Natural chunk-and-pass language processing: Just another joint source-channel coding model?

A recent theoretical treatment by Christiansen and Chater attempts to address fundamental challenges significant to language processing and evolution with one major operational constraint called the "Now-or-Never" bottleneck. The authors' "Chunk-and-Pass" processing putative...

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Published in:	Communicative & integrative biology Vol. 11; no. 2; pp. 1 - 2
Main Author:	Clark, Kevin B.
Format:	Journal Article
Language:	English
Published:	United States Taylor & Francis 04.03.2018 Taylor & Francis Ltd Taylor & Francis Group
Subjects:	Compression Computer applications Fault tolerance fault-tolerance and correction hierarchical language representation Language language coding and decoding language compression multilevel lossy code-transmission rate and security natural language acquisition and pattern duality Natural language processing shannon's noisy channel coding and separation theorems Speech shannon's noisy channel coding and separation theorems language coding and decoding language compression natural language acquisition and pattern duality hierarchical language representation fault-tolerance and correction multilevel lossy code-transmission rate and security
ISSN:	1942-0889, 1942-0889
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Abstract	A recent theoretical treatment by Christiansen and Chater attempts to address fundamental challenges significant to language processing and evolution with one major operational constraint called the "Now-or-Never" bottleneck. The authors' "Chunk-and-Pass" processing putatively mitigates the severe multilevel Now-or-Never bottleneck via fast linguistic coding and compression, hierarchical language representation and pattern duality, and incrementally learned item-based predictions useful for grammaticalization over wide spacetime scales. Despite being a promising explanation of language processes, structure, and development, the Chunk-and-Pass model manages the Now-or-Never constraint with seeming reliance on optimal joint source-channel coding, a set of computational attributes for natural and artificial speech based on Shannon's noisy channel theorems. Restating the Now-or-Never bottleneck with information-theoretic source-channel capacity limitations stresses tradeoffs inherent in the authors' model involving multilevel lossy code-transmission rate and security. Such attributes render evolvable associative networks capable of Chunk-and-Pass speech acquisition, recognition, generation, and adaptation, suggesting Chunk-and-Pass processing represents a special case of joint source-channel coding.
AbstractList	A recent theoretical treatment by Christiansen and Chater attempts to address fundamental challenges significant to language processing and evolution with one major operational constraint called the “Now-or-Never” bottleneck. The authors' “Chunk-and-Pass” processing putatively mitigates the severe multilevel Now-or-Never bottleneck via fast linguistic coding and compression, hierarchical language representation and pattern duality, and incrementally learned item-based predictions useful for grammaticalization over wide spacetime scales. Despite being a promising explanation of language processes, structure, and development, the Chunk-and-Pass model manages the Now-or-Never constraint with seeming reliance on optimal joint source-channel coding, a set of computational attributes for natural and artificial speech based on Shannon's noisy channel theorems. Restating the Now-or-Never bottleneck with information-theoretic source-channel capacity limitations stresses tradeoffs inherent in the authors' model involving multilevel lossy code-transmission rate and security. Such attributes render evolvable associative networks capable of Chunk-and-Pass speech acquisition, recognition, generation, and adaptation, suggesting Chunk-and-Pass processing represents a special case of joint source-channel coding. A recent theoretical treatment by Christiansen and Chater attempts to address fundamental challenges significant to language processing and evolution with one major operational constraint called the "Now-or-Never" bottleneck. The authors' "Chunk-and-Pass" processing putatively mitigates the severe multilevel Now-or-Never bottleneck via fast linguistic coding and compression, hierarchical language representation and pattern duality, and incrementally learned item-based predictions useful for grammaticalization over wide spacetime scales. Despite being a promising explanation of language processes, structure, and development, the Chunk-and-Pass model manages the Now-or-Never constraint with seeming reliance on optimal joint source-channel coding, a set of computational attributes for natural and artificial speech based on Shannon's noisy channel theorems. Restating the Now-or-Never bottleneck with information-theoretic source-channel capacity limitations stresses tradeoffs inherent in the authors' model involving multilevel lossy code-transmission rate and security. Such attributes render evolvable associative networks capable of Chunk-and-Pass speech acquisition, recognition, generation, and adaptation, suggesting Chunk-and-Pass processing represents a special case of joint source-channel coding.A recent theoretical treatment by Christiansen and Chater attempts to address fundamental challenges significant to language processing and evolution with one major operational constraint called the "Now-or-Never" bottleneck. The authors' "Chunk-and-Pass" processing putatively mitigates the severe multilevel Now-or-Never bottleneck via fast linguistic coding and compression, hierarchical language representation and pattern duality, and incrementally learned item-based predictions useful for grammaticalization over wide spacetime scales. Despite being a promising explanation of language processes, structure, and development, the Chunk-and-Pass model manages the Now-or-Never constraint with seeming reliance on optimal joint source-channel coding, a set of computational attributes for natural and artificial speech based on Shannon's noisy channel theorems. Restating the Now-or-Never bottleneck with information-theoretic source-channel capacity limitations stresses tradeoffs inherent in the authors' model involving multilevel lossy code-transmission rate and security. Such attributes render evolvable associative networks capable of Chunk-and-Pass speech acquisition, recognition, generation, and adaptation, suggesting Chunk-and-Pass processing represents a special case of joint source-channel coding.
Author	Clark, Kevin B.
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References	Clark KB (cit0003) 2017 cit0001 cit0012 Bernard A (cit0008) 1998; 1 cit0010 Nielson MA (cit0011) 2000 cit0009 cit0006 cit0007 cit0004 cit0015 cit0005 cit0002 cit0013 cit0014 22408560 - Sensors (Basel). 2009;9(6):4901-17 25514941 - Behav Brain Sci. 2014 Dec;37(6):551-2; discussion 577-604 16882728 - Proc Natl Acad Sci U S A. 2006 Aug 8;103(32):12203-8 21908257 - IEEE Trans Image Process. 2012 Mar;21(3):1111-22 7984711 - Psychol Rev. 1994 Oct;101(4):676-703 25869618 - Behav Brain Sci. 2016 Jan;39:e62
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SubjectTerms	Compression Computer applications Fault tolerance fault-tolerance and correction hierarchical language representation Language language coding and decoding language compression multilevel lossy code-transmission rate and security natural language acquisition and pattern duality Natural language processing shannon's noisy channel coding and separation theorems Speech
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Title	Natural chunk-and-pass language processing: Just another joint source-channel coding model?
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