# Key word "markov blanket"

Fields C., Hoffman D. D., Prakash C. & Prentner R. (2017) Eigenforms, Interfaces and Holographic Encoding: Toward an Evolutionary Account of Objects and Spacetime. Constructivist Foundations 12(3): 265–274. https://cepa.info/4168

Fields C., Hoffman D. D., Prakash C. & Prentner R.
(

2017)

Eigenforms, Interfaces and Holographic Encoding: Toward an Evolutionary Account of Objects and Spacetime.
Constructivist Foundations 12(3): 265–274.
Fulltext at https://cepa.info/4168
Context: The evolution of perceptual systems and hence of observers remains largely disconnected from the question of the emergence of classical objects and spacetime. This disconnection between the biosciences and physics impedes progress toward understanding the role of the “observer” in physical theory. Problem: In this article we consider the problem of how to understand objects and spacetime in observer-relative evolutionary terms. Method: We rely on a comparative analysis using multiple formal frameworks. Results: The eigenform construct of von Foerster is compared to other formal representations of observer-environment interactions. Eigenforms are shown to be encoded on observer-environment interfaces and to encode fitness consequences of actions. Space and time are components of observational outcomes in this framework; it is suggested that spacetime constitutes an error-correcting code for fitness consequences. Implications: Our results contribute to an understanding of the world in which neither objects nor spacetime are observer-independent. Constructivist content: The eigenform concept of von Foerster is linked to the concepts of decoherence and holographic encoding from physics and the concept of fitness from evolutionary biology.

Friston K., Sengupta B. & Auletta G. (2014) Cognitive dynamics: From attractors to active inference. Proceedings of the IEEE 102(4): 427–445. https://cepa.info/5359

Friston K., Sengupta B. & Auletta G.
(

2014)

Cognitive dynamics: From attractors to active inference.
Proceedings of the IEEE 102(4): 427–445.
Fulltext at https://cepa.info/5359
This paper combines recent formulations of self-organization and neuronal processing to provide an account of cognitive dynamics from basic principles. We start by showing that inference (and autopoiesis) are emergent features of any (weakly mixing) ergodic random dynamical system. We then apply the emergent dynamics to action and perception in a way that casts action as the fulfillment of (Bayesian) beliefs about the causes of sensations. More formally, we formulate ergodic flows on global random attractors as a generalized descent on a free energy functional of the internal states of a system. This formulation rests on a partition of states based on a Markov blanket that separates internal states from hidden states in the external milieu. This separation means that the internal states effectively represent external states probabilistically. The generalized descent is then related to classical Bayesian (e.g., Kalman-Bucy) filtering and predictive coding-of the sort that might be implemented in the brain. Finally, we present two simulations. The first simulates a primordial soup to illustrate the emergence of a Markov blanket and (active) inference about hidden states. The second uses the same emergent dynamics to simulate action and action observation.

Kirchhoff M., Parr T., Palacios E., Friston K. & Kiverstein J. (2018) The Markov blankets of life: Autonomy, active inference and the free energy principle. Journal of The Royal Society Interface 15(138): 20170792. https://cepa.info/5393

Kirchhoff M., Parr T., Palacios E., Friston K. & Kiverstein J.
(

2018)

The Markov blankets of life: Autonomy, active inference and the free energy principle.
Journal of The Royal Society Interface 15(138): 20170792.
Fulltext at https://cepa.info/5393
This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens, in terms of the presence of Markov blankets under the active inference scheme – a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets – all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment.

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