Cognition 110 (2009) 265–272
Contents lists available at ScienceDirect
Cognition
journal homepage: www.elsevier.com/locate/COGNIT
Brief article
Categorizing moving objects into film genres: The effect of animacy
attribution, emotional response, and the deviation from non-fiction
Valentijn T. Visch a,*, Ed S. Tan b
a
b
Industrial Design, Technical University Delft., Department of Arts, Vrije Universiteit Amsterdam, The Netherlands
Amsterdam School of Communications Research, University of Amsterdam, The Netherlands
a r t i c l e
i n f o
Article history:
Received 13 July 2006
Revised 10 October 2008
Accepted 24 October 2008
Keywords:
Genre
Movement
Emotion
Animacy
Categorization
Motion picture
.
a b s t r a c t
The reported study follows the footsteps of Heider, and Simmel (1944) [Heider, F., & Simmel, M. (1944). An experimental study of apparent behavior. American Journal of Psychology, 57, 243–249] and Michotte (1946/1963) [Michotte, A. (1963). The perception of
causality (T.R. Miles & E. Miles, Trans.). London: Methuen (Original work published
1946)] who demonstrated the role of object movement in attributions of life-likeness to
figures. It goes one step further in studying the categorization of film scenes as to genre
as a function of object movements.
In an animated film scene portraying a chase, movements of the chasing object were systematically varied as to parameters: velocity, efficiency, fluency, detail, and deformation.
The object movements were categorized by viewers into genres: non-fiction, comedy,
drama, and action. Besides this categorization, viewers rated their animacy attribution
and emotional response. Results showed that non-expert viewers were consistent in categorizing the genres according to object movement parameters. The size of its deviation
from the unmanipulated movement scene determined the assignment of any target scene
to one of the fiction genres: small and moderate deviations resulted in categorization as
drama and action, and large deviations as comedy. The results suggest that genre classification is achieved by, at least, three distinct cognitive processes: (a) animacy attribution,
which influences the fiction versus non-fiction classification; (b) emotional responses,
which influences the classification of a specific fiction genre; and (c) the amount of deviation from reality, at least with regard to movements.
Ó 2008 Elsevier B.V. All rights reserved.
1. Introduction
The reported study follows the footsteps of Heider and
Simmel (1944) who demonstrated the role of object movement in the attributions of life-likeness to figures. It goes
one step further in studying the categorization of film
scenes as to genre as a function of object movements. We
presented two animated abstract blocks involved in a
chase, varying five parameters of movement, and registered: (1) animacy rating, (2) emotional responses, and
* Corresponding author.
E-mail address: [email protected] (V.T. Visch).
0010-0277/$ - see front matter Ó 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.cognition.2008.10.018
(3) genre categorization as non-fiction, drama, action, and
comedy.
The importance of genre knowledge and recognition for
film viewers seems uncontested. Genre recognition primes
(Roskos-Ewoldsen, Roskos-Ewoldsen, & Dillman-Carpentier, 2002) and directs (Zwaan, 1994) attention, affects
modes of perception (Hawkins et al., 2005), generates narrative expectations (Grodal, 1997) and emotions (Smith,
2003; Tan, 1996; Zillmann, 1988). Genres can be accurately
recognized by stylistic features as Hayward (1994) showed
for the literature, Dalla Bella and Peretz (2005) for music
and Visch and Tan (2007, 2008) for film. However, the particular attributions and cues viewers use to arrive at a
genre classification remain unclear. This study focuses on
266
V.T. Visch, E.S. Tan / Cognition 110 (2009) 265–272
the role of perceptual movement characteristics, animacy
attribution, and emotional response in genre categorization. Four major genres were selected namely comedy, drama, action, and non-fiction. Originating in mythology
(Frye, 1957) and known at least since Aristotle ( Trans.,
1988), these genres have stood significant historical period
and media shifts.
It may be assumed that in categorizing film as to genre,
viewers use attributions to objects in motion at various
levels of complexity. At a low level, animacy is involuntarily attributed. Objects seen as animate are visually processed in the STS region (Beauchamp, Lee, Haxby, & Martin,
2003), which in turn facilitates higher-order attribution
such as emotions and intentions (Blakemore & Decety,
2001; Scholl & Tremoulet, 2000). Automated animacy
detection is an adaptive capacity, supporting early identification of living entities as prey, predators or mates (Allison,
Puce, & McCarthy, 2000; Schultz, Friston, O’Doherty, Wolpert, & Frith, 2005). Perceptual criteria proposed for animacy include changes of speed and direction (Tremoulet &
Feldman, 2000), intention (Dittrich & Lea, 1994), goal orientation (Opfer, 2002), and displayed interactivity between the objects ( Schultz et al., 2005). The stimuli we
used meet all proposed criteria for animacy attribution,
and in addition their motion patterns suggest a particular
intention, namely chasing (Barrett, Todd, Miller, & Blyth,
2005; Blythe, Todd, & Miller, 1999). A question this study
purports to answer is how animacy varies as a function
of movement characteristics. As to its role in genre categorization we predict that animacy enhances the recognition
of film realism, because moving objects that are clearly
artifacts seem to be realistically alive and socially aware
(Heider & Simmel, 1944; Michotte, 1946/1963, 1950/
1991). Hence, our animacy hypothesis: animacy attribution correlates positively to non-fiction genre attribution.
Emotions may serve as a somewhat higher level cue to
genre categorization. Genres are said to be organized
according to the particular emotions they typically elicit
(Aristotle, Trans., 1988; Carroll, 2003; Grodal, 1997). Comedy evokes mirth, action impresses the viewer, and drama
seeks to elicit tender emotions (Oliver, 2008), whereas
non-fiction does not try to evoke emotions, but instead
conveys an argument (Nichols, 2001). We expect that it
is not paired with any emotion, though in our case the
chase might invoke fear or admiration. Our emotion
hypothesis then reads: genre categorization (comedy, drama, action, and non-fiction) correlates with distinct emotion responses (respectively, funny, sad, impressive, and
scary). To complete the view on the emotional responses
to movements, three emotions (aesthetic liking, surprise,
and fascination) were added to the set of genre-specific
emotions.
Finally, at the highest complexity level we propose that
a distinction between fiction and non-fiction is crucial for
genre categorization. The distinction is pragmatically fundamental in that non-fiction might have direct implications for viewers’ personal lifes, whereas fiction allows
them to lean back and appreciate the story and aesthetic
properties of a film. The distinction may be based on the
perceived degree of the transformation of reality. Non-fiction transforms the actual world only minimally (Branigan,
1992; Corner, 1995; Nichols, 2001), whereas fiction is supposed to transform it to a higher degree, with references to
reality being indirect and metaphorical (Branigan, 1992;
Dewey, 1934; Goodman, 1984; Singer, 1998). We propose,
then, that the extent of transformation of the actual world
is perceived as a genre characteristic, with non-fiction acting as a baseline. Our realism hypothesis predicts that
untransformed movements will be categorized as non-fiction in the first place, and as fiction, i.e. drama, action, and
comedy, in the second.
In addition, a more precise ranking in terms of fidelity to
realism can be predicted. The drama genre’s transformation
of reality is slight, thus facilitating empathy (Gaut, 1999).
Drama portrays the reality of everyday life (Lacey, 2000;
Neale, 2000) and is characterized by realistic acting (Hallam
& Marshment, 2000). The action genre has a minimal similarity with reality allowing for thrilling conflicts, while certain elements are enhanced in comparison to non-fiction
(e.g. physical action, determination, and efficiency (Bordwell, 2006; Neale, 2000). The comedy genre’s relation to
non-fiction is the most remote: surprising deviations and
exaggerations of reality abound (Neale & Krutnik, 1990;
Sobchack, 2004). Our transformation hypothesis predicts
that moderate movement transformations from non-fiction
scenes cue drama and action categorizations, while strong
transformations cue comedy categorization.
2. Method
2.1. Materials
A Basic chase scene between two abstract blocks was
animated and varied as to five movement parameters. A
chase was selected as the animations’ action script, because the underlying intention is highly recognizable from
a simple motion pattern (Barrett et al., 2005; Blythe et al.,
1999). Animations produced in 3D MAYA 6.0 lasted 15 s
each. Camera movement served action visibility, and was
kept unobtrusive – see Fig. 1.
The choice of movement parameters was inspired by an
analysis of 80 chase scenes from the films of various fiction
and non-fiction genres (news, sport, documentaries, and
animals), personal interviews with animators, and the literature. Selected movement parameters were velocity, efficiency, fluency, detail, and deformation, for each of which
four versions were made deviating from the Basic scene
in one or the other direction. Deviations could be moderate
(+ or ) or strong (++ or
). Differences between the
deviations were exponential except for the efficiency
parameter.
2.2. Velocity
Velocity refers to the speed of the two blocks moving
over the track. All objects, blocks, floor, and camera were
scaled, but the track was not. Thus, perceptual sizes of
the blocks and the duration of the scenes remained constant over all versions, while perceptual velocity varied.
The variation factor was 1.5 between each two adjacent
levels.
V.T. Visch, E.S. Tan / Cognition 110 (2009) 265–272
267
Fig. 1. Filmstrip per second of animated running chase. The chase follows the following script: (a) A moves to the position of B, while B moves away from A;
(b) the distance between A and B decreases over time until A and B are unified at the end of the chase; and (c) as the distance between A and B decreases, B
takes increasingly more bends that become shorter and sharper toward the end of the chase.
Comedies, especially the slap-stick type, use extremely
fast or slow movements. Velocity is a strong factor in drama and comedy recognition (Visch & Tan, 2007) and in
emotion. Fast movements evoke joy, surprise and excitement, whereas slow movements evoke sadness, weakness,
gentleness and sympathy (Hille, 2001; De Meijer, 1989;
Michotte, 1963; Pollick, Paterson, Bruderlin, & Sanford,
2001; Scherer & Ellgring, 2007).
2.3. Efficiency
The ratio between the energy a character uses in pursuing a goal and the energy minimally needed to achieve it
determines perceived efficiency. In our stimuli, efficiency
relates to the directness of the chaser’s track. In a negative
efficiency version, the chaser’s zigzag movements are
wider than those of the chased – see Fig. 2.
The action genre displays highly efficient actions in order to impress viewers (Neale, 2000). Drama protagonists’
lack of resolution is reflected in less efficient movement.
Animation theory stresses inefficient actions’ potential
for comic effects (Thomas & Johnston, 1981).
2.4. Detail
Detail pertains to the temporal density of velocity
changes. The number of velocity alterations of the chaser
was manipulated through the number of ‘‘keys” in animation. (A ‘‘key” is a programmed change of velocity at a specific moment in time.) Detail level
has two keys set at
the beginning and at the end, with the block moving at a
single velocity. Level has five keys, the Basic scene eight,
detail + 14, and detail ++ 26 keys, giving rise to 25 velocity
alterations. High alteration densities seem to be characteristic of the chaotic character of movements in reality as
compared to the movements in fiction. Ashida, Lee, Allbeck, Sun, and Badler (2001) found that when artificial
agents displayed small and chaotic movements, they were
perceived as more natural than when showing less-detailed movements.
2.5. Fluency
Movement fluency was manipulated by varying the
smoothness of velocity transitions. The smoothest transition (fluency ++) was achieved by using a horizontal tangent at each velocity change, i.e. keypoint, resulting in a
gradual deceleration to an extremely short period of no
movement, immediately followed by a gradual acceleration. At the next two levels, the tangents become steeper,
until they are vertical at the fluency–level, resulting in no
acceleration nor deceleration. Further velocity abruptness
at this level ( ) was achieved by inserting duplicate frames
in between keys, resulting in movement arrests of 40 ms
each. In the fluency
condition, six frames were inserted, resulting in a 240 ms. stop of the chaser just before
the velocity change.
Abrupt movements seem to be characteristic for comedy (stop-and-go chases of Laurel and Hardy and Chuck
Jones’ Roadrunner) but also for the action genre where
the action hero moves like a robot (Neale, 2000). Fluent
movements, in contrast, appear to be more so for drama.
As regards expressive effects, Wallbott (1998) found that
non-fluent body movements express anger, fear, and joy,
while fluent ones express sadness, boredom, and
happiness.
2.6. Deformation
Deformation refers to dynamic shape alterations of the
chaser object when changing its course. The ratio of height
to the width of the object was exponentially scaled by onethird per step, keeping depth, and volume constant. Wide
objects, level ++, cause large wedge-shaped deformation
in negotiating a bend, see Fig. 3, whereas high and narrow
objects, level
, are minimally deformed.
268
V.T. Visch, E.S. Tan / Cognition 110 (2009) 265–272
Fig. 2. Three of five efficiency tracks of the chaser: the extremely inefficient track (
only follows the Basic track.
), the Basic track (0) and the extremely efficient track (++). The chased
(‘‘animacy study”) and the remaining measures (‘‘main
study”).
The design consisted of five non-crossed independent
variables, the movement parameters, each with five levels
(
, , 0, +, and ++). Level 0, the Basic scene, was one and
the same scene for all five parameters. Because of its anchor function, the Basic scene was presented and rated
twice by each participant, the average being taken as the
data. Thus, in all, the stimulus set consisted of 22 scenes.
Presentation order of scenes was randomized. For the main
study two orders were created, one the reverse of the
other. In the animacy study, the 22 scenes were randomly
presented to each participant.
2.8. Procedure and participants
Fig. 3. Example of deformation level ++.
Deformation is akin to the animation technique of
‘‘squash and stretch” (Thomas & Johnston, 1981), yielding
a funny impression of elasticity of cartoon bodies. This
is also a trademark of comedy actors, from Buster Keaton
to Jim Carrey. In contrast, action heroes seem to possess
extremely controlled and rigid bodies that scare and
impress.
2.7. Dependent measures and experimental design
There are 12 dependent variables grouped into four
types: genre categorization (comedy, drama, action, and
non-fiction), genre-related emotional responses (funny,
sad, impressive, and scary), aesthetic emotional responses
(fascination, aesthetic liking, and surprise) and animacy.
All dependent variables were measured using five-point
likert scales. Two data sets were obtained, animacy ratings
Each scene was shown twice followed by the rating of
the participants. The main study consisted of two rating
sessions each with a different randomized order, one for
genre categorization, and the other for the aesthetic and
emotional responses. The animacy study consisted of one
animacy rating session in which the stimuli were randomized. In all, each of the 22 scenes, including the dual Basic
scene, was presented four times to the participants in the
main study and two times to participants in the animacy
study.
Fifty-two participants (19 male, 33 female, ages ranging from 18 to 31), students of industrial design and of
arts, joined in the main study and were rewarded with
a cinema ticket. They were tested in groups from six to
ten. Stimuli were presented using a beamer and ratings
collected through questionnaires. Twenty-two psychology
students (11 male, 11 female, ages ranging from 17 to
29) participated in the animacy study and received
course credits. In the animacy study, the stimuli were
presented on 15.1 in. computer screen and ratings were
taken on-line.
269
V.T. Visch, E.S. Tan / Cognition 110 (2009) 265–272
3. Results
3.1. Animacy ratings
Data of the animacy study (N = 22) were analyzed using
repeated measures and showed no significant effect of
gender. The analysis did show that all movement parameter–level interactions, except that for ‘detail’, had significant effects on animacy ratings: velocity (F = 4.81,
p < .01); efficiency (F = 5.67, p < .001; fluency (F = 7.68,
p < .001); and deformation (F = 3.90, p < .01). Fig. 4 presents the animacy ratings as a function of movement
parameters and levels. Significant linear trends were obtained for velocity (F = 7.59, p < .02), fluency (F = 11.05,
p < .01), and deformation (F = 15.39, p < .01). Increases in
velocity and fluency resulted in higher animacy ratings
and increases in deformation resulted in lower animacy
ratings. Increases in the parameter efficiency from level 1
(
) to 4 (+) did not have a significant effect on animacy
ratings, but level 5 (++) was rated as being significantly less
animate than any of the former levels (F > 6.63, p < .018).
The effect of efficiency on the animacy ratings was best described by a quadratic contrast (F = 10.57, p < .01).
Table 1
Optimal Movements. Optimal levels within a specific genre or response are
those categorized significantly stronger, than at least one of the other levels
of that parameter. Horizontal ties do not differ in effect and represent
optimal sets. For instance, focusing on animacy ratings, velocity levels 3, 4,
and 5 do not differ among themselves, but all do have significantly higher
ratings than levels 1 and 2, rendering the set optimal for animacy
attribution. It should be noted that this Table does not present movement
optimal between the dependent variables.
Animacy
Comedy
Drama
Action
Non-fiction
Funny
Sad
Impressive
Scary
Velocity
Efficiency
Fluency
3, 4,
and 5
1
1
5
1, 2, 3,
and 4
1 and 5
2, 3, 4,
and 5
1
4
1 and 2
5
4 and 5
Fascinating
5
Aesthetic
liking
Surprise
1, 4,
and 5
5
1 and 2
3
1 and 5
5
1 and 5
1, 4, and
5
1 and 5
Deformation
1
4 and 5
1, 4, and 5
3
1
3
4 and 5
3
1, 4, and 5
15
4
1 and 2
2, 4,
and 5
1, 2, 4, and 5
1 and 5
1, 4, and
5
4 and 5
1 and 5
1 and 5
2, 4,
and 5
1 and 5
Legend: Level 1 =
; level 2 = ; level 3 = 0; level 4 = +; and level 5 = ++.
Note: Significance for the optimal levels is measured using MANOVA
Games Howell post-hoc tests with p < .05.
3.2. Genre and emotion ratings
The data of the main experiment (N = 52) were subjected to MANOVA showing that control variables gender
and order had no main effects on the dependent variables
(F = 2.27, p = .13; F = 0.63, p = .43). Table 1 presents the
optimal levels of the movement parameters for each of
the 11 categorizations and responses plus the animacy
ratings.
3.3. Correlations
Correlations between animacy-, emotion-, and genreratings were obtained by calculating Spearman rank corre-
lations between the means of the dependent variable for
each of the 25 level–movement parameter combinations.
Animacy correlated strongly and positively with non-fiction categorization, validating our first animacy hypothesis: (rs) = .63. An additional significant correlation of
animacy attribution was found with action categorization
(rs = .51). Animacy did not correlate significantly with
emotion ratings.
The emotion hypothesis predicting correlations between distinct emotions and genres was tested using not
3.8
3.6
3.4
Mean Animacy Rating
Detail
3.2
3
2.8
2.6
Velocity
2.4
Efficiency
2.2
Fluency
2
1.8
Deformation
1.6
--
-
0
+
Level of Movement Transformation
Fig. 4. Effect of movement parameters on animacy attribution.
++
270
V.T. Visch, E.S. Tan / Cognition 110 (2009) 265–272
Mean Genre Categorization Rating
3.4
3.2
*
3
*
*
*
*
2.8
2.6
*
*
*
* p < .03
*
Comedy
*
Drama
2.4
Action
Non-fiction
2.2
0
1
2
Extent of Movement Transformation
Fig. 5. Effect of movement transformation on genre categorization.
the means, but the individual ratings obtained in the main
study. Significant correlations were found with the fiction
genres only, for the pairs comedy genre and response
‘‘funny” (rs = .49), drama and ‘‘sad” (rs = .24), and action
and ‘‘impressive” (rs = .33). Non-fiction was not correlated
with any emotion, including ‘‘scary” or ‘‘impressive”.
3.4. Genre classification
Fig. 5 presents the effects of movement transformation
on genre categorizations. In order to test the realism prediction, the levels of all movement parameters were col-
lapsed from five to three levels: 0, 1 (+ and ) or 2 (++
and
). Significance was established using post hoc
Games Howell tests. Analysis showed that unmanipulated
movement scenes were categorized significantly stronger
as non-fiction than as any other genre. Moreover, they
were categorized as non-fiction stronger than were manipulated movement scenes. Our third realism hypothesis was
validated in that unmanipulated movement scenes were
categorized strongest as non-fiction, second to strongest
as drama, second to weakest as action, and weakest as
comedy. The transformation prediction also received support. Moderate transformations of movements increased
Mean Genre Categorization Rating
3.4
3.2
*
*
3
*
2.8
*
*
*
*
*
*
*
*
2.6
*
*
* p < .05
2.4
Non-fiction
Comedy
2.2
--
-
0
+
++
Level of Movement Transformation
Fig. 6. Effect of movement transformation on non-fiction and comedy categorization.
V.T. Visch, E.S. Tan / Cognition 110 (2009) 265–272
classification in all three fiction genres significantly compared to untransformed movements. Strong transformations only significantly increased categorization in the
comedy genre. It is noteworthy that categorization as the
comedy genre was affected by movement transformation
in exactly the opposite direction as non-fiction categorization (rs = .31) – see Fig. 6.
4. Discussion
This study showed that the movements of abstract objects, varying on velocity, efficiency, detail, fluency, and
deformation, elicit consistent higher-order categorizations
of film scenes as to genre, as well as lower-order emotion
responses and animacy attributions.
Animacy attributions increased, first, by a relatively
high velocity of the object probably resulting in more ‘lively’ movements. Second, animacy was increased by the
lack of deformation of the object when it negotiates a
bend. This result is unexpected in light of the widespread occurrence of squash and stretch deformation in
animated cartoons (cf. Thomas & Johnston, 1981). However, object deformation may have been confounded with
object proportion: strong deformations featured horizontally oriented object shapes, not resembling living bodies,
whereas weak deformations were paired with tall upright
objects, resembling human body shapes, which contributes to animacy attribution (Lange & Lappe, 2006). Third,
movement efficiency affected animacy. This could be expected since efficiency in a chase is closely tied to proposed animacy cues: (a) goal-directed movement
(Blakemore et al., 2003; Opfer, 2002) and (b) intentional
relations or interactivity between objects (Neri, Luu, &
Levi, 2006; Schultz, Friston, O’Doherty, Wolpert, & Frith,
2005). However, we found a significant drop of animacy
attribution when the chaser’s efficiency was extremely
high. This effect may be due to seeming anticipatory
capacities of the chaser enabling machinelike precision
in predicting where the chased will end. Fourth, a similar
effect of negative animacy attribution to machinelike
movements was found with extremely low-fluent movements, which featured exclusively abrupt starts and
stops. The lack of acceleration or deceleration also reminds one of machine behavior rather than the conduct
of sentient beings. The prediction that animacy attribution is associated with film realism, involving animate action, is validated by the considerable correlation between
animacy ratings and non-fiction categorization. We suggest that animacy attribution is not only functional for
adaptive purposes like proper social interaction (Allison
et al., 2000) and avoidances of dangerous species
(Schultz, Friston, O’Doherty, Wolpert, & Frith, 2005), but
it seems also to confer ‘reality status’ upon percepts of
motion pictures.
Specific stimuli movements that were varied in this
experiment induced specific emotional responses by the
viewers such as low velocity generating sadness – in line
with De Meijer (1989) and Pollick et al. (2001). Concerning
the role of emotion in genre categorization, we found that
each of the fiction genres correlated significantly with a
271
specific emotion, according to expectation: comedy with
funny, drama with sad, and action with impressive. As expected, non-fiction did not correlate with emotion ratings.
The contrast between functions of fiction and non-fiction,
one affective and the other informative, was already proclaimed by Aristotle (Trans., 1988).
As to the effects of movement transformations, untransformed movements seemed to give the viewer an impression of realism as they were categorized as an instance of
the non-fiction genre, as the realism hypothesis predicted.
Moreover, these realistic movements were categorized in
declining order from non-fiction to drama to action to
comedy, conforming to the transformation hypothesis.
The degree of transformation from the unmanipulated
‘‘realistic” movements determined as what particular fiction genre scenes were categorized: moderate deviations
resulted in categorization as drama and action, large deviations as comedy.
The results have two implications for a cognitive theory
of genre. Firstly, they suggest that genre knowledge consists in part of representations that can be evoked by the
specific types of movement. Secondly, the results suggest
that genres are not separate mental categories but organized as to their deviation from non-fiction. The knowledge involved may be expected to be implicit (Schacter,
1996): people might use it in classification without being
able to explicitly describe the movements or their transformative distance from non-fiction movements. In line with
theorizing by Mar and Oatley (2008) and Goldman (2006)
we believe that the embodied simulation of character
movement is inherent to typical responses to fiction. It
can be expected that film viewers match their perceptions
as a default with their large repertoire of non-fiction motor
experiences (Barsalou, 2008). The extent of successfulness
of such a match could function as an implicit cue for classification processes (Beilock, Lyons, Mattarella-Micke, Nusbaum, & Small, in press; Goldman & Sripada, 2005; Helbig,
Graf, & Kiefer, 2006) of fiction. Future research, using online measurements such as brain imaging, might reveal
whether matching with real life motor repertoires occurs
in fiction processing. On-line measurements could also
shed light on dependencies between genre categorization
and attributions to movement percepts.
In summary, we have shown that naïve subjects are
able to categorize genres, including fiction and non-fiction, consistently according to object movement parameters. Such a genre classification is achieved by, at least,
three distinct cognitive processes: (a) animacy attribution,
which influences the fiction versus non-fiction classification; (b) emotional responses, which influences the
classification of a specific fiction genre; and (c) the
amount of deviation from reality, at least with regard to
movements.
Acknowledgements
We would like to express our gratitude to the journal
editor Gerry Altman and three anonymous reviewers for
their stimulating and helpful comments, which have
greatly improved the quality of this manuscript.
272
V.T. Visch, E.S. Tan / Cognition 110 (2009) 265–272
References
Allison, T., Puce, A., & McCarthy, G. (2000). Social perception from visual
cues: Role of the STS region. Trends in Cognitive Sciences, 4(7),
267–278.
Aristotle (1988). Poetica (N. van der Ben & J.M. Bremer, Trans.)
Amsterdam: AthenaeumPolak and Van Gennep (Original work
written between 335-323 BC).
Ashida, K., Lee, S., Allbeck, J. M., Sun, H., & Badler, N. I. (2001). Pedestrians:
Creating agent behaviors through statistical analysis of observation
data. IEEE Computer Animation 2001: Proceedings of the 14th Conference
on Computer Animation, 84–92.
Barrett, H. C., Todd, P. M., Miller, G. F., & Blyth, P. W. (2005). Accurate
judgments of intention from motion cues alone: A cross-cultural
study. Evolution and Human Behavior, 26(4), 313–331.
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology,
59, 617–645.
Beauchamp, M. S., Lee, K. E., Haxby, J. V., & Martin, A. (2003). FMRI
responses to video and point-light displays of moving humans and
manipulable objects. Journal of Cognitive Neuroscience, 15, 991–1001.
Beilock, S. L., Lyons, I. M., Mattarella-Micke, A., Nusbaum, H. C., & Small, S.
L. (in press). Sports experience changes the neural processing of action
language. In Proceedings of the national academy of sciences, USA.
Blakemore, S.-J., Boyer, P., Pachot-Clouard, M., Meltzoff, A., Segebarth, C.,
& Decety, J. (2003). The detection of contingency and animacy from
simple animations in the human brain. Cerebral Cortex, 13(8),
837–844.
Blakemore, S., & Decety, J. (2001). From the perception of action to the
understanding of intention. Nature Reviews: Neuroscience, 2, 561–567.
Blythe, P. W., Todd, P. M., & Miller, G. F. (1999). How motion reveals
intention: Categorizing social interactions. In G. Gigerenzer & P. M.
Todd (Eds.), Simple heuristics that make us smart (pp. 257–285). NY:
Oxford University Press.
Bordwell, D. (2006). The way Hollywood tells it: Story and style in modern
movies. CA: University of California Press.
Branigan, E. (1992). Narrative comprehension and film. London: Routledge.
Carroll, N. (2003). Engaging the moving image. New Haven, CT: Yale
University Press.
Corner, J. (1995). Television form and public address. London: Edward
Arnold.
Dalla Bella, S., & Peretz, I. (2005). Differentiation of classical music
requires little learning but rhythm. Cognition, 96, 65–78.
De Meijer, M. (1989). The contribution of general features of body
movement to the attribution of emotion. Journal of Nonverbal
Behavior, 13(4), 247–268.
Dewey, J. (1934). Art as experience. NY: Berkley Publishing Group.
Dittrich, W. H., & Lea, S. E. (1994). Visual perception of intentional motion.
Perception, 23(3), 253–268.
Frye, N. (1957). Anatomy of criticism: Four essays. Princeton: Princeton
University Press.
Gaut, B. (1999). Identification and emotion in narrative film. In C. R.
Plantinga & G. M. Smith (Eds.), Passionate views: Film, cognition and
emotion (pp. 200–216). Baltimore: John Hopkins University Press.
Goldman, A. I. (2006). Imagination and simulation in audience responses
to fiction. In S. Nichols (Ed.), The architecture of the imagination
(pp. 41–56). Oxford: Oxford University Press.
Goldman, A. I., & Sripada, C. S. (2005). Simulationist models of face-based
emotion recognition. Cognition, 94, 193–213.
Goodman, N. (1984). Of mind and other matters. Cambridge, MA: Harvard
University Press.
Grodal, T. (1997). Moving pictures. Oxford: Clarendon Press.
Hallam, J., & Marshment, M. (2000). Realism and popular cinema.
Manchester, UK: Manchester University Press.
Hawkins, R. P., Pingree, S., Hitchon, J., Radler, B., Gorham, B. W., Kahlor, L.,
et al (2005). What produces television attention and attention style?
Genre, situation, and individual differences as predictors. Human
Communication Research, 31(1), 162–187.
Hayward, M. (1994). Genre recognition of history and fiction. Poetics, 22,
409–421.
Heider, F., & Simmel, M. (1944). An experimental study of apparent
behavior. American Journal of Psychology, 57, 243–249.
Helbig, H. B., Graf, M., & Kiefer, M. (2006). The role of action representations
in visual object recognition. Experimental Brain Research, 174, 221–228.
Hille, K. (2001). Synthesizing emotional behavior in a simple animated
character. Artificial Life, 7, 303–313.
Lacey, N. (2000). Narrative and genre: Key concepts in media studies. NY:
Palgrave.
Lange, J., & Lappe, M. (2006). A model of biological motion perception
from configural form cues. Journal of Neuroscience, 26(11),
2894–2906.
Mar, R. A., & Oatley, K. (2008). The function of fiction is the abstraction
and simulation of social experience. Perspectives on Psychological
Science, 3(3), 173–192.
Michotte, A. (1963). The perception of causality (T. R. Miles & E. Miles,
Trans.). London: Methuen (Original work published 1946).
Michotte, A. (1991). The emotions regarded as functional connections. In
G. Thinès, A. Costall, & G. Butterworth (Eds.), Michotte’s experimental
phenomenology of perception. Hillsdale: Erlbaum (Reprinted from
Feelings and emotions: The Mooseheart Symposium (pp. 114–125), by
M. L. Reymert (Ed.), 1950, New York: McGraw-Hill).
Neale, S. (2000). Genre and Hollywood. London: Routledge.
Neale, S., & Krutnik, F. (1990). Popular film and television comedy. London:
Routledge.
Neri, P., Luu, J. Y., & Levi, D. M. (2006). Meaningful interactions can
enhance visual discrimination of human agents. Nature Neuroscience,
9, 1186–1192.
Nichols, B. (2001). Introduction to documentary. Bloomington, IN: Indiana
University Press.
Oliver, M. B. (2008). Tender affective states as predictors of entertainment
preference. Journal of Communication, 58, 40–61.
Opfer, J. E. (2002). Identifying living and sentient kinds from dynamic
information: The case of goal-directed versus aimless autonomous
movement in conceptual change. Cognition, 86, 97–122.
Pollick, F. E., Paterson, H. M., Bruderlin, A., & Sanford, A. J. (2001).
Perceiving affect from arm movement. Cognition, 82, 51–61.
Roskos-Ewoldsen, D. R., Roskos-Ewoldsen, B., & Dillman-Carpentier, F. R.
(2002). Media priming: A synthesis. In J. Bryant & D. Zillmann (Eds.),
Media effects (2nd ed., pp. 97–120). Mahwah, NJ: Erlbaum.
Schacter, D. (1996). Searching for memory: The brain, the mind, and the past.
New York: Basic.
Scherer, K. R., & Ellgring, H. (2007). Multimodal expression of emotion:
Affect programs or componential appraisal patterns? Emotion, 7(1),
158–171.
Scholl, B. J., & Tremoulet, P. D. (2000). Perceptual causality and animacy.
Trends in Cognitive Sciences, 4(8), 299–309.
Schultz, J., Friston, K. J., O’Doherty, J., Wolpert, D. M., & Frith, C. D. (2005).
Activation in posterior superior temporal sulcus parallels parameter
inducing the percept of animacy. Neuron, 45(4), 625–635.
Singer, I. (1998). Reality transformed: Film as meaning and technique. MA:
MIT press.
Smith, G. M. (2003). Film structure and the emotion system. Cambridge, UK:
Cambridge University Press.
Sobchack, V. (2004). Thinking through Jim Carrey. In C. Baron, C. Diane, &
F. P. Tomasulo (Eds.), More than a Method: Trends and traditions in
contemporary film performance (pp. 275–296). MI: Wayne State
University Press.
Tan, E. S. (1996). Emotion and the structure of narrative film. Mahwah, NJ:
Erlbaum.
Thomas, F., & Johnston, O. (1981). The illusion of live: Disney animation.
New York: Disney Editions.
Tremoulet, P. D., & Feldman, J. (2000). Perception of animacy from the
motion of a single object. Perception, 29, 943–951.
Visch, V. T., & Tan, E. S. H. (2007). Effect of film velocity on genre
recognition. Media Psychology, 9(1), 59–75.
Visch, V. T., & Tan, E. S. H. (2008). Narrative versus style: Effect of genretypical events versus genre-typical filmic realizations on film viewers’
genre recognition. Poetics, 36, 301–315.
Wallbott, H. G. (1998). Bodily expression of emotion. European Journal of
Social Psychology, 28, 879–896.
Zillmann, D. (1988). Mood management: Using entertainment to full
advantage. In L. Donohew, H. E. Sypher, & E. Tory Higgins (Eds.),
Communication, social cognition and affect (pp. 147–171). Hillsdale, NJ:
Erlbaum.
Zwaan, R. A. (1994). Effect of genre expectations on text comprehension.
Journal of Experimental Psychology: Learning, Memory and Cognition,
20(4), 920–933.