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IEEE Transactions on Autonomous Mental Development

Issue 2 • Date Aug. 2009

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  • Table of contents

    Publication Year: 2009, Page(s): C1
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  • IEEE Transactions on Autonomous Mental Development publication information

    Publication Year: 2009, Page(s): C2
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  • Default Network and Intelligence Difference

    Publication Year: 2009, Page(s):101 - 109
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (466 KB) | HTML iconHTML

    In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task was performed. Some scientists believed that the resting state activity might reflect some neural functions that consolidate the past, stabilize brain ensembles, and prepare us for the... View full abstract»

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  • Connectionist Models of Reinforcement, Imitation, and Instruction in Learning to Solve Complex Problems

    Publication Year: 2009, Page(s):110 - 121
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1469 KB) | HTML iconHTML

    We compared computational models and human performance on learning to solve a high-level, planning-intensive problem. Humans and models were subjected to three learning regimes: reinforcement, imitation, and instruction. We modeled learning by reinforcement (rewards) using SARSA, a softmax selection criterion and a neural network function approximator; learning by imitation using supervised learni... View full abstract»

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  • Some Basic Principles of Developmental Robotics

    Publication Year: 2009, Page(s):122 - 130
    Cited by:  Papers (32)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (198 KB) | HTML iconHTML

    This paper formulates five basic principles of developmental robotics. These principles are formulated based on some of the recurring themes in the developmental learning literature and in the author's own research. The five principles follow logically from the verification principle (postulated by Richard Sutton) which is assumed to be self-evident. This paper also gives an example of how these p... View full abstract»

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  • Acquisition of the Head-Centered Peri-Personal Spatial Representation Found in VIP Neuron

    Publication Year: 2009, Page(s):131 - 140
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1419 KB) | HTML iconHTML

    Both body and visuo-spatial representations are supposed to be gradually acquired during the developmental process as described in cognitive and brain sciences. A typical example is face representation in a neuron (found in the ventral intraparietal (VIP) area) of which the function is not only to code for the location of visual stimuli in the head-centered reference frame, but also to connect vis... View full abstract»

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  • Active Information Selection: Visual Attention Through the Hands

    Publication Year: 2009, Page(s):141 - 151
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1066 KB) | HTML iconHTML

    An important goal in studying both human intelligence and artificial intelligence is to understand how a natural or an artificial learning system deals with the uncertainty and ambiguity of the real world. For a natural intelligence system such as a human toddler, the relevant aspects in a learning environment are only those that make contact with the learner's sensory system. In real-world intera... View full abstract»

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  • IEEE Transactions on Autonomous Mental Development (TAMD)

    Publication Year: 2009, Page(s): 152
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2009, Page(s): C3
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  • IEEE Transactions on Autonomous Mental Development Information for authors

    Publication Year: 2009, Page(s): C4
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Aims & Scope

IEEE Transactions on Autonomous Mental Development (TAMD) includes computational modeling of mental development, including mental architecture, theories, algorithms, properties, and experiments.

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Meet Our Editors

Editor-in-Chief
Angelo Cangelosi
Plymouth University