1. 2010 CRC PhD Student Conference
Investigating narrative ‘effects’: the case of suspense
Richard Doust, richard.doust@free.fr
Supervisors Richard Power, Paul Piwek
Department/Institute Computing
Status Part-time
Probation viva Before
Starting date October 2008
1 Introduction
Just how do narrative structures such as a Hitchcock film generate the well-known feeling known as suspense ? Our
goal is to investigate the structures of narratives that produce various narrative effects such as suspense, curiosity,
surprise. The fundamental question guiding this research could be phrased thus:
What are the minimal requirements on formal descriptions of narratives such that we can capture these
phenomena and generate new narratives which contain them ?
Clearly, the above phenomena may depend also on extra-narrative features such as music, filming angles, and so
on. These will not be our primary concern here. Our approach consists of two main parts:
1. We present a simple method for defining a Storybase which for our purposes will serve to produce different
‘tellings’ of the same story on which we can test our suspense modelling.
2. We present a formal approach to generating the understanding of the story as it is told, and then use the
output of this approach to suggest an algorithm for measuring the suspense level of a given telling of a story.
We can thus compare different tellings of a story and suggest which ones will have high suspense, and which
ones low.
2 Suspense
2.1 Existing definitions
Dictionary definitions of the word ’suspense’ suggest that there really ought to be several different words for what
is more like a concept cluster than a single concept. The Collins English dictionary gives three definitions:
1. apprehension about what is going to happen. . .
2. an uncertain cognitive state; "the matter remained in suspense for several years" . . .
3. excited anticipation of an approaching climax; "the play kept the audience in suspense" anticipation, ex-
pectancy - an expectation.
Gerrig and Bernardo (1994) suggest that reading fiction involves constantly looking for solutions to the plot-based
dilemmas faced by the characters in a story world. One of the suggestions which come out of this work is that
suspense is greater the lower the number of solutions to the hero’s current problem that can be found by the reader.
Cheong and Young’s (2006) narrative generating system uses the idea that a reader’s suspense level depends on
the number and type of solutions she can imagine in order to solve the problems facing the narrative’s preferred
character.
Generally, it seems that more overarching and precise definitions of suspense are wanting in order to connect
some of the above approaches. The point of view we will assume is that the principles by which literary narratives
are designed are obscured by the lack of sufficiently analytical concepts to define them. We will use as our starting
point work on stories by Brewer and Lichtenstein (1981) which seems fruitful in that it proposes not only a view of
suspense, but also of related narrative phenomena such as surprise and curiosity.
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2.2 Brewer and Lichtenstein’s approach
In Brewer and Lichtenstein (1981) propose that there are three major discourse structures which account for the
enjoyment of a large number of stories: surprise, curiosity and suspense. For suspense, there must be an initiating
event which could lead to significant consequences for one of the characters in the narrative. This event leads to
the reader feeling concern about the outcome for this character, and if this state is maintained over time, then the
reader will feel suspense. As Brewer and Lichtenstein say, often ‘additional discourse material is placed between
the initiating event and the outcome event, to encourage the build up of suspense’ (Brewer and Lichtenstein, 1981,
p.17).
Much of the current work can be seen as an attempt to formalise and make robust the notions of narrative
understanding that Brewer laid out. We will try to suggest a model of suspense which explains, for example, how
the placing of additional material between the initiating event and the outcome event increases the suspense felt in
a given narrative. We will also suggest ways in which curiosity and surprise could be formally linked to suspense.
We also hope that our approach will be able to shed some light on the techniques for creating suspense presented
in writer’s manuals.
3 The storybase
3.1 Event structure perception
Our starting point for analysing story structure is a list of (verbally described) story events. Some recent studies
(Speer, 2007) claim that people break narratives down into digestible chunks in this way. If this is the case, then
there should expect to discover commonalities between different types of narrative (literature, film, storytelling)
especially as regards phenomena such as suspense. One goal of this work is to discover just these commonalities.
3.2 Storybase : from which we can talk about variants of the ’same’ story.
One of the key points that Brewer and Lichtenstein make is that the phenomena of suspense depends on the order
in which information about the story is released, as well as on which information is released and which withheld.
One might expect, following this account, that telling ‘the same story’ in two different ways might produce different
levels of suspense.
In order to be able to test different tellings of the same story, we define the notion of a STORYBASE. This
should consist of a set of events, together with some constraints on the set. Any telling of the events which obeys
these constraints should be recognised by most listeners as being ‘the same story’. We define four types of link
between the members of the set of possible events:
• Starting points, Event links, Causal constraints, Stopping points.
The causal constraints can be positive or negative. They define, for example, which events need to have been
told for others to now be able to be told. Our approach can be seen as a kind of specialised story-grammar for
a particular story. The grammar generates ‘sentences’, and each ‘sentence’ is a different telling of the story. The
approach is different to story schemas. We are not trying to encode information about the world at this stage, any
story form is possible. With this grammar, we can generate potentially all of the possible tellings of a given story
which are recognisably the same story, and in this way, we can test our heuristics for meta-effects such as suspense
on a whole body of stories.
4 Inference
4.1 Inference types
To model the inferential processes which go on when we listen to or read a story, or watch a film, we define three
types of inference:
1. Inference of basic events from sensory input : a perceived action in the narrative together with an ‘event
classifier module’ produces a list of ordered events.
2. Inferences about the current state of the story (or deductions).
3. Inferences about the future state of the story (or predictions).
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Clearly these inferential processes also rely on general knowledge about about the world or the story domain, and
even about stories themselves.
So, for each new story event we build up a set of inferences STORYSOFAR of these three types. At each new
story event, new inferences are generated and old inferences rejected. There is a constant process of maintenance
of the logical coherence of the set of inferences as the story is told. To model this formally, we create a set of
‘inferential triples’ of the form: “if X and Y then Z” or X.Y->Z, where X, Y, and Z are Deductions or Predictions.
5 Measuring suspense
5.1 A ‘suspense-grammar’ on top of the storybase
To try to capture phenomena such as suspense, curiosity and surprise, we aim to create and test different algorithms
which take as their input the generated story, together with the inferences generated by the triples mentioned above.
A strong feature of this approach is that we can test our algorithms on a set of very closely related stories which
have been generated automatically.
5.2 Modelling conflicting predictions
Our current model of suspense is based on the existence of conflicting predictions with high salience. (This notion
of the salience of a predicted conflict could be defined in terms of the degree to which whole sets of following
predictions for the characters in the narrative are liable to change. For the moment, intuitively, it relates to how
the whole story might ‘flow’ in a different direction.) For the story domain, we construct the set INCOMP of pairs
of mutually conflicting predictions with a given salience:
INCOMP = { (P1,NotP1,Salience1), (P2,NotP2,Salience2), . . . }
We can now describe a method for modelling the conflicting predictions triggered by a narrative. If at time T, P1
and NotP1 are members of STORYSOFAR, then we have found two incompatible predictions in our ‘story-so-far’.
5.3 The predictive chain
We need one further definition in order to be able to define our current suspense measure for a story. For a given
prediction P1, we (recursively) define the ’prediction chain’ function C of P1:
C(P1) is the set of all predicted events P such that P.y -> P’ where P’ is a member of C(P1) for some
y.
5.4 Distributing salience as a rough heuristic for modelling suspense in a narrative
Suppose we have a predicted conflict between predictionA and predictionB which has a salience of 10. In these
circumstances, it would seem natural to ascribe the salience of 5 to each of the (at least) two predicted events
predictionA and predictionB which produce the conflict. Now suppose that leading back from predictionA there is
another predictionC that needs to be satisfied for the predictionA to occur. How do we spread out the salience of
the conflict over these different predicted events ?
5.5 A ’thermodynamic’ heuristic for creating a suspense measure
A predicted incompatibility as described above triggers the creation of CC(P1,P2,Z), the set of two causal chains
C(P1) and C(P2) which lead up to these incompatible predictions. Now, we have :
CC(P1,P2,Z) = C(P1) + C(P2)
To determine our suspense heuristic, we first find the size L of CC(P1,P2,Z). And at each story step we define the
suspense level S in relation to the conflicting predictions P1 and P2 as S = Z / L. Intuitively, one might say that
the salience of the predicted incompatibility is ’spread over’ or distributed over the relevant predictions that lead up
to it. We can call this a ‘thermodynamic’ model because it is as if the salience or ‘heat’ of one predicted conflicting
moment is transmitted back down the predictive line to the present moment. All events which could have a bearing
on any of the predictions in the chain are for this reason subject to extra attention.
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If the set of predictions stays the same over a series of story steps, and in a first approximation, we assume that
the suspensefulness of a narrative is equivalent to the sum of the suspense level of each story step, then we can say
that the narrative in question will have a total suspense level S-total relative to this particular predicted conflict of
S-total = Z/L + Z/(L-1) + Z/(L-2) + . . . + Z/L
as the number of predictions in CC(P1,P2,Z) decreases each time a prediction is either confirmed or annulled. To
resume we can a working definition of suspense as follows:
5.6 Definition of suspense
Definition : the suspense level of a narrative depends on the salience of predicted con-
flicts between two or more possible outcomes and on the amount of story time that these
predicted conflicts remain unresolved and ‘active’.
From this definition of suspense we would expect two results:
1. the suspense level at a given story step will increase as the number of predictions necessary to be confirmed
leading up to the conflict decreases, and
2. the way to maximise suspense in a narrative is for the narrative to ‘keep active’ predicted incompatibilities
with a high salience over several story steps.
In fact, this may be just how suspenseful narratives work. One might say,
suspenseful narratives engineer a spreading of the salience of key moments backwards in
time, thus maintaining a kind of tension over sufficiently long periods for emotional effects
to build up in the spectator.
6 Summary
We make two claims:
1. The notion of a storybase is a simple and powerful to generate variants of the same story.
2. Meta-effects of narrative can be tested by using formal algorithms on these story variants. These algorithms
build on modelling of inferential processes and knowledge about the world.
7 References
• Brewer, W. F. (1996). The nature of narrative suspense and the problem of rereading. In P. Vorderer,
H. J. Wulff, and M. Friedrichsen (Eds.), Suspense: Conceptualizations, theoretical analyses, and empirical
explorations. Mahwah, NJ: Lawrence Erlbaum Associates. 107-127.
• Brewer, W.F., and Lichtenstein, E. H. (1981). Event schemas, story schemas, and story grammars. In J.
Long and A. Baddeley (Eds.), Attention and Performance IX. Hillsdale, NJ: Lawrence Erlbaum Associates.
363-379.
• Cheong, Y.G. and Young, R.M. 2006. A Computational Model of Narrative Generation for Suspense. In
Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness: Papers from the 2006
AAAI Workshop, ed. Hugo Liu and Rada Mihalcea, Technical Report WS-06-04. American Association for
Artificial Intelligence, Menlo Park, California, USA, pp. 8- 15.
• Gerrig R.J., Bernardo A.B.I. Readers as problem-solvers in the experience of suspense (1994) Poetics, 22 (6), pp. 459-
472.
• Speer, N. K., Zacks, J. M., & Reynolds, J. R. (2007). Human brain activity time-locked to narrative event
boundaries. Psychological Science, 18, 449-455.
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