Thus, the Bayesian brain learns the causal structure of world events and refines its belief space. The Bayesian brain is thought to learn by updating its beliefs and the parameters of its GM. It is less clear whether Bayesian updating alone is sufficient to explain the genesis and evolution ...
The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. The resulting algorithms are theoretically grounded with ...
When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structu...
The first assumption is the free energy principle, which leads to active inference in the embodied context of action. This provides a principled (Bayes optimal) explanation for action and perception, in which both minimise a free energy bound on the (negative) Bayesian log evidence for a generat...
Active inference extends hierarchical predictive coding from the sensory to the motor domain; i.e., by equipping standard Bayesian filtering schemes (a.k.a. predictive coding) with classical reflex arcs that enable action (e.g., a hand movement) to fulfil predictions about hidden states of the...
The arrows represent causal relationships (i.e., conditional probability distributions). The variables highlighted in grey can be observed by the agent, while the remaining variables are inferred through approximate Bayesian inference (see Section 4) and called hidden or latent variables. Active ...
Thus, the Bayesian brain learns the causal structure of world events and refines its belief space. The Bayesian brain is thought to learn by updating its beliefs and the parameters of its GM. It is less clear whether Bayesian updating alone is sufficient to explain the genesis and evolution ...
In this paper, we introduce the Active Inference Framework (AIF), which casts the brain as a Bayesian “inference engine” that tests its “top–down” predictive models against “bottom–up” sensory error streams in its attempts to resolve uncertainty and make the world more predictable. ...
One possibility could be to integrate, in a hierarchical fashion, our AIF model with Bayesian causal inference models previously proposed for body ownership illusions8,42. These models are based on the assumption that the illusory sense of body ownership arises when the brain attributes the visual ...
Bayesian network. The step/operation of analyzing results of the execution of the one or more selected probes using a probabilistic inference may further comprise the step/operation of analyzing results of the execution of the one or more selected probes using one or more prior fault probabilities...