Active inference on discrete state-spaces: A synthesis

Journal of Mathematical Psychology, Journal Year: 2020, Volume and Issue: 99, P. 102447 - 102447

Published: Nov. 6, 2020

Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, associated process theory has grown to incorporate complex generative models, enabling simulation of wide range behaviours. Due successive developments active inference, it often difficult see how underlying relates theories practical implementation. In this paper, we try bridge gap by providing complete mathematical synthesis on discrete state-space models. This technical summary provides an overview the theory, derives neuronal dynamics from first principles processes. Furthermore, paper fundamental building block needed understand for mixed models; allowing continuous sensations inform representations. may be used as follows: guide research towards outstanding challenges, implement simulate experimental behaviour, pointer various in-silico neurophysiological responses that make empirical predictions.

Language: Английский

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A step-by-step tutorial on active inference and its application to empirical data

Journal of Mathematical Psychology, Journal Year: 2022, Volume and Issue: 107, P. 102632 - 102632

Published: Feb. 4, 2022

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity years useful approach for modeling neurocognitive processes. This framework is highly general flexible ability to be customized model any cognitive process, well simulate predicted neuronal responses based on accompanying neural theory. It also affords both simulation experiments proof of principle behavioral empirical studies. However, there are limited resources that explain how build run these models practice, which limits their widespread use. Most introductions assume technical background programming, mathematics, machine learning. In this paper we offer step-by-step tutorial POMDPs, simulations using standard MATLAB routines, fit data. We minimal programming thoroughly all equations, provide exemplar scripts can theoretical Our goal the reader with requisite knowledge practical tools apply own research. optional sections multiple appendices, interested additional details. should necessary use follow emerging advances

Language: Английский

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A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders

PLoS Computational Biology, Journal Year: 2020, Volume and Issue: 16(12), P. e1008484 - e1008484

Published: Dec. 14, 2020

Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate transdiagnostic emergence psychopathology. Specifically, it has been suggested that, certain psychiatric disorders, processing mechanisms either over-weight or under-weight from viscera (or both), leading to a failure accurately update about body. However, this not directly tested empirically. To evaluate potential roles and precision context, we fit Bayesian computational model behavior patient sample during an awareness (heartbeat tapping) task. Modelling revealed perturbation condition (inspiratory breath-holding heartbeat tapping), healthy individuals (N = 52) assigned greater ascending cardiac than with symptoms anxiety 15), depression 69), co-morbid depression/anxiety 153), substance use disorders 131), eating 14)–who failed increase their estimates resting levels. In contrast, did find strong evidence for differences beliefs. These results provide first empirical modeling selective dysfunction adaptive conditions, lay groundwork future studies examining how reduced influences visceral regulation interoceptively-guided decision-making.

Language: Английский

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Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Psychiatry and Clinical Neurosciences, Journal Year: 2020, Volume and Issue: 75(1), P. 3 - 13

Published: Aug. 29, 2020

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological psychiatric disorders. In recent years, has converged notion 'prediction machine,' it actively predicts sensory input receive if one or another course action chosen. These predictions are used select actions (most often, long run) maintain body within narrow range physiological states consistent with survival. This insight given rise an area computational research focuses characterizing neural circuit architectures can accomplish these predictive functions, how associated processes may break down become aberrant conditions. Here, we provide brief review examples work application processing models function study (psychiatric) disorders, aim highlighting current directions their potential utility. We offer conceptual models, formal mathematical applications such empirical populations, focus making this material accessible clinicians without expertise neuroscience. doing so, highlight insights opportunities as prediction machine practice.

Language: Английский

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Computational psychiatry: from synapses to sentience

Molecular Psychiatry, Journal Year: 2022, Volume and Issue: 28(1), P. 256 - 268

Published: Sept. 2, 2022

Abstract This review considers computational psychiatry from a particular viewpoint: namely, commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion generative model as underwriting (i) sentient processing brain, and (ii) scientific process psychiatry. The story starts with view brain—from cognitive neuroscience—as an organ inference prediction. offers formal description neuronal message passing, distributed belief propagation networks; how certain kinds dysconnection lead aberrant updating false inference. dysconnections question can be read pernicious synaptopathy that fits comfortably notions we—or our brains—encode uncertainty or its complement, precision . then ensuing theories are tested empirically, emphasis modelling circuits synaptic gain control mediates attentional set, active inference, learning planning. opportunities afforded by this sort considered light silico experiments; neuropsychology, phenotyping promises nosology for resulting survey approaches is not scholarly exhaustive. Rather, aim theoretical narrative emerging across subdisciplines within empirical scales investigation. These range epilepsy research neurodegenerative disorders; post-traumatic stress disorder management chronic pain, schizophrenia functional medical symptoms.

Language: Английский

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Play in predictive minds: A cognitive theory of play.

Psychological Review, Journal Year: 2022, Volume and Issue: 130(2), P. 462 - 479

Published: June 16, 2022

In this article, we argue that a predictive processing framework (PP) may provide elements for proximate model of play in children and adults.We propose is behavior which the agent, contexts freedom from demands certain competing cognitive systems, deliberately seeks out or creates surprising situations gravitate toward sweet-spots relative complexity with goal resolving surprise.We further experientially associated feel-good quality because agent reducing significant levels prediction error (i.e., surprise) faster than expected.We can unify range well-established findings developmental research highlights role learning, casts as Bayesian learners.The theory integrates positive valence explaining why fun); what it to be playful mood.Central account idea agents create establish an environment tailored generation resolution surprise uncertainty.Play emerges here variety niche construction where organism modulates its physical social order maximize productive potential surprise.

Language: Английский

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Subjective signal strength distinguishes reality from imagination

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: March 23, 2023

Humans are voracious imaginers, with internal simulations supporting memory, planning and decision-making. Because the neural mechanisms imagery overlap those perception, a foundational question is how reality imagination kept apart. One possibility that intention to imagine used identify discount self-generated signals during imagery. Alternatively, because internally generated generally weaker, sensory strength index reality. Traditional psychology experiments struggle investigate this issue as subjects can rapidly learn real stimuli in play. Here, we combined one-trial-per-participant psychophysics computational modelling neuroimaging show imagined perceived fact intermixed, judgments of being determined by whether intermixed signal strong enough cross threshold. A consequence account when virtual or enough, they become subjectively indistinguishable from

Language: Английский

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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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From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition

Biology & Philosophy, Journal Year: 2020, Volume and Issue: 35(3)

Published: April 29, 2020

Language: Английский

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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; Toward Solving the Hard Problem and Characterizing Agentic Causation

Frontiers in Artificial Intelligence, Journal Year: 2020, Volume and Issue: 3

Published: June 9, 2020

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

Language: Английский

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