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

Canadian Psychology, Feb 2003 by Robert G Lord, Paul J Hanges, Ellen G Godfrey

Integrating Neural Networks into Decision-Making and Motivational Theory: Rethinking VIE Theory*

Abstract

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 timeconsuming 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 are 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.

The transformation of social psychology by the information processing metaphor in the 1970s and 1980s affected applied psychology in a number of ways, influencing our understanding of critical applied processes such as performance appraisal (Ilgen, Barnes-Farrell, & McKellin, 1993) and leadership perceptions (Lord, Foti, & De Vader, 1984). Social psychology is currently being transformed by another metaphor in which the analogy to brain structure and processes serves to constrain both cognitive and social theory. This social-cognitive-neuroscience perspective uses cognitive processes as a key linkage between the social and neuropsychological levels of theory development (Ochsner & Lieberman, 2001). Applied psychologists, however, have rarely ventured into the area of neuroscience, in part because the methodologies of neuroscience are seen as being too intrusive for applied use and because the neuroscience knowledge base is generally unfamiliar to practitioners.

In the current article, we attempt to illustrate the potential gain from adopting a cognitive-neuroscience perspective for understanding one critical applied issue: motivation. We will leave it to subsequent theorists to elaborate the social extensions of this system. Our focus is more limited, namely, to show that neuropsychologically grounded theories of cognitive processes can be used to develop a better understanding of how human information processing capacities constrain motivation and decision-making. Specifically, we analyze the information processing mechanisms that may underlie one popular optimization theory - VIE theory (Vroom, 1964) - from a neural network perspective. We believe the resulting framework provides a clearer picture of when and how people are able to arrive at subjectively optimal decisions. Though our intent is not to dwell on the social extensions of this perspective, we should point out that optimality in decision-making (or lack of it) has long been at the heart of many macro-level theories. It is the foundation stone of neoclassical economic theory (Simon, 1991), a central aspect of transaction cost economics (Williamson, 1975), and the focus of bounded rationality theories of organizational decision-making (Cyert & March, 1963; March & Simon, 1958).

VALENCE, INSTRUMENTALITY, EXPECTANCY THEORY AND MOTIVATION

Expectancy theory and Vroom's (1964) ValenceInstrumentality-Expectancy (VIE) version of this theory, in particular, has motivated substantial research activity in the applied literature on motivation. According to VIE theory, people's actions and choices are lawfully related to the preferences and affective reactions they have for certain outcomes (i.e., valences), their beliefs about whether certain actions lead to particular outcomes or performance levels (i.e., expectancies), and their perception of the association between primary and secondary outcomes (i.e., instrumentalities). People are assumed to consciously review their valences, instrumentalities, and expectancies when deciding on a particular course of action, and it is believed that they will act in a subjectively optimal manner once these factors have been considered.

A substantial amount of research activity was stimulated by Vroom's ideas, and the influence of this theory extended beyond the motivation literature into many areas of decision-making. Indeed, VIE theory influenced scholars working on such diverse topics as leadership (e.g., House, 1971), compensation (e.g., Lawler, 1971), and turnover (e.g., Summers & Hendrix, 1991). Seminal reviews of the VIE research all noted some empirical support, but have also identified several problems. These problems have ranged from mismeasurement of the three key constructs (e.g., Ilgen, Nebeker, & Pritchard, 1981; Schwab, Olian-Gottlieb, & Heneman, 1979) to the use of an inappropriate experimental design (i.e., between-subjects design) to validate a within-subjects theory (e.g., Mitchell, 1974). A recent meta-analysis by Van Eerde and Thierry (1996) found that despite these problems, there is support for the theory. More specifically, Van Eerde and Thierry report that valence, instrumentality, and expectancy by themselves, as well as their combined VIE products, predicted performance, effort, intentions, preferences, and choice with average correlations between .33 and .65.