Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures

Albantakis, Larissa

doi:10.3390/e23111415

Cited by 3 publications

(3 citation statements)

References 91 publications

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“…Different information-theoretic formalisms of causal emergence, for example, may be applied to measuring higher-order causation within a system [82][83][84][85]. Similarly, attempts to formalise notions of individuality [81,86] and autonomy [87,88] can be used to measure how endogenously active and free from determinate external forces a system is, by quantifying the degree to which it may be more "interested in itself rather than the world outside" [89] (p. 3). Finally, the criteria of holistic integration and informational causation more generally fits neatly with the long-standing measures associated with Integrated Information Theory (IIT) [90][91][92].…”

Section: Discussionmentioning

confidence: 99%

Naturalising Agent Causation

Potter

Mitchell

2022

Entropy

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The idea of agent causation—that a system such as a living organism can be a cause of things in the world—is often seen as mysterious and deemed to be at odds with the physicalist thesis that is now commonly embraced in science and philosophy. Instead, the causal power of organisms is attributed to mechanistic components within the system or derived from the causal activity at the lowest level of physical description. In either case, the ‘agent’ itself (i.e., the system as a whole) is left out of the picture entirely, and agent causation is explained away. We argue that this is not the right way to think about causation in biology or in systems more generally. We present a framework of eight criteria that we argue, collectively, describe a system that overcomes the challenges concerning agent causality in an entirely naturalistic and non-mysterious way. They are: (1) thermodynamic autonomy, (2) persistence, (3) endogenous activity, (4) holistic integration, (5) low-level indeterminacy, (6) multiple realisability, (7) historicity, (8) agent-level normativity. Each criterion is taken to be dimensional rather than categorical, and thus we conclude with a short discussion on how researchers working on quantifying agency may use this multidimensional framework to situate and guide their research.

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Section: Discussionmentioning

confidence: 99%

Naturalising Agent Causation

Potter

Mitchell

2022

Entropy

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“…Individual neurons or neural populations filter and integrate incoming activity over multiple inputs and durations of time, performing non-linear transformations that extract multiply realisable higherorder patterns (from simple firing rates to more complex spatial arrangements). The causally effective elements are these macrostates, not the particular microstates that may realise any particular instance of them (Albantakis, 2021;Barack and Krakauer 2021;Ellis, 2008;Hoel at el., 2013;Klein and Hoel, 2020).…”

Section: Representations As Meaningful Cognitive Objectsmentioning

confidence: 99%

The origins of meaning – from pragmatic control signals to semantic representations

Mitchell

2023

Preprint

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The concept of representations is widely used across the cognitive sciences, but its meaning is highly contested. Representations are often thought of as “vehicles” with “content” – that is, internal physical patterns that are correlated with some state of affairs and that usefully convey that state of affairs to the rest of the neural system or the cognitive economy at large. This raises a number of problems: how does an internal pattern come to be correlated with something else? How does the rest of the system know what the pattern means? How does the system know what to do with that information? I suggest that thinking of representations as vehicles with content presents a stumbling block to answering these questions. Instead, thinking of them simply as meaningful patterns offers a more naturalistic framework for understanding their roles in perception, behavioural control, and cognition. Meaning is not contained within a vehicle – it is relational, contextual, and interpretive. Here, I explore the origins of meaningful patterns in simple behavioural control policies and the subsequent evolution of internalised representations, decoupled from obligate action. This transition from pragmatic to semantic meaning demystifies the notion of “content”, situates talk of grounding in referential, consequential, and relational terms, and highlights the dual roles of current activity and stored context.

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“…Existing work from IIT theorists already shows that IITanalyses can be used to reveal to what extent a system exhibits biological autonomy in the sense of self-defining and self-maintaining itself (75), and whether it should be regarded as a self-determining, enclosed agentive system (76). Maximally integrated complexes might thus correspond to autonomous organizations rather than to rich conscious experiences (77).…”

Section: Alternative Foundationsmentioning

confidence: 99%

We need to explain subjective experience, but its explanation may not be mechanistic

Signorelli¹,

Cea²,

Prentner³

2022

Preprint

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Models of consciousness need to explain both objective correlates of conscious experience as well as its subjective structure. However, such an explanation would not need to entail a reduction exclusively in terms of physical or neural systems. We briefly sketch a few points of contention with recent work on integrated information theory and some common assumptions made in the neuroscience of consciousness more generally.

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Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures

Cited by 3 publications

References 91 publications

Naturalising Agent Causation

Naturalising Agent Causation

The origins of meaning – from pragmatic control signals to semantic representations

We need to explain subjective experience, but its explanation may not be mechanistic

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