A World Unto Itself: Human Communication as Active Inference

Vasil, Jared; Badcock, Paul B.; Constant, Axel; Friston, Karl J.; Ramstead, Maxwell J. D.

doi:10.3389/fpsyg.2020.00417

Cited by 79 publications

(108 citation statements)

References 316 publications

(512 reference statements)

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“…Shared expectations, selective patterning of attention and behavior, cultural evolution, the formation of joint goals, implicit learning, and cultural inheritance underpin an active inference account of cognition [124]. Humans are predisposed to align their mental states with others of the same kind, and human communication can largely be cast as a cooperative activity that serves to reduce uncertainty [178]. In the process, communication and the culture that it spawns offloads some of the computational demand that an individual would otherwise face onto the larger cultural (and physical) niche.…”

Section: Self-similarity Cooperation Cognition and Communicationmentioning

confidence: 99%

Science-Driven Societal Transformation, Part I: Worldview

Boik

2020

Sustainability

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Humanity faces serious social and environmental problems, including climate change and biodiversity loss. Increasingly, scientists, global policy experts, and the general public conclude that incremental approaches to reduce risk are insufficient and transformative change is needed across all sectors of society. However, the meaning of transformation is still unsettled in the literature, as is the proper role of science in fostering it. This paper is the first in a three-part series that adds to the discussion by proposing a novel science-driven research-and-development program aimed at societal transformation. More than a proposal, it offers a perspective and conceptual framework from which societal transformation might be approached. As part of this, it advances a formal mechanics with which to model and understand self-organizing societies of individuals. While acknowledging the necessity of reform to existing societal systems (e.g., governance, economic, and financial systems), the focus of the series is on transformation understood as systems change or systems migration—the de novo development of and migration to new societal systems. The series provides definitions, aims, reasoning, worldview, and a theory of change, and discusses fitness metrics and design principles for new systems. This first paper proposes a worldview, built using ideas from evolutionary biology, complex systems science, cognitive sciences, and information theory, which is intended to serve as the foundation for the R&D program. Subsequent papers in the series build on the worldview to address fitness metrics, system design, and other topics.

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Section: Self-similarity Cooperation Cognition and Communicationmentioning

confidence: 99%

Science-Driven Societal Transformation, Part I: Worldview

Boik

2020

Sustainability

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“…Generative models are at the heart of things like the free energy principle and dynamic causal modelling (Friston, Moran, and Seth 2013;Friston 2019), in the sense that these approaches to 'sense-making' rest on model evidence: a function (i.e., marginal likelihood) of some data, under probabilistic beliefs about how those data were caused; i.e., a generative model. For example, dynamic causal modelling-originally developed in computational neuroscience (Friston 2019)-is gaining prominence in several other fields, such as cognitive and social psychology (e.g., Vasil et al 2020) and epidemiology (e.g., Friston et al 2020).…”

Section: Generative Modellingmentioning

confidence: 99%

From generative models to generative passages: A computational approach to (neuro)phenomenology

Mj¹,

Hesp²,

Sandved-Smith³

et al. 2021

Preprint

Self Cite

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This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. We call this approach computational phenomenology because it applies methods originally developed in computational modelling to phenomenology. The first section presents a brief review of the project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project, and situates our project with respect to these projects. The third section reviews the generative modelling framework. The following section presents our new approach to neurophenomenology based on generative modelling. We then discuss how this application of generative modelling differs from previous attempts to use it to explain consciousness. In summary, generative modelling allows us to construct a computational model of the inferential or interpretive process that best explain this or that kind of lived experience.

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“…Consequently, it has been suggested that the variational free energy of a system's sensory states is a measure of how well the system fits its niche. Since other systems are also part of the environment, social interaction can also be seen as Active Inference, including communication (Vasil, Badcock, Constant, Friston & Ramstead, 2020) and mental alignment (Friston & Frith, 2015;Ramstead, Veissière & Kirmayer, 2016) on a fast scale and formation of norms as a kind of social niche construction on longer timescales (Veissière, Constant, Ramstead, Friston, & Kirmayer, 2019).…”

Section: The Free Energy Principlementioning

confidence: 99%

Phi Fluctuates with Surprise: An empirical pre-study for the synthesis of the Free Energy Principle and Integrated Information Theory

Pt¹,

Cl²,

Ito³

et al. 2020

Preprint

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The Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical frameworks, the first aiming to make a general formal description of self-organization and life-like processes, and the second attempting a mathematical theory of conscious experience based on the intrinsic properties of a system. They are each concerned with complementary aspects of the properties of systems, one with life and behavior the other with meaning and experience, so combining them has potentially great scientific value. In this paper, we take a first step towards this synthesis by first partially replicating the results of the evolutionary simulation study by Albantakis et al. (2014) that show a relationship between IIT-measures and fitness in differing complexities of tasks. We then relate FEP-related information theoretic measures to this result, finding that the surprisal of simulated agents’ system states follows the general increase in fitness over evolutionary time, and that it fluctuates together with IIT-based consciousness measures in within-trial time. This suggests that the consciousness measures of IIT indirectly depend on the relation between the agent and the external world, and that they therefore should be related to concepts directly used in the FEP. Lastly, we suggest a future approach for investigating this relationship empirically.

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A World Unto Itself: Human Communication as Active Inference

Cited by 79 publications

References 316 publications

Science-Driven Societal Transformation, Part I: Worldview

Science-Driven Societal Transformation, Part I: Worldview

From generative models to generative passages: A computational approach to (neuro)phenomenology

Phi Fluctuates with Surprise: An empirical pre-study for the synthesis of the Free Energy Principle and Integrated Information Theory

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