Dr Just shows us the latest research on using fMRI machines to read
thoughts. Patterns in brain activity can be correlated to images and
more complex concepts independent of language and person using Machine Learning. Even emotions and semantic elements have surprisingly universal representations in the brain. With these tools, we can develop more effective ways of teaching and perhaps tackle mental illness. Dr. Marcel Just is the D.O. Hebb University Professor of Psychology and Director of the Center for Cognitive Brain Imaging at Carnegie Mellon University. His cognitive neuroscience research investigates the nature of high-level human thought (such as language and concept formation) by relating it to the mechanisms of the underlying brain infrastructure. The neural signatures of familiar concepts (measured with fMRI) like the thought of an apple or a hammer, or the experience of an emotion, are identifiable by machine learning programs, making it possible for the first time to tell whether someone is thinking of a hammer or an apple, or whether they are feeling happiness or disgust. One application of this “brain reading” approach is its potential to diagnose and suggest treatments for some psychiatric disorders. For example, in people with suicidal thoughts, the neural representations of death as well as thoughts of positive aspects of life (e.g. carefree) are systematically altered, and these alterations make it possible to diagnose suicidal ideation with very high accuracy. This research is in its infancy, but it is progressing rapidly and could enable a significant advance in understanding and enhancing human intellectual ability. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx