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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Active Inference: Demystified and Compared

Neural Computation, Journal Year: 2021, Volume and Issue: 33(3), P. 674 - 712

Published: Jan. 5, 2021

Active inference is a first principle account of how autonomous agents operate in dynamic, non-stationary environments. This problem also considered reinforcement learning (RL), but limited work exists on comparing the two approaches same discrete-state In this paper, we provide: 1) an accessible overview formulation active inference, highlighting natural behaviors that are generally engineered RL; 2) explicit comparison between and RL OpenAI gym baseline. We begin by providing condensed literature, particular viewing various through lens RL. show operating pure belief-based setting, can carry out epistemic exploration, for uncertainty about their environment Bayes-optimal fashion. Furthermore, reliance reward signal removed where simply be treated as another observation; even total absence rewards, agent learned preference learning. make these properties showing scenarios which infer reward-free environments compared to both Q-learning Bayesian model-based agents; placing zero prior preferences over rewards observations corresponding reward. conclude noting formalism applied more complex settings if appropriate generative models formulated. short, aim demystify behavior presenting discrete state-space time formulation, demonstrate environment, alongside agents.

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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In the Body’s Eye: The computational anatomy of interoceptive inference

PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(9), P. e1010490 - e1010490

Published: Sept. 13, 2022

A growing body of evidence highlights the intricate linkage exteroceptive perception to rhythmic activity visceral body. In parallel, interoceptive inference theories affective and self-consciousness are on rise in cognitive science. However, thus far no formal theory has emerged integrate these twin domains; instead, most extant work is conceptual nature. Here, we introduce a model cardiac active inference, which explains how ascending signals entrain sensory uncertainty. Through simulated psychophysics, reproduce defensive startle reflex commonly reported effects linking cycle behaviour. We further show that ‘interoceptive lesions’ blunt expectations, induce psychosomatic hallucinations, exacerbate biases perceptual synthetic heart-rate variability analyses, illustrate balance arousal-priors prediction errors produces idiosyncratic patterns physiological reactivity. Our offers roadmap for computationally phenotyping disordered brain-body interaction.

Language: Английский

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The free energy principle made simpler but not too simple

Physics Reports, Journal Year: 2023, Volume and Issue: 1024, P. 1 - 29

Published: June 1, 2023

This paper provides a concise description of the free energy principle, starting from formulation random dynamical systems in terms Langevin equation and ending with Bayesian mechanics that can be read as physics sentience. It rehearses key steps using standard results statistical physics. These entail (i) establishing particular partition states based upon conditional independencies inherit sparsely coupled dynamics, (ii) unpacking implications this inference (iii) describing paths variational principle least action. Teleologically, offers normative account self-organisation optimal design decision-making, sense maximising marginal likelihood or model evidence. In summary, world systems, we end up sentient behaviour interpreted self-evidencing; namely, self-assembly, autopoiesis active inference.

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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The Emperor's New Markov Blankets

Behavioral and Brain Sciences, Journal Year: 2021, Volume and Issue: 45

Published: Oct. 22, 2021

The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing objective function of variational energy. Following this premise, it claims to deliver a promising integration life sciences. In recent work, Markov blankets, one central constructs have been applied resolve debates philosophy (such as demarcating boundaries mind). aim paper is twofold. First, we trace development blankets starting standard application Bayesian networks, via inference, use literature on active inference. We then identify persistent confusion between formal epistemic tool for novel metaphysical demarcate physical boundary agent its environment. Consequently, propose distinguish "Pearl blankets" refer original "Friston new construct. Second, distinction critically assess resting philosophical problems. suggest would do well differentiating two different research programmes: "inference model" within model." Only latter capable doing work but requires additional premises cannot be justified appeal success mathematical alone.

Language: Английский

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Sophisticated Inference

Neural Computation, Journal Year: 2021, Volume and Issue: 33(3), P. 713 - 763

Published: Feb. 24, 2021

Active inference offers a first principle account of sentient behavior, from which special and important cases—for example, reinforcement learning, active Bayes optimal inference, design—can be derived. finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on same footing as reward or value. In brief, replaces value functions with functionals (Bayesian) beliefs, form an expected (variational) free energy. this letter, we consider sophisticated kind using recursive Sophistication describes degree agent has beliefs about beliefs. We agents counterfactual consequences action for states affairs those latent states. other words, move simply considering “what would happen if I did that” believe what that.” The energy functional effectively implements deep tree search over actions outcomes future. Crucially, is sequences belief opposed per se. illustrate competence scheme numerical simulations decision problems.

Language: Английский

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How particular is the physics of the free energy principle?

Physics of Life Reviews, Journal Year: 2021, Volume and Issue: 40, P. 24 - 50

Published: Nov. 23, 2021

The free energy principle (FEP) states that any dynamical system can be interpreted as performing Bayesian inference upon its surrounding environment. Although, in theory, the FEP applies to a wide variety of systems, there has been almost no direct exploration or demonstration concrete systems. In this work, we examine depth assumptions required derive simplest possible set systems – weakly-coupled non-equilibrium linear stochastic Specifically, explore (i) how general requirements imposed on statistical structure are and (ii) informative is about behaviour such We discover two Markov blanket condition (i.e. boundary precluding coupling between internal external states) stringent restrictions solenoidal flows tendencies driving out equilibrium) only valid for very narrow space parameters. Suitable require an absence perception-action asymmetries highly unusual living interacting with More importantly, observe mathematically central step argument, connecting variational inference, relies implicit equivalence dynamics average those states. This does not hold even since it requires effective decoupling from system's history interactions. These observations critical evaluating generality applicability indicate existence significant problems theory current form. issues make FEP, stands, straightforwardly applicable simple studied here suggest more development needed before could applied kind complex describe cognitive processes.

Language: Английский

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