Active inference as a theory of sentient behavior

Biological Psychology, Journal Year: 2024, Volume and Issue: 186, P. 108741 - 108741

Published: Jan. 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.

Language: Английский

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Deep kinematic inference affords efficient and scalable control of bodily movements

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(51)

Published: Dec. 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.

Language: Английский

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Embodied decisions as active inference

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Abstract Decision-making is often conceptualized as a serial process, during which sensory evidence accumulated for the choice alternatives until certain threshold reached, at point decision made and an action executed. This decide-then-act perspective has successfully explained various facets of perceptual economic decisions in laboratory, dynamics are usually irrelevant to choice. However, living organisms face another class – called embodied that require selecting between potential courses actions be executed timely dynamic environment, e.g., lion, deciding gazelle chase how fast do so. Studies reveal two aspects goal-directed behavior stark contrast view. First, processes can unfold parallel; second, action-related components, such motor costs associated with required “change mind” them, exert feedback effect on taken. Here, we show these signatures emerge naturally active inference framework simultaneously optimizes perception action, according same (free energy minimization) imperative. We optimizing choices requires continuous loop planning (where beliefs about guide dynamics) finesse alternatives). Furthermore, our simulations normative character ecological settings namely, achieving effective balance high accuracy low risk losing valid opportunities.

Language: Английский

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Predictive strategies for the control of complex motor skills: recent insights into individual and joint actions

Current Opinion in Behavioral Sciences, Journal Year: 2025, Volume and Issue: 63, P. 101519 - 101519

Published: April 5, 2025

Language: Английский

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Dynamic planning in hierarchical active inference

Neural Networks, Journal Year: 2025, Volume and Issue: 185, P. 107075 - 107075

Published: Jan. 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.

Language: Английский

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

Biomimetics, Journal Year: 2023, Volume and Issue: 8(5), P. 445 - 445

Published: Sept. 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.

Language: Английский

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The functional role of conscious sensation of movement

Neuroscience & Biobehavioral Reviews, Journal Year: 2024, Volume and Issue: 164, P. 105813 - 105813

Published: July 15, 2024

This paper proposes a new framework for investigating neural signals sufficient conscious sensation of movement and their role in motor control. We focus on proprioceptive awareness, particularly from muscle spindle activation primary cortex (M1). Our review vibration studies reveals that afferent alone can induce sensations movement. Similarly, employing peripheral nerve blocks suggest efferent M1 are On this basis, we show competing theories control assign different roles to According command theories, corresponds an estimation the current state based signals, predictions. In contrast, within active inference architectures, correspond predictions driven by M1. The provides way critically compare evaluate two theories. analysis offers insights into functional consciousness.

Language: Английский

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Slow but flexible or fast but rigid? Discrete and continuous processes compared

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e39129 - e39129

Published: Oct. 1, 2024

Language: Английский

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Modeling Motor Control in Continuous Time Active Inference: A Survey

IEEE Transactions on Cognitive and Developmental Systems, Journal Year: 2023, Volume and Issue: 16(2), P. 485 - 500

Published: Dec. 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.

Language: Английский

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Slow but flexible or fast but rigid? Discrete and continuous processes compared

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 21, 2023

A bstract tradeoff exists when dealing with complex tasks composed of multiple steps. High-level cognitive processes can find the best sequence actions to achieve a goal in uncertain environments, but they are slow and require significant computational demand. In contrast, lower-level processing allows reacting environmental stimuli rapidly, limited capacity determine optimal or replan expectations not met. Through reiteration same task, biological organisms tradeoff: from action primitives, composite trajectories gradually emerge by creating task-specific neural structures. The two frameworks active inference – recent brain paradigm that views perception as subject free energy minimization imperative well capture high-level low-level human behavior, how task specialization occurs these terms is still unclear. this study, we compare strategies on dynamic pick-and-place task: hybrid (discrete-continuous) model planning capabilities continuous-only fixed transitions. Both models rely hierarchical (intrinsic extrinsic) structure, suited for defining reaching grasping movements, respectively. Our results show perform better minimal resource expenditure at cost less flexibility. Finally, propose discrete might lead continuous attractors different motor learning phases, laying foundations further studies bio-inspired adaptation.

Language: Английский

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