Category Archives: pupillometry

Further progress towards disambiguating the effects of cognitive load and light on pupil diameter

In driving simulator studies participants complete both visual and aural task. The most obvious visual task is driving itself, but there are others such as viewing an LCD screen that displays a map. Aural tasks include talking to an in-vehicle computer. I am very interested in estimating the cognitive load of these various tasks. One way to estimate this cognitive load is through changes in pupil diameter: in an effect called the Task Evoked Pupillary Response (TEPR) [1], the pupil dilates with increased cognitive load.

However, in driving simulator studies participants scan a non-uniformly illuminated visual scene. If unaccounted for, this non-uniformity in illumination might introduce an error in our estimate of the TEPR. Oskar Palinko and I will have a paper at ETRA 2012 [2] extending our previous work [3], in which we established that it is possible to separate the pupil’s light reflex from the TEPR. While in our previous work TEPR was the result of participants’ engagement in an aural task, in our latest experiment TEPR is due to engagement in a visual task.

The two experiments taken together support our main hypothesis that it is possible to disambiguate (and not just separate) the two effects even in complicated environments, such as a driving simulator. We are currently designing further experiments to test this hypothesis.

References

[1] Jackson Beatty, “Task-Evoked Pupillary Responses, Processing Load, and the Structure of Processing Resources,” Psychological Bulletin, 276-292, 91(2)

[2] Oskar Palinko, Andrew L. Kun, “Exploring the Effects of Visual Cognitive Load and Illumination on Pupil Diameter in Driving Simulators,” to appear at ETRA 2012

[3] Oskar Palinko, Andrew L. Kun, “Exploring the Influence of Light and Cognitive Load on Pupil Diameter in Driving Simulator Studies,” Driving Assessment 2011

Towards disambiguating the effects of cognitive load and light on pupil diameter

Light intensity affects pupil diameter: the pupil contracts in bright environments and it dilates in the dark. Interestingly, cognitive load also affects pupil diameter, with the pupil dilating in response to increased cognitive load. This effect is called the task evoked pupillary response (TEPR) [1]. Thus, changes in pupil diameter are physiological measures of cognitive load; however changes in lighting introduce noise into the estimate.

Last week Oskar Palinko gave a talk at Driving Assessment 2011 introducing our work on disambiguating the effects of cognitive load and light on pupil diameter in driving simulator studies [2]. We hypothesized that we can simply subtract the effect of lighting on pupil diameter from the combined effect of light and cognitive load and produce an estimate of cognitive load only. We tested the hypothesis through an experiment in which participants were given three tasks:

  • Cognitive task with varying cognitive load and constant lighting. This task was adapted from the work of Klingner et al. [3]. Participants listened to a voice counting from 1 to 18 repeatedly. Participants were told that every sixth number (6, 12, and 18) might be out of order and were instructed to push a button if they detected an out-of-order number. This task induced increased cognitive load at every sixth number as participants focused on the counting sequence. A new number was read every 1.5 seconds, thus cognitive load (and pupil diameter) increased every 6 x 1.5 sec = 9 seconds.
  • Visual task with constant cognitive load (assuming no daydreaming!) and varying lighting. Participants were instructed to follow a visual target which switched location between a white, a gray and a black truck. The light reaching the participant’s eye varied as the participant’s gaze moved from one truck to another. Participants held their gaze on a truck for 9 seconds, allowing the pupil diameter ample time to settle.
  • Combined task with varying cognitive load and lighting. Participants completed the cognitive and visual tasks in parallel. We synchronized the cognitive and visual tasks such that increases in cognitive load occurred after the pupil diameter stabilized in response to moving the gaze between trucks. Synchronization was straightforward as the cognitive task was periodic with 9 seconds and in the visual task lighting intensity also changed every 9 seconds.

Our results confirm that, at least in this simple case, our hypothesis holds and we can indeed detect changes in cognitive load under varying lighting conditions. We are planning to extend this work by introducing scenarios in which participants drive in realistic simulated environments. Under such scenarios gaze angles, and thus the amount of light reaching participants’ eyes, will change rapidly, making the disambiguation more complex, and of course more useful.

References

[1] Jackson Beatty, “Task-Evoked Pupillary Responses, Processing Load, and the Structure of Processing Resources,” Psychological Bulletin, 276-292, 91(2)

[2] Oskar Palinko, Andrew L. Kun, “Exploring the Influence of Light and Cognitive Load on Pupil Diameter in Driving Simulator Studies,” Driving Assessment 2011

[3] Jeff Klingner, Rashit Kumar, Pat Hanrahan, “Measuring the Task-Evoked Pupillary Response with a Remote Eye Tracker,” ETRA 2008

Estimating cognitive load using pupillometry: paper accepted to ETRA 2010

Our short paper [1] on using changes in pupil size diameter to estimate cognitive load was accepted to the Eye Tracking Research and Applications 2010 (ETRA 2010) conference. The lead author is Oszkar Palinko and the co-authors are my PhD student Alex Shyrokov, my OHSU collaborator Peter Heeman and me.

In previous experiments in our lab we have concentrated on performance measures to evaluate the effects of secondary tasks on the driver. Secondary tasks are those performed in addition to driving, e.g. interacting with a personal navigation device. However, as Jackson Beatty has shown, when people’s cognitive load increases their pupils dilate  [2]. This fascinating phenomenon provides a physiological measure of cognitive load. Why is it important to have multiple measures of cognitive load? As Christopher Wickens points out [3] this allows us to avoid circular arguments such as “… saying that a task interferes more because of its higher resource demand, and its resource demand is inferred to be higher because of its greater interference.”

We found that in a driving simulator-based experiment that was conducted by Alex, performance-based and pupillometry-based (that is a physiological) cognitive load measures show high correspondence for tasks that lasted tens of seconds. In other words, both driving performance measures and pupil size changes appear to track cognitive load changes. In the experiment the driver is involved in two spoken tasks in addition to the manual-visual task of driving. We hypothesize that different parts of these two spoken tasks present different levels of cognitive load for the driver. Our measurements of driving performance and pupil diameter changes appear to confirm the hypothesis. Additionally, we introduced a new pupillometry-based cognitive load measure that shows promise for tracking changes in cognitive load on time scales of several seconds.

In Alex’s experiment one of the spoken tasks required participants to ask and answer yes/no questions. We hypothesize that different phases of this task also present different levels of cognitive load to the driver. Will this be evident in driving performance and pupillometric data? We hope to find out soon!

References

[1] Oskar Palinko, Andrew L. Kun, Alexander Shyrokov, Peter Heeman, “Estimating Cognitive Load Using Remote Eye Tracking in a Driving Simulator,” ETRA 2010

[2] Jackson Beatty, “Task-evoked pupillary responses, processing load, and the structure of processing resources,” Psychological Bulletin. Vol. 91(2), Mar 1982, 276-292

[3] Christopher D. Wickens, “Multiple resources and performance prediction,” Theoretical Issues in Ergonomic Science, 2002, Vol. 3, No. 2, 159-177