The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Processing of relative clauses is made easier by frequency of occurrence
Abstract
We conducted a large-scale corpus analysis indicating that pronominal object relative clauses are significantly more frequent than pronominal subject relative clauses when the embedded pronoun is personal. This difference was reversed when impersonal pronouns constituted the embedded noun phrase. This pattern of distribution provides a suitable framework for testing the role of experience in sentence processing: if frequency of exposure influences processing difficulty, highly frequent pronominal object relatives should be easier to process but only when a personal pronoun is in the embedded position. We tested this hypothesis experimentally: We conducted four self-paced reading tasks, which indicated that differences in pronominal object/subject relative processing mirrored the pattern of distribution revealed by the corpus analysis. We discuss the results in the light of current theories of sentence comprehension. We conclude that object relative processing is facilitated by frequency of the embedded clause, and, more generally, that statistical information should be taken into account by theories of relative clause processing.
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An integrated theory of language production and comprehension
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More than words: Frequency effects for multi-word phrases
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Cross-Linguistic Variation and Efficiency
Hawkins, J. A. (2014) · doi:10.1093/acprof:oso/9780199664993.001.0001 · Cited by 405
Frequency of basic English grammatical structures: A corpus analysis
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Metadata
- Authors
- Reali, F., & Christiansen, M. H.
- Venue
- Journal of Memory and Language
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 57
- Issue
- 1
- Pages
- 1-23
- ISSN
- 0749-596X (ISSN-L)
- OpenAlex
- W1987186529
- Cited by
- 310
- References
- 81
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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