Neural network principles for theoretical psychology

Abstract

Neural networks are an increasingly important tool for the mechanistic understanding of psychological phenomena. Three commonly used principles in neural-network design (associative learning, competition, and opponent processing) are outlined here, and two examples of their use in behavior-modeling architectures are discussed. One example relates to an instance of reinforcement learning; that is, of an organism controlling its environment to maximize positive reinforcement or to minimize negative reinforcement. The other example relates to some characteristic deviations from reinforcement learning that occur in people or monkeys with frontal-lobe damage. © 1989 Psychonomic Society, Inc.