Causal blankets : Theory and algorithmic framework
We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics. Our approach is based on the notion of causal blanket, which captures sensory and active variables as dynamical sufficient statistics—i.e. as the “differences that make a difference.” Furthermore, our theory provides a broadly applicable procedure to construct PALOs that requires neither a steady-state nor Markovian dynamics. Using our theory, we show that every bipartite stochastic process has a causal blanket, but the extent to which this leads to an effective PALO formulation varies depending on the integrated information of the bipartition.
Item Type | Other |
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Uncontrolled Keywords | Computational mechanics; Integrated information; Perception-action loops; Stochastic processes |
Subjects |
Computer Science(all) Mathematics(all) |
Date Deposited | 27 Jul 2024 00:23 |
Last Modified | 27 Jul 2024 00:23 |
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picture_as_pdf - Formalising_Markov_Blankets_IWAI_paper_1_.pdf
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- Department of Computer Science
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