PUPIL DILATION AND COGNITIVE WORKLOAD

Pupil size depends upon two types of muscles—radial muscles that dilate the pupil and circular muscles that constrict it.

Pupil size is constantly changing, influenced by two reflexes: the light reflex and the dilation reflex. The light reflex activates the circular muscles and inhibits the radial muscles, resulting in a smaller pupil. The dilation reflex does the opposite: it activates the radial muscles and inhibits the circular ones, which causes the pupil to enlarge.

Measurements of pupil size taken at a constant sampling rate (such as 30 Hz, 60 Hz, 120 Hz, or 250 Hz) produce a signal like the one here. This signal spans of two minutes.

It’s tempting to try to interpret the raw pupil size itself, but that’s not a good idea. You are really just measuring the impact of light (from the light reflex), not the impact of cognitive effort (from the dilation reflex).

Look at the graphs to the right. They plot raw pupil signals from one person under four different conditions: sitting quietly in the dark (A), sitting quietly in the light (B), answering math problems in the dark (C), and answering math problems in the light (D). Notice how similar panels B, C, and D look. You can’t tell which ones show cognitive effort from these raw pupil signals.

Now look at another set of graphs for the same pupil signals. These are the results of our analysis of the four signals above. Look how the impact of light has been negated (panels A and B look very similar now). And notice the impact of doing math problems (in C and D). It’s clear which signals reflect cognitive processing and which don’t.

We apply signal processing techniques and patented algorithms to create the Index of Cognitive Activity. The Index is scaled 0-1, with 0 indicating low workload and 1 indicating high workload. It can be produced for every observation in your data or for any time sequence you desire (i.e., every second, every 10 seconds, or averaged over the entire testing period).

Copyright ©2013 EyeTracking, Inc. www.eyetracking.com

How the Index of Cognitive Activity has been used:

• To monitor task difficulty • To compare novices and experts • To examine special populations • To assess interface usability • To evaluate team behavior

Where the Index has been used:

• Simulators • Laboratories • Operating rooms • Vehicles

Here are some references about the Index of Cognitive Activity:

Marshall, S. (2013). Interaction science and the aging user: Techniques to assist in design and evaluation. In Proceedings of the 15th International Conference on Human-Computer Interaction, San Diego, July 2013. Bartels, M., & Marshall, S. (2012). Measuring Cognitive Workload across Different Eye Trackers. In Proceedings of 2012 Eye Tracking Research and Applications (ETRA) Symposium. New York: ACM. 161-164. Bartels, M. & Marshall, S. (2011). Eye tracking and universal access: Three applications and practical examples. In C. Stephanidis (Ed.), Universal Access in HCI, Part II, HCII 2011, LNCS 6766, pp. 525-534. Richstone, L., Schwartz, M., Seideman, C., Cadeddu, J., Marshall, S., & Kavoussi, L. (2010). Eye metrics as an objective assessment of surgical skill. Annals of Surgery. Jul; 252(1):177-82. Jessee, M. S., (2010). Examining the convergent and discriminant validity of visual and mental workload using ocular activity variables. Technical Report ARL-TR-5132. Army Research Laboratory, Aberdeen Proving Ground, MD 21005-5066 Aberdeen Proving Ground, MD 21005-5066, March 2010.

Symank, B. G. (2010). Real-time workload monitoring: Improving cognitive process models. NATO Technical Report MP-HFM-202_P03. Available at ftp.rta.nato.int/public//PubFullText/RTO/...///MP-HFM-202-P03.doc.

Marshall, S. (2009). What the eyes reveal: Measuring the cognitive workload of teams. In Proceedings of the 13th International Conference on Human-Computer Interaction, San Diego, July 2009. Marshall, S. P. (2008). Measuring the eye for military applications. In R. Hammoud (Ed.,), Passive Eye Monitoring: Algorithms, Applications and Experiments (pp. 247-264). New York: Springer. Marshall, S. P. (2007). Identifying cognitive state from eye metrics. Aviation, Space, & Environmental Medicine, 78(5), 165-175. Marshall, S. P. (2007). Measures of Attention and Cognitive Effort in Tactical Decision Making. In M. Cook, J. Noyes, & V. Masakowski (Eds.), Decision Making in Complex Environments (pp. 321-332). Aldershot, Hampshire UK: Ashgate Publishing. Schwalm, M., Keinath, A., & Zimmer, H. (2007). Pupillometry as a method for measuring mental workload within a simulated driving task. In D. de Waard, F.O. Flemisch, B. Lorenz, H. Oberheid, and K.A. Brookhuis (Eds.) (2008), Human Factors for assistance and automation (pp. 75 - 87). Maastricht, the Netherlands: Shaker Publishing.

Copyright ©2013 EyeTracking, Inc. www.eyetracking.com