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.