Physiological indices of challenge and threat: A data‐driven investigation of autonomic nervous system reactivity during an active coping stressor task
Abstract
We utilized a data‐driven, unsupervised machine learning approach to examine patterns of peripheral physiological responses during a motivated performance context across two large, independent data sets, each with multiple peripheral physiological measures. Results revealed that patterns of cardiovascular response commonly associated with challenge and threat states emerged as two of the predominant patterns of peripheral physiological responding within both samples, with these two patterns best differentiated by reactivity in cardiac output, pre‐ejection period, interbeat interval, and total peripheral resistance. However, we also identified a third, relatively large group of apparent physiological nonresponders who exhibited minimal reactivity across all physiological measures in the motivated performance context. This group of nonresponders was best differentiated from the others by minimal increases in electrodermal activity. We discuss implications for identifying and characterizing this third group of individuals in future research on physiological patterns of challenge and threat.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 13, 2019
- Source ID
- 10.1111/psyp.13454
Entities
People
- Erika Siegel
- Jennifer Dy
- Jolie Wormwood
- Karen S Quigley
- Lisa Feldman Barrett
- Spencer K. Lynn
- Zulqarnain Khan
Organizations
- Massachusetts General Hospital
- National Institutes of Health
- Northeastern University
- U.S. Army Research Institute for the Behavioral and Social Sciences
- University of California, San Francisco
- University of New Hampshire