Momona Yamagami

SURF Mentoring

Potential projects/topics: The human brain can process a limited amount of information at a time, so we must choose only a small amount of available information to pay attention to. The process by which the brain computes which information should be prioritized for further processing is referred to as “attentional priority”. Here, we will leverage an interdisciplinary approach to address a fundamental limitation of current models of attentional priority: current models are built to predict where an observer will look in a 2-dimensional (2-D) image, but they do not account for actions that are available to an observer in an immersive 3-dimensional (3-D) environment. We hypothesize that being able to perform actions within an environment (e.g., reaching, grasping) will alter attentional priority. Towards testing this hypothesis, students will develop a testing platform in virtual reality (VR) using Unity3D that will allow us to systematically manipulate visual priority and available actions. Students will build on existing VR environments previously developed in our lab.

Potential skills gained: Unity3D development; C# coding; coding with a team; using git

Required qualifications or skills: Students who have taken introductory computational courses (e.g., COMP215 intro to programming, CAAM 210 intro to engineering computation) are desired so they have some prior experience with coding. Engineering or CS students who are interested in neuroscience / neuroengineering or health applications would be idea. However, there are parts of the project (e.g., scene development) that require no programming so interest in neuroscience / neuroengineering / health is more important than prior programming experience.

Direct mentor: Faculty/P.I., Graduate Student

Research Areas

Momona Yamagami is an Assistant Professor at Rice University in the Electrical and Computer Engineering department. Her research focuses on designing personalized human-machine interfaces for health and accessibility. She is interested in leveraging biosignals measured from sensors in, on, or around the body to develop closed-loop human-machine systems that are personalized to each user’s physical characteristics, with a focus on people with movement disabilities and/or chronic conditions.