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Cited by 1,675

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Toward an Interactive-Compensatory Model of Individual Differences in the Development of Reading Fluency

Stanovich, Keith E.·1980·Reading Research Quarterly

Abstract

INTERACTIVE MODELS OF READING appear to provide a more accurate conceptualization of reading performance than do strictly top-down or bottom-up models. When combined with an assumption of compensatory processing (that a deficit in any particular process will result in a greater reliance on other knowledge sources, regardless of their level in the processing hierarchy), interactive models provide a better account of the existing data on the use of orthographic structure and sentence context by good and poor readers. A review of the research literature seems to indicate that, beyond the initial stages of reading acquisition, superior reading ability is not associated with a greater tendency to use the redundancy inherent in natural language to speed word recognition. Instead, general comprehension strategies and rapid context-free word recognition appear to be the processes that most clearly distinguish good from poor readers.

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Metadata

Authors
Stanovich, K. E.
Venue
Reading Research Quarterly
Publisher
JSTOR
Published
1980
Volume
16
Issue
1
Pages
32
ISSN
0034-0553 (ISSN-L)
OpenAlex
W1989306084
Cited by
1,675
References
1
FWCI
44.07
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Toward an Interactive-Compensatory Model of Individual Differences in the Development of Reading Fluency. https://app.cochat.ai/papers/10.2307/747348
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Stanovich, K. E. (1980). Toward an Interactive-Compensatory Model of Individual Differences in the Development of Reading Fluency. Reading Research Quarterly. https://doi.org/10.2307/747348

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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.