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J Physiol 603.6 (2025) pp 1541–1566
The visual representation of 3D orientation in macaque
areas STPp and VPS
Rong Wang1 , Bin Zhao1,3 and Aihua Chen1,2
1
Key Laboratory of Brain Functional Genomics (Ministry of Education), East China Normal University, Shanghai, China
NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
3
Lingang Laboratory, Shanghai, China
2
Handling Editors: Nathan Schoppa & Jing-Ning Zhu
The Journal of Physiology
The peer review history is available in the Supporting Information section of this article
(https://doi.org/10.1113/JP287309#support-information-section).
Abstract figure legend Visual representation characteristic of 3D orientation in macaque areas STPp and VPS. The top
panel shows visual areas with 3D surface representation, with illustrations of the dorsal (blue arrows) and ventral (green
arrows) streams. The lower panels illustrate neuronal responses in macaque cortical areas STPp (purple box) and VPS
(blue box) to 3D orientation stimuli. In the STPp (purple box): the left plot shows neuronal tuning to tilt (red) and
mean disparity (blue), with different transparency levels reflecting varying Slant angles. The middle plot demonstrates
that motion signals enhance firing rates without changing preferences for tilt or mean disparity (dashed lines: tuning
curves under non-motion condition; continuous lines: tuning curves under motion condition). The right plot displays
TDD neurons, where preferences for tilt and mean disparity shift interdependently (black and red lines: first and second
response peaks). In the VPS (blue box): the left plot shows tuning to tilt (red) and mean disparity (blue). The middle
plot reveals that motion signals enhance firing rates, alter tilt preferences and diminish mean disparity tuning. The right
plot shows that TDD neurons in VPS exhibit interdependent response patterns similar to those in STPp.
R. Wang and B. Zhao contributed equally to this work.
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
DOI: 10.1113/JP287309
R. Wang and others
J Physiol 603.6
Abstract In the current study, we investigated the neural mechanisms underlying the representation
of three-dimensional (3D) surface orientation within the posterior portion of the superior temporal
polysensory area (STPp) and the visual posterior Sylvian area (VPS) in the macaque brain. Both
areas are known for their integration of visual and vestibular signals, which are crucial for visual
stability and spatial perception. However, it remains unclear how exactly these areas represent the
orientation of 3D surfaces. To tackle this question, we used random dot stereograms (RDS) to present 3D planar stimuli defined by slant and tilt, with depth via binocular disparity. Through this
method, we examined how STPp and VPS encode this information. Our results suggest that both
regions encode the orientation and depth of 3D surfaces, with interactions among these parameters
influencing neural responses. Additionally, we investigated how motion cues affect the perception
of 3D surface orientation. STPp consistently encoded plane orientation information regardless of
motion cue, whereas VPS responses showed less stability. These findings shed light on the distinct
processing mechanisms for 3D spatial information in different cortical areas, offering insights into
the neural basis of visual stability and spatial perception.
(Received 15 July 2024; accepted after revision 23 January 2025; first published online 14 February 2025)
Corresponding author A. Chen: Institute of Brain Functional Genomics, East China Normal University, 3663
Zhongshan Road N., Shanghai 200062, China. Email: ahchen@brain.ecnu.edu.cn
Key points
r Both STPp and VPS can encode 3D surface orientation.
r Slant is encoded independently from tilt and disparity in STPp and VPS areas.
r TDD neurons shift their depth preferences based on tilt in STPp and VPS areas.
r STPp maintains stable 3D orientation encoding under motion conditions, while VPS shows less
stability with changes in tilt and disparity preferences.
Introduction
Understanding how a flat retinal image transforms
into a three-dimensional (3D) surface orientation is
crucial for accurately assessing an object’s size, depth and
spatial positioning (Howard & Rogers, 2002). Various
visual cues like lines, textures, motion, shading and
especially binocular disparity, collectively contribute to
our perception of depth (Todd et al., 2016). Binocular
disparity, arising from the subtle disparities in eye
positions, is particularly important for precise depth
perception and spatial orientation (Stevenson, 2003;
Wheatstone, 1997). Apart from the visual system, the
vestibular system’s collaboration with visual signals
is indispensable for primates to navigate and interact effectively in 3D spaces (Brandt et al., 2005). Our
visual environment typically remains oriented relative
to gravity, regardless of our own spatial orientation,
indicating that signals from the vestibular system help
reinterpret encoded retinal images (De Vrijer et al.,
2008). These mechanisms, deeply tied to gravity and
0 Rong Wang is a PhD candidate at the Key Laboratory of Brain Functional Genomics, at East China Normal
University. She received her bachelor’s degree from Capital Normal University in 2020. Her research
focuses on how the 3D visual object orientation is encoded and decoded by the primate brain. In addition,
she is also involved in some research on the neural mechanism of multisensory integration of vestibular
and visual signals by using a combination of behavioral, cognitive and neural analyses. Bin Zhao is a
postdoctoral researcher at Lingang Laboratory. He earned his PhD in neuroscience in 2022 from the
Key Laboratory of Brain Functional Genomics at East China Normal University. His doctoral research
utilized primate models to investigate the neural mechanism of multisensory information integration
and stereoscopic vision. He has published papers in The Journal of Physiology and iScience. At Lingang
Laboratory, Dr Zhao focuses on neuroscience research related to visual prostheses and the development of brain-computer interface (BCI) medical devices.
His work involves training animal behavior models, collecting electrophysiological signals, and conducting in-depth data analysis to advance both the
understanding and clinical application of neural prosthetic technologies.
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1542
3D Orientation representation in macaque STPp and VPS
earth-vertical orientation (EVO), form the foundation
of spatial perception (Dakin & Rosenberg, 2018). Many
researchers have demonstrated that the integration of
vestibular inputs (pertaining to head motion perception)
with visual stimuli enhances the precision of the central
nervous system in discerning self-motion and spatial
orientation (Chepisheva, 2023; Cullen, 2019; Keshavarzi
et al., 2023; Zwergal et al., 2024). Thus, understanding
how we perceive objects in 3D and how our balance
system works together with our vision can help us understand how we see the world around us. However, little
is known about the underlying neural mechanism, and
the representation of 3D orientation in higher-level
visual-vestibular multisensory areas has not been
thoroughly investigated.
In the visual pathway, several cortical regions show
robust preferences for 3D surface orientation. These
include areas such as V3A (Georgieva et al., 2009), V4
(Umeda et al., 2007), the middle temporal area (MT)
(DeAngelis & Newsome, 1999; Maunsell & Van Essen,
1983), the caudal intraparietal area (CIP) (Chang et al.,
2022; Rosenberg et al., 2013), the inferior temporal
neurons area (IT) (Rosenberg et al., 2023), the dorsal subdivision of the medial superior temporal area (MSTd)
(Sugihara et al., 2002), ventral intraparietal area (VIP)
(Sasaki et al., 2020; Sunkara et al., 2015, 2016) and various
other IPS regions (Durand et al., 2007; Georgieva et al.,
2009; Sawamura et al., 2005). Given its proximity to
visuomotor areas within the parietal cortex (Thiele &
Hoffmann, 1996) and its input from areas MT, MSTd
and VIP (Asanuma et al., 1990; Baizer et al., 1991;
Boussaoud et al., 1990), the posterior portion of the
superior temporal polysensory area (STPp) could be a
crucial region for encoding 3D spatial features. The STP
complex, comprising sub-regions such as STPa and STPp
(Boussaoud et al., 1990; Goldman-Rakic & Rakic, 1991),
has been found to respond significantly to partial shape
stimuli and exhibits 3D structure tuning (Anderson &
Siegel, 1999; Bruce et al., 1981). STPa, in particular,
receives projections from STPp (Boussaoud et al., 1990;
Felleman & Van Essen, 1991) and can recognize 3D
spherical structures (Anderson & Siegel, 2005). However,
relatively little is known about the 3D surface orientation
encoding of STPp.
On the other hand, the visual posterior Sylvian area
(VPS) represents a high-level stage of the vestibular
pathway and responds selectively to heading direction
in both visual and vestibular modalities, with a preference for vestibular dominance (Chen et al., 2011a; Zhao
et al., 2023). It is plausible that VPS also plays a role in
processing 3D surface orientation.
In this study, we investigated the encoding capabilities
of STPp and VPS by using random dot stereograms (RDS)
to present 3D planar stimuli. By employing virtually
identical experiments and analysis methods in both areas,
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
1543
we conducted a controlled comparison. We examined
responses during conditions of varying slant angles and
disparity values, as well as the impact of motion cues on
3D plane orientation perception.
Methods
Ethical approval
Physiological experiments were performed with three
male rhesus monkeys (Monkeys A, K and M) weighing
6–10 kg. These animals were procured from ZHONGKE
(SuZhou XiShan ZongKe Laboratory Animal Co., Ltd)
and were subsequently accommodated in the Primate
Centre at East China Normal University, Shanghai, China.
The monkeys were not previously utilized in any research,
but the recordings presented here were gathered in parallel
to those appearing two published papers (Zhao et al.,
2021, 2023). All three monkeys were used in other projects
after this study. Monkey A is still alive and is currently
being utilized in ongoing studies. Monkeys K and M were
perfused as needed for other experiments approximately
1 year ago. The animals were housed in an environment
that maintained a 12-h alternating light/dark cycle, with
ambient temperature controlled between 20°C and 25°C
and relative humidity levels maintained between 40%
and 70%. The well-being of the monkeys was supervised by full-time, on-site primate veterinary staff, who
were available around the clock, along with veterinary
technicians.
Daily observations of the animals were conducted by
both laboratory personnel and veterinary technicians
to ensure their health and welfare. To encourage
participation in the experimental tasks, the monkeys
were managed on a customized controlled water intake
regimen. Training sessions, which typically occurred on
weekdays, were closely monitored for water consumption.
Once the animals were adept at the tasks, experimental
or training sessions, which included head restraint, were
conducted for a duration of 3 to 6 h, during which the
animals had access to ample water or juice. The monkeys
were also periodically rewarded with fruit-based treats
both during their time in the laboratory and prior to their
return to their enclosures. Furthermore, the animals were
granted an increased water allowance on at least 1 day
per week, typically falling on Friday and/or Saturday. The
precise volume of fluid intake was meticulously measured
and recorded, and body weights were documented at least
twice weekly. The animals were vigilantly monitored for
any indications of distress or dehydration on a daily basis.
All surgical and experimental protocols involving
the animals were granted approval by the Institutional
Animal Care and Use Committee at East China Normal
University, under license number Mo20211003. The
procedures were in strict accordance with the ethical
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J Physiol 603.6
R. Wang and others
guidelines of The Journal of Physiology and the animal
welfare principles delineated by Grundy (2015), as well as
the ARRIVE guidelines (Kilkenny et al., 2010).
Surgery
Three monkeys (A, K and M) underwent chronic
implantation of a head-restraint device, an electrode
placement platform, and a scleral search coil for eye
movement tracking. Prior to surgery, each animal was
induced with ketamine hydrochloride (15 mg/kg, intramuscularly) and maintained under anaesthesia with isoflurane (1%–3%) in a balanced mixture of nitrous oxide
and oxygen (in a 1:1 ratio). In addition to ketamine,
atropine sulfate (0.05 mg/kg, intramuscularly) was
administered pre-intubation to facilitate anaesthesia
induction. Throughout the surgical procedures, vital
signs, including heart rate (above 90 beats/min),
electrocardiogram (EKG), oxygen saturation (≥95%),
end-tidal carbon dioxide levels (20–60 mmHg) and
body temperature (36°C–38°C) were monitored
continuously. Physiological parameters were monitored
and documented at 15-min intervals throughout the
surgical procedures. In instances where the heart rate
surpassed 110 beats/min, indicating a potential response
to painful stimuli in rhesus monkeys, the isoflurane
concentration was carefully adjusted upwards. This
adjustment aimed to maintain an appropriate level of
anaesthesia while avoiding the risks associated with
over-sedation. Conversely, when the heart rate decreased
to less than 90 beats/min, the isoflurane concentration
was reduced to prevent excessive sedation. To further
assess the depth of anaesthesia, muscle tone was evaluated
by gently tugging on the leg, and the absence of corneal
reflex was confirmed by tapping on the side of the
eyebrow. These clinical indicators were used to ensure
that the animals remained in a state of deep anaesthesia,
effectively minimizing any response to surgical
stimuli.
For head-restraint implantation (Dickman & Angelaki,
2002; Gu et al., 2006), a scalp incision was first
made, the skin reflected back, and the wound margins
were protected with gauze. Periosteum and tissue were
removed completely from the bone. The periosteum and
surrounding tissues were meticulously removed from the
skull. Once the skull was exposed, six titanium screws
were affixed into the bone. Subsequently, a circular,
custom-moulded Delrin ring was attached to these screws,
and the assembly was cemented in place with dental
acrylic. An additional layer of acrylic was applied to the
central exposed skull area within the ring to ensure a
sealed implant environment. The implant was covered
with a protective cap when the animals were in their
enclosures to maintain cleanliness.
J Physiol 603.6
In conjunction with the head restraint device,
a recording platform, crafted from machinable
plastic-Delrin and measuring 56 mm × 33.5 mm with a
height of 5 mm, was affixed to the skull and positioned
within the ring. This platform featured staggered rows of
0.8 mm spaced holes, designed to accommodate 26-gauge
guide tubes, which were typically prepared by drilling on
a daily basis during experimental procedures.
For the sclera search coil implantation, a pre-fabricated,
sterilized coil was implanted subconjunctivally. The
procedure involved a circumferential incision of the
bulbar conjunctiva outside the corneal margin, followed
by careful dissection to separate the conjunctiva from the
sclera without damaging the ocular muscle insertions. The
coil was then affixed to the globe approximately 3–5 mm
from the corneal margin, anterior to the ocular muscle
insertions, using a non-absorbable suture (Ningbo microscopic instrument factory, China, 3/8 Spade needle, line
8-0). The conjunctiva was re-approximated to the limbus
with an absorbable suture (Shanghai Pudong Jinhuan
Medical Products Co. Ltd, China, R831). The coil’s lead
wires were then routed extraorbitally, beneath the muscle
and skin, guided by a surgical needle, to the top of the skull
within the Delrin ring. The lead wires were soldered to
a connector plug, which was secured within the implant
using dental acrylic.
All surgical interventions were conducted under strict
aseptic conditions within a dedicated sterile suite. Surgical
instruments were prepared through autoclaving or gas
sterilization. The surgical sites were disinfected with
Betadine, and the animals were draped in sterile cloth,
exposing only the area to be operated on. The surgical
team, including surgeons and assistants, adhered to
strict sterile attire, including masks, bonnets, booties
and sterile gloves and gowns. Prophylactic antibiotics
(Baytril, 5 mg/kg, intramuscularly) were administered
during and post-surgery for a minimum of 1 week to minimize infection risk. Analgesia (Tilidine, 4 mg/kg, intramuscularly) was provided every 8–12 h as required or
as directed by the attending veterinarian. Post-surgery,
the animals were allowed to recuperate with unrestricted
access to water and food. Following their recovery,
the animals were conditioned using standard operant
conditioning techniques to perform a fixation task, as
detailed in subsequent sections.
Apparatus
The visual stimuli were programmed by OpenGL and presented on a large LCD display in front of the macaque
(Philips BDL4225, refresh rate 60 Hz). The LCD display
is placed ∼30 cm (88° × 88°) in front of the monkey.
The monitor was connected to a stimulus-generating
computer via a VGA cable to generate visual stimuli in
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1544
3D Orientation representation in macaque STPp and VPS
real-time. It should be noted that the screen was mounted
in front of the magnetic field coil frame, and the top
and sides of the coil frame were black, so the macaques
could only see the visual stimuli presented on the screen
(Gu et al., 2006). Visual stimulation uses classical optical
flow stimulation (Chen et al., 2011a,b; Gu et al., 2006).
Figure 1B is a schematic diagram of the visual stimulus.
The entire stimulus consists of random cross dots with
a width of 100 cm, a height of 100 cm, a depth of
40 cm, and a density of 0.01/cm3 , each with a size of
0.15 cm × 0.15 cm. The stimuli were presented through
red and green dots, and the macaques were required
to wear red/green glasses made of for viewing Kodak
Wratten filters (red no. 29; green no. 61, Barrington, NJ,
USA).
Experimental protocol and behavioural tasks
The orientation of a 3D plane can generally be expressed
by two angle parameters, slant and tilt. Slant refers to the
angle between the surface normal and the line of sight,
representing the amount of depth variation. Slant ranges
from 0° (for a fronto-parallel surface) to 90° (approaching
Figure 1. Three-dimensional plane orientation and experimental stimuli
A, schematic of plane orientation. The blue and red angels represent the tilt and slant angles, respectively. N and
N’ represent the normal vector of the plane. B, example stimulus used in our experiments. The stimulus subtended
88° × 88° of visual angle, and the random dot size was 0.3° × 0.3° with a density of 64 dots/s/degree. The specific
parameters of the stimulus shown in the figure are as follows: slant = 30°, tilt = 0°, mean disparity = 0°. C, slant-tilt
angle diagram. Slant (radial axis; red) specifies the orientation in depth. Tilt (angular axis; blue) specifies which side
of the plane is nearest to the observer. Tilt ranges between 0° and 360° where 0°/360° indicates the right side of
the plane is closest, 90° indicates the top is closest, 180° indicates the left is closest and 270° indicates the bottom
is closest. Slant ranges between 0° and 90°, where 0° indicates fronto parallel and 90° approaches a ground plane.
The example plane is rendered with perspective and stereoscopic cues as red–green anaglyphs. [Colour figure can
be viewed at wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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J Physiol 603.6
R. Wang and others
a ground plane). Tilt describes the rotation of the surface
relative to the observer’s line of sight, indicating which
side of the surface is nearest. In our coordinate system,
tilt ranges between 0° and 360° where 0/360 indicates the
right side of the plane is closest, 90° indicates the top is
closest, 180° indicates the left is closest and 270° indicates
the bottom is closest. The slant-tilt stimulus consists of
three slant angles (0°, 30°, 60°) and eight tilt angles (0°, 45°,
90°, 135°, 180°, 225°, 270°, 315°). Figure 1C is a chessboard
diagram showing all of the 17 (2 × 8 + 1) slant-tilt
combinations. To explore the response characteristics of
neurons to 3D surfaces with different depths of field, we
also designed a total of five mean disparity angles (between
−2° and 2° in 1° steps). Negative mean disparity suggests
the 3D plane is closer to the observer than the fixation
point, while positive mean disparity indicates the stimulus
is further away, behind the fixation plane. So, there are a
total of 85 (5 mean disparity × 17 slant-tilt) combinations
of 3D plane stimuli. The mean disparity is produced by
rendering the stimulus into a contrast image of red and
green, and the macaques can see a solid, 3D plane by
wearing red and green glasses made in the laboratory.
The stimulation is a fixation-point task. For each trial,
the monkey first fixated at the yellow central target
(0.2° × 0.2°) for 300 ms, then maintained fixation for
1000 ms while one of 85 random combinations appeared,
and finally held fixation for 50 ms more. Each trial,
therefore, lasted 1350 ms, during which the macaques
had to maintain their fixation (fixation window: 3° × 3°),
and if they left the fixation frame at any time during the
course, the trial was judged to have failed, and then the
trial was stopped. Each trial was repeated five times, so
the macaques needed to complete 425 (5 × 85) trials in
a full 3D stimulus set. Based on the monkey and cell
reactions, there are 152 STPp cells and 99 VPS cells with
five repetitions. All recorded neurons in our study were
from the right hemispheres. All cells were used for further
analysis.
External objects are not always static, and the brain
needs to ignore the motion information of the object to
maintain the stability of the 3D perception. To explore the
influence of motion information on the 3D plane, different
‘speed’ parameters were set in the experiment: the random
dots in the stimulus moved uniformly towards the monkey
in the direction of the plane’s slant-tilt at the preferred
speed of neurons, specifically from a position farther away
from the monkey to one closer, and other parameters
were completely consistent with those in the non-motion
stimulus. Tilt is not defined at a slant of 0° and the dots
move in a direction of 0°, that is, along the plane from
the left to the right (Fig. 8A). The preferred speed of each
neuron was determined by roughly measuring its speed
tuning curve using a range of stimulus speeds, typically
between 1 and 100°/s.
J Physiol 603.6
As mentioned above, slant-tilt stimuli contain both
non-motion and motion conditions, which are consistent
in all conditions except speed. The monkeys first
completed 425 slant-tilt-disparity tests under non-motion
conditions. If the neurons continued to fire well, they
subsequently underwent 425 slant-tilt tests under motion
conditions. In total, 149 STPp cells and 95 VPS cells
completed motion stimulations.
Electrophysiological recordings
When the macaque performed the task, the neurons in
the brain region of the macaque were recorded in real
time. The experiment used a single electrode extracellular
recording method to collect the neuronal firing rates
(Chen et al., 2011a,b; Jia et al., 2021; Zhao et al.,
2021). Each animal was chronically implanted with a
head-restraint cap and with a scleral search coil in at least
one eye for monitoring eye movements inside a magnetic
field (Riverbend Instruments, Birmingham, AL, USA).
We used tungsten microelectrodes (Frederick Haer
Company; tip diameter 3 mm; impedance, 1–2 MU at
1 kHz) for electrical signal acquisition. When searching
for neurons in the cortex, a tiny hydraulic microdrive
(Frederick Haer Company, Bowdoin, ME, USA) was
used to drive the electrodes to move, and the electrodes entered the cortex through a guide tube. The neural
signals collected by the electrodes were amplified and
filtered (400–5000 Hz) by AlphaOmega Instruments
(Nazareth Illit, Israel), and digitally stored at a rate of
25 kHz. These signals were stored on a separate computer,
along with a numeric code generated by the Tempo
system that represents the time of the behavioural task
event, facilitating further offline neuronal action potential
(spike) classification. In addition, action spikes separated
online by the AlphaOmega system were collected by the
Tempo system at 1000 Hz system for offline analysis.
Areas STPp and VPS were identified using a
combination of magnetic resonance imaging scans,
stereotaxic coordinates, white/grey matter transitions and
multiunit responses to visual motion. We identified VPS
by its multiunit responses to visual motion stimuli.
As expected, it was found posterior to PIVC (the
parieto-insular vestibular cortex). The extent of this
region, which exhibited visual responses, was determined
to be between 4 to 5 mm along the anterior-posterior
dimension (for details, see Chen et al., 2010, 2011a). Our
approach involved the initial identification of the medial
tip of the lateral sulcus and subsequent lateral exploration
to ascertain the presence of directionally selective visual
responses with multiunit activity. The VPS area was
observed to blend into the PIVC at its anterior extremity,
while at the posterior end, the clarity of responses
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1546
diminished as the grey matter of the lateral sulcus became
less pronounced. Within the delineated VPS, we recorded
from neurons that were sensitive to vestibular signals. Our
recordings within the STPp were confined to the region
corresponding to the anterior bank of the caudal superior
temporal sulcus (Hikosaka et al., 1988). This STPp region,
which demonstrated visual responses, was mapped to
coordinates that extended from 0 to 8.8 mm anterior to
the interaural plane, 15.0–23.8 mm lateral to the midline,
and 6.2–23.5 mm ventral to these reference points. In
the STPp area, we specifically recorded neurons that
exhibited responses to a large-field flickering random-dot
stimulus. The neuroanatomical locations of the recorded
neurons from both STPp and VPS in three monkeys
were reconstructed from MRI scans and were graphically
represented on coronal sections of the model monkey
brain (Fig. 2).
Data analysis
All analyses except the normalization model were
performed using custom scripts written in MATLAB
(Mathworks, Natick, MA, USA).
Significance analysis of neuronal responses. In the 3D
planar stimulation, we calculated the average firing rates
(spikes/s) of neurons during the 1000 ms stimulation as
the neuronal response intensity. To calculate the tuning
significance of neurons for slant, tilt and mean disparity,
we first tested the tuning significance using three-way
ANOVA. PSlant_Three_Way < 0.05 indicates that neurons
are significantly tuned to slant, pTilt_Three_Way < 0.05
indicates that neurons are significantly tuned to tilt, and
PMean Disparity _Three_Way < 0.05 indicates that neurons are
significantly tuned to mean disparity.
Pooled processing of neuronal responses. To quantify
the overall neural tuning characteristics of tilt and mean
disparity, we pooled the tuning curves of tilt and mean
disparity, respectively (Nguyenkim & DeAngelis, 2003;
Sasaki et al., 2017). When the slant angle is fixed, mean
disparity tuning curves under different tilt angles are
aggregated, that is, firing rates of different tilt angles
are averaged under each mean disparity angle. The
mean disparity tuning curve after neuronal aggregation
processing was obtained. Similarly, we obtained the
tilt-tuning curves after aggregation processing by
averaging the responses of neurons to tilt angles under
different mean disparity conditions.
Determination of tilt and mean disparity preference. For
each neuron with significant tuning, tilt preferences were
calculated using the vector sum of mean responses. We
calculated the angle preferences of the pooled tuning
curves for further analysis. For the mean disparity
response, we selected the mean disparity angle with the
maximum response as the neuronal mean disparity preference (Pref Mean Disparity). Second, to quantify neuronal near or far preference, we calculated the disparity sign
discrimination index (DSDI) to quantify neuronal preference for depth, that is, the selectivity of mean disparity
symbols (Nadler et al., 2008, 2009; Yang et al., 2011).
Rfar(i) − Rnear(i)
1 .
DSDI =
n i=1 Rfar(i) − Rnear(i) + σavg
n
(1)
For each pair of depths symmetric around zero
(e.g. ±1°), we calculated the difference in response
between far (Rfar ) and near (Rnear ), relative to response
variability (σ avg , the average standard deviation of the
two responses). We then averaged across the two matched
pairs of depth to obtain the DSDI which ranges from −1 to
1. Neurons that respond more strongly to near stimuli will
have negative values, while neurons that prefer far stimuli
will have positive values.
Two types of interactions between disparity and
direction selectivity were classified (Yang et al., 2011). (1)
Tilt-dependent disparity tuned neurons (TDD): (i) The
mean disparity tuning was significant (P < 0.05) in the
two-way ANOVA at one of the slant angles. (ii) The DSDI
for at least two tilt angles was significantly different from
Figure 2. Reconstruction of recording sites in STPp and VPS
Inflated cortical surface illustrating the approximate locations of the coronal sections from the right hemispheres of
monkeys. Blue symbols: cells of area VPS (square: monkey A; circle: monkey K; triangle: monkey M); Red symbols:
cells of area STPp (square: monkey A; circle: monkey K; triangle: monkey M). [Colour figure can be viewed at
wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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3D Orientation representation in macaque STPp and VPS
J Physiol 603.6
R. Wang and others
zero (P < 0.05) and had opposite signs at that slant angle.
Using these criteria, we identified a class of neurons whose
preference sign for mean disparity (near or far) reversed
with tilt (positive to negative or negative to positive). (2)
Non-tilt-dependent disparity tuned neurons (Non-TDD):
(i) The mean disparity tuning was significant (P < 0.05)
in the two-way ANOVA at one of the slant angles. (ii)
The DSDI did not reverse with a tilt at that slant angle.
Non-TDD cells have separable coding of tilt and mean
disparity.
Determination of tilt and mean disparity tuning strength.
To quantify tilt and mean disparity tuning strength,
we defined the tilt discrimination index (TDI) and
mean disparity discrimination index (DDI), respectively
(DeAngelis & Uka, 2003; Prince et al., 2002).
TDI =
Rmax − Rmin
,
√
Rmax − Rmin + 2 SSE/ (N − M)
(2)
DDI =
Rmax − Rmin
,
√
Rmax − Rmin + 2 SSE/ (N − M)
(3)
where Rmax and Rmin are the maximum and minimum
responses from the tuning function, respectively. SSE is
the sum squared error around the mean response, N is the
total number of trials and M is the number of stimulus
directions (Tilt, M = 8; mean disparity, M = 5). The TDI
or MDI quantifies the reliability of a neuron to distinguish
tuning for tilt or mean disparity, with values ranging from
0 to 1. The greater value of DDI indicates the larger
response modulation of the tuning.
Tilt and mean disparity tuning significance under
non-motion and motion conditions. Two-way ANOVA
was used to assess the significance of tilt and mean
disparity tuning under non-motion and motion
conditions, and we divided the neurons into four classes:
‘Both_depth/tilt’ neurons with significant tuning to
tilt or mean disparity under both non-motion and
motion conditions (Pnon-motion < 0.05, Pmotion < 0.05);
‘Non-motion_depth/tilt’ neurons with significant tuning
to tilt or mean disparity only under non-motion
conditions (Pnon-motion < 0.05, Pmotion > 0.05); ‘Motion
only_depth/tilt’ neurons with significant tuning to
tilt or mean disparity only under motion conditions
(Pnon-motion > 0.05, Pmotion < 0.05); ‘Not tuned_depth/tilt’
neurons without significant tuning to tilt or mean
disparity under non-motion and motion conditions
(Pnon-motion > 0.05, Pmotion > 0.05).
Results
To characterize the 3D structure of a surface, we utilize
slant and tilt angles, denoted as the red and blue angles
J Physiol 603.6
in Fig. 1A. The slant angles vary from 0° to 60° in 30°
increments, resulting in three distinct slant positions. Tilt
angles range from 0° to 315° in 45° increments, yielding
eight tilt positions. Notably, at a slant of 0°, there is
no tilt, forming a base condition for examination. Slant
refers to the angle between the surface normal and the
line of sight, representing the amount of depth variation.
Slant, as an angular parameter, spans from 0° for surfaces
that are parallel to the fronto-parallel plane to 90° for
surfaces nearly touching the ground plane. Tilt describes
the rotation of the surface relative to the observer’s line
of sight, indicating which side of the surface is nearest.
Specifically, a tilt of 0°/360° signifies that the right side
of the surface is in closest proximity, 90° corresponds
to the top side, 180° to the left side and 270° to the
bottom side. Considering the importance of consistent
3D orientation tuning across different stimulus positions,
particularly in-depth perception, we implemented five
pedestal disparities, ranging from −2° to 2° in 1° steps, to
specify slant and tilt.
We examined a total of 17 slant-tilt combinations
(as shown in Fig. 1C) to evaluate surface orientation
tuning. This involved measuring responses from
152 neurons in STPp (high-level visual-vestibular
multisensory area, visual dominance) and 99 neurons
in VPS (high-level visual-vestibular multisensory area,
vestibular dominance) during a fixation task. The fixation
task consisted of presenting a plane across 85 stimulations,
resulting from the combination of 17 slant-tilt pairs and
five disparities. All the recorded neurons completed five
repetitions (at least 425 trials) for a full 3D stimulus set.
For stimulus generation, random dots were utilized
to create a 3D plane, and an illustrative example of the
stimulus is depicted in Fig. 1B, with a slant of 30°, tilt of
0° and mean disparity of 0°. Throughout the stimulus presentation, monkeys were tasked with maintaining fixation
at a central target on the screen for 1000 ms.
Additionally, we conducted a motion protocol wherein
random dots moved in the slant-tilt direction, each at
a preferred speed for the recorded neurons (motion
conditions, see Methods for details). A total of 149
neurons in STPp and 95 neurons in VPS were recorded
under motion conditions.
The neuroanatomical locations of all the neurons were
reconstructed using MRI scans and are illustrated in Fig. 2,
showing the recording sites mapped onto coronal sections
of the right hemisphere and the expanded cortical surface.
STPp and VPS neurons can encode 3D planes
To investigate whether both STPp and VPS regions
can encode 3D planes, we analysed the responses of
two representative neurons (one from STPp and one
from VPS) to 3D planar stimuli (Fig. 3). Contour maps
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1548
3D Orientation representation in macaque STPp and VPS
depicting tilt along the abscissa and mean disparity along
the ordinate are illustrated in Fig. 3A and B, showcasing
responses to various conditions at a slant of 30° from the
STPp neuron (Fig. 3A) and VPS neuron (Fig. 3B). The
tuning curves and contour maps at a slant of 30° reveal
that responses varied with tilt and mean disparity angles
in both neurons. We also added the mean disparity tuning
curves at a slant of 0° in Fig. 3A and B (left column).
Similarly, contour maps in Fig. 3C and D display responses
to different conditions at a slant of 60° from the same two
neurons in Fig. 3A and B. These figures also illustrate that
neuronal responses changed with tilt and mean disparity
at a slant of 60° in both neurons. Moreover, the tuning
curves of tilt and mean disparity also exhibited changes
from slant 30° to slant 60° for the same STPp and VPS
neurons. Collectively, these results indicate that responses
of neurons in both STPp and VPS are influenced by the
slant, tilt and mean disparity of a plane, highlighting their
essential role in visually encoding the 3D orientation of
objects.
Figure 3. Neural response example of STPp and VPS to three-dimensional plane stimuli
The left two columns (A and C) show the responses from the same STPp neuron at slants of 30° and 60°, while
the right two columns (B and D) show the neuronal responses from the same VPS neuron at slants of 30° and 60°.
A–D, the disparity-tilt joint tuning profile of the two neurons is shown as a colour-contour map, where the tilt is
plotted on the abscissa and binocular disparity on the ordinate. Tuning curves along the margins show tilt-tuning
for each disparity (top column) and disparity-tuning for each tilt and slant of 0° (left column). Dashed lines denote
spontaneous activity levels. The wine-red triangle curves represented the mean disparity tuning curves at a slant of
0°. Both neurons are sensitive to tilt (STPp, P = 1.0 × 10−5 ; VPS, P = 7.3 × 10−39 , three-way ANOVA) and mean
disparity (STPp, P = 3.6 × 10−56 ; VPS, P = 4.9 × 10−11 , three-way ANOVA). Error bars denote SD. [Colour figure
can be viewed at wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
1549
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J Physiol 603.6
R. Wang and others
To gain a more comprehensive understanding of the
shared and distinctive features in the characteristics of
3D orientation processing within the two areas, we
conducted three-way ANOVA tests to assess the tuning
significance of neurons to slant, tilt and mean disparity.
Results from the analysis of 152 recorded STPp neurons
revealed that 18.4% (28/152) were significantly tuned
to slant, 25.0% (38/152) to tilt and 70.4% (107/152) to
mean disparity (P < 0.05, three-way ANOVA). This
suggests a heightened sensitivity of STPp to the depth
information of a plane. Conversely, the proportions of VPS
neurons with significant tuning to slant (25.3%, 25/99)
and tilt (30.3%, 30/99) were slightly higher compared to
STPp neurons. However, the percentage of VPS neurons
significantly tuned to mean disparity (41.4%, 41/99) was
lower than that observed in STPp neurons, indicating
potential distinctive characteristics in the encoding of 3D
orientation between these two brain regions.
J Physiol 603.6
with a higher number of neurons exhibiting a preference
for the 0° mean disparity, representing the plane of the
fixation point (Fig. 4C). In contrast, the mean disparity
preference of VPS neurons was evenly distributed across
all depths (Fig. 4D). These findings suggest that STPp and
VPS neurons can encode a wide range of mean disparities
in our study and STPp neurons are more sensitive to the
fixation plane than VPS neurons.
STPp and VPS can encode mean disparity information
of 3D plane
For optimal utility in shape perception, it is imperative
that 3D orientation tuning remains consistent across
various stimulus positions, especially in depth. To capture
the overall mean disparity characteristics of neurons, we
pooled the mean disparity tuning curves among different
tilt angles at slants of 30° and 60° from the two neurons
presented in Fig. 3 (left curves), as illustrated in Fig. 4A
and B. To assess the depth preference for a 3D plane, we
employed the DSDI, which ranges from −1 (indicating a
strong near preference) to 1 (indicating a strong far preference). Figure 4A depicts three mean disparity tuning
curves of an STPp neuron at three slants. The example
neuron exhibited a maximum response when the mean
disparity was −2° at all slant angles. The DSDI values
were −0.61, −0.66 and −0.67 at slants of 0°, 30° and 60°,
respectively, indicating a consistent preference for near
disparities at all slant angles. Similarly, Fig. 4B presents
three mean disparity-tuning curves for the VPS neuron
at all three slants. The DSDI values of this neuron were
0.45, 0.35 and 0.47 at slants of 0°, 30° and 60°, indicating a
persistent preference for far disparities at all slant angles.
To further assess the mean disparity tuning
characteristics of the population neurons, we computed
the preferred mean disparity of each recorded neuron at
different slant angles. The population results, considering
only neurons with significant tuning to mean disparity
(slant 0°: P < 0.05, one-way ANOVA, black bars; slant 30°
and 60°: P < 0.05, two-way ANOVA, blue and red bars),
are presented in Fig. 4C (STPp result) and Fig. 4D (VPS
result). Our analysis revealed that the mean disparity preference of STPp neurons was distributed across all depths,
Figure 4. Mean disparity tuning characteristics at different
slant angles in STPp and VPS
A, the mean disparity tuning curves of STPp example neuron at
different slant angles. Black curve, slant 0°, DSDI = −0.61; blue
curve, slant 30°, DSDI = −0.66; red curve, slant 60°, DSDI = −0.67.
Error bars denote SD. B, the mean disparity tuning curves of VPS
example neuron at different slant angles. Black curve, slant 0°,
DSDI = 0.45; blue curve, slant 30°, DSDI = 0.35; red curve, slant 60°,
DSDI = 0.47. Error bars denote SD. C and D, mean disparity
preference distribution of neurons in STPp and VPS at three slant
angles. Black bars, slant 0°; blue bars, slant 30°; red bars, slant 60°.
Only cells with significant tuning to mean disparity were included
(slant 0°: P < 0.05, one-way ANOVA; slant 30°/60°: P < 0.05,
two-way ANOVA). E and F, the DDI at different slant angles of STPp
and VPS neurons. Only cells with significant tuning to mean disparity
were included (P < 0.05, three-way ANOVA). A total of 107 STPp
neurons and 41 VPS neurons are shown in the figure. [Colour figure
can be viewed at wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
14697793, 2025, 6, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP287309 by New York University, Wiley Online Library on [15/03/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
1550
3D Orientation representation in macaque STPp and VPS
Additionally, to measure the tuning strength of a
neuron to far and near planes, we utilized the mean DDI,
with values ranging from 0 to 1. A higher DDI value
implies a greater tuning strength to mean disparity. To
evaluate changes in mean disparity tuning strength at
different slant angles, we calculated the DDI of every
recorded neuron at slants of 0°, 30° and 60°. Subsequently,
we plotted the DDI tuning curves of neurons that
exhibited significant tuning to mean disparity (P < 0.05,
three-way ANOVA) in the STPp and VPS areas (Fig. 4E
and F). In STPp (Fig. 4E), the DDI values exhibited
a significant decrease from slant 0° (mean ± SD:
0.45 ± 0.15) to slant 30° (mean ± SD: 0.37 ± 0.13)
(P = 5.5 × 10−12 , Wilcoxon’s sign-rank test). Additionally,
there was a further significant decrease in DDI values
from slant 30° to slant 60° (mean ± SD: 0.31 ± 0.13)
(P = 1.3 × 10−9 , Wilcoxon’s sign-rank test). Similarly,
in VPS (Fig. 4F), the DDI values also decreased with
increasing slant angles (slant 0°, mean ± SD: 0.42 ± 0.10;
slant 30°, mean ± SD: 0.30 ± 0.09; slant 60°, mean ± SD:
0.25 ± 0.07; slant 0° vs. 30°, P = 4.5 × 10−7 , slant
30° vs. 60°, P = 0.035, Wilcoxon’s sign-rank test). Thus,
these results suggest that mean disparity tuning strength
decreases with increasing slant angles in both areas.
1551
VPS neurons remains consistent at the population level
between slant 30° and slant 60°.
To quantitatively assess whether the tilt-tuning strength
was affected by the slant, we defined the TDI, which ranges
from 0 to 1. A TDI closer to 1 indicates a higher tilt-tuning
strength. In STPp (Fig. 5A), the TDI was 0.35 at a slant
of 60°, which was greater than the TDI of 0.16 at a slant
of 30°. Similarly, in VPS (Fig. 5B), the TDI was 0.51 at
a slant of 60°, larger than the TDI of 0.46 at a slant of
30°. Therefore, the increase in slant did not alter the tilt
preferences but only enhanced the tilt-tuning strength in
STPp and VPS can encode tilt information of 3D plane
To better detect the encoding relationship between slant
and tilt, we pooled tilt responses from different disparity
angles of the two example neurons in Fig. 3 (top column)
(see Methods for details) and plotted the tilt-tuning curves
at slants of 30° and 60° in Fig. 5A and B. For the STPp
neuron in Fig. 5A, the preferred tilt computed from vector
sum was 147.9° at a slant of 30° and 160.1° at a slant of
60°, resulting in a shift of 12.2°, which is small enough
to fall within the sample tilt resolution (45°). Similarly,
the preferred tilt difference was 13.7° in VPS, with the
preferred tilt being 242.0° at a slant of 30° and 228.3°
at a slant of 60° (Fig. 5B). Thus, changing the slant did
not alter the tilt preferences of the two neurons. Across
38 STPp and 30 VPS neurons with significant tilt-tuning
(P < 0.05, three-way ANOVA), Fig. 5C and D summarized
how the tilt preferences of these neurons changed with
the slant. In STPp (Fig. 5C), the majority of neurons
(84.2%, 32/38) displayed a tilt preference shift of less than
45°. Furthermore, there was no significant difference in
tilt preference between slant 30° and slant 60° of STPp
population neurons (P = 0.856, Wilcoxon’s rank-sum
test). Similarly, in VPS (Fig. 5D), 90.0% (27/30) neurons
exhibited a preference shift of less than 45°, and there
was no significant difference in tilt preference between
slant 30° and slant 60° in the population of VPS neurons
(P = 0.994, Wilcoxon’s rank-sum test). Consequently,
these findings indicate that the tilt preference of STPp and
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
Figure 5. Tilt tuning characteristics at different slant angles in
STPp and VPS
A and B, an example neural response to different tilt angles at a slant
of 30° (blue lines) and slant of 60° (red lines) in STPp (A) and VPS (B).
Error bars denote SD. C and D, tilt preference shift at slants of 30°
and 60°. Only cells with significant tuning to tilt were included
(P < 0.05, three-way ANOVA). A total of 38 STPp neurons and 30
VPS neurons are shown in the figure. E and F, TDI of neurons with
significant tilt-tuning (P < 0.05, three-way ANOVA) at slants of 30°
and 60° in areas STPp (E) and VPS (F). Star symbols indicate
significant shifts (P < 0.01). Only cells with significant tuning to tilt
were included (P < 0.05, three-way ANOVA). A total of 38 STPp
neurons and 30 VPS neurons are shown in the figure. [Colour figure
can be viewed at wileyonlinelibrary.com]
14697793, 2025, 6, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP287309 by New York University, Wiley Online Library on [15/03/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
J Physiol 603.6
R. Wang and others
both STPp and VPS example neurons. Figure 5E and F
demonstrated the TDI of neurons that were significantly
tuned to tilt (P < 0.05, three-way ANOVA) in STPp and
VPS. The population TDI at a slant of 60° (mean ± SD:
0.34 ± 0.08) is significantly higher than the TDI at a
slant of 30° (mean ± SD: 0.27 ± 0.09) (P = 5.56 × 10−6 ,
Wilcoxon’s rank-sum test) in STPp (Fig. 5E). Similarly,
the population TDI at a slant of 60° (mean ± SD:
0.37 ± 0.09) is significantly higher than the TDI at a
slant of 30° (mean ± SD: 0.32 ± 0.08) (P = 3.71 × 10−4 ,
Wilcoxon’s rank-sum test) in VPS (Fig. 5F). These findings
are consistent with the example neurons, indicating that
the tilt-tuning strength in the STPp and VPS population
neurons increases as the slant angle increases.
The tilt-disparity interaction
To further delve into the specific interaction in tilt and
mean disparity, we compared the influence of disparity
on tilt preference and the influence of tilt angle on far
or near disparity preferences. Figure 6 displayed four
representative neurons from two distinct patterns of
tilt-disparity interaction. Neurons depicted in Fig. 6A
and B exemplify the first pattern, characterized by a strong
dependence of tilt preference on binocular disparity
(| preferred tilt| ≥ 90° for all disparities) and they also
displayed a clear sign reversal of DSDI for opposite tilts.
Specifically, the cell in Fig. 6A preferred near disparities
(DSDI = −0.619) when tilted at 315° (Fig. 6A, left
column), yet favoured far disparities (DSDI = 0.336)
when tilted at 225°. Similarly, this cell’s tilt preference also
depended highly on binocular disparity. The preferred tilt
was 225° for far disparities (Fig. 6A, top column), while
for near disparities, the neuron favoured a 0° tilt. We refer
to this property as tilt-dependent disparity (TDD) tuning
(for classification criteria, see Methods). This sort of interaction was reported previously in MSTd (Roy & Wurtz,
1990; Roy et al., 1992).
Figure 6C and D show another type of neuron that
exhibits a clear reversal in disparity preference (both far
and near disparities) for all tilt directions and had the same
preferred tilt (| preferred tilt| < 90°) for all disparities.
The cell in Fig. 6C preferred far disparity for tilt of 315°
(DSDI = 0.43; Fig. 6C, left column), but preferred near
disparity for tilt of 135° (DSDI = −0.646; Fig. 6C, left
column). However, what is different from the neuron in
Fig. 6A is that the preferred tilt was consistent across
all binocular disparities (Fig. 6C, top column) (| preferred tilt| < 90°). The cells that reversed their disparity
preference for different tilts were classified as TDD cells
and the others were classified as non-TDD cells (for
classification criteria, see Methods).
To visually demonstrate how the mean disparity preference changes with tilt, the DSDI curves with tilt of the
J Physiol 603.6
four neurons (Fig. 6A–D) were plotted in Fig. 6E and 6F.
For comparison, the two example non-TDD cells from
Fig. 3A and B are also drawn in Fig. 6E and F (black
lines). For the non-TDD cells in Fig. 3, the DSDI is fairly
constant across directions. In contrast, the DSDI varies
in a sinusoidal-like manner for the four TDD cells of
Fig. 6A–D.
To better explore the encoding properties of TDD
neurons, we identified the pair of opposite tilt directions
(separated by 180°) that resulted in the maximal absolute
difference in DSDI for each TDD cell at slants of 30°
and 60° separately. Therefore, we referred to the tilt with
the larger response as the ‘preferred tilt’ and its opposite
direction as the ‘null tilt’ (Yang et al., 2011). A total
of 40 STPp and 21 VPS neurons were TDD neurons
in both slant conditions. The majority of TDD cells
(STPp: 60.0%, 24/40; VPS: 61.9%, 13/21) also exhibited
a larger shift in their tilt preferences (| preferred
tilt| ≥ 90°) among all disparities. We calculated the average
difference in firing rates between the preferred and null
tilt curves, and graphed these values on the abscissa for
near disparities (less than 0°) and the ordinate for far
disparities (larger than 0°) in the scatter plot in Fig. 7,
respectively. Thus, neurons represented by data points
that fall in the upper-left or lower-right quadrant of the
scatter plot reversed both their tilt and disparity preferences, as exemplified in Fig. 7A, B, D and E. Those in
the upper-right or lower-left quadrants reversed disparity
preferences but not tilt preferences, as depicted in Fig. 7C.
Notably, 60% of neurons are located in the upper-left or
lower-right quadrants indicating a dual reversal in both
disparity and tilt preferences. This suggests that STPp and
VPS may be involved in more complex object recognition
(such as curved surfaces).
The effect of motion information on the perception
of 3D plane
In daily life, various factors can affect how we perceive the
3D structure of objects, including the motion information
of the object. In the process of perceiving the 3D structure
of an object, the brain needs to ignore the motion
information and maintain the stability of 3D object
perception. Therefore, we added ‘Speed’ parameters in the
experiment: all random dots moved uniformly towards
the monkeys in the direction of the slant-tilt plane at the
preferred speed of neurons specifically from a position
farther away from the monkey to one closer, as indicated
by the arrows in Fig. 8A (motion conditions, see Methods
for details).
By simulating visual stimulation under these natural
motion conditions, we can more accurately explore how
the brain processes and integrates visual information.
This approach provides deeper insight into the neural
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
14697793, 2025, 6, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP287309 by New York University, Wiley Online Library on [15/03/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
1552
3D Orientation representation in macaque STPp and VPS
Figure 6. TDD example neurons from areas STPp and VPS
A and B, for the two neurons, disparity preference reversed for opposite tilts (left column), and tilt preference
reversed for near vs. far disparities (top column). Error bars denote SD. The format is as in Fig. 3. C and D, for
the two neurons, disparity preference reversed for opposite tilts (left column), but tilt preference did not reverse
for near vs. far disparities (top column). The format is as in Fig. 3. Error bars denote SD. E, the DSDI is plotted as
a function of tilt for three disparity-selective neurons from the STPp, previously depicted in Figs 3A and 6A,C. In
non-TDD neurons, exemplified by the neuron from Fig. 3A, the DSDI exhibits minimal variation with changes in tilt
(black line). In contrast, TDD neurons exhibit a pronounced sign reversal in DSDI across varying tilts (red and blue
lines). F, the DSDI is plotted as a function of tilt for three disparity-selective neurons from the VPS, initially shown
in Figs 3B and 6B,D. The DSDI of non-TDD neurons, such as the one from Fig. 3B, remains largely unaffected by
tilt changes (black line), while TDD neurons demonstrate a strong sign reversal in DSDI with differing tilts (red and
blue lines). [Colour figure can be viewed at wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
1553
14697793, 2025, 6, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP287309 by New York University, Wiley Online Library on [15/03/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
J Physiol 603.6
R. Wang and others
mechanisms responsible for visual stability and spatial
perception. Comparing neural responses under static and
dynamic conditions allows us to identify differences in
how various brain regions process 3D spatial information.
These comparisons help clarify the specific roles different
brain regions play in maintaining visual stability and
spatial awareness.
We analysed and compared the responses of STPp and
VPS neurons under motion and non-motion conditions
to understand the stability of the neurons encoding a 3D
plane and explore the role of the two brain regions in the
3D object perception network.
There were 152 STPp neurons and 99 VPS neurons
recorded under the non-motion conditions. Among them,
149 STPp neurons and 95 VPS neurons were recorded
under stimulations with motion information. We first
tested the neuronal tuning significance to slant, tilt, and
mean disparity using three-way ANOVA under motion
conditions and compared the neuronal tuning ratio with
J Physiol 603.6
non-motion conditions. Across STPp neurons, 102 of 149
(68.5%) were tilt-selective under motion conditions, an
increase of 43.5% compared to non-motion conditions
(n = 38, 25%) (Table 1). The tilt tuning proportion of STPp
neurons has increased greatly, while the disparity tuning
proportion of STPp remained largely unchanged (Table 1).
This demonstrates that motion information can enhance
the encoding of 3D planes in STPp. Results from VPS
were markedly different from STPp (Table 1). First, there
were fewer disparity selective cells in VPS than STPp in
both conditions. Second, there was a decrease of 14.0% of
neurons with significant tuning to the mean disparity after
adding motion information (Table 1). The tuning ratio
suggests that motion information affects the 3D plane
encoding properties to some extent, and this influence
may vary between STPp and VPS.
Figure 8B–E compared the responses of two examples
STPp and VPS neurons to tilt and mean disparity under
motion and non-motion conditions. We first studied the
Figure 7. Population summary of response patterns for TDD neurons
Each data point in the scatter plot represents the difference in the average response between the preferred and null
tilt angles at far disparities (ordinate) vs. near disparities (abscissa) for TDD neurons. Separate data are shown for
TDD neurons from STPp (brown) and VPS (blue). Panels A–E display the disparity tuning profiles for the preferred
(pink curves) and null tilt angles (black curves) of five selected neurons, each aligned with the corresponding
markers on the scatter plot. Error bars denote SD. [Colour figure can be viewed at wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1554
3D Orientation representation in macaque STPp and VPS
Figure 8. Comparative analysis of neural responses in STPp and VPS neurons under motion and
non-motion conditions
A, stimulation direction under motion conditions. In the motion conditions, all random dots moved uniformly
towards the monkeys in the direction of the slant-tilt plane at the preferred speed of neurons, specifically from
a position farther away from the monkey to one closer (as indicated by the arrows in the figure). B and D, the
responses of STPp example neuron to tilt and mean disparity are shown under non-motion stimulations (B) and
motion stimulations (D). C and E, the responses of VPS example neuron to tilt and mean disparity are shown under
non-motion stimulations (C) and motion stimulations (E). The burgundy triangle curves (left column) represent the
mean disparity tuning curves at a slant of 0°. The format is as in Fig. 3. Error bars denote SD. [Colour figure can
be viewed at wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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J Physiol 603.6
R. Wang and others
Table 1. Summary of tuning in STPp and VPS
Non-motion
Motion
Slant
Tilt
Disparity
Total
28 (18.4%)
38 (25%)
107 (70.4%)
152
34 (22.8%)
102 (68.5%)
110 (73.8%)
149
Slant
Tilt
Disparity
Total
25 (25.3%)
33 (33.3%)
41 (41.4%)
99
24 (25.3%)
63 (66.3%)
26 (27.4%)
95
STPp
VPS
STPp: superior temporal polysensory area, VPS: visual posterior
sylvian area.
influence of motion information on the encoding of
the 3D plane by detecting the maximum firing rates of
neurons. From the STPp example neuron, it can be seen
that the contour map of neuronal responses in Fig. 8B
is similar to that in Fig. 8D, and the maximum firing
frequency of the neuron under non-motion conditions
is 64.0 spikes/s (Fig. 8B) and that of the neuron under
motion conditions is 58.8 spikes/s (Fig. 8D), indicating
that the maximum firing frequency of this neuron was
very close between the two conditions. For population
neurons, motion information had no significant effect on
the response intensity in STPp (P = 0.463, t test). In
VPS, the contour map in Fig. 8C is lighter than that in
Fig. 8E, and the maximum firing frequency of neurons in
Fig. 8C (51.2 spikes/s) is also lower than that in Fig. 8E
(74.5 spikes/s), indicating that the firing frequency of
the neuron increases greatly after the addition of motion
information. For population neurons, motion information
also had no significant effect on the response intensity in
VPS (P = 0.186, t test). The results show that motion did
affect the response intensity of the 3D plane in both areas.
Figure 4 indicates that different slant angles do not
affect the mean disparity characteristics of neurons. To
further understand how motion information specifically
affects the depth encoding in STPp and VPS, we compared
and analysed the mean disparity-tuning characteristics
(Fig. 9A–F) at a slant of 0° under motion and non-motion
conditions, separately. Figure 9A and B shows the mean
disparity-tuning curves of the example neurons shown
in Fig. 8B and C. The example STPp neuron showed
a maximum firing rate when the mean disparity was
−2° under both non-motion conditions (DSDI: 0.47,
Fig. 9A) and motion conditions (DSDI: 0.68, Fig. 9A).
In contrast, the VPS example showed a maximum firing
rate when the mean disparity was 0° under motion
conditions and when the mean disparity was −2° under
non-motion conditions (Fig. 9B), which indicated that the
J Physiol 603.6
mean disparity preferences of the VPS neuron changed
after adding motion information. To clearly show the
firing rate changes of neurons under different stimuli,
we divided the neurons significantly tuned to mean
disparity under different conditions into four classes
using one-way ANOVA: ‘Both_depth’ (Pnon-motion < 0.05,
Pmotion < 0.05), ‘Non-motion only_depth’ neurons
(Pnon-motion < 0.05, Pmotion > 0.05), ‘Motion only_depth’
neurons (Pnon-motion > 0.05, Pmotion < 0.05), ‘Not
tuned_depth’ neurons (Pnon-motion > 0.05, Pmotion > 0.05)
(for details, see Methods). We calculated and plotted
DSDI for each neuron under motion and non-motion
conditions at a slant of 0° in Fig. 9C and D. In STPp
(Fig. 9C), most of the data points are distributed
around the diagonal line, where 81.8% (54/66) neurons
significantly tuned to mean disparity under motion or
non-motion conditions (Both_depth: 25/28, Non-motion
only_depth: 10/15, Motion only_depth: 19/23) are
distributed in the upper-right or lower-left quadrants,
indicating that DSDI does not change sign after adding
motion information and these neurons maintained the
preference of far or near disparities. The data points
of VPS (Fig. 9D) are evenly distributed in the four
quadrants, indicating that about half of VPS neurons
displayed a clear reversal in disparity preference after the
addition of motion information.
We then compared the neuronal tuning strength to
the mean disparity between non-motion and motion
conditions in the two areas. In STPp (Fig. 9A), the DDI
of the neuron under motion conditions (0.68) was higher
than that under non-motion conditions (DDI: 0.37).
In VPS (Fig. 9B), the DDI value (0.53) under motion
conditions was also higher than that under non-motion
conditions (0.47). For population neurons, we calculated
and plotted the DDI of all neurons at a slant of 0°
under motion and non-motion conditions in Fig. 9E
and F. In STPp (Fig. 9E), ‘Both_depth’ neurons are mostly
located above the diagonal (67.9%, 19/28), and the DDI
(mean ± SD: 0.63 ± 0.08) under motion conditions
is significantly higher than the DDI (mean ± SD:
0.58 ± 0.09) under non-motion conditions (P = 0.011,
Wilcoxon’s sign-rank test). For all STPp neurons, the
mean DDI of neurons under non-motion conditions
(mean ± SD: 0.44 ± 0.15) was significantly higher than
that under motion conditions (mean ± SD: 0.41 ± 0.14,
P = 0.013, Wilcoxon’s sign-rank test), indicating that
the tuning strength for mean disparity of STPp neurons
was significantly improved after the adding motion
information while there was no significant difference
between the DDI (mean ± SD: 0.36 ± 0.12) under motion
conditions and that (mean ± SD: 0.38 ± 0.11) under
non-motion conditions (P = 0.27, Wilcoxon’s sign-rank
test) for all VPS neurons (Fig. 9F).
To detect the stability of neuronal encoding plane
direction and explore how motion information
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1556
3D Orientation representation in macaque STPp and VPS
Figure 9. Comparative analysis of mean disparity and tilt-tuning characteristics in STPp and VPS under
motion and non-motion conditions
The left two columns (A–F) compared the mean disparity-tuning characteristics at a slant of 0° between motion and
non-motion conditions in STPp and VPS, while the right two columns (G–L) compared the tilt-tuning characteristics
at a slant of 60° between the two conditions in both areas. A and B, the mean disparity tuning curves of example
neurons from STPp (A) and VPS (B) at a slant of 0°. Continuous lines: motion conditions; dashed lines: non-motion
conditions. A, non-motion: DSDI = 0.47, DDI = 0.37; motion: DSDI = 0.68, DDI = 0.68. B, non-motion: DDI = 0.47;
motion: DSDI = −0.23, DDI = 0.53. Error bars denote SD. C and D, the scatter plots show the DSDI under motion
conditions (ordinate) vs. the corresponding DSDI under non-motion conditions (abscissa) in STPp (C) and VPS (D)
at a slant of 0°. The horizontal and vertical continuous lines are straight lines through the origin, while the inclined
continuous lines represent diagonal lines. Cell types are represented as different colours. E and F, disparity tuning
strength (DDI) for the motion condition is plotted as a function of those for the non-motion condition in STPp (E)
and VPS (F). Continuous lines are diagonal lines. G and H, the tilt-tuning curves of example neurons from STPp
(G) and VPS (H) at a slant of 60°. Continuous lines: motion conditions; dashed lines: non-motion conditions. G,
non-motion: Pref tilt = 354.6°, TDI = 0.36; motion: Pref tilt = 332.8°, TDI = 0.48. H, non-motion: Pref tilt = 130.0°,
TDI = 0.51; motion: Pref tilt = 35.2°, TDI = 0.74. I and J, distributions of absolute differences in tilt preference (|
Pref Tilt|) between motion and non-motion conditions are shown as histograms in STPp (I) and VPS (J). Cell types
are represented as different colours. K and L, the TDI under motion stimulations are plotted as a function of the
TDI under non-motion stimulations of neurons in STPp (K) and VPS (L). The continuous lines are diagonal lines.
[Colour figure can be viewed at wileyonlinelibrary.com]
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
1557
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J Physiol 603.6
R. Wang and others
specifically affects the tilt coding, we next compared
the tilt preference shifts between motion and non-motion
conditions. To better understand the tilt-tuning
characteristics of the two areas, we pooled the tilt-tuning
curves under different disparities of two example neurons
shown in Fig. 8B–E and plotted in Fig. 9G and H (see
Methods for details). In STPp (Fig. 9G), the tilt preference
of this neuron under the non-motion condition is 332.8°
calculated by the vector sum, which is 21.8° less than the
tilt preference (354.6°) under the non-motion condition.
The shift was 21.8°, small enough to fall within the
sample tilt resolution (45°), indicating that the motion
information does not change the tilt preference of the
STPp neuron. In VPS (Fig. 9H), under motion conditions,
neurons have the maximum response at a tilt of 45°, and
the tilt preference of this neuron is 35.2°. However, the
neuron shows a maximum response at a tilt of 180° and
the tilt preference is 130.0° under non-motion conditions.
Thus, the tilt preference shift is 94.8°, which is greater
than 45°. This shows that the motion information changes
the tilt preference of the VPS example neuron. Next, we
compared the tilt preferences of population neurons
under non-motion and motion conditions at a slant of
60° and plotted the frequency distribution histogram
of the absolute shift of tilt preference between both
conditions (| Pref tilt|) (Fig. 9I and J). In STPp (Fig. 9I),
the shift distribution of ‘Both_depth’ neurons showed an
obvious unimodal distribution, and the shift of 75.8% of
neurons (113/149) was less than 45°, indicating that the
motion information hardly changed the tilt preference
of STPp neurons. In VPS (Fig. 9J), the distribution of
neurons showed a bimodal distribution, with peaks of
22.5° and 135°, respectively, indicating that tilt preference
of part VPS neurons changed greatly after adding motion
information. The findings suggest that STPp neurons
can ignore motion information and stably encode tilt
information of plane orientation, whereas VPS neurons
exhibit less consistent encoding of plane orientation after
adding motion.
Finally, we also compared the tilt-tuning strength
of STPp and VPS neurons under both conditions
at a slant of 60°. In Fig. 9G and H, the TDI values
of STPp and VPS example neurons under motion
conditions (STPp: 0.48, VPS: 0.74) were higher than
those under non-motion conditions (STPp: 0.36,
VPS: 0.51). To clearly show the firing rate changes of
neurons under different stimuli, we divided the neurons
significantly tuned to tilt under different conditions
into four classes using two-way ANOVA: ‘Both_tilt’
(Pnon-motion < 0.05, Pmotion < 0.05), ‘Non-motion only_tilt’
neurons (Pnon-motion < 0.05, Pmotion >0.05), ‘Motion
only_tilt’ neurons (Pnon-motion > 0.05, Pmotion < 0.05), ‘Not
tuned_tilt’ neurons (Pnon-motion > 0.05, Pmotion > 0.05)
(for details, see Methods). For population neurons, 69.7%
‘Both_tilt’ neurons (23/33) of STPp neurons that were
J Physiol 603.6
significantly tuned to tilt under two conditions were
distributed above the diagonal line (Fig. 9K), and the
mean TDI (mean ± SD: 0.40 ± 0.10) under motion
stimulations was significantly higher than that under
non-motion stimulations (mean ± SD: 0.35 ± 0.08)
(P = 3.1 × 10−4 , Wilcoxon’s sign-rank test), indicating
that tilt-tuning strength of STPp neurons was enhanced
after adding motion information. Similarly, the TDI
(mean ± SD: 0.51 ± 0.16) of VPS neurons under motion
stimulations was significantly higher than that under
non-motion stimulations (mean ± SD: 0.38 ± 0.07)
(P = 3.1 × 10−4 , Wilcoxon’s sign-rank test) (Fig. 9L),
indicating that tilt-tuning strength of VPS neurons is
also enhanced after adding motion information, which is
consistent with the example neuron in Fig. 9H. In general,
the proportion of neurons significantly tuned to tilt, and
the tilt-tuning strength was increased with motion in
both STPp and VPS by comparing the tilt encoding in the
non-motion and motion conditions.
In summary, the proportion of tilt-tuned cells and
the tuning strength of neurons in STPp and VPS both
increased. Unlike STPp, which can stably encode 3D plane
orientation after adding motion information, the tilt preferences of half of VPS neurons changed greatly after
adding motion information, indicating that the stability of
the VPS-encoded 3D plane was less than STPp.
Discussion
The study revealed that STPp and VPS can encode 3D
plane orientation defined by slant and tilt, with depth
via binocular disparity. While different slant angles may
influence the tuning strength of neurons for tilt and mean
disparity to some extent, they do not alter the overall
neuronal preferences. This implies that slant encoding
operates independently from tilt and mean disparity
encoding. Additionally, approximately 10% of cells in
both STPp (13.1%, 40/304) and VPS (10.6%, 21/198),
named TDD neurons, exhibited a unique characteristic:
their preference for planar depth varied with opposite tilt
angles. Finally, we investigated how motion cues affect the
perception of 3D surface orientation. STPp consistently
encoded plane orientation information regardless of
motion cue, whereas VPS responses showed less stability.
These findings shed light on the distinct processing
mechanisms for 3D spatial information in different
cortical areas, offering insights into the neural basis of
visual stability and spatial perception.
Tuning of 3D plane orientation information
In the context of 3D plane orientation, the study identified
that 25.0% and 30.3% of neurons in STPp and VPS,
respectively, exhibited tilt-tuning (P < 0.05, three-way
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1558
3D Orientation representation in macaque STPp and VPS
ANOVA). Notably, in the ventral pathway, the proportion
of tilt-tuned neurons in STPp was found to be lower
than in areas such as IT (68%) (Liu et al., 2004), while
being comparable to V4 (29%) (Hegdé & Van Essen,
2005a). However, these proportions were significantly
lower compared to dorsal pathway areas like MT (72%)
(Nguyenkim & DeAngelis, 2003) and CIP (54%) (Tsutsui
et al., 2002). One possible explanation is that the
stimulation did not efficiently activate STPp neurons.
It was observed that STP neurons generally respond
more favorably to stimuli containing motion information
(Oram & Perrett, 1994, 1996; Oram et al., 1993). The
inclusion of motion information in the study led to a substantial increase in the tilt-tuning ratio in both STPp and
VPS, bringing their ratios in line with those observed in
IT and MT and surpassing those in CIP. Moreover, the
findings indicated that STPp and VPS exhibit enhanced
encoding of plane orientation when motion information
is incorporated. Consequently, STPp was positioned
at a comparable level to IT and CIP in 3D plane
perception.
Furthermore, the study revealed that the tilt preferences of neurons in STPp and VPS spanned nearly all
directions, signifying their capacity to encode 3D plane
directions comprehensively. This observation aligns with
the characteristics of CIP, but diverges from IT. In a study
conducted by Liu et al. on IT neurons, the preference for
plane orientation, defined by texture and disparity cues,
was limited to only four tilt directions: left, right, up and
down (Liu et al., 2004). Notably, in IT, a consistent preference was observed for stimuli on the left (closer to the
observer) and the top (closer to the observer). In contrast,
our findings indicate that the preferred tilt of neurons
in STPp and VPS remains constant regardless of slant,
suggesting that the encoding of slant and tilt in these areas
may be separate. Moreover, the tilt-tuning strength in
STPp and VPS increases as the slant angle rises, potentially
influenced by more disparity information with an increase
in slant angle. This pattern is consistent with observations
made in MT, IT and CIP in previous studies (Liu et al.,
2004; Nguyenkim & DeAngelis, 2003; Rosenberg et al.,
2013; Sanada et al., 2012).
Tuning of 3D plane depth information
For the encoding of absolute disparity (slant 0°, zero-order
depth), 28.3% (43/152) of STPp neurons were tuned to
mean disparity, a proportion higher than VPS (13.1%,
13/99), but notably lower than V4 (35.3%) (Hegdé & Van
Essen, 2005b) and MT (92%) (DeAngelis & Uka, 2003).
Notably, V4 neurons displayed an increased disparity
tuning ratio of 80% when exposed to bar stimuli. Upon
adding relative disparity (slant 30°, first-order depth),
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
1559
both STPp and VPS showed a significant improvement in
the tuning ratio to mean disparity (STPp: 63.2%, 96/152;
VPS: 33.3%, 33/99). This suggests that both areas have an
elevated tuning ratio to the first-order depth with more
depth information. Psychological research also shows
that relative disparity plays a more substantial role in
depth perception (Neri, 2005). Consequently, STPp and
VPS may operate at a higher-level in-depth perception.
However, as the slant angle increases to 60°, the mean
disparity tuning ratio and strength in both STPp and VPS
decreases to some extent. This decline may be caused by
the pedestal disparities of the plane overlapping with the
total depth of the plane as the slant angle increases. This
overlap potentially makes it more challenging for neurons
to discriminate mean disparity accurately.
In primates, multiple brain regions contribute
to the processing of 3D visual spatial information,
including STPp, VPS, CIP, MT/V5, LIP, VIP and
PPRF (Bhattacharyya et al., 2009; Bremmer et al.,
2013; Caziot et al., 2024; Genovesio & Ferraina, 2004;
Gnadt & Mays, 1995; Ilg et al., 2004; Nakhla et al., 2021;
Sanada & DeAngelis, 2014). Neurons within STPp are
particularly specialized in encoding the fixation plane,
a function critical for integrating visual information
and coordinating eye movements to maintain a stable
perception of the environment. Given STPp’s position
higher in the visual hierarchy, it probably supports
advanced visual functions such as object recognition and
spatial relationship analysis.
In contrast, VPS neurons exhibit broader depth-tuning
ranges, suggesting a more generalized role in spatial
processing across various contexts. Neurons in MT and
MST, in particular, are highly sensitive to disparity within
dynamic visual stimuli, underpinning functions related to
visual motion and self-motion processing (Ilg et al., 2004;
Sanada & DeAngelis, 2014). LIP neurons, with their preference for near disparities, support eye movement control
and assist in planning saccades by emphasizing crossed
disparities (Genovesio & Ferraina, 2004; Gnadt & Mays,
1995). Neurons in VIP, which also show a strong preference for near disparities, are thought to be involved
in processing spatial information related to objects and
actions within peripersonal space, probably facilitating
precise hand movements and grasping (Bremmer et al.,
2013; Caziot et al., 2024; Nakhla et al., 2021). Similarly,
PPRF neurons exhibit disparity tuning that supports hand
movements by assisting in-depth assessments for precise
manual actions (Bhattacharyya et al., 2009).
In summary, the distinct disparity preferences across
these regions reflect specialized functions in spatial
information processing. The differences in disparity
sensitivity may also be associated with their connectivity
within the broader visual network, collectively forming an
integrated system for visual spatial processing in primates.
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J Physiol 603.6
R. Wang and others
The interaction of plane direction and depth
information
A distinct class of neurons, called TDD neurons, exhibited
a reversal in mean disparity preference with tilt. In
both STPp and VPS, 10% of TDD neurons showed
a reversal in preferred tilt, with the maximum shift
occurring around 180°. This finding is similar to
direction-dependent disparity-tuned neurons in area
MSTd, where approximately 30% of neurons exhibit a
reversal in disparity preference with the direction of
stimulation (Roy & Wurtz, 1990; Roy et al., 1992; Yang
et al., 2011). The lower proportion of TDD cells in STPp
and VPS compared to direction-dependent disparity
neurons in MSTd could be attributed to differences in
experimental stimuli. MSTd studies typically employ
optical flow stimuli or simple light spots and gratings.
In addition, it was hypothesized in these studies that
direction-dependent disparity neurons may be related
to motion disparity, and such cells may be involved in
the distinction between their motion and the motion
of external objects, so TDD neurons in STPp and VPS
may play a role in distinguishing self-motion from
external object motion. Nevertheless, further validation
is required. Notably, earlier research by Nguyenkim
and DeAngelis demonstrated that the depth preference
of MT neurons could reverse when stimuli were presented in the opposite region of the MT receptive field
(Nguyenkim & DeAngelis, 2003). However, their study
only examined tilt-tuning around the mean disparity
preference of neurons within the receptive field, which
was relatively small (4° × 4°) with peripheral inhibition
in some neurons. In contrast, STP receptive fields extend
to the contralateral region, and most cells have receptive
fields close to the macaque visual field (Bruce et al.,
1981), making our stimuli more aligned with the STPp
receptive field. Additionally, we hypothesized that TDD
cells may be involved in recognizing face orientation in
area STP. A study by Perrett et al. identified bimodal
viewer-centred cells in STPp, with approximately 10% of
cells responding significantly to two unconnected facial
stimuli (Perrett et al., 1991). These neurons can respond
to facial stimuli facing left or right. While faces typically
contain depth information, our experimental setup aligns
the tilt direction with the left and right sides. However,
their study involved facial stimuli within the same
plane, lacking depth information, and demonstrated a
reversal in neurons’ disparity preference compared to our
findings.
Finally, we propose that TDD cells may encode curved
planes defined by second-order disparity. In our study,
opposite tilt and mean disparity combinations form
a simplified second-order disparity surface. Previous
research has shown that both IT and AIP can encode such
simplified surfaces (Janssen et al., 2000a,b; Srivastava et al.,
J Physiol 603.6
2009), and since STP receives neural projections from IT,
TDD neurons in STPp and VPS may participate in the
encoding of curved planes. In summary, while there is
evidence of interaction between tilt and mean disparity
encoding in STPp, further investigation is required to
elucidate the specific functional characteristics of these
neurons.
Tuning of 3D plane motion information
In our experiment, we systematically investigated the
impact of motion information on 3D plane perception.
The results demonstrated a substantial effect on the VPS,
where only two neurons maintained their tuning to mean
disparity. This implies that motion information has a
dramatic influence on the coding of mean disparity in
VPS, almost entirely disrupting their ability to perceive
the 3D plane. Notably, approximately half of the VPS
neurons also exhibited a change in tilt preferences, with a
deviation exceeding 45°. These findings suggest that VPS
struggles to retain its initial perception of the 3D plane
when exposed to motion stimuli, underscoring the critical
role of VPS in self-motion perception (Chen et al., 2011a).
In contrast, STPp remarkably maintains the original 3D
perspective with high accuracy, and the introduction of
motion information serves to enhance the tuning strength
of neurons without altering their preferences. This
suggests that the responses of STPp under non-motion
conditions are not solely reliant on local visual data. In
other words, STPp can perceive and identify external
3D objects. Hietanen and Perrett observed that the area
STPa in macaque monkeys can discriminate self-motion
from object motion by computing correlations between
the observer’s motion and background signals (such
as vestibular, somatosensory or retinal information)
(Hietanen & Perrett, 1996). They also proposed that STPa
exhibits a preference for visual motion caused by external
objects, remaining less sensitive to changes in retinal optic
flow resulting from its own motion (Hietanen & Perrett,
1996). This observation may provide an explanation
for why STPp neurons exhibit reduced response to
self-motion. Therefore, considering the low proportion of
tilt-tuned neurons in STPp during self-orientation tasks, it
is reasonable to infer that STPp has undergone the process
of object recognition and can discriminate between self
and external objects.
In summary, both STPp and VPS can represent the
direction and depth of a 3D plane based on binocular
disparity. However, the responses in VPS seem to be
influenced by visual local cues, while STPp appears to
play a pivotal role in the perception and identification
of external objects. These regions complement each
other in 3D perception, with VPS resembling early
motion-processing areas like MT (DeAngelis et al., 1998),
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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1560
3D Orientation representation in macaque STPp and VPS
while STPp aligns with higher-order areas such as
CIP (Alizadeh et al., 2018), which handle sophisticated
integration of motion, depth and multisensory data.
Together, they highlight the complexity of 3D perception
and the neural mechanisms supporting spatial awareness
and navigation.
Population coding in STPp and VPS
Each neuron within the STPp and the VPS could have
specific tuning properties and respond optimally to
different features of visual stimuli, such as orientation,
disparity or motion. In our study, we found that both
STPp and VPS contain neurons tuned to slant, tilt or mean
disparity. Furthermore, some neurons exhibit tuning to
multiple types of 3D planar information simultaneously.
Such diversity allows the brain to capture various aspects
of complex visual scenes (DeYoe & Van Essen, 1988;
Livingstone & Hubel, 1988). When these neurons work
together, their collective responses form a comprehensive
representation of the visual input (Jazayeri & Movshon,
2006). The brain might decode this collective activity by
analysing patterns of firing rates and synchrony (Engel
et al., 2001; Singer, 1999). Through this analysis, it extracts
relevant features from stimuli. If several neurons tuned
to specific orientations respond together, the brain could
interpret this synchronized activity as an indication of
the object’s position or movement in space (Fries, 2005).
Importantly, when populations of neurons are activated,
they can represent both slant and tilt simultaneously. The
brain could decode this information by analysing the
distinct firing patterns associated with each dimension.
For example, if certain neurons indicate a specific
slant while others indicate a particular tilt, the brain
can integrate these signals to form a comprehensive
representation of the surface’s orientation. Our findings
indicate that the VPS and STPp encode slant, tilt and
mean disparity independently. Similarly, some studies
also have shown that the brain can represent slant, tilt
and distance independently, which enhances flexibility in
visual perception and allows for accurate interpretation of
a wide variety of orientations (Chang et al., 2020; Doudlah
et al., 2022). This independence is crucial in real-world
scenarios, such as navigation and object manipulation,
where understanding precise surface orientation and
position is necessary. This contributes to a nuanced
representation of 3D pose (the position and orientation
of an object or person in three-dimensional space),
improving the brain’s ability to interpret complex visual
information effectively (Warren Jr, 1998).
Collective encoding also enhances robustness in
sensory processing (Averbeck et al., 2006; Shadlen &
Newsome, 1998; Zohary et al., 1994). The visual system
relies on populations of neurons rather than single
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
1561
neurons, making it resilient to noise and variability.
Our results show that in both VPS and STPp, multiple
neurons are tuned to the same 3D planar parameter.
If one neuron becomes less responsive due to fatigue
or input variability, other neurons can still provide
reliable information, ensuring stable overall perception
(Britten et al., 1992). Furthermore, the collective nature
of encoding allows for dynamic adjustments based on
contextual information and prior experiences (Engel
& Singer, 2001; Gilbert & Sigman, 2007; Singer, 1999).
Neurons can recalibrate their responses depending on the
environment or task. For instance, as an observer moves
through space, neural populations in STPp and VPS
can adjust their activity patterns to optimize perception
based on changing visual and vestibular inputs. Our
study demonstrates that, upon the introduction of
visual motion information, STPp neurons maintain a
robust representation of 3D planar orientation, whereas
the responses of VPS neurons are influenced by the
motion information. In addition to visual information,
STPp and VPS also integrate signals from other sensory
modalities, such as auditory and vestibular cues (Chen
et al., 2011a,b; Zhao et al., 2021, 2023). This multisensory
approach enhances collective encoding, allowing the
brain to create a coherent representation of 3D space that
incorporates multiple sources of information. During
motion, for example, integrating vestibular cues with
visual signals helps maintain an accurate perception of
spatial orientation and object movement (Chen et al.,
2011a,b; Fetsch et al., 2010; Gu et al., 2008).
In summary, collective encoding in STPp and VPS
might involve the collaborative activity of diverse neuron
populations responding to different visual features. This
process enhances the robustness and reliability of spatial
representations, allows for dynamic adjustments based
on context, and ultimately leads to a more accurate and
nuanced understanding of 3D space.
Similarities in 3D surface perception in human and
monkeys
There are significant similarities in the mechanisms of
3D surface perception between humans and monkeys.
Both species engage both the dorsal and ventral visual
pathways to extract 3D shapes, utilizing critical cues
such as disparity, motion and texture in both pathways
(Ungerleider, 1982).
As primary processing centres for visual information,
V1 and V2 are responsible for processing basic
visual features such as absolute disparity and motion
information in both humans and non-human primates
(Cumming & Parker, 1999; Thomas et al., 2002). Neurons
in these regions are sensitive to depth, providing the basis
for subsequent 3D shape perception.
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J Physiol 603.6
R. Wang and others
Area V3A shows strong activation in response to
stereoscopic stimuli especially in processing disparity
information in human and non-human primates,
suggesting that V3A is involved in 3D visual processing
in both species (Bhattacharyya et al., 2009; Nakhla et al.,
2021).
In the dorsal pathway, the CIP region is responsible for
the analysis of 3D object features in monkeys, especially
the axis and surface orientation of the object (Rosenberg
& Angelaki, 2014; Sakata, 2003; Tsao et al., 2003).
In humans, homologous region CPDR (caudal parietal
disparity region) is activated in 3D surface perception
tasks (Shikata et al., 2001).
The occipito-temporal cortex demonstrates a more
segregated processing of motion, texture and shading in
areas such as MT/V5, while the presence of homologous
regions hMT/V5+ includes homologies MT/V5 and the
FST (Kolster et al., 2009; Kolster et al., 2010). This suggests
that, despite species differences, the function of these
regions is conserved in both species.
STPp is a multisensory integration region in
non-human primates that integrates visual and vestibular
signals and is critical for visual stability and spatial
perception (Zhao et al., 2021). There is no known region
in the human brain that corresponds exactly to STPp, but
there may be functional similarities with pSTS (posterior
superior temporal sulcus) and STGa (superior temporal
gyrus, anterior portion) (Vanduffel et al., 2001).
In macaques, VPS is associated with the convergence
of visuomotor information (optic flow) and vestibular
signals, which are important for self-motor perception
and 3D vision (Chen et al., 2011a). In humans, VPS may
correspond to the posterior insular cortex (PIC), an area
that also plays a role in processing visual motion and
perception of self-motion. fMRI studies in humans have
shown activity in the PIC region in response to both visual
and vestibular stimuli (Claeys et al., 2003).
Moving forward, we will investigate how the identified
regions contribute to various aspects of human 3D
perception, particularly in situations where depth cues
may be ambiguous or conflicting. We propose future
studies that utilize neuronal recordings in monkeys to
further elucidate the functional roles of STPp and VPS,
allowing us to determine how these findings can be
extrapolated to improve our understanding of human
visual processing.
Limitations and future directions
This research focused on investigating the role of macaque
regions STPp and VPS in encoding information related
to 3D orientation, providing valuable insights into the
response properties of these areas in object recognition.
STPp and VPS were found to be actively involved in 3D
J Physiol 603.6
plane perception defined by binocular disparity, capable of
encoding both planar orientation and depth information.
In this study, the sole visual cue employed was binocular
disparity; however, it is worth noting that the ventral
visual pathway often relies on monocular static cues
for abundant 3D perception, with texture gradient-tuned
neurons outnumbering disparity-tuned neurons in area
IT (Liu et al., 2004). Similarly, MSTd, in the dorsal
visual pathway, can generate robust 3D planar perception
through speed gradients (Georgieva et al., 2009). Future
investigations in areas of STPp and VPS could explore
the detection of other visual cues relevant to 3D plane
perception. Additionally, considering the cue integration
abilities observed in MT and CIP, where neurons linearly
combine responses from different cues (Sanada et al.,
2012; Tsutsui et al., 2001), further research could explore
how STPp integrates responses to various cue stimuli.
Our experiments focused on analyzing neuron
responses to zero and first-order planes; however, these
planes offer limited information on complete depth
structures. The zero-order plane merely signifies the order
and depth interval of an object, while the first-order plane
represents the 3D direction of the boundary surface or the
flat object. In contrast, second-order surface structures
are more closely associated with the depth structure of an
object (Orban, 2011). Previous studies have successfully
detected responses to second-order curved surfaces in
both ventral and dorsal higher-level regions such as IT
and CIP (Janssen et al., 2001, 2000a; Katsuyama et al.,
2010; Tsutsui et al., 2001). Therefore, future research could
explore STPp’s response to higher-order curved structures
to enhance our understanding of its characteristics in
perceiving 3D objects. Additionally, considering that STP
neurons can encode complex face and body shape stimuli,
and also integrate motion direction information with 3D
structure (Bruce et al., 1981; Oram & Perrett, 1994, 1996),
future studies might investigate STPp’s response to more
intricate stimuli containing richer biological information.
While our findings suggest that STPp and VPS encode
orientation and depth of 3D surfaces, the experiments
were not designed to evaluate whether selectivity for
local disparity signals might explain the responses of
TDD neurons, despite the fact that they have large
receptive fields. This complicates the interpretation of the
responses to tilt for that subpopulation, as it is challenging
to disentangle the effects of local disparity from true
tilt encoding (Rosenberg et al., 2023). Nevertheless, the
tolerance of surface orientation preferences to the pedestal
disparity for non-TDD neurons indicates robust tilt
selectivity for that subpopulation. Further research that
differentiates tilt encoding and local disparity encoding
will contribute to understanding the complexity of 3D
visual information processing in the brain and may have
significant implications for theoretical models of visual
perception and spatial cognition.
© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
14697793, 2025, 6, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP287309 by New York University, Wiley Online Library on [15/03/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
1562
3D Orientation representation in macaque STPp and VPS
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Additional information
Data availability statement
All raw data have been deposited at GitHub (https:
//github.com/RongWang58/3D-orientation) and are publicly
available as of the date of publication.
Competing interests
None declared.
Author contributions
A.C.: designed the experiments. B.Z.: performed data collection.
B.Z. and R.W.: analyzed the data. A.C. and R.W.: drafted the
manuscript. All authors have read and approved the final submission and agree to be accountable for all aspects of the work.
All persons designated as authors qualify for authorship, and all
those who qualify for authorship are listed.
Funding
This work was supported by grants from the ‘STI2030-major
projects’ (No. 2021ZD0202600), the National Basic
Research Program of China (No. 32171034) and Shanghai
Municipal Science and Technology Major Project (Grant No.
2021SHZDZX).
Acknowledgements
We would like to thank Minhu Chen for the software
development. We thank Prof. Dora Angelaki, Prof. Greg
DeAngelis and Prof. Ari Rosenberg for their helpful comments.
Keywords
3D orientation, mean disparity, motion, STPp, VPS
Supporting information
Additional supporting information can be found online in the
Supporting Information section at the end of the HTML view of
the article. Supporting information files available:
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© 2025 The Authors. The Journal of Physiology © 2025 The Physiological Society.
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