text: In this section, we outline how to use 
  parameter recovery and predictive checks. 
  ActiveInference for simulation and model fitting in conjunction with ActionModels. In the 
  following section, we show how to achieve this on a concrete worked example. 
  
  **4. Usage Example**
  
  In this section, we demonstrate a full usage example of how to create an AIF agent, sim- 
  ulate behaviour in a classic T-maze environment and fit the AIF agent to a simulated exam- 
  ple dataset. We provide the necessary code to run this simulation. All code required to repro- 
  duce the example simulation can be found in an open source OSF repository osf.io/j3k5q/. 
  This example was performed with the current version of ActiveInference.jl (0.1.1); the 
  newest version can be found at github.com/ilabcode/ActiveInference.jl. 
  
  4.1. Setting Up Environment and Agent 
  
  A T-maze is a simple task commonly employed in the behavioural sciences, as well 
  as in the AIF literature [14,54–57]. It is a minimal type of task that requires balancing 
  exploration and exploitation, or epistemic and pragmatic value, respectively. It is also 
  suitably represents in a discrete state space. Together, this makes it easily compatible with 
  a POMDP-based AIF approach. 