ORIGINAL RESEARCH
published: 03 March 2020
doi: 10.3389/fnbeh.2020.00025
On the Face of It: No Differential
Sensitivity to Internal Facial Features
in the Dog Brain
Dóra Szabó 1*, Anna Gábor 1,2 , Márta Gácsi 1,3 , Tamás Faragó 1 , Enikő Kubinyi 1 ,
Ádám Miklósi 1,3 and Attila Andics 1,2
Department of Ethology, Eötvös Loránd Univesity, Budapest, Hungary, 2 MTA-ELTE ‘Lendület’ Neuroethology of
Communication Research Group, Budapest, Hungary, 3 MTA-ELTE Comparative Ethology Research Group, Budapest,
Hungary
1
Edited by:
Pietro Pietrini,
IMT School for Advanced Studies
Lucca, Italy
Reviewed by:
Andrés Antonio González-Garrido,
Universidad de Guadalajara, Mexico
Gopikrishna Deshpande,
Auburn University, United States
*Correspondence:
Dóra Szabó
szaboodoora@gmail.com
Specialty section:
This article was submitted to
Behavioral Endocrinology,
a section of the journal
Frontiers in Behavioral Neuroscience
Received: 09 May 2019
Accepted: 04 February 2020
Published: 03 March 2020
Citation:
Szabó D, Gábor A, Gácsi M, Faragó T,
Kubinyi E, Miklósi Á and Andics A
(2020) On the Face of It: No
Differential Sensitivity to Internal Facial
Features in the Dog Brain.
Front. Behav. Neurosci. 14:25.
doi: 10.3389/fnbeh.2020.00025
Dogs are looking at and gaining information from human faces in a variety of contexts.
Next to behavioral studies investigating the topic, recent fMRI studies reported face
sensitive brain areas in dogs’ temporal cortex. However, these studies used whole heads
as stimuli which contain both internal (eyes, nose, mouth) and external facial features (hair,
chin, face-outline). Behavioral studies reported that (1) recognition of human faces by
dogs requires visibility of head contour and that (2) dogs are less successful in recognizing
their owners from 2D pictures than from real human heads. In contrast, face perception
in humans heavily depends on internal features and generalizes to 2D images. Whether
putative face sensitive regions in dogs have comparable properties to those of humans
has not been tested so far. In two fMRI experiments, we investigated (1) the location
of putative face sensitive areas presenting only internal features of a real human face
vs. a mono-colored control surface and (2) whether these regions show higher activity
toward live human faces and/or static images of those faces compared to scrambled
face images, all with the same outline. In Study 1 (n = 13) we found strong activity
for faces in multiple regions, including the previously described temporo-parietal and
occipital regions when the control was a mono-colored, homogeneous surface. These
differences disappeared in Study 2 (n = 11) when we compared faces to scrambled
faces, controlling for low-level visual cues. Our results do not support the assumption
that dogs rely on a specialized brain region for processing internal facial characteristics,
which is in line with the behavioral findings regarding dogs inability to recognize human
faces based on these features.
Keywords: dog, fMRI, visual, face-sensitive, face processing, inner face, DFA, face area
1. INTRODUCTION
While object perception is based on thedistributed response from neurons coding different aspects
of an object, the neural response to faces in humans (Andrews et al., 2010) and in some other
primate species (Burke and Sulikowski, 2013) seems to be somewhat exceptional. The holistic
processing of faces is manifested at the behavioral level via three main phenomena. The inversion
effect corresponds to impaired identification of upside-down faces compared to upright faces
(Kanwisher et al., 1998). The part-whole effect implies better discrimination performance of two
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Internal Facial Features in the Dog Brain
face parts when the parts are presented in the context of
a whole face than when presented in isolation (Tsao and
Livingstone, 2008). Finally, the composite effect, which means
slower identification of half of a chimeric face if it is aligned
with an inconsistent other half-face than if the two half-faces
are misaligned (Tsao and Livingstone, 2008). These effects
show that in the human brain, faces are represented not only
as a combination of the parts (eyes, nose, mouth), but as a
non-decomposable whole. These effects are disproportionately
present for faces in contrast to e.g., everyday objects.
More than 20 years of research localized several brain areas in
humans which play specific roles in face processing. Kanwisher
et al. (1997) described the fusiform face area at the border of
the temporal and occipital lobe as a region showing higher
activations to faces than various non-face objects and scrambled
images. Since then, researchers carried out a large number of
studies to examine its characteristics. Based on the literature,
the observed effect in face selective areas cannot be explained
by differences in low-level spectral features (Andrews et al.,
2010) or expert individuation (Rhodes et al., 2004). Despite
the holistic representation of faces, this higher activation is
more pronounced toward internal facial features (eyes, nose,
mouth) when compared to external features (hair, chin, faceoutline)(Kanwisher and Yovel, 2006; Andrews et al., 2010). Facesensitive areas in humans are considered as being right lateralized
in general (Kanwisher and Yovel, 2006), although a more recent
study suggested that the response profiles of the left and right
fusiform gyri differ, with the right fusiform gyrus being more
involved in categorical face/non-face judgements, while the left
fusiform gyrus reacting more to the level of similarity to faces in
case of non-face images (face-semblance) (Meng et al., 2012).
Face processing has been also investigated in a wide range
of non-human animals (mainly using faces of conspecifics as
stimuli), both at the behavioral and neural level (Leopold and
Rhodes, 2010; Burke and Sulikowski, 2013). Certain phenomena
such, as the face inversion effect, were also observed in
phylogenetically distant taxa (e.g., in a recent study on fish
Kawasaka et al., 2019). However, the presence of a composite
effect was reported only for primates (Burke and Sulikowski,
2013).
Family dogs are living in close day-to-day contact with
humans, easily developing a cross-species, individualized
communicative relationship with them. The properties of their
visual communication (including initiating and maintaining
eye contact) with humans is well-studied (Miklósi, 2009).
Human facial cues are thought to be important in dog-human
communication. Some recent behavioral studies showed dogs’
ability to use human facial cues for inferring attentional
states (Gácsi et al., 2004) and discriminate between emotional
expressions (Nagasawa et al., 2011; Müller et al., 2015).
A study in dogs (Huber et al., 2013), where the authors trained
dogs to discriminate between human heads based on visual
information, reported that transitioning from live presentation
to pictures of heads of the same individuals, and from these head
pictures to pictures of faces showing only internal features of
the head required additional training session before some of the
subjects could master the new task.
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Dilks et al. (2015) and Cuaya et al. (2016) compared portraits
of humans to everyday objects in fMRI studies and reported
higher activity toward faces in the right temporal regions (Dilks
et al., 2015) or in bilateral temporal regions with greater extent
in the left temporal lobe (Cuaya et al., 2016). Thompkins et al.
(2018) compared dog and human portraits and reported separate
brain areas in the left temporal cortex showing higher activity
to portraits of humans and dogs, resepctively. Comparing heads
with a wide variety of objects/scenes instead of a more restricted
category (e.g., houses), may intoduce an unintended bias, as
heads are more uniform in regard to their shape than the shape of
different everyday objects. All of the above mentioned dog fMRI
studies presented two-dimensional still images stimuli, but it has
not been established whether or not dogs (whose visual capacities
differ from that of humans) perceive basic two-dimensional
stimuli in the same way as they do in case of three-dimensional
stimuli (Byosiere et al., 2018).
In humans (who constantly look at images from a very early
age), there is evidence that photos of objects are processed
differently on the neural level (Snow et al., 2011), using real
items amplifies value-based fMRI responses (Culham et al., 2013)
and video-mediated faces do not elicit the physiological response
(measured via skin conductance responses) detectable in case of
live faces (Riby et al., 2012).
Presenting two-dimensional visual stimuli to animals is
methodologically challenging (Leopold and Rhodes, 2010; Burke
and Sulikowski, 2013). One of the concerns is whether
in studies which rely on photographs, one can reasonably
expect that animals see the image as a depiction of a real
object without explicit training and/or a significant amount
of previous experience with this type of stimuli (Burke and
Sulikowski, 2013). The other concern is whether static stimuli
has comparable ecological validity to dynamic stimuli in case of a
carnivorous species.
In the current study, our aim was to investigate face sensitivity
in dogs via fMRI by using similar stimuli as in the study of Huber
et al. (2013). Dogs were presented with features of the inner face
(see Figure 1). To determine the level of similarity between face
processing in dogs and humans, it is important to investigate
what aspects of the stimuli are necessary to elicit a response in
the analogous brain areas in dogs. The novelty of our design was
that it allowed us to compare face and non-face stimuli, while all
stimuli had the same size and shape. To make sure that dogs see
portraits as a depiction of a real person/face (and to avoid explicit
training and its potential impact), we decided to rely on live faces.
First, as a proof of concept to test the viability of our setup and to
locate candidate brain areas which respond with higher activity
toward visually complex stimuli, we compared a live human face
to a simple control with markedly different visual characteristics
(Study 1, a mono-colored surface). Study 2 had two goals: (1) to
test whether the regions showing higher activity toward a face in
Study 1 also do so in case of a more stringent control (scrambled
images), and (2) to compare the BOLD response when using a
portrait (photo) and a live face as stimuli.
Our hypothesis was that, if the regions in the dogs’ brain,
which were previously reported as face-sensitive (Dilks et al.,
2015; Cuaya et al., 2016; Thompkins et al., 2018), react to the
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Internal Facial Features in the Dog Brain
life faces, a pictures of the same faces and a scrambled images of
the same faces, see Figure 3). A professional photographer took
the pictures used in this study, in the actual study setup in the
scanner to ensure that the conditions are as similar as possible.
Stimulus set consisted of three separate series (1 male, 2 females,
all Caucasian, all familiar to the dogs). In a single run, we used
a single series (all three stimuli showing the same person) and
every subject was tested with two different sets. The size and
shape of all stimuli were the same: oval-shaped, with a height
of 22.57 cm and width of 14.97 cm. Box scrambling was carried
out in Matlab via randomly rearranging pixels with a pixel size
of 1.5 by 1.5 mm. Video illustrations of stimulus presentation
in Study 2 are available at the following links: (https://www.
youtube.com/watch?v=tNaKRsG72b0)(view from the subject’s
side) (https://www.youtube.com/watch?v=DO1ELrupxmc)(view
from the Experimenters’ side).
In Study 1 (2 conditions), one run contained 50 stimulus
presentations, while in Study 2 (3 conditions), one run
contained 51 presentations. The stimuli were presented in a
counterbalanced, semi-random order. Controlled presentation
timing and duration were achieved via pre-recorded instructions
which were played to the Experimenter (see below) through
headphones during the runs. The instructed duration of stimulus
presentation was 3,000 ms. The actual duration may have
slightly varied across trials (see below). Inter-stimulus interval
was jittered between 4,000–6,000 ms. When comparing our
designs’ detection power to the top 5 randomizations generated
by optseq2 with these settings post-hoc (duration of run, range
of ITI, number of stimuli) for the two studies combined,
the detection power was nearly identical [optseq2 generated:
5.30 ± 0.31 (mean ± SD), our randomization: 5.31 ± 0.27
(mean ± SD)].
FIGURE 1 | Stimuli used in Study 1. The individual shown on the figure is an
author of this paper and consented to the publication of their photographs.
configural information present in inner faces, this area would
show higher activity toward faces even when we control for low
level visual characteristics. Alternatively, if the involved brain
areas react toward the variation in visual properties instead
of face configuration/holistic face processing, these brain areas
would not show higher activity for faces when compared to
scrambled images with the same shape and color pattern.
2. MATERIALS AND METHODS
2.1. Participants
In Study 1, we measured 13 family dogs (Canisfamiliaris) (age
5.31 ± 2.69 years (mean ± SD), range 1–11 years, 6 females and 7
males, 6 golden retrievers, 5 border collies, 1 Chinese crested dog,
1 Cairn terrier. Only subjects which already passed a run wise
3 mm motion threshold criteria during a 6-min-long functional
data collection run for a previous study were enrolled. In addition
to the MRI acoustic training, the dogs were prepared for the
visual study design as follows: We gradually (with increasing
durations) removed the visible human who had been sitting at the
end of the scanner bore. The next step was that the human was
eventually walking over in front of the dog lying in the scanner,
to get the dogs used to a disappearing and reappearing human
while they lied still in the scanner. Completing these steps took
2–6 sessions. We concluded their training when they were able to
remain still for 6 min under these conditions. In Study 2, the same
subjects participated, except 2 dogs (a 5 years old female Border
collie and a 4 years old male golden retriever was not part of the
second study). The training procedure was described in Andics
et al. (2014), and it was based on individual and social learning
using positive reinforcement.
2.3. Procedure
We constructed a dismountable equipment for the purpose of
this study. In Study 1, this included a plastic wall covering the MR
scanner bore and a rotating plastic wheel for the presentation of
the visual stimuli (a hole in case of a live face, see Figure 2). For
Study 2, an additional lever was added to the equipment, which
could be lifted to cover and uncover the stimuli. This assured that
the presentations of stimuli started only after the person/stimuli
reached a stationary position.
We utilized different wheels for Study 1 and Study 2, the
former containing two stimulus presentation segments, while
the latter containing three such segments. Stimulus presentation
in Study 1 involved a single Experimenter, sitting at the end
of the scanner bore, who served as the face stimulus and
also operated the wheel based on pre-recorded instructions
played via earphones. In Study 2, Experimenter 1 served as the
stimulus just like in Study 1, while an additional Experimenter
operated the equipment (both the lever and the wheel)
based on the pre-recoded instructions played via earphones.
Timing and instructions of the stimulus presentation were
delivered binaurally through MRI-compatible sound-attenuating
headphones (MR Confon, Magdeburg, Germany) and were
controlled using Matlab (version 7.9) Psychophysics Toolbox
2.2. Stimuli
For Study 1, the stimuli consisted of a real-life face (the female
trainer of the dogs) and a piece of yellow, mono-colored textured
neoprene rubber sheet mounted on the stimulus presenting wheel
(see Figure 2). For Study 2, we used three types of stimuli (real
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FIGURE 2 | In-scanner setup of the equipment and stimulus presentation. The individual shown on the figure is an author of this paper and consented to the
publication of their photographs.
were normalized and transformed manually (linear, nonrigid transformation) to a custom-made individual template
anatomical image (Czeibert et al., 2019) in Amira. The
resliced images were then coregistered and normalized to
this transformed mean functional image via SPM’s standard
nonlinear warping function with 16 iterations and smoothed with
an FWHM of 5 mm.
Data were analyzed via a general linear model and statistical
parametric mapping in SPM. Condition regressors were created
for each run and for each condition. We modeled the trials
as 3 s long events. We included realignment regressors for
each run to model potential movement artifacts. We utilized
a high-pass filter with a cycle-cutoff of 128 s to remove lowfrequency signals. Regressors were convolved with the canonical
haemodynamic response function in SPM. Single-subject fixed
effect analyses were followed by group level, whole-volume
random effects analyses. For the GLM, we calculated the average
smoothness of the residuals using 3dFWHMx and then used
3dClustsim to determine the cluster threshold with. For Study
2, in case of higher-level contrasts, next to the whole-brain
analysis, results where also evaluated in a restricted brain search
space, including only the regions which higher activity for the
face stimulus in Study 1, via an inclusive mask thresholded
at p < 0.001. Lateralization indices (LI) were calculated using
a bootstrapping method implemented in SPM’s LI-Toolbox
(Wilke and Lidzba, 2007). Subject-specific contrast images for
each condition (compared to baseline) served as input, with an
exclusive midline mask of 11 mm. The process resulted in an
overall weighted bootstrapped LI per subject and per contrast.
These LIs across subjects and conditions were then compared
in one-sample T-tests to assess condition-specific hemispheric
bias. To control for false positives, we performed familywise error (FWE) correction for multiple comparisons using
3dFWHMx/3dClustSim modules of AFNI (http://afni.nimh.nih.
gov/afni; “fixed” version, compiled Oct, 2019). Specifically, we
run 10,000 Monte Carlo simulations to estimate the cluster size
3. Dogs were provided with noise protection through MRIcompatible sound-attenuating headphones. All procedures were
approved by the Government Office of Pest County Directorate
of Food Chain Safety and Animal Health (XIV-I-001/5204/2012), and conducted in accordance with the national and
European guidelines for animal care. Owners of the pet dogs
participated in the study on a voluntary basis with their dogs
and gave their written informed consent to the MR scanning of
their dogs.
2.4. Image Acquisition
MRI measurements were taken at the MR Research Centre of the
Semmelweis University Budapest on a Philips Achieva 3 T whole
body MR unit (Philips Medical Systems, Best, The Netherlands)
with a Philips SENSE Flex Medium coil. This coil consisted of
two elliptical elements that were 14 × 17 cm. One element was
placed below the dog’s head while the other element was fixed
with plastic strips above the dog’s head. In both experiments, we
obtained EPI-BOLD fMRI time series via continuous scanning
with the following parameters: TE = 36 msec, TR = 2,035 ms,
FOV = 224 × 224 × 101 mm, Voxel size = 3.5 × 3.5 × 3.5 mm
(including 0.5 mm slice gap). We collected 29 transverse slices
in ascending order covering the whole brain. One run consisted
of 180 scans and lasted for 360 s. For Study 1, we collected 1
run/subject, while Study 2 consisted of 2 runs.
2.5. Image Processing and Analysis
Image pre-processing and statistical analysis were performed
using SPM12 (http://www.fil.ion.ucl.ac.uk/spm/). FMRI preprocessing and statistical analysis were performed using SPM12.
Pre-processing included affine realignment (6 parameters, least
square approach) and reslicing of the images of the individual
runs, followed by manual coregistration of the mean image
to the individuals’ own structural T1 image in Amira 6.0
(Thermo Fisher Scientific). The individual structural images
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Internal Facial Features in the Dog Brain
displacement within our data was 0.21 ± 0.25 mm (mean ± SD).
The distribution of framewise displacement values showed that
the majority of the framewise displacement values fell well below
0.5 mm (the human standard for task-based fMRI studies). We
censored runs if the total, cumulative motion during did exceed 3
mm in any direction. In this case we truncated the run, discarding
scans from the end of the run. In Study 1, two runs were involved
(after 120 and 160 scans out of 180, respectively), while in Study
2, a single run was involved (after 125 scans out of 180).
In Study 1, we found higher activity for the face condition
compared to the control condition (a mono-colored surface)
in multiple temporal and occipital regions (see Figure 4 and
Table 1, results reported with an uncorrected peak-level p <
0.0001, clusters of min. 2 voxels), while we found no suprathreshold regions which would have shown higher activity to the
control with the same threshold. The regions showing higher
activity toward the face condition included the bilateral caudal
and rostral regions of the Sylvian gyrus, the bilateral marginal
gyrus, the left mid-suprasylvian gyrus and the insular cortex.
The caudal part of the marginal gyrus is involved specifically in
the visual processing, as containing the primary visual area, the
insular cortex is a primary viscerosensory area receiving inputs
from the internal organs, whilst the Slyvian gyrus and the middle
suprasylvian gyrus are association areas next to the primary
somatosensory and auditory cortexes, thus having the capability
to integrate stimuli from different sensory modalities.
However, in Study 2 we found no supra-threshold voxels
with a threshold of p < 0.001 in any of the investigated
contrasts. No regions showed higher activity toward a face when
compared to a more stringent control (scrambled images), and
no differences emerged between the portrait (photo) and the live
face presentations. This was also the case when we restricted
brain search space to the inclusive mask based on the results of
Study 1. With a weaker threshold of p(uncorrected) < 0.05, still no
regions in any of the investigated contrasts showed higher activity
toward faces (neither the live nor the portrait face conditions). A
cluster in the left caudate nucleus showed higher activity toward
scrambled images in both the combined faces vs. scrambled
images (coordinates −2, 6, 6, size: 62 voxels) and in the portraits
vs. scrambled images contrast (coordinates −2, 6, 6, size: 45
voxels). In the portraits vs. scrambled images contrast, an other
cluster located in the right sensorymotoric cortex also showed
higher activity toward scrambled images (coordinates 13, 1, −20,
size: 36 voxels). The boot-strapping approach revealed no group
level lateralization of cortical responses in any of the conditions
within the two experiments (ps > 0.1).
FIGURE 3 | Stimuli used in Study 2. Each stimulus set contained a live
presentation, a portrait from the same individual, and the scrambled stimuli
generated from the portrait. The individuals shown on the figure are authors of
this paper and consented to the publication of their photographs. A video
demonstrating stimulus presentation in Study 2 is available at the following
link:(https://www.youtube.com/watch?v=tNaKRsG72b0).
4. DISCUSSION
above which the false positive probability was below a given level
(FWE = 0.05) for a given cluster defining threshold (p < 0.0001
in Study 1, p < 0.001, and p < 0.05 in Study 2, respectively).
To our knowledge, this is the first study which investigated
human face processing in dogs via fMRI using exclusively inner
face stimuli. In Study 1 we found multiple, bilateral brain regions
in the temporal and occipital cortex which showed higher activity
toward the inner parts of a single face than to a mono-colored
control surface. This is in line with previous human results,
reporting higher activity toward faces than surfaces/textures in
3. RESULTS
We only report clusters surviving family-wise error (FWE)
correction for false-positives with alpha <0.05. The framewise
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Internal Facial Features in the Dog Brain
similar visually to the live condition and (2) appropriately
controlled by the scrambled face. Note that we did not find
significant differences between brain activities for processing live
faces and portraits (both conditions containing faces, but one
containing more variability regarding the stimuli) and neither
of them differed from the scrambled condition in our study.
While it is theoretically possible that the minimum cluster size
determined by 3dFWHMx and 3dClustsim (2 voxels in case of
Study 1) represent an area larger than dogs face processing area,
the previous studies reported significant activation toward faces
in areas larger than this, using similar voxel sizes and FWHMs
(Dilks et al., 2015; Cuaya et al., 2016).
Both the temporal and occipital regions detected in Study 1
showed similar localization to those reported by previous fMRI
studies investigating face sensitive areas in dogs (Dilks et al.,
2015; Cuaya et al., 2016; Thompkins et al., 2018), confirming that
we measured a similar phenomenon with our setup. However,
Study 2 showed that these regions do not show significantly
higher activity toward faces when we control for low level visual
characteristics via scrambling. This finding is in line with the
results of Dilks et al. (2015), who also did not find significant
difference in dogs’ brain activity between images of faces vs.
scrambled faces. In a behavior study by Pitteri et al. (2014), where
the authors had trained dogs for discriminating the picture of
their owner, they found that dogs experienced in this type of task
were also able to pick their owners image from scrambled images,
while naive dogs could not. This suggests that when presented
with a similar visual discrimination training situation, dogs may
learn to master such tasks relying on other local visual processes
not specific to face processing.
Although Pitteri et al. (2014) trained dogs to discriminate
isolated face parts, a comparison between their performance
related to isolated face parts vs. whole faces is not possible, as
every subject was trained/tested with whole heads only after they
mastered all three isolated face part discrimination tasks, which
means that the presence of a part-whole effect could not be tested.
Currently, there are no publications showing disproportionate
inversion effect for faces (Racca et al., 2010 reported a nonspecific-inversion effect; dogs showed the same viewing pattern
to inverted objects than to human faces), part-whole effect or
composite effect in dogs, which would signal configural/holistic
processing of faces (Burke and Sulikowski, 2013).
Similarly to Cuaya et al. (2016), but unlike Thompkins et al.
(2018) we could not record the dogs’ in-scanner viewing behavior
to evaluate the spatial viewing pattern of our participants
(although the presentation of live stimuli allowed us to have
some feedback regarding the dogs’ attentional state and they
seemed to be attentive). In Study 2, we used a more diverse
stimulus set, multiple runs, more conditions and a smaller sample
size than in Study 1, which may have affected statistical power.
Our study differed in several aspects from previous designs.
We utilized an event-related design, while Dilks et al. (2015)
and Cuaya et al. (2016) utilized a block design. While previous
studies had a systematic difference between face and non-face
objects regarding shape and ratio of real-size to projected size
(more variability in shape and other visual characteristics among
objects, faces are in general projected to be approximately
FIGURE 4 | GLM results in study 1 (n = 13). Regions showing higher activity to
the face stimulus compared to the mono-colored control. Color heatmap and
color bar indicates t-values, displayed on selected slices overlaid on the
template brain. Thresholdet at uncorrected voxel threshold p < 0.0001, FWE
corrected at the cluster level. RSG, right Sylvian gyrus; LmSSG, left mid
suprasylvian gyrus; LMG, left marginal gyrus; RMG, right marginal gyrus; LSG,
left Sylvian gyrus.
humans, showing that even contrasts with such low level controls
activate these higher-level regions in the human fusiform face
areas (Puce et al., 1996). In contrast, we found no such effect
in Study 2 when the stimuli contained images from multiple
individuals and we also controlled for color and brightness.
This was also the case when we restricted our analysis to the
regions of interests based on the findings of the first study. The
temporal regions displaying higher activity toward faces in Study
1 did not show higher activity toward faces when compared
to the scrambled images in Study 2. We found no differences
in activity between the live faces and portraits conditions. Live
presentation might have shown small deviations from the static
portrait (even though the human demonstrator always presented
a neutral facial expression). We addressed this issue by including
an “intermediate” portrait condition which is (1) arguably highly
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TABLE 1 | Main GLM results for Study 1 with a threshold of peak-level uncorrected p < 0.0001, showing the results of the face > controll contrast.
Brain region
R rostral Sylvian gyrus,
caudal Sylvian gyrus
cluster-level p
Cluster size (voxels)
Peak T
Peak-level p (FWE-corrected)
Coordinates (x, y, z)
<0.001
28
8.74
0.002
23, −12, 6
<0.001
14
8.65
0.002
−16, −12, 9
0.003
7
(FWE-corrected)
Insular cortex,
L rostral Sylvian gyrus,
caudal Sylvian gyrus
L mid suprasylvian gyrus
L marginal gyrus
R marginal gyrus
0.003
7
7.91
0.005
−19, −29, 23
6.27
0.046
−12, −36, 20
7.32
0.010
6, −33, 23
We found no significant cluster in case of control > face contrast. One cluster may contain multiple peaks. In this case, providing cluster sizes for the peaks separately is not possible. L
mid suprasylvian gyrus and L marginal gyrus belong to the same cluster, hence we could not report the cluster sizes separately for these two peaks.
DATA AVAILABILITY STATEMENT
real sized while objects not), we controlled for this effect in
our study, which may in part explain the different findings.
While our sample size may be considered low compared to the
standards of human studies, it exceeds the sample size utilized
in previous similar studies and considerably larger than several
primate study’s. We included dogs varying in breeds, sex and
age: this includes sample variability and therefore increases
generalizability of our findings, but it potentially also leads to
larger individual variability, perhaps reducing power.
We found no signs of lateralized brain responses in either
study. Previous dog face fMRI studies reported varying effects
regarding face sensitive areas: only in the left hemisphere
(Thompkins et al., 2018), only in the right hemisphere (Dilks
et al., 2015) or bilateral activation with greater extent in the left
lobe (Cuaya et al., 2016).
Dogs are looking at and gaining information from human
faces in a variety of contexts (Adachi et al., 2007; Huber et al.,
2013), and they are able to rely on cues provided by a (lifesize) projected human (Pongrácz et al., 2003). We do not debate
that dogs are able to process and extract relevant information
from (projected) faces, but based on our results we claim
that the current evidence is not sufficient to argue that dogs
utilize holistic face processing with a specialized brain region
showing higher activity selectively to faces. Alternatively, family
dogs without explicit, specialized training may rely on other
characteristics (head shape), context (dogs in general have limited
experience with isolated heads without a body) or combined
information from different modalities to detect relevant social
cues. This is also supported by a behavioral study showing
that dogs are only able to spontaneously discriminate between
their owner’s and a stranger’s head if outer face elements are
also visible (Mongillo et al., 2017). A distributed response from
neurons coding different aspects of the stimuli is proved to be
sufficient in a variety of contexts in humans from solving an
individuation task of Lepidoptera images (Rhodes et al., 2004)
to processing Chinese characters (Fu et al., 2012), showing that
a highly specialized, brain level adaptation is not a prerequisite
for compelling cognitive performance. In a similar manner, dogs’
expertise when engaging in social interactions with humans do
not necessary require the presence of the same highly specialized
brain adaptations found in humans.
Frontiers in Behavioral Neuroscience | www.frontiersin.org
The datasets ANALYZED for this study can be found
in the OSF repository (https://osf.io/jvct4/?view_only=
7a665871ce8b4181afe364bec78f5f3d).
ETHICS STATEMENT
This study was carried out in accordance with the
recommendations of the national and European guidelines
for animal care. The protocol was approved by the Government
Office of Pest County Directorate of Food Chain Safety and
Animal Health (XIV-I-001/520-4/2012).
AUTHOR CONTRIBUTIONS
DS: conceptualization, methodology, formal analysis,
investigation, writing original draft, writing review and editing,
project administration. AG: methodology, writing review and
editing. MG: conceptualization, investigation, writing review
and editing, visualization. TF: methodology, writing review and
editing. EK: writing review and editing, supervision, and funding
acquisition. ÁM: writing review and editing, supervision, and
funding acquisition. AA: conceptualization, methodology,
formal analysis, investigation, and supervision.
FUNDING
This project has received funding from the European Research
Council (ERC) under the European Union’s Horizon 2020
research and innovation programme (Grant Agreement No.
680040), was supported by the National Research, Development
and Innovation Office (Grant No. 115862K) for MG, the
Hungarian Academy of Sciences [MTA-ELTE Comparative
Ethology Research Group (Grant No. F01/031), MTA-ELTE
Lendület Neuroethology of Communication Research Group
(Grant No. 95025), the János Bolyai Research Scholarship
of the Hungarian Academy of Sciences for AA and EK]
the Bolyai+ ÚNKP-18-4 New National Excellence Program of
the Ministry of Human Capacities for EK and the Eötvös
Loránd University. ÁM was supported by the Hungarian Brain
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March 2020 | Volume 14 | Article 25
Szabó et al.
Internal Facial Features in the Dog Brain
Research Program (Grant No. 2017-1.2.1-NKP-2017-00002), by
the Program of National Excellence (NKP_17, 2017-1.2.1-NKP2017-00002), by the ELTE Institutional Excellence Program (7833/2018/FEKUTSRAT) and supported by the Hungarian Ministry
of Human Capacities. We thank all dog owners participating in
our study.
ACKNOWLEDGMENTS
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Frontiers in Behavioral Neuroscience | www.frontiersin.org
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Szabó, Gábor, Gácsi, Faragó, Kubinyi, Miklósi and Andics. This
is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums
is permitted, provided the original author(s) and the copyright owner(s) are credited
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