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A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain

Fig 2

Results of the Kalman filter simulations of chronic pain.

The model produces output reflecting chronic pain when there is elevated uncertainty in the sensory input, in combination with the internal model parameter (solid blue line in upper plots). Left: when R (dash-dotted, purple line in upper plot) is low the perceived pain, , (solid red line in lower plot, the shaded area indicates the interquartile range) will primarily be influenced by the sensory input and closely correspond to the true level of tissue damage, x, (dash-dotted black line in lower plot).Right: for a larger value of R, predictions from the internal model have a stronger influence on the perceived level of pain, possibly resulting in a chronically elevated level of pain even after the tissue damage has recovered.

doi: https://doi.org/10.1371/journal.pcbi.1012097.g002