Abstract
Background: Web cameras are increasingly part of the standard hardware of most smart devices. Eye movements can often provide a noninvasive “window on the brain,” and the recording of eye movements using web cameras is a burgeoning area of research.Objective: This study investigated a novel methodology for administering a visual paired comparison (VPC) decisional task using a web camera.To further assess this method, we examined the correlation between a standard eye-tracking camera automated scoring procedure [obtaining images at 60 frames per second (FPS)] and a manually scored procedure using a built-in laptop web camera (obtaining images at 3 FPS).Methods: This was an observational study of 54 clinically normal older adults.Subjects completed three in-clinic visits with simultaneous recording of eye movements on a VPC decision task by a standard eye tracker camera and a built-in laptop-based web camera. Inter-rater reliability was analyzed using Siegel and Castellan's kappa formula. Pearson correlations were used to investigate the correlation between VPC performance using a standard eye tracker camera and a built-in web camera.Results: Strong associations were observed on VPC mean novelty preference score between the 60 FPS eye tracker and 3 FPS built-in web camera at each of the three visits (r = 0.88–0.92). Inter-rater agreement of web camera scoring at each time point was high (κ = 0.81–0.88). There were strong relationships on VPC mean novelty preference score between 10, 5, and 3 FPS training sets (r = 0.88–0.94). Significantly fewer data quality issues were encountered using the built-in web camera.Conclusions: Human scoring of a VPC decisional task using a built-in laptop web camera correlated strongly with automated scoring of the same task using a standard high frame rate eye tracker camera.While this method is not suitable for eye tracking paradigms requiring the collection and analysis of fine-grained metrics, such as fixation points, built-in web cameras are a standard feature of most smart devices (e.g., laptops, tablets, smart phones) and can be effectively employed to track eye movements on decisional tasks with high accuracy and minimal cost.
Highlights
Web cameras are increasingly part of the standard hardware of most smart devices
After detailing the system components for both the standard eye tracking camera system and the web-camera, we will detail the test construction of the VPC task, including the visual calibration, data acquisition, and scoring methods associated with each eye tracking system
We found that mean novelty preference scores calculated using human coding of 3 frames per second (FPS) data from the built-in web camera could substitute for eye movement data captured at 60 FPS using a standard eye tracker camera
Summary
Web cameras are increasingly part of the standard hardware of most smart devices. The quality and cost of these devices has allowed for their increased use worldwide and are a standard feature on most smart devices, including desktop and laptop computers, tablets, and smart phones. Because eye movements can often provide a noninvasive “window on the brain,” the recording of eye movements using web cameras is a burgeoning area of research including both online and offline system development (Wang and Sung, 2001; Hansen and Pece, 2005; Vivero et al, 2010; Anderson et al, 2011; Lin et al, 2013; Petridis et al, 2013). Visual paired comparison (VPC) task paradigms assess recognition memory through comparison of the proportion of time an individual spends viewing a new picture compared to a picture they have previously seen, i.e., a novelty preference (Fantz, 1964; Fagan, 1970). Eye movements can often provide a noninvasive “window on the brain,” and the recording of eye movements using web cameras is a burgeoning area of research
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