This article indicates how competition between afferent data and learned feedback expectancies can stabilize a developing code by buffering committed populations of detectors against continual erosion by new environmental demands. Tille gating phenomena that result lead to dynamically maintained critical peri(Jlds, and to attentional phenomena such as overshadowing in the adult. The fuillctional unit of cognitive coding is suggested to be an adaptive resonance, or amplification and,prolongation of neural activity, that occurs when afferent data and efferent expectancies reach consensus through a matching process. The resonant state embodies the perceptual event, or attentional focus, and its amplified and sustained activities are capable of driving slow changes of long-term memor:r"' Mismatch between afferent data and efferent expectancies yields a global sulppression of activity and triggers a reset of short-term memory, as well as raJ~id parallel search and hypothesis testing for uncommitted cells. These mechanisms help to explain and predict, as manifestations of the unified theme of stable code development, positive and negative aftereffects, the McCollough effect, spatial frequency adaptation, monocular rivalry, binocular rivalry and hysteresis, pattern completion, and Gestalt switching; analgesia, partial reinforcement acquisition effect, conditioned reinforcers, underaroused versus overaroused depression; the contingent negative variation, P300, and pontoge]lliculo-occipital waves; olfactory coding, corticogeniculate feedback, matching of proprioceptive and terminal motor maps, and cerebral dominance. The psychophysiological mechanisms that unify these effects are inherently nonlinear and parallel and are inequivalent to the computer, probabilistic, and linear models currently in use.

Abstract—Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without

Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies. 1

Four-month-old infants sometimes can perceive the unity of a partly hidden object. In each of a series of experiments, infants were habituated to one object whose top and bottom were visible but whose center was occluded by a nearer object. They were then tested with a fully visible continuous object and with two fully visible object pieces with a gap where the occluder had been. Patterns of dishabituation suggested that infants perceive the boundaries of a partly hidden object by analyzing the movements of its surfaces: infants perceived a connected object when its ends moved in a common translation behind the occluder. Infants do not appear to perceive a connected object by analyzing the colors and forms of surfaces: they did not perceive a connected object when its visible parts were stationary, its color was homogeneous, its edges were aligned, and its shape was simple and regular. These findings do not support the thesis, from gestalt psychology, that object perception first arises as a consequence of a tendency to perceive the simplest, most regular configuration, or the Piagetian thesis that object perception depends on the prior coordination of action. Perception of objects

Data and models about recognition and recall of words and non words are unified using a real-time network processing theory. Lexical decision and word frequency effect data are analyzed in terms of theoretical concepts that have unified data about development of circular reactions, imitation of novel sounds, the matching of phonetic to articulatory requirements, serial and paired associate verbal learning, free recall, unitization, categorical perception, selective adaptation, auditory contrast, and word superiority effects. The theory, called adaptive resonance theory, arose from an analysis of how a language system self-organizes in real time in response to its complex input environment. Such an approach emphasizes the moment-by-moment dynamical interactions that control language development, learning, and stability. Properties of language performance emerge from an analysis of the system constraints that govern stable language learning. Concepts such as logogens, verification, automatic activation, interactive activation, limited-capacity processing, conscious attention, serial search, processing stages, speed-accuracy trade-off, situational frequency, familiarity, and encoding specificity are revised and developed using this analysis. Concepts such as adaptive resonance, resonant equilibration of short-term memory, bottom-up adaptive filtering, tQp-down adaptiveteml'late matching, competitive masking field, unitized list representation, temporal order information over item representations,

Our physical bodies play a central role in shaping human experience in the world, understanding of the world, and interactions in the world. This paper draws on theories of embodiment — from psychology, sociology, and philosophy — synthesizing five themes we believe are particularly salient for interaction design: thinking through doing, performance, visibility, risk, and thick practice. We introduce aspects of human embodied engagement in the world with the goal of inspiring new interaction design approaches and evaluations that better integrate the physical and computational worlds. Author Keywords Embodiment, bodies, embodied interaction, ubiquitous

The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments. In this paper we introduce the concepts of online and offline sampling and highlight the role of online sampling in agent-based models. After comparing the strengths of each approach for modeling particular developmental phenomena and research questions, we describe a recent agent-based model of infant causal perception. We conclude by discussing some of the present limitations of agent-based models and suggesting how these challenges may be addressed. © 2001 Academic Press Computational models of learning and development are playing an increasingly critical role in child development research (Cassidy, 1990;

This article addresses these questions by examining the interaction between representation and intelligence in user interfaces. The rubric "representation" covers at least three topics in this context: how a computational system is represented in its user interface, how the interface conveys its representations of information and of the world to human users, and how the system's internal representation affects the human user's interaction with that system. I will argue that each of these kinds of representations (of the system, of information and the world, of the interaction) is key to how users make the kind of attributions of intelligence that facilitate their interactions with intelligent systems. I will argue for representing a system as a human in those cases where social collaborative behavior is key, and I will argue for the system representing its knowledge to humans in multiple ways on multiple modalities. I will demonstrate my claims by discussing issues of representation and intelligence in an embodied conversational agent -- an interface in which the system is represented as a person, in which information is conveyed to human users via multiple modalities such as voice and hand gestures, and in which the internal representation is modality-independent, and both propositional and non-propositional.

This paper discusses the concept of joint attention and the di#erent skills underlying its development. We argue that joint attention is much more than gaze following or simultaneous looking because it implies a shared intentional relation to the world. The current state-of-the-art in robotic and computational models of the di#erent prerequisites of joint attention is discussed in relation with a developmental timeline drawn from results in child studies.

Abstract not found