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Narrative theory and the dynamics of popular movies

Overview of attention for article published in Psychonomic Bulletin & Review, May 2016
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Title
Narrative theory and the dynamics of popular movies
Published in
Psychonomic Bulletin & Review, May 2016
DOI 10.3758/s13423-016-1051-4
Pubmed ID
Authors

James E. Cutting

Abstract

Popular movies grab and hold our attention. One reason for this is that storytelling is culturally important to us, but another is that general narrative formulae have been honed over millennia and that a derived but specific filmic form has developed and has been perfected over the last century. The result is a highly effective format that allows rapid processing of complex narratives. Using a corpus analysis I explore a physical narratology of popular movies-narrational structure and how it impacts us-to promote a theory of popular movie form. I show that movies can be divided into 4 acts-setup, complication, development, and climax-with two optional subunits of prolog and epilog, and a few turning points and plot points. In 12 studies I show that normative aspects in patterns of shot durations, shot transitions, shot scale, shot motion, shot luminance, character introduction, and distributions of conversations, music, action shots, and scene transitions reduce to 5 correlated stylistic dimensions of movies and can litigate among theories of movie structure. In general, movie narratives have roughly the same structure as narratives in any other domain-plays, novels, manga, folktales, even oral histories-but with particular runtime constraints, cadences, and constructions that are unique to the medium.

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The data shown below were compiled from readership statistics for 237 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Germany 1 <1%
Unknown 234 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 16%
Student > Bachelor 33 14%
Researcher 20 8%
Student > Master 14 6%
Student > Doctoral Student 12 5%
Other 52 22%
Unknown 69 29%
Readers by discipline Count As %
Arts and Humanities 38 16%
Psychology 37 16%
Social Sciences 21 9%
Computer Science 14 6%
Business, Management and Accounting 10 4%
Other 45 19%
Unknown 72 30%