Computational neuroscience of human decision-making and social interaction

Abstract: 

My talk will explore the computational foundations of human complex cognition, emphasising how internal models guide decision-making and information acquisition, with a focus on the prefrontal cortex. Bayesian approaches—particularly partially observable Markov decision processes (POMDPs)—provide a framework for linking these processes to brain activity measured with fMRI. I will outline the theoretical background and present applications including navigation under uncertainty, social decision-making, and observational learning. I will also describe recent work on hierarchical models of navigation, confidence, and avoidance learning. Finally, I will discuss future directions, including clinical applications and neurotechnology, where adaptive paradigms such as neurofeedback and closed-loop interventions offer new ways to shape behaviour and support rehabilitation.

Bio: 

Dr Wako Yoshida is a researcher at the University of Oxford. She has previously held research positions at Kyoto University, ATR Japan, and University College London. Her work focuses on the computational neuroscience of human decision-making and social interaction, with particular emphasis on the role of the prefrontal cortex in resolving uncertainty and constructing beliefs. She has also studied social cognition, including Theory of Mind and cooperative group decision-making, using approaches such as fMRI hyperscanning. Her research has been supported by grants from EPSRC, MEXT, JSPS, and ARIA.