Human children 18 – 24 months of age and 3 young chimpanzees interacted in 4 cooperative activities with a human adult partner. The human children successfully participated in cooperative problem-solving activities and social games, whereas the chimpanzees were uninterested in the social games. As an experimental manipulation, in each task the adult partner stopped participating at a specific point during the activity. All children produced at least one communicative attempt to reengage him, perhaps suggesting that they were trying to reinstate a shared goal. No chimpanzee ever made any communicative attempt to reengage the partner. These results are interpreted as evidence for a uniquely human form of cooperative activity involving shared intentionality that emerges in the second year of life. From soon after birth, human infants interact with other persons dyadically in coordinated, turn-taking sequences (Trevarthen, 1979). From about 6 to 9 months of age, infants ’ social interactions become more complex, as they often incorporate outside objects and so become triadic (Tomasello, 1995). Some of these triadic interactions are relatively extended and maintain a turn-taking structure, for example, rolling a ball back and forth or taking turns beating a drum (Gustafson, Green, & West, 1979; Ratner & Bruner, 1978). Most of these early triadic interactionsFsometimes called cooperative gamesFseem to rely on adult scaffolding in fairly ritualized situations, because the introduction of novel toys or a peer partner disrupts them almost totally until 18 months of age (Hay, 1979; Ross, 1982). In a series of longitudinal studies, Eckerman and colleagues have investigated the emergence of young children’s skills in cooperative games of a less ritualized

The acquisition of causal knowledge is a primary goal of childhood; yet most of this knowledge is known already to adults. We argue that causal learning which leverages social reasoning is a rapid and important route to knowledge. We present a computational model integrating knowledge about causality with knowledge about intentional agency, but using a domaingeneral mechanism for reasoning. Inference in this model predicts qualitatively different learning than an equivalent model based on causality alone or a hybrid causal-encoding model. We test these predictions experimentally with adult participants, and discuss the relation of these results to the developmental phenomenon of over-imitation.

This chapter reviews several approaches to the problem of learning by imitation in robotics. We start by describing several cognitive processes identified in the literature as necessary for imitation. We then proceed by surveying different approaches to this problem, placing particular emphasys on methods whereby an agent first learns about its own body dynamics by means of self-exploration and then uses this knowledge about its own body to recognize the actions being performed by other agents. This general approach is related to the motor theory of perception, particularly to the mirror neurons found in primates. We distinguish three fundamental classes of methods, corresponding to three abstraction levels at which imitation can be addressed. As such, the methods surveyed herein exhibit behaviors that range from raw sensory-motor trajectory matching to high-level abstract task replication. We also discuss the impact that knowledge about the world and/or the demonstrator can have on the particular behaviors exhibited.

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Abstract—This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, unbounded and non-preset environments. After introducing social learning and intrinsic motivation, we describe the design of our algorithm, before showing through a fishing experiment that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation to gain a wide repertoire while being specialised in specific subspaces. I. APPROACHES FOR ADAPTIVE PERSONAL ROBOTS The promise of personal robots operating in human environments to interact with people on a daily basis points out the importance of adaptivity of the machine to its environment and

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.

People are amateur social psychologists: they explain other people’s behavior, infer what other people are thinking and feeling, and predict how other people will act. I will refer to this sort of psychologizing as social reasoning in order to highlight the fact that it involves reasoning about people. Social reasoning often requires significant leaps of inductive inference: people infer others ’ mental states, such as their preferences, goals, and beliefs, from relatively sparse information, such as others ’ choices and actions. The capacity to reason about mental states and about how mental states relate to behavior is often referred

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