A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing.

ABSTRACT: Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quantification of the influence of prior expectations versus current nociceptive input during perception. Using a Pavlovian-learning task, we investigated the influence of prior expectations on the belief about the varying strength of association between a painful electrical cutaneous stimulus and a visual cue in healthy subjects (N = 70). The belief in cue-pain associations was examined with computational modelling using a Hierarchical Gaussian Filter (HGF). Prior weighting estimates in the HGF model

SUBMITTER: Drusko A 

PROVIDER: S-EPMC9950064 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature