The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Consequences of the Serial Nature of Linguistic Input for Sentenial Complexity
Abstract
All other things being equal the parser favors attaching an ambiguous modifier to the most recent possible site. A plausible explanation is that locality preferences such as this arise in the service of minimizing memory costs—more distant sentential material is more difficult to reactivate than more recent material. Note that processing any sentence requires linking each new lexical item with material in the current parse. This often involves the construction of long‐distance dependencies. Under a resource‐limited view of language processing, lengthy integrations should induce difficulty even in unambiguous sentences. To date there has been little direct quantitative evidence in support of this perspective. This article presents 2 self‐paced reading studies, which explore the hypothesis that dependency distance is a fundamental determinant of reading complexity in unambiguous constructions in English. The evidence suggests that the difficulty associated with integrating a new input item is heavily determined by the amount of lexical material intervening between the input item and the site of its target dependents. The patterns observed here are not straightforwardly accounted for within purely experience‐based models of complexity. Instead, this work supports the role of a memory bottleneck in language comprehension. This constraint arises because hierarchical linguistic relations must be recovered from a linear input stream.
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Cites
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Anderson, J. R. (2013) · doi:10.4324/9781315799438 · Cited by 6,761
Halliday, M. A. K., & Hasan, R. (2014) · Routledge eBooks · doi:10.4324/9781315836010 · Cited by 6,082
(1983) · Cited by 5,041
Speaking: from intention to articulation
(1989) · Choice Reviews Online · doi:10.5860/choice.27-1947 · Cited by 5,003
Speaking: From Intention to Articulation
Levelt, W. J. M. (1989) · doi:10.7551/mitpress/6393.001.0001 · Cited by 4,804
A theory of reading: From eye fixations to comprehension.
Just, M. A., & Carpenter, P. A. (1980) · Psychological Review · doi:10.1037/0033-295x.87.4.329 · Cited by 3,887
Integration of Visual and Linguistic Information in Spoken Language Comprehension
Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M., & Sedivy, J. C. (1995) · Science · doi:10.1126/science.7777863 · Cited by 2,929
The Mental Representation of Grammatical Relations
Baltin, M., & Bresnan, J. (1985) · Language · doi:10.2307/414493 · Cited by 2,783
The problem of serial order in behavior
Lashley, K. S. (1951) · MPG.PuRe (Max Planck Society) · Cited by 2,734
Cited by
Computational principles of working memory in sentence comprehension
Lewis, R. L., Vasishth, S., & Van Dyke, J. A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.08.007 · Cited by 853
How Efficiency Shapes Human Language
Gibson, E., Futrell, R., Piantadosi, S. P., Dautriche, I., Mahowald, K., Bergen, L., & Levy, R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.003 · Cited by 485
Self-citationLarge-scale evidence of dependency length minimization in 37 languages
Futrell, R., Mahowald, K., & Gibson, E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1502134112 · Cited by 455
Self-citationProbabilistic models of language processing and acquisition
Chater, N., & Manning, C. D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.006 · Cited by 411
The linguistic processes underlying the P600
Gouvea, A. C., Phillips, C., Kazanina, N., & Poeppel, D. (2010) · Language and Cognitive Processes · doi:10.1080/01690960902965951 · Cited by 344
Metadata
- Authors
- Grodner, D., & Gibson, E.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2005-03
- Volume
- 29
- Issue
- 2
- Pages
- 261-290
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2129849249
- Cited by
- 455
- References
- 101
- FWCI
- 5.14
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.