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PseudoSloan: A perimetric-complexity and area-controlled font for vision and reading research
Vladimir Y Vildavski1,2, Luca Lo Verde1,3, Gail Blumberg4,5
1Wu Tsai Neurosciences Institute, Department of Psychology, Stanford University, California, USA.
Journal of Vision
|September 8, 2022
Summary
Researchers developed "PseudoSloan," a novel artificial orthography based on Sloan optotypes. This scalable font aids in isolating orthographic effects from lexical processing in reading and learning studies.
Area of Science:
- Vision Science
- Cognitive Psychology
- Linguistics
Background:
- Artificial orthographies (pseudo/false fonts) are crucial for isolating orthographic effects in reading and verbal learning research.
- Optotype design for visual acuity testing focuses on character complexity and blur degradation.
- Existing research lacks a unified approach combining these principles.
Purpose of the Study:
- To design a novel, scalable pseudofont, "PseudoSloan," merging principles of artificial orthography and optotype design.
- To create a font based on the widely used Sloan optotypes, facilitating direct comparison and research integration.
- To provide a versatile tool for studying visual perception and reading processes.
Main Methods:
- Developed "PseudoSloan" based on Sloan optotype design guidelines.
- Included 26 Latin letters and two sets of letter-like symbols.
- Quantitatively matched Sloan and PseudoSloan glyphs using ink area and perimetric complexity.
Main Results:
- Successfully designed and created the scalable "PseudoSloan" font.
- Provided PseudoSloan glyphs with controlled variations in ink area and perimetric complexity.
- Font available in TrueType, OpenType, and .svg formats for broad accessibility.
Conclusions:
- "PseudoSloan" offers a valuable, standardized resource for research in reading, verbal learning, and vision science.
- The font's design facilitates the separation of orthographic from lexical processing effects.
- Availability of diverse glyphs supports research on visual acuity and character recognition.
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