Modeling reading, spelling, and past tense learning with artificial neural networks

The connectionist modeling of reading, spelling, and past tense acquisition is discussed. We show how the same simple pattern association network for all three tasks can achieve perfect performance on training data containing many irregular words, provide near human level generalization performance, and exhibit some realistic developmental and brain damage effects. It is also shown how reaction times (such as naming latencies) can be extracted from these networks along with independent priming and speed-accuracy trade-off effects. We argue that all the remaining problems with these models will be solved by supplementing them with an appropriate connectionist semantic route.