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
Internet-related prefixes Internet-related prefixes Internet-related prefixes such as "e-", "i-", "cyber-", "info-", "techno-" and "net-" are added to a wide range of existing words to describe new, Internet- or computer-related flavors of existing concepts, often electronic products and services that already have a non-electronic counterpart. The adjective "virtual" is often used in a similar manner. "Cyber-" is derived from "cybernetic," which comes from the Greek word κυβερνητικός meaning "skilled in steering or governing". It is mainly used in the terms "cyberspace", "cyberlaw", "cyberbullying", "cybercrime", "cyberwarfare", "cyberterrorism", "cybersex", and "cyberdelic" among others. Although it is more commonly used to describe policies and politics