UC Berkeley

Many models posit the use of distinctive spatial features to recognize letters of the alphabet, a fundamental component of reading. It has also been hypothesized that when letters are in close proximity, visual crowding may cause features to mislocalize between nearby letters, causing identification errors. Here, we took a data-driven approach to investigate these aspects of textual processing. Using data collected from subjects identifying each letter in thousands of lower-case letter trigrams presented in the peripheral visual field, we found characteristic error patterns in the results suggestive of the use of particular spatial features. Distinctive features were seldom entirely missed, and we found evidence for errors due to doubling, masking, and migration of features. Dependencies both amongst neighboring letters and in the responses revealed the contingent nature of processing letter strings, challenging the most basic models of reading that ignore either crowding or featural decomposition.