Uncertainty, epistemics and active inference

• Published in: Journal of the Royal Society interface Vol. 14; no. 136; p. 20170376
• Main Authors: Parr, Thomas, Friston, Karl J.
• Format: Journal Article
• Language: English
• Published: England The Royal Society 01.11.2017; The Royal Society Publishing
• Subjects: Acetylcholine; Acetylcholine - metabolism; Acetylcholine - physiology; Active Inference; Bayes Theorem; Bayesian; Computer Simulation; Forage; Learning - physiology; Life Sciences–Mathematics interface; Markov Chains; Models, Neurological; Neuromodulation; Noradrenaline; Norepinephrine - metabolism; Norepinephrine - physiology; Systems Biology; Uncertainty; Visual aspects; Volatility; neuromodulation; noradrenaline; acetylcholine; active inference; uncertainty; Bayesian
• ISSN: 1742-5689; 1742-5662; 1742-5662
• DOI: 10.1098/rsif.2017.0376

Biological systems—like ourselves—are constantly faced with uncertainty. Despite noisy sensory data, and volatile environments, creatures appear to actively maintain their integrity. To account for this remarkable ability to make optimal decisions in the face of a capricious world, we propose a generative model that represents the beliefs an agent might possess about their own uncertainty. By simulating a noisy and volatile environment, we demonstrate how uncertainty influences optimal epistemic (visual) foraging. In our simulations, saccades were deployed less frequently to regions with a lower sensory precision, while a greater volatility led to a shorter inhibition of return. These simulations illustrate a principled explanation for some cardinal aspects of visual foraging—and allow us to propose a correspondence between the representation of uncertainty and ascending neuromodulatory systems, complementing that suggested by Yu & Dayan (Yu & Dayan 2005 Neuron 46, 681–692. (doi:10.1016/j.neuron.2005.04.026)).