text: the current model generate significant surprise, and sometimes, 
  the surprise can remain relatively high for long periods before the 
  model adapts (or the world changes), especially with some param- 
  eterizations of the generative model. This is particularly relevant 
  in this context since active inference agents strive to minimize 
  their surprise (and the long-term average of surprise, entropy, 
  which is a measure of uncertainty) by changing their model, or 
  changing the world, or both.  Second, these examples illustrate the importance of precision 
  control and the appropriate setting of precision parameters in 
  guiding inference. Remarkably, the inference can be more or less 
  accurate or fast using the same data, depending on the precision 
  parameters. Note that in Fig. 2, we manipulated only the precision 
  of the likelihood. However, it would also be possible to manipulate 
  the precision of the prior, together or in alternative to the precision 
  of the likelihood. Generally speaking, when the precision of the 