A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain
Fig 4
Results of the hierarchical Kalman filter simulations of classical conditioning.
We simulate the conditioning procedure similar to the learning phase of the experimental paradigm described by Jepma et al., [20]. During conditioning previously neutral cues (‘high cue’ and ‘low cue’ in the figures) are repeatedly paired with high or low noxious stimuli, resulting in diverging values of internal model parameters and
(panel a)), and creating expectations of high pain associated with the high cue, and low pain associated with the low cue (panel b)). In testing the effect of conditioning the cues are paired with intermediate intensity noxious stimuli (47°C for ‘low heat’ and 48°C for ‘high heat’). Note that the during the test trials the level of noxious stimuli is independent from the cue, i.e., the low cue is paired with both high and low thermal stimulation. a) median value of
(dashed red line) and
(dashed blue line) across conditioning trials. Shaded areas indicate the interquartile range. b) average expected pain for high-cue trials (red) and low-cue trials (blue) during conditioning. Open triangles indicate the expected pain for each participant on each conditioning trial. c) the average expected (
, open triangles) and perceived (
, filled circles) pain as a function of cue type on each test trial. d) perceived (filled circles) and e) expected (open triangles) pain as a function of stimulus temperature and cue type. Error bars indicate inter-individual standard errors.