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Related Experiment Video

Updated: Sep 7, 2025

Mindfulness in Motion MIM: An Onsite Mindfulness Based Intervention MBI for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

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Published on: July 1, 2015

Human Mental Workload: A Survey and a Novel Inclusive Definition.

Luca Longo¹, Christoper D Wickens², Gabriella Hancock³

¹Artificial Intelligence and Cognitive Load Lab, The Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland.

Frontiers in Psychology

|June 20, 2022

Summary

Assessing human mental workload (MW) requires a unified framework. This study synthesizes current MW assessment methods, proposing an inclusive definition and integrated measurement approach for improved human performance prediction.

Keywords:

definitions measures mental workload models novel framework novel inclusive definition survey theories

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Area of Science:

Human Factors and Ergonomics
Neuroscience
Neuroergonomics
Computer Science

Background:

Human mental workload is a key construct in Human Factors and Ergonomics, with growing interest in Neuroscience and Neuroergonomics.
Current characterization uncertainties necessitate computational models, attracting Computer Science contributions.
Mental workload's role in predicting human performance is well-established.

Purpose of the Study:

To synthesize the state-of-the-art in human mental workload assessment.
To address the lack of a universally accepted framework for mental workload research.
To propose an inclusive definition and integrated measurement strategy for mental workload.

Main Methods:

Review of existing literature on mental workload definitions, measurement techniques, and applications.
Analysis of the limitations of current assessment frameworks.
Proposal of a novel, inclusive definition of mental workload.
Advocacy for the combined use of self-report, task performance, and physiological measures.

Main Results:

A single, reliable, and generalizable framework for mental workload research is not yet established.
Divergent operational definitions and isolated use of measurement types contribute to this gap.
Existing measurement classes (self-report, task performance, physiological indices) are often used in isolation or pairs, not comprehensively.

Conclusions:

An inclusive definition of mental workload is proposed to guide future empirical research.
Comprehensive assessment using physiological, task-performance, and self-report measures can yield more robust results.
Further research is needed to establish a unified framework for mental workload assessment.