July 2013
Volume 13, Issue 9
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Predicting reading performance for different fonts using physical and perceptual properties of letters
Author Affiliations
  • Jean-Baptiste Bernard
    School of Optometry, UC Berkeley
  • Daniel R. Coates
    Vision Science Graduate Program, UC Berkeley
  • Susana T. L. Chung
    School of Optometry, UC Berkeley\nVision Science Graduate Program, UC Berkeley
Journal of Vision July 2013, Vol.13, 1300. doi:https://doi.org/10.1167/13.9.1300
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      Jean-Baptiste Bernard, Daniel R. Coates, Susana T. L. Chung; Predicting reading performance for different fonts using physical and perceptual properties of letters. Journal of Vision 2013;13(9):1300. doi: https://doi.org/10.1167/13.9.1300.

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

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Previous studies have reported differences in reading speed depending on the font used. The dependence of reading speed on letter recognition (Legge et al, 2001) predicts that poor letter recognition performance would lead to low reading speed. In this study, we investigated if physical attributes of letters and/or perceptual properties of letter recognition can predict reading performance for different fonts. Reading speed was measured using the rapid serial visual presentation paradigm, for a range of print sizes (x-height: 0.7° – 2.8°) and for 13 different fonts in 8 subjects. For each font, we derived the two most important characteristics that summarize the reading performance: the maximum reading speed (MRS) and the critical print size (CPS), the smallest print that can be read with maximum speed. The letter size that enabled subjects to identify single letters at 80 percent-correct at 100ms was also determined ("letter acuity"). For each font matched in x-height, the following physical attributes of letters were measured based on font letter templates: x-width, average letter width, average letter height, ink area, skeleton length, perimeter, perimetric complexity, stroke width, inter-letter similarity and stroke regularity. Principal Component Analysis was performed to extract the principal components that captured the physical variations across fonts. Averaged across subjects, CPS (range: 0.09° – 0.16°) and MRS (range: 459 wpm – 713 wpm) were different across fonts. Principal components of the physical attributes failed to predict CPS and MRS using a multiple regression model (adjusted R2<0.14). However, there was a correlation between letter acuity and CPS (r=0.94; p<0.001), and between letter acuity and MRS (r=-0.80; p=0.03). Our results suggest that linear relations of physical measurements are not able to predict the differences between reading speeds across fonts. However, letter acuity, a perceptual measure, seems to be a good predictor of maximum reading speed.

Meeting abstract presented at VSS 2013


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