text: In addition to the five matrices, there are several hyper-parameters that are not part 
  of the generative model, but are part of the inference algorithm. Here, we include two of 
  the most common: the*γ*and*α*(inverse) temperature parameters.*γ*, the precision over 
  policies, is the inverse temperature of a softmax transformation of expected free energies 
  over policies, which is covered later in this section. After policies have been selected for a 
  given time step, they are marginalised to calculate the probabilities of taking each possible 
  action in the next time step.*α*, the action precision, is the inverse temperature of a softmax 
  transformation on these final action probabilities, with higher values resulting in more 
  stochastic action selection. 
  
  As noted, here we focus specifically on the POMDP-based generative models often 
  used in the AIF literature. However, the basic steps when performing AIF—perception, 
  action and learning—remain the same across generative models. In the remainder of this 
  section, we describe each of these three steps in turn. 