Measuring cognitive load in real time

How Tobii and SOMAREALITY are shaping the future of eye tracking

In high-stakes environments, cognitive overload isn’t just a challenge — it’s a risk. Traditionally, detecting when someone is mentally overworked has been difficult, requiring intrusive equipment like EEG. But thanks to advances in
eye tracking and cognitive algorithms, it’s now possible to monitor mental workload non-invasively and in real time.

When mental effort becomes a matter of safety 

Picture a surgeon deep into a six-hour operation. Every decision counts, but fatigue slowly creeps in, narrowing focus and increasing the risk of error. Or a pilot navigating turbulence at night, where every second of reaction time make the difference between safety and disaster. 

The problem: Why overload goes unnoticed 

The mental effort we invest, cognitive load, can build silently. Drivers grow drowsy, surgeons lose focus, pilots face split-second decision fatigue. Without clear warning signs, performance can drop before anyone realizes. 

The result? Delayed reactions, increased mistakes, and higher risk in exactly the environments where precision matters most. 

The fix: Eye tracking as a cognitive window 

Our eyes tell the story of our brains. Changes in pupil size, fixation patterns, and
saccades act as digital biomarkers of mental workload. Larger pupils often signal increased mental effort. Longer fixations reveal deeper processing. Rapid gaze shifts suggest scanning under pressure. 

This is where the pupillary light reflex (PLR) comes in. While our pupils respond to light automatically, they also respond to cognitive effort. In fact, subtle dilations can reveal when someone’s brain is working harder than usual.  

By measuring these signals continuously, eye tracking provides a non-invasive, real-time window into mental effort. It recognizes overload before it becomes a problem. 

Why data quality matters: Tobii’s advantage 

But here’s the catch: not all eye tracking data is created equal. Small errors in gaze tracking or inconsistent pupil measurement can lead to misleading results. 

This is where Tobii comes in. With decades of leadership in high-precision
eye tracking hardware, Tobii ensures data accuracy, stability, and reliability across environments — from research labs to real-world applications. 

Turning data into insight: SOMAREALITY’s unique approach 

If Tobii provides the raw clarity of vision, SOMAREALITY provides the intelligence to interpret it. 

SOMAREALITY has developed advanced cognitive algorithms that transform Tobii’s precise eye tracking signals into real-time indicators of workload, attention, and fatigue. What makes the approach unique is their real-time brightness model – enabling them to filter out all non-cognitive activity during runtime as well as their scientific focus. With over 10 peer-reviewed publications, and customers such as Intuitive Surgical, Lufthansa and the Austrian Armed Forces, they can provide their customers with significant support from implementation to interpretation. 

• Pupillary Light Reflex (PLR): Distinguishes light-driven changes from effort-driven dilations, ensuring reliable cognitive load detection. 

• Fixation behavior: Longer fixations suggest deeper processing, while shorter ones can indicate scanning or overload. 

• Saccadic movements: Eye jumps between fixations reveal search strategies and stress levels. 

Real-world use cases 

Surgery & Healthcare 

• Real-time feedback helps surgeons maintain focus during long procedures. 

• Algorithms detect overload early, reducing the chance of mistakes. 

• Training programs adapt dynamically to each trainee’s cognitive state. 

Aviation & Flight safety 

• Monitors pilot workload during critical phases like take-off and landing. 

• Helps instructors identify moments of high stress in simulators. 

• Improves both safety and training efficiency. 

Automotive & Driver monitoring 

• Detects early signs of drowsiness or distraction. 

• Acts as an additional safety layer, reducing accidents. 

• Offers real-time feedback for both consumer and industrial driving contexts. 

VR & Training environments 

• Adaptive simulations that adjust difficulty based on workload. 

• Deeper insights into how people learn, focus, and retain knowledge. 

• Applications in defense, medical training, and workforce development. 

Academic validation & industry momentum 

Eye tracking isn’t just theory. It’s already being validated across cognitive psychology, human-computer interaction, and neuroscience. Research shows strong correlations between pupil responses and mental workload, while applied studies prove its impact in training, safety, and performance. 

The partnership between Tobii and SOMAREALITY represents the next step: Bridging academic rigor with practical, real-world deployment.

Looking ahead: The future of cognitive monitoring

Eye tracking combined with cognitive load monitoring will define the next wave of human-machine collaboration, evident already in cars that detect driver fatigue, adaptive training that prevents overload or operating rooms that safeguard against human error. 

With Tobii’s unmatched data quality and SOMAREALITY’s pioneering algorithms, the future of trusted, real-time cognitive monitoring is already here. 

Publications

Stolte, M., Gollan, B., & Ansorge, U. (2020). Tracking visual search demands and memory load through pupil dilation. Journal of Vision, 20(6), 21-21. Gollan, B. (2017). 

Sensor-based Online Assessment of Human Attention. Submitted by Benedikt Gollan (Doctoral dissertation, Universität Linz). Gollan, B., Haslgrübler, M., & Ferscha, A. (2016, September). 

Demonstrator for extracting cognitive load from pupil dilation for attention management services. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 1566-1571). Gollan, B., & Ferscha, A. (2016). 

Modeling pupil dilation as online input for estimation of cognitive load in non-laboratory attention-aware systems. COGNITIVE. Gollan, B., Wally, B., & Ferscha, A. (2011). 

Automatic human attention estimation in an interactive system based on behavior analysis. Proc. EPIA 2011. Jungwirth, F., Gollan, B., Breitenfellner, M., Elancheliyan, P., & Ferscha, A. (2019, September). 

EyeControl: wearable assistance for industrial maintenance tasks. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (pp. 628-632). Haslgrübler, M., Gollan, B., & Ferscha, A. (2018). 

A cognitive assistance framework for supporting human workers in industrial tasks. IT Professional, 20(5), 48-56. Ferscha, A., Zia, K., & Gollan, B. (2012, September). 

Collective attention through public displays. In 2012 IEEE Sixth International Conference on Self-Adaptive and SelfOrganizing Systems (pp. 211-216). IEEE. Thomay, C., Gollan, B., Ferscha, A., & Spiess, M. (2019, November).  

Taking Off: Towards Real Competence-Based Flight Pilot Training. In 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA) (pp. 764-769). IEEE. Thomay, C., Gollan, B., Haslgrubler, M., Ferscha, A., & Heftberger, J. (2018, November). 

The Other Kind of Machine Learning: Modeling Worker State for Optimal Training of Novices in Complex Industrial Processes. In 2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA) (pp. 577-582). IEEE