Becker Friedman Institute

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A neural network model of the structure and dynamics of human personality

We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two overarching motivational systems, an approach and an avoidance system, as well as a general disinhibition and constraint system. Each overarching motivational system influences more specific motives. Traits are modeled in terms of differences in the sensitivities of the motivational systems, the baseline activation of specific motives, and inhibitory strength. The result is a motive-based neural network model of personality based on research about the structure and neurobiology of human personality. The model provides an account of personality dynamics and person–situation interactions and suggests how dynamic processing approaches and dispositional, structural approaches can be integrated in a common framework.

Authors: 
Stephen Read, University of Southern California
Brian M. Monroe, University of Southern California
Aaron L. Brownstein, University of Southern California
Yu Yang, University of Southern California
Gurveen Chopra, University of Southern California
Lynn C. Miller, University of Southern California
Publication Date: 
January, 2010
HCEO Working Groups: 
Publication Type: 
Journal: 
Psychological Review
Volume: 
117
Issue Number: 
1
Pages: 
61-92