Human and Artificial Agents Learning Categories in Interaction

We present a set of experiments on category learning in which a human or artificial agent has to learn to master a task that requires to properly manipulate unknown objects. The agent learns to master the task by exploiting the feedback provided by a tutor. These experiments extend Morlino et al (2010) to take into account how social interaction bootstraps the acquisition of semantic knowledge. They aim at showing how a tutor can actually aid and improve the learning of categories in both artificial and human agents. We hypothesize that the social interaction required for the acquisition of categories needs to be established via contingency (DeJaegher, Di Paolo and Gallgher 2010). We operationalise contingency as synchronous feedback as opposed to asynchronous feedback. We aim to analyse the characteristics of the feedback provided by the tutors and the way in which such feedback is beneficial to the agent’s learning process. In the human experimental scenario, the participants are a human tutor and a human learner. The learners are asked to interact with 16 2D-objects using a mouse device. The objects vary with respect to four perceptual binary features (colour, shape, weight and colour blinking). They are grouped into four categories, formed by four objects each, on the basis of the values of two of the four features. Each object has to be manipulated on the basis of the category it belongs to (i. e. it has to be shaken horizontally or vertically or placed in the right or lower-left position of the screen). The experiment involves a learning agent, located in front of a computer, who should discover how th