A neural network model of foraging decisions made under predation risk.
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.
Coleman
Scott L
SL
University of North Texas Health Science Center, Fort Worth, Texas, USA.
Brown
Vincent R
VR
Levine
Daniel S
DS
Mellgren
Roger L
RL
D000818:Animals; D001070:Appetitive Behavior; D001362:Avoidance Learning; D001364:Awareness; D003657:Decision Making; D005239:Fear; D006815:Hunger; D009042:Motivation; D016571:Neural Networks, Computer; D011235:Predatory Behavior; D011337:Probability Learning