text: generative models, or even (deep learning-based) amortised inference models. These vari- 
  ous extensions could provide valuable tools for using AIF models in both theoretical and 
  applied research. 
  
  **Author Contributions:**Conceptualisation, S.W.N., J.E.L. and P.T.W.; methodology, S.W.N., J.E.L. and 
  P.T.W.; software, S.W.N., J.E.L. and P.T.W.; formal analysis, S.W.N. and J.E.L.; writing—original draft 
  preparation, S.W.N. and J.E.L.; writing—review and editing, C.H., K.F., C.M. and P.T.W.; visualisation, 
  S.W.N. and J.E.L.; supervision, C.M. and P.T.W.; project administration, P.T.W. All authors read and 
  agreed to the published version of this manuscript. 
  
  **Funding:**C.M. acknowledges funding from Aarhus Universitets Forskningsfonds (grant no. AUFF- 
  E-2019-7-10) and from the Carlsberg Foundation (grant no. CF21-0439). 
  
  **Institutional Review Board Statement:**Not applicable. 
  
  **Informed Consent Statement:**Not applicable. 
  
  **Data Availability Statement:**The original data presented in this study are openly available in 
  ActiveInferenceJuliaPaper at URL: https://osf.io/j3k5q/. 