Connectionist Simulation of Attitude Learning: Asymmetries in the Acquisition of Positive and Negative Evaluations

Abstract

Connectionist computer simulation was employed to explore the notion that, if attitudes guide approach and avoidance behaviors, false negative beliefs are likely to remain uncorrected for longer than false positive beliefs. In Study 1, the authors trained a three-layer neural network to discriminate "good" and "bad" inputs distributed across a two-dimensional space. "Full feedback" training, whereby connection weights were modified to reduce error after every trial, resulted in perfect discrimination. "Contingent feedback," whereby connection weights were only updated following outputs representing approach behavior, led to several false negative errors (good inputs misclassified as bad). In Study 2, the network was redesigned to distinguish a system for learning evaluations from a mechanism for selecting actions. Biasing action selection toward approach eliminated the asymmetry between learning of good and bad inputs under contingent feedback. Implications for various attitudinal phenomena and biases in social cognition are discussed.