Modelling narrative comprehension in film studies
In an essay titled ‘Can scientific models of theorizing help film studies’, Malcolm Turvey (2005: 25) asks why ‘is there a lack of systematic empirical research in film theory if the explanatory principles governing the cinema are like the explanatory principles governing natural phenomena?’
Turvey’s answer is that theories in the humanities do not need empirical validation in the same way as a theory in the sciences because ‘cinema is a human creation’ and is ’embedded in human practices and institutions,’ and researchers do not need to empirically validate their theory because they already know a great deal about such practices and institutions. Humanistic theories clarify our knowledge about things we already know, whereas scientific theories are generate new knowledge about subjects we have no prior knowledge about.
One of the examples of just such a humanistic theory accepted without validation cited by Turvey is David Bordwell’s (1985) constructivist account of the viewer’s comprehension of narrative. However, empirical research on narrative comprehension is to be found sociology, psychology, linguistics, and many other disciplines; but this is not the case in film studies. It is not because there is something special about film theories that means they do not require empirical validation – it is simply because no one has ever tried. Turvey does not address the fundamental reason for the lack empirical research in film studies: namely, that the vast majority of film scholars simply do not know how to design and conduct a piece of empirical research. There is a lack of basic empirical research skills in film studies, but this is unsurprising given that theory courses dominate film degrees while empirically based courses are few and far between. It is of course important that students study theory, if only so that they may have an understanding of the history of film studies as an academic discipline; but this should be accompanied with an emphasis on empirical research. Until this is generally the case, empirical validation of film theories will not be achieved.
We may have not trouble understanding a narrative film, but there are many questions about narrative comprehension in the cinema that can only be addressed through empirical research, including:
- Which pieces of evidence in a narrative do viewer’s consider salient?
- What hypotheses do viewers form to explain narrative events, and what role do they play in creating expectations?
- How do different viewers weight the same piece of evidence in their reasoning, and why do they differ?
- To what extent is the weighting of evidence determined by regimes of generic and cultural verisimilitude?
- Is the viewer sceptical, withholding belief until the end of a narrative when its conclusion becomes apparent; or is she credulous, committing belief early on in a narrative only for her assumptions to be overturned?
- What impact does the way in which evidence is presented to viewer have on her belief? For example, does it make a difference if a piece of evidence is presented visually with accompanying dialogue from a character, if it is presented visually only, or if it is presented as dialogue only?
- Does the viewer strive to achieve the local or global coherence of a narrative?
- Does the viewer make inferences that are unnecessary to the successful comprehension of the narrative?
Although we have no problem in understanding narratives, we do not have answers to these questions. If, as Turvey claims, film theories concern ‘what human beings already know and do‘ (25), it begs the question how do we know what human beings already know and do? How could we determine what human beings already know and do if we do not research (empirically) what it is that human beings know and do? We do not have answers to the above questions because no one has done the research, not because humanistic theories can be accepted without empirical validation. As the reader will soon learn, the plausibility of a theory is the not the same as truth.
Bordwell D 1985 Narration in the Fiction Film. London: Methuen.
Turvey M 2005 Can scientific models of theorizing help film theory?, in TE Wartenberg and A Curran (eds.) The Philosophy of Film: Introductory Texts and Readings. Malden, MA: Blackwell Publishing: 21-32.
In order to conduct research into narrative comprehension in the cinema we need some methods that will allow us to model the reasoning of viewers, and some methods are discussed in the my paper ‘Modelling inference in the comprehension of cinematic narratives.’ The abstract is below, and the pdf file can be downloaded beneath that. There are also some links to papers that provide the background theory to the concepts used in this article below.
Modelling inference in the comprehension of cinematic narratives
The purpose of this paper is to outline some models of inference that can be used in describing and analysing the behaviour of real viewers in comprehending a cinematic narrative. The viewer’s processes of inference making in the cinema involve the framing of hypotheses about the world of the narrative which may be overturned by subsequent information and are, therefore, nonmonotonic. The viewer’s reasoning can be modelled mathematically, and two approaches are discussed here: the use of Bayes’ Theorem to represent and update the subjective and conditional probabilities of an agent is summarised, and Peter Abell’s Bayesian approach to the sociological understanding of narratives is outlined; and the transferable belief model developed by Phillipe Smets, in which the beliefs of an agent are represented by belief functions that do not assume an underlying probability distribution. In understanding narratives and the understanding of narratives it is useful to represent information visually, and an analytic and synthetic method of representing inference via Wigmore charts is outlined.
In reading this paper a little set theory would not go amiss, and the basics can be found here.
Some of the references in this paper can be accessed freely online, and I have included links to many of them below. They are organised under broad categories of relevance to the paper. (NB: some of these links may not be to the final published versions of these papers).
Goldstein M 2006 Subjective Bayesian analysis: principles and practice, Bayesian Analysis 1 (3): 403-420.
Evidence: method, philosophy, and theory
The UCL web page devoted to the research of evidence and inquiry has many interesting pieces that describes methods and philosophies of evidence that are worth reading with a view to exploring cognition and reasoning in the cinema, and may be accessed here.
Man as intuitive statistician
Brunswick E 1943 Organismic achievement and environmental probability, Psychological Review 50 (3): 255-272.
Kelley HH 1973 The process of causal attribution, American Psychologist 28: 107-128.
Peterson CR and Beach LR 1967 Man as intuitive statistician, Psychological Bulletin 68 (1): 29-46.
Cosmides L and Tooby J 1996 Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty, Cognition 58: 1-73. (NB: an online version of this paper is available but there is no URL associated with it, but you can find it by searching for the title easily enough).
Narrative action theory
Abell P 2007 Narratives, Bayesian narratives, and narrative actions, Sociologica 3: http://www.sociologica.mulino.it/journal/article/index/Article/Journal:ARTICLE:122/Item/Journal:ARTICLE:122.
Brewka G, Niemalä I, and Truszczyński M 2007 Nonmonotonic reasoning, in F van Harmelen, V Lifschitz, and B Porter (eds.) Handbook of Knowledge Representation. Amsterdam: Elsevier: 239-284.
Josephson JR 1994 Conceptual analysis of abduction, in JR Josephson and SG Josephson (eds.) Abductive Inference: Computation, Philosophy, Technology. New York: Cambridge University Press: 5-30.
Reiter R 1980 A logic for default reasoning, Artificial Intelligence 13: 81-132.
Transferable belief model
Smets P 1990a The combination of evidence in the transferable belief model, IEEE-Pattern Analysis and Machine Intelligence 12 (5): 447-458.
Smets P 1991 About updating, in BD D’Ambrosio, P Smets, and PP Bonisone (eds.) Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufman: 378-385.
Smets P 2002 Decision making in a context where uncertainty is represented by belief functions, in RP Srivastava and TJ Mock (eds.) Belief Functions in Busines Decisions. New York: Physica-Verlag: 17-61.
Smets P and Kennes R 1994 The transferable belief model, Artificial Intelligence 66 (2): 191–234.
Srivastava RP 1997 Decision making under ambiguity: a belief-function perspective, Archives of Control Sciences 6 (1-2): 5-27.