David C. Somers, PhD
Professor & Chair,
Department of Psychological & Brain Sciences
Member, Cognition & Perception Study Section, NIH/CSR
Post Doctoral Associates
I’m interested in the ways in which humans rely on predictability to support cognitive processing. Most of my work has been in the realm of visual short-term memory and attention. My graduate work was in EEG, psychophysics, and computational approaches to describing human behavior. Here in the Somers Lab I’m delighted to be extending my methods toolkit to include MRI, and to be working on the interactions between visual and auditory information, temporal and spatial sequences, and short-term and long-term memory.
The way a brain region is wired determines the sorts of computational operations it can perform. Connectivity patterns define the type of information available to a region, and thus, connectivity should principally govern neural responses. We can measure connectivity patterns noninvasively with diffusion-weighted imaging (an MRI technique), alongside patterns of neural responses with fMRI. In my research, I model neural responses as a function of connectivity patterns in order to identify the connectivity patterns that support attention and cognitive control.
My research interests are focused on how long-term memory and visual attention interact with one another. Human attentional capacities are severely limited, and yet, our visual performance far exceeds that of powerful supercomputers. The paradox of high real-world performance and limited capacity can be reconciled by considering the important role that long-term memory (LTM) plays in guiding visual attention. I have used both functional MRI and behavioral experiments to investigate the mechanisms by which LTM can be used to guide spatial attention.
I am broadly interested in human visual attention. My current work concerns fMRI investigation of human cortical attention networks and their interaction under various types of behavioral training. Formerly, I used fMRI to investigate the stimulus selectivity profiles of areas within human and macaque IT cortex.
I love working with giant magnets and pushing the limits of what we can do with these immensely powerful machines. My current interests involve the integration of cognitive neuroscience, connectomics and diagnostic medicine in order to investigate basic network characteristics and their disruption due to disease. I am interested in the development of computational neuroimaging tools capable of aiding differential diagnosis by classifying disease states and predicting future disfunction at the level of the individual. I also hope to increase the rate at which we validate and integrate of these tools into medicine.
My research explores the mechanisms supporting visual working memory and visuo-spatial attention as well as the interaction between these processes using a combination of psychophysical and fMRI methods. I am interested in investigating factors that limit or enhance working memory and attentional performance.
I am interested in examining the neural processes underlying behavioral-related changes in cortical attention networks using fMRI. My current research focuses on investigating attentional differences between groups as well as employing resting-state functional connectivity to localize task-related activation in individual subjects.
- Samantha Michalka, Ph.D., Boston University
- Katie Bettencourt, Ph.D., Harvard University
- Mark Halko, Ph.D., Harvard Medical School/Beth Israel Hospital
- Lingqiang Kong, Ph.D.
- Stephanie McMains, Ph.D., Harvard University
- Lotfi Merabet, O.D., Ph.D., Harvard Medical School / Mass. Eye and Ear Infirmary
- Summer Sheremata, Ph.D., George Washington University
- Jascha Swisher, Ph.D., Vanderbilt University
- Dan Bireley
- Rachel Franklin
- Stephanie Bachewski
- Eli Fredman
- Christie Gamble
- Julia Ladna