PseudoSloan: A perimetric-complexity and area-controlled font for vision and reading research.

Vladimir Y Vildavski, Luca Lo Verde, Gail Blumberg, Joss Parsey, Anthony M Norcia
Author Information
  1. Vladimir Y Vildavski: Wu Tsai Neurosciences Institute, Department of Psychology, Stanford University, California, USA.
  2. Luca Lo Verde: Wu Tsai Neurosciences Institute, Department of Psychology, Stanford University, California, USA.
  3. Gail Blumberg: Blumberg Communication Design, 309 Arden Road, Menlo Park, California, USA.
  4. Joss Parsey: Freelance Designer, 3810 Page Mill Road, Los Altos Hills, California, USA.
  5. Anthony M Norcia: Wu Tsai Neurosciences Institute, Department of Psychology, Stanford University, California, USA.

Abstract

Artificial orthographies have long been used in studies of verbal learning and reading. These orthographies, also known as pseudo or false fonts, are designed to match the letters of an existing alphabet on a range of visual features, isolating effects of orthography from those owing to lexical processing. In a parallel line of research, there has been much interest in the design of optotypes for measuring visual acuity that have good properties in terms of character complexity and graceful degradation under blur. Here we merge these two traditions by designing a fully scalable pseudofont, "PseudoSloan," that is based on the design rubric of the widely used Sloan optotypes. The font includes 26 Latin letters as well as two sets of letter-like symbols matching the Latin alphabet on a letter-by-letter basis. Quantitative matching of the pairs of Sloan and PseudoSloan glyphs is done on the basis of ink area and perimetric complexity. We provide the installable PseudoSloan font in TrueType and OpenType formats, plus a large number of PseudoSloan glyphs in .svg format that vary over wide ranges in their perimetric complexity and ink area (https://osf.io/qhj2b/).

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Grants

  1. P30 EY026877/NEI NIH HHS

MeSH Term

Humans
Pattern Recognition, Visual
Reading
Vision, Ocular
Visual Acuity
Visual Field Tests

Word Cloud

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