Emergence of Orienting Behavior in Ecological Neural Networks

We investigated the emergence of orienting behavior in artificial organisms that evolved following a genetic algorithm. These organisms live in a simulated environment containing food and danger elements and reproduce selectively based on the capacity of each individual to eat food while avoiding danger. When the amount of computational resources (number of hidden units) is adequate to the difficulty of the perceptual discrimination between food and danger, peripheral vision is sufficient to trigger stimulus identification. When the resources are scarce, the central portion of the sensory surface becomes a ‘fovea’, and the presence of a stimulus in peripheral vision triggers an orienting movement (foveation), before the organism can decide whether to eat or to avoid the object. Thus, orienting movements, as well as the segregation of processing resources into a high-definition fovea and a poor-definition periphery, may originate from a disproportion between complex perceptual tasks and (relatively) scarce computational resources.