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.
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Coleman, S.L., Brown, V.R., Levine, D.S. et al. A neural network model of foraging decisions made under predation risk. Cognitive, Affective, & Behavioral Neuroscience 5, 434–451 (2005). https://doi.org/10.3758/CABN.5.4.434
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DOI: https://doi.org/10.3758/CABN.5.4.434