Literature DB >> 19795221

Unifying syntactic theory and sentence processing difficulty through a connectionist minimalist parser.

Sabrina Gerth, Peter Beim Graben.   

Abstract

Syntactic theory provides a rich array of representational assumptions about linguistic knowledge and processes. Such detailed and independently motivated constraints on grammatical knowledge ought to play a role in sentence comprehension. However most grammar-based explanations of processing difficulty in the literature have attempted to use grammatical representations and processes per se to explain processing difficulty. They did not take into account that the description of higher cognition in mind and brain encompasses two levels: on the one hand, at the macrolevel, symbolic computation is performed, and on the other hand, at the microlevel, computation is achieved through processes within a dynamical system. One critical question is therefore how linguistic theory and dynamical systems can be unified to provide an explanation for processing effects. Here, we present such a unification for a particular account to syntactic theory: namely a parser for Stabler's Minimalist Grammars, in the framework of Smolensky's Integrated Connectionist/Symbolic architectures. In simulations we demonstrate that the connectionist minimalist parser produces predictions which mirror global empirical findings from psycholinguistic research.

Year:  2009        PMID: 19795221      PMCID: PMC2777194          DOI: 10.1007/s11571-009-9093-1

Source DB:  PubMed          Journal:  Cogn Neurodyn        ISSN: 1871-4080            Impact factor:   5.082


  21 in total

1.  Word order in sentence processing: an experimental study of verb placement in German.

Authors:  Helga Weyerts; Martina Penke; Thomas F Münte; Hans-Jochen Heinze; Harald Clahsen
Journal:  J Psycholinguist Res       Date:  2002-05

Review 2.  How the brain solves the binding problem for language: a neurocomputational model of syntactic processing.

Authors:  Peter Hagoort
Journal:  Neuroimage       Date:  2003-11       Impact factor: 6.556

3.  Inverse problems in dynamic cognitive modeling.

Authors:  Peter beim Graben; Roland Potthast
Journal:  Chaos       Date:  2009-03       Impact factor: 3.642

4.  Harmony in linguistic cognition.

Authors:  Paul Smolensky
Journal:  Cogn Sci       Date:  2006-09-10

5.  Uncertainty about the rest of the sentence.

Authors:  John Hale
Journal:  Cogn Sci       Date:  2006-07-08

Review 6.  Optimality: from neural networks to universal grammar.

Authors:  A Prince; P Smolensky
Journal:  Science       Date:  1997-03-14       Impact factor: 47.728

7.  Syntactic structure assembly in human parsing: a computational model based on competitive inhibition and a lexicalist grammar.

Authors:  T Vosse; G Kempen
Journal:  Cognition       Date:  2000-05-15

8.  Brain potentials elicited by garden-path sentences: evidence of the application of verb information during parsing.

Authors:  L Osterhout; P J Holcomb; D A Swinney
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1994-07       Impact factor: 3.051

9.  Neural networks and physical systems with emergent collective computational abilities.

Authors:  J J Hopfield
Journal:  Proc Natl Acad Sci U S A       Date:  1982-04       Impact factor: 11.205

10.  The Unification Space implemented as a localist neural net: predictions and error-tolerance in a constraint-based parser.

Authors:  Theo Vosse; Gerard Kempen
Journal:  Cogn Neurodyn       Date:  2009-09-26       Impact factor: 5.082

View more
  3 in total

1.  Prolegomena to a neurocomputational architecture for human grammatical encoding and decoding.

Authors:  Gerard Kempen
Journal:  Neuroinformatics       Date:  2014-01

2.  A dynamical systems perspective on the relationship between symbolic and non-symbolic computation.

Authors:  Whitney Tabor
Journal:  Cogn Neurodyn       Date:  2009-11-07       Impact factor: 5.082

3.  A modular latching chain.

Authors:  Sanming Song; Hongxun Yao; Alessandro Treves
Journal:  Cogn Neurodyn       Date:  2013-06-19       Impact factor: 5.082

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.