Perception of Biological Motion in Schizophrenia and
Healthy Individuals: A Behavioral and fMRI Study
Jejoong Kim1,2*, Sohee Park1,2, Randolph Blake1,2,3
1Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Korea, 2Department of Psychology, Vanderbilt University, Nashville, Tennessee, United
States of America, 3Vanderbilt Vision Research Center, Vanderbilt University, Nashville, Tennessee, United States of America
Background: Anomalous visual perception is a common feature of schizophrenia plausibly associated with impaired social
cognition that, in turn, could affect social behavior. Past research suggests impairment in biological motion perception in
schizophrenia. Behavioral and functional magnetic resonance imaging (fMRI) experiments were conducted to verify the
existence of this impairment, to clarify its perceptual basis, and to identify accompanying neural concomitants of those
Methodology/Findings: In Experiment 1, we measured ability to detect biological motion portrayed by point-light
animations embedded within masking noise. Experiment 2 measured discrimination accuracy for pairs of point-light
biological motion sequences differing in the degree of perturbation of the kinematics portrayed in those sequences.
Experiment 3 measured BOLD signals using event-related fMRI during a biological motion categorization task. Compared to
healthy individuals, schizophrenia patients performed significantly worse on both the detection (Experiment 1) and
discrimination (Experiment 2) tasks. Consistent with the behavioral results, the fMRI study revealed that healthy individuals
exhibited strong activation to biological motion, but not to scrambled motion in the posterior portion of the superior
temporal sulcus (STSp). Interestingly, strong STSp activation was also observed for scrambled or partially scrambled motion
when the healthy participants perceived it as normal biological motion. On the other hand, STSp activation in schizophrenia
patients was not selective to biological or scrambled motion.
Conclusion: Schizophrenia is accompanied by difficulties discriminating biological from non-biological motion, and
associated with those difficulties are altered patterns of neural responses within brain area STSp. The perceptual deficits
exhibited by schizophrenia patients may be an exaggerated manifestation of neural events within STSp associated with
perceptual errors made by healthy observers on these same tasks. The present findings fit within the context of theories of
delusion involving perceptual and cognitive processes.
Citation: Kim J, Park S, Blake R (2011) Perception of Biological Motion in Schizophrenia and Healthy Individuals: A Behavioral and fMRI Study. PLoS ONE 6(5):
Editor: Hans P. Op de Beeck, University of Leuven, Belgium
Received November 26, 2010; Accepted April 19, 2011; Published May 20, 2011
Copyright: ? 2011 Kim et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by NARSAD, and WCU program (R31-10089) through the National Research Foundation of Korea funded by the Ministry of
Education, Science and Technology, Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: Jejoong3@snu.ac.kr
Humans are remarkably adept at perceiving the actions and
intentions of others, an especially important skill befitting out
highly social nature . Called biological motion perception, this
skill has been extensively studied in the laboratory using point-light
(PL) animations of human activity portrayed exclusively by dots of
light depicting the trajectories of the limbs of an actor’s body .
Upon viewing PL animations, most people have no trouble
perceiving subtle characteristics of the PL actor including the
actor’s gender [3,4], identity , and social signals such as mood
. This paper deals with perception of biological motion in
people with schizophrenia, a psychotic disorder characterized by
debilitating deficits in a multitude of cognitive and social domains.
Psychophysical studies indicate that schizophrenia patients
exhibit deficits on a variety of visual tasks including judgment of
spatial location , discrimination of spatial frequencies , and
detection of visual motion [9–12]. One particularly intriguing
deficit uncovered in recent work from our laboratory was that
schizophrenia patients exhibit impaired performance on a task
involving discrimination of ordinary PL sequences of biological
motion from sequences in which the spatial location of the dots
were perturbed . In this study, we used a discrimination task in
which, on each test trial, patients viewed either a PL animation of
a person engaged in one of several, familiar activities (e.g. walking
or running) or an animation consisting of the same PL motions
spatially and temporally scrambled to perturb the normal
kinematics of the activity; the order of animations over trials was
random and following each trial the patient categorized the
animation as biological or perturbed. Signal detection analyses
revealed significantly lower categorization performance (d’) by the
schizophrenia patients compared to matched control participants,
and this reduction in performance arose primarily from their
abnormally high false alarm rates (i.e., judging a scrambled
PLoS ONE | www.plosone.org1 May 2011 | Volume 6 | Issue 5 | e19971
sequence as normal biological). These results imply that patients
may be generally less sensitive to the kinematics defining normal,
coordinated motion of the human body. If people with
schizophrenia are indeed less sensitive to the kinematics defining
human social actions, this could represent a significant perceptual
component related to the multiple deficits in the social domain in
schizophrenia . Such a social perceptual deficit could also
imply the existence of abnormalities in brain structures thought to
be involved in perception of biological motion .
Because of the potentially important implications of our initial
results, we performed three experiments aimed at documenting
the nature and possible neural bases of impaired perception of
biological motion in schizophrenia, using refined psychophysical
techniques coupled with fMRI brain imaging. Experiments 1 and
2 were designed to elucidate the perceptual bases of impaired
biological motion perception in schizophrenia. Results from those
two experiments, in turn, set the stage for Experiment 3. This was
a brain imaging study focusing on the posterior portion of the
superior temporal sulcus (STSp), a brain region widely considered
to be a lynchpin in a network of areas involved in registration of
socially relevant sensory information [16–21].
Experiment 1: Detection of biological motion
embedded in noise
In Experiment 1 we used a two-alternative, forced-choice
method (2AFC) to estimate thresholds for detection of biological
motion perception for PL sequences in noise dots that obscured
the spatio-temporal coherence of the dozen or so PL dots
describing human activity [22–24]. Two successive motion
sequences were presented, one in each of two intervals defining
a trial: one interval contained a dot sequence defining a biological
activity in noise and the other contained a scrambled version of
that sequence also embedded in noise, and the participants
indicated which of the two intervals contained a biological
sequence. By varying the number of noise dots over trials using
a staircase procedure, we determined the minimum signal-to-
noise-ratio supporting above chance performance on this 2AFC
task requiring discrimination of PL biological motion.
Materials and Methods
written informed consent was obtained from all participants after
they were given a complete description of the study. The
Institutional Review Board of Vanderbilt University approved
the protocol and consent procedure.
Fifteen outpatients (7 females and 8 males) who
met the DSM-IV  criteria for schizophrenia were recruited
from private psychiatric facilities in Nashville, Tennessee.
Exclusion criteria were head injury, neurological disorders,
substance use within the past 6 months, and IQ,85. Clinical
symptoms were assessed with the Brief Psychiatric Rating Scale
(BPRS). Positive and negative symptoms were assessed using
the Scale for Assessment of Positive Symptoms (SAPS) and the
Scale for Assessment of Negative Symptoms, respectively . All
patients were taking atypical antipsychotic drugs (risperidone,
olanzapine, or clozapine) at the time of testing.
Twelve healthy and medication-free controls (5 females and 7
males) were recruited from the same local community. They had
no DSM-IV Axis I diagnosis based on the Structured Clinical
Interview for DSM IV (SCID) . Exclusion criteria were history
of schizophrenia in themselves or in their families, head injury,
neurological disorders, substance use within the past 6 months,
and IQ,85. Control participants were also screened before the
In this and the following two experiments,
experiment to rule out elevated schizotypy using the Schizotypal
Personality Questionnaire (SPQ) ; none of those volunteers
had to be rejected on those grounds. Mean (SD) SPQ score was
All participants had normal or corrected-to-normal visual
acuity, and they wore their refractive correction during testing.
There were no statistically significant group differences in age, IQ,
handedness, or education level. Demographic information is
summarized in Table 1.
Animations consisting of black dots presented
against a white background were presented on a CRT monitor
(120 Hz, TOTOKU Calix CDT2141A, Japan) controlled by a
PowerMac G5 computer (Apple Inc, Cupertino, CA) running
Matlab? (Mathworks Inc. Natick, MA) and the Psychophysics
Toolbox [30,31]. The experiment was conducted in a dark room
illuminated by the screen only, with a 64 cm viewing distance
maintained by stabilizing the observer’s head using a chin/head
rest. Biological motion animations consisted of 12 dots denoting
the locations of the head, torso and joints of a human body
engaged in one of 24 distinct activities. Scrambled motion
sequences of each of those 24 activities were created by
randomizing the spatial locations of the dots in the first frame of
a sequence. The difficulty in discriminating biological from
scrambled animations was manipulated by presenting each
animation within a field of noise dots (see Figure 1A). The
motion trajectories of the noise dots corresponded to those of
biological or scrambled motion sequences on the same trial. This
form of masking is particularly effective in degrading perception of
biological motion [24,32].
On each trial of this 2AFC task, the participant
maintained fixation on a small cross located at the center of the
display monitor while viewing two successive, 1 sec sequences
(separated by 0.5 sec blank interval) defining a trial. One interval
contained the dot sequence defining a biological activity in noise
and the other contained a corresponding scrambled sequence
within the same level of noise. Following the two successive
presentations constituting a trial, the participant pressed one of two
keys on a computer keyboard to indicate which of the two intervals
contained the biological sequence, guessing if necessary. Auditory
feedback was provided following incorrect responses. The number
Table 1. The demographic data.
Age 34.0 (7.8)A
Sex (M/F) 7/58/70.79
Education (years) 15.9 (2.1)14.4 (1.7)0.063
WASI IQ Score104.4 (14.1) 95.9 (17.1)0.19
SAPS n/a14.21 (11.66)
SANS n/a19.85 (16.63)
Handedness (L/R/Bi) &
Illness duration (years) n/a14.7 (8.9)
AMean (standard deviation).
Biological Motion Perception
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of noise dots presented on a given trial was governed by a two-up/
one-down staircase procedure that converges onto the noise level
producing approximately 71% correct performance. The staircase
was terminated after 16 reversals, and the threshold was estimated
as the average number of noise dots over the last six reversals. A
sequence of trials began with20 noise dots, and the noise levels were
incremented and decremented in steps of 6 noise dots per change
for the first 12 reversals in the staircase and in steps of 3 noise dots
per change after that.
The size of each dot was 5-arc min, and the average dot speed
within a sequence was 4u/sec. The entire array of dots, noise dots
included, appeared within a virtual square region approximately
11u on a side, and the cluster of 12 dots defining biological or
scrambled motion fell within a square region subtending
approximately 7u on side centered on the fixation mark. The
exact spatial location of the biological figure and the correspond-
ing scrambled figure was varied from trial to trial by 1.4 deg visual
angle around the center of the noise field; this maneuver made it
impossible for participants to monitor just a small subset of dots to
judge which interval contained the biological sequence.
Mean (SE) noise levels (estimated threshold) are shown in
Figure 1B, and those values were were 40.83 (4.39) and 55.96
(4.36) for the schizophrenia group and for the control group,
respectively. This difference is statistically significant (t(26)=2.43,
p,0.03). Response times were not recorded on a trial-by-trial
basis, but the total elapsed time was not significantly different
between groups (t=1.22, p=0.23). It is unlikely, therefore, that
the performance differences are attributable to differences in the
length of time taken to arrive at a decision following each trial. We
also analyzed the trial-by-trial performance of each participant, to
learn more about the pattern of correct and error responses. The
mean (SE) number of trials for all staircases was 62.31 (2.31) in the
control group and 56.33 (2.11) in the schizophrenia group; this
difference is not statistically significant (t=1.91, p=0.067). A
participant had to be correct on the first 2 trials of the session for
the staircase to proceed to the next level of noise. Eight out of 12
control participants responded correctly on the first two trials,
compared to only 5 out of the 15 patients. This difference was
significant (Pearson x2=4.41, p=0.036). Thus individuals in the
schizophrenia group not only had more difficulty discriminating
PL sequences in noise, they also performed worse on the easier
trials. These early errors are not surprising, since our earlier study
 found that patients tended to confuse scrambled and
biological motion even in the absence of noise.
There were no significant correlations between performance on
the task and 1) symptom severity (BPRS: r=0.17, p=0.57; SAPS:
Figure 1. Experiment 1: Detection of biological motion in noise. A. Each trial consisted of two successive 1 sec presentations of PL animations
(separated by a 0.5 sec blank period), with one interval containing biological motion in noise and the other interval containing scrambled motion in
noise; the scrambled motion on each trial was always derived from the biological motion presented on that trial. The left panel shows one frame
depicting biological motion in the first interval (black dots indicate biological motion) and the right panel a frame of scrambled motion in the second
interval. In the actual experiment all dots appeared as black against a white background. Noise dots had the same local motion trajectories as those
of the biological or scrambled motion on that trial. The set of biological motion sequences totaled 24 distinct activities: 5 walking (stairway walking,
climbing, crossing a small object, and 2 plain walking with different viewing angle), 4 jumping (standing jump, leaping, rope-jumping, and high-
jumping), 4 kicking (toward front, side, and 2 soccer kicking), 2 running (plain and turning around), 6 throwing (3 overhead and 3 under-throwing),
and 3 crouching. B: Results of the biological motion detection task. Mean detection thresholds for the two groups are shown, together with error bars
indicating 61 standard error of the mean (SE).
Biological Motion Perception
PLoS ONE | www.plosone.org3 May 2011 | Volume 6 | Issue 5 | e19971
r=0.16, p=0.58; r=20.14, p=0.63) 2) other demographic
variables (age: r=20.22, p=0.43; education: r=0.24, p=0.39;
IQ: r=20.02, p=0.95; illness duration: r=20.23; p=0.46;
Edinburgh: r=0.10, p=0.72) and 3) medication (r=20.09;
These results indicate that schizophrenia patients experienced
greater difficulty distinguishing biological from non-biological
motion sequences when those sequences appeared within an array
of distracting noise dots, compared with healthy controls.
Specifically, the level of noise had to be approximately 30%
lower for schizophrenia patients to perform at the same level of
accuracy as controls. One could argue that this deficit is
attributable to a more general impairment involving perceptual
organization and figure/ground segmentation. Indeed, schizo-
phrenia patients exhibit impaired performance on some tasks
requiring integration of spatially distributed visual features [33,34],
although they perform equivalently to healthy individuals on other
perceptual organization tasks [13,35,36]. Successful discrimination
performance in the present experiment requires spatiotemporal
integration of the PL motion tokens signifying a given human
activity, and that integration process also depends on successful
figure (PL motion)/ground (noise) segregation.
To target the process of spatiotemporal integration that is not
confounded by figure/ground segregation, we employed an entirely
different task without noise elements in the second psychophysical
experiment: a more subtle, challenging task that depends crucially
on the ability to judge spatiotemporal coherence in PL animations
that are uncontaminated by extraneous noise dots. Because it was
impossible to create these kinematically perturbed animations
adaptively inrealtime, we had to administer thistaskas a method of
constant stimuli, not an adaptive staircase procedure.
Experiment 2: Perceptual discrimination of
perturbations in biological motion sequences
In Experiment 2, we compared how well schizophrenia patients
performed, relative to healthy controls, on a task involving
discrimination of pairs of PL sequences that differed in their
degrees of spatial perturbation of the dots defining a biological
activity. With these kinds of sequences, small amounts of
perturbation preserve the gross impression of biological motion
only up to some degree of perturbation, after which the sequences
look incoherent. On each trial, two differently perturbed PL
sequences generated from the same normal biological motion were
presented simultaneously and the participants were asked to
indicate which one of the two motion sequences looked more
Materials and Methods
Experiment 1 along with 6 new participants (2 patients and 4
controls). The two groups were matched demographically, and
those demographic data are shown in Table 2.
A seriesof parametrically
sequences was created from 10 different PL animations (listed in
the caption for Figure 2). The graded degrees of perturbation were
produced in the following way (see Figure 2A). The starting frame
of a given sequence (black dots in Figure 2A) was used to create a
corresponding 100% scrambled animation frame in which the
initial positions of each dot were spatially randomized within the
confines of a virtual display window (gray dots). Next, varying
degrees of perturbation from a normal sequence were created by
locating each and every dot of an animation sequence a given
distance between its normal and scrambled location; animations
were generated for each of four values of perturbation ranging
from 15% to 60% in steps of 15% (these values were selected
based on pilot work). For each of the 10 biological activities we
created exemplars of each of the 4 degrees of perturbation, and
these exemplars were combined to create the pairs (Figure 2B).
Participants were tested using a two-alternative, spatial
forced-choice procedure. On each trial, two PL sequences were
presented simultaneously for 1 sec, to the left and right of a central
fixation mark. Each pair always comprised the same biological
activity but the two sequences always differed by 15% in degree of
perturbation. Thus on each trial, the participant saw pairs
comprising 1 of 4 possible conditions: 0% vs. 15%, 15% vs. 30%,
30% vs. 45%, and 45% vs. 60%. Following each presentation, the
participant indicated which motion sequence looked more normal
by button press, guessing if necessary. Forty test trials were devoted
to each pair of perturbation differences, with the order of trials
randomized. Prior to formal testing, each participant viewed
multiple examples of the various degrees of perturbation as well
as examples of all of the normal biological motion. The two motion
sequences presented on each trial fell within a rectangular region
subtending approximately 9u (width) and 6u (height). During the
presentation of the PL pairs, the participant was allowed to
successively fixate the two sequences if desired.
Participantsincluded all individualsin
Mean (SE) accuracy levels for each perturbation condition are
shown in Figure 2C, and here it can be seen that even with small
degrees of perturbation participants in both groups made errors. A
repeated measures ANOVA revealed a significant main effect of
perturbation (F(3,93)=99.93, p,0.001), confirming that all
participants had increased difficulty discriminating pairs of
animation containing greater degrees of perturbation. A significant
main effect of diagnosis was also confirmed by ANOVA
(F(1,31)=28.99, p,0.001): schizophrenia patients were less
accurate in discriminating two differently perturbed motion
sequences compared to healthy controls. The interaction between
diagnosis and perturbation condition was also statistically
significant (F(3,93)=5.17, p,0.01), and this is obvious from the
graph: healthy controls showed an approximately linear decrease
in discrimination accuracy that fell to the chance level only for the
Table 2. The demographic data.
Sex (M/F) 8/810/70.61
Education (years) 15.5 (2.23)14.4 (1.7)0.12
IQ103.8 (12.9)101.8 (22.7)0.76
SAPS n/a14.9 (11.1)
SANS n/a21.1 (15.9)
SPQ13.3 (7.2) n/a
Illness duration (years) n/a15.1 (8.5)
CPZ equivalent (mg/day)n/a246.97 (119.3)
AMean (standard deviation).
Biological Motion Perception
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pair of 45% vs. 60%, whereas schizophrenia patients fell to chance
for the 30% vs. 45% stimulus pair. We also looked at each
individual’s performance at each of the four perturbation
conditions, to calculate how many observers in the two groups
performed above chance as defined by binomial distribution (i.e.
26/40 or greater % correct). Results shown in Table 3 point to the
same conclusion: patients found this task generally more difficult
than controls except at the highest degree of perturbation where
nearly all individuals found the task to be impossible.
In this experiment each trial involved presentation of a pair of
PL animations portraying the same activity at two different levels
of perturbation, and those PL animations could be any one of ten
different activities. Are the group differences in performance on
this task dependent on the particular activity being portrayed? To
answer that question we computed for each observer the percent-
correct performance for each of the ten animations separately,
pooling over the different degrees of perturbation (except for the
pair of sequences where performance was at chance for both
groups). The results of that analysis, shown in Figure 3, confirm
that degree of perturbation was more difficult to distinguish for
some PL animations compared to others, but the pattern of results
was the same for healthy controls and patients, with the
Figure 2. Experiment 2: Discrimination of perturbation of biological motion. A: A series of parametrically perturbed motion sequences was
created from 10 different PL animations each depicting a different human activity. These ten different PL animations comprised 2 portraying jumping
(standing jump, rope-jumping), 3 kicking (toward front, toward side, and soccer kicking), 3 throwing (tossing, bowling, overhead throwing), 1
crouching for high jump, and 1 backward walking. In this example, black dots indicate the dots forming a single frame of a normal biological PL
sequence and gray dots illustrate the corresponding frame of spatially scrambled version of this sequence. For example, dot A’ indicates a new
location of dot A when the motion is 100% spatially scrambled. The position denoted as (a) corresponds to an intermediate position that divides the
distance between A and A’ in the ratio of 15:85. Therefore, when the position ‘(a)’s are taken from all the other pairs of biological-scrambled dots, a
sequence containing 15% perturbed biological motion is generated. In the same way, (b),(c), and (d) represent the dot positions of 30%, 45%, and
60% perturbed motion. B: Single frame exemplars of the four discrimination conditions. Over trials, these pairs of animations portraying differing
degrees of perturbation were presented in random order, and following each trial the participant indicated which one (left or right) was closer to
unperturbed human motion. The % values below each figure refer to the percent of spatial perturbation. C: Performance (accuracy of discrimination)
on the task in the schizophrenia group (filled symbols) and the healthy control group (open symbols). Error bars indicate 61 standard error of the
mean (SE). Chance performance on this 2AFC task corresponds to 50% correct.
Biological Motion Perception
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correlation between groups being highly significant (r=0.85,
p=0.002). The variations in task difficulty associated with the
different animations, in turn, led us to wonder whether those
variations were related to the amount of body and limb motion
associated with the different activities. To estimate the overall
amount of motion in each of the ten motion exemplars, we derived
an index of motion energy for each exemplar defined as the total
angular deviation produced by each dot of a given unscrambled
animation during one cycle of the activity being portrayed. Those
index values, also shown in Figure 3 for each motion type, confirm
what is obvious from visual inspection of the animations, namely
that some entail larger body and limb motions than others. But the
correlation between these index values for each motion type and
the associated percent-correct performance for each type indicates
that the two factors are unrelated, both for healthy participants
(r=20.11, p=0.76) and for patients (r=20.03, p=0.92).
We examined whether patient performance was related to
symptom severity. Here we found that performance (mean accuracy
of the scrambling conditions excluding 45% vs. 60% condition) and
symptom severity were not significantly correlated (BPRS: r=0.31,
p=0.91, SAPS: r=20.13, p=0.96, SANS: r=0.37, p=0.15).
Other demographic variables were also uncorrelated with perfor-
mance (age: r=0.198, p=0.45, education: r=0.24, p=0.36, IQ:
r=0.39, p=0.13, handedness: r=0.17, p=0.51, illness duration:
r=0.09, p=0.74, medication: r=20.12, p=0.63).
One possible cause of the observed deficit of biological motion
perception is that patients may be generally less sensitive to the
spatio-temporal coherence defining normal body movements.
According to this view, schizophrenia patients might need more
salient spatio-temporal coherence to gain an impression of biological
motion; sequences with relatively large degrees of perturbation
appear equally disordered and therefore indiscriminable. Alterna-
tively, it could be that perturbed sequences strongly resemble
coherent biological motion to the patients, to the extent that both
sequences of a pair look normal and hence indistinguishable. The
higher false alarm rates exhibited by schizophrenia patients in our
previous study and in the behavioral task of Experiment 3 are
certainly consistent with this second alternative. In the Discussion we
consider this second alternative in greater detail.
The results from Experiments 1 and 2 set the stage for
examining possible neural concomitants of the impaired ability of
schizophrenia patients to discriminate biological motion sequenc-
es, a heretofore-unexamined question.
Experiment 3: An event-related fMRI study of
biological motion perception
From human brain imaging studies, there is a growing body of
evidence for the existence of a network of dorsal and ventral
stream cortical areas involved in the analysis of kinematic
information defining human action . One key component in
that network is found in the posterior portion of the superior
temporal sulcus (STSp). Within this area, neural responses are
stronger when one views motion of a human figure or human-like
robots , PL biological sequences [16,17,38,39] or biological
motion in noise . In contrast, STSp is not strongly activated by
scrambled PL sequences, by isolated pendular motions or by
mechanical motions lacking purposeful meaning . The
behavioral results from Experiments 1 and 2 naturally lead to
the following question: Are patterns of brain activation in
schizophrenia different from those in healthy individuals?
In Experiment 3, we used event-related fMRI to measure the
BOLD activity levels associated with viewing biological motion
sequences while participants–schizophrenia patients and normal
controls–performed a biological motion discrimination task that
allowed us to analyze separately brain activations measured on
correct trials and error trials. For brain scanning we targeted STSp
as well as motion-sensitive area MT, a neighboring visual area that
presumably implicated in deficient motion perception in schizo-
Materials and Methods
and 6 males) and ten healthy controls (5 females and 5 males)
participated in the experiment. Summary of demographic
information is shown in Table 4.
The same series of 24 distinct biological activities
Experiment 1 were presented at the center of the screen (note
that, unlike in Experiment 1, these sequences were not embedded
Ten outpatients with schizophrenia (4 females
scrambled motionsused in
Table 3. The number of subject who performed above
chance accuracy of binomial distribution in each perturbation
0% vs. 15%15% vs. 30%30% vs. 45% 45% vs. 60%
15 (75.44)*13 (69.12)3 (51.88)2 (51.88)
16 (89.06)16 (82.19)9 (65.47)1 (52.5)
*Mean accuracy (% correct).
Figure 3. Experiment 2: Performance for each distinct activity.
Each activity animation was quantified in terms of the total motion
energy in that animation (defined by the total excursion of dots over
space and time during the 1 sec presentation). The motion energy of
each animation is speicified by the y-axis on the left-hand side of the
graph, and the animations are ordered from most to least motion
energy along the x-axis. (1=standing jump; 2= kicking side;
3=crouching jump; 4=soccer kicking; 5=kicking front; 6=backward
walking; 7=bowling; 8=rope jumping; 9=tossing; 10=overhead
throwing). The histogram bars show average discrimination perfor-
mance associated with each activity pooled across all perturbation
pairings except the most extreme perturbations where performance on
the task was impossible. Filled bars are for schizophrenia patients and
open bars for healthy controls. There is no correlation between
performance and total motion energy.
Biological Motion Perception
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in noise). Each PL animation fell within a virtual rectangular
Animations consisted of 20 frames displayed within a 1 sec
period (50 msec/frame). In addition to the normal biological
motion and completely scrambled motion sequences, a series of
partially (37%) perturbed motion sequences was also used. The
spatial perturbation value of 37% was selected based on the result
from Experiment 2: schizophrenia patients performed at chance
level when required to discriminate 30% vs. 45% perturbed
biological motion, whereas controls exhibited above chance
accuracy (65.47%) for this pair of perturbations (Figure 2C).
Since only one sequence was displayed per trial, we decided to use
the degree of scrambling falling midway between 30% and 45%
Functional localization of STSp and MT.
event-related fMRI scans, we used conventional displays and
subtraction techniques to localize areas STSp and MT. STSp was
identified by comparing the BOLD signals associated with viewing
biological and scrambled motion animations in a block-designed
procedure (Figure 4A). Each participant viewed alternating
biological and scrambled motion blocks (7 blocks each lasting 14
sec). In each block, seven 1 sec animations were displayed with an
inter-stimulus interval of 1 sec. To maintain the observers’
attention, each block required performance of a 1-back task in
Table 4. The demographic data.
Controls (n=10) Patients (n=10)C
Age 38.7 (7.2)A
Sex (M/F) 5/5 6/40.65
Education (years)15.7 (2.7) 14.3 (2.45)0.24
IQ101.9 (11.8)100.3 (27.89) 0.87
BPRS 14.9 (6.6)
53.0 (59.6) 0.053
BSchizotypal Personality Questionnaire.
CTwo patients out of twelve were excluded from analyses because of lack of
behavioral response on the task during fMRI scans.
Figure 4. Experiment 3: Localization of regions of interest. A. Stimuli and procedures used to localize area STSp. Shown on the left are
examples of PL biological motion and scrambled motion, and on the right is shown schematically the block-designed runs for STSp localization. B.
Stimuli and procedures used to localize area MT. Dots moving radially inward and outward and static dots were presented in block-designed runs for
MT localization. C. Inflated whole-brain images (both hemispheres for one patient and for one healthy control) showing regions of interest identified
using the localizers described above.
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which observers were required to press a button whenever the
current motion sequence was identical to the one appearing in the
immediately preceding 1-sec presentation; the probability of a
repeated sequence was 0.50. The scan lasted 316 sec, with the
initial 8 sec (4 volumes, 1TR=2 sec) being discarded from
analyses to allow for MR saturation.
To localize MT, the participants viewed fourteen motion blocks
interleaved with fourteen static dot blocks and pressed a button at
every point of block switching (Figure 4B). The scan lasted 300 sec.
The motion sequence consisted of 380 dots (black against a light
gray background) that moved inward and outward from the center
of the display. The entire array of the dots fell within a virtual
circular region subtending 13u visual angle. The static dot field
had the same number of dots, but consisted of only 1 frame. Each
dot was approximately 6-arc min in size.
Event-related fMRI task.
The event-related design for the
biological motion task comprised nine runs each containing 24
trials consisting of eight biological, scrambled, and 37% scrambled
motion sequences in random order (Figure 5A). We elected to use
a constant inter-stimulus interval of 11 sec, to insure that the
hemodynamic response associated
presentation . The participant was always aware of the
timing of the next, forthcoming stimulus because the fixation cross
changed size 2 sec before that event. Immediately following each
witha given stimulus
stimulus presentation, participants judged whether the given
motion depicted a human activity or not by pressing one of two
pre-assigned buttons of the hand-puck being worn in the scanner.
The total number of trials was 216, and participants were allowed
to rest between runs if they so requested.
All brain images were collected on a
Philips Intera Achieva 3T MRI scanner located at the Vanderbilt
University Medical Center, Nashville, TN. High-resolution T1
anatomical images were collected for each participant (170
slices, 1.061.061.0 mm). Functional images (single-shot EPI,
TR=2000 ms, TE=25 ms, flip angle=90u, matrix=1286128,
FOV=2406240 mm) were acquired over the whole brain,
parallel to AC-PC line (25 slices, 1.87561.875 mm in plane,
4.5 mm thick with 0.45 mm gap). Visual stimuli were presented
using a DLP projector connected to a Macintosh G4 computer
(Apple Inc., Cupertino, USA). The projector’s image was back-
projected onto a screen located at the observer’s feet and viewed
through a periscope mirror attached to the head coil.
Imaging data were preprocessed and
analyzed using Brain Voyager QX 1.10 (Brain Innovations,
Maastricht, The Netherlands). The anatomical volumes were
transformed into stereotaxic space , and functional volumes
for each participant were aligned to these transformed anatomical
volumes. Functional volumes were also preprocessed following
procedures including realignment, three-dimensional motion
Figure 5. Experiment 3: Event-related portion of brain imaging study. A. The schematics of successive animation frames shown on the left
are examples of the three categories of PL animations presented in an event-related fMRI design shown on the right. B. Mean(SE) d’ on the biological
motion task performed during the event-related functional scan. C. Hit and false alarm rates associated with the d’ values shown in panel B.
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correction, linear de-trending, high-pass temporal frequency
filtering, and spatial smoothing with a 4 mm FWHM spatial filter.
To localize regions of interest (ROIs), the general linear model
(GLM) was applied to the time-series of task-related functional
volumes. ROIs for each individual were then defined as
contiguous voxels within the anatomical region of cortex
corresponding to the caudal portion of the superior temporal
sulcus that were significantly activated by biological motion
relative to scrambled motion at a false discovery rate (FDR) of
q,0.05, or p-value (uncorrected) lower than 0.003 (if STSp is not
successfully localized at the given FDR). The same analysis was
applied to voxels in the general anatomical region of the human
MT+ complex, this time contrasting activations to optic flow and
static dots. Among observers the numbers of voxels identified by
these methods ranged from 15–174 in STSp and 382263 in MT+.
To analyze the functional imaging data, the design matrix
(reference time course) was defined to include 4 predictors based on
each individual’s behavioral response: (1) activation associated with
hits (‘‘biological’’ response to biological motion), (2) activation
associated with correct rejection (‘‘scrambled’’response toscrambled
motion), (3) activation associated with false alarms (‘‘biological’’
response to scrambled motion), and (4) overall activation to 37%
scrambled motion. While there is no objectively correct answer for
trials involving 37% scrambled motion, we initially intended to
analyze the fMRI results for those trials based on observers’
perceptual judgement (‘‘biological’’ vs. ‘‘scrambled’’); as reported in
the Results, however, there were too few ‘‘biological’’ responses to
make that possible. Miss trials (‘‘scrambled’’ responses to biological
motion) also had to be excluded from fMRI analyses because of the
paucity of these trials. Within the defined ROIs, the voxels coupled
with the event-related trials were averaged to create a single time
series for each condition (predictors) in each individual. MR signal
levels coupled with each condition were averaged to create an
estimate of BOLD activity through the process of event-related
averaging in Brain Voyager QX. Percent change in BOLD signal
associated with each condition was defined as difference between
baseline (activation at the stimulus onset) and the peak activity
following stimulus onset. That peak was identified from the actual
BOLD signal values plotted over time, not estimated from fitted
hemodynamic response functions.
motion trials, schizophrenia patients had significantly lower
discrimination sensitivity (d’) compared to healthy controls
(Figure 5B), consistent with our earlier study . Mean (SE) d’
was 2.54 (0.4) in patients and 3.82 (0.32) in controls (t(18)=2.45,
p=0.024). Both groups had high hit-rates (controls: 98.4 (1.08)%,
On the biological motion and scrambled
patients: 94.6(3.42)%, p=0.2). The difference in the incidence of
false alarms was large (Cohen’s d=0.75) but failed to achieve
statistical significance (controls: 19.7(5.8)%, patients: 37.7(9.7)%,
p=0.13,) (see Figure 5C). Behavioral results from the 37%
scrambled motion trials reveal that patients and controls tended to
categorize these animations as scrambled, although the incidence
of biological responses was larger in the patient group (19%, on
average) compared to the control group (4%, on average). Strictly
speaking, these ‘‘biological’’ responses cannot be categorized as
incorrect, because sequences with 37% scrambling do look more
biological than 100% scrambled sequences. (In informal pilot
testing of controls and schizophrenia patients, all participants rated
both 30% and 45% sequences as more human-like than 60%
scrambled sequences, so 37% is undoubtedly seen as different from
scrambled.) Still, the higher incidence of ‘‘biological’’ responses
from schizophrenia patients viewing the 37% scrambled sequences
certainly comports with results from our earlier study  where
patients had higher false alarm rates than normal controls.
Unfortunately, the number of these kind of trials was insufficient to
permit analysis of imaging data on trials where 37% scrambled
was judged biological.
The behavioral performance (as indexed by d’) of the patients
measured while they were in the scanner was not significantly
correlated with the severity of their clinical symptoms as indexed
by rating scales (BPRS, SAPS, and SANS).
Localization of STSp and MT.
the STSp was functionally localized by subtracting activation to
scrambled motion from activation to biological motion at the
threshold of q(FDR),0.05. In the tenth healthy control partici-
pant, STSp was localized by applying p,0.003 (uncorrected)
because the area was not clear at q(FDR),0.05. Among the ten
members of the schizophrenia group, STSp was localized in six
individuals at the threshold of q(FDR),0.05. For two other
patients, that threshold level had to be adjusted to p,0.003
(uncorrected) to localize STSp. For the remaining two patients,
STSp could not be localized even by this more lax criterion. As an
alternative, the location of STSp in their brains was estimated by
identifying significant BOLD activations during biological motion
blocks relative to baseline and then delimiting the activated voxels
to just those successfully localized in other eight patients.
Area MT was successfully localized in all participants by con-
trasting activation to optic flow stimuli with that to a static dot field.
Mean (SD) Talairach coordinates of the two ROIs are shown in
Table 5, which is similar to those of a previous study . For
reference, Figure 4C shows those ROIs–STSp and MT–in one
normal control and one schizophrenic patient.
Event-related activity in STSp.
BOLD responses for hit, correct rejection, and false alarm trials
In nine healthy participants,
Group averaged peak
Table 5. Talairach coordinates of the defined ROIs.
Left hemisphereRight hemisphere
258.0 (8.72)10.3 (5.7) 49.5 (7.6)
253.5 (8.9)9.6 (5.3)
20.9 (5.0) 44.1 (1.9)
263.8 (5.6)0.4 (5.2)
255.6 (8.1)8.6 (5.1)48.1 (9.1)
254.6 (10.8)9.1 (3.9)
21.11 (4.63)43.0 (3.68)
Mean coordinates and standard deviations (in parentheses) for the two ROIs (posterior STS and MT) in each group. CO: controls, SZ: schizophrenia patients.
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are shown in Figure 6A. We have no control, of course, over the
number of trials contributing to these three categories, for those
categories are defined by the stimulus and by the participants’
responses. Because hit rates were higher than false alarm rates for
both groups, more fMRI BOLD signal estimates comprise the
responses associated with hits than with the other two categories,
but this is true for both healthy controls and patients. Moreover, a
multifactorial repeated measures ANOVA confirmed that overall
activations across these three categories did not differ significantly
between groups (F(1,30)=0.031, p=0.86). The main effect of the
signal detection category was not significant, either (F(2,60)=0.93,
p=0.40), but the interaction between signal detection category
and diagnosis was significant (F(2,60)=4.57, p=0.014). ANOVA
with two selected categories also revealed significant interaction
effects (hit vs. correct rejection: F(1,30)=6.89, p=0.01; correct
rejection vs. false alarm: F(1,30)=9.63, p=0.004). These statistical
analyses confirm the impressions portrayed by the patterns of
results seen in the summary data in Figure 6B.
Summarizing those results for the two groups separately, healthy
individuals produced significantly greater STSp activation on hit
trials (biological motion perception) than on correct rejection trials
(scrambled motion perception) (F(1,16)=12.05, p=0.003). Interest-
ingly, STSp activations on false alarm trials were not significantly
different from activations on hit trials (F(1,16)=1.22, p=0.29),
suggesting that people with strong STSp activation on a given trial
motion. This correlation between perceptual state and brain
activation has been reported for other visual tasks as well , and
it is a point we return to in the Discussion. On the other hand,
schizophrenia patients did not show differential activation for the
three signal detection categories: levels of STSp activation within
patients were not significantly different across hits, correct rejections
and false alarms (F(2,24)=0.512, p=0.65). The same conclusion is
reached when we perform pair-wise comparisons of hits to false
alarms (t(12)=0.21, p=0.84), hits to correct rejections ( t(12)=
20.72, p=0.49) and correct rejections to false alarms (t(12)=1.107,
p=0.29). This absence of differential activation in patients quite
plausibly could contribute to their poor ability to discriminate
biological motion from scrambled motion, for within STSp those
two categories of animations produce highly similar levels of activity.
It is natural to wonder why STSp in patients showed no
differential activation on hit and correct rejection trials event though
the two categories of PL animations presented on those trials–
biological and scrambled–were used successfully in 8 out of 10
patients to identify STSp on the localizer trials. These differential
results, we surmise, are attributable to the fact that block designs
typically generate more robust BOLD signals than do event-related
designs. This was certainly true for our experiment: average peak
activations (biological and scrambled) for STSp in patients averaged
0.59% in the block design but only 0.4% in the event-related design.
Figure 6. Experiment 3: Brain imaging results from STSp. A: Average time-series associated with each of three signal detection categories (hit,
correct rejection, false alarms) in controls (left) and patients (right). Time value 1 denotes stimulus onset (TR=2 sec). B: Each histogram plots, for
patients and healthy controls, the peak BOLD signal levels (1 SE) associated with each of the three signal detection categories. C. Same as panel B,
with data removed for the two schizophrenia patients for whom STSp localization was based on anatomy, not differences in activations on the STSp
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The tasks, too, were different for the two designs, although it is not
obvious why the one-back task (used in the block design) would
promote differences between scrambled and biological whereas the
categorization (used in the event-related design) would not. We will
consider the general question of task performance and event-related
imaging results further in the Discussion.
As mentioned earlier, STSp was not successfully localized using
conventional statistical methods in two of the ten schizophrenia
patients. To be sure their results were not responsible for the lack of
BOLD signal differences between scrambled and biological
sequences in the schizophrenia group, we reanalyzed the group
data with those two individuals’ data removed (n=8). This
additional analysis yielded the same pattern of results (Figure 6C):
the main effects of diagnosis and of signal detection category were
not significant (F(1,28),0.001, p=0.99; F(2,56)=0.67, p=0.52,
respectively) and the interaction effect was significant (F(2,56)=4.22,
p=0.02). Interaction effects between two signal detection categories
(hit vs. correct rejection; correct rejection vs. false alarm) were also
significant (F(1,28)=5.2; p=0.03; F(1,28)=9.32, p,0.01, respec-
tively). We are thus confident that the two patients in whom STSp
was not conventionally localized were not the sole source of the
overall differences between patients and normal controls.
As mentioned before, the paucity of ‘‘biological’’ responses in
the 37% scrambled condition precluded statistical analyses of the
fMRI results for this condition contingent on the perceptual
report. We were able, however, to perform group comparisons of
the overall activation levels for this stimulus condition irrespective
of response category, and those activations did not differ between
the groups (t(30)=0.93, p=0.36).
Clinical symptom scores from the schizophrenia patients were
not significantly correlated with STSp peak activation in any signal
procedures were applied to MT activations measured during the
biological motion task (see Figure 7). There was no significant
group (diagnosis) difference in overall activation (F(1,35)=2.24,
p=0.14), nor a significant main effect of signal detection category
(F(2,70)=1.43, p=0.25). Unlike STSp activation, the diagnosis6
signal detection category interaction effect was not significant
(F(2,70)=0.18, p=0.84), indicating that MT activation is not
associated with stimulus type or observer’s response.
To learn whether MT activity level is related to STSp activation,
we computed the correlation between peak activations between the
two areas. In healthy controls, there were no significant correlation
between STSp and MT activation in any of signal detection
categories. In patients, the correlations between STSp activity and
MT activity for hit trials and correct rejection trials were not
significant. There was a significant correlation on false-alarm trials,
but it was restricted to the left STSp and MT (r=20.88, p=0.047
in left; r=0.25, p=0.51 in right). However, the sample size was
small: left STSp was localized in only 5 patients, including one who
exhibited extraordinarily strong activation. In general, we see no
strong indication that MT activation predicts responses in STSp
when results are analyzed contingent on the participants’ responses
to given categories of stimuli.
The behavioral experiments confirm that schizophrenia pa-
tients, compared to healthy controls, experience difficulty
distinguishing biological motion from non-biological motion
sequences. We have now seen these group differences on three
complementary tasks: simple categorization of sequences as
biological or scrambled (Reference 13, and the behavioral
Figure 7. Experiment 3: Brain imaging results from MT. Same format as in Figure 6.
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component of Experiment 3), discriminating biological from
scrambled in the presence of distracting noise (Experiment 1)
and discriminating biological motion sequences in which the
spatio-temporal coherence of the dots defining kinematics is
perturbed (Experiment 2). Moreover, we have identified a
potential neural correlate of this deficit in the BOLD signals
measured from STSp, a brain area known to be involved in
perception of biological motion.
Can these results be attributed to the fact that all patients in this
study were taking antipsychotic medication at the time of testing and
scanning? Past perceptual and cognitive studies with schizophrenia
patients have not found significant differences in performance
between medicated and non-medicated patients (e.g. ), nor have
they found a significant correlation between medication and
performance , which is what we too observed in the present
study. We are, therefore, disinclined to believe that medication alone
is the sole factor responsible for our patients’ performance deficits.
What, then are the reasons for these deficits? In the following
paragraphs we consider alternative interpretations of these findings.
Starting with the psychophysically measured perceptual deficits
in schizophrenia patients, it is reasonable to ask whether they are
unique to biological motion or, instead, stem from a more general
problem in motion perception. Indeed, earlier work has shown that
schizophrenia patients require stronger translational motion signals
to discriminate direction of motion in random-dot cinematograms
(RDC) containing signal and noise dots . And it is true that our
masking study involved detecting biological motion figures
embedded in dynamic noise dots, similar to the conventional
RDC task. For several reasons, however, we believe that the deficits
perceiving biological motion go beyond simply a deficit in
perceiving signal dots within noise. First, the discrimination task
(Experiment 2) did not involve noise dots, yet deficient performance
was observed. The same is true for our earlier task  and for the
behavioral task employed in our brain imaging study (Experiment
3). Second, the stimulus information supporting detection of weak
translational motion within fields of random dots is fundamentally
different from the information specifying the hierarchical, pendular
motions of dots creating the vivid impression of biological motion.
Third, these two disparate forms of motion perception appear to be
mediated by distinct neural mechanisms as evidenced by their
different integration time constants , their dissociation conse-
quent to brain damage [48–50], and the different activations they
produce duringimaging studies innormal people (e.g. ). Fourth,
neither controls nor patients showed differential MT activation
contingent on signal detection categories, and furthermore, we
found no meaningful correlations between MT and STSp peak
activations ineithergroup.Theseobservationsimplythat the neural
at least in part, from those involved in biological motion perception
[1,51]. Based on four these reasons, we believe the deficits observed
on these various tasks involving biological motion sequences are not
attributable solely to difficulties perceiving motion in general but,
instead, arise from impairments in extracting the kinematics unique
to biological motion and effectively isolated using PL animations.
Is it possible that this deficit in perception of biological motion
perception in schizophrenia patients is related to a more general
problem involving visual grouping of spatially distributed visual
elements? We know, for example, that chronic schizophrenic
patients are impaired in their ability to recognize objects portrayed
in fragmented images in which portions of the contours defining
the objects are invisible . This task presumably taps into an
ability to fill in missing information using a contour interpolation
processes. Perceiving biological activity from PL animations could
also be construed as involving interpolation of missing informa-
tion, in this instance information ordinarily available when viewing
whole-body movements and not just the movements of select
portions of the body designated by PLs. We have no quarrel with
this way of characterizing the nature of the task, and we are
intrigued by findings implicating impaired dorsal stream process-
ing as a correlate of deficits in perceiving fragmented objects by
schizophrenia patients . After all, STSp is a component of this
broad dorsal stream network. We are reluctant to conclude,
however, that the neural processes involved in perceiving static
fragmented figures are the same as those responsible for
perception of dynamic activity portrayed by PL sequences since
the latter, but not the former, requires integration of information
over time as well as over space. It would be interesting indeed to
examine correlations in performance on these rather different tasks
in schizophrenia patients and, for that matter, in healthy controls.
In a related vein, it is conceivable that the difficulties
experienced by schizophrenia patients when viewing PL anima-
tions is somehow related to the well-established abnormalities in
temporal integration in these patients [54,55]. Perhaps in our tasks
the 1-sec presentation durations, while adequate for healthy
controls, are simply too brief for sufficient visual processing by the
patients. We doubt that the presentation duration limited their
ability to fixate the displays, for in two of our tasks (Experiments 1
and 3), the PL animations were presented at fixation. In the task
involving simultaneous presentation of two animations, saccadic
eye movements would be required to achieve successive glances of
the stimuli, but existing evidence indicates that simple saccadic eye
movements are intact in schizophrenia patients [56–58]. But it is
possible that limitations in integration of visual information over
time contribute to the perceptual deficits documented in our study.
Indeed, it is known that temporal summation for perception of
biological motion extends beyond one second , so an
impairment in temporal integration could place patients at a
disadvantage relative to healthy controls. It could be informative
to assess temporal integration in perception of biological motion in
patients, by systematically varying exposure duration.
Turning now to the brain imaging results, in healthy control
participants STSp activation was stronger when biological motion
was perceived correctly (hits) than when scrambled motion was
perceived correctly (correct rejection). This merely confirms what
was already known, namely that STSp selectively responds to
biological relative to scrambled PL sequences [2,47,50,59]. In
contrast, however, schizophrenia patients showed comparable
levels of event-related activations in STSp across all signal
detection categories, including those where the stimulus involved
presentation of scrambled motion. For that matter, we also had
more difficulty pinpointing STSp in a couple of our schizophrenia
patients using our localization procedure that contrasts biological
and scrambled sequences in a simple block design. At present we
can only speculate about possible reasons why STSp in these
patients produced strong, undifferentiated responses to these
different categories of PL animations. It is well known that
schizophrenia is characterized by reduced grey matter volume in a
variety of brain areas including the superior temporal lobe.
Moreover, there is growing evidence that schizophrenia is
associated with disordered neural connectivity among brain areas
(see review ). To the extent that those connections mediate
inhibition of during task-related activities , we might expect
schizophrenia patients to exhibit reduced suppression of activity
within brain areas important for registering information kinemat-
ics, just as a lack of suppression may underlie their poorer
performance on working memory tasks .
Given these event-related results from schizophrenia patients, it is
natural to wonder what neural information they were using when
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trying to perform the behavioral tasks we administered to them.
After all, their behavioral performance, while reduced relative to
normal controls, implies that they could distinguish scrambled from
biological sequences at above chance levels. One possibility is that
brain areas other than STSp contain neural responses sufficient to
signify the nature of the PL animation being viewed . To
evaluate that possibility, we looked throughout all brain volumes
scanned during event-related brain imaging in our schizophrenia
patients, in search of voxels showing reliable signal differences
between hit trials (when biological sequences were judged biological)
and correct rejection trials (when scrambled sequences were judged
scrambled). That whole brain analysis turned up just a few, small
clusters of voxels showing significant activation differences. None of
those clusters, however, were located within neural structures
associated with biological motion activations in normal individuals,
leading us to conclude thattheywere chancedifferences arisingfrom
the multiple comparisons we performed in this analysis.
Alternatively, it is conceivable that on given trials STSp in
schizophrenia patients can produce patterns of neural activity that
correctly signify the category of animation being viewed (e.g.,
biological). This possibility is not incompatible with our event-
related fMRI results, for those results comprised peak levels of
activation averaged over multiple trials for each of the signal
One potential clue about the possible involvement of STSp in the
performance of schizophrenic patients may come from reconsider-
ation of thefalse alarmtrialsand the accompanying brainactivations
in STSp. Recall that strong STSp activation was observed in healthy
individuals on false alarm trials, i.e., error trials on which scrambled
motion sequences were seen as biological. Perhaps, then, perceptual
errors on false alarm trials-seeing something that is not actually
there-are manifestations of neuronal activity ordinarily involved in
registering the presence of biological motion. Continuing this line of
reasoning, we now know that in schizophrenia patients scrambled
sequences produce activations as large as those produced by
biological motion sequences, which could well be responsible for
their higher false alarm rates on the biological vs. scrambled
categorization task (Experiment 3 and Reference 13) and for their
in noise (Experiment 1) or discriminating sequences differing in
degree of scrambling (Experiment 2). What we are suggesting,
therefore, is that the deficits in biological motion perception in
patients are an exaggerated manifestation of the neural events within
STSp associated with perceptual errors sometimes made by healthy
observers on these same tasks.
Given this possibility, what can we conclude about the origins of
the strong STSp activations on false alarm trials? First, it is possible
that intrinsic neural noise causes activity levels in STSp to fluctuate
spontaneously over time, the consequence being that activity
induced by a suboptimal stimulus achieves abnormal levels that
mimic activity patterns ordinarily associated with a coherent
biological event. This account, however, cannot explain why, in
healthy individuals, STSp activity is elevated during visual imagery.
Instead, imagery and false alarm-associated activations could result
from top-down influences on perception of biological motion, of the
sort suggested by earlier work [64–68]. For example, efficiency of
biological motion processing is strongly influenced by action
categories: certain familiar actions (e.g. walking) are generally
recognized more quickly and more accurately. This has led to the
proposal that high-level vision contains ‘‘selective movement filters’’
 or ‘‘sprites’’  that embody models of common actions
exhibited by familiar objects including people. Through top-down
processes such as attention, these high-level schemas can modulate
weak, ambiguous or noisy motion signals and, thereby, bias
perception in favor of familiar actions under conditions like those
used in our studies (e.g. ) One possible candidate for the neural
locus of these high-level representations is the inferotemporal sulcus
(ITS), an area implicated in object recognition [69,70], visual
known to be responsive to biological motion , suggesting that
reciprocal connections between STSp and ITS could form at least
partofthe networkinvolved intop-downinfluencesonperceptionof
biologicalmotion.Regardlessofthe details ofthat network,itisclear
that such top-down influences may well mediate the strengthened
activation within STSp associated with false alarm trials.
To end on a speculative note, our results may fit into the larger
discussion about the nature of delusion, a discussion that centers
around two themes: faulty perception vs. faulty cognition. The
perceptual account explains delusions as the rational explanation of
anomalous perception (in other words, the best, correct interpre-
tation of noisy, poor quality sensory data: see ). On the other
hand, more cognitive account posits that those with abnormal
beliefs tend to have cognitive biases that result in faulty hypothesis
testing and jumping to conclusions (e.g. [75–77]). In fact, however,
the two accounts could go hand in hand: poor quality sensory data
necessitate increased involvement of cognitive processes to make
sense of the world. Indeed there is evidence for complex interaction
between cognitive and perceptual information processing that may
account for hallucinatory and delusional experiences (e.g. [78–80]).
Visual information processing is abnormal in schizophrenia (see
[81–83]) and structural abnormalities have also been observed in
the visual cortex . Given that the quality of sensory data in these
patients is compromised, they may need to rely more heavily on
higher cortical regions (e.g., frontotemporal regions) to make sense
of theirvisual world.IndeedIt hasbeen observed that schizophrenic
patients give greater weight to top-down expectations on perception
than normal controls do .
Construed in this context, what we have found in our study is
that people with schizophrenia tend to ‘‘see’’ living things in
randomness and this subjective experience is correlated with an
increased activity in the STSp. This finding is broadly in
agreement with past behavioral results suggesting that psychotic
or psychosis-prone individuals tend to see meaning where there is
none (e.g. ), perhaps because they adopt a more lenient
criterion for distinguishing perception and imagination owing to
abnormal up-regulation of dopamine neurotransmitter . In the
case of biological motion perception, these self-generated, false
impressions of meaning can have negative social consequences, in
that schizophrenia patients may misconstrue the actions or
intentions of other people.
We are grateful to Crystal Gibson, Amanda Cumming, Katy Thakkar and
Randall Minas for their help with aspects of this project.
Conceived and designed the experiments: JK RB SP. Performed the
experiments: JK. Analyzed the data: JK. Contributed reagents/materials/
analysis tools: RB SP JK. Wrote the paper: JK RB SP.
1. Blake R, Shiffrar M (2007) Perception of human motion. Annu Rev Psychol 58:
2. Johansson G (1973) Visual perception of biological motion and a model for its
analysis. Percept Psychophys 14: 201–211.
Biological Motion Perception
PLoS ONE | www.plosone.org13 May 2011 | Volume 6 | Issue 5 | e19971
3. Kozlowski LT, Cutting JE (1977) Recognising the sex of a walker from a
dynamic point-light display. Percept Psychophys 21: 575–580.
4. Pollick FE, Lestou V, Ryu J, Cho S (2002) Estimating the efficiency of
recognizing gender and affect from biological motion. Vision Res 42:
5. Troje NF, Westhoff C, Lavrov M (2005) Person identification from biological
motion: Effects of structural and kinematics cues. Percept Psychophys 67:
6. Loula F, Prasad S, Harber K, Shiffrar M (2005) Recognizing people from their
movement. J Exp Psychol Hum Percept Perform 31: 21–30.
7. Cadenhead KS, Serper Y, Braff DL (1998) Transient versus sustained visual
channels in the visual backward masking deficits of schizophrenia. Biol
Psychiatry 43: 132–138.
8. O’Donnell BF, Potts GF, Nestor PG, Stylianopoulos KC, Shenton ME, et al.
(2002) Spatial frequency discrimination in schizophrenia. J Abnorm Psychol 111:
9. Chen Y, Palafox GP, Nakayama K, Levy DL, Mathysse S, et al. (1999) Motion
perception in schizophrenia. Arch Gen Psychiatry 56: 149–154.
10. Chen Y, Levy DL, Nakayama K, Matthysse S (1999) Dependence of impaired
eye tracking on deficient velocity discrimination in schizophrenia. Arch Gen
Psychiatry 56: 155–161.
11. Chen Y, Nakayama K, Levy D, Mattysse S, Holzman PS (2003) Processing of
global, but not local, motion direction is deficient in schizophrenia. Schizophr
Res 61: 215–227.
12. Kelemen O, Erdelyi R, Pataki I, Benedek G, Janka Z, et al. (2005) Theory of
mind and motion perception in schizophrenia. Neuropsychology 19: 494–500.
13. Kim J, Doop ML, Blake R, Park S (2005) Impaired visual recognition of
biological motion in schizophrenia. Schizophr Res 77: 299–307.
14. Green M, Horan WP (2010) Social cognition in schizophrenia. Curr Dir Psychol
Sci 19: 243–248.
15. Allison T, Puce A, McCarthy G (2000) Social perception from visual cues: role of
the STS region. Trends Cogn Sci 4: 267–278.
16. Vaina LM, Solomon J, Chowdhury S, Sinha P, Belliveau JW (2001) Functional
neuroanatomy of biological motion perception in humans. Proc Natl Acad Sci
USA 98: 11656–11661.
17. Grossman ED, Blake R (2002) Brain areas active during visual perception of
biological motion. Neuron 35: 1157–1165.
18. Michels L, Lappe M, Vaina LM (2005) Visual areas involved in the perception
of human movement from dynamic form analysis. NeuroReport 16: 1037–1041.
19. Peuskens H, Vanrie J, Verfaillie K, Orban GA (2005) Specificity of regions
processing biological motion. Eur J Neurosci 21: 2864–2875.
20. Peelen MV, Morris J, Michelich C, Allison T, McCarthy G (2005) Functional
anatomy of biological motion perception in posterior temporal cortex: an fMRI
study of eye, mouth and hand movements. Cereb Cortex 15: 1866–1876.
21. Jastorff J, Orban GA (2009) Human functional magnetic resonance imaging
reveals separation and integration of shape and motion cues in biological motion
processing. J Neurosci 29: 7315–7329.
22. Cutting JE, Moore C, Morrison R (1988) Masking the motions of human gait.
Percept Psychophys 44: 339–347.
23. Grossman ED, Blake R, Kim CY (2004) Learning to see biological motion: brain
activity parallels behavior. J Cogn Neurosci 16: 1669–1679.
24. Hiris E, Humphrey D, Stout A (2005) Temporal properties in masking biological
motion. Percept Psychophys 67: 435–443.
25. American Psychiatric Association (1994) DSM-IV: Diagnostic and Statistical
Manual of Mental Disorders (Ed. 4). Washington DC: American Psychiatric
26. Overall JE, Gorham DR (1962) The brief psychiatric rating scale. Psychol Rep
27. Andreason NC, Olsen S (1982) Negative v positive schizophrenia. Definition
and validation. Arch Gen Psychiatry 39: 789–794.
28. First MB, Spitzer RL, Gibbon M, Williams JBW (2002) Structured Clinical
Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition.
(SCID-I/P) New York: Biometrics Research, New York State Psychiatric
29. Raine A (1991) The SPQ: a scale for the assessment of schizotypal personality
based on DSM-III-R criteria. Schizophr Bull 17: 556–564.
30. Brainard DH (1997) The psychophysics toolbox. Spat Vis 10: 443–446.
31. Pelli DG (1997) The video toolbox software for visual psychophysics:
transforming numbers into movies. Spat Vis 10: 437–442.
32. Bertenthal BI, Pinto DR (1994) Global processing of biological motions. Psychol
Sci 5: 221–225.
33. Silverstein S, Knight RA, Schwarzkopf SB, West LL, Osborn LM, et al. (1996)
Stimulus configuration and context effects in perceptual organization in
schizophrenia. J Abnorm Psychol 105: 410–420.
34. Silverstein S, Hatashita-Wong M, Schenkel M, Wilkniss S, Kova ´cs I, et al. (2006)
Reduced top-down influences in contour detection in schizophrenia. Cogn
Neuropsychiatry 11: 112–132.
35. Herzog M, Kopmann S, Brad A (2004) Intact figure-ground segmentation in
schizophrenia. Psychiatry Res 129: 55–63.
36. Knight RA, Manoach DS, Elliot DS, Hershenson M (2000) Perceptual
organization in schizophrenia: The processing of symmetrical configurations.
J Abnorm Psychol 109: 575–587.
37. Pelphrey KA, Mitchell TV McKeown MJ, Goldstein J, Allison T, et al. (2003)
Brain activity evoked by the perception of human walking: Controlling for
meaningful coherent motion. J Neurosci 23: 6819–6825.
38. Grossman ED, Blake R (2001) Brain activity evoked by inverted and imagined
biological motion. Vision Res 41: 1475–1482.
39. Saygin AP, Wilson SM, Hagler DJ, Bates E (2004) Point-light biological motion
perception activates human premotor cortex. J Neurosci 24: 6181–6188.
40. Li CS (2002) Impaired detection of visual motion in schizophrenia patients. Prog
Neuropsychopharmacol Biol Psychiatry 26: 929–934.
41. Stuve TA, Friedman L, Jesberger JA, Gilmore GC, Strauss ME, et al. (1997)
The relationship between smooth pursuit performance, motion perception and
sustained visual attention in patients with schizophrenia and normal controls.
Psychol Med 27: 143–152.
42. Bandettini PA, Cox RW (2000) Event-related fMRI contrast when using
constant interstimulus interval: Theory and experiment. Magn Reson Med 43:
43. Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain.
Thieme, New York.
44. Grill-Spector K (2003) The neural basis of object perception. Curr Opin
Neurobiol 13: 159–166.
45. Green MF, Nuechterlein KH, Breitmeyer B (1997) Backward masking
performance in unaffected siblings of schizophrenia patients: evidence for a
vulnerability indicator. Arch Gen Psychiatry 54: 465–472.
46. Butler PD Javitt DC (2005) Early-stage visual processing deficits in schizophre-
nia. Curr Opin Psychiatry 18: 151–157.
47. Neri P, Morrone C, Burr DC (1998) Seeing biological motion. Nature 395:
48. Schenk T, Zihl J (1997) Visual motion perception after brain damage: I. Deficits
in global motion perception. Neuropsychologia 35: 1285–1297.
49. Schenk T, Zihl J (1997) Visual motion perception after brain damage: II. Deficits
in form-from-motion perception. Neuropsychologia 35: 1299–1310.
50. Vaina LM, Lemay M, Bienfang D, Choi A, Nakayama K (1990) Intact
‘biological motion’ and ‘structure from motion’ perception in a patient with
impaired motion mechanisms: A case study. Visual Neurosci 5, 353-369.
51. Sekuler R, Watamaniuk SNJ, Blake R (2002) : Visual motion perception. In:
Pashler H, series editor, Yantis S, vol. editor. Stevens’ handbook of experimental
psychology. Sensation and perception. Vol. 1. 3rdedition. New York: Wiley.
52. Doniger GM, Foxe JJ, Schroeder CE, Murray MM, Higgins BA, et al. (2001)
Visual perceptual learning in human object recognition areas: A repetition
priming study using high-density electrical mapping. Neuroimage 13: 305–313.
53. Doniger GM, Foxe JJ, Murray MM, Higgins BA, Javitt DC (2002) Impaired
visual object recognition and dorsal/ventral stream interaction in schizophrenia.
Arch Gen Psychiatry 59: 1011–1020.
54. Schwartz BD, Winstead DK, Adinoff D (1983) Temporal integration deficit in
visual information processing by chronic schizophrenics. Biol Psychiatry 18:
55. Izawa R, Yamamoto S (2002) Spatio-temporal disintegration of visual
perception in schizophrenia as revealed by a novel cognitive task, the Searchlight
Test. Schizophr Res 53: 67–74.
56. Clementz B (1996) Saccades to moving targets in schizophrenia: Evidence for
normal posterior cortex functioning. Psychophysiology 33: 650–654.
57. Gale HJ, Holzman PS (2000) A new look at reaction time in schizophrenia.
Schizophr Res 46: 149–165.
58. Thakkar KN, Schall JD, Boucher L, Logan GD, Park S (2011) Response
inhibition and response monitoring in a saccadic countermanding task in
schizophrenia. Biol Psychiatry 69: 55–62.
59. Pavlova M, Sokolov A (2000) Orientation specificity in biological motion
perception. Percept Psychophys 62: 889–898.
60. Karlsgodt KH, Sun D, Cannon TD (2010) Structural and functional brain
abnormalities in schizophrenia. Curr Dir Psychol Sci 19: 226–231.
61. Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, Milham MP (2008)
Competition between functional brain networks mediates behavioral variability.
Neuroimage 39: 527–537.
62. Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV,
et al. (2009) Hyperactivity and hyperconnectivity of the default network in
schizophrenia and in first-degree relatives of persons with schizophrenia. Proc
Natl Acad Sci USA 106: 1279–1284.
63. Jung WH, Gu BM, Kang DH, Park JY, Yoo SY, et al. (2008) BOLD response
during visual perception of biological motion in obsessive-compulsive disorder:
An fMRI study using the dynamic point-light animation paradigm. Eur Arch Psy
Clin N 259: 46–54.
64. Cavanagh P (1991) Short-range vs long-range motion: not a valid distinction.
Spat Vis 5: 303–309.
65. Cavanagh P, Labianca A, Thornton IM (2001) Attention-based visual routines:
Sprites. Cognition 80: 47–60.
66. Dittrich W (1993) Action categories and the perception of biological motion.
Perception 22: 15–22.
67. McLeod P, Dittrich W, Driver J, Perrett D, Zihl J (1996) Preserved and impaired
detection of structure from motion by a ‘motion blind’ patient. Vis Cogn 3:
68. Thornton IM, Rensink RA, Shiffrar M (2002) Active versus passive processing of
biological motion. Perception 31: 837–853.
Biological Motion Perception
PLoS ONE | www.plosone.org 14May 2011 | Volume 6 | Issue 5 | e19971
69. Malach R, Reppas JB, Benson RR, Kwong KK, Jiang H, et al. (1995) Object-
related activity revealed by functional magnetic resonance imaging in human
occipital cortex. Proc Natl Acad Sci USA 92: 8135–8139.
70. Pietrini P, Furey ML, Ricciardi E, Gobbini MI, Wu WH, et al. (2004) Beyond
sensory images: object-based representation in the human ventral pathway. Proc
Natl Acad Sci USA 101: 5658–5663.
71. Ishai A, Ungerleider LG, Haxby JV (2000) Distributed neural systems for the
generation of visual images. Neuron 22: 189–199.
72. Kourtzi Z, Kanwisher N (2001) Representation of perceived object shape by the
human lateral occipital complex. Science 293: 1506–1509.
73. Thompson JC, Clarke M, Stewart T, Puce A (2005) Configural processing of
biological motion in human superior temporal sulcus. J Neurosci 25: 9059–9066.
74. Maher BA (2002) Delusions. Comprehensive Handbook of Psychopathology
Part II. pp 309–339, Springer.
75. Brugger P, Graves R (1997) Testing vs. believing hypotheses: Magical Magical
Ideation in the Judgement of Contingencies. Cogn Neuropsychiatry, 2: 251–272.
76. Moritz S, Woodward TS (2005) Jumping to conclusions in delusional and non-
delusional schizophrenic patients. Br J Clin Psychol 44(Pt 2): 193–207.
77. Blackwood NJ, Howard RJ, Bentall RP, Murray RM (2001) Cognitiveneur-
opsychiatric models of persecutory delusions. Am J Psychiatry.158: 527–39.
78. Collerton D, Perry E, McKeith I (2005) Why people see things that are not
there: A novel Perception and Attention Deficit model for recurrent complex
visual hallucinations. Behav Brain Sci 28: 737–794.
79. Spencer KM, Nestor PG, Perlmutter R, Niznikiewicz MA, Klump MC, et al.
(2004) Neural synchrony indexes disordered perception and cognition in
schizophrenia. Proc Natl Acad Sci USA 101: 17288–17293.
80. Langdon R, Coltheart M (2000) The Cognitive Neuropsychology of Delusions.
Mind Lang 15: 183–216.
81. Foxe JJ, Doniger GM, Javitt DC (2001) Visual processing deficits in
schizophrenia: Impaired P1 generation revealed by high-density electrical
mapping. NeuroReport 12: 3815–3820.
82. Tadin D, Kim J, Doop ML, Gibson C, Lappin JS, et al. (2006) Weakened
Center-Surround Interactions in Visual Motion Processing in Schizophrenia.
J Neurosci, 26(44): 11403–11412.
83. Kantrowitz JT, Butler PD, Schecter I, Silipo G, Javitt DC (2009) Seeing the
world dimly: the impact of early visual deficits on visual experience in
schizophrenia. Schizophr Bull 35: 1085–1094.
84. Selemon LD, Rajkowska G, Goldman-Rakic PS (1995) Abnormally high
neuronal density in the schizophrenic cortex. A morphometric analysis of
prefrontal area 9 and occipital area 17. Arch Gen Psychiatry 52: 805–818.
85. Aleman A, Bocker KBE, Hijman R, de Haan EHF, Khan RS (2003) Cognitive
basis of hallucinations in schizophrenia: Role of top-down information
processing. Schizophr Res 64: 175–85.
86. Brugger P, Regard M, Landis T, Cook N, Krebs D, et al. (1993) ‘‘Meaningful’’
patterns in visual noise: effects of lateral stimulation and the observer’s belief in
ESP. Psychopathology, 26: 261–265.
87. Lou HC, Skewes JC, Thomsen KR, Overgaard M, Lau HC, et al. (2011)
Dopaminergic stimulation enhances confidence and accuracy in seeing rapidly
presented words. J Vis 11(2): 15, 1–6.
Biological Motion Perception
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