Some notes on cinemetrics

Over the past couple of weeks various issues have been raised regarding my posts on cinemetrics. Hopefully, today’s post will go some way to addressing points that have been raised, whilst also bring to you attention some things you hadn’t previously considered.

Statistics and the internet

I don’t use expensive software such as SAS or SPSS because there is no need – between MS Excel and open source statistical software you can do pretty much any statistical analysis you want to.

(If you do have access to expensive statistical software, then don’t feel bad – make the most of it. We don’t accept arguments from authority, and your analysis won’t carry more weight just because you’re the underdog).

Learning about statistics

Before using whatever software you have to hand, it is best to understand something about statistics. There are lots of good introductory books to statistics that you can find in any half-decent bookshop or library, and Google books will give you a chance to browse these before you commit your hard earned cash to a purchase (or just use Google books and keep your money for something more exciting). Don’t forget that many non-statisticians have learn something about means and medians (sociologists, doctors, engineers, etc), and as a result there are lots of introductory texts aimed at non-specialists that are often good places to start.

Perhaps the best resource available freely on the internet is Gerard E. Dallal’s The Little Handbook of Statistical Practice, which provides a comprehensive introduction to statistics while at the same time being clear and simple to understand. It’s aimed at bioscience researchers and so all the examples are drawn from this discipline, but they are easy to grasp quickly.

Most universities have some sort of introductory materials for their students online, and you can access most of these for free without any problem. Good ones include Glasgow – which has a glossary of statistical terms; Leicester – which goes through examples of how to do statistical tests and is aimed at biologists; and Vassar – which has an introduction and lots of free online calculators you can use.

Finally, NIST provides a very comprehensive introduction to statistics. This is a bit more technical than the others I have listed above, and it does assume that you have access to some reasonably powerful software – but it does cover just about everything. And it’s free!

Finally, don’t forget that if you want to know about a particular topic in statistics you can just search using Google. If you want to know about Normal Probability Plots then search for it, and you will find a host of websites devoted to this subject.

Statistics is not difficult – but you should understand it before using it.

Statistics software

I mentioned about that Vassar’s stats pages have online calculators, and there are many such calculators on the internet that you can use for free.

Index of online calculators: this site has calculators for descriptive statistics, 2-sample Kolmogorov-Smirnov test, Chi-square, Fisher Exact Test, ANOVA. It is very easy to use, and there are explanations of how each test works and how to interpret the results. You can also use it to draw graphs (although once drawn you can’t do much with them as they come off as gs or pdf files).

GraphPad: GraphPad produce statistical software that is very easy to use and you can download demos and use those (they are cheaper than most stats software, but still pricey). They also have an online introduction to statistics, and a range of stats calculators you can use for free.

Statspages: an index of online calculators, although some of the links don’t always work. Handy if you’re looking for a particular test.

Daniel Soper Statistical Calculators: an index of 45 calculators for computing a whole range of things, though you will probably need to know what you’re doing before you try them. Once you do, very easy to use and tastefully presented in black and green.

SOCR: this is a website with a whole range of statistical tests, but I find it fussy and irritating. It also seems to take a long time to load. I avoid it, but you might it useful. Again, it’s free.

A very useful resource is Free Statistics, which will direct you to websites that will teach you about stats, to sources of data, and to free software that you can download. Some of this software requires a reasonably sophisticated knowledge of programming, whilst others are very simple, but the range is impressive and there is something for everyone.

I use PAST, which is a free statistical software package aimed at palaeontologists and provides a wide range of tests. Obviously some of these are not needed for Cinemetrics (you won’t need to study cladistics if you’re doing film studies), but it is incredibly easy to use, and there is an online manual that explains everything. The only downside is that is a pain to enter data into PAST, and so I usually enter data into Excel and then paste it into PAST before running the analysis.

Play

The best thing to do is to get some data and a little understanding and then play with the software. The best way to learn is to try.

Getting data out of cinemetrics

Of course, if you want to use any of the above you need to have some data. The Cinemetrics database has lots, but you need to get it into some useful form before you can do something with it. Here is a simple process of getting the data from those graphs into your software.

Cinemetrics has two parts: the data and the software. When you change the look of the graph on the page of a film to view the cutting swing, the two parts work in unison. You may have noticed that a lot of red text appears when you change the graph and then disappears. That is the data, and if you can separate it from the software that draws the graph you can see it all. To do this save the page to a directory on your computer and then reopen it when you are not connected to the internet (if you’re on a network you will probably have to set your browser to ‘Work Offline,’ which will be under the File menu  the precise details will depend on how your computer is set up). Tell the page to redraw the graph (set the height to 300 and click Redraw). You will see the red data text appear, but because it cannot connect to the software it needs to draw the graph it gets stuck and stays on the screen. Open your spreadsheet software and make the window small enough you that you can see the data from the webpage on the same screen (see Figure 1). You will find that with only a little practice, you can enter the shot lengths very quickly (or you can save what you’re doing and go and have a rest if there are lots of shots). You now have data is a form that is easier to manipulate.

Figure 1 Entering Cinemetrics data for The Lady Lies (1929) into MS Excel

Using Excel

I use Excel 2007 as my main software package. All versions of Excel have a good range of statistical tests, although they are easier to use in the latest versions. The thing with Excel is that you have to remember that it is simple: on the one hand it is easy to use; and on the other, it is not very intelligent and won’t necessarily do things in the easiest way possible. For example, most people don’t know that Excel comes with a statistical analysis package built in – why would they: Excel doesn’t tell them this. In order to install the analysis package you need load the Analysis Toolpack from the Add-ins menu (where this will be depends on the version of Excel you are using, but search the help file for add-ins and you’ll find it). Once installed, the toolpack gives you an automated means of accessing the statistical commands that you normally have to type into the spreadsheet. (Actually using the commands in the spreadsheet is often quicker, easier, and gives you a little bit more control but this depends on how comfortable you are in using the software and statistics. To find out which statistical functions are built into Excel, search the help file for statistical functions). The Analysis Toolpack gives you access to descriptive statistics, ANOVA, z-tests, t-tests, F-tests, correlation, regression, and many other functions, but they are all parametric tests (they depend on the parameters of a distribution and make certain assumptions about the nature of that distribution). If you want nonparametric tests (which make fewer assumptions and don’t rely on parameters), then you can find these in PAST. Alternatively, you can set up your own spreadsheet to do things like the Mann Whitney U test or Kruskal-Wallis ANOVA once you’ve grasped how these tests work and the idiotic way Excel sometimes requires you to do things (see Figure 2).

Figure 2 My spreadsheet for Kruskal-Wallis ANOVA of the films of Terence Davies

Normal Probability Plots

Above I mentioned two types of statistical tests: parametric and nonparametric. It is important to choose the right test to get the most out of your data, and picking the wrong approach may lead you to the wrong conclusion. What distinguishes these two types of tests are the assumptions you can make about the data:

• Parametric tests assume that data is distributed according to an underlying probability distribution (of which there are several, but I’ll only mention a couple here), that data sets have equal and/or independent variances, that the data is at least interval or ratio data, etc. The precise assumptions needed will depend on which test you are using. If the assumptions about the data hold, then parametric tests are more powerful than nonparametric tests.
• Nonparametric tests require fewer assumptions about the nature of the data and do not depend on an underlying probability distribution. They are often referred to as ‘distribution free.’ There is usually a nonparametric equivalent that can be used when a parametric test is inappropriate (for example, Mann Whitney U is the nonparametric equivalent of a t-test for independent samples).

Typically, the distribution of shot lengths in a motion picture is positively skewed with a number of outlying data points: as such, it does not follow a normal distribution. HOWEVER, we could still use parametric tests if the data is normally distributed after a transformation has been applied. Usually, such a transformation involves using logarithms. Once the data has been transformed to its logarithm, we can then run tests to see if the data now follows a normal distribution: if it does, then we say that the data is lognormally distributed. (A random variable is lognormally distributed if its logarithm is normally distributed).

How, then, do we test data to see if it comes from an underlying normal distribution? Well, there are several tests that can be applied: the 1-sample Kolmogorov-Smirnov Test, Shapiro-Wilk*, Cramer-von Mises, Anderson-Darling, Pearson’s Chi-Square*, Jarque-Bera*, and Lillefor’s tests can all be used. (Tests marked * can be found in the PAST software I mentioned above). These tests can be used in varying circumstances – it depends on what you are trying to do.

A simple method which provides both a visual and numerical measure of normality is to use normal probability plots and the probability plot correlation coefficient (PPCC), that I have described elsewhere. (Both PAST and Excel will produce normal probability plots, and PAST also calculates the PPCC). By comparing the value of the PPCC of your data for a specified significance level with the critical value of the PPCC for the size of the dataset you are using you can see if the data is normally distributed: if the observed PPCC is greater than the critical value, then the data is normally distributed; and if it is less than the critical value then it is not normally distributed. For example, The Immigrant (1917) has 159 shots (n = 159): for a sample of this size the critical value of the PPCC is 0.9923. For the untransformed data the observed value of the PPCC is 0.8420 – clearly not normally distributed; and for the data transformed to its common logarithm (log₁₀), the PPCC is 0.9715 – so not lognormally distributed either.

Now, 0.9715 is not much less 0.9923 so maybe there is not a big difference. BUT, in statistics numbers are never just numbers – they have meaning within a specific context. In interpreting the result of the PPCC we need to remember that it comes from a distribution of critical values that is ASYMPTOTIC – that is, it approaches a limit (in this case 1.0) as the sample size grows. It will never actually reach 1, because you can always have a bigger sample, and so the value of the PPCC will get ever close requiring ever more decimal places. Look at Figure 3: this graph plots the critical value (the solid lines) and the observed values (the dotted lines) of the PPCC for His New Job (1915) and Verboten! (1952) using log-transformed data. For His New Job, the observed value is greater than the critical value (the dotted line is to right of the solid line) and so this data is lognormally distributed. For Verboten!, the opposite is true, and the data is not lognormally distrubuted. The sample size (number of shots) is 502 with a critical value of ~0.9971, but the observed value is only 0.9575, which equates to a sample size of only 25. There is only a small numerical difference between 0.9971 and 0.9575, but looking at Figure 3, we can see that actually this is quite a large difference in the context of the asymptotic distribution of the PPCC.

Figure 3 The distribution of the probability plot correlation coefficient for sample sizes n =5 to n = 1000.

What, then, does this mean for The Immigrant? The observed value of the PPCC corresponds to a sample size of ~41, which is nearly four times smaller than the sample size used (n = 159) – a much larger difference than the numbers (if taken at their face value) would appear to imply.

Why is this relevant? In statistics we are estimating outcomes – we rarely know the complete data for any situation, and if we are using the Cinemetrics tool then some error in the data will always be present (you can only press that space bar so quickly in response to observing a cut). If we rely on parametric statistics for assessing shot length distributions when we know the data is not normally or lognormally distributed then we run the risk of saying that there is a difference between two data sets (i.e., the shot lengths of two films) when in fact there isn’t ( a Type I error – false positive), or saying that no difference exists when if fact it does (a Type II error – false negative). Using nonparametric tests is a way around this problem – but will not eliminate the possibility of making an error completely.

I have looked at the PPCC for normal and lognormal distributions of 40 films from the Cinemetrics database, and, while these films cannot be considered a representative sample of the database, half (20) are not lognormally distributed. Some miss their critical value by only  small margin but others miss by quite some distance: Man with a Movie Camera has a lognormal PPCC of 0.9639, which would be the critical value for a sample of size 30 but the film has 1729 shots! Of the six reels for this film, only reel 5 is lognormally distributed. Verboten! is worth a mention here – for most films the PPCC test for untransformed data usually produces a value between 0.7000 and 0.9000, while for this film it is only 0.5495 and this deserves closer attention.

Of course, all this assumes that you want to use frequentist statistics. You could adopt a Bayesian approach …

UK film tax relief, 1992 to 2008

In order to boost investment in the British film industry the Conservative government introduced new tax measures in 1992 in order to ease the cash flow difficulties of producers. Section 42 of the Finance (No.2) Act 1992 introduced tax relief for preliminary expenditure incurred ‘for the purpose of enabling a decision to be taken as to whether or not to make a film,’ and applied only to those films that were qualified, or were likely to qualify, as ‘British’ under Schedule 1 of the Films Act 1985. Section 42 provided for the accelerated write-off of production expenditure over a three-year period for a qualifying film once the film has been completed. Under section 42 it was expected that film producers could generate an immediate cash sum of between four and eight per cent of a film’s budget [1].

However, the Advisory Committee on Film Finance, chaired by Sir Peter Middleton, reported in 1996 that the tax arrangements and definition of a British film were a deterrent to potential investors. The Committee noted that it was widely believed in the British film industry that tax incentives needed to be at least ten per cent of a film’s budget before they had an impact on investment decisions, and, therefore, the relief available under the Finance (No.2) Act 1992 was inadequate [2]. The Committee also cited poor communication between the film and financial sectors as a contributing factor to the limited growth of investment in the film industry after 1992. The Committee proposed that the most effective means of stimulating investment was to introduce a 100 per-cent write-off of production costs in the year that they were incurred, and the establishment of a film finance forum. These recommendations were intended to reduce the risk-reward ratio for investors, and were introduced by the 1997 Labour government.

Section 48 of the Finance (No. 2) Act 1997 introduced a 100 per cent write-off against taxable profits once a film is completed for production and acquisition expenditure on films certified as British that cost £15 million or less to produce. The one-third relief system was retained for films with budgets over £15 million. The 100 per cent write-off was initially introduced for three years, but was extended to July 2005 by Section 72 of the Finance Act 2001. Anne Creigh-Tyte and Barry Thomas suggest that ‘nearly 200 certificates of nationality were issued in the three years since the concession in order for films to qualify as “British,” compared to under 20 per annum in the years from 1993 to 1996’ [3]. In 1999 to 2000 alone, the Department of Culture, Media, and Sport (DCMS) issued certificates to seventy-six ‘British qualifying’ films costing less than £15 million, at an estimated cost of tax relief of £85 million, which, it was claimed, represents ‘over double the amount that would have been available under the previous arrangement’ [4]. HM Treasury’s figures for tax relief for the British film industry in the New Labour puts the total cost at £2 billion (see Table 1).

Table 1 Film tax relief in the United Kingdom, 1997-1998 to 2005-2006 (£ million)

Data from the UK Film Council puts the total number of certifications of British films under Schedule 1 of the Films Act 1985 for the period 1998 to 2006 at 658 with a total expenditure of film production in the UK of £3583.4 million [5]. The £2000 million in tax relief therefore represents approximately 55 per cent of the total UK expenditure on film production. This data does not include co-productions. Over the period 1995 to 2005, goverment subsidies for film production administered through the National Lottery (by the Arts Council from 1995 to 2001 and the UK Film Council between 2000 and 2005) and the British Screen Finance Group from 1996-2000 totalled £448 million [6]. Subisidies have long been a significant part of British film policy, but it is clear that over the past decade it is the tax regime that has become the more significant policy. In 2007, Oxford Economics estimated that without tax relief for films, production in the UK would be 75 per cent smaller [7].

In reporting the figures in Table 1, it was noted that they included the costs of ‘substantial mis-use’ of the tax relief introduced in 1992 and 1997, and these measures were judged to be a failure in a report published by HM Treasury in July2005. This report defined the core objective of film tax relief as being ‘to promote the sustainable production of culturally British films;’ and that underpinning this was the encouragement of the production of films that might otherwise not be made, the promotion of sustainability in British film production, and the maintenance of a ‘critical mass’ of production infrastructure and of creative and technical expertise in the UK to facilitate the production of culturally British films [8]. Section 42 and section 48 did not contribute to this core objective for four reasons. First, producers were unable to predict future income with any certainty and so were unable to anticipate tax benefits in decisions relating to the budget of a film and the location of filming. This led to the development of sale-and-leaseback deals in which producers sold a completed film to a third party and then leased the film back over a fixed period. The third party claimed the tax relief against income from other non-filmmaking activities, while the producer received an upfront payment that could used for production financing. Consequently, the tax relief was shared by the producer and the third party, reducing the benefit to the producer and restricting the level of investment in film production. Second, sale-and-leaseback deals were linked with high levels of tax avoidance. Third, the end of the tax year came to determine production schedules as the number of sale-and-leaseback deals peaked at this time of year as third party investors sought to gain a tax benefit before this deadline expired. Fourth, tax relief under section 42 and section 48 applied to all eligible expenditure on a film and not just expenditure incurred in the UK providing the qualifying criteria were met. ‘Relief tourism’ became an issue as producers would meet only the minimum requirements for certification before moving on to access incentives elsewhere. The UK’s tax relief measures became a disincentive to produce films in the UK and the level of investment in UK-based productions was low [9]. These problems led to a flurry of corrective legislation between 2000 and 2006 aimed primarily at eliminating the use of sale-and-leaseback deals for tax avoidance. The constant revision of tax incentives by the government destabilised the UK production sector; and in 2006 Lord Northbrook, commented that:

The continual cycle of change has produced huge uncertainty in the film industry and has jeopardised some important projects. For instance, I understand that the filming of the latest James Bond film [Casino Royale] has moved to the Czech Republic, so even the Government’s most famous civil servant has moved offshore, partly as a result of the instability caused by changes in the film tax regime [10].

These issues were dealt with in the Finance Act 2006 (sections 31 to 48), which replaced section 42 and section 48, introduced a minimum level of 25 per cent of core expenditure to be incurred in the UK for both certified British films and qualifying co-productions, and ensured that tax benefits were only available to the production company involved in making the film. For films with a budget under £20 million, up to 25 per cent of the total UK expenditure may be claimed as cash rebate; while films with a budget greater than £20 million can claim a rebate of up to 20 per cent. Additionally, tax relief can be claimed on up to 80 per cent of the total qualifying UK spend. The first figures of the tax relief under the 2006 regulations were published by HM Treasury in October 2008, and showed that £104 million of relief for the production of around 100 films from January 2007 to March 2008 [11].

Notes

1. Anne Creigh-Tyte and Barry Thomas, ‘Taxation,’ in Sara Selwood (ed.) The UK Cultural Sector: Profile and Policy Issues. London: Policy Studies Institute, 2001: 131.
2. Advisory Committee on Film Finance, Report to the Secretary of State for National Heritage, July 1996. London: Department of National Heritage, 1996: 33.
3. Creigh-Tyte and Thomas, ‘Taxation:’ 133.
4. Hansard, HC vol. 377, 8 January 2002, cols. 806w-807w.
5. UK Film Council, UK Certification Data Q1 1998 to Q4 2008, http://www.ukfilmcouncil.org.uk/media/excel/h/a/British_film_certification__Jan98_-_Dec08__public_version.xls, accessed 21 May 2009.
6. Hansard, HC vol. 433, 27 February 2006, col. 328w.
7. Oxford Economics, The Economic Impact of the UK Film Industry. Oxford: Oxford Economics, 2007: 6.
8. HM Treasury, Reform of Film Tax Incentives: Promoting the Sustainable Production of Culturally British Films. London: HM Treasury, 2005.
9. For a more detailed discussion of these issues see HM Treasury, Reform of Film Tax Incentives: 12-15.
10. Hansard, HL vol. 684, 17 July 2006: col. 1065.
11. HM Treasury, Film tax relief supporting UK film industry, http://www.hm-treasury.gov.uk/press_109_08.htm, accessed 20 May 2009.

Power functions and the mean relative frequency of shot scales in motion pictures

UPDATE (26 October 2009): This post has been getting a lot of traffic recently, and I think it is important to point the reader in the direction of a follow up post (here) where I point out that while the use of rank-frequency plots is useful for analysing film style (see here, for example) the power laws approach is not. This is not to say that films or groups of films will not be described by a non-linear power model, but other non-linear models (logarithmic, exponential) are also evident and there does not seem to be any general rule for which model can be applied to specific types of films (e.g. genres, eras, etc.). In general, a power laws approach to the distribution of shot scales is not going to get you anywhere – certainly not to the extent that I suggest in this post. I’ll leave this post here because there is still some useful information, and it’s also nice to see how wrong you can be.

Power functions describe a wide range of social phenomena, from the distributions of city size to the popularity of websites, the citations of academic papers, and the frequency of words in the corpus of a language (Schroeder 1991: 33-38, 103-119; Newman 2005). While power functions have been used for over half a century in analysing language and communication (e.g. Zipf’s law) they have yet to be applied to the empirical analysis of film style. This brief survey looks at the applicability of power functions in describing the distribution of the mean relative frequency of shot scales in the films of two directors – Alfred Hitchcock and Fritz Lang – whose careers encompass both European and Hollywood filmmaking.

Data on the frequency of shot scales was collected from the Cinemetrics database. Seven shot scales were used – big close-up, close-up, medium close-up, medium shot, medium-long shot, long shot, and very long shot (see Salt 2006). The relative frequency of shot scales in a motion picture was calculated by dividing the frequency with which each scale occurred by its normalising value (~500), and this data was then ranked from the event of the highest frequency to the lowest. The average value of the seven ordered relative frequencies was taken to give the mean relative frequency of each shot scale, thereby removing the problem of films that have zero frequency for a particular shot size. This data was then used to calculate predicted values for linear regression (f(x) = ax+b, where a is the slope and b is the intercept of the regression line) and power regression (f(x) = cx^α, where c is a constant equal to the frequency of the most frequently occurring scale and α is the exponent of the distribution such that Σ f(x) = 1). The coefficient of determination (R²) was used as a measure of goodness-of-fit for the predicted mean relative frequencies to the empirically observed values.

Data on shot scales was taken from the Cinemetrics database for 43 films directed by Alfred Hitchcock between 1925 and 1963, of which 23 were produced in the UK between 1925 and 1939; and from 21 films directed by Fritz Lang between 1919 and 1955, of which 11 were produced in Germany between 1919 and 1933. The results are presented in Table 1, and show that power regression provides the better model only for Lang’s German films, while for the other classes the linear model is superior. These results can be seen clearly in Figures 1-4, in which the observed values and the linear and power regression lines are plotted on linear axes. (The power regression line is straight when the log rank is plotted against the log frequency).

Table 1 Linear and power regression for the films of Alfred Hitchcock and Fritz Lang

Table 1 also shows that while the distribution of the mean relative frequencies of shot scales in the films of Alfred Hitchcock are consistent for his British and Hollywood films, there is a change in Lang’s style in his shift from Germany to Hollywood. It is also worth noting the similarity in the figures of R² for both linear and power regression for Hitchcock’s and Lang’s Hollywood films, which suggests that both filmmakers are working within a consistent institutional style (such as classical Hollywood cinema) rather than auteurist idiosyncrasies. The value c is the mean relative frequency with a rank of 1, and this too is similar for Hitchcock and Lang’s Hollywood films, reinforcing the idea of an institutional style. This does not, however, account for why Hitchcock’s British films are so similar to his Hollywood movies. It is possible that German cinema in the 1920s was different from British and Hollywood cinema in general, and that British films style was influenced by Hollywood, but a larger scale study is needed to resolve these questions.

Figure 1 Linear and power regression for the mean relative frequency (MRF) of shot scales in Alfred Hitchcock British films, 1925-1939

Figure 2 Linear and power regression for the mean relative frequency (MRF) of shot scales in Alfred Hitchcock’s Hollywood films, 1940-1963

Figure 3 Linear and power regression for the mean relative frequency (MRF) of shot scales in Fritz Lang’s German films, 1919-1933

Figure 4 Linear and power regression for the mean relative frequency (MRF) of shot scales in Fritz Lang’s Hollywood films, 1936-1956

Table 1 also shows that while the distribution of the mean relative frequencies of shot scales in the films of Alfred Hitchcock are consistent for his British and Hollywood films, there is a change in Lang’s style in his shift from Germany to Hollywood. It is also worth noting the similarity in the figures of R² for both linear and power regression for Hitchcock’s and Lang’s Hollywood films, which suggests that both filmmakers are working within a consistent institutional style (such as classical Hollywood cinema) rather than auteurist idiosyncrasies. The value c is the mean relative frequency with a rank of 1, and this too is similar for Hitchcock and Lang’s Hollywood films, reinforcing the idea of an institutional style. This does not, however, account for why Hitchcock’s British films are so similar to his Hollywood movies. It is possible that German cinema in the 1920s was different from British and Hollywood cinema in general, and that British films style was influenced by Hollywood, but a larger scale study is needed to resolve these questions.

This survey has shown that while power functions can be used to describe the distribution of mean relative frequencies of shot scales in motion pictures, they cannot be applied universally and linear functions may provide a better means of modelling film style. These distributions may be used as a measure of film style in order to distinguish between different groups of films. However, this approach cannot tell us what changes in the use of shot scales have occurred. It is necessary, then, to look more closely at where the continuities and discontinuities lie. As I have shown elsewhere, in there is a shift in the use of particular shot scales in the films in the films of Hitchcock and Lang when they arrive in Hollywood (Redfern 2009, unpublished). For both directors we find that there is a shift from distant shots to closer framing. Armed with the knowledge that different regression models explain the distribution of the mean relative frequencies of shot scales for Hitchcock and Lang prior to their arrival in Hollywood, we can extend this argument to state that: (1) Hitchcock’s Hollywood films feature closer framing than his British films, but there is no change in the distribution of the mean relative frequencies of the scale overall; and, (2) Lang’s Hollywood films feature a large change in the distribution of the mean relative frequencies as well as a shift to closer framing. In Lang’s German films it is the long shot that dominates, while in his Hollywood films there is no single shot scale that determines the films’ style.

References

Newman, M.E.J. (2005) Power laws, Pareto distributions, and Zipf’s law, Contemporary Physics 46 (5): 323-351.

Redfern, N. (2009) Shot scales in the films of Fritz Lang.

Redfern, N. (unpublished) Cinemetric analysis of shot types in the films of Alfred Hitchcock.

Salt, B. (2006) Moving into Pictures: More on Film History, Style, and Analysis. London: Starwood.

Schroeder, M. (1991) Fractals, Chaos, and Power Laws: Minutes from an Infinite Paradise. New York: W.H. Freeman & Co.

Emotion, Genre, and the Hollywood Paranoid Film

This piece was originally presented as a paper at the New Nightmares Conference at Manchester Metropolitan University in April 2008. I am currently working on an expanded version which will look more closely at anxiety in The X-Files, while also examining the role of anxiety in some other Hollywood genres.

The mental flow model of the viewer’s experience of the cinema proposed by Torben Grodal (1997, 1999, 2004) takes film genres to be forms constructed in order to evoke characteristic emotions that are intimately connected to generic themes and narrative structures. In this model, the horror film evokes the emotion ‘fear,’ producing autonomic responses of crying, shivering, and screaming in the viewer. Paranoia is frequently cited as a recurrent generic theme of the horror film (e.g., Orr 2000, Pratt 2001); and Kim Newman identifies a sub-genre of the ‘paranoid horror’ film in which ‘the Establishment is a monolithic, all-encompassing Evil’ (1988: 79). Grodal (1997: 172-173, 250-252) also links horror and paranoia. In this paper I argue that characteristic emotion of paranoia is not fear but anxiety, and films that evoke this emotional state in the viewer demonstrate numerous differences from the horror genre. These differences are evident in the type of emotional responses experienced by the viewer and the level of their intensity, as well as in the generic themes, and narrative structure of the paranoid film. The purpose of this paper is to distinguish paranoid films from the horror genre in Hollywood cinema, and I utilise the mental flow model in drawing this distinction. Consequently, this paper is at once an implementation of this model and a refinement of it.

The mental flow model of the film experience

Cognitive approaches to emotion in the cinema reject romantic and psychoanalytical theories of emotion as irrational negations of reality to assert that cognitions and emotions work together in allowing us to evaluate our environment and as a basis for adaptive behaviours. Emotions are action tendencies that require cognition to recognise the cause of emotions and to evaluate appropriate motor responses. Emotions are structured states that consist of physiological changes, feelings, and thinking; and have a particular object as their focus or target (Plantinga and Smith 1999).

For Grodal, the viewer’s experience of a film must be described as a temporal flow that proceeds from perception to (simulated) motor actions and is mediated by innate emotional functions and cognitive schemata. There are three modes of emotional functions: the telic mode consists of voluntary, goal-directed actions and thoughts; the paratelic mode consists of experiences, actions, and thoughts that take place without a specific goal; and the autonomic mode, which consists of non-voluntary emotional responses and is activated when the subject is unable to exert control over his or her situation. The activation of these modes is related to the forward flow of the narrative so that there is ‘a systemic relation between embodied mental processes and configurations activated in a given type of visual fiction and the emotional “tone” and “modal qualities” of the experienced affects, emotions, and feelings in the viewer’ (Grodal 1997: 3). The forward flow of narrative events in the diegetic world of a film guides the viewer through a sequence of emotional reactions, and is structured by canonical narratives that ‘consist of one or several central [characters], a series of emotion-evoking conditions, and a series of actions to alter conditions and to evoke preferred states’ (Grodal 1999: 137). In this model, the main film genres are based on innate features of mind and body, which presuppose specific mental mechanisms as their necessary (but not sufficient) conditions for their existence. Consequently, genres – as prototypical narratives – are constructed in such a way as to evoke a characteristic emotion (see Table 1).

Table 1 Some genres and their characteristic emotions (Grodal 2004)

An example of the cognitive approach to emotion and genre is the horror film, which may be described as a prototypical narrative in which a character (or characters) is confronted with a hypernatural antagonist producing autonomic responses that are transformed into telic emotional states that are the basis for (simulated) motor actions leading to the destruction of the antagonist. The viewer identifies with a character that is initially marked by an inability to act when confronted with a monstrous threat. This antagonist is hypernatural, deviating from norms of behaviour, the laws of physics, the known facts of history, and is usually supernatural or possesses seemingly superhuman qualities. Faced with such a threat, the character – and by identification, the viewer – experiences a state of paralysis, in which the inability to act triggers responses of shivering, trembling, crying, vaso-motor constrictions, breaking out in goose-pimples, and other autonomic responses (Grodal 1997: 172). This inability to act gives way to a telic mode of experience as the character overcomes the cognitive dissonances created by the antagonist to understand the nature of the threat encountered, devises a pan to eliminate that threat, and successfully executes it. The narrative structure of the horror film guides the viewer through a progression from autonomic to telic modes of experience, from the unwanted emotional state of fear to the preferred emotional state of safety. Dracula (Tod Browning, 1931), for instance, is a prototypical horror narrative, in which the heroes are confronted with an immortal vampire capable of changing his physical form and therefore represents a hypernatural threat, and can only be destroyed once the lore of the vampire has been understood and a plan of action based on symbolic-ritual codes of behaviour is carried out.

Paranoia and anxiety

Anxiety as an emotional state can be distinguished from fear – though it is not always easy to do so (Edelmann 1995). Like fear, anxiety is a state characterised by ‘subjective, consciously perceived feelings of tension and apprehension, and heightened autonomic nervous system activity’ (Spielberger et al. 1970: 3); but where fear is a rational response to a specific and identifiable threat producing intense emergency reactions that recede with the removal of the threat, anxiety is ‘the tense anticipation of a threatening but vague event; a feeling of uneasy suspense … In its purest form anxiety is diffuse, objectless, unpleasant, and persistent. … Anxiety is a state of heightened vigilance rather than an emergency reaction’ (Rachman 1998: 2-3). The differences between fear and anxiety are summarised in Table 2.

Table 2 Characteristics of fear and anxiety (Rachman 1998)

Paranoia is ‘a mental disorder characterised by the presence of persistent non-bizarre delusions of persecutory, grandiose, or other self-referential content’ (Scheuer 2001: 1134). This content typically takes the form of a perceived ‘loss of autonomy, the conviction that someone’s actions are being controlled by someone else, or that one has been “constructed” by powerful, external agents’ (Melley 2000: vii), and reflects an uncertainty about the causes of individual action in the face of a persistent but vague threat to the self. Consequently, paranoia is characterised by an emotional state of anxiety.

The Hollywood paranoid film

The distinction between the emotional states of fear and anxiety makes it possible to nuance the mental flow model, and to make a distinction between the horror genre characterised by fear and the paranoid film characterised by anxiety. The differences between the Hollywood paranoid film and the horror genre are evident in the level of intensity of emotional responses experienced by the viewer, the generic themes of the paranoid film, and the structure of the narrative. In the paranoid film, the unwanted emotional state of anxiety is not dispelled, and, unlike the horror film, autonomic responses do not give way to telic modes of experience. These differences are summarised in Table 3. Examples of these differences can be identified in Enemy of the State (1998) and The X-Files (1998).

Table 3 The horror film and the paranoid film

Enemy of the State

In Enemy of the State, Robert Clayton Dean (played by Will Smith), a Washington, D.C., attorney finds himself in the midst of a frantic search for a recording of the assassination of a U.S. congressman (Jason Robbards) he is unaware he possesses. The film presents a vision of how anyone – accidently and innocently – can be become the victim of a conspiracy, and in doing so presents the viewer with a diegetic world in which seemingly everyday objects become sources of anxiety. Tracking and bugging devices are planted inside shoes, pagers, pens, etc. Telephones are tapped and private conversations recorded without the knowledge of the participants. The film has numerous action sequences, but derives its emotional impact from the suggestion that this could – indeed, very well might – happen to the viewer. The film is thus characterised by a pervasive sense of unease, producing apprehension, tension, and autonomic responses albeit without the emotional outbursts of crying, shivering, etc. The emotional response of the viewer is then similar to that experienced in watching a horror film, but has a lower level of intensity.

This lower level of intensity is, in part, generated by Dean’s lack of awareness of the dangerous circumstances in which he finds himself – repeatedly throughout the film he states that he does not know why he is being pursued. The viewer, however, is aware of the nature of the conspiracy, and tensely anticipates the dangers that will befall Dean. It is this tense anticipation that produces a state of anxiety, and is predicated on the viewer’s identification with Dean as the hero and this excess of narrative knowledge. The viewer is thus placed in a position of hypervigilance. This state of anxiety is not transformed into telic modes of action until the very end of the film, and then only in a limited fashion. With the assistance of an anonymous former conspirator, Brill (Gene Hackman), Dean is able to eliminate the immediate threat posed by his pursuers. However, the central issue of the film is not resolved. The threat of the unregulated and nightmarish applications of surveillance technologies and information gathering systems remains. The narrative thus remains unresolved, and the closing shots of the film echo the opening titles with shots of a satellite orbiting the earth, gathering information, and a montage of surveillance images. The threat posed by the conspirators is only temporary, and is only the visible threat – the unseen surveillance technologies (e.g., satellites, computer tracking systems, directional microphones, etc) remain invisible and remain a threat to society. The need for hypervigilance on the part of the viewer continues, but is rendered problematic by the failure of the film to provide a solution to the regulation of surveillance.

The X-files

In The X-files, FBI Agents Mulder (David Duchovny) and Scully (Gillian Anderson) investigate the apparent cover-up of the existence of extra-terrestrial life on earth. The film presents an alternative history of the Earth in which aliens arrived on the planet millions of years ago, and which challenges the viewer’s knowledge of the history of civilisation. This questioning of accepted knowledge is a theme that is continued throughout the film, as the viewer is forced to reassess the significance of narrative events according to the interpretations put forward by various characters – the destruction of a federal office building, for example, is either an act of domestic terrorism or the cover up of bodies infected with an alien virus. This frustrates the forward flow of the narrative, blocking the narrative drive of the film. In contrast to Enemy of the State, where anxiety is the product of the viewer’s knowledge exceeding that of Dean, in The X-Files it is the product of cognitive dissonances that cannot be fitted into narrative schemata. As Mulder observes at the end of the film, ‘They’ll never believe you – not unless your story can be easily programmed, categorised, or easily referenced.’

The capacity for voluntary modes of behaviour is again transposed to the field of spectacular action – for example, the escape from the research laboratory or Mulder’s rescue of Scully from the alien ship – and not the resolution of the narrative. As in Enemy of the State, the nature of the conspiracy at the heart of the film remains elusive. It is difficult in this film to determine who or what the antagonist is. The aliens are never clearly presented on the screen, and we learn nothing of their nature or their goals – there is no ‘alien lore’ that can form the basis for the heroes’ destruction of the enemy. The conspirators lack personality, and are identified only by vague descriptions (e.g., the cigarette-smoking man, the well-manicured man, etc.), and the film provides only tantalising glimpses that the conspiracy even exists. The viewer is thus left in a state of confusion with their paranoid suspicions intact, but without the possibility of a resolution. Again, the ending to this film – a hearing to determine the reasons behind the destruction of the office building – mirrors an earlier scene, and explicity rejects the narrative of the Agent’s investigation in the absence of ‘hard evidence.’ The conclusion of the film sees Agents Mulder and Scully reassigned to the X-files in order to investigate inexplicable phenomena, and the reconstitution of the conspiracy at a new location, creating a looped and unending narrative structure that is common to paranoid thrillers (e.g., The Parallax View, 1974).

Conclusion

In the early-1970s, Tony Tanner wrote that ‘the possible nightmare of being controlled by unseen agencies and powers is never far away in contemporary American fiction’ (1971: 15, my emphasis). It is certainly the case that paranoia has been a constant element of American popular culture in the post-war era, but while the paranoid scenarios of Enemy of the State and The X-files might be described as nightmarish they should not be seen as a part of the horror genre. Applying the mental flow model to these films, it is possible to identify a genre of the paranoid film in which the characteristic emotion evoked is anxiety, and which shows marked differences from the horror film.

Filmography

Enemy of the State (Touchstone Pictures\Jerry Bruckheimer Films\Scott Free, 1998) prod. Jerry Bruckheimer, dir. Tony Scott, wr. David Marconi, ph. Daniel Mindel, ed. Chris Lebenzon, m. Harry Gregson-Williams, Trevor Rabin. Cast: Will Smith (Robert Clayton Dean), Gene Hackman (Brill), John Voight (Reynolds), Lisa Bonet (Rachael Banks), Regina King (Carla Dean), Loren Dean (Hicks), Barry Pepper (Pratt), Ian Hart (Bingham), Stuart Wilson (Congressman Albert).

The X-Files (Twentieth Century-Fox, Ten Thirteen Productions, 1998) prod. Chris Carter, Daniel Sackheim, dir. Rob Bowman, wr. Chris Carter, Frank Spotnitz, ph. Ward Russell, ed. Stephen Mark, m. Mark Snow. Cast: David Duchovny (Special Agent Fox Mulder), Gillian Anderson (Special Agent Dana Scully), John Neville (Well-manicured Man), William B. Davis (Cigarette-smoking Man), Martin Landau (Alvin Kurtzweil, MD), Mitch Pileggi (Assistant Director Walter Skinner).

References

Edelmann, R.J. (1995) Anxiety: Theory, Research, and Intervention in Clinical and Health Psychology. Chichester: John Wiley and Sons.

Grodal, T. (1997) Moving Pictures: A New Theory of Film Genres, Feeling, and Cognition. Oxford: Clarendon Press.

Grodal, T. (1999) Emotions, cognition, and narrative patterns in film, in C. Plantinga and G.M. Smith (eds.) Passionate Views: Film, Cognition, and Emotion. Baltimore and London: Johns Hopkins University Press: 127-145.

Grodal, T. (2004) Frozen flows in von Trier’s oeuvre, in T. Grodal, B. Larsen, and I.T. Laursen (eds.) Visual Authorship: Creativity and Intentionality in Media. Copenhagen: Museum Tusculanum Press\University of Copenhagen: 129-167.

Melley, T. (2000) Empire of Conspiracy: The Culture of Post-war paranoia in Post-war America. Ithaca: Cornell University Press.

Newman, K. (1988) Nightmare Movies: A Critical History of the Horror Movies. New York: Harmony Books.

Orr, J. (2000) The Art and Politics of Film. Edinburgh: Edinburgh University Press.

Plantinga, C. and Smith, G.M. (1999) Introduction, in C. Plantinga and G.M. Smith (eds.) Passionate Views: Film, Cognition, and Emotion. Baltimore and London: The Johns Hopkins University Press: 1-17.

Pratt, R. (2001) Projecting Paranoia: Conspiratorial Visions in American Film. Lawrence, Kan.: University Press of Kansas.

Rachman, S. (1998) Anxiety. Hove: Psychology Press.

Scheuer, A.D. (2001) Paranoia, in The Corsini Encyclopedia of Psychology and Behavioural Science – Volume Three, third edition, edited by W.E. Craighead and C.B. Nemeroff. New York: John Wiley & Sons: 1133-1135.

Spielberger, C.D., Gorush, R.L., and Lushene, R.E. (1970) Manual for State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychology Press.

Tanner, T. (1971) City of Words. London: Jonathan Cape.