Integrating neural networks into decision-making and motivational theory: Rethinking VIE theory

This manuscript uses a reformulation of Vroom's (1964) VIE theory to illustrate the potential value of neuropsychologically based models of cognitive processes. Vroom's theory posits that people's decisions are determined by their affective reactions to certain outcomes (valences), beliefs about the relationship between actions and outcomes (expectancies), and perceptions of the association between primary and secondary outcomes (instrumentalities). One of the major criticisms of this type of theory is that the computations it requires are unrealistically time-consuming and often exceed working memory capacity. In this paper, we maintain that if an individual has extensive experience with a problem situation, he or she can process decisions about that situation using neural networks that operate implicitly so that cognitive resources are not exhausted by simple computations; instead, the computations arc performed implicitly by neural networks. By thinking about VIE from a neural network standpoint, at least one of its problems is eliminated, and several new insights into decision-making are provided. We use simulation methodology to show that such a model is both viable and can reflect the effects of current goals on choice processes.