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.

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