Daniel Alexander Braun, Institute of Neural Information Processing Faculty of Engineering, Computer Science and Psychology, Ulm University | Bounded rationality in sensorimotor learning and decision-making

Abstract

Expected utility maximization is the foundation of statistical decision-making, but ignores the problem of limited computational resources. Here we consider information-theoretic approaches for decision-making with limited resources including limited sample size or model uncertainty. We discuss sensorimotor learning and decision-making experiments and investigate how such limitations can lead to interesting deviations from Bayes-optimal behavior including robustness, the formation of abstractions and the coupling of perception and action.

Literature

• Structure Learning in Bayesian Sensorimotor Integration (Link)
• Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context. Experiments and an Information-Theoretic Ambiguity Model (Link)
• Thermodynamics as a theory of decision-making with information-processing costs (Link)