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
Transposed letter effect the two middle letters with different letters (double substitute non-words). Baboons classified TL non-words as words significantly more often than double substitute non-words. There are a number of theories that have been challenged by the effects shown with transposition-letter priming. These theories mainly have to do with how letters are used to process words. Slot-based coding theory states that each letter in a word is connected to a particular location, or slot, within that word. One of the major theories that predict letter slots is the interactive activation model created by McClelland and Rumelhart (1981). This model assumes that people