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.
KeywordsHuman foraging Optimal foraging Search termination Reward pattern Visual search
This research was supported by grants to J.M.W. from ONR MURI (N000141010278), NIH-NEI (EY017001), Hewlett-Packard, Google, CELEST (NSF SBE-0354378) and by the National Natural Science Fund of China (Grant numbers 61233011, 61374006, 61473086); Major Program of National Natural Science Foundation of China (Grant number 11190015); Natural Science Foundation of Jiangsu (Grant number BK20170692, BK20131300); the Innovation Fund of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (Nanjing University of Science and Technology, Grant number JYB201601); the Innovation Fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering(Southeast University, Grant number MCCSE2017B01); the Fundamental Research Funds for the Central Universities (2242016k30009).
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