text: prior is very high, the posterior will closely reflect the prior, ren- 
  dering the inference rigid and incapable of adapting to changing 
  environmental conditions—which might be especially problem- 
  atic in periods of significant changes, such as adolescence or more 
  simply when one changes city, working environment, and friends. 
  Furthermore, as shown in Fig. 1, hierarchical predictive coding 
  architectures have precision values associated with every hierar- 
  chical level (whereas, for simplicity, the inference shown in Fig. 2 
  is not hierarchical). The correct balance of precision parameters 
  within and across layers is crucial for accurate inference, as it 
  ensures that the correct levels of confidence are assigned to data 
  and prior information. 
  
  because they could contribute to reducing the (otherwise unman- 
  ageable) uncertainty about bodily and emotional states or the self. 
  In other words, in some conditions, the self-injuring pain could be 
  more than compensated by the information gain—and the pos- 
  sibility to generate precise sensations about one’s bodily state. 
  By harming the body, we turn it into a very precise source of 
  sensations that relieves us from excessive uncertainty about the 
  present state and the future course of action. Our (simple) exam- 
  ple, therefore, illustrates a possible way paradoxical actions could 
  be pursued by active inference agents who endure to minimize 
  their uncertainty. While self-injuries and other similar behav- 
  iors are maladaptive in the sense of reducing the fitness of an 
  organism, they can still emerge as a result of a correct inference 
  that tries to minimize the uncertainty of one’s model of the body 
  and the self. This case could particularly fit when some of the 
  (precision) parameters of one’s model of the body and the self 
  are not appropriately tuned (Fig. 2), producing excessive levels of 
  uncertainty. 