where a ir is the reward forager i expects from resource type r (personal information based on reinforcement learning), a ie is an assessment of the quality of resources that can be found in the environment (see below), and σ i scales selectivity, i.e. how likely an individual selects when the expected reward a ir<a ie (the expected quality of resources in the environment). Since associations are initially zero (a ir=0), unknown resource types can only be sampled via exploration (P E) or social stimulation (P S). For solitary foragers this means that the exploration rate P E must be greater than zero. For grouping foragers, social stimulation P S could in principle replace exploration P E as the means to sample unknown resources. Once expected rewards a ir>0, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\left (\frac {a_{ir}}{a_{ie}} \right)^{\sigma _{i}}$\end{document}airaieσi contributes to the probability of choosing a certain resource type, which is maximal when a ir>a ie and less than one if a ir<a ie. If a ir>a ie, the forager is certain to choose the resource item, irrespective of P E and P S. The impact of P E and P S is therefore greatest when resource are relatively unfamiliar (a ir<a ie).