BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Chris F. Westbury and Michelle Yang (Chris F. Westbury (University of Alberta) and Michelle Yang (McGill University)) DTSTART:20250611T150000Z DTEND:20250611T161500Z DTSTAMP:20260423T035959Z UID:AAIT/47 DESCRIPTION:Title: Or thographic uncertainty: An entropy-based measure of word form typicality.< /a>\nby Chris F. Westbury and Michelle Yang (Chris F. Westbury (University of Alberta) and Michelle Yang (McGill University)) as part of Seminar on Algorithmic Aspects of Information Theory\n\n\nAbstract\nMeasures of ortho graphic typicality have long been studied as predictors of lexical access. The best-known orthographic typicality measure is orthographic neighbourh ood size (Coltheart’s N or ON)\, the number of words that are one letter different\, by substitution\, from the target word. A more recent related measure of orthographic typicality is orthographic Levenshtein distance 2 0 (OLD20)\, the average Levenshtein orthographic edit distance of a target word from its 20 closest neighbours (Yarkoni\, Balota\, and Yap\, 2008). Both measures have been implicated in lexical access. We will discuss a fa mily of measures of word form similarity we call orthographic uncertainty. These measures are based on Shannon entropy (Shannon\, 1948)\, which has a long history of being considered psychologically relevant. Orthographic uncertainty measures are superior to ON and OLD20 at predicting word/nonwo rd decision times and word reading times and accuracies. They are also sup erior to the older measures insofar as they are naturally tied to the wide ly-accepted quantification using Shannon Entropy of the psychological func tions of familiarity\, uncertainty\, learnability\, and representational a nd computational efficiency.\n LOCATION:https://researchseminars.org/talk/AAIT/47/ END:VEVENT END:VCALENDAR