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Cited by 637

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

DISCOURSE COMPREHENSION

Graesser, Arthur C., Millis, Keith K., Zwaan, Rolf A.·1997·Annual Review of Psychology

Abstract

▪ Abstract The field of discourse processing has dissected many of the levels of representation that are constructed when individuals read or listen to connected discourse. These levels include the surface code, the propositional textbase, the referential situation model, the communication context, and the discourse genre. Discourse psychologists have developed models that specify how these levels are mentally represented and how they are dynamically built during comprehension. This chapter focuses on the meaning representations that are constructed when adults read written text, such as literary stories, technical expository text, and experimenter-generated “textoids.” Recent psychological models have attempted to account for the identification of referents of referring expressions (e.g. which person in the text does she refer to), the connection of explicit text segments, the establishment of local and global coherence, and the encoding of knowledge-based inferences.

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Crossref · 10.1146/annurev.psych.48.1.163

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Metadata

Authors
Graesser, A. C., Millis, K. K., & Zwaan, R. A.
Venue
Annual Review of Psychology
Published
1997-02
Volume
48
Issue
1
Pages
163-189
ISSN
  • 0066-4308 (ISSN-L)
  • 1545-2085
OpenAlex
W4251323989
Cited by
637
References
6
FWCI
9.79
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: DISCOURSE COMPREHENSION. https://app.cochat.ai/papers/10.1146/annurev.psych.48.1.163
The paper
Graesser, A. C., Millis, K. K., & Zwaan, R. A. (1997). DISCOURSE COMPREHENSION. Annual Review of Psychology. https://doi.org/10.1146/annurev.psych.48.1.163

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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.