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.
In Experiment 1, pairs of naive zebra finches were allowed to find food in a same (local enhancement) or a different (local interference) location and in the presence of the same (stimulus enhancement) or different (stimulus interference) stimulus situation. The local interference condition resulted in significantly more time taken to find food and more search errors compared with both local enhancement and control (visually isolated) conditions. The stimulus interference condition resulted in searches of the stimulus associated with the partner's food source on an extinction posttest. In Experiment 2 the foraging behavior of a naive conspecific disrupted the performance of its knowledgeable partner and had stimulus interference effects on the knowledgeable partner in an extinction posttest like those found in Experiment 1.
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