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Cited by 733

The DOI matches its Crossref record; 153 of the 153 references it cites were found in a registry or an index.

Redundancy and reduction: Speakers manage syntactic information density

Open access
Florian Jaeger, T.·2010·Cognitive Psychology
DOI 10.1016/j.cogpsych.2010.02.002
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Abstract

A principle of efficient language production based on information theoretic considerations is proposed: Uniform Information Density predicts that language production is affected by a preference to distribute information uniformly across the linguistic signal. This prediction is tested against data from syntactic reduction. A single multilevel logit model analysis of naturally distributed data from a corpus of spontaneous speech is used to assess the effect of information density on complementizer that-mentioning, while simultaneously evaluating the predictions of several influential alternative accounts: availability, ambiguity avoidance, and dependency processing accounts. Information density emerges as an important predictor of speakers’ preferences during production. As information is defined in terms of probabilities, it follows that production is probability-sensitive, in that speakers’ preferences are affected by the contextual probability of syntactic structures. The merits of a corpus-based approach to the study of language production are discussed as well.

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Metadata

Authors
Florian Jaeger, T.
Venue
Cognitive Psychology
Publisher
Elsevier BV
Published
2010-08
Volume
61
Issue
1
Pages
23-62
ISSN
0010-0285 (ISSN-L)
DOI
10.1016/j.cogpsych.2010.02.002
OpenAlex
W2129955048
Cited by
733
References
153
FWCI
313.90
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Redundancy and reduction: Speakers manage syntactic information density. https://app.cochat.ai/papers/10.1016/j.cogpsych.2010.02.002
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Florian Jaeger, T. (2010). Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychology. https://doi.org/10.1016/j.cogpsych.2010.02.002

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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.