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In 2002, Dr. Sandra Marshall presented a landmark paper at the IEEE 7th Conference on Human Factors and Power Plants, introducing the Index of Cognitive Activity (ICA). This innovative technique “provides an objective psychophysiological measurement of cognitive workload” from pupil-based eye tracking data. In the decade since this conference, the ICA has been used by eye tracking researchers all over the world in a wide variety of contexts.
In this installment of the EyeTracking blog, we’ll take a look at some of the most interesting applications of the ICA. There are many to choose from, but here are a few of the greatest hits…
The ICA in Automotive Research
Understanding the workload of drivers is central to automotive design and regulation. Schwalm et al. collected ICA data during a driving simulation including lane changes and secondary tasks. Analyses of workload for the entire task and on a second-by-second basis indicated that the ICA (a) responded appropriately to changes in task demands, (b) correlated well with task success and self-reported workload and (c) identified shifts in participant strategy throughout the task. The researchers conclude that the ICA could be a valuable instrument in driver safety applications including learning, skill acquisition, drug effects and aging effects.
The ICA in Surgical Skill Assessment
Currently, surgical skill assessments rely heavily on subjective measures, which are susceptible to multiple biases. Richstone et al. investigated the use of the ICA and other eye metrics as an objective tool for assessing skill among laparoscopic surgeons. In this study, a sample of surgeons participated in live and simulated surgeries. Non-linear neural network analysis with the ICA and other eye metrics as inputs was able to classify expert and non-expert surgeons with greater than 90% accuracy. This application of the ICA may play an integral role in future documentation of skill throughout surgical training and provide meaningful metrics for surgeon credentialing.
The ICA in Military Team Environments
Many activities require teams of individuals to work together productively over a sustained period of time. Dr. Sandra Marshall describes a networked system for evaluating cognitive workload and/or fatigue of team members as they perform a task. The research was conducted at the Naval Postgraduate School in Monterey, CA under the Adaptive Architectures for Command and Control (A2C2) Research Program sponsored by the Office of Naval Research. Results demonstrated the viability of the ICA as a real-time monitor of team workload. This data can be examined by a supervisor or input directly into the operating system to manage unacceptable levels of workload in individual team members.
The ICA Across Eye Tracking Hardware Systems
Different research scenarios demand different eye tracking equipment. Because the ICA is utilized in so many disparate fields of study, it is important to validate this metric across different hardware systems. Bartels & Marshall evaluated four eye trackers (SMI’s Red 250, SR Research’s EyeLink II, Tobii’s TX 300 and Seeing Machines’ faceLAB 5) to determine the extent to which manufacturer, system type (head-mounted vs. remote) and sampling rate (60 Hz vs. 250 Hz) affected the recording of cognitive workload data. Each of the four systems successfully captured the ICA during a workload-inducing task. These results demonstrate the robustness of the ICA as a valid workload measure that can be applied in almost any eye tracking context.
The Index of Cognitive Activity is offered as part of EyeTracking, Inc.’s research services. It is also available through the EyeWorks Cognitive Workload Module.
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.
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, CA July 2009.
Schwalm, M., Keinath A. & Zimmer, H. (2008). Pupillometry as a Method for Measuring Mental Workload within a Simulated Driving Task. In Human Factors for Assistance and Automation. Shaker Publishing, 75–87.
Bartels, M. & Marshall, S. (2012). Measuring Cognitive Workload Across Different Eye Tracking Hardware Platforms. Paper presented at 2012 Eye Tracking Research and Applications Symposium, Santa Barbara, CA March 2012
Methods, processes and technology in this document are protected by patents, including US Patent Nos.: 6,090,051, 7,344,251, 7,438,418 and 6,572,562 and all corresponding foreign counterparts.