Beyond environmental stress: A demand-capacity framework for assessing and predicting extremeness in human performance research.

The term extreme environment is widely used in applied human performance research and is often assigned on the basis of environmental features alone, such as temperature, altitude, noise, isolation, or threat. Although convenient for designing controlled studies, this environment-centric convention limits generalization by overlooking individual differences, adaptation, compensation, and cases in which performance remains near typical while internal costs and vulnerability increase. We argue that extremeness should instead be treated as a dynamic, relational demand-capacity phenomenon in which external and internal demands push individuals or teams beyond normative physiological, cognitive, emotional, or behavioral operating ranges, constraining adaptive capacity and increasing instability, error risk, and delayed recovery. Building on theories of compensatory control, allostasis, workload, resilience, and adaptation, we propose a working definition centered on departures from normative operating envelopes across neurophysiology, cognition, affect, behavior, and recovery. Moving beyond mean performance change, we outline an operationalization strategy emphasizing variability, state dependence, strategy shifts, and hysteresis. We also describe experimental designs and computational approaches for quantifying extremeness before overt failure and conclude with a translational agenda for improving readiness, safety, and human-machine teaming.