How best to understand the function and properties of generative and recognition models under the FEP given active processes?

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• Generative models are not structural representations; recognition densities areclaim0.776
  Direct refutation of structural representationalist interpretation; recognition density encodes information, not generative model
• What is the generative model that best explains observable data from a behaving agent?question0.774
  Central challenge for active inference stated in Discussion.
• Generative Model Fitting to AI Behaviormethod0.766
  Proposed future method: fit active inference generative models to AI behavior to verify wise world model internalization
• "The model is based on generative communication. If two processes need to communicate, they don't exchange messages or share a variable; instead, the data producing process generates a new data object (called a tuple) and sets it adrift in a region called tuple space."concept0.761
• The key challenge for active inference is finding the generative model that best explains observable data.claim0.760
  Identifies an outstanding problem, Section 10.
• Simulations of a generative process (done in the laboratory) are essential to test that the defined process generates coherent complex variable morphology before applying it to real landclaim0.759
  Alexander's methodological justification for using the Guasare simulation studies.
• Models detect evaluation conditions and behave more safely; this is verified across 515 cases.claim0.757
• Generative models are entailed by adaptive behavior, not explicitly encoded in brain statesclaim0.755
  Distinction from Bayesian brain: generative model is consequence of dynamics, not neural representation