Extending the MNREAD sentence corpus: Computer-generated sentences for measuring visual performance in reading. (May 2019)

• Record Type: Journal Article
• Authors: Mansfield, J.S.; Atilgan, N.; Lewis, A.M.; Legge, G.E.
• Is Part Of: Vision research. Volume 158(2019)
• Page Start: 11
• Page End: 18
• Publication Date: 2019-05
• Subjects: MNREAD -- Reading acuity -- Critical print size -- Maximum reading speed -- Blur; Vision -- Periodicals; 573.88
• DOI: 10.1016/j.visres.2019.01.010
• Languages: English
• ISSNs: 0042-6989

Abstract

Abstract: The MNREAD chart consists of standardized sentences printed at 19 sizes in 0.1 logMAR steps. There are 95 sentences distributed across the five English versions of the chart. However, there is a demand for a much larger number of sentences: for clinical research requiring repeated measures, and for new vision tests that use multiple trials at each print size. This paper describes a new sentence generator that has produced over nine million sentences that fit the MNREAD constraints, and demonstrates that reading performance with these new sentences is comparable to that obtained with the original MNREAD sentences. We measured reading performance with the original MNREAD sentences, two sets of our new sentences, and sentences with shuffled word order. Reading-speed versus print-size curves were obtained for each sentence set from 14 readers with normal vision at two levels of blur (intended to simulate acuity loss in low vision) and with unblurred text. We found no significant differences between the new and original sentences in reading acuity and critical print size across all levels of blur. Maximum reading speed was 7% slower with the new sentences than with the original sentences. Shuffled sentences yielded slower maximum reading speeds and larger reading acuities than the other sentences. Overall, measures of reading performance with the new sentences are similar to those obtained with the original MNREAD sentences. Our sentence generator substantially expands the reading materials for clinical research on reading vision using the MNREAD test, and opens up new possibilities for measuring how text parameters affect reading.