Predictive sequence models in the generative modality are simulators of a learned distribution.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Generative models are entailed by adaptive behavior, not explicitly encoded in brain statesclaim0.794
  Distinction from Bayesian brain: generative model is consequence of dynamics, not neural representation
• Generative models are not structural representations; recognition densities areclaim0.778
  Direct refutation of structural representationalist interpretation; recognition density encodes information, not generative model
• A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model's power).claim0.760
  Prediction orthogonality thesis.
• Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model.quote0.756
  Opening sentence defining self-evidencing.
• A generalized model where Level 2 modulates all Level 1 precision parameters would capture mood, emotion, and broader phenomenology of mental states.hypothesis0.754
  Future work direction; extends current model beyond attentional precision to full space of emotional and metacognitive phenomena.
• These dynamics are consistent with predictive processing since active inference prescribes dynamics that minimise prediction error.concept0.753
  Connection between active inference neuronal dynamics and predictive processing theory.
• Predictive Processing Theoryframework0.752
  Theory that perception and cognition involve predictive coding; listed with indicators in Butlin et al. 2023.
• "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."concept0.749