Detecting Cheating when Testing Vision: Variability in Acuity Measures Reveals Misrepresentation

Optom Vis Sci. 2018 Jun;95(6):536-544. doi: 10.1097/OPX.0000000000001227.

Abstract

Significance: In certain scenarios, it is advantageous to misrepresent one's ability and "cheat" on vision tests. Our findings suggest that increased variability when testing visual acuity holds promise as a novel means to help detect this cheating and may generalize to other subjective tests of visual function.

Purpose: People who cheat on vision tests generally do so to make their vision appear better than it actually is (e.g., for occupational or driving purposes). However, there are particular settings in which it is advantageous for their vision to appear to be worse than is the case (e.g., to qualify for benefits available to people with low vision). Therefore, a method to help detect cheating in these scenarios is desirable. The aim of this study was to investigate whether the intentional underrepresentation of vision could be detected when testing visual acuity.

Methods: We tested the visual acuity of 13 participants with simulated vision impairment using the Berkeley Rudimentary Vision Test. Participants were tested in an honest condition when providing their best effort and in a cheating condition when attempting to make their visual acuity appear to be markedly worse. We also tested visual acuity of 17 participants with a wide range of vision impairments.

Results: Participants were successfully able to "cheat" on the tests; however, their responses were significantly more variable when cheating (P < .001). Although the variability in visual acuity was larger in individuals with actual vision impairment compared with those providing honest answers with simulated impairment (P < .01), their responses remained significantly less variable than those for individuals in the cheating condition (P = .01).

Conclusions: The variability in the estimations of vision provides a promising novel means of detecting the intentional underrepresentation of vision and could help to minimize the chance of successfully cheating on tests of vision.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Automobile Driving
  • Female
  • Humans
  • Lie Detection*
  • Male
  • Vision Disorders / diagnosis*
  • Vision Disorders / physiopathology
  • Vision Tests / methods*
  • Vision, Low / physiopathology
  • Visual Acuity / physiology*