Learning to identify crowded letters: does it improve reading speed?

Abstract

Crowding, the difficulty in identifying a letter embedded in other letters, has been suggested as an explanation for slow reading in peripheral vision. In this study, we asked whether crowding in peripheral vision can be reduced through training on identifying crowded letters, and if so, whether these changes will lead to improved peripheral reading speed. We measured the spatial extent of crowding, and reading speeds for a range of print sizes at 10 degrees inferior visual field before and after training. Following training, averaged letter identification performance improved by 88% at the trained (the closest) letter separation. The improvement transferred to other untrained separations such that the spatial extent of crowding decreased by 38%. However, averaged maximum reading speed improved by a mere 7.2%. These findings demonstrated that crowding in peripheral vision could be reduced through training. Unfortunately, the reduction in the crowding effect did not lead to improved peripheral reading speed.

Figure 1

A schematic cartoon illustrating the basic experimental design of the study.

Figure 2

Samples of trigrams rendered at different letter separations, specified as the distance between centers of adjacent letters and expressed as multiples of x-height (shown on the left). Each row shows two sample trigrams rendered at the specified letter separation.

Figure 3

Proportion-correct of identifying a letter flanked by two other letters at the closest letter separation (0.8× the x-height) is plotted as a function of training blocks, for each individual observer. Filled symbols in each panel represent measurements obtained at pre- and post-tests (not included in the fitting of the regression line). The solid line in each panel represents the best-fit regression line to the 60 blocks of training data. The correlation coefficient of the line is given in each panel. For all observers, the slope of the line is significantly different from 0 (p < 0.0001), implying a significant amount of improvement following training.

Figure 4

Proportion-correct of identifying the middle letter of trigram is plotted as a function of letter separation (multiples of x-height) for each observer. Data for pre- and post-tests are represented by unfilled and filled symbols, respectively. Each set of data was fit with a cumulative-Gaussian function. Error bars represent ± 1 standard error of the proportion. The spatial extent of crowding is defined as the letter separation that yields 50% correct of letter identification (after correction for guessing) on the cumulative-Gaussian function. Dashed lines represent the performance for identifying unflanked (single) letters at pre-test (performance at post-test was either identical to pre-test or better). Arrows indicate the trained letter separation (0.8× the x-height).

Figure 5

RSVP reading speed (wpm) is plotted as a function of print size (deg) for each observer. Data for pre- and post-tests are represented by unfilled and filled symbols, respectively. Each set of data was fit with a two-line fit on log-log axes (see text for details) from which the maximum reading speed and critical print size were determined. Error bars represent ± 1 SEM.

Figure 6

Ratios of post-test and pre-test reading speed were plotted as a function of individual print sizes normalized to each observer’s critical print size. The vertical dashed line divides the print size axis into those smaller (left of the line) or larger (right of the line) than the critical print size. The horizontal dashed line divides the reading speed ratio axis into those showing an improvement following training (ratios >1) and those showing a decline in performance following training (ratios <1).