WIN: a Window Into Neuroregulation
The pace of our digital-era society and the circumstances in which people live are diverse and can produce expectations and situations requiring children to be extremely flexible and resilient. In order to maximally benefit from learning experiences in a classroom setting, children must have the capacity to “self-regulate,” regardless of their circumstances, presenting an increasingly relevant challenge. From a biological perspective, the ability to self-regulate is dealt with through the balance of the brain and bodily systems by effectively balancing the sympathetic and parasympathetic nervous systems. The primary perpetrator responsible for maintaining this balance is thought to be the vagus nerve, and its efficacy can be approximated through a variety of heart rate and respiration rate comparisons often referred to as “vagal tone”. Because of the need for continual measurement and analysis of the impact of the environment, social others, and activities in context, our understanding of the biology of self-regulation is extensive; our understanding, however, of self-regulation in the rich contextual social setting of the classroom is limited to non-existent. A multi-disciplinary team will converge on design and practices for accomplishing this work in order to create technology and best practices for conducting science that is situated in the classroom setting. This will, for the first time, allow a theoretically driven collection of large-scale, temporally synchronized, biological and behavioral data in the classroom context that will actually be mapped onto the environment in which it occurs, allowing development of new algorithms for measurement of the system in context.
Duration:
August 15, 2019 - July 31, 2022
Additional Non-UMD Investigator(s):
Joanna Christodoulou, Jay Giedd, Todd Coleman
Partnering Organization(s):
University of California-San Diego & Harvard University
Project Website:
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932619&HistoricalAwards=false
Total Award Amount:
$1,000,000