Resource depletion accelerates rate learning but not composition learning in patch foraging
Foraging is a universal animal behavior that has increasingly attracted the interest of both experimentalists and theorists. Most prior models assume an animal knows the distribution of resources in its environment, but this structure must be learned as the animal explores its environment. Foraging can thus be regarded as a hierarchical inference problem. We develop a normative Bayesian account of an agent learning a patchy environment while exploiting it, and show that resource depletion shapes the levels of that hierarchy differently. Within a patch, depletion accelerates rate learning, since successive encounters occur at falling rates whose spacing pins down the initial rate. Across patches, composition learning, inferring the fraction of patches that are high yield, is slow, set by the number of patches sampled rather than the time spent in each, and unaffected by depletion once the rates are known. Reward-maximizing and information-seeking strategies therefore diverge, a forager resolving the composition underharvesting rich patches because learning it requires departures, most sharply early in exposure. When a fixed set of patches replenishes between visits, the reward-maximizing policy collapses onto a stable orbit over the high-yield patches, and the replenishment rate sets whether the forager maps the whole environment or locks onto a rich subset. Which departure rule maximizes intake is itself set by how variable the patches are, switching from counting prey to timing the gaps between them once richness varies by more than about a quarter. Learning the environment thus buys significant intake over learning a rule from reward alone, as long as its assumptions about depletion leave room for the truth.