ON THE PROBABILISTIC MODELLING OF PAIN

Pain is a complex subjective experience encompassing sensory, affective, and cognitive dimensions. Evidence challenges the linear relationship between nociceptive activation and pain perception, revealing scenarios where pain is felt without nociceptive input or vice versa. This research is based on the understanding that pain arises not merely from bottom-up stimuli but also from a blend of subjective components and contextual evaluations. The aim of this study is to develop a probabilistic and computational model of pain that aligns with both behavioural data and neurobiological constraints, aspiring to represent pain at various levels. This includes incorporating perceptual, affective, motivational, and social aspects into a comprehensive framework, and proposing implementation models tailored to specific contexts and varying levels of abstraction. The model uses Bayesian approaches to capture the probabilistic and dynamic nature of the phenomenon. The upcoming discussions will adopt a multidisciplinary lens, converging philosophical, psychological, and neurobiological insights to shape the envisaged model.