Word recognition can also be called lexical access or lexical selection because it is the process of going from a printed letter string to the selection of a single item stored in lexical memory. However, Seidenberg and McCleland did not agree with this definition because it assumes that words are represented as lexical entries in memory. They argued that representations were distributed across sets of simple subsymbolic processing units. They said that semantics should be integrated into the word recognition system like orthographic and phonological.
Word recognition should be able to account for eight phenomena. First of all is the word superiority effect. This phenomenon deals with phonology more than lexical processing. Word superiority effect is that when letter is presented in a word, they will be recognized faster than in isolation. The second is the word frequency effect. Words that are seen more often is recognized rapidly. The third is the semantic priming effect. Here, there is experimental task which provide prime and target. If the target is “dog”, the prime which is semantically related, like “cat” will be recognized faster. The next is the masked repetition priming effect. In this phenomenon, a prime word is briefly presented followed immediately in the same position on the target, for example dog-DOG. It will be responded rapidly if the words are similar. The fifth is familiarity. If the word is familiar to us, we will recognize faster. The sixth is length effect. Length can be measured based on how many letters, syllables, phonemes and spoken word. The shortest the word, the faster we recognize it. The next phenomenon is neighbourhood effect. If the word has consistent neighbour, it will be identified faster. The last phenomenon is word and non word effect where word will be made out faster.
Using DRC model, regular words are recognized faster than irregular words. In DRC, there is information conflict at the phoneme level when a word is irregular. It takes times to solve the conflict so the speed of reading will be slower. Frequency effect in DRC model defines accessing high frequency words in mental lexicon is faster than access for low frequency words. It also shows that low frequency words will have larger regularity effect. For example the word “wave” and “have”. “Wave” is a low frequency word and it is a regular word because it will be pronounced same as the neighbour like “cave” and “brave”. However, “have” is a high frequency word and irregular word. Lexical processing will be slow to process irregular high frequency words and there will be more time for the conflicting information from the non lexical route. In a person with surface dyslexia, non words and regular words will be read with normal accuracy because the non lexical route can do the job. However, irregular word will fail to be pronounced correctly. Another thing that DRC model account for is non words. The evidence is that a person with phonological dyslexia can pronounce irregular words and regular words accurately but they can not pronounce non words.
To sum up, DRC model is able to explain irregular and regular words, frequency, word and non word effect.