I present a neural network model of the complex interactions between filial imprinting and a naive preference for heads and necks in domestic chicks. The fixed weights in the network were evolved in a genetic algorithm simulation which emphasized the survival value of both innate and learned information: having the networks recognize their “mothers”. The architecture and genetic algorithm regimen were able to produce five different behaviors exhibited by chicks in laboratory experiment emulations dissimilar to the training regimen used in the genetic algorithm simulations. These networks learn about their own biases, given random inputs, as well as learning about their environments through simulated visual inputs, and these kinds of learning interact to produce the behaviors seen in chicks. Thus, this architecture, in the context of selection for an ability to distinguish something that might be a mother from other objects in the environment, is a candidate architecture for the neural system underlying the full range of imprinting-related behaviors in chicks.
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