A minimal model of the interaction of social and individual learning

Learning is thought to be achieved by the selective, activity dependent, adjustment of synaptic connections. Individual learning can also be very hard and/or slow. Social, supervised, learning from others might amplify individual, possibly mainly unsupervised, learning by individuals, and might underlie the development and evolution of culture. We studied a minimal neural network model of the interaction of individual, unsupervised, and social supervised learning by communicating "agents". Individual agents attempted to learn to track a hidden fluctuating "source", which, linearly mixed with other masking fluctuations, generated observable input vectors. In this model data are generated linearly, facilitating mathematical analysis. Learning was driven either solely by direct observation of input data (unsupervised, Hebbian) or, in addition, by observation of another agent's output (supervised, Delta rule). To make learning more difficult, and to enhance biological realism, the learning rules were made slightly connection-inspecific, so that incorrect individual learning sometimes occurs. We found that social interaction can foster both correct and incorrect learning. Useful social learning therefore presumably involves additional factors some of which we outline.