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In discussions of the ontogenetic origins of numerical abilities to date, most of the research suggests that infants establish representations of numbers of objects presented visually, auditorily, and haptically. The question is not whether babies are sensitive to differences in number. The focus lies on what models can better account for the wealth of data. Some have suggested that the infant number capacity is in the format of object file representations, and is characterized by a small set size - ones, twos, threes, perhaps fours, predicted by limitations of short-term storage of an object file model (Uller et al., 1999). Others have proposed that the infant counting abilities are in the format of analog representations, roughly in a format comparable to that of rats and pigeons (Xu & Spelke 2000), where limit is not a concern. The task is to bring data to bear on this question to help decide between the two alternative models. Uller and Leslie (2000) began to address this issue. Utilizing the looking time technique, they showed 12- month-olds 2+1 and 2+0 event where two objects go behind a screen, then either 1 is added or 0 is added. The outcome is always 2. Another group of 12-month-olds see parallel events with 3, namely, 3+1 and 3+0. Babies are able to understand 'exactly 2', but not 'exactly 3', which provides support for an object file model. More recently, Uller (2001) investigated whether 12-month-olds understand exactly two using reaching and searching time as dependent measures. Twelve-month-old infants were shown 2 objects go into a box, then either a third one being added (2+1) or nothing being added (2+0) into the box. The outcome was always 2. In this study, she confirmed Uller and Leslie's (2000) looking time findings that 12-month-olds understand what 'exactly two' means. Parallel results were found by Feigenson, Carey & Hauser (in press) in a forced choice experiment testing 10- and 12-month-old infants. Here, babies had to choose between different numbers of graham crackers, namely, 2 vs 3, 3 vs 4, and 3 vs 6. The researchers found that babies only chose the bigger number in the small discrimination cases. When they saw 3 vs 6 and 4 vs 6, they randomly picked one or the other. In the current paper, I make the case for focusing on the young infant capacity for small number representation. I will argue that this is a capacity that can be found spontaneously in humans, nonhuman primates, avians and even lower vertebrates. I will also argue that this is a capacity that should be considered primitive and foundational, perhaps at the core of cognition. I will review the proposals available to date on the young infant representation of small number, and examine possible implementations that can be derived from these considerations.

Young children perform well on a wide range of tasks involving the attribution of goals/desires. For instance, two- and three-year old children are told that a boy wants to find his rabbit, which is in one of two locations. If the puppet fails to find the rabbit in the first location, children predict that he will look in the second location; if the puppet finds the rabbit in the first location, however, children predict that he will not look in the second location (Wellman and Woolley 1990). Although there is plenty of evidence that children can reason about other people’s desires, the origins of this ability is still very much unknown. In one study, Repacholi and Gopnik (1997) studied 18-month old children in a food-request procedure in which they asked whether the children could correctly understand people’s desires by predicting specific reactions to desired/undesired objects. The children saw the experimenter express disgust as she tasted one kind of food, and happiness as she tasted a different kind of food. They were then asked to give the experimenter some food. The children correctly gave her the food towards which she had expressed positive affect. This led researchers to conclude that the children not only inferred that another person held a desire, but they also recognized how desires relate to emotions. More recently, researchers have looked at the origins of other people’s mental states in even younger infants using visual attention measures. For instance, Woodward (1998) has presented results that indicate that 12-month-old infants detect goals. The origins of subjective desire, however, is still an open question. Here we proposed to start addressing the issue of subjective desire in young infants. In this task, thirty-two 10- and 11-month-old infants are familiarized with two scenarios. In (1), infants see the experimenter express disgust towards cracker/broccoli (‘yuk’), and in (2), infants see the experimenter express happiness towards broccoli/cracker (‘wow’). After the familiarization phase, the infants are then tested in one of two outcomes, the experimenter has a neutral expression and either grabs the broccoli, or the cracker. Parental reports on infants’ preferences are obtained to check their behavior against their performance in the task. If the infants have a psychological understanding of subjective desire, then they will expect the experimenter to grab the food towards which she expressed positive affect; if, on the other hand, the infants only have a behavioral understanding of desire, then they will be unable to determine the object of the experimenter’s desire because no specific cues are provided in the test phase. They will tend to respond egocentrically, and give the experimenter what they themselves like. Preliminary results reveal that 10- and 11-month-old infants will look longer when the experimenter grabs the object towards which she expressed disgust. Data collection continues. Further steps towards the characterization of such ability are discussed.

While computational models are playing an increasingly important role in developmental psychology, at least one lesson from robotics is still being learned: modeling epigenetic processes often requires simulating an embodied, autonomous organism. This paper first contrasts prevailing models of infant cognition with an agent-based approach. A series of infant studies by Baillargeon (1986; Baillargeon & DeVos, 1991) is described, and an eye-movement model is then used to simulate infants' visual activity in this study. I conclude by describing three behavioral predictions of the eyemovement model, and discussing the implications of this work for infant cognition research.

We suppose the functional parts combination (FPC) model, whereby a problem solving strategy is acquired depending on the tasks given. The model is based on the neuroscientific fact that each cerebral cortical area has a different role and is selectively activated depending on the task. FPC model is a meta learning model that consists of a set of functional parts and a sequence of control signals that specifies their combination. The functional parts are combined depending on the situation, to realize a processing circuit required for the situation. We use genetic algorithm for searching the control signals. We examine the model by evaluating the difference in acquired behavior of (1) two agents with different functional parts working on the same navigational task and (2) two agents with the same functional parts working on different tasks. We show that the agent using FPC model acquires learning strategies suitable for the given problems.

We present experiments in which a group of autonomous mobile robots learn to perform fundamental sensor-motor tasks through a collaborative learning process. Behavioural strategies, i.e. motor responses to sensory stimuli, are encoded by means of genetic strings stored on the individual robots, and adapted through a genetic algorithm (Mitchell, 1998) executed by the entire robot collective: robots communicate their own strings and corresponding fitness to each other, and then execute a genetic algorithm to improve their individual behavioural strategy. The robots acquired three different sensormotor competences, as well as the ability to select one of two, or one of three behaviours depending on context ("behaviour management"). Results show that fitness indeed increases with increasing learning time, and the analysis of the acquired behavioural strategies demonstrates that they are effective in accomplishing the desired task.

For the purposes of manipulation, we would like to know what parts of the environment are physically coherent ensembles - that is, which parts will move together, and which are more or less independent. It takes a great deal of experience before this judgement can be made from purely visual information. This paper develops active strategies for acquiring that experience through experimental manipulation, using tight correlations between arm motion and optic flow to detect both the arm itself and the boundaries of objects with which it comes into contact. We argue that following causal chains of events out from the robot's body into the environment allows for a very natural developmental progression of visual competence, and relate this idea to results in neuroscience.

An automatic choreography method to generate lifelike body movements is proposed. This method is based on somatics theories that are conventionally used to evaluate human’s psychological and developmental states by analyzing the body movement. The idea of this paper is to use the theories in the inverse way: to facilitate generation of artificial body movements that are plausible regarding evolutionary, developmental and emotional states of robots or other non-living movers. This paper reviews somatic theories and describes a strategy for implementations of automatic body movement generation. In addition, a psychological experiment is reported to verify expression ability on body movement rhythm. This method facilitates to choreographing body movement of humanoids, animal-shaped robots, and computer graphics characters in video games.

We present a system that attempts to model the functional role of mirror neurons, namely the activation of structures in response to both the observation of a demonstrated task, and its generation. Through social situatedness and a set of innate skills, perceptual and motor structures develop for recognition and reproduction of demonstrated actions. We believe this is an implementation towards a mirror system, and we test it on two platforms, one in simulation involving imitation of object interactions, the second on a physical robot learning from a human to follow walls.

The concept of ‘social situatedness’, i.e. the idea that the development of individual intelligence requires a social (and cultural) embedding, has recently received much attention in cognitive science and artificial intelligence research. The work of Lev Vygotsky who put forward this view already in the 1920s has influenced the discussion to some degree, but still remains far from well known. This paper therefore aims to give an overview of his cognitive development theory and discuss its relation to more recent work in primatology and socially situated artificial intelligence, in particular humanoid robotics.

Given a neural control structure, what would be the impact of body growth on control performance? This question, which addresses the issue of the interaction between innate structure, ongoing developing structure and experience, is very relevant to the field of epigenetic robotics. Much of the early social interaction is done as the body develops and the interplay cannot be ignored. We hypothesize that starting with fewer degrees of freedom enables a more efficient exploration of the sensorimotor space, that results in multiple directions of stability. While not necessarily corresponding to optimal task performance, they will guide the coordination of additional degrees of freedom. These additional degrees of freedom then allow for optimal task performance as well as for more tolerance and adaptation to environmental interaction. We propose a simple case-study to validate our hypothesis and describe experiments with a small humanoid robot.

Philippe Rochat and his colleague tentatively proposed that young infants' propensity to engage in self-perception and systematic exploration of the perceptual consequences of their own action plays and is probably at the origin of an early sense of self: the ecological self. Rochat and Hespos (1997) reported that neonates discriminate between external and self-stimulation. Neonate tended to display significantly more rooting responses (i.e., head turn towards the stimulation with mouth open and tonguing) following external compared to self-stimulation. Rochat et al. (1998) also reported that 2-month-olds showed clear sign of modulation of their oral activity on the pacifier as a function of analog versus non-analog condition. Rochat and his colleague concluded that these observations are interpreted as evidence of self-exploration and the emergence of a sense of self-agency by 2-month-olds. We tried to replicate these findings in infant chimpanzees. We observed rooting responses of three baby chimpanzees in two condition, self-stimulation and external stimulation. In external stimulation condition, the index finger of the experimenter or small stick touched one of the infant's cheeks. In self-stimulation condition, the experimenter took infant's hand and touched his or her cheek with their fingers. In Rochat and Hespos, they recorded and analyzed several measures such as state, head movement, mouth activity and so on. How ever, we analyzed only mouth activities tentatively. We found infant chimpanzees tended to show more rooting responses following external stimulation compared to self-stimulation as well as human infants. We also carried out sucking experiment with two baby chimpanzees. The experimenter held the pacifier and put the artificial nipple into the infant's mouth. A session started when the infant take the nipple inside the his or her mouth. Auditory stimulus, which was a complex tone comprised of six harmonics with equal intensity, was given to the chimpanzee according to the test condition during their sucking. There were four test conditions and each condition consisted with three types of feedback as follows: 1) silent baseline, contingent, and steady, 2) contingent baseline, 1-sec delay, and 3-sec delay, 3) contingent baseline, 6-sec delay, and 12-sec delay, 4) contingent baseline, 1/2 efficiency, and 1/4 efficiency. In test 1, one infant chimpanzee showed decrease of the minimum pressure of sucking in the contingent condition. In test 2, one subject showed shorter intervals of sucking in 3-sec delay condition. This seems to be similar to human infant's. We may be able to postulate ecological self in baby chimpanzees according to the self-exploration. In test 3 and 4, we did not obtain any effects of stimulus conditions. Results of these studies. These studies were conducted as the parts of the chimpanzee development project in Primate Research Institute, Kyoto University, organized by Professor Tetsuro Matsuzawa.

The value system of a developmental robot signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. In the work reported here, a low level value system is modeled and implemented. It simulates the non-associative animal learning mechanism known as habituation effect. Reinforcement learning is also integrated with novelty. Experimental results show that the proposed value system works as designed in a study of robot viewing angle selection.

In the study of systems the function of the system is often a good hint to how it works. In the following paper I would like to suggest that in studying or modeling a cognitive system our pre-knowledge of their functions should be treated carefully. We should focus on the statistical distribution of the system's environment and the ways this distribution affects the behavior and development of the cognitive system. I will show an example of how such a focus changes the view of the immune system. I would also like to show how this new outlook on the study of cognitive systems could affect attempts at creating artifcial cognitive system.

In this paper we expose the theoretical background underlying our current research. This consists in the development of behaviour-based knowledge systems, for closing the gaps between behaviour-based and knowledge-based systems, and also between the understandings of the phenomena they model. We expose the requirements and stages for developing behaviour-based knowledge systems and discuss their limits. We believe that these are necessary conditions for the development of higher order cognitive capacities, in artificial and natural cognitive systems.

Science, like any other cognitive activity, is grounded in the sensorimotor interaction of our bodies with the environment. Human embodiment thus constrains the class of scientific concepts and theories which are accessible to us. The paper explores the possibility of doing science with artificial cognitive agents, in the framework of an interactivist-constructivist cognitive model of science. Intelligent robots, by virtue of having different sensorimotor capabilities, may overcome the fundamental limitations of human science and provide important technological innovations. Mathematics and nanophysics are prime candidates for being studied by artificial scientists.

A crucial aspect of the development of language in children has been concerned with pragmatics - a field which explores the ways in which interaction is successfully accomplished. One aspect of this is concerned with the sequential implicativeness of our actions - that is what do particular behaviours accomplish given the specific turn by turn interactive sequence in which they occur. This paper seeks to consider some aspects of this by reference to conversation analytic work on gaze in adult interaction. In this way the paper attempts to provide a brief overview of some of the ways that our thinking about robot - human interaction can be deepened by an appreciation of conversation analytic work. In particular it argues that the empirical basis of conversation analysis (henceforth CA) offers a wonderful treasure-trove of understandings about how humans accomplish social interaction. The understanding that CA provides is derived from careful empirical scrutiny and therefore it is able to offer a perspective on interaction that is sensitive to minute detail rather than crude applications of global concepts. Thus this paper provides a provisional inspection of a small fraction of CA literature concerning the use of gaze in interaction and thinks through the relevance that this might have for the design and understanding of interacting robots. Whilst CA provides a complex understanding of human interaction, predominantly derived from the everyday talk of adults, this paper argues that the approach can provide both an idealised target of communication competence and perhaps more important a means of understanding instances of human-robot interaction. In this way CA may usefully supplement other approaches to communicative competence in work on interacting robots.

In this paper we propose a description language for specifying motions for humanoid robots and for allowing humanoid robots to acquire motor skills. Locomotion greatly increases our ability to interact with our environments, which in turn increases our mental abilities. This principle also applies to humanoid robots. However, there are great difficulties to specify humanoid motions and to represent motor skills, which in most cases require four-dimensional space representations. We propose a representation framework that includes the following attributes: motion description layers, egocentric reference system, progressive quantized refinement, and automatic constraint satisfaction. We also outline strategies for acquiring new motor skills by learning from trial and error, macro approach, and programming. Then, we outline the development of a new humanoid motion description language called Cybele.

This paper discusses the concept of Volitron - a controller to make its host robot increase its competence in such activities as self-initiated exploration of an environment, new goal acquisition, and planning/executing of actions while taking into account predicted behaviors of objects of interest. There are four key elements in Volitron's structure: a model of perceived reality, a model of desired reality, a model of ideal reality and a model of anticipated reality. The task of a robot's working memory includes producing images of the robot itself imitating another subject's activities and sending the images to a model of desired reality. A tension (a concept borrowed from psychoanalysis) arising from the differences between a perceived reality and a desired reality is a source of a motivation toward action. The final decision to take an action is based on a comparison of the model of anticipated reality with that of ideal reality. The interaction of Volitron's elements are described in the paper. Furthermore, a computational model of working memory (WM) and its psychological justification are provided.

We present the first stages of the developmental course of a robot using vision and a 5 degree of freedom robotic arm. During an exploratory behavior, the robot learns visuo-motor control of its mechanical arm. We show how a simple neural network architecture, combining elementary vision, a self-organized algorithm, and dynamical Neural Fields is able to learn and use proper associations between vision and arm movements, even if the problem is ill posed (2-D toward 3-D mapping and also mechanical redundancy between different joints). Highlighting the generic aspect of such an architecture, we show as a robotic result that it is used as a basis for simple gestural imitations of humans. Finally we show how the imitative mechanism carries on the developmental course, allowing the acquisition of more and more complex behavioral capabilities.