Literature DB >> 15193971

Semantic integration and syntactic planning in language production.

Eric S Solomon1, Neal J Pearlmutter.   

Abstract

Five experiments, using a subject-verb agreement error elicitation procedure, investigated syntactic planning processes in production. The experiments examined the influence of semantic integration--the degree to which phrases are tightly linked at the conceptual level--and contrasted two accounts of planning: serial stack-based systems and parallel activation-based systems. Serial stack-based systems rely on memory-shifting processes to coordinate ongoing planning. Memory-shifting should be easier for more integrated phrases, resulting in fewer errors. Parallel, activation-based systems, on the other hand, maintain multiple representations simultaneously in memory. More integrated phrases will be more likely to be processed together, resulting in increased interference and more errors. Participants completed stimuli like The drawing of/with the flower(s), which varied local noun number (flower(s)) and the relationship between the head (drawing) and local noun. In some constructions, the nouns were tightly integrated (e.g., of), whereas in others the relationship was looser (e.g., with, specifying accompaniment). In addition to the well-established local noun mismatch effect (more errors for plural than for singular local nouns), all experiments revealed larger mismatch error effects following tightly integrated stimuli. These results are compatible with parallel activation-based accounts and cannot be explained by serial, memory-shift-based accounts. The experiments and three meta-analyses also ruled out alternative accounts based on plausibility, argumenthood, conceptual number, clause packaging, or hierarchical feature-passing, reinforcing the general finding that error rates increase with degree of semantic integration.

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Year:  2004        PMID: 15193971     DOI: 10.1016/j.cogpsych.2003.10.001

Source DB:  PubMed          Journal:  Cogn Psychol        ISSN: 0010-0285            Impact factor:   3.468


  22 in total

1.  Late L2ers can acquire grammatical features that do not occur in their L1: Evidence from the effect of animacy on verb agreement in L1 Chinese.

Authors:  Henrietta Lempert
Journal:  Mem Cognit       Date:  2016-05

2.  Subject-verb agreement in children and adults: serial or hierarchical processing?

Authors:  Isabelle Negro; Lucile Chanquoy; Michel Fayol; Maryse Louis-Sidney
Journal:  J Psycholinguist Res       Date:  2005-05

3.  Notional number agreement in English.

Authors:  Karin R Humphreys; Kathryn Bock
Journal:  Psychon Bull Rev       Date:  2005-08

4.  The time-course of feature interference in agreement comprehension: Multiple mechanisms and asymmetrical attraction.

Authors:  Darren Tanner; Janet Nicol; Laurel Brehm
Journal:  J Mem Lang       Date:  2014-10-01       Impact factor: 3.059

5.  Against structural constraints in subject-verb agreement production.

Authors:  Maureen Gillespie; Neal J Pearlmutter
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2012-06-25       Impact factor: 3.051

6.  Resolving Conflicts in Natural and Grammatical Gender Agreement: Evidence from Eye Movements.

Authors:  Maya Dank; Avital Deutsch; Kathryn Bock
Journal:  J Psycholinguist Res       Date:  2015-08

7.  Hierarchical structure and memory mechanisms in agreement attraction.

Authors:  Julie Franck; Matthew Wagers
Journal:  PLoS One       Date:  2020-05-19       Impact factor: 3.240

8.  Hierarchy and scope of planning in subject-verb agreement production.

Authors:  Maureen Gillespie; Neal J Pearlmutter
Journal:  Cognition       Date:  2010-11-27

9.  When language comprehension reflects production constraints: resolving ambiguities with the help of past experience.

Authors:  Maryellen C MacDonald; Robert Thornton
Journal:  Mem Cognit       Date:  2009-12

10.  When Singular and Plural are Both Grammatical: Semantic and Morphophonological Effects in Agreement.

Authors:  Jelena Mirković; Maryellen C Macdonald
Journal:  J Mem Lang       Date:  2013-10       Impact factor: 3.059

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