Megan E. Hillis

Megan E. Hillis

she/her

Ph.D. in Cognitive Neuroscience

Teaching

Courses

COGS 50.08 Modeling Mind and Behavior

Fall 2025 - MWF 10
Course Description: You will actively engage in the scientific process of hypothesis testing that combines the disciplines of neuroscience, behavioral science, statistics/machine learning to understand the mechanisms of mind and behavior. The course will review computational approaches to modeling the mind by walking through the steps of hypothesis formation, experimental design, statistical analysis and theory development requiring skills in research methods, programming and scientific writing.

Guest Lectures

Spring 2025 - COGS 80 Major Seminar in Cognitive Science
Talk title: Initial signs of learning: Decoding newly learned vocabulary from neural patterns in novice sign language learners
Abstract: Learning a new language requires the brain to map new perceptual cues onto real-world semantic concepts. Studies of semantic processing suggest that these representations are at least partially language-independent: despite differing low-level perceptual features, homologous words can evoke overlapping neural patterns associated with a shared underlying meaning. I will present two different experiments in which hearing English speakers underwent brief training in American Sign Language (ASL) before undergoing fMRI scanning. In the first study, participants learned a short list of nouns in a single training session. Using representational similarity analysis (RSA) we identify several frontal, temporal, and occipital regions where neural patterns reflect semantic relationships between nouns in both languages, and the degree of correlation in ASL reflects individual differences in comprehension. In the second study, participants completed approximately 15 hours of lessons over three weeks. I’ll present preliminary results suggesting that in these participants, a voxelwise encoding model trained on fMRI data from English stimuli is able to predict brain responses to the ASL stimuli.

Fall 2022 - COGS 80 Major Seminar in Cognitive Science
Talk title: Characterizing changes in knowledge and learning with multivariate fMRI
Abstract: A crucial part of understanding how people learn is the ability to detect and measure learning over time. Traditional methods of measuring learning include a wide array of pencil-and-paper tests, essays, and oral or discussion-based assignments. Prior research has suggested that neuroimaging techniques like functional magnetic resonance imaging (fMRI) can pick up on neural activity patterns that reflect shifts in understanding of new concepts. This talk will focus on two domains of learning as examples: foreign language and physics. In both cases, using multivariate pattern analysis and decoding techniques, we identified regions of the brain where neural responses during a conceptual task were meaningfully related to how well the participants understood the material, including how well they performed on pencil-and-paper assessments. This work can provide insight into how learning shapes the representation of concepts in the brain, and also has potential applications for intelligent teaching systems which can use neural signals to make inferences about the learner’s needs.

Teaching Apprenticeship