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.
University of North Texas Health Science Center, Fort Worth, Texas, USA.
Scott L
SL
Coleman
Vincent R
VR
Brown
Daniel S
DS
Levine
Roger L
RL
Mellgren
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