A neural network model of foraging decisions made under predation risk

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.

Neural network modeling of emotion

“…8), simulated short-term emotional reactions that could at times include risk aversion for gains and risk seeking for losses. The survival rate of the "cognitive-emotional forager" of [90] was less than that of optimal foraging models but surprisingly close to the optimal rate.…”

“…Behavioral output is based on positive emotional signals from both dipoles. (Adapted from [90] with the permission of the Psychonomic Society. )…”

“…Coleman et al [90], using a variant of the gated dipole (see Fig. 8), simulated short-term emotional reactions that could at times include risk aversion for gains and risk seeking for losses.…”

“…As reviewed in [88][89][90], optimal foraging theories have been based on the assumption that an animal's foraging decisions are the ones that maximize its lifetime survival probability. Yet there is considerable evidence that foraging decisions may deviate from lifetime fitness maximization due to consideration of short-term risks, both of starvation and predation [91][92][93].…”

“…For example, an animal must sometimes decide between one patch that contains a highly desirable food but entails a significant predation risk, and another patch that contains a less desirable but still nourishing food and is relatively safe from predators. This has generated some neural network models of foraging behavior based both on temporal differences [88,89] and on gated dipoles [90].…”