text: the behaviour (Figure 8). We saw that the estimation successfully captured the difference 
  between the two groups, and that the*α*parameter recovered fairly well. Note that the 
  ability to recover parameters depends on the specific model and task, as well as on the 
  specific values of the parameters (when*α*is very high, for example, the behaviour becomes 
  essentially deterministic; further increases in*α*would then not have any effect on the 
  behaviour, and therefore, not be estimable). A subtle issue here is that the parameters 
  that best explain some data are not necessarily the parameters used to generate those 
  data. This is because the best parameters are those that maximise the marginal likelihood 
  of the data (also known as the model evidence); because the model evidence includes 
  a complexity term, the parameter recovery will often recover parameters that provide a 
  simpler explanation for the data relative to the parameters used to generate these data. 
  (cid:7)  (cid:4) 
  
  # Extract quantities from the fitted model 
  agent_parameters = extract_quantities ( model , renamed_posterior_chains ) 
  # Extract posterior estimates 
  posterior_estimates = get_estimates ( agent_parameters ) 
  (cid:6)  (cid:5) 