USING NEURAL NETWORKS TO MODEL PERSONALITY-DEVELOPMENT

A neural network approach to modeling the development of personality t raits through social learning is presented. From the more general mode l the special case of a network mapping four situation dimensions (inp ut neurons) onto 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 personal ity disorder. The network's ability to learn these pattern pairs is de monstrated. The trained network is then presented with new input (situ ational) patterns and is shown to respond to these new situations with output patterns consistent with a borderline personality disorder. Th e 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 passi ve. This model provides a quantitative and reproducible framework with in which to discover and test theories of personality development. It is hoped that it will extend our ability to predict human behavior.