Minimal physicalism as a scale-free substrate for cognition and consciousness - PubMed (original) (raw)

. 2021 Aug 2;2021(2):niab013.

doi: 10.1093/nc/niab013. eCollection 2021.

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Minimal physicalism as a scale-free substrate for cognition and consciousness

Chris Fields et al. Neurosci Conscious. 2021.

Abstract

Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, nonneural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and function of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our "minimal physicalist" approach makes no assumptions beyond those of quantum information theory, and hence is applicable from the molecular scale upwards. We show that standard concepts including integrated information, state broadcasting via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phenomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of human autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bacteria and other basal systems.

Keywords: active inference; aneural systems; basal cognition; classical computation; integrated information; memory; quantum computation; self-representation; state broadcasting.

© The Author(s) 2021. Published by Oxford University Press.

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Figures

Figure 1.

Figure 1.

The interaction HAB specified by Equation (5) can, without loss of generality, be implemented by alternating preparation and measurement of N mutually noninteracting qubits. Each cycle of interaction has two phases: first, A prepares the N qubits and then B measures them, then B prepares the N qubits and A measures them. The qubit array defines a holographic screen B separating A from B. This screen enforces conditional independence between A and B, and hence functions as a MB. There is no source of classical noise in the interaction; however, there is quantum noise, and hence potential classical communication error, whenever A and B employ different reference frames (e.g. different _z_-axes) to prepare and measure the qubits. Adapted from Fields and Marcianò (2020b); CC BY license.

Figure 2.

Figure 2.

Simplified cartoon of feature or object perception in MP. The depicted relationship between A and B is topological: they are separated by the boundary B. There is no implied geometry, and the interaction is bipartite: there is no third system “outside” U = AB with which A or B interact. Features or objects “embedded” in the environment B are perceptible only by systems A equipped with QRFs and property detectors that render the features/objects both detectable and meaningful, and are defined only relative to such systems; this lack of observer-independent ontology is indicated here by dashed boundaries. Triangles within A suggest the form of classifier cocones when drawn as diagrams (Fields and Glazebrook 2019a,b; 2020a,b,c); arrows indicate binding operations. The analogy with mammalian visual feature detection is obvious; see Fields and Glazebrook (2019b) for a detailed formal construction.

Figure 3.

Figure 3.

Cartoon representation of a system A with an internal boundary C and hence a separable state |_A_> = |_A_1>|_A_2>. Again the relationships depicted are topological, not geometric. Triangles represent QRFs; f and g are internal informational states. Information flow across C is bidirectional by Equation (5); information also flows through the environment (dashed arrow). The communication loop is closed, generating positive Φ (Oizumi et al. 2014).

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