The effect of normal aging and age-related macular degeneration on perceptual learning

Abstract

We investigated whether perceptual learning could be used to improve peripheral word identification speed. The relationship between the magnitude of learning and age was established in normal participants to determine whether perceptual learning effects are age invariant. We then investigated whether training could lead to improvements in patients with age-related macular degeneration (AMD). Twenty-eight participants with normal vision and five participants with AMD trained on a word identification task. They were required to identify three-letter words, presented 10° from fixation. To standardize crowding across each of the letters that made up the word, words were flanked laterally by randomly chosen letters. Word identification performance was measured psychophysically using a staircase procedure. Significant improvements in peripheral word identification speed were demonstrated following training (71% ± 18%). Initial task performance was correlated with age, with older participants having poorer performance. However, older adults learned more rapidly such that, following training, they reached the same level of performance as their younger counterparts. As a function of number of trials completed, patients with AMD learned at an equivalent rate as age-matched participants with normal vision. Improvements in word identification speed were maintained at least 6 months after training. We have demonstrated that temporal aspects of word recognition can be improved in peripheral vision with training across a range of ages and these learned improvements are relatively enduring. However, training targeted at other bottlenecks to peripheral reading ability, such as visual crowding, may need to be incorporated to optimize this approach.

Figure 1

Example of stimulus used to test and train word identification in participants with normal vision. A random three-letter word (e.g., DAM) was displayed 10° above a fixation dot and flanked either side by a random letter. Arrows and labels in blue were not displayed during the experiment.

Figure 2

Example of stimulus used to test and train word identification in participants with AMD. A random three-letter word (e.g., LID) was displayed 10° above or below a fixation cross and flanked either side by a random letter. Arrows and labels in blue were not displayed during the experiment.

Figure 3

Example of stimuli used to test visual acuity. Participants were required to identify the gap in a Landolt C, which was presented in a line of five Landolt Cs positioned 10° above fixation and randomly set to one of the four cardinal directions. Arrows and labels in blue were not displayed during the experiment.

Figure 4

(a) Mean peripheral word identification duration as a function of training session for all participants with normal vision. (b) Mean peripheral word identification duration for younger (median age of 64 years) participants with normal vision as a function of training session. Smooth curves through the data points are best fittings solutions of Equation 1. Error bars show 1 standard error of the mean (SEM).

Figure 5

(a) Peripheral word identification speed on session 1 and session 10 as a function of age. Solid and dashed lines show linear regression curves fitted through the duration threshold data for session 1 and session 10, respectively. (b) Change in word identification threshold (threshold on session 10 − threshold on session 1) as a function of age. The line shows a linear regression curve fitted through the data. (c) Change in peripheral word identification threshold (threshold on session 10 − threshold on session 1) as a function of threshold on session 1, including linear regression curve fit.

Figure 6

(a) Mean peripheral word identification speed for younger (median age of 64 years) participants with normal vision as a function of training session. Circular points represent mean peripheral word identification speed on each run. Solid and dashed lines show linear regression curves fitted through the word identification data for each session for the younger and older groups, respectively. (b) Within-session learning rate as a function of session. A negative slope corresponds to a within-session improvement in performance, whereas a positive slope indicates that participants got worse on the task during the session. Error bars show R² of linear regression curves fitted to mean threshold data for each run on individual sessions (see lines in panel a).

Figure 7

Peripheral word identification speed on session 1, session 10, and 6 months following training in five participants with normal vision (48 ± 18 years of age). Error bars show SEM.

Figure 8

Word identification threshold as a function of training session for each participant with AMD (76 ± 6 years of age) and five age-matched participants (71 ± 2 years of age) with normal vision. Error bars show SEM.

Figure 9

Word identification duration thresholds for participants with AMD (n = 5, 76 ± 6 years of age) and age-matched participants with normal vision (n = 5, 71 ± 2 years of age) as a function of (a) training session, (b) number of runs/staircases completed, and (c) number of trials completed. Error bars show SEM.