After all the evidence has been presented, we will evaluate the models in terms of their ability to support the data, the
reader here is processing the letter T in the first position in a word. Every node in the visual feature detector level is connected to every node in the letter detector level. The letters seen here apply only to the first letter of a word. The connections between the visual feature detector level and the letter level are all either excitatory (represented with an arrow at the end of the connection) or
inhibitory (represented with a circle at the end of the connection). The inhibitory connections between each of the letters will result in the Tbeing the most activated letter node because it has the most incoming excitatory
activation. The letter node for T will then send excitatory activation to all the words that start with T and inhibitory activation to all the other words. For the fourth letter position the computer simulation is told that there is a vertical line on the left, a crossbar in the middle, and a diagonal pointing towards the bottom right. Time in the computer is measured in epochs of activation events. The early activation equally is rising for the kand r letter nodes. During the early epochs the letter nodes are only receiving activation from the visual feature nodes, but later activation is provided by the word nodes. Since the first three letters of the word are not degraded, the letter nodes easily recognized them as w, o,and r for the first three positions respectively. This allows the k letternode and the word work to continuously increase in activation and send inhibitory activation to their competitors, the letter r and the word word. The Seidenberg & McClellandand Plaut et. After seeing a correct sample, the network will calculate the error in its guess of the pronunciation, and then modifies the strength of each of the nodes that are connected to it so that the error will be slightly less next time. This is analogous to what the brain does. He found that: A) Subjects are more successful at naming letters to the right of fixation than to the left of fixation. B) When distance to the right of the fixation point is controlled, subjects are better able to recognize the last letter of a word than the first letter of word. This data explains why it is that we tend to fixate just to the left of the middle of a word. Bouwhuis & Bouma (1979) extended the Bouma (1973) paper by not only finding the probability of recognizing the first and last letters of a word, but also the middle letters. They used this data todevelop a model of word recognition based on the probability of recognizing each of the letters within a word.