University of North Texas Health Science Center, Fort Worth, Texas, USA.
Abstract:
This article develops the cognitive—emotional forager (CEF) model, a novel application of a neural network to dynamical processes in foraging behavior. The CEF is based on a neural
network known as the gated dipole, introduced by Grossberg, which is capable of representing short-term affective reactions in a manner similar to Solomon
and Corbit’s (1974) opponent process theory. The model incorporates a trade-off between approach toward food and avoidance
of predation under varying levels of motivation induced by hunger. The results of simulations in a simple patch selection
paradigm, using a lifetime fitness criterion for comparison, indicate that the CEF model is capable of nearly optimal foraging
and outperforms a run-of-luck rule-of-thumb model. Models such as the one presented here can illuminate the underlying cognitive
and motivational components of animal decision making.