The method of computationally simulating evolutionary processes provides a unique opportunity for the automated development of models and hypotheses on cognitive and affective processes and their underlying neural mechanisms. The role of the modeler is limited to setting up the evolutionary selection procedure (e.g., a genetic algorithm; Holland, 1975), the initial state, and the environmental conditions. This has several advantages, of which the opportunity for innovation is not the least. Novel models with mechanisms and functions that have not been previously considered may emerge from these undirected optimization procedures.In previous simulation work (Heerebout & Phaf, 2010), we serendipitously discovered "artificial neural oscillations" in agents that inhabited a virtual environment (see Figure 1). The oscillations were an emergent property, not intentionally built in, and not even thought of beforehand, which proved to be highly adaptive. These simulations extended our investigation of LeDoux's (1996) evolutionary justification of his dual-pathway model for the processing of emotional stimuli (den Dulk, Heerebout, & Phaf, 2003). The agents, which were controlled by artificial neural networks, increased their chances of reproduction and survival by collecting food while avoiding predators. Analyses of the oscillating agents showed that the evolutionary advantage was conferred by an enhanced capacity for attentional switching when in an oscillatory mode.To test the switching efficacy of an oscillating agent, it was compared with a nonoscillating agent from a control simulation with a simpler network. Both agents responded to a plant that was placed in front of them at an angle of 45º to its left, which would, as the agent approached the plant, suddenly be replaced by a predator. The agents' phenotypic behavior was described in terms of speed of movement and rotational speed, and revealed distinct approach and avoidance behaviors. The oscillating agent would first "cautiously" approach the plant following a swerving, "zig-zag" trajectory, and then, as it detected the predator, would make a sharp turn (at 0.042º per time step) and accelerate strongly (the speed increased 87% in the first 10 time steps). The nonoscillatory agent kept a more constant speed. When it detected a predator, it accelerated only slightly (its speed increased only 13% in the first 10 time steps) while turning away (with 0.034º per time step). In addition, this appeared to be a robust finding, because fast switching oscillating agents turned up in five out of the seven replications.Although the behavioral consequences of the oscillations were evident, the underlying mechanism demanded further analysis. Moreover, the networks from the replicated simulations exhibited oscillations at different frequencies. If the oscillations are adaptive because they enhance the ability to quickly switch between competing behaviors, then the evolutionary demands imposed by the two types of stimuli might induce different oscillation frequencies. After al...