Titlebook: Active Inference; First International Tim Verbelen,Pablo Lanillos,Cedric De Boom Conference proceedings 2020 Springer Nature Switzerland A

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Die internationale Ebene (UNESCO)ur previous finding that healthy individuals adaptively adjust prior expectations and interoceptive sensory precision estimates based on task context. This offers further support for the utility of computational approaches to characterizing the dynamics of interoceptive processing.

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https://doi.org/10.1007/978-3-531-93289-7el evidence. Control-as-Inference (CAI) is a framework within reinforcement learning which casts decision making as a variational inference problem. While these frameworks both consider action selection through the lens of variational inference, their relationship remains unclear. Here, we provide a

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Autobiographical Memory and Survey Researchoundation that is often expressed in terms of the Fokker-Planck equation. Easy-to-follow examples of this formalism are scarce, leaving a high barrier of entry to the field. In this paper we provide a worked example of an active inference agent as a hierarchical Gaussian generative model. We proceed

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Affect and Memory in Retrospective Reports input and output signals. Here, the nonlinear stochastic differential equation of a Duffing oscillator is cast to a generative model and dynamical parameters are inferred using variational message passing on a factor graph of the model. The approach is validated with an experiment on data from an e

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Telescoping and Temporal Memoryintended to minimize future surprise. We show that surprise minimization relying on Bayesian inference can be achieved by filtering of the sufficient statistic time series of exponential family input distributions, and we propose the hierarchical Gaussian filter (HGF) as an appropriate, efficient, a

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Introduction: Remembering Alpurrurulam,t models are limited to fully observable domains. In this paper, we describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. The deep learning architecture optimizes a variant of the expected free energy and encodes the continuous state