Matthew Schlesinger

Abstract: We present the broad outlines of a roadmap toward human-level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological and information processing perspectives. The challenge of identifying… Show more

Abstract: We present the broad outlines of a roadmap toward human-level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological and information processing perspectives. The challenge of identifying appropriate tasks and environments for measuring AGI will be addressed, and seven scenarios will be presented as milestones suggesting a roadmap across the AGI landscape along with directions for future research and collaboration. Show less

First comprehensive introduction to the emerging discipline of developmental robotics.

Abstract: Does modeling matter? We address this question by providing a broad survey of the computational models of cognitive development that have been proposed and studied over the last three decades. We begin by noting the advantages and limitations of computational models. We then describe four key dimensions across which models of development can be organized and classified. With this taxonomy in hand, we focus on how the modeling enterprise has evolved over time. In particular, we… Show more

Abstract: Does modeling matter? We address this question by providing a broad survey of the computational models of cognitive development that have been proposed and studied over the last three decades. We begin by noting the advantages and limitations of computational models. We then describe four key dimensions across which models of development can be organized and classified. With this taxonomy in hand, we focus on how the modeling enterprise has evolved over time. In particular, we separate the timeline into three overlapping historical waves and highlight how each wave of models has not only been shaped by developmental theory and behavioral research, but in return also provided valuable insights and innovations to the study of cognitive development. Show less

Abstract: This chapter provides an optimistic forecast for the future of connectionism and dynamic systems theory (DST). In particular, it focuses on the idea that regardless of how similar or dissimilar connectionism and DST appear to be at this moment in their development, there are numerous signs that hybridization of the two approaches is not only possible, but also has begun to occur. The chapter begins by reviewing three major, crosscutting themes that are shared by connectionism and DST.… Show more

Abstract: This chapter provides an optimistic forecast for the future of connectionism and dynamic systems theory (DST). In particular, it focuses on the idea that regardless of how similar or dissimilar connectionism and DST appear to be at this moment in their development, there are numerous signs that hybridization of the two approaches is not only possible, but also has begun to occur. The chapter begins by reviewing three major, crosscutting themes that are shared by connectionism and DST. It then highlights the evidence for an optimistic outlook by describing recent work in the field of adaptive behavior and robotics, which is illustrated by numerous examples of models that blend elements of connectionism and DST. Finally, it returns to the crosscutting themes and elaborates on each in light of the progress that robotics researchers have made toward a hybrid approach. Show less

Abstract: Recent work by Amso and Johnson (Developmental Psychology, 42(6), 1236–1245, 2006) implicates
the role of visual selective attention in the development of perceptual completion during early infancy. In the current article, we extend this finding by simulating the performance of 3-month-old infants on a visual search task, using a multi-channel, image-filtering model of early visual processing. Model parameters were systematically varied to simulate developmental change in three… Show more

Abstract: Recent work by Amso and Johnson (Developmental Psychology, 42(6), 1236–1245, 2006) implicates
the role of visual selective attention in the development of perceptual completion during early infancy. In the current article, we extend this finding by simulating the performance of 3-month-old infants on a visual search task, using a multi-channel, image-filtering model of early visual processing. Model parameters were systematically varied to simulate developmental change in three neural components of visual selective attention: degree of oculomotor noise, growth of horizontal connections in visual cortex, and duration of recurrent processing in parietal cortex. While two of the three components—horizontal connections and recurrent parietal processing—are each able to account for the visual search performance of 3-month-olds, recurrent parietal processing also suggests a coherent pattern of developmental change in visual selective attention during early infancy. We conclude by highlighting plausible neural mechanisms for modulating recurrent parietal activity, including the development of feedback from prefrontal cortex. Show less

Since the publication in 1986 of Rumelhart and McClelland’s Parallel distributed processing: Explorations in the microstructure of cognition, neural network or connectionist models have become an increasingly common method for studying learning and development. A wide range of developmental domains have been investigated with connectionist models, including language acquisition, perceptual development, object permanence, developmental psychopathology and motor skill acquisition. Many of these… Show more

Since the publication in 1986 of Rumelhart and McClelland’s Parallel distributed processing: Explorations in the microstructure of cognition, neural network or connectionist models have become an increasingly common method for studying learning and development. A wide range of developmental domains have been investigated with connectionist models, including language acquisition, perceptual development, object permanence, developmental psychopathology and motor skill acquisition. Many of these models rely on the backpropagation-of-error learning algorithm, a form of supervised learning in which a ‘teacher’ shapes the output of the network by providing it with desired responses. Show less

Abstract: Although 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 article 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… Show more

Abstract: Although 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 article 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 eye-movement model and discussing the implications of this work for infant cognition research. Show less

Abstract: Artificial Life is the study of all phenomena of the living world through their reproduction in artificial systems. We argue that Artificial Life models of evolution and development offer a new set of theoretical and methodological tools for investigating Piaget’s ideas. The concept of an Artificial Life Neural Network (ALNN) is first introduced, and contrasted with the study of other recent approaches to modeling development. We then illustrate how several key elements of Piaget’s… Show more

Abstract: Artificial Life is the study of all phenomena of the living world through their reproduction in artificial systems. We argue that Artificial Life models of evolution and development offer a new set of theoretical and methodological tools for investigating Piaget’s ideas. The concept of an Artificial Life Neural Network (ALNN) is first introduced, and contrasted with the study of other recent approaches to modeling development. We then illustrate how several key elements of Piaget’s theory of cognitive development (e.g., sensorimotor schemata, perception-action integration) can be investigated within the Artificial Life framework. We conclude by discussing possible new directions of Artificial Life research that will help to elaborate and extend Piaget’s developmental framework. Show less

Abstract: 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… Show more

Abstract: 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. Show less