Short-term gains, long-term pains: how cues about state aid learning in dynamic environments

Successful investors seeking returns, animals foraging for food, and pilots controlling aircraft all must take into account how their current decisions will impact their future standing. One challenge facing decision makers is that options that appear attractive in the short-term may not turn out best in the long run. In this paper, we explore human learning in a dynamic decision making task which places short- and long-term rewards in conflict. Our goal in these studies was to evaluate how people's mental representation of a task affects their ability to discover an optimal decision strategy. We find that perceptual cues that readily align with the underlying state of the task environment help people overcome the impulsive appeal of short-term rewards. Our experimental manipulations, predictions, and analyses are motivated by current work in reinforcement learning which details how learners value delayed outcomes in sequential tasks and the importance that "state" identification plays in effective learning.