We present a unified model of the dynamics of goal-directed motivation and decision making. The model—referred to as the GOAL architecture—provides a quantitative framework for integrating theories of goal pursuit and for relating their predictions to different types of data. The GOAL architecture proposes that motivation changes over time according to three gradients that capture the effects of the distance to the goal (i.e., the progress remaining), the time to the deadline, and the rate of progress required to achieve the goal. We use the model to integrate and compare six theoretical perspectives that make different predictions about how these dynamics unfold when pursuing approach and avoidance goals. We use the architecture within a hierarchical Bayesian framework to analyze data from three experiments which manipulate distance to goal, time to deadline, and goal type (approach versus avoidance), and data from the naturalistic context of professional basketball. The results show that people rely on all three gradients when making resource allocation decisions during goal pursuit, but that the relative influence of the gradients depends on the goal type. We also demonstrate how the GOAL architecture can be used to answer questions about the effectiveness of people's goal pursuit strategies and the influence of goal importance. Our findings suggest that goal pursuit unfolds in a complex manner that cannot be accounted for by any one previous theoretical perspective, but that is well-characterized by our unified framework. This research highlights the importance of theoretical integration for understanding motivation and decision-making during goal pursuit.
A general architecture for modeling the dynamics of goal-directed motivation and decision-making.
