IEEE Transactions on Autonomous Mental Development 

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Table of Contents

Volume: 1  Issue: 4   Date: December 2009


(Previous issue:Vol. 1, No. 3, October 2009)

How Caregiver's Anticipation Shapes Infant's Vowel Through Mutual Imitation
Ishihara, H.; Yoshikawa, Y.; Miura, K.; Asada, M.;
Page(s): 217-225
Digital Object Identifier 10.1109/TAMD.2009.2038988

Abstract: The mechanism of infant vowel development is a fundamental issue of human cognitive development that includes perceptual and behavioral development. This paper models the mechanism of imitation underlying caregiver–infant interaction by focusing on potential roles of the caregiver's imitation in guiding infant vowel development. Proposed imitation mechanism is constructed with two kinds of the caregiver's possible biases in mind. The first is what we call “sensorimotor magnets,” in which a caregiver perceives and imitates infant vocalizations as more prototypical ones as mother-tongue vowels. The second is based on what we call “automirroring bias,” by which the heard vowel is much closer to the expected vowel because of the anticipation being imitated. Computer simulation results of caregiver–infant interaction show the sensorimotor magnets help form small clusters and the automirroring bias shapes these clusters to become clearer vowels in association with the sensorimotor magnets.

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A Computational Model of Acoustic Packaging
Schillingmann, L.; Wrede, B.; Rohlfing, K.J.;
Page(s): 226-237
Digital Object Identifier 10.1109/TAMD.2009.2039135

Abstract: In order to learn and interact with humans, robots need to understand actions and make use of language in social interactions. The use of language for the learning of actions has been emphasized by Hirsh-Pasek and Golinkoff (MIT Press, 1996), introducing the idea of acoustic packaging . Accordingly, it has been suggested that acoustic information, typically in the form of narration, overlaps with action sequences and provides infants with a bottom-up guide to attend to relevant parts and to find structure within them. In this article, we present a computational model of the multimodal interplay of action and language in tutoring situations. For our purpose, we understand events as temporal intervals, which have to be segmented in both, the visual and the acoustic modality. Our acoustic packaging algorithm merges the segments from both modalities based on temporal overlap. First evaluation results show that acoustic packaging can provide a meaningful segmentation of action demonstration within tutoring behavior. We discuss our findings with regard to a meaningful action segmentation. Based on our future vision of acoustic packaging we point out a roadmap describing the further development of acoustic packaging and interactive scenarios it is employed in.

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Developmental Stereo: Emergence of Disparity Preference in Models of the Visual Cortex
Solgi, M.; Juyang Weng;
Page(s): 238-252
Digital Object Identifier 10.1109/TAMD.2009.2038360

Abstract: How our brains develop disparity tuned V1 and V2 cells and then integrate binocular disparity into 3-D perception of the visual world is still largely a mystery. Moreover, computational models that take into account the role of the 6-layer architecture of the laminar cortex and temporal aspects of visual stimuli are elusive for stereo. In this paper, we present cortex-inspired computational models that simulate the development of stereo receptive fields, and use developed disparity sensitive neurons to estimate binocular disparity. Not only do the results show that the use of top-down signals in the form of supervision or temporal context greatly improves the performance of the networks, but also results in biologically compatible cortical maps-the representation of disparity selectivity is grouped, and changes gradually along the cortex. To our knowledge, this work is the first neuromorphic, end-to-end model of laminar cortex that integrates temporal context to develop internal representation, and generates accurate motor actions in the challenging problem of detecting disparity in binocular natural images. The networks reach a subpixel average error in regression, and 0.90 success rate in classification, given limited resources.

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Learning Posture Invariant Spatial Representations Through Temporal Correlations
Spratling, M. W.;
Page(s): 253-263
Digital Object Identifier 10.1109/TAMD.2009.2038494

Abstract: A hierarchical neural network model is used to learn, without supervision, sensory-sensory coordinate transformations like those believed to be encoded in the dorsal pathway of the cerebral cortex. The resulting representations of visual space are invariant to eye orientation, neck orientation, or posture in general. These posture invariant spatial representations are learned using the same mechanisms that have previously been proposed to operate in the cortical ventral pathway to learn object representation that are invariant to translation, scale, orientation, or viewpoint in general. This model thus suggests that the same mechanisms of learning and development operate across multiple cortical hierarchies.

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