Transposed letter effect are letter-position specific when detecting words, so our recognition of words is based on what letters it contains, where the letters are placed within that word and the length of the word itself. Another example is the bayesian reader model created by Norris (2006) which also assumes that the letters in a word are associated with their specific location. Many experiments (for example, Perea and Lupker, 2003) have shown if you use transposed-letter priming, (for example, priming judge with jugde) you will see a priming effect but if the prime is a word where two letters are changed to other
Teeline Shorthand a streamlined way to transcribe the spoken word quickly by removing unnecessary letters from words and making the letters themselves faster to write. Vowels are often removed when they are not the first or last letter of a word, and silent letters are also ignored. Common prefixes, suffixes, and letter groupings (such as "sh" and "ing") are reduced to single symbols. The symbols themselves are derived from the old cursive forms of the letter and the unnecessary parts are again stripped leaving only the core of the letter left. Unlike phonetics based shorthands, such as Pitman, Teeline is a spelling