Active inference as a theory of sentient behavior

Biological Psychology, Год журнала: 2024, Номер 186, С. 108741 - 108741

Опубликована: Янв. 4, 2024

This review paper offers an overview of the history and future active inference—a unifying perspective on action perception. Active inference is based upon idea that sentient behavior depends our brains' implicit use internal models to predict, infer, direct action. Our focus conceptual roots development this theory (basic) sentience does not follow a rigid chronological narrative. We trace evolution from Helmholtzian ideas unconscious inference, through contemporary understanding In doing so, we touch related perspectives, neural underpinnings opportunities for development. Key steps in include formulation predictive coding theories neuronal message passing, sequential planning policy optimization, importance hierarchical (temporally) deep (i.e., generative or world) models. has been used account aspects anatomy neurophysiology, offer psychopathology terms aberrant precision control, unify extant psychological theories. anticipate further all these areas note exciting early work applying beyond neuroscience. suggests just biology, but robotics, machine learning, artificial intelligence.

Язык: Английский

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The evolution of brain architectures for predictive coding and active inference

Philosophical Transactions of the Royal Society B Biological Sciences, Год журнала: 2021, Номер 377(1844)

Опубликована: Дек. 27, 2021

This article considers the evolution of brain architectures for predictive processing. We argue that mechanisms perception and action are not late evolutionary additions advanced creatures like us. Rather, they emerged gradually from simpler loops (e.g. autonomic motor reflexes) were a legacy our earlier ancestors—and key to solving their fundamental problems adaptive regulation. characterize simpler-to-more-complex brains formally, in terms generative models include increasing hierarchical breadth depth. These may start simple homeostatic motif be elaborated during four main ways: these multimodal expansion control into an allostatic loop; its duplication form multiple sensorimotor expand animal's behavioural repertoire; gradual endowment with depth (to deal aspects world unfold at different spatial scales) temporal select plans future-oriented manner). In turn, elaborations underwrite solution biological regulation faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising—about processing—with comparative data on animal species. is part theme issue ‘Systems neuroscience through lens theory’.

Язык: Английский

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Generating meaning: active inference and the scope and limits of passive AI

Trends in Cognitive Sciences, Год журнала: 2023, Номер 28(2), С. 97 - 112

Опубликована: Ноя. 15, 2023

Prominent accounts of sentient behavior depict brains as generative models organismic interaction with the world, evincing intriguing similarities current advances in artificial intelligence (AI). However, because they contend control purposive, life-sustaining sensorimotor interactions, living organisms are inextricably anchored to body and world. Unlike passive learned by AI systems, must capture sensory consequences action. This allows embodied agents intervene upon their worlds ways that constantly put best test, thus providing a solid bedrock is – we argue essential development genuine understanding. We review resulting implications consider future directions for AI.

Язык: Английский

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Neural representation in active inference: Using generative models to interact with—and understand—the lived world

Annals of the New York Academy of Sciences, Год журнала: 2024, Номер 1534(1), С. 45 - 68

Опубликована: Март 25, 2024

Abstract This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that learns navigate world adaptively, not (or solely) understand it. Different may possess an array models, spanning from those support action‐perception cycles underwrite planning imagination; namely, explicit entail variables predicting concurrent sensations, like objects, faces, or people—to action‐oriented predict action outcomes. then elucidates belief dynamics might link implications different types agent's cognitive capabilities in relation its ecological niche. concludes open questions regarding evolution development advanced abilities—and gradual transition pragmatic detached representations. on offer foregrounds diverse roles play processes representation.

Язык: Английский

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Active inference unifies intentional and conflict-resolution imperatives of motor control

PLoS Computational Biology, Год журнала: 2022, Номер 18(6), С. e1010095 - e1010095

Опубликована: Июнь 17, 2022

The field of motor control has long focused on the achievement external goals through action (e.g., reaching and grasping objects). However, recent studies in conditions multisensory conflict, such as when a subject experiences rubber hand illusion or embodies an avatar virtual reality, reveal presence unconscious movements that are not goal-directed, but rather aim at resolving conflicts; for example, by aligning position person’s arm with embodied avatar. This second, conflict-resolution imperative movement did emerge classical adaptation online corrections, which allow to reduce been largely ignored so far formal theories. Here, we propose model grounded theory active inference integrates intentional imperatives. We present three simulations showing is able characterize guided intention achieve goal, necessity resolve both. Furthermore, our fundamental difference between (active) underlying imperatives it driven two different (model sensory) kinds prediction errors. Finally, show only conflict resolution, incorrectly infers velocity zero, if was moving. result suggests novel speculative explanation fact people unaware their subtle compensatory avoid conflict. can potentially help shed light deficits awareness arise psychopathological conditions.

Язык: Английский

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Deep kinematic inference affords efficient and scalable control of bodily movements

Proceedings of the National Academy of Sciences, Год журнала: 2023, Номер 120(51)

Опубликована: Дек. 12, 2023

Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models produce sensory predictions, allows a cheaper inversion However, devising complex kinematic chains like human body challenging. We introduce an architecture that affords simple but effective via easily scales up drive chains. Rich can be specified in both using attractive or repulsive forces. The proposed model reproduces sophisticated bodily paves way for efficient biologically plausible of actuated systems.

Язык: Английский

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Dynamic planning in hierarchical active inference

Neural Networks, Год журнала: 2025, Номер 185, С. 107075 - 107075

Опубликована: Янв. 8, 2025

By dynamic planning, we refer to the ability of human brain infer and impose motor trajectories related cognitive decisions. A recent paradigm, active inference, brings fundamental insights into adaptation biological organisms, constantly striving minimize prediction errors restrict themselves life-compatible states. Over past years, many studies have shown how animal behaviors could be explained in terms inference - either as discrete decision-making or continuous control inspiring innovative solutions robotics artificial intelligence. Still, literature lacks a comprehensive outlook on effectively planning realistic actions changing environments. Setting ourselves goal modeling complex tasks such tool use, delve topic keeping mind two crucial aspects behavior: capacity understand exploit affordances for object manipulation, learn hierarchical interactions between self environment, including other agents. We start from simple unit gradually describe more advanced structures, comparing recently proposed design choices providing basic examples. This study distances itself traditional views centered neural networks reinforcement learning, points toward yet unexplored direction inference: hybrid representations models.

Язык: Английский

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Active Vision in Binocular Depth Estimation: A Top-Down Perspective

Biomimetics, Год журнала: 2023, Номер 8(5), С. 445 - 445

Опубликована: Сен. 21, 2023

Depth estimation is an ill-posed problem; objects of different shapes or dimensions, even if at distances, may project to the same image on retina. Our brain uses several cues for depth estimation, including monocular such as motion parallax and binocular diplopia. However, it remains unclear how computations required are implemented in biologically plausible ways. State-of-the-art approaches based deep neural networks implicitly describe a hierarchical feature detector. Instead, this paper we propose alternative approach that casts problem active inference. We show can be inferred by inverting generative model simultaneously predicts eyes' projections from 2D belief over object. Model inversion consists series homogeneous transformations Predictive Coding principles. Under assumption nonuniform fovea resolution, favors vision strategy fixates object with eyes, rendering more accurate. This not realized first fixating target then estimating depth; instead, combines two processes through action-perception cycles, similar mechanism saccades during recognition. The proposed requires only local (top-down bottom-up) message passing, which circuits.

Язык: Английский

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Modeling Motor Control in Continuous Time Active Inference: A Survey

IEEE Transactions on Cognitive and Developmental Systems, Год журнала: 2023, Номер 16(2), С. 485 - 500

Опубликована: Дек. 4, 2023

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus stimulus-response mappings that optimize cost functions. Ideomotor theory cybernetics propose a different perspective: they suggest are selected controlled by activating action effects continuously matching internal predictions with sensations. Active Inference offers modern formulation of these ideas, in terms inferential mechanisms prediction-error-based control, which can be linked to neural living organisms. This article provides technical illustration models continuous time brief survey solve four kinds control problems; namely, goal-directed reaching movements, active sensing, resolution multisensory conflict during movement integration decision-making motor control. Crucially, Inference, all facets emerge from same optimization process - minimization Free Energy do not require designing separate Therefore, unitary perspective various aspects inform both study biological design artificial robotic systems.

Язык: Английский

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Detection of Barely Visible Impact Damage in Polymeric Laminated Composites Using a Biomimetic Tactile Whisker

Polymers, Год журнала: 2021, Номер 13(20), С. 3587 - 3587

Опубликована: Окт. 18, 2021

This is a novel investigation on the possibility of detecting barely visible impact damage (BVID) in composite materials by whisking across surface via tactile whisker sensors that resemble rats' whiskers. A series drop tower low-velocity tests were performed quasi-isotropic plates. The plates made from unidirectional T800 carbon/MTM49-3 epoxy prepregs with stacking sequence [45/0/90/-45]4S. Investigating specimens' naked eye does not reveal any significant damage, rather than small dent surface, no tangible difference different energy levels. Ultrasonic C-scan observations showed existence BVID all levels, an increasing trend size level. collected data analyzed using support vector machine classifier, based their vibrational properties, to identify impacted region and classify severity. It was observed after training for 13 contacts, severity can be classified accuracy 100%. offering new detection technique, high potential automation reliability used as alternative or combined available inspection systems.

Язык: Английский

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