Modeling human performance with neural networks

A neural network architecture derived from recurrent back propagation which learns to mimic human behavior and performance in a sample task is presented. It shows operating characteristics similar to those of human subjects and even makes the same kinds of mistakes. Possible applications are discussed. In particular, this type of model could be used where realistic behavior is required from a simulation of a human-operated system