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Foraging in humans and other animals requires a delicate balance between exploitation of current resources and exploration for new ones. The tendency to overharvest—lingering too long in depleting patches—is a routine behavioral deviation from predictions of optimal foraging theories. To characterize the computational mechanisms driving these deviations, we modeled foraging behavior using a virtual patch-leaving task with human participants and validated our findings in an analogous foraging task in two monkeys. Both humans and monkeys overharvested and stayed longer in patches with longer travel times compared to shorter ones. Critically, patch residence times in both species declined over the course of sessions, enhancing reward rates in humans. These decisions were best explained by a logistic transformation that integrated both current rewards and information about declining rewards. This parsimonious model demystifies both the occurrence and dynamics of overharvesting, highlighting the role of information gathering in foraging. Our findings provide insight into computational mechanisms shaped by ubiquitous foraging dilemmas, underscoring how behavioral modeling can reveal underlying motivations of seemingly irrational decisions.