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On-line grammaticality judgments in French children and adults: a crosslinguistic perspective

Published online by Cambridge University Press:  04 November 2004

MICHELE KAIL
Affiliation:
Laboratoire Cognition et Développement, Université Paris V – CNRS
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Abstract

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This study examined the on-line processing of French sentences in a grammaticality judgment experiment. Three age groups of French children (mean age: 6;8, 8;6 and 10;10 years) and a group of adults were asked to detect grammatical violations as quickly as possible. Three factors were studied: the violation type: agreement violations (number and gender) vs. word order violations; the violation position: early vs. late in the sentence; the target type of the violations: intra vs. interphrasal. An example of an early interphrasal verbal agreement violation follows: ‘Chaque semaine la voisine remplissent le frigo après avoir fait les courses au marché’ (Every week the neighbour fill the fridge after shopping at the market). The main developmental results were the following: not surprisingly, children were always slower than adults in the detection of grammatical violations. At each age level, morphological violations were more rapidly detected than word order violations. Each age group was faster at judging sentences with later occurring violations and the position effect was especially strong in the youngest groups. Finally, intraphrasal violations were more rapidly detected than interphrasal ones, this effect being observed only in the oldest groups (i.e. 10;10 years and adults). The results were compared to previous on-line data obtained in modern Greek (Kail & Diakogiorgi, 1998) showing strong similarities, even though Greek is a very rich morphological language. These results are discussed within the framework of the Competition Model, outlining the necessity to incorporate new processing constraints into the model.

Type
Research Article
Copyright
2004 Cambridge University Press

Footnotes

I acknowledge Philippe Bonnet and Madeleine Léveillé for their contribution in programming experiments and analysing data.