How humans react to changing rewards during visual foraging.

Much is known about the speed and accuracy of search in single-target search tasks, but less attention has been devoted to understanding search in multiple-target foraging tasks. These tasks raise and answer important questions about how individuals decide to terminate searches in cases in which the number of targets in each display is unknown. Even when asked to find every target, individuals quit before exhaustively searching a display. Because a failure to notice targets can have profound effects (e.g., missing a malignant tumor in an X-ray), it is important to develop strategies that could limit such errors. Here, we explored the impact of different reward patterns on these failures. In the Neutral condition, reward for finding a target was constant over time. In the Increasing condition, reward increased for each successive target in a display, penalizing early departure from a display. In the Decreasing condition, reward decreased for each successive target in a display. The experimental results demonstrate that observers will forage for longer (and find more targets) when the value of successive targets increases (and the opposite when value decreases). The data indicate that observers were learning to utilize knowledge of the reward pattern and to forage optimally over the course of the experiment. Simulation results further revealed that human behavior could be modeled with a variant of Charnov's Marginal Value Theorem (MVT) (Charnov, 1976) that includes roles for reward and learning.