The combination of (i) food choice biased to resource types with high expected reward a ir (selective foraging), and (ii) learning via updating of a ir and experience t ir, generates a positive feedback. This positive feedback generates a familiarity bias and a development process that is contingent on stochastic initial conditions, leading to idiosyncratic learning histories and somewhat arbitrary variation between foragers in their knowledge of the environment. Therefore, while learning is biased towards high quality resources, due to an intrinsic familiarity bias in the process, learning could get ‘stuck’ on a self-stabilizing repertoire as soon as this repertoire fulfills the intake needs of the forager . Previous work has shown that this familiarity bias becomes strong in environments with pure patches, and when foragers do not become satiated after eating a lot of a given food type [21, 24]. We therefore focus on patches with several resources and satiation as a default case, so that the familiarity bias is not unreasonably strong such that foragers only end up consuming a few resource types.