text: The second panel of Fig. 2 shows the Shannon surprise of 
  an inference model that estimates the current heart rate using 
  the two standard components of a generative model. The for- 
  mer component is the prior, which encodes the person’s a priori 
  probabilistic belief (i.e. probability distribution) about her “nor- 
  mal” heart rate range; here, the prior is a Gaussian centered on 
  67 and has a precision of 0.11. The latter component is the likeli- 
  hood, which encodes the probabilistic mapping between sensory 
  (heartbeat) observations and the hidden state (heart rate); here, 
  the likelihood is a Gaussian centered on the current heart rate 
  with an additional bias of 15 pulses, and the panel shows the 
  results for 10 values for precision obtained by subdividing the 
  range [0.1,10] into equal intervals. The results shown in the second 
  panel of Fig. 2 show that Shannon surprise increases dramatically 
  during episodes of tachycardia and bradycardia, which are far 
  from the normal range. The pattern of results is the same across 
  all levels of likelihood precision. However, the inference with a 
  very high precision (a precision of 10) tracks more closely the noise 
  sensory signals and can therefore lead to more extreme results. 