Abstract not found

The identification of any form of social learning, imitation, copying or mimicry presupposes a notion of correspondence between two autonomous agents. Judging whether a behavior has been transmitted socially requires the observer to identify a mapping between the demonstrator and the imitator. If the demonstrator and imitator have similar bodies, e.g. are animals of the same species, of similar age, and of the same gender, then to a human observer an obvious correspondence is to map the corresponding body parts: left arm of demonstrator maps to left arm of imitator, right eye of demonstrator maps to right eye of imitator, tail of demonstrator maps to tail of imitator. There is also an obvious correspondence of actions: raising the left arm by the model corresponds to raising the left arm by the imitator, production of vocal signals by the model corresponds to the production of acoustically similar ones by the imitator, picking up a fruit by the demonstrator corresponds to picking up a fruit of the same type by the imitator. Furthermore, there is a correspondence in sensory experience: audible sounds, a touch, visible objects and colors, and so on evidently seem to be detected and experienced in similar ways. What to take as the correspondence seems relatively clear in this case. As humans, we are good at imitating and at recognizing such correspondences. It is also clear that most other animals, robots, and software programs may in fact generally fail to recognize any such correspondences. To judge a produced behavior to be a copy of an observed one, we require at least that it respects some such correspondence. The faithfulness or precision of the behavioral match can obviously vary, and no absolute cutoff or threshold exists defining success as opposed to failure of behavioral matching. But one can study the degree of success using various metrics and measures of correspondence (Nehaniv & Dautenhahn, 2001; also see below). Moreover, it turns out that the “obvious” correspondences between similar bodies mentioned above are not the only ones possible. Consider a human imitating another one that is facing her: if the demonstrator raises her left arm, should the imitator raise her own left arm? Or should she raise her right, to make a "mirror image" of the demonstrator's actions? If the demonstrator picks up a brush, should an imitator pick up the same brush? Or just another brush of the same type? If the demonstrator opens a container to get at chocolate inside, should the imitator open a similar container in the same way – e.g. by unwrapping but not tearing the surrounding paper?, or is it enough just to open the container somehow? The different possible answers to these questions presuppose different correspondences. If a child watches a teacher solving subtraction problems in arithmetic, and then solves for the first time similar but not identical problems on its own, social learning has occurred. But what type of correspondence is at work here? In China and Japan, the ideographic character for “to imitate” also means “to learn” or “to study”. By going through the motions of an algorithm for solving sample problems, students everywhere are able to learn how to solve similar ones, of course without necessarily gaining understanding of why the procedures they have learned work. In this chapter, for lack of a better term, we shall use the word “imitator” to refer to any autonomous agent performing a candidate behavioral match. The use of this word here does not entail any particular mechanism of matching or any particular type of social learning. In what follows, we shall describe how different matching phenomena arise depending on the criteria employed in generating the behavior of the imitator. For example, goal emulation, stimulus enhancement, mimicry, and so on, will all be cast as solutions to correspondence problems with different particular selection criteria.

Introduction This chapter presents the agent-based perspective on imitation. In this perspective, imitation is best considered as the behavior of an autonomous agent in relation to its environment, including other autonomous agents. We argue that such a perspective helps unfold the full potential of research on imitation and helps in identifying challenging and important research issues. We first explain the agent-based perspective and then discuss it in the context of particular research issues in studies with animals and artifacts, with reference to chapters presented in this book. At the end of the chapter we briefly introduce the individual contributions to this book and provide a roadmap that helps the reader in navigating through the exciting and highly interwoven themes that are presented in this book. In order to focus discussions, we explain the agent-based perspective with particular consideration of the correspondence

I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars.

markj @ oregon.uoregon.edu and rohrer @ cogsci.ucsd.edu © 2003-2007 by the authors, pre-press final draft 4/7/07 citation information:

Imitative learning has recently piqued the interest of various fields including neuroscience, cognitive science and robotics. In computational behavior modeling and development, it promises an accessible framework for rapidly forming behavior models without tedious supervision or reinforcement. Given the availability of lowcost wearable sensors, the robustness of real-time perception algorithms and the feasibility of archiving large amounts of audio-visual data, it is possible to unobtrusively archive the daily activities of a human teacher and his responses to external stimuli. We combine this data acquisition/representation process with statistical learning machinery (hidden Markov models) as well as discriminative estimation algorithms to form a behavioral model of a human teacher directly from the data set. The resulting system learns audio-visual interactive behavior from the human and his environment to produce an interactive autonomous agent. The agent subsequently exhibits simple audio-visual behaviors that appear coupled to real-world test stimuli.

Social learning is assumed to underlie traditions, yet evidence indicating social learning in capuchin monkeys (Cebus apella), which exhibit traditions, is sparse. The authors tested capuchins for their ability to learn the value of novel tokens using a previously familiar token-exchange economy. Capuchins change their preferences in favor of a token worth a high-value food reward after watching a conspecific model exchange 2 differentially rewarded tokens, yet they fail to develop a similar preference after watching tokens paired with foods in the absence of a conspecific model. They also fail to learn that the value of familiar tokens has changed. Information about token value is available in all situations, but capuchins seem to pay more attention in a social situation involving novel tokens. Social learning, or the ability to learn from others (Whiten, 2000), is in evidence in many animal species. Such diverse taxa as

Evolutionary developmental psychology involves the expression of evolved, epigenetic programs, as described by the developmental systems approach, over the course of ontogeny. There have been different selection pressures on organisms at different times in ontogeny, and some characteristics of infants and children were selected in evolution to serve an adaptive function at that time in their life history rather than to prepare individuals for later adulthood. Examples of such adaptive functions of immaturity are provided from infancy, play, and cognitive development. Most evolved psychological mechanisms are proposed to be domain specific in nature and have been identified for various aspects of children’s cognitive and social development, most notably for the acquisition of language and for theory of mind. Differences in the quality and quantity of parental investment affect children’s development and influence their subsequent reproductive and childcare strategies. Some sex differences observed in childhood, particularly as expressed during play, are seen as antecedents and preparations for adult sex differences. Because evolved mechanisms were adaptive to ancestral environments, they are not always adaptive for contemporary people, and this mismatch of evolved mechanisms with modern environments is seen in children’s maladjustment to some aspects of formal schooling. We argue that an evolutionary perspective can be valuable for developing a better understanding of human ontogeny in contemporary society and that a developmental perspective is important for a better understanding of evolutionary psychology.

The authors evaluated the ontogenetic performance of a grey parrot (Psittacus erithacus) on object permanence tasks designed for human infants. Testing began when the bird was 8 weeks old, prior to fledging and weaning. Because adult grey parrots understand complex invisible displacements (I. M. Pepperberg & F. A. Kozak, 1986), the authors continued weekly testing until the current subject completed all of I. C. Uzgiris and J. Hunt's (1975) Scale 1 tasks. Stage 6 object permanence with respect to these tasks emerged at 22 weeks, after the bird had fledged but before it was completely weaned. Although the parrot progressed more rapidly overall than other species that have been tested ontogenetically, the subject similarly exhibited a behavioral plateau part way through the study. Additional tests, administered at 8 and 12 months as well as to an adult grey parrot, demonstrated, respectively, that these birds have some representation of a hidden object and understand advanced invisible displacements. In children, object permanence—the notion that objects are separate entities that continue to exist when out of sight of the observer—is neither innate nor unitary, but develops

Research in robotics has moved away from its primary focus on industrial applications. The New Robotics is a vision that has been developed in past years by our own university and many other national and international research institutions and addresses how increasingly more human-like robots can live among us and take over tasks where our current society has shortcomings. Elder care, physical therapy, child education, search and rescue, and general assistance in daily life situations are some of the examples that will benefit from the New Robotics in the near future. With these goals in mind, research for the New Robotics has to embrace a broad interdisciplinary approach, ranging from traditional mathematical issues of robotics to novel issues in psychology, neuroscience, and ethics. This paper outlines some of the important research problems that will need to be resolved to make the New Robotics a reality.