Constructing inferences during narrative text comprehension.
Abstract
The authors describe a constructionist theory that accounts for the knowledge-based inferences that are constructed when readers comprehend narrative text. Readers potentially generate a rich variety of inferences when they construct a referential situation model of what the text is about. The proposed constructionist theory specifies that some, but not all, of this information is constructed under most conditions of comprehension. The distinctive assumptions of the constructionist theory embrace a principle of search (or effort) after meaning. According to this principle, readers attempt to construct a meaning representation that addresses the reader's goals, that is coherent at both local and global levels, and that explains why actions, events, and states are mentioned in the text. This study reviews empirical evidence that addresses this theory and contrasts it with alternative theoretical frameworks.
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Cited by (63)
- Situation models in language comprehension and memoryZwaan, Rolf A., Radvansky, Gabriel A. · 1998 · Psychological Bulletin
- A model of dual attitudes.Wilson, Timothy D., Lindsey, Samuel, Schooler, Tonya Y. · 2000 · Psychological Review
- A meta-analysis of the cognitive and motivational effects of serious games.Wouters, Pieter, van Nimwegen, Christof, van Oostendorp, Herre, van der Spek, Erik D. · 2013 · Journal of Educational Psychology
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Constructing inferences during narrative text comprehension. https://app.cochat.ai/papers/10.1037//0033-295x.101.3.371
- The paper
- Graesser, A. C., Singer, M., & Trabasso, T. (1994). Constructing inferences during narrative text comprehension. Psychological Review. https://doi.org/10.1037//0033-295x.101.3.371
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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.