Further Thoughts on Measuring Narrational Complexity in Fiction Film
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Abstract
Cutting (2021) argues that the narrational complexity of fiction film can be quantified similarly to computational measures of text complexity. Narrational complexity refers to the structure that arises from how a story is told. This article expands upon Cutting's proposal by taking inspiration from contemporary approaches for measuring text complexity. These approaches reject the notion that complexity can be measured via a limited set of indices as Cutting proposed for narrational complexity. Similarly, we argue that narrational complexity for fiction films should be multi-dimension and include indices that are associated with events, characters, and the rules that govern the fictional world. We discuss the viability of using computational approaches to analyze video and natural language processing to develop approaches to measuring narrational complexity.
Contributor Notes
Joseph P. Magliano, PhD, is a professor of educational psychology in the Department of Learning Sciences at Georgia State University. He studies the cognitive processes involved in the comprehension of media, such as texts, comics, and films. He uses a variety of empirical approaches in his program of research, such as think aloud protocols, timed responses, and event segmentation judgments. He assesses the convergence in these approaches to learn about the nature of comprehension. He also studies what it means to be ready to read for school, and in particular, college and how to support underprepared college readers. E-mail: jmagliano@gsu.edu (address for correspondence)
Lingfei Luan, PhD, is a cognitive and consulting psychologist with multiple backgrounds: film research and practice, media and communication, and experimental research. She currently works as a visiting professor of cognitive science at Case Western Reserve University. Her goal is to provide filmmakers and content makers with scientific feedback by identifying the factors that impact viewers’ interpretations and preferences. E-mail: lallen@umn.edu
Laura K. Allen, PhD, is a Bonnie Westby Huebner Chair in the Department of Educational Psychology at the University of Minnesota Twin Cities. Her research examines how individuals most effectively learn and communicate through text and discourse. Underlying this work is a focus on the use of natural language processing and data science methodologies to glean more insights into the fine-grained and multi-dimensional nature of discourse.