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Linguistic attractors : the cognitive dynamics of language acquisition and change

Author: D L Cooper
Publisher: Amsterdam ; Philadelphia, Pa. : J. Benjamins Pub. Co., ©1999.
Series: Human cognitive processing, v. 2.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
The interdisciplinary linguistic attractor model portrays language processing as linked sequences of fractal sets, and examines the changing dynamics of such sets for individuals as well as the speech community they comprise. Its motivation stems from human anatomic constraints and several artificial neural network approaches. It uses general computation theory to: (1) demonstrate the capacity of Cantor-like fractal  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Cooper, D.L.
Linguistic attractors.
Amsterdam ; Philadelphia, Pa. : J. Benjamins Pub. Co., ©1999
(DLC) 99010761
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: D L Cooper
ISBN: 9789027299383 9027299382 9781556192029 1556192029
OCLC Number: 70764678
Description: 1 online resource (xv, 375 pages) : illustrations.
Contents: Linguistic Attractors; Editorial page; Title page; LCC data; Table of contents; Preface; Introduction. Abstractions, Universals, Systems, and Attractors; Chapter 1. Human Architecture: Physical Constraints and Special Accommodations; Chapter 2. Possible Neural Network Implementations: General Network Properties and Requirements for Computation; Chapter 3. Representations; Chapter 4. Attractor Dynamics on Semantic Fields; Chapter 5. Toward an Attractor Grammar; Chapter 6. The Dynamics of Language Change: Beowulf, the Tatian, and German Biblical Texts; References; Index.
Series Title: Human cognitive processing, v. 2.
Responsibility: David L. Cooper.

Abstract:

The interdisciplinary linguistic attractor model portrays language processing as linked sequences of fractal sets, and examines the changing dynamics of such sets for individuals as well as the speech community they comprise. Its motivation stems from human anatomic constraints and several artificial neural network approaches. It uses general computation theory to: (1) demonstrate the capacity of Cantor-like fractal sets to perform as Turing Machines; (2) better distinguish between models that simply match outputs (emulation) and models that match both outputs and internal dynamics (simulation.

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