A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Strayer, David L. · 2019 · Frontiers in Human Neuroscience

DOI: 10.3389/fnhum.2019.00057

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Summary

This review addresses the critical need for objective, real-time assessment of motorists’ cognitive states, such as workload, inattention, and fatigue, particularly as driving automation increases. The authors argue that traditional subjective self-reports and behavioral measures are insufficient because drivers often misjudge their own vigilance and because behavioral metrics (e.g., steering reversals) may not capture covert cognitive changes. To mitigate traffic safety risks associated with suboptimal cognitive functioning, the paper evaluates psychophysiological measures that can detect internal states non-invasively and in real-world driving environments. The study provides a selective review of eight commonly used physiology-based indices: electroencephalography (EEG) and event-related potentials (ERPs), optical imaging, heart rate and heart rate variability (HRV), blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each measure, the authors analyze the underlying physiological mechanisms, methods for measurement in driving contexts, and the specific cognitive states they index. The review distinguishes between measures suitable for controlled laboratory settings versus those viable for applied, real-world scenarios. It synthesizes empirical research to determine how these metrics correlate with arousal states, ranging from under-arousal (drowsiness, low workload) to over-arousal (high stress, high workload). Key findings highlight the utility of EEG and ERPs as robust indicators of neural activity. For instance, increased alpha power and decreased theta power are associated with drowsiness and attentional withdrawal, whereas increased theta power and suppressed alpha power indicate high mental workload. The P3b ERP component is identified as a sensitive marker for attentional allocation, with reduced amplitudes observed during distraction or fatigue. Peripheral measures also show promise; heart rate and HRV changes correlate with workload and stress, while pupillometry reflects cognitive load through pupil dilation. However, the authors note significant limitations for real-world application, including signal artifacts from movement, sensitivity to environmental factors like lighting (for pupillometry), and the complexity of setup for contact sensors like EEG. The significance of this work lies in its framework for advancing Advanced Driver Assistance Systems (ADAS) and human-machine interfaces. By identifying which psychophysiological measures offer the best balance of reliability and practicality, the review supports the development of systems capable of monitoring driver state in dynamic, ecologically valid settings. The authors conclude that while no single measure provides a one-to-one mapping to specific psychological constructs, a multi-method approach combining several physiological indices can effectively delineate net cognitive states. This synthesis aims to facilitate the transition from lab-based research to real-time, in-vehicle monitoring systems that can predict and augment risky driving behavior.

Key finding

Psychophysiological measures provide objective, real-time data on driver cognitive states that complement behavioral metrics, though their practical application requires addressing signal quality and environmental variability.

Methodology

review

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• workload measurement
• stress driving
• drowsiness detection algorithms
• mental demand
• drowsiness
• cognitive capacity variation

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data
• Methodological Resource: validation psychometrics
• Theoretical Contribution: theory or model