Generative models are not structural representations; recognition densities are

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• Generative models are entailed by adaptive behavior, not explicitly encoded in brain statesclaim0.824
  Distinction from Bayesian brain: generative model is consequence of dynamics, not neural representation
• Predictive sequence models in the generative modality are simulators of a learned distribution.claim0.778
  Naming of generative sequence models as simulators.
• How best to understand the function and properties of generative and recognition models under the FEP given active processes?question0.776
  Core research question motivating the paper's dual-interpretation investigation
• Representation may be a fundamental principle of living systems, but it is difficult to generalize formally because it appears to vanish at the molecular level.claim0.771
  Central open theoretical tension raised in the discussion.
• "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."concept0.767
• Neural representations carry rich geometric structure; but does that structure causally shape behavior?quote0.765
  Opening sentence framing the paper's core inquiry.
• We hypothesize that representation geometry drives model behavior — the geometric structure of internal representations causally shapes what models do externally.hypothesis0.765
  The causal hypothesis motivating the use of causality (intervention) as the lens connecting representation and behavior geometry.
• There are fewer representations competent for N tasks than M<N tasks, so training more general models should yield fewer possible solutionshypothesis0.757
  Selective pressure toward convergence via task generality