Wako Yoshida
Researcher
Research interests
My research addresses the computational neuroscience of human cognitive decision making and social interaction, with a particular focus on the function of the prefrontal cortex.
Every day we try our best to make optimal decisions, but the information we get from our environment is often uncertain and incomplete. In machine learning, these situations are referred to as partially observable decision-making problems, in which the true (or hidden) state of the world cannot be directly observed. Instead, we can only estimate the hidden state of the world using available observations, and make decisions based on our inferred 'beliefs'. I’m studying the mechanisms by which uncertainty is resolved and beliefs constructed in the brain.
Another especially interesting type of cognitive decision-making problem occurs in social communication, as the ability to optimise mutual interactions requires the ability to read each others’ minds. My previous work probes some of the complexities of how Theory of Mind is achieved in the brain, and at the moment I am studying human brain activity during group decision-making tasks, including 'hyper-scanning' two subjects as they interact (cooperate) to solve a task together in separate fMRI scanners.
Key publications
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Confidence modulates the decodability of scene prediction during partially-observable maze exploration in humans.
Journal article
Katayama R. et al, (2022), Commun Biol, 5
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Pain Control by Co-adaptive Learning in a Brain-Machine Interface
Journal article
Zhang S. et al, (2020), Current Biology, 30, 3935 - 3944.e7
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Game Theory of Mind
Journal article
Yoshida W. et al, (2008), PLoS Computational Biology, 4, e1000254 - e1000254
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Resolution of Uncertainty in Prefrontal Cortex
Journal article
Yoshida W. and Ishii S., (2006), Neuron, 50, 781 - 789
Recent publications
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Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks
Journal article
Schreiber S. et al, (2024), Wellcome Open Research, 9, 423 - 423
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Belief inference for hierarchical hidden states in spatial navigation
Journal article
Katayama R. et al, (2024), Communications Biology, 7
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THE NEURAL BASES OF PRIOR AND LIKELIHOOD UNCERTAINTY
Journal article
Hayashi K. et al, (2023), IBRO Neuroscience Reports, 15, S687 - S687
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Deep learning-based image deconstruction method with maintained saliency.
Journal article
Fujimoto K. et al, (2022), Neural Netw, 155, 224 - 241
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Confidence modulates the decodability of scene prediction during partially-observable maze exploration in humans.
Journal article
Katayama R. et al, (2022), Commun Biol, 5