Random word recognition chart helps scotoma assessment in low vision.

A limiting factor in reading with low vision can be visual acuity that is routinely tested by the MNread test.1 Loss of the central field and interference by a central scotoma can further impair word recognition and localization of the following line. The goal of this work was to evaluate the use of the SKread test in exposing vision problems that may impair reading. The test requires recognition of printed words but does not allow prediction of the next word or letter. The concept is similar to the existing Pepper test2 but in a format designed for straightforward analysis in the clinic, as exemplified by MNread. The resulting SKread test minimizes the influence of linguistics, reading experience, and education level on test outcome. Background Printed words as test material have been common in vision care for at least 150 years because of the importance of reading in cultural and educational contexts.3–6 Two aspects of vision are most frequently tested6: reading acuity and critical print size and reading performance, that is, speed and accuracy The former stresses the point that reading depends on physical dimensions, for example, letter size and contrast.7 In countries with a predominantly English-speaking population, the standard tools are the MNread test charts.1 The latter aspect stresses the fact that reading also depends on cognitive, educational, and linguistic components.2,8 The two most important elements of the linguistics of reading are syntax and semantics.9 Both can transform a random string of words into continuous text, which increases reading speed and fluency by making contextual and lexical inference possible. Thus, they allow associating a probability with each word that determines the likelihood that a certain word may follow.10–13 The ability to use this prediction determines reading speed and is the basis of the difference between fluent and nonfluent reading. Theoretical Considerations If a sequence of words loses the syntactic and semantic coherence, reading becomes more difficult and slows down.14,15 In the simplest case, reading can be performed letter-by-letter, which is very slow because only one letter is recognized at a time. Examples are children who just start to learn how to read or patients with a ring scotoma and a small central seeing island.15,16 Recognizing a group of letters as a word presents an advantage. Consequently, reading word-by-word is faster because it allows a limited degree of prediction of upcoming words. For instance, the ascenders or descenders of letters in an upcoming word can function as guides for rapid recognition of a word shape, as in “reap” vs. “read.” There is evidence that word recognition involves visual cortex that is also sensitive to other shapes as well as colors and faces.17 A further mechanism can contribute to predictions of longer words where recognizing a few letters in the beginning may reduce the range of possibilities to just a few words by lexical inference.10,13,18 The fastest reading speed can be achieved with the highest degree of prediction by using the relationships between words (linguistic inference). This does not even require precise recognition of an upcoming word. Consider the common phrase “for all intents and purposes,” where “purposes” is very predictable once the first three words are recognized. In contrast, the phrase “for all intents and programs” is highly unlikely. Interactions of various linguistic elements are so complex that reading has been used to estimate levels of intelligence.19 There is evidence that prediction of upcoming words during reading is used to determine the landing positions of reading eye movements.20 In real-life horizontal reading, upcoming words are visible as long as a horizontal “perceptual span” of 15 to 20 letters is not obstructed.21–26 The functional importance of this span becomes evident by the poor reading performance of patients with restricted visual fields 27,28 and with central scotomata.29,30 Two established vision tests in the English language focus on the difference between reading with or without linguistic inference. The MNread test uses simple sentences with intact grammar and meaning. Each is composed of 60 letters, including spaces, that are printed on three lines.1 The standardized format allows quick comparisons between reading performance of paragraphs printed in different sizes. It is the preferred instrument to determine reading speed and critical print size and is a well-used clinical tool to evaluate reading performance in low vision.31 Note that the original publication also explored using sequences of unrelated words.1 On the other hand, the Pepper Visual Skills for Reading (VSR) test uses random sequences of words and single letters in a paragraph of 13 lines, which prevents prediction of the next word based on the occurrence of the previous one.2,32 The lines are of different lengths (29 to 45 letters, including spaces). This test allows differentiating nine error types so that accurate scoring requires off-line data analysis from an audio recording. The special feature of this test is that it does not allow prediction based on linguistic inference. Its disadvantage is that the results need to be analyzed and quantified off-line, which is too time-consuming in a clinical setting. To overcome this disadvantage, we applied the proven standardized format of the MNread test to groups of unrelated words and letters, similar to the content of the VSR test to create the SKread test. The gained advantage is that, during the test, the examiner can annotate a score sheet showing the same print material as the test charts without the need for off-line data analysis. For this study, we related data from the de facto standard MNread test to those from the SKread test to investigate the following questions: In what way do the differences in test material influence reading speed and error rate? Does the presence and location of a central scotoma in patients with a maculopathy influence the characteristics of reading errors? A subset of data presented here has been previously published in conference proceedings.33–36