Figure 3.

Computational models for studies of pain

(A) Dynamic causal modeling (DCM) for identifying plausible brain nodes of effective connectivity among value-encoding and aversive PE-encoding regions (Roy et al., 2014, ref. ¹³⁵, figure reprinted with permission, Springer Nature).

(B) Computational models to characterize self-fulfilling prophecies in pain (Jepma et al., 2018, ref. ¹², figure adapted with permission, Springer Nature). (i) RL model for capturing effects of cued-based expectations on pain and confirmation bias on expectation updating. Perceptual inference of pain is jointly determined by nociceptive input, expected pain, and initial belief of the cue. (ii) Predictive coding is visualized by a graphical model and formulated as a linear Gaussian state-space model (parameterized by three variables $\left\{\mu ,{\sigma }_{\psi },{\sigma }_{\epsilon }\right\}$). Each node is a random variable, and the arrow indicates the statistical dependency between random variables, the uncertainty is characterized by a Gaussian distribution with mean and SD parameters. Bayesian inference produces iterative updating of the posterior distribution of pain. (iii) Expectation updating as a function of PE sign. Negative (aversive) and positive (appetitive) PEs represent the lower-than-expected and higher-than-expected pain, respectively. There was a significant main effect of cue type but no significant interaction between PE and cue type.

(C) Bayesian model prediction for placebo hypoalgesia (Büchel et al., 2014, ref. ⁴⁹, figure adapted with permission, Elsevier). The uncertainty of expectation or pain stimulus is characterized by the precision (inverse of variance) parameter. (i) Impact of the precision of prior expectation on posterior prediction in placebo hypoalgesia experiments. Distributions of prior expectation (red), sensory observation (blue), and posterior of perceived pain (green) are shown with respect to the visual analog scale (VAS) rating. (ii) Impact of the precision of sensory input on posterior prediction in placebo hypoalgesia experiments.

(D) Effects of attention increases orientation selectivity (response profile amplitude) and mismatch selectivity (putative PE) for four test conditions. Time 0 denotes the stimulus onset (Smount et al., 2019, ref. ¹³⁹, CC BY license).