An objective measurement approach to quantify the perceived distortions of spectacle lenses.

The eye's natural aging influences our ability to focus on close objects. Without optical correction, all adults will suffer from blurry close vision starting in their 40s. In effect, different optical corrections are necessary for near and far vision. Current state-of-the-art glasses offer a gradual change of correction across the field of view for any distance-using Progressive Addition Lenses (PALs). However, an inevitable side effect of PALs is geometric distortion, which causes the swim effect, a phenomenon of unstable perception of the environment leading to discomfort for many wearers. Unfortunately, little is known about the relationship between lens distortions and their perceptual effects, that is, between the complex physical distortions on the one hand and their subjective severity on the other. We show that perceived distortion can be measured as a psychophysical scaling function using a VR experiment with accurately simulated PAL distortions. Despite the multi-dimensional space of physical distortions, the measured perception is well represented as a 1D scaling function; distortions are perceived less with negative far correction, suggesting an advantage for short-sighted people. Beyond that, our results successfully demonstrate that psychophysical scaling with ordinal embedding methods can investigate complex perceptual phenomena like lens distortions that affect geometry, stereo, and motion perception. Our approach provides a new perspective on lens design based on modeling visual processing that could be applied beyond distortions. We anticipate that future PAL designs could be improved using our method to minimize subjectively discomforting distortions rather than merely optimizing physical parameters.

Estimating the perceived dimension of psychophysical stimuli using triplet accuracy and hypothesis testing.

Vision researchers are interested in mapping complex physical stimuli to perceptual dimensions. Such a mapping can be constructed using multidimensional psychophysical scaling or ordinal embedding methods. Both methods infer coordinates that agree as much as possible with the observer's judgments so that perceived similarity corresponds with distance in the inferred space. However, a fundamental problem of all methods that construct scalings in multiple dimensions is that the inferred representation can only reflect perception if the scale has the correct dimension. Here we propose a statistical procedure to overcome this limitation. The critical elements of our procedure are i) measuring the scale's quality by the number of correctly predicted triplets and ii) performing a statistical test to assess if adding another dimension to the scale improves triplet accuracy significantly. We validate our procedure through extensive simulations. In addition, we study the properties and limitations of our procedure using "real" data from various behavioral datasets from psychophysical experiments. We conclude that our procedure can reliably identify (a lower bound on) the number of perceptual dimensions for a given dataset.