In this paper we propose a computational model for learning from demonstration. By adequate adjustment of a few parameters, our model is able to produce di erent learning behaviours, taking into account di erent elements of the demonstration. In particular, our model takes into consideration the actions of the demonstrator, its effects on the environment/surroundings, the demonstrator's inferred goals, and the interests and preferences of the learner itself. We present results where we show that our model can reproduce (in simulation) several well-known results from standard experimental paradigms in developmental psychology and also an application to a real robotic imitation learning task.

Associative accounts of goal-directed action, developed in the fields of human ideomotor action and that of animal learning, can capture cognitive belief-desire psychology of human decision-making. Whereas outcome-response accounts can account for the fact that the thought of a goal can call to mind the action that has previously procured this goal, response-outcome accounts capture decision-making processes that start out with the consideration of possible response alternatives followed only in the second instance by evaluation of their consequences. We argue that while the outcome-response mechanism plays a crucial role in response priming effects, the response-outcome mechanism is particularly important for action selection on the basis of current needs and desires. We therefore develop an integrative account that encapsulates these two routes of action selection within the framework of the associative cybernetic model. This model has the additional benefit of providing mechanisms for the incentive modulation of goal-directed action and for the development of behavioural autonomy, and therefore provides a promising account of the multi-faceted process of animal as well as human instrumental decision-making.