June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Computer-generated MNREAD sentences for measuring acuity and reading-speed: A comparison of Times and Courier.
Author Affiliations & Notes
  • John Stephen Mansfield
    Psychology, SUNY Plattsburgh, Plattsburgh, New York, United States
  • Angela M Lewis
    Psychology, SUNY Plattsburgh, Plattsburgh, New York, United States
  • Footnotes
    Commercial Relationships   John Stephen Mansfield, Precision Vision (R); Angela Lewis, None
  • Footnotes
    Support  NIH EY002934
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 3274. doi:
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      John Stephen Mansfield, Angela M Lewis; Computer-generated MNREAD sentences for measuring acuity and reading-speed: A comparison of Times and Courier.. Invest. Ophthalmol. Vis. Sci. 2017;58(8):3274.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : The MNREAD acuity chart consists of standardized sentences printed at a wide range of print sizes. Each sentence has 60 characters, uses 3rd-grade vocabulary, and is typeset using the Times font on 3 lines of left-right justified text with minimal adjustment of the space between words. It has been challenging to write sentences that meet these constraints; there are just 95 sentences distributed across five versions of the chart. However, there is a demand for many more sentences meeting the MNREAD constraints: for clinical research requiring repeated measures, and for new vision tests that use multiple trials at each print size.

Methods : We have created an algorithmic sentence generator that uses phrase-structure grammars to produce sentences that fit the MNREAD linguistic, length, and typesetting constraints. So far, the generator has produced 116,000 sentences (see Figure 1). Many of these sentences also fit the MNREAD constraints in fonts other than Times (e.g., 20% fit the constraints for Courier.) This allows for comparisons of reading performance across different fonts using identical sentences. To demonstrate: we compared reading speed in 50 college students for sentences printed in Times and Courier (print size = 1M). Students read booklets of 8 pages with 60 sentences per page. Students were randomly assigned to either of two versions of the booklet, the sentence order was the same in each, but the font order (alternating pages of Times and Courier) was counterbalanced. For each page, the students were given 2 minutes to read as many sentences as they could, indicating whether or not each sentence “made sense”.

Results : Reading speeds were calculated from the number of words read in each 2-minute interval. A mixed-effects model with participant and sentence-set as random effects showed a significant fixed effect of font — Times was read 3.5% faster than Courier [CI=0.6%–6.4%, p<0.05]. This outcome is consistent with findings using earlier versions of the MNREAD chart showing a reading speed advantage for Times over Courier in readers with normal vision (Mansfield, Legge, & Bane, IOVS 37, 1996).

Conclusions : Our sentence generator substantially expands the reading materials for clinical research on reading vision using the MNREAD test.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Figure 1. Example algorithmically-generated sentence printed in Times and Courier.

Figure 1. Example algorithmically-generated sentence printed in Times and Courier.

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