Applying perceptual learning to achieve practical changes in vision

Abstract

Research of visual perceptual learning has illuminated the flexibility of processing in the visual system and provides insights into therapeutic approaches to remediating some components of low vision. A key observation from research of perceptual learning is that effects of training are often highly specific to the attributes of the trained stimuli. This observation has been a blessing to basic research, providing important constraints to models of learning, but is a curse to translational research, which has the goal of creating therapies that generalize widely across visual tasks and stimuli. Here we suggest that the curse of specificity can be overcome by adopting a different experimental framework than is standard in the field. Namely, translational studies should integrate many approaches together and sacrifice mechanistic understanding to gain clinical relevance. To validate this argument, we review research from our lab and others, and also present new data, that together shows how perceptual learning on basic stimuli can lead to improvements on standard vision tests as well as real world vision use such as improved reading and even improved sports performance. Furthermore, we show evidence that this integrative approach to perceptual learning can ameliorate effects of presbyopia and provides promise to improve visual function for individuals suffering from low vision.

Keywords: applied vision; perceptual learning; presbyopia; reading; visual therapy.

FIGURE 1

Data from Deveau et al. (2014a). Left, for acuity, Landolt C size thresholds were measured at different locations in the visual field (with an eye-tracker to enforce fixation). Middle, contrast sensitivity thresholds were measured by varying the contrast of an “O” presented at visual field locations. Right, an Optec Visual Analyzer (Stereo Optical Company, Chicago, IL, USA) measured foveal visual acuity and contrast sensitivity. Data from pre-training tests (black) is shown against data of post-training tests (gray). In the left two graphs, lower values represent better performance. Acuity values (left) are based on standard 20/20 scores in the fovea (peripheral scores values are poorer). Weber Contrast (middle). Contrast Sensitivity (right) shows contrast as a function of spatial frequency in central vision (higher values are better). Training-induced benefits are all significant at least to the p< 0.05 levels. Error bars represent SE of the mean.

FIGURE 2

(A) Mean difference of reading acuity, critical print size, and reading speed using MNREAD acuity charts in healthy young adults. Trained participants were tested binocularly before and after vision training. Untrained data reproduced from Subramanian and Pardhan (2006) where two different versions of the MNREAD acuity charts were used to measure learning effects. Error bars represent subject SE. (B) Mean difference in LogMAR acuity measurements taken in the right eye, left eye, and binocularly in presbyopic individuals. Participants were tested before and after vision training. Error bars represent within subject SE.