title: Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment.
abstract: Pain is a complex and multidimensional perception, embodied in our daily experiences through interoceptive appraisal processes. The article reviews the recent literature about interoception along with predictive coding theories and tries to explain a missing link between the sense of the physiological condition of the entire body and the perception of pain in chronic conditions, which are characterized by interoceptive deficits. Understanding chronic pain from an interoceptive point of view allows us to better comprehend the multidimensional nature of this specific organic information, integrating the input of several sources from Gifford's Mature Organism Model to Melzack's neuromatrix. The article proposes the concept of residual interoceptive images (ghosts), to explain the diffuse multilevel nature of chronic pain perceptions. Lastly, we introduce a treatment concept, forged upon the possibility to modify the interoceptive chronic representation of pain through external input in a process that we call interoceptive modeling, with the ultimate goal of reducing pain in chronic subjects.
authors.affiliation: Department of Psychology, Università Cattolica del Sacro Cuore Milan, Italy.
authors.forename: Daniele
authors.initials: D
authors.lastname: Di Lernia
authors.affiliation: Department of Psychology, Università Cattolica del Sacro CuoreMilan, Italy; Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico ItalianoMilan, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Serino
authors.affiliation: Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano Milan, Italy.
authors.forename: Pietro
authors.initials: P
authors.lastname: Cipresso
authors.affiliation: Department of Psychology, Università Cattolica del Sacro CuoreMilan, Italy; Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico ItalianoMilan, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Riva
keywords: chronic pain; free energy; interoception; interoceptive modeling; predictive coding