NDLI: Tutorial on compositionality and self-organization in cognitive minds: Dynamic neural network models and robotics experiments (ICDL-EPIROB 2014, Genova, Oct. $13^{th},$ 2014) Jun Tani, KAIST, South Korea

Content Provider	IEEE Xplore Digital Library
Author	Jun Tani
Copyright Year	2014
Description	Author affiliation: KAIST, Daejeon, South Korea (Jun Tani)
Abstract	The most pressing question about cognitive brains is how they support the compositionality that enables combinatorial manipulations of images, thoughts and actions. When addressing this problem with synthetic modeling, the conventional idea prevalent in artificial intelligence and cognitive science, generally, is to assume hybrid systems and corresponding neural network models, where higher-order cognition is realized by means of symbolic representation and lower sensory-motor processes by analogue processing. However, the crucial problem with such approaches is that the symbols represented at higher order cognitive levels cannot be grounded naturally in sensory-motor reality. The former are defined in a discrete space without any metric and the latter are defined in a continuous space with a physical metric. These, therefore, cannot directly interact with each other, regardless of the interface that is assigned between them. In facing with this problem, a recent promising proposal in the community of embodied cognition and developmental robotics is to reconstruct higher-order cognition by means of continuous neuro-dynamic systems that can elaborate delicate interactions with the sensory-motor level while sharing the same metric space. In fact a set of neuro-robotics experiments - including hierarchical learning of compositional action skills, associative learning between proto-language - have shown that the compositionality necessary for higher-order cognitive tasks can be acquired by means of self-organizing dynamic structures, via interactive learning between the top-down intentional process of acting on the physical world and the bottom-up recognition of perceptual reality.
Starting Page	1
Ending Page	3
File Size	67784
Page Count	3
File Format	PDF
ISBN	9781479975402
DOI	10.1109/DEVLRN.2014.6982942
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-13
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Brain models Robot sensing systems Cognition Biological neural networks
Content Type	Text
Resource Type	Article

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