||Recent neuroimaging work is pushing in the direction of predictive science with the help of computational and machine learning modeling. Statistical pattern recognition algorithms have been applied to single-trial multivoxel patterns of fMRI data to make predictions about behavior and cognition including seen stimuli, motor intention, recalled memory, and dreamed contents, realizing a primitive form of “neural mind-reading”. The scientific approach using this method is now widely recognized as “multivoxl (multivariate) pattern analysis (MVPA)” or “brain decoding”. In this talk, I will present methodological principles and technical limitations of this approach, while highlighting the gap between the state of the art and what general people think “mind-reading” is like. I discuss new approaches that could help to fill the gap and enable us to read out a wider variety of mental states experienced in daily life.
Ph.D. Professor at Graduate School of Informatics, Kyoto University and Head of Department of Neuroinformatics at ATR Computational Neuroscience Laboratories, Kyoto, Japan. He received B.A. in Cognitive Science from University of Tokyo in 1993, M.S. in Philosophy of Science from University of Tokyo in 1995, and Ph.D. in Computation and Neural Systems from California Institute of Technology in 2001. He continued his research in cognitive and computational neuroscience at Harvard Medical School and Princeton University. In 2004, he joined ATR Computational Neuroscience Laboratories, where he heads Department of Neuroinformatics. In 2015, he started a new lab at Kyoto University. As a pioneer in brain decoding, he brought a new paradigm to brain imaging in which mental contents are predicted from brain activity patterns using machine learning models. He was named Research Leader in Neural Imaging on “Scientific American 50” in 2005. He has received several distinguished awards including Tsukahara Memorial Award (2013), and JSPS Award (2014).