Mapping the Human Performance Envelope via PID-LASSO: A Multidimensional Causal Connectivity Framework

Background/Objectives: The Human Performance Envelope (HPE) is a multidimen-sional model that represents the range in which an individual operator's performance is acceptable or begins to become dangerous. Although several alternative models have been proposed, HPE currently remains primarily a theoretical concept. The goal of the study was therefore to translate this theoretical concept into practical applications, seeking to characterise and measure how HPE manifests itself in real-world contexts. Methods: Multivariate Autoregressive Models (MVAR) have been used in the analysis of complex systems in which variables are interdependent and mutually influence their dynamics over time. Professional Air Traffic Controllers (ATCOs) were involved in the study and asked to deal with realistic traffic scenarios while their behavioural, subjec-tive and neurophysiological data were collected. Partial Information Decomposition - Least Absolute Shrinkage and Selection Operator (PID – LASSO) model was then em-ployed to estimate the interactions among ATCO’s Human Factors (HFs) and identify the most appropriate characterisation of the HPE. Results: The results showed high and significant correlations among each ATCO’s performance and the corresponding neu-rophysiological – based HPE values. Furthermore, high-performance conditions (Best) were characterized by a significantly higher HPE values and a higher inter-HFs con-nections compared to low-performance (Worst) states. This suggested that a densely interconnected network of HFs is a prerequisite for operational resilience. Conclusions: The study provides the first application of a neurophysiological framework to model the causal interactions between HFs, translating the theoretical HPE into a quantifiable model validated against operator performance.