A computational model of avoidance behavior

Learned avoidance behavior is critical to animal survival but has proven difficult for animal learning theorists to model. The authors propose a computational model of the highest layer of an hierarchical control system responsible for learning and behavior. The proposed layer consists of a network of associative control processes (ACPs) that employ the drive-reinforcement learning mechanism. A network of ACPs has been shown to successfully predict both classically and instrumentally conditioned behavior. The output of the authors´ model is a goal-directed, whole-animal behavioral command that is used in lower layers of the control system to guide motor responses. In computer simulations, the authors´ model developed, through trial and error learning, the active responses necessary to avoid shock in a one-way shuttlebox experiment. The model extends Mowrer´s (1960) revised two-factor theory of learning