text: free energy having some claims to being a better approximation than the information 
  criteria classically used with MCMC methods (although see other approximations, like the 
  Pareto-Smoothed Importance Sampling [59] or Thermodynamic Integration methods [60]; 
  see [35] for a further review). Note that independently of which of these approaches one 
  might take, the process involves inverting a generative model of the mental processes 
  underlying the behaviour of a given subject, a generative model which itself is an inversion 
  of the subject’s generative model of the environment. We can call the generative model that 
  the agent has of its environment the subjective generative model, and the model we have of 
  the agent the objective generative model, in what has been called a meta-Bayesian approach 
  or “observing the observer” [1,61]. 
  
  Here, we demonstrated model fitting by fitting the POMDP model to the synthetic 
  behaviour that it generated; this is called a parameter recovery study since we can then 
  compare the estimated parameters to the generative values used for creating the simulated 
  data [62,63]. Here, we used the simulation method shown in the previous section to 
  produce a synthetic dataset with known parameter values for each agent (in practice, these 
  are often participants in an experiment), here with a focus on estimating the*α*parameter. 
  We then used MCMC methods to estimate the parameters for each agent and compared 
  the estimated values with the correct values. Here, we simulated two groups of five 
  synthetic subjects agents with different*α*values (the parameters for the first group were 
  sampled from a Gaussian distribution with mean = 8 and SD = 2, and the second group 
  with with mean = 24 and SD = 2). Each agent interacted with the T-maze environment for 
  300 time steps. We produced the following data frame, containing the data of each of the 
  agents: their observations, actions and an identifier, a format suitable for cognitive and 
  behavioural modelling. 