Mapping and correcting the influence of gaze position on pupil size measurements

Abstract

Pupil size is correlated with a wide variety of important cognitive variables and is increasingly being used by cognitive scientists. Pupil data can be recorded inexpensively and non-invasively by many commonly used video-based eye-tracking cameras. Despite the relative ease of data collection and increasing prevalence of pupil data in the cognitive literature, researchers often underestimate the methodological challenges associated with controlling for confounds that can result in misinterpretation of their data. One serious confound that is often not properly controlled is pupil foreshortening error (PFE)—the foreshortening of the pupil image as the eye rotates away from the camera. Here we systematically map PFE using an artificial eye model and then apply a geometric model correction. Three artificial eyes with different fixed pupil sizes were used to systematically measure changes in pupil size as a function of gaze position with a desktop EyeLink 1000 tracker. A grid-based map of pupil measurements was recorded with each artificial eye across three experimental layouts of the eye-tracking camera and display. Large, systematic deviations in pupil size were observed across all nine maps. The measured PFE was corrected by a geometric model that expressed the foreshortening of the pupil area as a function of the cosine of the angle between the eye-to-camera axis and the eye-to-stimulus axis. The model reduced the root mean squared error of pupil measurements by 82.5 % when the model parameters were pre-set to the physical layout dimensions, and by 97.5 % when they were optimized to fit the empirical error surface.

Dissociable saccadic suppression of pupillary and perceptual responses to light

“…However, further studies are necessary to investigate this eye movement-related pupil modulation, especially since its cause and function are at present unknown. It cannot be entirely explained either by 1) the eye-position artifact (Hayes and Petrov 2015), evident as a rapid, small pupil change during the saccade, or 2) the effort of preparing the saccade execution , which consists of a progressive dilation preceding the saccade. Neither of these effects explains the prolonged constriction after the eye has reached its final postsaccadic position.…”

Pupillary response to complex interdependent tasks: A cognitive-load theory perspective

“…For example, in a reading task the stimulus (the paragraph) runs from top left to bottom right, which might create a problem, because the pupil diameters could be systematically overestimated at the top left corner and underestimated at the bottom right, or vice versa (Brisson et al, 2013), thereby leading to a pupillometry profile that would show a gradual decrease (or increase) regardless of the cognitive-load profile of the passage. Such errors can lead to Type I errors (Hayes & Petrov, 2016). In this study, the areas of interest for each task were spread across the screen, and the visits/revisits to these areas by the participants were haphazard.…”

A Review of Pupillometry for Human-computer Interaction Studies

“…With growing processing power, even long standing issues such as the pupil foreshortening error [39] are becoming feasible to solve even in realtime. The future of the field is in highly adaptable, self-correcting image processing algorithms, that work in tandem with other sources of information (heart rate, electrodermal activity changes and behavioral data), to identify problems in HCI or to provide feedback for both the users and the developers.…”

“…The third method is to measure the area of the pupil, but that is vulnerable to both eyelid closure and the pupil foreshortening error. There are attempts to attenuate these sources of error: the pupil foreshortening error can be lessened by using a geometric eye model based correction routine [39]. Fig.…”