Exp Brain Res (2006) 170: 457–463
DOI 10.1007/s00221-005-0229-1
R ES E AR C H A RT I C L E
Alexandra Séverac Cauquil Æ Yves Trotter
Margot J. Taylor
At what stage of neural processing do perspective depth cues make
a difference?
Received: 19 January 2005 / Accepted: 13 September 2005 / Published online: 24 November 2005
Springer-Verlag 2005
Abstract The present study investigated the cortical
processing of three-dimensional (3D) perspective cues in
humans, to determine how the brain computes depth
from a bidimensional retinal image. We recorded visual
evoked potentials in 12 subjects in response to flat and
in-perspective stimuli, which evoked biphasic potentials
over posterior electrodes. The first, positive component
(P1, at 90 ms) was not sensitive to perspective, while the
second, negative peak (N1 at 150 ms) was significantly
larger for 3D stimuli, regardless of attention. The
amplitude increase due to perspective was seen on all
posterior electrodes, but was largest over the right
hemisphere, particularly at parietal sites. Source modeling low-resolution electromagnetic tomography (LORETA) confirmed that among the different areas
participating in two- and three-dimensional stimuli
processing, the right parietal source is the most enhanced by perspective depth cues. We conclude that the
extraction of depth from perspective cues occurs at a
second level of stimulus processing, by increasing the
activity of the regions involved in 2D stimuli processing,
particularly in the right hemisphere, possibly through
feedback loops from higher cortical areas. These modulations would participate in the fine-tuned analysis of
the 3D features of stimuli.
Keywords Depth perception Æ Human
electrophysiology Æ Linear perspective Æ Source
localization
Abbreviations 2D: Two-dimensional Æ 3D:
Three-dimensional Æ CIP: Caudal part of the lateral
bank of the intraparietal sulcus Æ LORETA:
Low-resolution electromagnetic tomography Æ RDS:
A. Séverac Cauquil (&) Æ Y. Trotter Æ M. J. Taylor
Centre de Recherche Cerveau et Cognition,
UMR 5549 UPS/CNRS, Université Paul Sabatier Toulouse III,
133 route de Narbonne, 31062 Toulouse cedex 4, France
E-mail: [email protected]
Tel.: +33-5-62172835
Fax: +33-5-62172809
Random-dot stereogram Æ STS: Superior temporal
sulcus Æ VEPs: Visual evoked potentials
Introduction
Three-dimensional (3D) vision relies on monocular cues
such as perspective, shading and texture as well as binocular cues; understanding the chronology and location
of 3D processing is a major research area in visual science. The cortical processing of monocular depth cues
has been less studied when compared with the processing
of binocular cues such as disparity. Yet monocular
depth cues are basic and critical to normal vision, for
example, we still have efficient depth perception if we
close one eye.
As shown in monkeys, neurons sensitive to retinal
disparity are found from the striate cortex (Trotter 1995;
Gonzalez and Perez 1998; Cumming and Parker 2000) to
the frontal eye fields (Ferraina et al. 2000), and most
parts of the dorsal pathway are involved in depth perception, based on both monocular and binocular cues
(Tsutsui et al. 2001, 2002; Uka and DeAngelis 2003).
The ventral pathway is also involved in disparity coding
(Janssen et al. 1999, 2000; Uka et al. 2000).
In human fMRI studies, areas in the intra-parietal
sulcus were found activated by stimuli with monocular
depth cues such as shading (Taira et al. 2001) and shading
plus perspective (Shikata et al. 2001). In a study of Necker
cube perception, Inui et al. (2000) suggested that ‘‘monocular stereopsis’’ activates the bilateral occipito-temporal gyrus as well as premotor and lateral parietal cortices.
Moore and Engel (2001) found that the lateral occipital
complex (LOC), the lateral and ventral region of the
human occipital cortex, is responsive to both the knowledge-based and the image-based monocular components
of a 3D object structure. They presented strictly identical
stimuli, which gave rise to a significantly stronger activity
when, owing to gray-scale primes, the stimuli were perceived as a volume rather than as a flat object. Thus, the
LOC could be a region where information from different
458
sources is integrated to support the subjective experience
of perceiving volume. Another fMRI study demonstrated
an increase of activity in this ventral region (plus parietally) for the presentation of 3D versus 2D shape stimuli
(Murray et al. 2003). In contrast, Kourtzi and Kanwisher
(2000) failed to find a stronger activity in the LOC, when
processing 3D rather than 2D objects, and claimed that
the region was more involved in processing shape than
depth.
For binocular depth cues, both static and dynamic
solid stereograms induce fMRI activity in the dorsal
occipital portion and the superior parietal lobule with
right hemisphere dominancy (Iwami et al. 2002). Random-dot stereogram (RDS) presentation induced a
widespread activity within the extrastriate cortex, particularly area V3A (Backus et al. 2001).
Very few studies have used visual evoked potentials
(VEPs), which provide detailed information on the
timing of cortical activation, to study monocular depth
perception. Jeffreys (1996) found that the ‘‘later negative
potential’’ present on occipital VEPs around 170 ms was
sensitive to depth cues, particularly monocular depth
cues: the activity recorded on the three occipital electrodes investigated was consistently enhanced (seen in 42
out of 49 subjects) although not statistically tested. The
largest amplitude was recorded over the right electrode.
Skrandies and Jedynak (1999) reported that in subjects
learning to see sub-threshold 3D stimuli, the pattern of
activation shifted towards the right hemisphere. Previous VEPs studies using RDS have noted the opposite
(Manning et al. 1992) or the absence of (Lehmann and
Julesz 1978) hemisphere dominancy.
In summary, most of the aforementioned studies
suggest that the exposure to visual stimuli with monocular perspective depth cues could increase cortical
activity in occipital, temporal and parietal regions. Thus,
we hypothesized that with 3D stimuli there would be
increased activation in posterior temporal regions compared to 2D stimuli, and that this would occur early in
processing. We investigated the precise timing and the
organization of specific cortical network of 3D processing in humans using VEPs evoked by 2D and 3D
shape stimuli (with or without perspective). This method
allowed us to determine the extent of the activity and the
time-structure of the monocular-based processing of 3D
objects.
Visual stimuli and procedure
Figure 1 presents the procedure and the set of stimuli, 20
different monochromatic images: ten with perspective
monocular cues (noted 3D; e.g. a cube) and ten matching without (noted 2D; e.g. a flattened cube), the 2D
being the flat projection of the 3D. No shading or other
monocular cues were present on the stimuli, which were
monochromatic (bright green on a black background)
and the same size across 2D and 3D sets (20 wide).
They were presented binocularly one at a time on a
computer screen 60 cm away from the seated subject.
Subjects were instructed to maintain visual fixation on
the central target during stimulus presentation.
A session contained 12 blocks of 80 stimuli: the set of
20 stimuli (Fig. 1) presented four times in a pseudorandom order. Stimuli were presented for 200 ms, with
an inter-stimulus interval varying between 700 and
1,400 ms. In order to control attention effects linked to
the task, during the blocks, subjects were asked to
respond by pressing the mouse button whenever they
saw either a 2D (‘‘2D attended’’ condition) or a 3D
stimulus (‘‘3D attended’’ condition). Therefore, during
six blocks, subjects responded to 2D stimuli, and during
six other blocks they responded to 3D stimuli; this order
200 ms
+
700-1400 ms
200-ms stimulus
presentation
+
x 40 = 1 block
inter-stimulus interval
varying between 700
and 1400 ms
Materials and methods
Experimental subjects
Twelve adults were included in the study
(38.1 years±2.8; four females); all were right-handed
and had normal or corrected to normal vision, and gave
informed consent. The experiments were performed in
compliance with institutional guidelines, and the COPE
ethics committee Centre National de la Recherche
Scientifique (CNRS) approved the protocol.
Fig. 1 Upper part: experimental procedure. Lower part complete
set of the stimuli, ten 2D (upper two rows) and ten matched 3D
images
459
was counterbalanced across subjects. Short pauses were
given between blocks.
VEP recording
The VEPs were recorded with a NeuroScan system 4.1
(NeuroScan, Sterling, VA, USA, www.neuro.com) from
33 EEG electrodes applied with an EasyCap electrode
cap (Falk Minow Services, Herrsching-Breitbrunn,
Germany, www.easycap.de) according to the 10–10
system. Cz served as the reference lead during data
collection. The EOG was monitored with three electrodes, at the outer canthi and the left supra-orbital
ridge. The epochs lasted 1.1 s, with a 100 ms pre-stimulus baseline, and were recorded with a bandpass filter
of 0.1–100 Hz. The EEG was digitally re-referenced offline to an average-reference montage. Trials were rejected for EOG or movement artifact (>±90 lV). For
each trial, the baseline, estimated as the mean amplitude
of the 100 ms pre-stimulus epoch, was subtracted and
then trials were averaged according to the stimulus
categories. The components of interest were P1 and N1
measured at the posterior electrodes: O1, O2, PO9,
PO10, P3, P4, P7, P8, TP9 and TP10, where the largest
and clearest VEPs were observed. Broadly distributed
frontal activity was seen but likely reflected the other
pole of the posterior activity (George et al. 1996).
Analyses of variance with repeated measures were
conducted on latency and amplitude data by depth
stimuli (two levels: 3D and 2D), attention condition (two
levels: 3D and 2D attended), electrodes (five levels) and
hemisphere (two levels). When appropriate (e.g. with
repeated measures, to reduce non-sphericity of the variance/covariance matrix) GreenhouseGeisser adjustments were used. Post hoc tests using the methods of
contrasts investigated the electrode effects.
Source modeling was performed using LORETA, lowresolution tomography (www.unizh.ch/keyinst/NewLORETA; (Pascual-Marqui et al. 1994). LORETA computes the smoothest 3D intracerebral distribution of
current density (in lA/mm2) by minimizing the norm of
the Laplacian of the current vectors without assuming a
specific a priori location or number of sources. LORETA
computations are restricted to 2,394 voxels in the cortical
gray matter and hippocampus, based on the Talairach
atlas (Talairach and Tournoux 1988), giving a 7 mm
voxel resolution. In the version used here, the head model
was three-shell spherical, using reported EEG electrode
coordinates (Towle et al. 1993), based on several individual measurements from which the mean best-fitting
sphere was determined. Then a surface-fitting algorithm
was performed to transfer the best-fitting sphere to the
mean MRI brain. The source locations were given as xyz
coordinates, with an estimate of the corresponding brain
region and Brodmann area obtained after the transformation of MNI coordinates into Talairach space.
It has been shown that dipole localization on grand
averages was a valid approach and could be more stable
than on individual averages because of the better signalto-noise ratio (Giard et al. 1994; Pantev et al. 1995; Clark
and Hillyard 1996). We therefore opted for a 2-step
process: first, to work with a better signal-to-noise ratio,
we entered our grand mean data into LORETA, which
gave us a solution at both the P1 and N1 latencies. Second, to be able to assess the statistical significance of the
modeling, we ran LORETA on the individual subject’s
data and then performed a paired t-test voxel by voxel, on
the set of 2,394 voxels, and for each time frame, to compare individual LORETA models in both the 2D and the
3D conditions. We obtained a statistical parametric map
and used P<0.0001 as a correction for multiple testing.
Results
The stimuli evoked a biphasic potential (P1–N1) recorded over the posterior scalp. The positive deflection
P1 was not seen for inferior electrodes PO9, PO10, TP9
and TP10, but appeared clearly on P3, P4, P7, P8 and
O1, O2.
The general distribution, as seen on grand average
maps, was fairly symmetrical (Fig. 2), but with an
asymmetry to the right at P1 latency. Stimuli with 3D
perspective evoked a much larger activity than did 2D
stimuli, whereas the task did not influence the recorded
activity: the presentation of 2D stimuli evoked a similar
response whether attended or not, as did 3D stimulation.
At the N1 latency a simultaneous large frontal positivity
was recorded.
Peak analyses
The P1 and N1 latencies did not differ between 2D and
3D stimuli: P1 at 90.1±1.4 ms and N1 at 148.8±4.6 ms.
Neither the attention task (target vs. non-target) nor the
P1
N1
attended
P1
N1
non attended
P1
N1
P1
N1
-7.9 -7 -6.2 -5.3 -4.5 -3.6 -2.7 -1.9 -1 0 0.7 1.6 2.4 3.3 4.2 5 5.9 (µV)
Fig. 2 Grand average scalp voltage maps (n=12) at P1 (90 ms) and
N1 (150 ms) mean latencies, to the 2D and 3D stimuli (left and
right panels, respectively), in the two attended/non-attended
conditions (upper and lower panels, respectively). The 2D and 3D
labels were stimuli in the corresponding category
460
presence of perspective influenced latencies significantly
(for P1 P=0.582 and P=1; for N1 P=0.101 and
P=0.508, for the ‘‘attention’’ and ‘‘depth’’ factors,
respectively). Figure 3 presents the grand averages of
waveforms obtained for the four conditions, 2D/3D,
attended/non-attended, at the electrodes where the effect
was the strongest.
The larger waveforms were seen at the occipital and
parieto-occipital electrodes. Waveforms obtained in the
target and non-target conditions were perfectly superimposable until the end of the N1 component (Fig. 3).
Then, 200 ms after the stimulus onset, an influence of
the attentional task could be observed, especially on the
left hemisphere electrodes, but as it occurred after the
early biphasic event sensitive to perspective, this was not
further studied here.
Figure 4 presents bar graphs (mean±SEM) of P1 and
N1 amplitudes for the electrodes of interest. The P1
amplitude was similar for 2D and 3D stimuli, regardless
of the task, but it was significantly larger over the right
hemisphere (‘‘hemisphere’’ factor P<0.05) for the
parietal electrodes (P8 vs. P7: +92%, P4 vs. P3: +74%;
‘‘electrode·hemisphere’’ interaction: P<0.05).
In contrast, N1 was clearly larger (‘‘depth’’ factor,
P<0.001) for the 3D than for the 2D stimuli ( 4.9±0.8
vs. 3.4±0.6 lV) and the increase due to perspective
cues was maximal over the parietal, mostly the right
parietal (see bar graphs on Fig. 4: +30% on P3
Fig. 3 Grand average (n=12)
waveforms recorded over the
six electrodes of interest in the
four conditions: 2D (green) and
3D (purple) for attended (dark)
and non-attended (pale) stimuli
and +150% on P4), rather than ventral sites (+10 and
+37% at TP and PO sites, respectively, not shown, and
+50% at O1, O2 and P7, P8 sites) (‘‘electrode·depth’’
interaction: P<0.003; ‘‘depth·hemisphere’’ interaction,
P<0.04).
Source modeling
As it was evident that the attended/non-attended status
of the stimulus did not yield differences, we collapsed the
data from the two conditions to improve the signal-tonoise ratio for the source analyses.
Grand average modeling
At both P1 and N1 latencies, the proposed model of the
grand average data presented the same pattern, with the
same source localization, for both 2D and 3D stimuli.
The model included five main regions (Table 1): two
lateral, symmetrical sources at the level of occipitotemporal junctions (left BA39 and right BA22), two
medial: one occipital (right/left BA18) and one occipitoparietal (right/left BA7), and one in the region of the
right superior parietal lobule (right BA7). A further
source was located in the region of premotor area (right/
left BA6).
P3
P4
P1
P8
N1
P7
O2
O1
+
-
1 µV
100 ms
2D attended
2D non-attended
3D attended
3D non-attended
461
then, between 162 and 170 ms, below in the temporal
lobe (x=46, y= 60 and z=22). This difference between
2D and 3D models occurred during the N1 time range
and was congruent with the larger N1 increase we
observed over the right parietal and parieto-occipital
electrodes. No statistical difference was found either in
the left hemisphere or in the medial parietal region,
between 2D and 3D stimuli.
P4
P3
P8
P7
Discussion
O2
O1
1 γV
2-D attended
3-D attended
2-D non-attended
3-D non-attended
Fig. 4 Bar graphs representing the mean amplitude±SEM of P1
(the four left-most bars at each of the six electrode locations) and
N1 (four bars on the right for each electrode) amplitudes for the
four conditions at each electrode of interest
Subject by subject modeling
The sources modeled on grand averages were found in
most of the individual modeling (Table 1). We analyzed
the individual results of the LORETA inversion using a
paired t-test design, at each time frame and for each
voxel. This statistical approach assesses the ‘‘depth effect’’ without any a priori assumption on the timing and
locations of the sources as it is performed at each time
frame and for each voxel (see Materials and methods).
For P<0.0001, one cluster of significantly different
voxels emerged at N1 latency in the right hemisphere,
around the region of the angular gyrus (see Fig. 5),
appearing first (156–162 ms post-stimulus) slightly
above, in the parietal lobe (x=46, y= 67 and z=36),
Adding 3D perspective cues to visual stimuli significantly modified the second component of the cortical
response: the posterior N1 peak was always larger for
the 3D than the 2D stimuli but the processing of perspective cues did not delay the cortical response to the
visual stimuli. Attention to the stimulus category modified the waveforms only at a later, post-N1 stage. The
two approaches used to analyze the cortical activity led
to the same conclusion: processing 3D information from
perspective cues involves the same cortical areas as
matched 2D stimuli, with an increase in activity of the
regions contributing to the N1 component, and a preferentially greater increase in right parieto-occipital
areas. Perspective cues comparable to those used here
may initiate vergence eye movements (Enright 1987) and
an N1 component related to vergence eye movements
has been recently reported (Tzelepi et al. 2004). However, the possibility of the involvement of oculomotor
cues in our results appears unlikely. In the Tzelepi et al.
(2004) study, the amplitude of vergence movement that
evoked a comparable N1 amplitude was 13. Monocular
exploration of a drawing with perspective cues seen at
comparable viewing distance to the present study yielded
much smaller (0.5–1.5) vergence movements. Furthermore, this size of vergence movements disappear in
binocular vision (Enright 1987), and we used binocular
viewing for the present study.
As expected, the early stages of cortical processing of
the visual stimuli occurred in posterior areas, as shown
Table 1 Grand average and individual modeling
x
y
z
Number of subjects
P1 (86 ms)
N1 (156 ms)
2D
3D
2D
3D
2D
3D
2D
3D
2D
3D
2D
3D
12
12
11
11
10
10
7
9
11
12
10
10
11
10
12
12
9
10
10
11
9
11
7
6
L BA 39
45
67
15
R BA 22
53
60
15
R/L BA 18
3
88
6
R/L BA 7
3
74
43
32
74
43
3
11
64
R BA 7
R/L BA 6
Co-ordinates (mm) in the digitized atlas of Talairach and Tournoux space (1988) of the LORETA peaks of activation processed from
grand averages for both the 2D and 3D conditions and number of individual subjects showing activity in the corresponding area, at P1
and N1 latency. Approximation of the localization in Brodmann areas is given
462
Fig. 5 Results of the t-test on the individual LORETA modeling. Localization of the voxels that were significantly more active
(P<0.0001) for 3D versus 2D stimuli, at 158 ms post-stimulus latency
by the sharply focal posterior VEPs compared to the
broad waves recorded frontally. The fact that LORETA
modeling also localized the sources in the posterior
cortex reinforces the hypothesis of 3D processing
occurring in visual regions and that there was not a
contributing frontal source. The synchronous dispersed
frontal activity was likely the positive ends of the N1
dipoles. Source modeling, although requiring caution in
its interpretation when based on 32-channel recordings,
suggests the following cortical network is engaged in
perspective processing.
The first positive component, P1, reflects early cortical activation by the stimuli. The voltage topography
and the source modeling at this latency suggest occipital,
possibly BA 18 or 17, bilateral occipito-temporal and
medial to right parieto-occipital localization of this
activity. This is compatible with physiological data
based on studies with monkeys showing V1, V3 and
MST as regions where neurons have the shortest latencies (Nowak and Bullier 1997; Schmolesky et al. 1998;
Bullier 2001). Foxe and Simpson (2002) suggested in a
study presenting parafoveal stimuli, that multiple visual
generators are active in that latency range. Using highdensity mapping, they provided evidence that despite a
timing advantage for dorsal over ventral regions, temporal stream areas are active as early as 82 ms. This is
consistent with the bilateral source LORETA proposed
around area 37 at P1 latency. This first group of active
areas, the ‘‘fast brain’’, or the first wave of information
through the visual pathway, was not differentially active
for 2D and 3D stimuli since no difference, either in P1
amplitude or in amount of source activity, was observed
between the two conditions.
It was for the second, negative component, that
perspective cues produced an effect. The significantly
larger amplitude of N1 to 3D than to 2D stimuli is
attributed to enhanced activity in occipital, right and left
occipito-temporal regions, and particularly in the right
parietal region, around BA7. The symmetrical occipitotemporal sources found at N1 and P1 latencies are
possibly related to the V3A and/or MT/MST activation
found in fMRI studies of human (Backus et al. 2001;
Iwami et al. 2002) and monkey stereopsis (Uka and
DeAngelis 2003). These studies investigated depth perception based on binocular cues, but 3D vision based on
monocular cues, such as perspective, could share the
same processing areas. In addition to these bilateral
occipito-temporal junction areas, the different analysis
methods used in the present study converge on the
finding that the parieto-occipital region is where differences between 2D and 3D stimuli are the most marked:
the greatest difference in N1 amplitude between 2D and
3D was found over the right parietal electrodes, where,
at this latency, LORETA analyses located a cluster of
voxels statistically different between 2D and 3D. This
emphasizes that the parietal region, particularly in the
right hemisphere, plays a major role in the detection of
3D shape, as Taira et al. (2001) found in an fMRI study
based on shading. Other fMRI studies, with RDS or
with solid stereograms, suggested that stereopsis processes were dorsally located in the parieto-occipital
cortex, particularly on the right hemisphere (Nishida
et al. 2001; Iwami et al. 2002). In light of the increased
amplitude to perspective cues that we found over the
right compared to the left parietal electrode, that region
likely processes monocular as well as binocular depth
cues, as suggested above for the occipito-temporal
sources. Tsutsui et al. (2002) reported that among the
many neurons of the caudal part of the lateral bank of
the intraparietal sulcus selective to 3D surface orientation, most are sensitive to retinal disparity. The same
result was recently obtained in neurons of the lower
bank of STS (Liu et al. 2004).
In summary, the difference between the processing of
2D and 3D stimuli appeared at the N1 component of the
VEP response, and not at the earlier P1 stage. Source
analyses allowed us to localize the brain areas implicated.
A hypothesis is that the first structures activated, lying
mainly but not only on the dorsal, magnocellular pathway, send feedback loops to early processing areas to
reinforce their activity for 3D stimulus processing. The
increased activity at N1 in occipital, occipito-temporal
463
and right parietal regions would reflect this neural network for refining the ongoing 3D sensory construction,
based on monocular as well as binocular 3D cues.
Acknowledgements The authors are grateful to Renaud Lestringant
for his constructive comments and highly valuable contribution to
the statistical analysis of LORETA models, to Laurent Le Guyader
for helping with LORETA inversion, and to the 12 subjects who
participated in the study. This work was supported by grants from
CNRS (Centre National de la Recherche Scientifique) and CNES
(Centre National d’Etudes Spatiales).
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