Influence of Pointing on Learning to Count: A Neuro-Robotics Model
This addresses the problem of understanding gesture-based learning in counting for developmental robotics and cognitive science, but it appears incremental as it builds on existing neuro-robotics approaches.
The paper tackles how gestures influence learning to count by introducing a neuro-robotics model trained with pointing data from an iCub robot simulator, showing that the model's performance changes with gesture production align with human children's behavior.
In this paper a neuro-robotics model capable of counting using gestures is introduced. The contribution of gestures to learning to count is tested with various model and training conditions. Two studies were presented in this article. In the first, we combine different modalities of the robot's neural network, in the second, a novel training procedure for it is proposed. The model is trained with pointing data from an iCub robot simulator. The behaviour of the model is in line with that of human children in terms of performance change depending on gesture production.