Communication as Socially Extended Active Inference: An Ecological Approach to Communicative Behavior

Tison, Rémi; Poirier, Pierre

doi:10.1080/10407413.2021.1965480

Cited by 17 publications

(9 citation statements)

References 209 publications

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“…The majority of applications have focused on cognitive neuroscience, with a particular focus on modelling decision-making under uncertainty. Nonetheless, the framework has broad applicability and has recently been applied to diverse disciplines, ranging from computational models of psychopathology [5,6,7,8], control theory [9,10,11] and reinforcement learning [12,13,14,15,16], through to social cognition [17,18,19] and even real-world engineering problems [20,21,22]. While in recent years, some of the code arising from the active inference literature has been written in open source languages like Python and Julia [23,24,25,26,16], to-date, the most popular software for simulating active inference agents is the DEM toolbox of SPM [27,28].…”

Section: Statement Of Needmentioning

confidence: 99%

pymdp: A Python library for active inference in discrete state spaces

Heins,

Millidge,

Demekas

et al. 2022

Preprint

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Section: Statement Of Needmentioning

confidence: 99%

pymdp: A Python library for active inference in discrete state spaces

Heins,

Millidge,

Demekas

et al. 2022

Preprint

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“…Many of the theoretical claims of 4E cognitive science can be operationalized in terms of current models of active inference in computational psychiatry ( Badcock et al, 2019 ; Hipólito and van Es, 2022 ). This allows us to build models of cognitive function and adaptation that include both the brain and the social world, through interactions with other people and institutions that present cultural affordances ( Ramstead et al, 2016 ; Kirmayer and Ramstead, 2017 ; Veissière et al, 2020 ; Tison and Poirier, 2021 ; Constant et al, 2022 ).…”

Section: Toward a Person-centered Ecosocial Neuroscience For Precisio...mentioning

confidence: 99%

Restoring the missing person to personalized medicine and precision psychiatry

et al. 2023

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Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute of Mental Health Research Domain Criteria (RDoC), multilevel biological “omics” data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.

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“…From this point of view, to select an action is to infer 'what I must be doing, given what I believe and what I sense'. Extensive work has been done in the field of active inference to study social systems and the way in which the minimisation of free energy could give rise to (eventually large-scale) behavioural coordination [3,15,[78][79][80][81][82][83]. However, much of this work is still theoretical.…”

Section: An Active Inference Model Of Epistemic Communitiesmentioning

confidence: 99%

Epistemic Communities under Active Inference

Albarracin

Daphne

Ramstead

et al. 2022

Entropy

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The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

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Communication as Socially Extended Active Inference: An Ecological Approach to Communicative Behavior

Cited by 17 publications

References 209 publications

pymdp: A Python library for active inference in discrete state spaces

pymdp: A Python library for active inference in discrete state spaces

Restoring the missing person to personalized medicine and precision psychiatry

Epistemic Communities under Active Inference

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