Using neural networks to model personality development.

A neural network approach to modeling the development of personality traits through social learning is presented. From the more general model the special case of a network mapping four situation dimensions (input neurons) into seven dimensional personality traits (output neurons) is described. This network is allowed to learn with input/output sets representing conditions suspected of leading to a borderline personality disorder. The network's ability to learn these pattern pairs is demonstrated. The trained network is then presented with new input (situational) patterns and is shown to respond to these new situations with output patterns consistent with a borderline personality disorder. The neural network model is thus shown to have important advantages over other personality models in that it can predict what situations will produce shifts in personality traits, for example from active to passive. This model provides a quantitative and reproducible framework within which to discover and test theories of personality development. It is hoped that it will extend our ability to predict human behavior.