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Synapse
May 31, 2026Cognition Technology & Work0 citationsOpen Access

A methodology for standardized mental workload through taskload levels characterization

BSBertille SomonNMNicolas MailleMMMauro Marchitto

Key Points

  • This work aims to identify objective markers for standardized mental workload (MWL) and their relationship with subjective measures.
  • Data was collected using the MATB-II task, simulating pilots’ activities, with pre-standardized taskload levels.
  • Physiological measures, including cardiac, ocular, and EEG, were assessed under varying taskload conditions.
  • Correlation analyses were conducted between physiological measures and subjective MWL estimations.
  • Participants’ subjective workload and performances corresponded to the manipulated taskload levels.
  • Cardiac and EEG measures consistently correlated with subjective MWL estimations, particularly in high workload conditions.
  • Inconsistencies were found when randomizing taskload levels for physiological measures like heart rate and variability.

Abstract

Abstract The existing literature on Mental Workload (MWL) has used subjective and behavioral measures to understand processes involved in MWL. More recently, physiological measurements have also shown promising perspectives in this regard. However, their heterogeneity and interindividual variability leads to question their reliability, dependencies and covariations for daily use. The present work aims to identify objective psychophysiological markers that reliably reflect standardized MWL variations and their relationship with subjective and behavioral responses. We defined pre-standardized taskload levels to assess the stability of physiological markers, across MWL variations. Data was collected under a computer-based controlled task (MATB-II) mimicking aircraft pilots’ activity during flight. Standardization procedures were applied to the MATB-II, allowing the identification of scenarios inducing low, medium and high MWL, keeping constant the number and type of subtasks involved. Results showed that participants’ subjective workload and performances followed the taskload manipulation. Then, we identified at least three types of physiological measures (cardiac, ocular and electroencephalographic) affected by taskload, notably discriminating high workload conditions. Correlation analyses showed that cardiac and EEG measures are consistently associated with subjective MWL estimations. Inconsistencies were also observed when randomizing taskload levels for several physiological measures (heart rate, heart rate variability, engagement ratio). These results extend our knowledge of MWL to varying levels of taskload, while questioning the reliability of psychophysiological markers for the detection of evolving MWL. Ultimately, this knowledge could be applied in industries, supporting product development, to assess new designs with regards to their impact on users’ workload and performances, throughout the design cycle.

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Cite This Study

Somon et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd0845783ba022b6fc3f9https://doi.org/10.1007/s10111-026-00873-6
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