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IEEE Transactions on Autonomous Mental Development

Issue 4 • Date Dec. 2013

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  • Table of Contents

    Publication Year: 2013, Page(s): C1
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  • IEEE Transactions on Autonomous Mental Development publication information

    Publication Year: 2013, Page(s): C2
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  • Computational Audiovisual Scene Analysis in Online Adaptation of Audio-Motor Maps

    Publication Year: 2013, Page(s):273 - 287
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1927 KB) | HTML iconHTML

    For sound localization, the binaural auditory system of a robot needs audio-motor maps, which represent the relationship between certain audio features and the position of the sound source. This mapping is normally learned during an offline calibration in controlled environments, but we show that using computational audiovisual scene analysis (CAVSA), it can be adapted online in free interaction w... View full abstract»

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  • Modeling Cross-Modal Interactions in Early Word Learning

    Publication Year: 2013, Page(s):288 - 297
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1085 KB) | HTML iconHTML

    Infancy research demonstrating a facilitation of visual category formation in the presence of verbal labels suggests that infants' object categories and words develop interactively. This contrasts with the notion that words are simply mapped “onto” previously existing categories. To investigate the computational foundations of a system in which word and object categories develop simu... View full abstract»

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  • Learning to Reproduce Fluctuating Time Series by Inferring Their Time-Dependent Stochastic Properties: Application in Robot Learning Via Tutoring

    Publication Year: 2013, Page(s):298 - 310
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2949 KB) | HTML iconHTML

    This study proposes a novel type of dynamic neural network model that can learn to extract stochastic or fluctuating structures hidden in time series data. The network learns to predict not only the mean of the next input state, but also its time-dependent variance. The training method is based on maximum likelihood estimation by using the gradient descent method and the likelihood function is exp... View full abstract»

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  • Conceptual Imitation Learning Based on Perceptual and Functional Characteristics of Action

    Publication Year: 2013, Page(s):311 - 325
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1751 KB) | HTML iconHTML

    This paper presents a conceptual model for imitation learning to abstract spatio-temporal demonstrations based on their perceptual and functional characteristics. To this end, the concepts are represented by prototypes irregularly scattered in the perceptual space but sharing the same functionality. Functional similarity between demonstrations is understood by reinforcements of the teacher or reco... View full abstract»

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  • A Robotic Model of Reaching and Grasping Development

    Publication Year: 2013, Page(s):326 - 336
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2133 KB) | HTML iconHTML

    We present a neurorobotic model that develops reaching and grasping skills analogous to those displayed by infants during their early developmental stages. The learning process is realized in an incremental manner, taking into account the reflex behaviors initially possessed by infants and the neurophysiological and cognitive maturation occurring during the relevant developmental period. The behav... View full abstract»

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  • 2013 Index IEEE Transactions on Autonomous Mental Development Vol. 5

    Publication Year: 2013, Page(s):337 - 341
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2013, Page(s): C3
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  • IEEE Transactions on Autonomous Mental Development information for authors

    Publication Year: 2013, 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