Implementation of fNIRS for Monitoring Levels of Expertise and Mental Workload

Bunce, Scott C.; Izzetoglu, Kurtulus; Ayaz, Hasan; Shewokis, Patricia; Izzetoglu, Meltem; Pourrezaei, Kambiz; Onaral, Banu · 2011 · Crossref

DOI: 10.1007/978-3-642-21852-1_2

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Summary

This study investigates the utility of functional near-infrared spectroscopy (fNIRS) as a physiological measure for monitoring mental workload and expertise levels in complex command and control environments. The research is motivated by the need for accurate, real-time assessment of operator cognitive load to support adaptive aiding systems, which can prevent performance errors in high-stakes scenarios like air traffic control or naval operations. While electroencephalography (EEG) offers high temporal resolution, it lacks spatial precision; fNIRS provides a portable, non-invasive alternative with better spatial localization, specifically targeting the dorsolateral prefrontal cortex (DLPFC), a region associated with attentional control and working memory. The authors aim to determine if fNIRS can differentiate between experts and novices and track neural efficiency during tasks of varying difficulty. The experimental design involved eight healthy adult participants, divided into two groups based on prior experience with the Warship Commander Task (WCT): four with high practice (up to 300 hours) and four novices (3–4 hours). Participants performed the WCT, a quasi-realistic naval simulation requiring spatial and verbal working memory, decision-making, and divided attention. Task difficulty was manipulated by varying the number of incoming aircraft (6, 12, 18, or 24 planes per wave) and the proportion of unidentified aircraft. A secondary auditory memory task was also introduced to increase cognitive load. fNIRS monitored hemodynamic responses (oxygenation changes) in the prefrontal cortex throughout the task. Performance was measured using a percentage game score, while neural activity was analyzed using mixed-model ANOVA to assess the effects of practice level, task complexity, and secondary task presence. The results revealed distinct patterns of neural activity and performance based on expertise. Experts consistently achieved higher performance scores than novices, particularly as task difficulty increased. Regarding neural activity, experts exhibited lower oxygenation levels in the DLPFC during low-to-moderate workload conditions, indicating greater neural efficiency and reduced reliance on attentional control resources. However, at the highest difficulty level (24 planes), experts showed increased oxygenation, reflecting the recruitment of additional cognitive resources to maintain high performance. In contrast, novices displayed higher oxygenation at moderate loads but experienced a precipitous drop in both oxygenation and performance scores at high loads. This decline suggests that novices disengaged from the task when cognitive demands exceeded their capacity, whereas experts sustained effort. These findings support the "scaffolding-storage" framework of expertise development, where practice reduces the need for generic attentional scaffolding, freeing up cognitive reserves for more demanding situations. The study demonstrates that fNIRS can effectively distinguish between expertise levels and detect signs of cognitive overload or disengagement. The authors conclude that fNIRS is a viable tool for adaptive automation systems, capable of providing real-time feedback on operator mental workload. However, they note that accurate interpretation requires distinguishing between low effort due to efficiency versus disengagement, suggesting that future systems should integrate fNIRS with other measures or task difficulty indicators to optimize adaptive aiding interventions.

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• neuro workload indices
• workload measurement

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• Empirical Findings: physiological data