Neural network models of personality structure and dynamics

Abstract

Abstract Although personality theory and research have recently made considerable progress, this work has also highlighted a number of unresolved issues in personality theory and measurement. We present a model of personality, embodied in a neural network, that addresses many of these issues. After first presenting our goal/motive-based model of personality, and then describing its implementation as a neural network, we address six key questions: (1) How can we integrate the structural and dynamic traditions in a single model? (2) How can a single model explain both the ideographic and nomothetic structure of personality? (3) How can stable personality structures nevertheless generate higher levels of within-subject variability than between subject variability? (4) What are the neurobiological systems that underlie personality? (5) How can a model of personality account for the effects of learning and experience on personality change? and (6) How should personality and situation be measured in order to maximize our ability to predict behavior? We argue that the current model can successfully answer all of these questions, providing a unified account of a number of core issues in personality psychology.