About me

Hi! I'm Lisa (aka Elizabeth Musz) and I'm a cognitive neuroscientist interested in understanding how the human brain represents abstract information about the world (how do we identify objects and people?), and how these representations are modulated by contextual factors (how do our brains and our thoughts differ depending on our individual experiences?). For specific examples of how I study these big-picture questions, check out my Research page.

I received my PhD in Psychology at the University of Pennsylvania. During my graduate research with Sharon Thompson-Schill, I studied the neural bases of lexical representations (word meanings), semantic memory, and cognitive control. Most recently, I worked as a postdoctoral fellow at the Department and Psychological Brain Sciences at Johns Hopkins University in collaboration with Marina Bedny and Janice Chen. My postdoctoral research leveraged brain-to-brain neuroimaging analyses and naturalistic experiment paradigms to explore how neural representations and subjective interpretations of realistic stimuli (e.g., movies and artwork) converge and diverge across people, depending on their experiences.

My research draws on a variety of experimental methods and analysis techniques, including behavioral testing, eyetracking, natural language processing tools, noninvasive brain stimulation (tDCS), and functional neuroimaging (including event-related univariate analysis, fMRI adaptation, and multi-voxel pattern analysis). My graduate and postdoctoral studies have been supported by fellowships from the National Science Foundation.

My work in the interconnected fields of psychology, cognitive science, and neuroscience began during my undergraduate studies at Carleton College. Since then, I have collaborated with several students and faculty at the University of Iowa, Harvard University, the University of Pennsylvania, and Johns Hopkins University. More details about my research experiences and my body of scholarly work are available on my CV and my Publications page.