( Friston et al., 2014 FitzGerald et al., 2015a) Illustration of risk sensitive or KL control in an engineering benchmarkĭemonstration of how beliefs states are absorbed into a generative modelĪssociating dopamine with the encoding of (expected) precision provides a plausible account of dopaminergic discharges Optimal control (the mountain car problem) Initial formulation of active inference for Markov decision processes and sequential policy optimisation Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations reproducing mismatch negativity and P300 responses respectively. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models.
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