Wilding the predictive brain

Nave, Kathryn; Deane, George; Miller, Mark M.; Clark, Andy

doi:10.1002/wcs.1542

Cited by 44 publications

(35 citation statements)

References 73 publications

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“…The basic predictive processing story so far casts the brain as a hierarchical prediction machine using belief updating schemes to approximate Bayesian inference by utilizing ‘priors’ (probability distributions about hidden states of the world) and incoming sensory data (‘prediction errors’) to arrive at a posterior estimate: a ‘best guess’ of the hidden causes of sensory signals ( Clark 2013 , 2015 ; Hohwy 2013 ; Aitchison and Lengyel 2017 ; Nave et al. 2020 ).…”

Section: Consciousness In Active Inferencementioning

confidence: 99%

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Deane

2021

Neuroscience of Consciousness

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Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by ‘subjective valuation’—a deep inference about the precision or ‘predictability’ of the self-evidencing (‘fitness-promoting’) outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of ‘selflessness’; in particular the ‘totally selfless’ states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology.

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Section: Consciousness In Active Inferencementioning

confidence: 99%

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Deane

2021

Neuroscience of Consciousness

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“…We can also use AMs to understand the effects of feedback at multiple temporal scales. This might be useful in the approximation of phenomena such as predictive processing [33]. Indeed, AMs allow for both minimal representational capacity and evaluation of error minimization [34].…”

Section: Discussionmentioning

confidence: 99%

Allostasis Machines: a model for understanding internal states and technological environments

Alicea¹,

cialfi²,

Lim³

et al. 2021

Preprint

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In the present paper we will approach enactivism from the perspective of internal regulation: while the environment shapes the organism, it is also true that organisms have complex internal states with regulatory machinery with a set of continuous phenotype-environment interactions. The aim of the present paper is to provide a visual means to analyze these interactions in individuals and computational agents alike. An essential component of our approach is the representation of continuous internal states through the usage of the single continuous indicator we call an Allostasis Machine (AM). Consequently, we consider potential perturbation regimes for both naturalistic and virtual environments: within the naturalistic cases, it is possible to observe the effects of perturbations in isolation, or as overlapping, multiplicative events. In virtual cases, we can observe perturbations as the outcome of both realistic and fantastical environments. To conclude, we discuss how AMs can be utilized to improve our understanding of both the theoretical basis of embodied interaction and the dynamic regulation of complex psychophysiological states.

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“…Active inference, its extensions (like variational ecology [59]), and related frameworks (like predictive processing/coding [60][61][62]) are highly relevant to discussions of societal transformation. If societal systems are viewed as a cognitive architecture, then theories of cognition would naturally play critical roles in the design, testing, and operation of new systems.…”

Section: Active Inferencementioning

confidence: 99%

“…All biological organisms (including superorganisms) are fundamentally anticipatory in nature [65,66]. Much of what we take to be central to intelligence-perception, attention, emotion, learning, and language-can be understood within a framework of prediction, action, error correction, and uncertainty reduction [61,67]. Not surprisingly, narrative too, and communication more generally, can be cast in an action-cognition framework [68][69][70].…”

Section: Active Inferencementioning

confidence: 99%

Science-Driven Societal Transformation, Part III: Design

Boik

2021

Sustainability

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Climate change, biodiversity loss, and other major social and environmental problems pose severe risks. Progress has been inadequate and scientists, global policy experts, and the general public increasingly conclude that transformational change is needed across all sectors of society in order to improve and maintain social and ecological wellbeing. At least two paths to transformation are conceivable: (1) reform of and innovation within existing societal systems (e.g., economic, legal, and governance systems); and (2) the de novo development of and migration to new and improved societal systems. This paper is the final in a three-part series of concept papers that together outline a novel science-driven research and development program aimed at the second path. It summarizes literature to build a narrative on the topic of de novo design of societal systems. The purpose is to raise issues, suggest design possibilities, and highlight directions and questions that could be explored in the context of this or any R&D program aimed at new system design. This paper does not present original research, but rather provides a synthesis of selected ideas from the literature. Following other papers in the series, a society is viewed as a superorganism and its societal systems as a cognitive architecture. Accordingly, a central goal of design is to improve the collective cognitive capacity of a society, rendering it more capable of achieving and sustainably maintaining vitality. Topics of attention, communication, self-identity, power, and influence are discussed in relation to societal cognition and system design. A prototypical societal system is described, and some design considerations are highlighted.

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Wilding the predictive brain

Cited by 44 publications

References 73 publications

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Allostasis Machines: a model for understanding internal states and technological environments

Science-Driven Societal Transformation, Part III: Design

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