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Visual realism and character identification in science comics

An empirical study testing McCloud’s ‘masking effect’ theory

Bibliographic Data

ID12700237
AuthorsShiyu Yang (0000-0002-6222-0389, University of Tennessee at Knoxville), Leonardo Barolo Gargiulo (0000-0001-6084-6372, University of Wisconsin–Madison), Nan Li (0009-0000-4562-451X, University of Wisconsin–Madison, corresponding author), Dominique Brossard (0000-0002-9188-8388, University of Wisconsin–Madison)
Year2024
Publication date2024-10-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueVisual Communication (JOURNAL)
Journal identifiersISSN: 1470-3572 • E-ISSN: 1741-3214
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/14703572241258896
OpenAlexW4403107858
LanguageEN
Citations received1
References cited49

Comics have increasingly served as a tool for delivering scientific information to diverse audiences. However, little is known about how visual elements in comics influence their persuasive potential. Inspired by McCloud (1993)’s ‘masking effect’ theory featured in Understanding Comics: The Invisible Art, this study examined how different degrees of realism in comic characters, combined with background details, affected readers’ character identification. The findings suggested that exposure to comics featuring unrealistic, cartoonish characters minimized the gap in character identification between Black and white US participants. Contrary to McCloud’s hypotheses, exposure to comics with less detailed backgrounds resulted in higher character identification across conditions, regardless of character realism. The authors discuss the implications for future research and practice of using comics for public communication of science

Art · Character (mathematics · Comics · Empirical research · Epistemology · Identification (biology · Literature · Masking (illustration · Realism · Sociology · Comics and Graphic Narratives · Digital Storytelling and Education · Mathematics · Media, Gender, and Advertising · Philosophy · Psychology

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Unique citing works1
Citations per year1
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

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