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

  title={Modeling reading, spelling, and past tense learning with artificial neural networks.},
  author={John A. Bullinaria},
  journal={Brain and language},
  volume={59 2},
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… CONTINUE READING

From This Paper

Topics from this paper.


Publications citing this paper.
Showing 1-10 of 45 extracted citations

Multi-variable Neural Network Forecasting Using Two Stage Feature Selection

2014 13th International Conference on Machine Learning and Applications • 2014
View 1 Excerpt

Text to phoneme alignment and mapping for speech technology: A neural networks approach

The 2011 International Joint Conference on Neural Networks • 2011
View 10 Excerpts

Similar Papers

Loading similar papers…