Gist Inference Scores Predict Cloze Comprehension "In Your Own Words" for Native, Not ESL Readers.

Three patient education texts from the National Cancer Institute (NCI) were subjected to a Coh-Metrix analysis, then further analyzed to obtain Gist Inference Scores (GIS), a new measure of the likelihood that readers will make appropriate inferences about a text's bottom-line meaning. In the GIS formula, the Coh-Metrix psycholinguistic variables referential cohesion, deep cohesion, and latent semantic analysis (LSA) verb overlap increase GIS because cohesive texts that describe related actions are likely to induce gist representations. The Coh-Metrix variables word concreteness, imagability for content words, and hypernymy for nouns and verbs are negatively weighted because they tend to promote verbatim mental representations. NCI texts were modified for a cloze procedure with every tenth word replaced by a blank starting with the second sentence. Participants in two studies received all three cloze-modified texts. Fuzzy-Trace Theory suggests that people are more likely to comprehend high GIS texts "in their own words," and thus fill-the-blanks with multiple words that differ from those omitted by the cloze procedure expressing comparable meaning. In Study One, non-native English speakers appropriately filled blanks with different words at the same rate for all three texts of low-, medium-, and high-GIS. In Study Two, replicating previous findings, for high GIS texts, native English speakers filled blanks appropriately with words other than those removed significantly more often than for medium- or low-GIS texts. High GIS texts apparently afford readers more semantic and lexical flexibility, but non-native English speakers may be ill-equipped to capitalize on this characteristic of high GIS texts.

Citation

Christopher R Wolfe, Mitchell Dandignac, Cynthia Wang, Savannah R Lowe. Gist Inference Scores Predict Cloze Comprehension "In Your Own Words" for Native, Not ESL Readers. Health communication. 2021 May 04:1-8

PMID: 33947301

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