D O Hebb Professor, Director CCBI , Psychology
BioMy research uses fMRI and other technologies to uncover the structure of human thought. The fMRI studies track the brain activity that occurs during a wide range of cognitive and social thought, such as language comprehension, visual thinking, problem-solving, working memory, social judgment, and multi-tasking.
One current interest is in identifying the neural basis of concept representations using fMRI in the new area of neurosemantics. In collaboration with colleagues in the School of Computer Science, we have developed experimental paradigms and machine-learning techniques (multi-voxel pattern analysis) that are being applied to the study of lexical, perceptual, and social concepts (identifying the neural signature of that object and the components of the signature). We can identify the thought of a concrete object, social interaction, and digit, and we are moving on to propositions. This is leading us to a specification of how simple thoughts are neurally coded.
A second research area examines how scientific concepts are learned. As we listen to a lecture or read a textbook, neural representations of the new knowledge are being established. We hope to specify the processes by which new concepts come to be neurally represented.
Another important area of our research is in understanding the brain functioning in autism and relating it to the social and cognitive impairments that sometimes arise in the disorder. Our work has led to a new perspective, expressed as the underconnectivity theory of autism. This work uses fMRI and high angular resolution diffusion imaging (HARDI) to relate several levels of analysis: anatomical connectivity, informational (functional) connectivity, and behavioral performance. We are also starting to apply our neurosemantics (machine learning) methods to concept representations in autism.
The findings are being used to continuously develop a comprehensive theory of how brain function is related to thought, often expressed in terms of the 4CAPS computational theory.
Just, M. A., Cherkassky, V. L., Aryal, S., & Mitchell, T. M. (2010). A neurosemantic theory of concrete noun representation based on the underlying brain codes. PLoS ONE, 5, e8622.
Kassam, K. S., Markey, A. R., Cherkassky, V. L., Loewenstein, G., & Just, M. A. (2013). Identifying emotions on the basis of neural activation. PLoS ONE, 8, e66032.
Just, M. A., Keller, T. A., & Kana, R. K. (2013). A theory of autism based on frontal-posterior underconnectivity. In M. A. Just & K. A. Pelphrey (Eds.), Development and brain systems in autism (pp. 35-63). New York: Psychology Press.
Mason, R. A., Prat, C. S., & Just, M. A. (2013). Neurocognitive brain response to transient impairment of Wernicke's area. Cerebral Cortex; doi: 10.1093/cercor/bhs423