Abstract
Collecting data on performance on multiple visual tasks from multiple, diverse samples is useful for examining individual differences of various functions. As a means of large-scale data collection, we developed a set of vision tests embedded in short video games that operate using web browsers and evaluated contrast sensitivity (CS), multiple-object detection (MOD), multiple-object tracking (MOT), and visual crowding (VC). This study aimed to confirm the usability of these tests with large datasets in a variety of contexts outside the laboratory. We conducted experiments using the test set in two contexts with large populations of laypersons. The first was during a digital content convention with primarily young adults in attendance (DC dataset). The other was collected at an eye hospital visited by older adults and their families (EH dataset). Overall, data were available from 1332 participants (DC: n=1256; EH: n=76). The EH and DC datasets were analyzed separately. The two groups showed similar results, which were roughly comparable to the results reported in previous studies. The results of the CS test showed an inverted-U curve peaking at around 2.4 cpd, with a sensitivity of approximately 250. On the VC test, more than half of participants could distinguish target letters at 10 deg eccentricity when they were surrounded by four letters with 5 deg spacing. On the MOT test, participants could track 3–4 moving targets from among 4–5 distractors. On the MOD test, detection sensitivity was consistent between the16 positions within a field of 40 dva. The average time taken by participants to complete was 2–3 min for each test. These findings suggest that the existing tests are a useful assessment for large-scale data collection that involves the examination of the variability and associations between individual differences and multiple visual functions.