Walk this way: approaching bodies can influence the processing of faces.
ABSTRACT A highly familiar type of movement occurs whenever a person walks towards you. In the present study, we investigated whether this type of motion has an effect on face processing. We took a range of different 3D head models and placed them on a single, identical 3D body model. The resulting figures were animated to approach the observer. In a first series of experiments, we used a sequential matching task to investigate how the motion of an approaching person affects immediate responses to faces. We compared observers' responses following approach sequences to their performance with figures walking backwards (receding motion) or remaining still. Observers were significantly faster in responding to a target face that followed an approach sequence, compared to both receding and static primes. In a second series of experiments, we investigated long-term effects of motion using a delayed visual search paradigm. After studying moving or static avatars, observers searched for target faces in static arrays of varying set sizes. Again, observers were faster at responding to faces that had been learned in the context of an approach sequence. Together these results suggest that the context of a moving body influences face processing, and support the hypothesis that our visual system has mechanisms that aid the encoding of behaviourally-relevant and familiar dynamic events.
- SourceAvailable from: Timothy F Brady[Show abstract] [Hide abstract]
ABSTRACT: As we move through the world, information about objects moves to different spatial frequencies. How the visual system successfully integrates information across these changes to form a coherent percept is thus an important open question. Here we investigate such integration using hybrid faces, which contain different images in low and high spatial frequencies. Observers judged how similar a hybrid was to each of its component images while walking toward or away from it or having the stimulus moved toward or away from them. We find that when the stimulus is approaching, observers act as if they are integrating across spatial frequency separately at each moment. However, when the stimulus is receding, observers show a perceptual hysteresis effect, holding on to details that are imperceptible in a static stimulus condition. Thus, observers appear to make optimal inferences by sticking with their previous interpretation when losing information but constantly reinterpreting their input when gaining new information.Frontiers in Psychology 01/2012; 3:462. · 2.80 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Typically developing individuals show a strong visual preference for faces and face-like stimuli; however, this may come at the expense of attending to bodies or to other aspects of a scene. The primary goal of the present study was to provide additional insight into the development of attentional mechanisms that underlie perception of real people in naturalistic scenes. We examined the looking behaviors of typical children, adolescents, and young adults as they viewed static and dynamic scenes depicting one or more people. Overall, participants showed a bias to attend to faces more than on other parts of the scenes. Adding motion cues led to a reduction in the number, but an increase in the average duration of face fixations in single-character scenes. When multiple characters appeared in a scene, motion-related effects were attenuated and participants shifted their gaze from faces to bodies, or made off-screen glances. Children showed the largest effects related to the introduction of motion cues or additional characters, suggesting that they find dynamic faces difficult to process, and are especially prone to look away from faces when viewing complex social scenes-a strategy that could reduce the cognitive and the affective load imposed by having to divide one's attention between multiple faces. Our findings provide new insights into the typical development of social attention during natural scene viewing, and lay the foundation for future work examining gaze behaviors in typical and atypical development.Frontiers in Psychology 03/2014; 5:193. · 2.80 Impact Factor
Walk this way: Approaching bodies can influence the processing of facesq
Karin S. Pilza,⇑, Quoc C. Vuongb, Heinrich H. Bülthoffc,d, Ian M. Thorntone
aDepartment of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Canada
bInstitute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
cMax Planck Institute for Biological Cybernetics, Tübingen, Germany
dDepartment of Brain and Cognitive Engineering, Korea University, Seoul 136-713, Republic of Korea
eDepartment of Psychology, Swansea University, Swansea, UK
a r t i c l ei n f o
Received 21 February 2010
Revised 20 September 2010
Accepted 24 September 2010
a b s t r a c t
A highly familiar type of movement occurs whenever a person walks towards you. In the
present study, we investigated whether this type of motion has an effect on face process-
ing. We took a range of different 3D head models and placed them on a single, identical 3D
body model. The resulting figures were animated to approach the observer. In a first series
of experiments, we used a sequential matching task to investigate how the motion of an
approaching person affects immediate responses to faces. We compared observers’
responses following approach sequences to their performance with figures walking back-
wards (receding motion) or remaining still. Observers were significantly faster in respond-
ing to a target face that followed an approach sequence, compared to both receding and
static primes. In a second series of experiments, we investigated long-term effects of
motion using a delayed visual search paradigm. After studying moving or static avatars,
observers searched for target faces in static arrays of varying set sizes. Again, observers
were faster at responding to faces that had been learned in the context of an approach
sequence. Together these results suggest that the context of a moving body influences face
processing, and support the hypothesis that our visual system has mechanisms that aid the
encoding of behaviourally-relevant and familiar dynamic events.
? 2010 Elsevier B.V. All rights reserved.
Movement can cause frequent and often quite dramatic
changes to the visual information available to us. Such
changes arise not only because of our own actions, but also
because other humans, animals, objects and natural phe-
nomena move when we stand still. A vast amount of
research has concentrated on the low-level or early visual
processing of motion (e.g., Bülthoff & Bülthoff, 1987;
Hassenstein & Reichardt, 1956; Krekelberg & Albright,
2005; Poggio & Reichardt, 1973). In terms of ‘‘visual cogni-
tion”, however, the impact of motion is still relatively
under-explored, and the use of static stimuliis still far more
common than the use of dynamic stimuli.
In recent years, studies of object (e.g., Chuang, Vuong,
2004, 2006), face (e.g., Christie & Bruce, 1998; Knappmeyer,
Thornton, & Bülthoff, 2003; Lander & Bruce, 2000, 2003;
O’Toole, Roark, & Abdi, 2002; Pike, Kemp, Towell, & Phillips,
1997; Pilz, Thornton, & Bülthoff, 2006; Wallis & Bülthoff,
2001), body (e.g., Johansson, 1973; Knoblich, Thornton,
Benjamin, & Osborne, 2007; Vuong, Hof, Bülthoff, &
0010-0277/$ - see front matter ? 2010 Elsevier B.V. All rights reserved.
qThis work was conducted while the first author was at the Max Planck
Institute for Biological Cybernetics, Tübingen, Germany and the Graduate
School for Neural and Behavioural Sciences, Tübingen, Germany.
⇑Corresponding author. Address: Department of Psychology, Neuro-
science and Behaviour, McMaster University, 1280 Main Street West,
Hamilton, ON, Canada L8S 4K1. Tel.: +1 905 525 9140x24489; fax: +1 905
E-mail address: firstname.lastname@example.org (K.S. Pilz).
Cognition 118 (2011) 17–31
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/COGNIT
Thornton, 2006) have begun to take into account the conse-
quences of motion. Precisely how the cognitive system
treats ‘‘dynamic” versus ‘‘static” information, however,
remains an open question (Freyd, 1987; Matthews et al.,
The focus of the current paper is on the perception and
representation of facial identity. Until the 1990s, it was
commonly assumed that facial form and facial motion
served complementary functions during face processing.
Facial features and their configuration provided static,
invariant cues to identity, gender, race and age. The move-
ment of the head and face were concerned with emotion
and communication, via expressions, gestures and visible
speech (e.g., Bruce & Young, 1986). More recently, how-
ever, such a simple dichotomy has been called into ques-
tion. For example, it has been shown that both rigid and
non-rigid facial motion can serve as reliable cues to indi-
vidual identity (e.g., Hill & Johnston, 2001; Knappmeyer
et al., 2003; Lander & Bruce, 2000, 2003; O’Toole, Roark,
& Abdi, 2002; Pilz, Bülthoff, & Vuong, 2009; Pilz et al.,
2006). Findings suggest that facial motion not only pro-
vides additional cues (e.g., structure from motion) but
can also generally influence the efficiency with which we
encode and process facial information (Pilz et al., 2006;
Thornton & Kourtzi, 2002).
In the current work we take one particular dynamic con-
text – a person walking towards you – and explore how this
might affect face processing, in particular, the processing of
facial identity. Although the vast majority of research treats
faces as isolated objects, our real-world experience typically
involves seeing a head in the context of a body that moves
in space. Does this dynamic ‘‘body context” have any influ-
ence on the way we process faces? Could variations in view-
point, changes in information availability (e.g., as people
approach and recede) or simply the familiarity of certain
events have a reliable impact on face processing? Here, we
take a very simple and highly familiar event and examine
whether seeing a face in this dynamic context, as compared
to static snapshots, affects performance in an immediate face
matching task (Experiments 1–3) or a long-term memory vi-
sual search task (Experiments 4–5).
quences of a liquor store hold-up to static shots taken from
for people that had been learned from dynamic video se-
tic mug shots. Vicki Bruce and colleagues (e.g., Bruce,
Henderson, Newman, & Burton, 2001; Bruce et al., 1999;
Burton, Wilson, Cowan, & Bruce, 1999; Henderson, Bruce,
& Burton, 2001) have used a range of paradigms to test
observers’ ability to identify people depicted in CCTV foot-
previously unfamiliar people from such videos. Familiar
individuals, however, can be recognized quite successfully,
an important role in person recognition. Roark, O’Toole, and
to recognize individuals whom they had previously learned
from either static photographs or facial speech videos. They
found that observers were better at recognizing individuals
namic video clips rather than static snapshots. In a similar
study O’Toole and colleagues showed observers real videos
of moving faces, static faces and whole bodies with faces.
eos. They demonstrated that human identification is best
when the whole person was seen in motion (O’Toole et al.,
These studies represent an important advance in study-
ing person recognition by taking into account the moving
body instead of exclusively concentrating on the isolated
face. Their findings clearly suggest that observers are influ-
enced by seeing faces in the context of a moving body.
However, although the use of video-based stimuli has the
advantage of capturing naturalistic movement sequences,
such stimuli also have some important limitations. That
is, it becomes very difficult to separately assess or control
for the variety of identity cues that may be available in
such clips. Recognition may be based on facial change, on
individual body motion or walking style, or other individ-
ually distinct features such as hairstyle or clothing.
Another general goal of the current paper is to demon-
strate that computer animated, or virtual stimuli, can be
used to overcome such limitations. Near-photorealistic vir-
tual characters or avatars are quickly becoming common-
place on game consoles and movie screens. Previous
research on facial motion has already shown how similar
stimuli can be used to explore individual identity (e.g., Hill
& Johnston, 2001; Knappmeyer et al., 2003). Here, we com-
bined different 3D head models (Blanz & Vetter, 1999; Troje
& Bülthoff, 1996) with a single 3D body model taken from a
popular animation package Poser?. This single body model
was used to create animation sequences in which the ava-
tar approached the observer using an identical walk pat-
tern. In this way we were able to vary the facial
information but to precisely equate the body and how it
moved. This is a manipulation that is simply not possible
with real-world stimuli.
To explore the impact of viewing faces in the context of
body motion, we chose two different paradigms that had al-
ready been successfully used to study facial motion. The first,
an immediate matching task (Experiments 1–3), explored the
brief, short-term representations we might form during a
natural approach sequence (Pilz et al., 2006; Thornton &
Kourtzi, 2002). Because the nature of an immediate matching
paradigm as applied in Experiments 1–3, makes it possible
for observers to adopt a speed-accuracy tradeoff as part of
their response strategy, we also decided to use an additional
task to investigate the effects of body motion on face recog-
nition. In Experiments 4 and 5 we used a delayed visual
search paradigm to test whether an advantage for encoding
approaching over static presentations persisted over longer
periods of time (Pilz et al., 2006, 2009).
2. General methods
Fifteen male heads from the MPI 3D head database
(Blanz & Vetter, 1999; Troje & Bülthoff, 1996) were
K.S. Pilz et al./Cognition 118 (2011) 17–31
mounted onto a 3D avatar using Poser?, a commercially
available animation package. The resulting figures were
animated to approach the observer on a straight walk path
using the Poser? built-in walk designer. The virtual cam-
era took perspective views of the animated figures. Since
all stimuli had identical bodies and were animated with
the same walk pattern and walk path, the only feature that
distinguished one stimulus from another was the head it-
self. The final movie clips were converted to grayscale
and edited to contain 30 frames. For each initial or ‘prime’
sequence 15 consecutive frames were taken from the clip
(i.e., 600 ms at a frame rate of 25 fps) according to proce-
dures described in the specific methods section for each
experiment. The display area subtended a visual angle of
15.7? ? 11.7? (width ? height). Fig. 1 gives an example of
the stimuli. Dynamic prime bodies varied in size from
6.2? ? 6.2? (Fig. 1A) to 13.3? ? 4.3? (Fig. 1C) during the
course of the animation, i.e., the whole upper part of the
body in the initial frames up to only the head and shoul-
ders in the final frames. The heads of those animated fig-
ures varied from 2.0? ? 2.9? to 5.0? ? 7.2?. The static
prime frame was a frame taken from the dynamic se-
quence. It was always chosen to match the end-point of
the dynamic sequence. This was either the last frame of
the animation sequence, when only the approach sequence
was shown, or the middle frame, when both approaching
and receding sequences were used. In addition to animat-
ing the avatars, we rendered the same fifteen 3D heads
without the body. These images were shown in frontal
view (Experiments 1–5), as well as 22? to the right and left
(Experiments 2–5) with their original color pigmentation
and served as target stimuli for all five experiments. The
targetdisplay area subtended
10.5? ? 11? (width ? height). The heads themselves sub-
tended a visual angle of 6.2? ? 8.6?. The slight variations
in size and color between prime and test images were de-
a visual angleof
signed to increase the difficulty of the task and to minimize
the possibility of picture matching.
All of the following experiments were conducted on a
Macintosh G4 computer under the control of customized
software using the PsychToolBox extension for MATLAB
(Brainard, 1997; Pelli, 1997). Stimuli were presented on a
21 in. monitor with a resolution of 1152 ? 864 pixels and
a frame rate of 75 Hz. Observers were seated 60 cm from
2.3. Data analysis
In all experiments we examined both the speed and
accuracy of responses. We used the median reaction time
(RT) of single subjects for each condition, because the med-
ian is less affected by outliers than the mean and provides
a better estimate of the true average for skewed distribu-
tions, which RT data often have (Ratcliff, 1979a, 1979b,
1993). As match and non-match decisions were likely to
represent different processes, our experimental predic-
tions relate to same trials. Complete RT data are presented
in Tables 1 (Experiments 1–3) and 3 (Experiments 4–5).
For Experiments 1–3, accuracy data were transformed into
d0values, which measures sensitivity to our stimulus
manipulations. It is computed from the hits (correctly de-
tected same trials) and false alarms (incorrectly denoted
different trials). For completeness, we report the accuracy
data to show performance for same (hits) and different tri-
als (correct rejections) separately for Experiments 1–3 in
The main focus of analysis for this and all experiments
of the current paper will reaction time (RT). We did not
anticipate differences in accuracy, because in both tasks
Fig. 1. Example pictures of (A) the first (B) the 15th and (C) the 30th frame of the rendered movie sequence.
Reaction time (ms) data for same and different trials for Experiments 1–3. SE refers to 95% confidence intervals calculated with the method described by Loftus
and Masson (1994).
Same trials Different trials
Looming probe Receding probeStatic probe Looming probe Receding probeStatic probe
MSEMSEM SEMSEMSEM SE
Note. Receding probe stimuli were not tested in Experiment 1.
K.S. Pilz et al./Cognition 118 (2011) 17–31
used here, target faces are clearly visible and often re-
peated. Furthermore, in the case of sequential matching,
recognition occurs directly after exposure to the two faces
with participants only having to indicate same or different.
In the delayed visual search task, faces are learned to such
a high degree that differences in accuracy between condi-
tions are not usually detectable. In previous studies using
similar paradigms we did not find accuracy differences be-
tween conditions (Pilz et al., 2006; Thornton & Kourtzi,
The current RTs were recorded using a standard USB
keyboard. It has been shown previously that the delay be-
tween the actual key press and the finger touching the key
is about 2 ms. In addition, the scanning time of a standard
USB keyboard is between 18 and 32 ms (Shimizu, 2002)
and may vary depending on key position. This scanning
time might potentially introduce noise to the data. In addi-
tion, it has been shown that faster movements and RTs oc-
cur with the dominant hand (e.g., Shen & Franz, 2005).
In the current work, it is important to note that RTs for
the critical conditions were all measured with the same
key to minimize the noise introduced by the choice of
answering device and handedness. The only differences be-
tween hand and key were for target conditions (same/dif-
ferent (Experiment 1–3), present/absent (Experiment 4–5)
but not between the critical prime/learning conditions.
3. Experiment 1
In a first series of experiments, we used an immediate
matching paradigm to investigate whether the motion of
an approaching person affects the short-term encoding
and recognition of unfamiliar faces. The purpose of Exper-
iment 1 was to directly compare matching performance in
trials with dynamic approach sequences to those in which
the prime was a single static snapshot. Based on previous
research using moving faces (Pilz et al., 2006; Thornton &
Kourtzi, 2002), our prediction was that dynamic prime
stimuli would lead to better matching performance than
static prime stimuli.
3.1. Materials and methods
Twelve right-handed observers aged 21–39 (mean age:
27.0 years) participated in this study (five females and se-
ven males). Observers were either volunteers from the MPI
community or were recruited from the MPI subject pool in
return for 8 €/h. All observers had normal or corrected-to-
normal vision and were naïve regarding the purpose of the
experiment. Observers did not participate in more than
one experiment. All observers gave informed consent.
In Experiment 1, 15 video clips of an approaching per-
son, as well as the corresponding 15 target faces in frontal
view were used. In addition, 15 static target images of dif-
ferent identities served as distractors for ‘different’ trials.
The static prime face was chosen to be the ending frame
of the dynamic sequence as described above.
Observers were seated in front of a computer screen at a
viewing distance of 60 cm. They were told that each trial
would involve the presentation of two faces, a prime face
followed by a target face. They were instructed to pay close
attention to the identity of the prime face so that they were
able to decide if the target face showed the same or a dif-
ferent person. Observers were told that the target face
would always be a static image, but the prime face would
sometimes be a short video clip of an approaching person
(dynamic prime) and sometimes a single static frame (sta-
tic prime). It was emphasized that this video/static manip-
ulation was not relevant to the identity decision they were
required to make. The target face stayed on the screen until
the observer responded and auditory feedback was given
whenever observers made an incorrect response or took
longer than 800 ms to respond. The whole experiment took
about 10 min to complete.
3.1.4. Task and design
The experiment consisted of 144 trials divided into four
blocks of trials. Each block consisted of 36 trials, half of
which contained dynamic stimuli (18 trials) and half of
which contained static stimuli (18 trials). After each block,
observers were encouraged to take a small break. They ini-
tiated the next block by pressing ‘n’ on the keyboard.
Within this motion factor, there were equal numbers of
same trials (nine trials) and different trials (nine trials).
for prime and target face. ‘Different’ trials were constructed
by randomly selecting different identities for prime and
separately for each observer on a block-by-block basis. On
Accuracy (% correct) data for same and different trials for Experiments 1–3. The data shown in the table are combined in the d0-analysis described in the text as
hits (correctly detected same trials) and false alarms (incorrectly denoted different trials). SE refers to 95% confidence intervals calculated with the method
described by Loftus and Masson (1994).
Same trialsDifferent trials
Looming probeReceding probe Static probeLooming probeReceding probeStatic probe
M SEM SEM SEM SEM SEM SE
Note. Receding probe stimuli were not tested in Experiment 1.
K.S. Pilz et al./Cognition 118 (2011) 17–31
each trial a prime appeared in the middle of the screen for
600 ms. The prime was either a dynamic animation se-
quence of a person approaching the observer or a static pic-
ture. After a blank of 300 ms the target face appeared in the
middle of the screen. The target face was always a static im-
hair in front of a black background. On each trial the prime
stimulus was randomly drawn from the stimulus set. The
task of the observers was to determine if the prime and tar-
get faces was of same individual or of different individuals.
They were asked to respond as quickly and as accurately
as possible using one of two marked keys. This was the ‘s’
key for ‘same’ and the ‘l’ key for ‘different’. An example of
the trial sequence can be seen in Fig. 2.
3.2.1. Reaction times
Fig. 3 shows mean RTs for correct responses for both
same trials and different trials. A 2 (prime condition (static,
dynamic)) ? 2 (target condition (same, different)) repeated
measures ANOVA showed a main effect of prime condition,
F(1, 11) = 15.11, p < 0.01, with a reliable overall RT advan-
tage of 25.8 ms for dynamic compared to static primes,
and no effect of target condition F(1, 11) = 0.18, p = 0.6.
The interaction between prime and target condition was
marginally significant, F(1, 11) = 4.32, p = 0.06. Post hoc
tests showed that for same trials, there was a consistent
RT advantage of 36 ms for dynamic compared to static
primes, t(11) = 22.0, p < 0.001. The difference between sta-
tic and dynamic prime stimuli in the different trials of
15 ms was not reliable, t(11) = 3.2, p = 0.1.
Accuracy remained at about 80% in all conditions as can
be seen in Table 2. There was no variation in sensitivity be-
tween conditions with an average d0of 1.8, t(11) = 1.9,
p > 0.1.
The results of Experiment 1 showed that dynamic stim-
ulican lead tofastermatchingresponsesthanstaticstimuli.
This advantage was more pronounced when the prime and
the target showed the same person. This suggests that the
advantage does not arise solely from a general increase in
arousal or alertness for moving primes, but rather relates
to the representation of a particular individual. Such an
identity specific RT advantage has previously been shown
with non-rigid facial motion (Pilz et al., 2006; Thornton &
The next two experiments investigate the origin of this
dynamic advantage in further detail.
4. Experiment 2
The results of Experiment 1 showed that an animated
figure approaching in depth can speed identity decisions
relative to a static snapshot. Does this dynamic advantage
relate directly to the salience of an approach sequence, or
would other body movements in depth have a similar ef-
fect? The purpose of Experiment 2 was to directly contrast
the dynamic approach sequence used in the Experiment 1
with the same type of sequence played backwards. Such a
control condition closely equates the amount of available
facial information, while varying the familiarity and sal-
ience of the observed action.
Previous research has shown a particular sensitivity for
approaching – but not receding – stimuli in a number of
animal species (e.g., Ball & Tronick, 1971; Bower, Brough-
ton, & Moore, 1970; Maier, Neuhoff, Logothetis, & Ghazan-
far, 2004; Schiff, 1965; Schiff, Caviness, & Gibson, 1962;
Fig. 2. Example of a trial sequence in Experiment 1. Observers saw either
an approaching or a static probe person for 600 ms. After a blank of
300 ms, the target face (shown from frontal view) was presented until the
observer responded to whether the probe and the target face showed the
same or different identities.
Fig. 3. Reaction times for dynamic and static same and different trials for
Experiment 1. Error bars represent 95% confidence intervals calculated
with the method described by Loftus and Masson (1994).
K.S. Pilz et al./Cognition 118 (2011) 17–31
Tronick, 1967). For humans, assessing the identity and/or
the intentions of an approaching person is also highly sig-
nificant. Compared to a sequence where a person is walk-
ing backwards and away from the observer – the control
condition introduced here – such an approach sequence
also varies in terms of the frequency, naturalness and sal-
ience of the action. We thus predicted that responses to dy-
namic approach sequences would be faster than responses
to dynamic receding sequences. If, on the other hand, the
two conditions were to give rise to similar patterns of per-
formance, this would suggest the results of Experiment 1
relate more to general alerting or arousal effects of motion
(e.g., Driver & Baylis, 1989; Franconeri & Simons, 2003;
Hillstrom & Yantis, 1994), rather than being specific to
the act of approach.
In addition to the new receding motion condition, we
also repeated the static prime condition, to provide a gen-
eral baseline, and introduced viewpoint changes between
prime stimulus and test. Target faces could now appear
in either frontal view or rotated by 22? to either the left
or right. Changes in viewpoint are a useful manipulation,
as they increase the difficulty of the task and lessen the
possibility of picture matching. Although Experiment 1
had involved changes from grayscale to color and the for-
mat of the prime and test images were quite different,
we felt that increasing the task demands even further
may help provide more evidence for a dynamic advantage
for face recognition. More generally, showing that a match-
ing advantage is maintained across viewpoints would also
strengthen the hypothesis that motion in depth facilitates
the encoding of identity.
Sixteen right-handed observers aged 19–26 (mean age:
22 years) participated in this study (seven females and
Video clips of 15 approaching avatars, and their corre-
sponding target faces in frontal view, as well as 22? on
either side of the face were used in the current experiment.
In addition, static target images from 15 individuals shown
from the three different viewpoints served as distractors
for ‘different’ trials.
Twenty-nine consecutive frames were taken from the
video clips of the approaching figures. For each presenta-
tion of an approaching stimulus, the first 15 sequential
frames were selected out of these 29 and played forwards.
For the receding stimuli, the last 15 frames were taken out
of the 29 and played backwards. Thus, the end frame of
both the approaching and the receding sequences was
the same as the static frame, i.e., the middle frame of the
29 rendered ones. The facial image in the stopping frame
for both conditions subtended 3.3? ? 4.3? (width ? height)
(Fig. 1B), the body subtended 8.6? ? 5.7?. The static prime
stimulus was chosen to be the stopping frame of both se-
The procedure was the same as in Experiment 1 except
that observers were told that the prime would sometimes
be a short video clip of an approaching person (approach-
ing prime), a person moving away from the camera (reced-
ing prime) or a single static frame (static prime).
4.1.4. Task and design
The experiment consisted of 144 trials, separated into
three blocks of 48 trials each. After each block, observers
were encouraged to take a small break. They initiated the
next block by pressing ‘n’ on the keyboard. Across the three
blocks, observers completed 72 same and 72 different tri-
als of which a third (24 trials) showed approaching, reced-
ing, or static primes. Out of these 24 trials, eight showed
frontal view faces as targets (frontal targets), eight showed
left view faces (left targets) and eight showed right view
faces (right targets). ‘Same’ trials were constructed by
showing faces of the same identity for prime and target.
‘Different’ trials were constructed by randomly selecting
from the 15 additional target faces. The order of trials
within each block was randomized separately for each ob-
server on a block-by-block basis. On each trial a prime ap-
peared in the middle of the screen for 600 ms. The prime
was either an approaching animation sequence, a receding
animation sequence or a static picture. After a blank of
300 ms the target face appeared in the middle of the
screen. The target face was always a still image showing
the head from one of the three viewpoints. On each trial
the prime stimulus was randomly drawn from the stimu-
lus set. The task of the observers was to determine if the
prime and target faces came from the same individual.
They were asked to respond as quickly and as accurately
as possible using one of two marked keys. This key was
the ‘s’ key for ‘same’ and the ‘l’ key for ‘different’. Fig. 2
shows the presentation sequence for a typical trial.
4.1.5. Data analysis
Repeated measures analyses of variance (ANOVAs)
were used to compare the factors of interest (prime condi-
tion (approaching, receding, static) ? target condition
(same, different) ? target viewpoint (front, left, right)).
Additional post hoc contrasts were used to test for specific
differences across conditions.
4.2.1. Reaction times
Fig. 5 shows RT across set size for both target types. A 3
(prime condition) ? 2 (target condition) ? 3 (target view-
point) repeated measures ANOVA revealed main effects
of prime condition, F(2, 30) = 8.3, p < 0.01, target condition,
F(1, 15) = 5.4, p < 0.05, target viewpoint, F(2, 30) = 9.5,
p < 0.001, and an interaction between prime condition
and target condition, F(2, 30) = 3.7, p < 0.05. There were
no further interactions. To investigate the prime ? target
condition interaction in further detail, we performed sepa-
rate ANOVAs on same and different trials.
For same trials, a two-way repeated measures ANOVA
(prime condition ? target viewpoint) revealed main effects
of both prime condition, F(2, 30) = 13.9, p < 0.001, and
K.S. Pilz et al./Cognition 118 (2011) 17–31
target viewpoint, F(2, 30) = 3.5, p < 0.05, but no interaction
between these two conditions. Importantly, post hoc
analyses of the prime condition showed that the approach-
ing prime stimuli gave rise to faster responses than the
receding trials, F(1, 15) = 4.3, p < 0.05. Additionally, both
F(1, 15) = 27.6,
F(1, 15) = 10.2, p < 0.001, trials were faster than static tri-
als. These effects are illustrated in Fig. 4. The main effect
of target viewpoint appeared to be driven by responses
to frontal view faces (M = 649 ms, SE = 49 ms) being faster
than responses to target faces oriented 22? to the right
(M = 684 ms, SE = 56 ms) or 22? to the left (M = 683 ms,
SE = 43 ms).
For different trials, there was no effect of prime condi-
tion, F(2, 30) = 1.14, p = 0.2, but a main effect of target
viewpoint, F(2, 30) = 4.4, p < 0.05. The effect of viewpoint
arose because responses to target faces oriented 22? to
the right (M = 733 ms, SE = 38 ms), were slower than re-
(M = 711 ms, SE = 40 ms), F(1, 15) = 9.9, p < 0.01, and to
frontal view faces (M = 699 ms, SE = 34 ms), F(1, 15) = 7.1,
p < 0.01. There was no interaction between prime condi-
tion and viewpoint for the different trials.
p < 0.001,and receding,
Accuracy was again about 80% on average (see Table 2).
A 2 (prime condition) ? 3 (viewpoint) repeated measures
same ANOVA on d0showed a significant effect of prime
condition, F(2, 30) = 4.8, p < 0.05. This was due to a slight
drop in sensitivity for approaching (M = 1.5, SE = 0.2),
F(1, 15) = 5.0, p < 0.05, and receding prime stimuli
(M = 1.5, SE = 0.2), F(1, 15) = 6.8, p < 0.05, compared to sta-
tic prime stimuli (M = 1.9, SE = 0.2). Importantly, there was
no difference in sensitivity between approaching and
receding prime stimuli, F(1, 15) = 0.02, p = 0.9.
Thenovel finding fromExperiment 2 wasthat
approaching trials gave rise to a reliable 25 ms advantage
compared to receding trials. Although both types of dy-
namic primes were responded to more quickly than the
static prime, suggesting that any form of motion in depth
may be helpful to encode identity, the familiarity and/or
salience of looming motion clearly has an additional effect.
We note that as the final frame was equated between the
approach and the receding sequence, the receding condi-
tion provided larger and higher resolution faces on average
than the former during the initial phase of the prime stim-
ulus. Thus, if performance depended on the quality of facial
snapshots seen during the prime sequence, the receding
condition would have been expected to lead to better
Alternatively, because we used an identical stopping
point, the images within the receding sequence were sub-
jected to a larger overall change in size compared to the
looming sequence. Such a change between prime and tar-
get could have disadvantaged receding trials (Kolers,
Duchnicky, & Sundstroem, 1985). To explore this issue,
we conducted a control experiment in which the looming
and receding sequences were identical but were simply
played forwards or backwards. That is, they neither started
nor finished in the same place, but contained equal image
variation. All six of the additional, naïve participants we
ran in this condition continued to show an advantage of
looming (M = 562 ms) over receding stimuli (M = 618 ms),
(F(1, 5) = 29, p < 0.01).
Finally, some caution needs to be observed when inter-
preting the general advantage of the dynamic primes over
the static prime conditions in this experiment. Although
Fig. 4. Reaction time differences in ms for the three probe conditions
‘looming’ (white bars), ‘receding’ (dark gray bars) and ‘static’ (black bars)
for Experiments 2 (left panel) and 3 (right panel). All RT results represent
same trials. Error bars represent 95% confidence intervals calculated with
the method described by Loftus and Masson (1994).
dynamically learned target
statically learned target
Fig. 5. Reaction times for correct trials in Experiment 4 for the target
learned in motion (gray) and the target learned as static (black) across set
size. Error bars represent 95% confidence intervals calculated with the
method described by Loftus and Masson (1994).
K.S. Pilz et al./Cognition 118 (2011) 17–31
the size of this advantage – 63 ms for approaching, 38 ms
for receding primes – was considerably larger than that ob-
served in Experiment 1, it was accompanied by a slight
drop in sensitivity. This pattern may simply reflect an over-
all increase in the difficulty associated with the variety of
prime type and target viewpoints, a notion supported by
a general slowing of RT relative to Experiment 1. However,
the nature of the immediate matching task does make it
possible for observers to adopt a speed-accuracy tradeoff
as part of their response strategy, a limitation that partly
motivated our decision to seek converging evidence from
a different task in Experiments 4 and 5.
5. Experiment 3
Experiments 1 and 2 suggest that body motion can af-
fect the processing of faces. More specifically, the highly
familiar event of seeing a person approach can improve
identity matching performance relative to either static
(Experiment 1) or receding (Experiment 2) baselines. An
obvious question to ask next is whether this advantage re-
lies on the presence of the body, or whether simply moving
an isolated head in depth would produce a similar effect.
To test this idea, in Experiment 3 we used exactly the same
stimuli and design as in Experiment 2, but simply masked
the bodies so that they were invisible. As mentioned in
Section 1, the use of computer-generated figures provides
the opportunity to easily design and create control condi-
tions, such as this.
We should note that as the heads were identical to
those used in Experiment 2, they not only scaled in size
and resolution as they approached, but also translated with
the slight variations in horizontal and vertical positions
that are characteristic of human walking. Phenomenologi-
cally, even in the absence of body form information, these
stimuli continue to convey a compelling sense of a person
moving in depth. Our question was whether this sense of
human motion would still influence the pattern of match-
ing results in the absence of an explicit body.
Fourteen right-handed observers aged 20–36 (mean
age: 24.7 years) participated in this study (six females
and eight males).
The same set of stimuli was used as in Experiment 2.
However, rather than presenting the whole figures we oc-
cluded the bodies so that only the heads could be seen
from the original sequences. To do this, we simply masked
the bodies so that they were invisible.
5.1.3. Task, design and data analysis
The task, design and data analysis were identical to
5.2.1. Reaction times
The data wereanalyzedusingthe same ANOVAmodelas
in Experiment 2. A 3 (prime condition) ? 2 (target condi-
tion) ? 3 (target viewpoint) repeated measures ANOVA re-
vealed main effects of target condition, F(1, 13) = 8.1,
p < 0.05, and target viewpoint, F(2, 26) = 14.5, p < 0.001,
and a target condition ? target viewpoint interaction,
F(2, 26) = 14, p < 0.001, but no effect of prime condition,
F(2, 26) = 3, p = 0.07. Analysing same and different trials
separately to investigate the origin of the interaction term
we found no effect of motion in the same, F(2, 26) = 1.7,
p = 0.5, or different, F(2, 26) = 1.8, p = 0.5, trials. The
ANOVAsdid not show a viewpoint effect in the differenttri-
als, F(2, 26) = 0.02, p = 0.9, but in the same trials, F(2, 26) =
35.0, p < 0.001. This effect was due to an advantage of
Reaction time (ms) data for Experiments 4 and 5. 95% Confidence intervals calculated with the method described by Loftus and Masson (1994) in brackets.
Target learned as loomingTarget learned as static Target absent
Experiment 41108 (71) 1412 (75) 1768 (77)1218 (40) 1601 (60)2115 (84)1458 (45) 2331 (67)3217 (132)
Target learned from multiple views Target learned from a single image
Experiment 51432 (129 1871 (115)2120 (58) 1200 (119)1688 (105) 2138 (181)1654 (67) 2584 (112)3593 (187)
Accuracy (% correct) data for Experiments 4 and 5. 95% Confidence intervals calculated with the method described by Loftus and Masson (1994) in brackets.
Target learned as looming Target learned as staticTarget absent
Experiment 489 (3) 92 (2) 91 (3)84 (2) 85 (2)86 (2) 85 (2)76 (3) 71 (3)
Target learned from multiple viewsTarget learned from a single image
Experiment 587 (3) 85 (3)90 (2)84 (4) 87 (3)88 (2) 84 (2)78 (2)69 (4)
K.S. Pilz et al./Cognition 118 (2011) 17–31
frontal targets (M = 579 ms, SE = 56 ms), over targets ori-
to the right
F(2, 26) = 69.7, p < 0.001, and targets oriented 22? to the left
(M = 621 ms, SE = 57 ms), F(2, 26) = 22.0, p < 0.001.
(M = 653 ms,
SE = 54 ms),
The ANOVA on d0s revealed both prime condition,
F(2, 26) = 5.00, p < 0.05, and target viewpoint effects,
F(2, 26) = 6.9, p < 0.01, but no interactions between these
factors. The viewpoint effect was due to decreasing sensi-
tivity with increasing viewpoint differences between
prime and target. The effect of prime condition was due
to a disadvantage in performance for looming primes,
M = 1.9, SE = 0.2, compared to both receding (M = 2.1,
SE = 0.2),
F(1, 13) = 9.3,
p < 0.01)
(M = 2.2, SE = 0.2), F(1, 13) = 9.5, p < 0.01. See Table 2 for
The isolated heads used in this experiment failed to
show the dynamic advantage observed in Experiments 1
and 2, despite the use of identical motion patterns. This
suggests that the context of the body plays a vital role in
the previous matching advantages. Fig. 4 indicates faster
responses to static compared to moving prime stimuli. This
trend, however, is not reliable. More generally, Fig. 4 indi-
cates that observers are faster and more accurate when the
body is absent (Experiment 3) than when it is present
(Experiment 2). The additional RT cost in Experiment 2
may reflect some sort of mandatory processing of the body
information (Thornton & Vuong, 2004). An alternative
explanation might be that the absence of the body in
Experiment 3 reduced the image difference between learn-
ing and test stimuli. Hence, the matching of the learning
and test stimuli might be facilitated in Experiment 3, lead-
ing to faster overall responses. However, given that reac-
tion times were fastest in Experiment 1, in which the
body was also present, another explanation for the RT cost
in Experiment 2 is simply that this set of observers was
generally responding more slowly compared to those in
Experiments 1 and 3.
The drop in sensitivity in Experiment 2 compared to
Experiment 3 could be due to a decreased signal-to-noise
ratio as all stimuli had the same body, thus providing no
useful matching information for the observer. Interest-
ingly, in the current experiment, observers were less accu-
rate at matching looming primes compared to both
receding and static primes. The difference is small but reli-
able and might arise due to the fact that the average size of
the looming heads was generally smaller than the size of
both the static and the receding heads. This did not affect
observers’ performance in Experiments 1 and 2, in which
the bodies were present but might have led to the de-
creased accuracy for looming primes in the current exper-
iment, in which the heads were presented without the
Fig. 6. Example stimulus as shown in the learning phase of Experiment 5. The matrix on the left depicts the one consisting of multiple static images, the one
on the right the one of a single image shown repeatedly.
different static frames
same static frame
Fig. 7. Reaction times for correct trials in Experiment 5 for the target
learned from different static pictures (gray) and the target learned from
same static pictures (black) across set size. Error bars represent 95%
confidence intervals calculated with the method described by Loftus and
K.S. Pilz et al./Cognition 118 (2011) 17–31
6. Experiment 4
In the first series of experiments (Experiments 1–3), we
used an immediate matching task to investigate whether
the approach of a person facilitates the short-term encod-
ing of their identity. The purpose of the next series of
experiments (Experiments 4–5), was to determine if this
dynamic advantage also holds across long-term encoding
of identity. Previously we used a delayed visual search par-
adigm to show that the matching advantages seen for non-
rigid facial motion (Thornton & Kourtzi, 2002) extended
across time (Pilz et al., 2006, 2009). Here, we used the same
delayed visual search paradigm to further investigate the
advantage seen in Experiments 1–2.
In the delayed visual search paradigm, observers are
first familiarized with two target faces using an incidental
learning technique (Knappmeyer et al., 2003). In the cur-
rent experiments, this involved images of two avatars
alternating on the screen while observes filled out a de-
tailed questionnaire about them. One sequence always
consisted of a single static image, the other was in motion
or contained relevant control manipulations. During test, a
visual search array of two, four or six static faces was pre-
sented. The task of the observer was simply to decide
whether one of the previously learned faces was present.
Previously, we have suggested that such delayed visual
search paradigms may be very useful for studying the dy-
namic aspects of face recognition over time (Pilz et al.,
2006, 2009). That is, extended exposure to a small set of
target identities may provide a better opportunity for dy-
namic information to have an effect, compared to brief
exposure to multiple targets, as is usually the case with
traditional old-new recognition tasks.
In Experiment 4, one target was presented in the con-
text of the approach sequence used in Experiments 1–3.
The other target was presented as static snapshots taken
from the approach sequence. Our question was whether
the dynamic context during learning would affect subse-
quent search behaviour.
Twenty-two right-handed observers aged 22–29 (mean
age: 23.5 years) participated in this study (16 females and
six males). None of the observers had participated in any of
the other experiments presented in this paper.
The stimuli used were as described in Section 3.1. For
learning, two avatars were randomly selected out of the
set of 15 rendered animation sequences for each observer,
one as a static frame, the other, as a video sequence of an
approaching person. For testing, 15 heads, those of the
two targets and thirteen additional ones, were used as dis-
tractor faces, each rendered from frontal view and 22? to
the right and 22? to the left.
6.1.3. Task and design
The delayed visual search task used in this experiment
consisted of a learning phase and a test phase. In the learn-
ing phase, observers were familiarized with two avatars as
targets. One of the targets was presented dynamically in an
approach sequence, whereas the other target was pre-
sented as static snapshot. These avatars alternated 100
times on the screen, each time presented for 600 ms with
an inter stimulus interval of 2 s. While watching the ava-
tars, observers filled out a questionnaire. They were asked
to rate factors such as the apparent attractiveness, age,
kindness, aggressiveness and intelligence of the two per-
sons, as well as to describe their prominent facial features.
After the avatars stopped alternating, observers were re-
quired to take a short break of approximately 3 min. This
method of learning has been shown to be effective in sev-
eral previous studies (Knappmeyer et al., 2003; Pilz et al.,
On each trial of the test phase, two, four or six static
faces were shown in a circular search array. Observers
were asked to respond as quickly and accurately as possi-
ble to whether either one of the learned faces (targets) was
present in the search pattern or not. The targets were pres-
ent on 66% of trials, with each of the familiarized faces
appearing equally often. Distractor faces were randomly
selected from the set described above. Observers re-
sponded ‘target present’ by pressing the ‘s’ key and ‘target
absent’ by pressing the ‘l’ key. Auditory feedback was given
for incorrect responses. Each trial started automatically
after a response was given. The experiment consisted of
450 trials, in which each target was present on 150 of
the trials. In the remaining 150 trials no target was pre-
sented. All set size by target-type and viewpoint trials oc-
curred with equal frequency and were randomized for
each observer individually.
6.1.4. Data analysis
In this and subsequent experiments, we examined both
the speed and accuracy of responses as a function of set
size on target-present trials. RTs from correct trials are re-
ported for trials in which one of the target faces was pres-
ent in the visual search array. Repeated measures were
used to compare the factors of interest (target type ? view-
point ? set size). Table 3 shows correct RTs and Table 4 the
accuracy data for both target-present and target-absent
trials for Experiments 4 and 5.
6.2.1. Reaction times
A 2 (target type) ? 3 (set size) ? 3 (viewpoint) repeated
measures ANOVA was used to explore correct target-
present trials. There was a main effect of target type,
F(1, 21) = 5.3, p < 0.05, with the target learned from
approaching sequences leading to faster visual search re-
sponses than the target learned from static snapshots.
There was also a main effect of set size, F(2, 42) = 90.0,
p < 0.001, with longer responses for arrays containing more
items. There was no main effect of viewpoint, F(2, 42) = 1,
p = 0.3, but a significant target type ? set size interaction,
K.S. Pilz et al./Cognition 118 (2011) 17–31
F(2, 42) = 3.0, p < 0.05. This interaction was due to a steeper
search slope for the target learned from static snapshots.
A 2 (target types) ? 3 (set size) ? 3 (viewpoint) repeated
measures ANOVA on accuracy did not reveal any main ef-
fects of target condition, F(1, 21) = 3, p = 0.1, viewpoint,
F(2, 42) = 2, p = 0.1, or set size, F(2, 42) = 1, p = 0.3, but an
interaction between viewpoint and set size, F(2, 42) = 5.5,
p < 0.01, which was mainly due to lower accuracy scores
for faces angles to the right at set size 2 and faces angles
to the left for set size 6.
Observers were significantly faster at finding a target
face in the search array if that face had been learned in
the context of a moving rather than a static avatar. This
advantage held across viewpoints. In addition, we found
a target type ? set size interaction. Search slopes for stati-
cally learned targets were steeper than slopes for dynami-
cally learned targets. This difference in slopes suggests that
the search for the dynamically learned target was more
efficient than the search for the statically learned one. Ta-
ken together these results suggest, that in addition to
short-term advantages, as shown in Experiments 1–2,
looming can also facilitate long-term encoding of identity
and can facilitate later recognition.
As the stimulus parameters for both learned avatars
were identical during the test phase, the origin of any dif-
ference between conditions must have occurred during
learning. As in Experiments 1 and 2, the stimulus motion
per se did not contain any information about the identity
of the moving person. Therefore, the present results under-
line the hypothesis that our visual system has mechanisms
that facilitate the encoding of stimuli that move in a rele-
vant and familiar way without containing relevant dy-
namic information about the specific identify of the
7. Experiment 5
Experiment 4 showed a clear advantage for recognizing
targets learned from an approach sequence in a visual
search task across viewpoints. In Experiment 5, we investi-
gate whether this advantage is due to the motion of the
face and body or simply due to the additional static infor-
mation contained in the individual frames of the moving
sequence. Observers were trained on static frames from
the moving sequence presented in a 4 ? 4 matrix. For
one target identity, this matrix contained the same static
view repeated 16 times. For the other target, 16 different
frames were randomly selected from the moving sequence
used in Experiment 4. Our question was whether this addi-
tional static information would lead to a speed and/or
7.1. Materials and methods
Thirteen right-handed observers aged 19–30 (mean
age: 27 years) participated in this study (ten females and
three males). One observer had to be excluded from further
analysis, because he had already participated in one of the
The stimuli were the same as in Experiment 4 except
that observers were familiarized with a matrix of 16 static
pictures during the learning phase. In one condition, the 16
pictures showed the same static frame as used in Experi-
ment 4 (single-picture condition). By comparison, in the
other condition, the matrix showed 16 different frames
from the moving sequences used in Experiment 4 (multi-
ple-pictures condition). These 16 frames were randomly
arranged in a different order on each presentation. This
manipulation ensured that observers saw more than one
static picture even if they tended to look at a preferred
location in the array. Fig. 6 gives an example of the stimuli
used during the learning phase.
7.1.3. Task, design and data analysis
The task, design, and data analyses were the same as in
7.2.1. Reaction times
A 2 (target type) ? 3 (set size) ? 3 (viewpoint) repeated
measures ANOVA revealed no main effects of target type,
F(1, 11) = 0.8, p = 0.4, or viewpoint, F(2, 22) = 2.3, p = 0.1,
but an effect of set size, F(2, 22) = 18, p < 0.001. Fig. 7
shows reaction times across set size for both target types.
2 (target type) ? 3 (set size) ? 3 (viewpoint) repeated
measures ANOVA revealed no main effects of target type,
F(1, 11) = 0.05, p = 0.8, or viewpoint, F(2, 22) = 0.9, p = 0.4,
but an effect of set size, F(2, 22) = 3.5, p < 0.05. Observers’
performance was best for set size 6 compared to set sizes
2 and 4 (see Table 4 for further detail).
The results of Experiment 5 suggest that the advantage
for learning approaching stimuli over static ones found in
Experiment 4 cannot be solely due to additional static
information provided in the looming sequence as com-
pared to a single static snapshot. Observers did not show
any difference in performance for the individuals they
learned from multiple static pictures versus the individual
they learned from a single static snapshot.
8. General discussion
In the current paper, we investigated whether the con-
text of an approaching person affected subsequent identity
K.S. Pilz et al./Cognition 118 (2011) 17–31
decisions. We examined both short retention intervals,
using a sequential matching paradigm (Experiments 1–3),
and long retention intervals, using a delayed visual search
paradigm (Experiments 4–5). In the first series of experi-
ments, we obtained a response time advantage for trials
in which a human figure was animated to approach the ob-
server, compared to static (Experiment 1) or receding
(Experiment 2) trials. We did not find an advantage of
approaching versus receding primes in which only isolated
heads were used (Experiment 3). The movement of the
whole body in depth thus seems to be an important factor
in obtaining the dynamic reaction time advantage.
The second series of experiments showed that the dy-
namic advantage for approaching figures obtained in
Experiments 1–3 persists over long retention intervals.
Viewing an approach sequence in the learning phase
speeded up performance in a subsequent visual search task
compared to a static learning phase (Experiment 4). Search
performance was also not influenced when additional sta-
tic information was provided in the learning phase, equat-
ing the views seen during static and dynamic approach
sequence (Experiment 5).
While the majority of research continues to treat faces
as isolated objects, the evidence presented here suggests
that the context of a moving body can directly influences
the processing of facial identity. If such context effects
can be found with a wider range of stimuli – using real vi-
deo and more natural actions, for instance – and if such ef-
fects also generalize to other tasks and dependent
measures (e.g., old/new recognition, accuracy), then this
could have important implications for the way faces are
studied, both in the laboratory and in applied, forensic set-
tings. We return to these issues below. First though, it is
important to consider how the dynamic effects observed
in the current experiments may have come about:
Previous studies have suggested that the addition of
motion might affect face processing in at least two differ-
ent ways (Lander & Bruce, 2000; O’Toole et al., 2002).
The ‘‘supplemental information hypothesis” suggests that
advantages for a moving face may arise when a particularly
distinctive smile, expression of surprise, or a nod becomes
represented as a characteristic pattern of movement (e.g.,
Knappmeyer et al., 2003; Knight & Johnston, 1997; Lander
& Bruce, 2000). In the current study, however, the moving
faces were not animated with expressive gestures and the
body motion was identical for all stimuli. Thus, there were
no characteristic dynamic cues to identity. The ‘‘represen-
tational enhancement hypothesis” suggests that the addi-
tionof motion indirectly
facilitating the recovery of facial structure. Changes of head
position during walking, and scaling of feature resolution
during approach, could potentially provide additional
information, compared to a single snapshot. However, the
lack of an advantage for isolated approaching heads
(Experiment 3) or multiple snapshots (Experiment 5)
would seem to argue against this explanation.
It seems almost certain that attention plays an impor-
tant role in the current findings. Increased deployment of
attention in dynamic conditions could affect both the qual-
ity and quantity of information encoded about the target
faces, thus leading to the observed pattern of results.
Motion alone is known to be a very effective cue for atten-
tion (e.g., Driver & Baylis, 1989; Franconeri & Simons, 2003;
Hillstrom & Yantis, 1994), although this explanation might
also predictan advantage for isolatedmovingheads(Exper-
iment 3). The human body, however, is likely to be very
effective at attracting and holding attention. It has been
shown that bodies are processed very rapidly (e.g.,
Johansson, 1975; Thorpe, Fize, & Marlot, 1996), and even
when the figure is not relevant to the current task (e.g.,
Bosbach, Prinz, & Kerzel, 2004; Thornton & Vuong, 2004),
which is also the case in the current study. In addition, body
orientation has been shown to be one of the primary cues
for joint attention (e.g., Lawson, Clifford, & Calder, 2009;
Nummenmaa & Calder, 2009) and the extraction of such
cues may well demand attention (Cavanagh, Labianca, &
Thornton, 2001; Chandrasekaran, Turner, Bülthoff, &
Thornton, 2010; Thornton,
Thornton, Rensink, & Shiffrar, 2002). For the stimuli in the
current study, this might suggest that if the moving figure
is attracting and holding attention, the related head and
face may also benefit relative to the static condition.
Aside from attracting and holding attention, the moving
body may also provide a context that influences the encod-
ing of information about the face. There are many exam-
ples in the literature showing that context can improve
the recognition of objects (e.g., Biederman, Glass, & Stacy,
1973; Chun & Jiang, 1999; Davenport & Potter, 2004; Pal-
mer, 1975; see Oliva & Torralba, 2007 for a recent review),
particularly when image resolution is low or target objects
are degraded (e.g., Biederman, 1981; Torralba, 2009).
While ‘‘context” typically refers to the background of a
scene, the functional relationship between co-occurring
objects has also been shown to be relevant (e.g., Chun &
Jiang, 1999; Green & Hummel, 2006). For example, a table
can facilitate recognition of a chair, and a nail, the recogni-
tion of a hammer (Green & Hummel, 2006). Within the
context of faces, de Gelder and colleagues have demon-
strated that whole body signals help to facilitate the pro-
cessing of expressions (de Gelder, 2006, 2009). In another
study O’Toole and colleagues showed observers real videos
of moving and static faces and whole persons and demon-
strated that human identification is best when the whole
person was seen in motion (O’Toole et al., in press). Even
though the moving body in our experiments is not infor-
mative, its presence may help guide face perception in
some way, particularly in early frames of the animation
where image resolution would be relatively low.
There is an additional sense in which a moving body
may provide a contextual advantage: Considerable evi-
dence suggests that the motor system is directly involved
in the visual perception of other people’s bodies. Theoreti-
cally, studies of ‘‘embodied cognition” have long postu-
lated that we use our bodies and actions to make sense
of the world (e.g., Clark, 1997; Lakoff & Johnson, 1999;
see Wilson, 2002, for a review) and the tight coupling be-
tween perception and action is extremely well docu-
mented (e.g., Humphreys & Riddoch, 2001; Prinz, 1997;
Riddoch, Humphreys, Edwards, Baker, & Willson, 2003;
Schutz-Bosbach & Prinz, 2007). The discovery of so-called
‘‘mirror neurons” in primates – cells that respond both to
the observation and execution of actions – have provided
K.S. Pilz et al./Cognition 118 (2011) 17–31
particularly important evidence in this regard (Kurata &
Tanji, 1986; Rizzolatti et al., 1988; for review see Rizzolatti
& Craighero, 2004). Functionally equivalent networks of
areas have also been proposed in humans (e.g., Decety
et al., 1997; Gazzola & Keysers, 2009; Kilner, Neal,
Weiskopf, Friston, & Frith, 2009; Rizzolatti et al., 1996). If
additional action-related processing occurred in the cur-
rent work when a moving body was present, this could
have strengthened or supplemented face-related informa-
tion contained in our dynamic stimuli.
The results of Experiment 2 showed that a looming hu-
man figure leads to better performance than a receding fig-
ure. Such an advantage is consistent with the finding that
many animals have evolved a bias for detecting and
responding to looming events due to their relevance for
survival (e.g., Maier & Ghazanfar, 2007; Maier et al.,
2004; Schiff, 1965; Schiff et al., 1962). Both the familiarity
and predictability of looming events could contribute to
this processing bias. For example, in primates, it has been
shown that more neurons are tuned to familiar views of
objects and faces than unfamiliar views (Wachsmuth,
Oram, & Perret, 1994). There is also evidence that the selec-
tivity of cells in temporal cortex is biased towards stimuli
experienced as an adult (Logothetis, Pauls, & Poggio,
1995, Perrett, Oram, & Ashbridge, 1998) and that spatio-
temporal predictability of such representations may en-
hance firing rates (Perrett, Xiao, Barroclough, Keysers, &
Oram, 2009). Similar experience-based neuronal plasticity
might also contribute to the current dynamic advantage.
Elsewhere, we have suggested that advantages for mov-
ing over static stimuli might reflect the involvement of
what Freyd (1987) termed, ‘‘dynamic mental representa-
tions”. The central notion here is that by retaining both
spatial and temporal dimensions relating to an event some
behavioural advantage may be achieved (e.g., allowing you
to anticipate the arrival of a dangerous projectile). Such dy-
namic mental representations have been introduced in a
number of perceptual domains including face recognition
(Thornton & Kourtzi, 2002), object recognition (Kourtzi &
Nakayama, 2001; Stone, 1998) and biological motion pro-
cessing (Cavanagh et al., 2001). Hubbard (2005) presents
a very comprehensive review of many of the issues relating
to this area.
Traditionally, such dynamic mental representations
were thought to influence short-term rather than long-
tem memory processes (Freyd, 1987; Freyd & Johnson,
1987; Kourtzi & Nakayama, 2001; Thornton & Kourtzi,
2002). Recently, however, Matthews et al. (2007) re-
ported memory advantages for dynamic versus static
scenes that persisted over 7 and 28-day retention inter-
vals. The delay of several minutes that we introduced
between study and test in the current delayed visual
search paradigm, although not nearly as dramatic, also
suggests the involvement of long-term memory systems.
Matthews et al. (2007) suggest that a spatiotemporal
version of ‘‘long-term” object file theory (Hollingworth
& Henderson, 2002; Kahneman, Treisman, & Gibbs,
1992) may be a useful framework within which to model
and explore the influence of dynamic mental representa-
tions. Relating this idea to the current work, it suggests
that during encoding information about position and
motion are explicitly stored along with other object
properties. During retrieval, such information may act
as additional cues to the identity of an object, leading
to performance advantages.
In this paper we have presented some initial evidence
that the motion of an approaching person can affect later
facial identity decisions. While we have tried to suggest
several potential explanations for this effect, clearly, more
research is necessary. It is quite possible, for example, that
other complex, multi-part objects, such as animals, bicy-
cles, or cars, would show a similar dynamic context effect.
We do believe that our results relate to the close functional
relationship between the body and the face. Other relation-
ships such as between a bicycle and its saddle, or a boat
and its sails might produce a similar advantage. This is
clearly a useful area of future research.
There are several other directions in which we feel this
work could be usefully extended. For example, it would be
interesting to establish whether other types of human ac-
tion afford a similar advantage. Would observing faces in
the context of bodies engaged in sports activities or every-
day actions, such as making a cup of tea, also lead to dy-
namic advantages? While we have tried to focus on tasks
that are appropriate for use with dynamic stimuli, it would
also be interesting to explore other paradigms, such as tra-
ditional old/new recognition. Tasks that focus more on
accuracy, rather than speed of response, would be particu-
larly interesting with a view to developing forensic or
other real-world applications.
On a related note, we mentioned in the introduction
that another important goal of the current paper was
to demonstrate the potential of using computer graphics
and virtual reality techniques in an experimental context.
We firmly believe that the added control and flexibility
offered by these methods make them invaluable tools.
Clearly though, our stimuli cannot be considered natural.
While photo-realistic animated figures may soon be
available, it may always make sense to confirm novel
findings, such as ours, with live-action video (e.g., Burton
et al., 1999; Roark, O’Toole, & Abdi 2003; Schiff et al.,
1986), even though the range of control conditions can-
not be replicated. Alone, neither approach may be suffi-
cient to fully understand the interaction between face
Finally, it will be interesting to probe into the neural
underpinning of the current dynamic advantage. The supe-
rior temporal sulcus (STS) has already been implicated in
studies of facial motion (e.g., Haxby, Hoffman, & Gobbini,
2000; Hoffman & Haxby, 2001; Puce, Allison, Bentin, Gore,
& McCarthy, 1998; Schultz & Pilz, 2009), body motion (e.g.,
Beauchamp, Lee, Haxby, & Martin, 2003; Grossman &
Blake, 2002; Pelphrey et al., 2003; Puce & Perrett, 2003;
Saygin, Wilson, Hagler, Bates, & Sereno, 2004), action
understanding, and social attention (Allison, Puce, &
McCarthy, 2000). In both tasks as used in the current paper,
we might predict increased activation in STS during the
presentation of the moving body, relative to the static con-
ditions. Of additional interest would be whether the static
facial images used as targets in both sets of studies would
also evoke responses in STS, even when there is no physical
K.S. Pilz et al./Cognition 118 (2011) 17–31
We would like to thank Isabelle Bülthoff and Lewis
Chuang for helpful discussions on earlier drafts of the man-
uscript. We gratefully acknowledge the support of the
Humboldt Foundation (Feodor-Lynen Stipend to KSP), the
Max Planck Society and the WCU (World Class University)
program through the National Research Foundation of Kor-
ea funded by the Ministry of Education, Science and Tech-
Allison, T., Puce, A., & McCarthy, G. (2000). Social perception from visual
cues: Role of the STS region. Trends in Cognitive Science, 4(7), 267–268.
Ball, W., & Tronick, E. (1971). Infant responses to impending collision –
Optical and real. Science, 171(3973), 818–820.
Beauchamp, M. S., Lee, K. E., Haxby, J. V., & Martin, A. (2003). FMRI
responses to video and point-light displays of moving humans and
Biederman, I. (1981). On the semantics of a glance at a scene. In M.
Kubovy& J. R. Pomerantz
(pp. 213–263). Hillsdale, NJ: Erlbaum.
Biederman, I., Glass, A. L., & Stacy, W. (1973). Searching for objects in real-
world scenes. Journal of Experimental Psychology, 97, 22–27.
Blanz, V., & Vetter, T. (1999). A morphable model for the synthesis of 3D
faces. In Proceedings, SIGGRAPH, Vol. 99, pp. 187–194.
Bosbach, S., Prinz, W., & Kerzel, D. (2004). A Simon-effect with stationary
moving stimuli. Journal of Experimental Psychology: Human Perception
and Performance, 30(1), 39–55.
Bower, T. G. R., Broughton, J. M., & Moore, M. K. (1970). Infant responses to
approaching objects: An indicator for response to distal variables.
Perception and Psychophysics, 9, 193–196.
Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10,
Bruce, V., Henderson, Z., Greenwood, K., Hancock, P. J. B., Burton, A. M., &
Miller, P. (1999). Verification of face identities from images captured
on video. Journal of Experimental Psychology – Applied, 5(4), 339–360.
Bruce, V., Henderson, Z., Newman, C., & Burton, A. M. (2001). Matching
identities of familiar and unfamiliar faces caught on CCTV images.
Journal of Experimental Psychology – Applied, 7(3), 207–218.
Bruce, V., & Young, A. (1986). Understanding face recognition. British
Journal of Psychology, 77(Pt. 3), 305–327.
Bülthoff, H. H., & Bülthoff, I. (1987). Combining neuropharmacology and
behavior to study motion detection in flies. Biological Cybernetics, 55,
Burton, M. A., Wilson, S., Cowan, M., & Bruce, V. (1999). Face recognition
in poor-quality video. Psychological Science, 10, 243–248.
Carney, T. (1997). Evidence for an early motion system which integrates
information from the two eyes. Vision Research, 37, 2361–2368.
Cavanagh, P., Labianca, A., & Thornton, I. M. (2001). Attention-based visual
routines: Sprites. Cognition, 80, 47–60.
Chandrasekaran, C., Turner, L., Bülthoff, H. H., & Thornton, I. M. (2010).
Attentional networks and biological motion. Psihologija, 43(1), 5–20.
Christie, F., & Bruce, V. (1998). The role of dynamic information in the
recognition of unfamiliar faces. Memory and Cognition, 26, 780–790.
Chuang, L., Vuong, Q. C., Thornton, I. M., & Bülthoff, H. H. (2006).
Recognising novel deforming objects. Visual Cognition, 14, 85–88.
Chun, M. M., & Jiang, Y. (1999). Top-down attentional guidance based on
implicit learning of visual covariation. Psychological Science, 10,
Clark, A. (1997). Being there: Putting brain, body, and world together again.
Cambridge, MA: MIT Press.
Davenport, J. L., & Potter, M. C. (2004). Scene consistency in background
and scene perception. Psychological Science, 15(8), 559–564.
de Gelder, B. (2006). Towards the neurobiology of emotional body
language. Nature Reviews, Neuroscience, 7, 242–249.
de Gelder, B. (2009). Why bodies? Twelve reasons for including bodily
expressions in affective neuroscience. Philosophical Transactions of the
Royal Society B, 364, 3475–3484.
Decety, J., Grézes, J., Costes, N., Perani, D., Jeannerod, M., Procyk, E.,
et al. (1997).Brain activity
Influence of action content and subject’s strategy. Brain, 120,
of Cognitive Neuroscience,15(7),
during observationof actions.
Derrington, A. M., Allen, H. A., & Delicato, L. S. (2004). Visual mechanisms
of motion analysis and motion perception. Annual Review of
Psychology, 55, 181–205.
Driver, J., & Baylis, G. C. (1989). Movement and visual attention: The
spotlight metaphor breaks down. Journal of Experimental Psychology:
Human Perception and Performance, 15, 448–456.
Franconeri, S. L., & Simons, D. J. (2003). Moving and looming stimuli
capture attention. Perception and Psychophysics, 65, 1–12.
Freyd, J. J. (1987). Dynamic mental representations. Psychological Review,
Freyd, J. J., & Johnson, J. Q. (1987). Probing the time course of
representational momentum. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 13, 259–268.
Gazzola, V., & Keysers, C. (2009). The observation and execution of actions
share motor and somatosensory voxels in all tested subjects: Single-
subject analyses of unsmoothed fMRI data. Cerebral Cortex, 19,
Green, C., & Hummel, J. E. (2006). Familiar interacting object pairs are
perceptually grouped. Journal of Experimental Psychology: Human
Perception and Performance, 32(5), 1107–1119.
Grossman, E. D., & Blake, R. (2002). Brain areas active during visual
perception of biological motion. Neuron, 35(6), 1167–1175.
Hassenstein, B., & Reichardt, W. (1956). Systemtheorische analyse der
Bewegungsperzeption des Rüsselkäfers Chlorophanus. Zeitschrift für
Naturforschung, 11b, 513–524.
Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human
neural system for face perception. Trends Cognitive Science, 4,
Henderson, Z., Bruce, V., & Burton, A. M. (2001). Matching the faces of
Hill, H., & Johnston, A. (2001). Recognizing sex and identity from the
biological motion of faces. Current Biology, 5(11), 880–885.
Hillstrom, A. P., & Yantis, S. (1994). Visual motion and attentional capture.
Perception and Psychophysics, 55, 399–411.
Hoffman, E. A., & Haxby, J. V. (2000). Distinct representation of eye gaze
and identity in the distributed human neural system for face
perception. Nature Neuroscience, 3(1), 80–84.
Hollingworth, A., & Henderson, J. M. (2002). Accurate visual memory
for previously attended objects in natural scenes. Journal of
Experimental Psychology: Human Perception and Performance, 28,
Hubbard,T.L. (2005). Representational
displacements in spatial memory: A review of the findings.
Psychonomic Bulletin & Review, 12, 822–851.
Humphreys, G. W., & Riddoch, M. J. (2001). Detection by action:
Neuropsychological evidence for action-defined templates in search.
Nature Neuroscience, 4, 84–88.
Johansson, G. (1973). Visual perception of biological motion and a model
for its analysis. Perception and Psychophysics, 14, 201–211.
Johansson, G. (1975). Visual motion perception. Scientific American, 232,
Kahneman, D., Treisman, A., & Gibbs, B. J. (1992). The reviewing of object
files: Object-specific integration of information. Cognitive Psychology,
Kilner, J. M., Neal, A., Weiskopf, N., Friston, K. J., & Frith, C. D. (2009).
Evidence of mirror neurons in human inferior frontal gyrus. The
Journal of Neuroscience, 29(32), 10153–10159.
Knappmeyer, B., Thornton, I. M., & Bülthoff, H. H. (2003). Facial
motion biases the perception of facial form. Vision Research, 43,
Knight, B., & Johnston, A. (1997). The role of movement in face
recognition. Visual Cognition, 4, 265–273.
Knoblich, G., Thornton, I. M., Grosjean, M., & Shiffrar, M. (2006). The
human body. Perception from the inside out. New York, NY: Oxford
Kolers, P. A., Duchnicky, R. L., & Sundstroem, G. (1985). Size in the visual
processing of faces and words. Journal of Experimental Psychology:
Human Perception and Performance, 11(6), 726–751.
Kourtzi, Z., & Nakayama, K. (2001). Dissociable signatures of processing
for moving and static objects. Visual Cognition, 9, 248–264.
Krekelberg, B., & Albright, T. D. (2005). Motion mechanisms in macaque
MT. Journal of Neurophysiology, 93(5), 2908–2921.
Kurata, K., & Tanji, J. (1986). Premotor cortex neurons in macaques:
Activity before distal and proximal forelimb movements. Journal of
Neuroscience, 6(2), 403–411.
Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The embodied mind
and its challenge to western thought. New York: Basic Books.
Applied CognitivePsychology, 15,
K.S. Pilz et al./Cognition 118 (2011) 17–31
Lander, K., & Bruce, V. (2000). Recognizing famous faces: Exploring the
benefits of facial motion. Ecological Psychology, 12, 259–272.
Lander, K., & Bruce, V. (2003). The role of motion in learning new faces.
Visual Cognition, 10, 897–921.
Lawson, R. P., Clifford, C. W. G., & Calder, A. J. (2009). About turn: The
visual representation of human body orientation revealed by
adaptation. Psychological Science, 20(3), 363–371.
Loftus, G. R., & Masson, M. E. J. (1994). Using confidence intervals
in within-subject designs. Psychonomic Bulletin and Review, 1,
Logothetis, N. K., Pauls, J., & Poggio, T. (1995). Shape representation in the
inferior temporal cortex of monkeys. Current Biology, 5, 552–563.
Maier, J. X., & Ghazanfar, A. (2007). Looming biases in monkey auditory
cortex. Journal of Neuroscience, 27(15), 4093–4100.
Maier, J. X., Neuhoff, J. G., Logothetis, N. K., & Ghazanfar, A. A. (2004).
Multisensory integration of looming signals by rhesus monkeys.
Neuron, 43, 177–181.
Matthews, W. J., Benjamin, C., & Osborne, C. (2007). Memory for moving
and static images. Psychonomic Bulletin and Review, 14(5), 989–993.
Nummenmaa, L., & Calder, A. J. (2009). Neural mechanisms of social
attention. Trends in Cognitive Science, 13(3), 135–143.
Oliva, A., & Torralba, A. (2007). The role of context in object recognition.
Trends in Cognitive Sciences, 11, 520–527.
O’Toole, A. J., Phillips, P. J., Weimer, S., Roark, D. A., Ayyad, J., Barwick, R.,
et al. (in press). Recognizing people from dynamic and static faces and
bodies: Dissecting identity with a fusion approach. Vision Research.
O’Toole, A. J., Roark, D., & Abdi, H. (2002). Recognizing moving faces: A
psychological and neural synthesis. Trends in Cognitive Science, 6,
Palmer, S. E. (1975). The effects of contextual scenes on the identification
of objects. Memory and Cognition, 3, 519–526.
Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics:
Transforming numbers into movies. Spatial Vision, 10, 437–442.
Pelphrey, K. A., Mitchell, T. V., McKeown, M. J., Goldstein, J., Allison, T., &
McCarthy, G. (2003). Brain activity evoked by the perception of
human walking: Controlling for meaningful coherent motion. Journal
of Neuroscience, 23(17), 6819–6825.
Perrett, D. I., Oram, M. W., & Ashbridge, E. (1998). Evidence accumulation
in cell populations responsive to faces: An account of generalisation
of recognition without mental transformations. Cognition, 67(1–2),
Perrett, D. I., Xiao, D., Barroclough, N. E., Keysers, K., & Oram, M. W. (2009).
Seeing the future: Natural image sequences produce ‘anticipatory’
neuronal activity and bias perceptual report. Quarterly Journal of
Experimental Psychology, 61, 2081–2104.
Pike, G. E., Kemp, R. I., Towell, N. A., & Phillips, K. C. (1997). Recognizing
moving faces: The relative contribution of motion and perspective
view information. Visual Cognition, 4, 409–437.
Pilz, K. S., Bülthoff, H. H., & Vuong, Q. C. (2009). Learning influences the
encoding of static and dynamic faces and their recognition across
different spatial frequencies. Visual Cognition, 17(5), 716–735.
Pilz, K. S., Thornton, I. M., & Bülthoff, H. H. (2006). A search advantage for
faces learned in motion. Experimental Brain Research, 171, 436–447.
Poggio, T., & Reichardt, W. (1973). Considerations on models of movement
detection. Kybernetik, 13, 223–227.
Prinz, W. (1997). Perception and action planning. European Journal of
Cognitive Psychology, 9, 129–154.
Puce, A., Allison, T., Bentin, S., Gore, J. C., & McCarthy, G. (1998). Temporal
cortex activation in humans viewing eye and mouth movements.
Journal of Neuroscience, 18(6), 2188–2199.
Puce, A., & Perrett, D. (2003). Electrophysiology and brain imaging of
biological motion. Philosophical Transactions of The Royal Society of
London Series B: Biological Sciences, 358(1431), 435–445.
Ratcliff, R. (1979a). Group reaction time distribution and an analysis of
distribution statistics. Psychological Bulletin, 86(3), 446–461.
Ratcliff, R. (1979b). Methods for dealing with reaction time outliers.
Psychological Bulletin, 114(3), 510–523.
Ratcliff, R. (1993). Methods for dealing with reaction time outliers.
Psychological Bulletin, 114(3), 510–532.
Riddoch, M. J., Humphreys, G. W., Edwards, S., Baker, T., & Willson, K.
(2003). Seeing the action: Neuropsychological evidence for action-
based effects on object selection. Nature Neuroscience, 6, 82–89.
Rizzolatti, G., Camarda, R., Fogassi, L., Gentilucci, M., Luppino, G., &
Matelli, M. (1988). Functional organization of inferior area 6 in the
macaque monkey. II. Area F5 and the control of distal movements.
Experimental Brain Research, 71(3), 491–507.
Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual
Review of Neuroscience, 27, 169–192.
Rizzolatti, G., Fadiga, L., Matelli, M., Bettinardi, V., Paulesu, E., Perani, D.,
et al. (1996). Localization of grasp representations in humans by PET:
1. Observation versus execution. Experimental Brain Research, 111,
Roark, D., O’Toole, A. J., & Abdi, H. (2003). Human recognition of familiar
and unfamiliar people in naturalistic video. In Proceedings of the IEEE
international workshop on analysis and modeling of faces and gestures,
Saygin, A. P., Wilson, S. M., Hagler Jr., D. J., Bates, E., & Sereno, M. I. (2004).
Poin-light biological motion perception activates human premotor
cortex. Journal of Neuroscience, 24(27), 6181–6188.
Schiff, W. (1965). Perception of impending collision—A study of visually
directed avoidant behavior. Psychological Monographs, 79, 1–26.
Schiff, W., Banka, L., & Galdi, G. D. (1986). Recognizing people seen in
events via dynamic ‘‘mug shots’’. American Journal of Psychology, 99,
Schiff, W., Caviness, J. A., & Gibson, J. J. (1962). Persistent fear responses in
rhesus monkeys to the optical stimulus of ‘‘looming”. Science, 136,
Schultz, J., & Pilz, K. S. (2009). Natural facial motion enhances the cortical
responses to faces. Experimental Brain Research, 194(3), 465–475.
Schutz-Bosbach, S., & Prinz, W. (2007). Perceptual resonance: Action-
induced modulation of perception. Trends in Cognitive Sciences, 11(8),
Shen, Y. C., & Franz, E. A. (2005). Hemispheric competition in left-handers
on bimanual reaction-time tasks. Journal of Motor Behaviour, 37, 3–9.
Shimizu, H. (2002). Measuring keyboard response delays by comparing
keyboard and joystick inputs. Behaviour Research Methods, Instruments
and Computers, 34(2), 250–256.
Stone, J. V. (1998). Object recognition using spatiotemporal signatures.
Vision Research, 38, 947–951.
Stone, J. V. (1999). Object recognition: View-specificity and motion-
specificity. Vision Research, 39, 4032–4044.
Thornton, I. M., & Kourtzi, Z. (2002). A matching advantage for dynamic
faces. Perception, 3, 1113–1132.
Thornton, I. M., Pinto, J., & Shiffrar, M. (1998). The visual perception of
human locomotion. Cognitive Neuropsychology, 15, 535–552.
Thornton, I. M., Rensink, R. A., & Shiffrar, M. (2002). Active versus passive
processing of biological motion. Perception, 31, 837–853.
Thornton, I. M., & Vuong, Q. C. (2004). Incidental processing of biological
motion. Current Biology, 14(12), 1084–1089.
Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human
visual system. Nature, 381, 520–522.
Torralba, A. (2009). How many pixels make an image? Visual Neuroscience,
Troje, N., & Bülthoff, H. H. (1996). Face recognition under varying poses:
The role of texture and shape. Vision Research, 36, 1761–1771.
Tronick, E. (1967). Approach response of domestic chicks to an optical
display. Journal of Comparative Physiology and Psychology, 64,
Vuong, Q. C., Hof, A. F., Bülthoff, H. H., & Thornton, I. M. (2006). An
advantage for detecting dynamic targets in natural scenes. Journal of
Vision, 6, 87–96.
Vuong, Q. C., & Tarr, M. J. (2004). Rotation direction affects object
recognition. Vision Research, 44, 1717–1730.
Vuong, Q. C., & Tarr, M. J. (2006). Structural similarity and spatiotemporal
noise effects on learning dynamic novel objects. Perception, 35,
Wachsmuth, E., Oram, M. W., & Perret, D. I. (1994). Recognition of objects
and their component parts – Response of single units in the temporal
cortex of macaque. Cerebral Cortex, 4, 509–522.
Wallis, G., & Bülthoff, H. H. (2001). Effects of temporal association on
recognition memory. Proceeding of the National Academy of Sciences,
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin
and Review, 9, 625–636.
K.S. Pilz et al./Cognition 118 (2011) 17–31