The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition

Linson, Adam; Clark, Andy; Ramamoorthy, Subramanian; Friston, Karl J.

doi:10.3389/frobt.2018.00021

Cited by 73 publications

(63 citation statements)

References 142 publications

(184 reference statements)

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“…Active inference also provides a suitable framework for investigating the emergence of action-oriented models. Previous work has highlighted the fact that active inference is consistent with, and necessarily prescribes, frugal and parsimonious generative models, thus providing a potential bridge between 'representation-hungry' approaches to cognition espoused by classical cognitivism and the 'representation-free' approaches advocated by embodied and enactive approaches (Kiverstein, 2018;Kirchhoff and Robertson, 2018;Ramstead et al, 2019;Negru, 2018;Linson et al, 2018;Clark, 2015;Williams, 2018;Kirchhoff Michael et al, 2018;Kirchhoff and Froese, 2017;Friston, 2013;Baltieri and Buckley, 2019a,c,b).…”

Section: Active Inferencementioning

confidence: 99%

Learning action-oriented models through active inference

Tschantz

Seth

Buckley

2019

Preprint

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Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

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Section: Active Inferencementioning

confidence: 99%

Learning action-oriented models through active inference

Tschantz

Seth

Buckley

2019

Preprint

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“…3 See Linson et al (2018), for a lucid explication of the deep continuities between thermodynamics and the free energy principle. For a more technical exposition, see Sengupta et al 2013. agent's phenotype (Friston et al, , 2010a, insofar as the phenotype is simply a description of the organism's characteristic (i.e.…”

Section: Life Formalised: Thermodynamics Attracting Sets and (Un)cmentioning

confidence: 99%

From Allostatic Agents to Counterfactual Cognisers: Active Inference, Biological Regulation, and The Origins of Cognition

Corcoran

Pezzulo

Hohwy

2019

Preprint

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What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity (Godfrey-Smith 1996). Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems – even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology and computational neuroscience, we cash these distinctions out in terms of the representation and resolution of different varieties of uncertainty. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, one that enables the agent to detach from the present and engage in counterfactual (active) inference.

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“…This hypothesis is consistent with Clark's (2018) suggestion that coherent and precise inference stems from requirements for engaging with environments via sensorimotor couplings (Clark, 2016). Along these lines, by enabling the generation of inferences with rapidity and reliability, SOHMs could afford approximate models capable of guiding action-perception cycles and decision-making (Von Uexküll, 1957;Fuster, 2009;Madl et al, 2011;Vul et al, 2014;Linson et al, 2018;Parr and Friston, 2018b). Further, these sensorimotor engagements may promote SOHM-formation by providing coherent sources of correlated information, so affording the possibility of learning even more sophisticated models (Pfeifer and Bongard, 2006;Safron, 2019cSafron, , 2019a.…”

Section: Sohms As Dynamic Cores Of Integrated Information and Workpacesmentioning

confidence: 99%

“…Self-processes may be practically necessary for consciousness because the integration of largescale brain activity may be required for the coherent regulation of action-perception cycles, and thereby cybernetic sense-making. Self-processes could allow for selection of specific models on the basis of relevance (Shanahan and Baars, 2005;Davey and Harrison, 2018;Linson et al, 2018;Hattori et al, 2019), with stable self-models extending this organization across time (Hirsh et al, 2013;Dennett, 2014;Buonomano, 2017), thereby enabling the learning required to construct experienceable world models. In brief, IWMT proposes that Kant's preconditions for judgment are also necessary preconditions for consciousness (Northoff, 2012;De Kock, 2016).…”

Section: Iwmt and Maximizing Sohms: Bringing Forth Worlds Of Experiencementioning

confidence: 99%

An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation

Safron¹

2019

Preprint

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The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that connection with external reality, systems could have arbitrarily high amounts of integrated information, but nonetheless would not entail subjective experience. I further describe how an integration of these frameworks may contribute to their evolution as unified systems theories and models of emergent causation. Then, inspired by both Global Neuronal Workspace Theory (GNWT) and the Harmonic Brain Modes framework, I describe how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs). These integrating dynamics may be particularly likely to occur via richly connected subnetworks affording body-centric sources of phenomenal binding and executive control. Along these connectivity backbones, SOHMs are proposed to implement loopy message passing (cf. turbo-codes) over predictive (autoencoding) networks, so generating maximum a posteriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, so affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, so enabling inferential synergy.

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The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition

Cited by 73 publications

References 142 publications

Learning action-oriented models through active inference

Learning action-oriented models through active inference

From Allostatic Agents to Counterfactual Cognisers: Active Inference, Biological Regulation, and The Origins of Cognition

An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation

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