A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain
Fig 9
Schematic figure of the hierarchical Kalman filter model, depicting the evolution of states and internal model parameters across iterations, and the relationship between model variables and parameters.
The related variance/noise/covariance to each parameter is indicated next to the arrows. State estimation (lower part of the figure with arrows in solid black lines) is comparable to the single-layer Kalman filter model depicted in Fig 8. The estimation error, i.e., the difference between the predicted and perceived pain, , acts as the “bottom-up” input to the parameter estimation Kalman filter (upper part of the figure with arrows in dashed grey lines).The estimated parameters from the previous iteration (and additive internal model noise, Qp) act as the prior estimate,
. Finally, the prior estimate,
, and the estimation error,
, are combined to form the new estimated internal model parameters.