Statistical information about reward timing is insufficient for promoting optimal persistence decisions

Resumen

When deciding how long to keep waiting for delayed rewards that will arrive at an uncertain time, different distributions of reward times dictate different optimal strategies for maximizing reward. For example, when reward timing distributions are heavy-tailed (e.g., waiting on hold with customer service) there is a point in time at which waiting is no longer advantageous because the opportunity cost of waiting is too high. Alternatively, when reward timing distributions have a more predictable time scale (uniform or Gaussian), it can be advantageous to wait as long as necessary for the reward to arrive. Although people learn to approximate optimal strategies, little is known about how this learning occurs. One possibility is that people form a general cognitive representation of the probability distribution that governs reward timing and then infer a strategy from that model of the environment. Another possibility is that they learn an action policy more directly, without explicitly representing the reward timing distribution. Here, in a series of studies in which participants decided how long to persist for delayed rewards before quitting, we provided participants with general information about the reward timing distribution in several ways. Whether the information was provided through counterfactual feedback (Study 1), previous exposure (Study 2), or description (Study 3), it did not obviate the need for direct, feedback-driven learning in the decision context. These results suggest that learning when to quit waiting for delayed rewards might depend on task-specific experience, not solely on general probabilistic reasoning.

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Cómo citar (APA 7)

Lempert, K., Schaefer, L., Breslow, D., Peterson, T. D., Kable, J., & McGuire, J. T. (2022). Statistical information about reward timing is insufficient for promoting optimal persistence decisions. https://doi.org/10.31234/osf.io/3yezf