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

Area of Science:

• Applied human performance research
• Human factors and ergonomics
• Environmental psychology

Background:

• Current 'extreme environment' definitions rely solely on external features (e.g., temperature, altitude).
• This environment-centric view overlooks individual variability, adaptation, and internal physiological/cognitive costs.
• Such limitations hinder the generalizability of findings and practical applications in high-demand settings.

Purpose of the Study:

• To propose a new, dynamic, relational definition of 'extremeness' in human performance.
• To shift the focus from environmental features to the interplay between external/internal demands and individual capacity.
• To provide a framework for operationalizing and quantifying 'extremeness' beyond overt performance decrements.

Main Methods:

• Conceptual framework integrating theories of compensatory control, allostasis, workload, resilience, and adaptation.
• Proposed definition centers on dynamic departures from normative operating envelopes across multiple domains (neurophysiology, cognition, affect, behavior, recovery).
• Operationalization strategy emphasizes variability, state dependence, strategy shifts, and hysteresis, moving beyond mean performance metrics.

Main Results:

• A relational demand-capacity model of extremeness is proposed, accounting for internal costs and individual differences.
• The framework highlights the importance of quantifying deviations within normative operating ranges, not just overt failure.
• Methods for experimental design and computational analysis are suggested for measuring extremeness dynamically.

Conclusions:

• Redefining extremeness as a dynamic, relational phenomenon enhances the understanding of human performance in challenging conditions.
• This approach offers improved methods for assessing risk, optimizing training, and ensuring safety in applied settings.
• The proposed framework supports advancements in human-machine teaming and overall operational readiness.