What are the neuronal mechanisms by which prior beliefs from one agent's model are received and properly implemented by a naive agent (neuronal hermeneutics)?

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• What is the nature of the shared model space or lexicon that enables naive agents to properly implement received priors from experienced agents?question0.811
  Prerequisite for model-level communication; raises issues of neural hermeneutics
• Biological agents use a process theory of active inference where neuronal dynamics correspond to variational free energy minimisation for perception and expected free energy minimisation for action.hypothesis0.810
  The core process theory hypothesis set up in the paper.
• Can process theories implementing Bayesian models be derived and shown to explain empirical neuronal phenomena?question0.805
  Motivating question: bridging normative Bayesian theory and testable neuroscience predictions.
• Neuronal dynamics for state estimation coincide with variational message passing.claim0.798
  Connection between active inference and message passing algorithms.
• The results of abductive reasoning (reduced model priors) can be communicated to other agents as prior beliefs, provided all agents share the same model lexicon or hypothesis space.claim0.797
  Explanation of how knowledge (not just parameters) is shared between agents; links to pre-Cartesian consciousness
• Results may not fully generalize to all models and scenarios because the model organism relies on hints and nudges and Llama Nemotron cannot consistently distinguish evaluation/deployment based on subtle cuesclaim0.789
  Key limitation acknowledged by authors.
• Neural networks, trained with different objectives on different data and modalities, are converging to a shared statistical model of reality in their representation spaces.quote0.789
  The paper's central thesis statement, presented prominently after the abstract
• A naive agent equipped with reduced priors from an experienced agent performs perfectly with maximum confidence from the first trial.finding0.788
  Demonstration that model-level priors (not parameter-level knowledge) suffice for immediate transfer