text: inference that tracks the noise rather than the correct state of 
  the estimated variable system (i.e. overfitting), whereas assigning 
  excessively low weight to sensations (or excessively high weight to 
  prior knowledge) makes the system poorly responsive to incom- 
  ing observations that might signal a change in the state of the 
  system—and both are examples of aberrant inference (Friston 
  et al. 2014). 
  
  Figure 2 provides a formal illustration of the above by plot- 
  ting some examples of Bayesian inference using generative models 
  under various levels of precision of the model components. For 
  simplicity, we focus on a simplified example of inference of an 
  interoceptive variable: one’s heart rate. Heart rate is a “hidden 
  variable” in Bayesian parlance since it is not directly observable 
  but needs to be inferred through two sources of information: 
  prior knowledge about the most likely heart rate and sensory 
  (heartbeat) observations. The top panel of Fig. 2 shows a series 
  of (noisy) heartbeat observations. In the beginning, they are 
  in the normal range for an adult (time steps 1–10), then they 
  increase significantly, simulating tachycardia (time steps 11–20), 
  then they go back to the normal range (time steps 21–30), then 
  they decrease significantly, simulating bradycardia (time steps 
  31–40), and finally, they go back to the normal range (time steps 
  41–50). 