Human decision-making is a complex computational process involving prediction of possible outcomes, evaluation of alternatives, selection of action, and learning with feedback from the outcomes. In addition, impaired decision-making which results in fatal behaviors like suicide is frequently observed in diverse neuropsychiatric disorders such as addiction, ADHD, schizophrenia. Economists who have examined human rational choice as well as neuroscientists who have studied related issues such as reward, learning, and emotion are converging into the science of decision-making, neuroeconomics. Equipped with high spatiotemporal resolution of neuroimaging methods recently developed, it becomes possible to investigate neural mechanisms underlying the dynamic decision-making processes. We have world-wide recognition in the development of nonlinear signal analysis methods and dynamical modeling tools applicable to brain signals ranging from single-neuron activity to field potentials (i.e. EEG) based on nonlinear dynamics, information theory, and stochastic processes.
Hence, we are (1) developing experimental designs with well-established theoretical framework like behavioral economics, game theory, cognitive psychology (2) recording neural activities with EEG/fMRI (3) analyzing the multiple time series with nonlinear-dynamical or information-theoretic measures (4) establishing computational models and simulating it with the experimental data. We finally aim at understanding neural substrates and its dynamical principles of human decision-making and providing an integrative model of the decision-making, which lead to broad social impact and contribute to diagnosis, treatment of psychiatric patients. We are also developing therapeutic tools for detecting and recovering impaired decision-making processes in brain disorders. These techniques will be readily applied to neurofinance, neuromarketing, and neuroconsulting in the finance industry with the highest social impact.