Neural network based human performance modeling

A neural network architecture, derived from recurrent backpropagation, 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. The goal of this task was to develop a neural network which, when trained on data derived from a human subject performing a task, emulates that subjects´s performance and style. The task was to be interactive, and the conditions controlled. The task the subjects performed was based on a popular neural network demonstration concept. It is a computer generated display showing a two-lane circular track and five cars. One car is controlled by the subject, and the other four are controlled by the computer. The cars all travel in a counter-clockwise direction. The perspective is adjusted so that the subject´s car is always at the 3 o´clock position on the track, and everything else moves relative to the controlled car. The subject´s task was to drive his car around the track, switching lanes and adjusting speed as necessary to avoid collisions. Results and possible applications are discussed