| 研究実績の概要 |
Uncertainty is quantified via Bayesian models of optimal behaviour, studying their relations to stochastic (thermo)dynamics in physics, symplectic structures in information geometry and goal-directed agents in optimal control/reinforcement learning via their consistent mathematical formulation.
The project includes mathematical analysis and computational models of decision making mechanisms under uncertainty. This uncertainty is quantified via Bayesian models of optimal behaviour, studying their relations to stochastic (thermo)dynamics in physics, symplectic structures in information geometry and goal-directed agents in optimal control/reinforcement learning via their consistent mathematical formulation.
Our work presents an analysis of an emergent framework in neuroscience, cognitive science and biology: the free energy principle. This framework is a popular way of describing behaviour and neural activity in terms of predictive models encoding information about the world. More recently, this mathematical framework has been proposed to account for models of individuality and origins of life, casting the definition of “individual” (or even “agent”) in terms of statistical separations from the outside world. Our paper provides an extensive review and analysis of mathematical assumptions, philosophical commitments and applications to neuroscience of this frameworks, identifying significant flaws that still remain at the moment.
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