Tuesday 13 December 2016, 4.00PM to 5.00pm
Speaker(s): Professor Yuki Kamitani, Kyoto University
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 “multivoxel (multivariate) pattern analysis (MVPA)” or “brain decoding”. In this talk, I will present methodological principles and limitations of this approach, and discuss new methods that could enable us to read out a wider variety of mental states experienced in real-life settings.
Kyoto University and ATR Computational Neuroscience Laboratories
Host: Dr Jonny Smallwood