A model compresses a state space by capturing a set of invariances that predict the variance in the states. Its free parameters define the latent space of the model and should ideally fully correspond to the variability, the not-invariant (= unexplained) remainder of the state.
To know where your body is, you need a model of the whole world around it. You may start with the body surface as ground zero, then map it as a suitably deformed volume into a flat space, and then populate that space with other objects, before you make a global map of all objects
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Ok yes makes sense! Each model is enmeshed in others and they’re sorta mutually contingent. And I suppose each model is also a composition of other models (or automonia?).
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Yes. Most models work by reducing the data to a few free parameters, by identifying trends. Where the trends change, they find meta-trends describing where the local trends apply, which leads to nested models.
End of conversation
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