Continuing In-person Motor Neuroscience Research Virtually During COVID-19

Kristen Twigg, Trevor Maco, Cameron Perrie, Jerome Harmon, Jason Lee, Ryan Low, Yvanne Seuhou, Abby Snellman, Alexandra Shaver, Dr. Garrett E Katz, Dr. James A Reggia, Dr. James Purtilo, Dr. Rodolphe J. Gentili, Department of Computer Science, University of Maryland College Park, 20742

The study of human learning of action sequences involving motor precision components is traditionally conducted in an in-person laboratory setting. However, the ongoing COVID-19 pandemic places a unique challenge on researchers studying this. To continue research during the pandemic, VLEARN (Virtualized Learning) was developed to allow users to both develop and complete virtual motor sequence tasks. VLEARN provides a web-based environment for experiment design, creation, and execution. Prior to COVID-19, two maintenance tasks were developed, a hard drive replacement (previously conducted in-person) and a pipe cleaning (which has been stopped due to the pandemic but was planned to be conducted in-person later). By virtually implementing these two tasks, VLEARN allows researchers to examine the underlying human cognitive-motor processes during learning of action sequences involving various levels of motor demands. Although VLEARN does not exactly replicate the real-world settings, relative to the actual experimental platform, it provides several critical advantages including more precise, real-time data collection, and the capability to enroll a larger pool of participants who can be remotely tested. VLEARN also enhances data processing by accelerating it and also avoids errors due to manually encoding the action sequences to assess performance. Qualitative and quantitative methods will be used to measure user-experience and efficiency of data collection with VLEARN. First, after completing both maintenance tasks, participants will complete surveys from which usability and mental states scores will be derived in order to assess user-experience. Second, researchers who conducted the in-person experiment will also be surveyed for user experience and data accuracy relative to the original data set collected in-person. It is expected that VLEARN will be favorably assessed by most users. Future work includes assessing this virtual experimental platform via additional user-validity and motor sequence tasks.

Acknowledgments: This work is supported by The Office of Naval Research (N00014-19-1-2044).

Additional Abstract Information

Presenters: Kristen Twigg, Cameron Perrie, Trevor Maco

Institution: University of Maryland College Park

Type: Poster

Subject: Computer Science

Status: Approved

Time and Location

Session: Poster 5
Date/Time: Tue 12:30pm-1:30pm
Session Number: 4005