| Literature DB >> 35490544 |
Anna Ciaunica1, Anil Seth2, Jakub Limanowski3, Casper Hesp4, Karl J Friston5.
Abstract
This paper considers the phenomenology of depersonalisation disorder, in relation to predictive processing and its associated pathophysiology. To do this, we first establish a few mechanistic tenets of predictive processing that are necessary to talk about phenomenal transparency, mental action, and self as subject. We briefly review the important role of 'predicting precision' and how this affords mental action and the loss of phenomenal transparency. We then turn to sensory attenuation and the phenomenal consequences of (pathophysiological) failures to attenuate or modulate sensory precision. We then consider this failure in the context of depersonalisation disorder. The key idea here is that depersonalisation disorder reflects the remarkable capacity to explain perceptual engagement with the world via the hypothesis that "I am an embodied perceiver, but I am not in control of my perception". We suggest that individuals with depersonalisation may believe that 'another agent' is controlling their thoughts, perceptions or actions, while maintaining full insight that the 'other agent' is 'me' (the self). Finally, we rehearse the predictions of this formal analysis, with a special focus on the psychophysical and physiological abnormalities that may underwrite the phenomenology of depersonalisation.Entities:
Keywords: Active inference; Agency; Depersonalisation; Predictive processing; Sense of self; Sensory attenuation
Mesh:
Year: 2022 PMID: 35490544 PMCID: PMC9130736 DOI: 10.1016/j.concog.2022.103320
Source DB: PubMed Journal: Conscious Cogn ISSN: 1053-8100
Fig. 1This simplified generative model illustrates the inferential process of explaining multi-modal percepts (; blue) in terms of deep temporal models ( orange) for which the precisions are set by higher-level states of attention and attenuation (; green). Self and Others are models of agency (or intuitive psychology), which often exhibit large degrees of overlap (Friston & Frith, 2015), while one’s model of the inanimate world is governed by intuitive physics (see Ullman, Spelke, Battaglia, & Tenenbaum, 2017). The highest level performs Bayesian model selection to guide inferences about which combination of the deep temporal models (Friston et al., 2017) provides the best explanation of the multi-modal percepts of one’s body (interoception; Seth et al. 2011; Allen, Levy, Parr, & Friston, 2019), world (exteroception; Parr, Corcoran, Friston, & Hohwy, 2019). For a computational implementation of Bayesian filtering with multiple internal models, see the work by Isomura, Parr, and Friston (2019). Such models are temporally deep in the sense that they involve Bayesian inference on multiple time scales (Hesp et al., 2020, Ramstead et al., 2018): observations in ‘real-time’ inform beliefs about lower-level parameters (intermediate time scales), which in turn allow for updating beliefs about higher-level parameters (successively larger time scales). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)