Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

Abstract. This paper proposes cognitive developmental robotics as a new principle for the design of humanoid robots. This principle may provide ways of understanding human beings that go beyond the current level of explanation found in the natural and social sciences. Furthermore, a methodological emphasis on humanoid robots in the design of artificial creatures holds promise because they have many degrees of freedom and sense modalities and, thus, must face the challenges of scalability that are often side stepped in simpler domains. We examine the potential of this new principle as well as issues that are likely to be important to CDR in the future. 1

Abstract. This paper presents a constructive model by which a robot acquires the ability of joint attention with a human caregiver based on its embedded mechanisms of visual attention and learning with self-evaluation. The former is to look at a salient object in the robot’s view, and the latter is to learn sensorimotor co-ordination when visual attention has succeeded. Since the success of visual attention does not always correspond to the success of joint attention, the robot has incorrect learning data for joint attention as well as correct data. However, the robot is expected statistically to lose incorrect data as outliers since such data do not have any correlation in the sensorimotor co-ordination while correct data have a correlation. The robot consequently acquires the ability of joint attention by finding the correlation in the sensorimotor co-ordination even if multiple objects are placed at random positions in an environment and a human caregiver does not provide any task evaluation to the robot. The experimental results show that the proposed model makes the robot reproduce the developmental process of infants ’ joint attention. Therefore, the proposed model could be one of the models to explain how infants develop the ability of joint attention.

There is strong evidence that face processing in the brain is localized. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kinds of complex objects, indicates that face and non-face object recognition may be served by partially independent neural mechanisms. In this paper, we use computational models to show how the face processing specialization apparently underlying prosopagnosia and visual object agnosia could be attributed to (1) a relatively simple competitive selection mechanism that, during development, devotes neural resources to the tasks they are best at performing, (2) the developing infant's need to perform subordinate classification (identification) of faces early on, and (3) the infant's low visual acuity at birth. Inspired by de Schonen, Mancini and Liegeois' arguments (1998) [de Schonen, S., Mancini, J., Liegeois, F. (1998). About functional cortical specializat...

The term “aphasia ” refers to acute or chronic impairment of language, an acquired condition that is most often associated with damage to the left side of the brain, usually due to trauma or stroke. We have known about the link between left-hemisphere damage and language loss for more than a century (Goodglass, 1993). For almost as long, we have also known that the lesion/symptom correlations observed in adults do not appear to hold for very young children (Basser, 1962; Lenneberg, 1967). In fact, in the absence of other complications, infants with congenital damage to one side of the brain (left or right) usually go on to acquire language abilities that are well within the normal range (Eisele & Aram, 1995; Feldman, Holland, & Janosky, 1992; Vargha-Khadem, Isaacs, & Muter,

A constructivist neural network is presented that models the acquisition of the past tense of English verbs. The network constructs its architecture in response to the learning task, corresponding to neurobiological and psychological evidence. The model outperforms other connectionist and symbolic models in learning and in displaying psychologically realistic learning and generalization behavior. It is argued that the success of the network is due to its constructivist nature, and that the distinction between fixed architecture and constructivist models is fundamental. Given this distinction, constructivist systems constitute better models of cognitive development. 1. Introduction The acquisition of the English past tense has in the past years become a touchstone for different theories of language acquisition and of cognition in general. Different theories and models have not only been used in the debate between proponents of symbolic and connectionist accounts of language learning, ...

This study argues how a human infant acquires the ability of joint attention through interactions with its caregiver from the viewpoint of a constructive approach. This paper presents a constructive model by which a robot acquires a sensorimotor coordination for joint attention based on visual attention and learning with self-evaluation. Since visual attention does not always correspond to joint attention, the robot may have incorrect learning situations for joint attention as well as correct ones. However, the robot is expected to statistically lose the data of the incorrect ones as outliers through the learning, and consequently acquires the appropriate sensorimotor coordination for joint attention even if the environment is not controlled nor the caregiver provides any task evaluation. The experimental results suggest that the proposed model could explain the developmental mechanism of the infant's joint attention because the learning process of the robot's joint attention can be regarded as equivalent to the developmental process of the infant's one.

We propose that the development of causality can be seen as a primitive for understanding and constructing complex systems either biological or artificial. Furthermore, we put forward a view of development in terms of the control of complexity. Although some of these elements are at the moment speculative or barely outlined, the theoretical test and verification are part of the ongoing research. On the artificial side, we will show how developmental principles are used within the architecture of a humanoid robot. The reference problem is the ontogenesis of sensori-motor coordination. Visual, acoustic and inertial cues constitute the sensory repertoire of the robot; computation, in the form of mappings, represents its brain activity. The continuous and meaningful adaptation during the natural interaction of the robot with the environment is one of the key aspects of the implementation.

The paper focuses on the problem how a community of distributed agents may autonomously invent and coordinate lexicons and grammars. Although our earlier experiments have shown that a communication system can indeed emerge in a socio-cultural dynamics, it relies on the control of complexity by the experimenter, so that agents first acquire words, then simple constructions, and then more complex ones. This paper addresses the question how agents could themselves regulate the complexity both of the mechanisms they bring to bear to the language task and on the semantic complexity of what they want to express. We make use of the autotelic principle, coming from psychology. It requires monitoring challenge and skill (based on actual performance) and maintaining a ’flow ’ regime balancing the two. We show in computational experiments that the autotelic principle is able to explain autonomous scaffolding towards greater complexity in the emergence of language.

cognitive science: Integrative approaches to learning and development, Neurocomputing, doi:10.1016/j.neucom.2006.06.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.