What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication.

Developmental psychology and developmental neuropsychology have traditionally focused on the study of children. But these two fields are also supposed to be about the study of change, i.e. changes in behavior, changes in the neural structures that underlie behavior, and changes in the relationship between mind and brain across the course of development. Ironically, there has been relatively little interest in the mechanisms responsible for change in the last 15–20 years of developmental research. The reasons for this de-emphasis on change have a great deal to do with a metaphor for mind and brain that has influenced most of experimental psychology, cognitive science and neuropsychology for the last few decades, i.e. the metaphor of the serial digital computer. We will refer to this particu-

in this article we present symmetric diffusion networks, a family of networks that instantiate the principles of continuous, stochastic, adaptive and interactive pro-pagation of information. Using methods of Markovlon diffusion theory, we for-malize the activation dynamics of these networks and then show that they can be trained to reproduce entire muitivariote probability distributions an their outputs using the contrastive Hebbian learning rule (CHL).,We show that CHL performs gradient descent on an error function that captures differences between desired and obtolned continuous multivoriate probability distributions. This allows the learning algorithm to go beyond expected values of output units and to approxi-mate complete probability distributions on continuous muitivariote activation spaces. We argue that learning continuous distributions is an important task underlying a variety of real-life situations that were beyond the scope of previous connectionist networks. Deterministic networks, like back propagation, cannot ieorn this task because they ore limited to learning average values of indepen-dent output units. Previous stochastic connectionist networks could learn pro-bobility distributions but they were limited to discrete variables. Simulations show that symmetric diffusion networks can be trained with the CHL rule to op-proximate discrete and continuous probability distributions of various types. 1.

. We used cascade-correlation to model human cognitive development on a well studied psychological task, the balance scale. In balance scale experiments, the child is asked to predict the outcome of placing certain numbers of equal weights at various distances to the left or right of a fulcrum. Both stage progressions and information salience effects have been found with children on this task. Cascade-correlation is a generative connectionist algorithm that constructs its own network topology as it learns. Cascade-correlation networks provided better fits to these human data than did previous models, whether rule-based or connectionist. The network model was used to generate a variety of novel predictions for psychological research. Keywords: cognitive development, balance scale, connectionist learning, cascade-correlation 1. Introduction Although connectionist network models have become well known for their ability to simulate low level perceptual, learning, and memory phenomena, it h...

Abstract. The embodiment hypothesis is the idea that intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity. In this paper we offer six lessons for developing embodied intelligent agents suggested by research in developmental psychology. We argue that starting as a baby grounded in a physical, social and linguistic world is crucial to the development of the flexible and inventive intelligence that characterizes humankind.

We show that by introducing an appropriate schema concept and exploiting the higher-level features of a resonance function in a neural network it is possible to define a form of nonmonotonic inference relation between the input and the output of the network. This inference relation satisfies some of the most fundamental postulates for nonmonotonic logics. The construction presented in the paper is an example of how symbolic features can emerge from the subsymbolic level of a neural network.

Introduction Since the time that "God made man in His own image", humans have been fascinated by stories about artifacts coming to life. Ancient myth, fairytales, literature, and science fiction abound with stories of artificial beings. In the older stories, although it is people who make the beings, it is the power of supernatural forces that bestows life. Ovid's story of Pygmalion is perhaps one the most famous from mythology; a sculptor falls in love with his sculpture of a woman which the goddess Venus then brings to life. Then there is the ancient Guianan Indian fairytale about a witch doctor who carved himself a daughter out of a plum tree because he needed a son-in-law to look after him. Similarly, there is the story of the wooden puppet Pinocchio who desires and eventually obtains boyhood (a desire that parallels that of the android Commander Data in "Star Trek: the Next Generation"). In days of old, the breath of life into the inanimate was a mystery that extolled th

Simulating a human figure performing a task requires that the agent interact with objects in the environment in a realistic manner. In this paper we describe a system which directs task-level, general-purpose, object grasping for a simulated human agent. The Object Specific Reasoner (OSR) generates parameters for underspecified task-level instructions such as (pickup jack hammer). The Grasp behavior manages simultaneous motions of the joints in the hand, wrist and arm. When composed hierarchically, the OSR and the Grasp behavior interpret task-level commands to the animation system. These modules are implemented as part of the Jack project at the University of Pennsylvania.

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Four-month-old infants can perceive bimodally specified events. They respond to relationships between the optic and acoustic stimulation that carries information about an object. Infants can do this by detecting the temporal synchrony of an object's sounds and its optically specified impacts. They are sensitive both to the common tempo and to the simultaneity of such sounds and visible impacts. These findings support the view that intermodal perception depends at least in part on the detection of invariant relationships in patterns of light and sound. Humans live in a world of objects and events that can be seen, heard, and felt. When mature perceivers look and listen to an event simultaneously, they experience a unitary episode. When they look at one event while listening to another, they are aware of two separate happenings. These experiences are possible because adults can determine if simultaneous patterns of light and sound are produced by a single object. Adults can perceive bimodally specified events. What are the origins of this capacity? Many philosophers and psychologists have suggested that it arises from experience. Perceivers come to relate visual and auditory sensations through direct association (Berkeley, 1709/1910; Birch & Lefford, 1967; Mill, 1829), verbal mediation (Blank & Bridger, 1964), or the integration of schemes for look-Portions of this research are based on a thesis submitted