A single computational model for many learning phenomena

Simplicity is a basic principle of science and this implies that, if we want to explain the behaviour of animals by constructing robots that behave like real animals, one and the same robot should reproduce as many behaviours and as many behavioural phenomena as possible. In this paper we describe robots that both evolve and learn in their natural environment and, in addition, learn in the equivalent of an experimental laboratory and reproduce a variety of results of experiments on learning in animals. We introduce a new model of learning in which the weights of the connections that link the units of the robots neural network are genetically inherited and do not change during the robots life but what changes during life and makes the robots learn new behaviours is the synaptic receptivity of a special set of network units which we call learning units. The robots evolve in a variety of different environments and they learn in a variety of different ways including imprinting and learning by imitating the behaviour of others. Then we test the robots in the controlled conditions of an artificial laboratory and we reproduce a number of experimental results on both operant learning and classical conditioning, including learning and extinction curves, the role of the temporal interval between conditioned and unconditioned stimuli, and the influence of motivation on learning.