Linking Epistemic Monitoring to Perceived Realism: the Impact of Story-World Inconsistency on Realism and Engagement

ABSTRACT

Discourse comprehension research demonstrates that understanding new information as it arises in a text, requires that readers retrieve information from earlier in the text and from preexisting knowledge brought to the reading experience, known as bridging and elaboration, respectively. Epistemic monitoring may detect inconsistencies that arise during bridging and elaboration, and these inconsistencies may interfere with comprehension and the construction of mental models. The present study links these processes with readers’ perceptions of narrative and external realism. It investigates the influence of inconsistencies – references to modern technologies in written short stories set before those technologies existed – on two types of realism judgments and on five dimensions of narrative engagement. Experimentally introduced inconsistencies designed to interfere with elaboration and, subsequently with the construction of a story world model, reduced perceived external realism and narrative realism. The effect on narrative realism was mediated by external realism. Results further indicate that narrative realism causally preceded imagery production, and that imagery production fully mediated the relation between narrative realism and emotional engagement, and partially mediated the relation between narrative realism and a sense of presence in the story.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The data described in this article are openly available in the Open Science Framework at https://osf.io/fvzre/?view_only=bf79f2c71b8f478f9515afd1aa288e62.

Open Scholarship

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Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. Models of comprehension offer different conceptualizations of active memory. However, all share the premise that mental models may be too complex to hold entirely in working memory and that if information presented in the text is unnecessary for immediate comprehension it moves out of working memory, but remains more accessible for future comprehension of the narrative than other irrelevant information (McNamara & Magliano, 2009).

2. On-Line Appendices are located in the Open Science Framework Repository and can be found at the following link: https://osf.io/fvzre/?view_only=f8e5786135b14b919bfcdc73c4a47049

3. We failed to conduct an a priori power analysis for this study. While the utility of post hoc power analyses are controversial (e.g., Dziak, Dierker, & Abar, 2020; Yuan & Maxwell, 2005), we can identify two ways to estimate the power of the study post hoc. One method is to calculate power based on observed effect size (ɳ_p²) for each analysis. This method suggests that even statistically significant differences between means, such as the influence of the manipulation on external realism (M_cont = 5.55, M_manip = 4.93, (F (1, 176) = 9.920, p < .02, ɳ_p² = .057), were slightly under powered, 1-β err prob = 0.727 as calculated using G*Power (Faul, Erdfelder, Lang, & Buchner, 2007). Non-significant observed mean differences were extremely underpowered, as would be expected. A second method is to treat our main result as a pilot test and use the observed statistics in predicting required sample size. Using the same ɳ_p² value of .06 to estimate the number of participants required to achieve a power of 0.8 suggested an N of 216. Our active sample of 182 appears to have resulted in a slightly under powered study.

4. We recognize that race and ethnicity are important aspects of all social interactions. However, at the time of data collection we anticipated, based on the population from which participants were drawn, that the majority of respondents would be white. We could find no justification to exclude or analyze separately the responses of nonwhite participants.

5. Principle component analysis with varimax rotation including the understanding and narrative realism items resulted in two separate factors with narrative realism items loading cleanly on the first factor and understanding items loading cleanly on the second factor, with eigenvalues of each factor above 1.0.

6. All items are available in the Open Science Framework Repository at the following link: