Robotic model of the contribution of gesture to learning to count

In this paper a robotic connectionist model of the contribution of gesture to learning to count is presented. By formulating a recurrent artificial neural network model of the phenomenon and assessing its performance without and with gesture it is demonstrated that the proprioceptive signal connected with gesture carries information which may be exploited when learning to count. The behaviour of the model is similar to that of human children in terms of the effect of gesture and the size of the counted set, although the detailed patterns of errors made by the model and human children are different.