Graham Goddard Lecture
Time & Location
About the event
The new science of thought imaging: Using machine learning to break the brain’s code for representing concepts
Recent computational techniques, particularly machine learning, are being applied to fMRI brain imaging data, making it possible for the first time to relate patterns of brain activity to specific thoughts. Our early work focused on the identification of the neural signatures of individual concrete concepts, like the thought of an apple or a hammer. It progressed to identifying many other types of concepts, such as emotions, numbers, abstract concepts, and sentences. The more recent work is progressing towards understanding the structure of thought in terms of its neural components. One facet of this approach consists of identifying the neurosemantic dimensions that underlie the representation of concepts in a given domain. For example, in the domain of elementary physics concepts, the dimensions of periodicity, energy flow, and causal motion emerge. In the case of advanced physics concepts in the minds of professional physicists, more abstract dimensions emerge. One application of this approach is to STEM instruction, where it is possible to assess the neural structure of STEM concepts. Another application is to neuropsychiatry, where it has been possible to identify suicidal ideation in terms of alterations of a normative pattern of concept representation. The scientific significance is that we are beginning to understand the basic neurocognitive building blocks of more and more types of thought from simple to complex. This research is in its infancy, but it is advancing rapidly and is providing a new perspective on the brain’s information organization system for representing individual concepts and larger constellations of thought and knowledge.