Oscillations

“…While little empirical work exists on predictive processing and panic attack disorder, maladaptive, highly negative expectations are considered to be both a core symptom and aggravating cause of the disorder (Reiss, 1991). This idea has been further developed in several theoretical and computational works (Allen et al, 2022a;Lyndon and Corlett, 2020;Maisto et al, 2021;Smith et al, 2021a). For example, Allen and colleagues (2022a) developed a computational model of interoceptive and affective inference, in which a simulated agent's fear expectations were modulated by rhythmic cardiac information.…”

Active inference unifies intentional and conflict-resolution imperatives of motor control

“…In these conditions, an incorrect inference that one is moving (or not moving) could arise from a model that does not properly monitor its model errors and hence relies too strongly on prior expectations, which in turn fail to be updated in the light of novel evidence and become excessively rigid [51,67]. This idea would align well with computational theories of various other psychopathological conditions, such as panic disorder [68], psychosis [69] and eating disorders [70,71], which focus on incorrect prioritization (and precision-weighting) of prior information and prediction errors during inference [72]. All these are speculative hypotheses that remain to be tested in future studies.…”