ORIGINAL RESEARCH
published: 22 December 2015
doi: 10.3389/fpsyg.2015.01944
Electroencephalographic Correlates
of Sensorimotor Integration and
Embodiment during the Appreciation
of Virtual Architectural Environments
Giovanni Vecchiato 1*, Gaetano Tieri 2, 3 , Andrea Jelic 4 , Federico De Matteis 4 ,
Anton G. Maglione 1 and Fabio Babiloni 5
1
Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy, 2 Laboratory of Social
Neuroscience, IRCCS Fondazione Santa Lucia, Rome, Italy, 3 Department of Psychology, Sapienza University of Rome,
Rome, Italy, 4 Department of Architecture and Design, Sapienza University of Rome, Rome, Italy, 5 Department of Molecular
Medicine, Sapienza University of Rome, Rome, Italy
Edited by:
Isabella Pasqualini,
Ecole Polytechnique Fédérale de
Lausanne, Switzerland
Reviewed by:
Mark A. Elliott,
National University of Ireland Galway,
Ireland
Eddy J. Davelaar,
Birkbeck, University of London, UK
*Correspondence:
Giovanni Vecchiato
giovanni.vecchiato@uniroma1.it
Specialty section:
This article was submitted to
Cognitive Science,
a section of the journal
Frontiers in Psychology
Received: 13 July 2015
Accepted: 03 December 2015
Published: 22 December 2015
Citation:
Vecchiato G, Tieri G, Jelic A, De
Matteis F, Maglione AG and Babiloni F
(2015) Electroencephalographic
Correlates of Sensorimotor Integration
and Embodiment during the
Appreciation of Virtual Architectural
Environments. Front. Psychol. 6:1944.
doi: 10.3389/fpsyg.2015.01944
Frontiers in Psychology | www.frontiersin.org
Nowadays there is the hope that neuroscientific findings will contribute to the
improvement of building design in order to create environments which satisfy man’s
demands. This can be achieved through the understanding of neurophysiological
correlates of architectural perception. To this aim, the electroencephalographic (EEG)
signals of 12 healthy subjects were recorded during the perception of three immersive
virtual reality environments (VEs). Afterwards, participants were asked to describe their
experience in terms of Familiarity, Novelty, Comfort, Pleasantness, Arousal, and Presence
using a rating scale from 1 to 9. These perceptual dimensions are hypothesized to
influence the pattern of cerebral spectral activity, while Presence is used to assess the
realism of the virtual stimulation. Hence, the collected scores were used to analyze
the Power Spectral Density (PSD) of the EEG for each behavioral dimension in the
theta, alpha and mu bands by means of time-frequency analysis and topographic
statistical maps. Analysis of Presence resulted in the activation of the frontal-midline
theta, indicating the involvement of sensorimotor integration mechanisms when subjects
expressed to feel more present in the VEs. Similar patterns also characterized the
experience of familiar and comfortable VEs. In addition, pleasant VEs increased
the theta power across visuomotor circuits and activated the alpha band in areas
devoted to visuospatial exploration and processing of categorical spatial relations.
Finally, the de-synchronization of the mu rhythm described the perception of pleasant
and comfortable VEs, showing the involvement of left motor areas and embodied
mechanisms for environment appreciation. Overall, these results show the possibility
to measure EEG correlates of architectural perception involving the cerebral circuits
of sensorimotor integration, spatial navigation, and embodiment. These observations
can help testing architectural hypotheses in order to design environments matching the
changing needs of humans.
Keywords: electroencephalography, immersive virtual reality, presence, architecture, embodiment, sensorimotor
integration, spatial navigation, affordances
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EEG Correlates of Architectural VEs Perception
INTRODUCTION
demonstrated when familiar items were presented during the
active exploration of new environments.
In addition, evidence of the cerebral responses underpinning
perceptual dimensions such as pleasantness is also found in
studies on art appreciation, showing the activation of the
motor system through embodied mechanisms, encompassing
the simulation of actions, emotions, and corporeal sensations,
which are also postulated to play an important role in the
perception of architecture (Freedberg and Gallese, 2007). The
idea of the involvement of non-overt bodily reactions in the
perception and the experience of architectural spaces can be
traced back from late nineteenth century “Einfühlung” theories.
Such hypotheses suggest that the observation of architectural
forms may lead to corporeal responses establishing a relationship
between the aesthetic and emotional dimension, as well as
bodily engagement with space (Mallgrave and Ikonomou, 1994).
These assumptions have been validated by recent neuroscientific
findings, highlighting the crucial role of sensorimotor areas in the
appreciation of works of art (Kawabata and Zeki, 2004; Umilta’
et al., 2012; Sbriscia-Fioretti et al., 2013). Freedberg and Gallese
(2007) proposed a theoretical framework based on the role of
embodied simulation and empathy in the aesthetic experience of
art resulting in tactile sensations, implied gestures and actions.
Additionally, the interaction with the built environment as well
as its appreciation could also involve motivational and affective
factors. In fact, the perception of visual artwork (Sbriscia-Fioretti
et al., 2013) and environments characterized by curvilinear
contours (Vartanian et al., 2013) activates reward circuits formed
by medial orbitofrontal and anterior cingulate cortices (Vartanian
and Goel, 2004). Moreover, observation of art or architecture
may be accompanied by activations of neural networks regulating
reward and judgment, suggesting the involvement of emotional,
cognitive and contextual factors mediating aesthetic appreciation
(Chatterjee and Vartanian, 2014).
However, very few neuroscientific studies have been
conducted so far that investigate the modulation of the
cerebral activity during perception of real-like architectural
environments. One of the reasons for this lack of knowledge
consists in a methodological gap. Concretely, there is a difficulty
in reproducing the qualitative richness of architectural spaces in
highly controlled environments such as a laboratory, allowing
systematic neurophysiological investigation. However, a growing
number of studies in psychology and neuroscience demonstrate
that such difficulties can be surmounted using the Immersive
Virtual Reality (IVR; Sanchez-Vives and Slater, 2005; Bohil et al.,
2011). The IVR technologies create a high sensorial immersion
within a fictive three dimensional scenario, inducing in the
observer the sense of presence defined as the “sense of being
in the virtual environment” (Slater and Wilbur, 1997) that is, a
psychophysiological state which reproduces realistic behaviors
and physiological responses as if the subject was experiencing a
real-life situation (Diemer et al., 2015). Hence, by using the IVR
it is possible to create highly controlled real-size architectural
environments, allowing the measurement of reliable behavioral
and neurophysiological indices.
The objective of this study is to investigate whether
simple architectural scenarios are able to modify perception
Despite increasing evidence of the influence physical features
in the built environments have on our psychophysiological
states (Stamps, 1999; Lindal and Hartig, 2013), systematic
research on the cerebral networks activated by perception and
appreciation of architectural spaces is still scarce. At the same
time, there is a growing trend in architectural practice of
employing the evidence-based insights for creating environments
capable of satisfying the need for variety and improving people’s
psychological, biological and social lives. In this regard, the
present study aims to illustrate the potential that neuroscientific
findings have for describing the impact of architecture on
people.
First of all, buildings have to respond to various functional
requirements such as adequate lighting, heating, and cooling
systems as well as public safety provisions. These functional
aspects of architectural design are strongly supported
by contemporary technological advances. However, the
understanding of how the aesthetic perception of living
environments affects people’s cognitive and emotional states
primarily relies on the architect’s intuition and experience. In
this context, available research provides good indications that
personal sensory motor perception of the environment could
play an important role in cognitive and emotional interactions
within the environment itself. Most importantly, these attributes
can now be evaluated from the neurophysiological point of view.
Therefore, it becomes relevant to understand the human cerebral
reactions produced by the perception of architecture. This
claim is generally supported by different studies from various
disciplines, including environmental psychology, behavioral
research, and biophilic design.
More specifically, Appleton’s habitat theory (Appleton, 1996)
states that considering an environment as emotionally and
aesthetically pleasing is indicative of its favorability to survival.
In fact, since spatial features influence the activities and social
interactions to be performed in a specific environment (i.e.,
sleeping in a bedroom or entertaining guests in a living
room), architecture can affect cognitive and emotional states
of inhabitants as well as their mood and productivity (Graham
et al., 2015). These authors take into account different perceptual
dimensions, highlighting the role of comfort as an important
factor in both home and work environments in order to
promote well-being and productivity. Additionally, architectural
design can either limit or facilitate the social interactions and
the dynamics that take place at home (Graham et al., 2015).
Furthermore, studies show that the perception of different
kinds of environments can have a beneficial impact on the
observer’s cognitive ability and task performance. For instance,
the exploration of an environment can promote long-term
potentiation in the hippocampus, improving memory encoding.
Although direct evidence of a link between exploration of
environments and increased plasticity comes from studies on
animals, such a link has been also found in humans. Indeed, an
improvement of the recall of both allocentric spatial information
(Plancher et al., 2013) and words (Schomaker et al., 2014),
as well as memory encoding (Bunzeck and Düzel, 2006) was
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EEG Correlates of Architectural VEs Perception
as well as to explore its corresponding cerebral activity. An
IVR paradigm was used to highlight the neurophysiological
features underpinning architectural perception by analyzing
the electroencephalographic (EEG) and autonomic reactions
elicited by the perception of highly immersive real-size Virtual
Environments (VEs). With the working hypothesis that changing
interior design features could activate the cerebral circuits
involved in mechanisms of embodiment in different ways,
we compared the spectral activity of the EEG using a set
of subjective dimensions describing the perception of VEs
such as Pleasantness, Novelty, Familiarity, Comfort, Arousal,
and Presence. In addition, the hypothesis that architectural
perception could involve cerebral circuits regulating reward
and affective processes was also advanced basing on a broad
literature on aesthetic judgments (for a review, see Chatterjee and
Vartanian, 2014).
The VEs were presented by means of a CAVE automatic
virtual environment system (3 × 3 × 2.5 m; Cruz-Neira et al.,
1993) composed by three rear-projection screens for the walls
and a down- projection screen for the floor, as illustrated in
Figure 2. Viewsonic projectors (1024 × 768 pixels) displayed
images on the screens through mirrors and an Nvidia 3d vision
wireless glass provided the image of 3D graphics generated by the
CAVE. An Intersense 900 ultrasonic head tracking system (6◦ of
Freedom, DoF) was used to record in real time the subject’s head
position and orientation (with a frequency of 120 Hz) and thus to
anchor the 3D images to his/her point of view. As a special feature
of the CAVE system, the four 3D images were joined together so
that subjects could not see the edges of the adjacent walls. Hence,
the active stereo projection was perceived as a continuous virtual
world. This setup enabled a high level of sensorial immersion so
that the subject could feel herself/himself physically present in the
VE (Sanchez-Vives and Slater, 2005).
Subjects sat on a chair placed in the middle of the CAVE
and immersed in each VE for 4 min. VEs presentation was
counterbalanced among subjects. Their task was (i) to visually
explore the surrounding environment and, at the end of
the exposure, (ii) to verbally express their judgment about
the perceptual dimensions of Familiarity, Novelty, Comfort,
Pleasantness, Arousal, and Presence using a 9-point rating scale
(1, lowest score; 9, highest score). These items were arranged
in two short questionnaires, as shown in Table 1, each related
to perception and sense of presence induced by the VEs,
respectively. The items used to measure the sense of Presence
were adapted from Sanchez-Vives and Slater (Sanchez-Vives and
Slater, 2005). Subjects had 2 min to answer the questionnaires
before experiencing the next VE. The whole procedure lasted
around 20 min during which the EEG and autonomic activity
were continuously collected, as described in the following section.
At the beginning, each subject experienced a familiarization
period (Slater et al., 2010; Tieri et al., 2015a,b) within an
MATERIALS AND METHODS
Participants
Twelve healthy volunteers were involved in the study (five
females; mean ± SD 26.8 ± 2.4). All subjects had normal or
corrected-to-normal vision and were not familiar with the IVR
experience. The study was approved by the ethics committee of
Fondazione Santa Lucia, according to the ethical standards of the
1964 Declaration of Helsinki.
Procedure
Three VEs were designed in real size (5 × 5 m) and tested using
different interior designs. The first one represented an empty
room (VE1), which was in turn equipped with a modern design
(VE2) and then with cutting-edge furniture (VE3) as shown in
Figure 1. They were designed using 3DS Max 2011 (Autodesk,
Inc.) and implemented in XVR (http://www.vrmedia.it/en/xvr.
html; Tecchia et al., 2010).
FIGURE 1 | Rendering of the VEs that were used as stimuli. VEs were counterbalanced across subjects: empty room (A), modern furniture (B), and cutting edge
design (C).
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EEG Correlates of Architectural VEs Perception
FIGURE 2 | Virtual reality cave system setup. It is possible to appreciate two rooms with different interior design: modern furniture (A) and cutting edge design (B).
The subject is placed in the middle of the cave, wearing the EEG cap, for the whole duration of the experiment.
that had the highest and lowest scores, respectively, according
to each subject. Therefore, positive (z > 0) scores represented
VEs which were highly rated (e.g., High Pleasantness VEs) while
negative (z < 0) scores were associated with lowly rated VEs (e.g.,
Low Pleasantness VEs). These z-scores were used to contrast
the neurophysiological data: interiors rated with positive scores
(e.g., High Pleasantness) were compared with the ones rated
with negative scores (e.g., Low Pleasantness) for each perceptual
dimension and presence.
According to this procedure, the analysis was driven by the
subjective scores across all VEs instead of investigating the
cerebral activity related to the perception of single VEs (see
Vecchiato et al., 2010a, 2011b for a similar statistical approach).
The subjective scores related to Presence were first averaged
across the three items and then z-scored across stimuli. These
were first used to separate VEs that induced either high or
low presence and, subsequently, to group and contrast the
neurophysiological activity. The data associated to z = 0 were
not considered for the following analysis (6.48% of total scores).
TABLE 1 | Items used to assess dimensions of perception and presence.
Perception
Presence
Dimension
Item
Familiarity
How much did this VE remind you of
environments in which you lived?
Novelty
How much did this VE provide elements of novelty
with respect to environments in which you lived?
Comfort
How much did you feel at your ease during the
perception of this VE?
Pleasantness
How much did you like this VE?
Arousal
How much did this VE arouse you?
To what extent did you have the sense of being in
the VE?
To what extent were there times during the
experience when the VE became the reality for
you and you almost forgot about the real world of
the laboratory in which the whole experience was
really taking place?
When you think about this experience, do you
think of the VE more as images that you saw or
more as somewhere that you visited?
Electroencephalographic and Autonomic
Signal Recording and Processing
Each dimension was evaluated according to a 9-point scoring scale (1, lowest; 9, highest).
The EEG activity was recorded by means of a portable 24channel system (BEmicro, EBneuro, Italy). Nineteen electrodes
were disposed according to the 10–20 I.S. The signals acquired at
a sampling rate of 256 Hz with sensors impedances kept below
10 k. Raw EEG traces were band pass filtered (hp = 0.5 Hz;
lp = 45 Hz) and the Independent Component Analysis (ICA;
Hyvärinen and Oja, 2000) was then applied to detect and remove
components generated by eye movements and blinks. For this
purpose, the infomax ICA algorithm was used, provided by
EEGLAB (Delorme and Makeig, 2004). EEG data were extracted
to take into account the perception of the three VEs (three
stimuli per subject) and further segmented into 1-s epochs (240
epochs per stimulus defining the 4 min exposition). Muscular
and environmental artifacts were detected and removed with
a semi-automatic procedure based on two different criteria:
threshold (traces which exceeded a threshold of ±80 µV were
rejected) and gradient (traces in which the difference between
two consecutive samples exceeded ±50 µV were rejected). Only
artifact-free trials (92.72%) were considered for this analysis. The
additional neutral VE that represented a generic laboratory
setting composed by different objects including a chair, a desk,
some computers, and books. Subjects were asked to look around
the environment and verbally describe the virtual scenario. This
preliminary phase ended when they reported to feel present in
the environment (all participants experienced presence within
81.28 ± 37.62 s). The neurophysiological activity acquired during
this phase was not used for further analysis.
Behavioral Data Analysis
With the aim to investigate the relationship among each
of the perceptual dimensions—Familiarity, Novelty, Comfort,
Pleasantness, Arousal—the subjective behavioral scores were
transformed in z-scores and then used to perform Pearson’s
correlation analysis (Bonferroni corrected due to multiple tests).
Afterwards, two datasets for each perceptual dimension were
created using the z-scores in order to identify and group VEs
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b and channel ch. For each increasing tN time window of length
N, with N ǫ [1, T], the autocorrelation function was computed as:
extra-cerebrally referred EEG signals were transformed by means
of the Common Average Reference (CAR). Afterwards, the Power
Spectral Density (PSD) was computed for each epoch according
to the Welch method (Welch, 1967) with Hanning window in a
Matlab environment (The MathWorks, Inc.). Individual Alpha
Frequency (IAF) was calculated for each subject to perform timefrequency analysis according to individually defined bands and
widths (Doppelmayr et al., 1998). Therefore, in this study the
bands of interest were defined as theta, ranging from IAF × 0.4
to IAF × 0.8 Hz, alpha (IAF × 0.8, IAF × 1.2) Hz and mu (IAF,
IAF × 1.2) Hz.
In all subjects we achieved an IAF = 10.54 ± 0.80 Hz. Then,
the PSD was band averaged to obtain data structures comprising
T = 240 time-frequency bins per EEG channel and subject,
for the three frequency bands. The whole dataset was pooled
according to the behavioral z-scores in order to contrast the
positive against the negatively judged VEs. This comparison was
performed for each perceptual dimension (Familiarity, Novelty,
Comfort, Pleasantness, Arousal) and Presence. Hence, a PSD
time-frequency series was obtained for each condition.
Autonomic activity, such as Electrodermal Activity (EDA)
and Heart Rate (HR), was recorded by means of a NeXus-4
(Mindmedia, The Netherlands) system with a sampling rate of
256 Hz. Skin conductance was acquired by the constant voltage
method (0.5 V). Ag-AgCl electrodes were attached to the palmar
side of the middle phalanges of the second and third fingers
of the participant’s non-dominant hand with a Velcro fastener,
following published procedures (Boucsein et al., 2012). In order
to retrieve the tonic component of the skin conductance (Skin
Conductance Level, SCL) the LEDAlab software was employed
(Benedek and Kaernbach, 2010). Disposable Ag-AgCl electrodes,
which were provided by the Mindmedia company, were applied
to the subject’s wrist to collect cardiac activity. The PanTompkins algorithm was then used to calculate the HR (Pan
and Tompkins, 1985). Both EDA and HR were analyzed to asses
Presence.
Rxx [m] = Rxx [−m] =
x [n] x [n − m]
n=1
with m ǫ [1-N, N-1]. Here are considered only values of Rxx
with m ≥ 0. At each iteration tN , LN equals the length of the
time window, after which the autocorrelation function of the time
series dropped to 1/e of its maximum value (Stam, 2005). Then,
the weighted average of the PSD for each tN was computed as:
PSDw [tN ] =
PLN
n = 1 x [n] w [n]
PLN
n=1 w [n]
where wn are the selected LN larger coefficients of the
autocorrelation function. This calculation was performed for
each subject and condition. Afterwards, the subjective z-score of
PSDw spectral values for each time bin was calculated, as similarly
done with behavioral scores, to perform the mass univariate
analysis described in the following paragraph.
Statistical Mass Univariate Analysis
EEG features are typically analyzed via statistical methods on
average activity in a priori windows. Mass univariate analyses
were born thanks to the advances in computing power and
statistics (Blair and Karniski, 1993). They consist of hundreds
or thousands of statistical univariate tests, e.g., Student’s t-test,
which are applied to a large number of time points or cerebral
locations accompanied by corrections for multiple comparisons.
Such analyses are very useful when there is little a priori
knowledge of effect locations or latencies, as well as to delineate
effect boundaries. For instance, conducting statistical analysis on
particular cerebral features such as average or peak amplitude
does not take into account the whole time-series of observations
and, therefore, cannot provide information about when and/or
where an effect occurs (Dien and Santuzzi, 2005). This analysis
can be applied to EEG, magnetoencephalographic (MEG), and
functional Magnetic Resonance Imaging (fMRI) data, as reviewed
in Groppe et al. (2011). Here, this methodology is applied to the zscored PSDw time series in order to contrast the spectral activity
for each perceptual dimension and presence. This is made using
multiple Student’s t-test (significance level of 0.05) and the False
Discovery Rate (FDR) correction for multiple comparisons to
minimize type I errors (Benjamini and Yekutieli, 2001; Vecchiato
et al., 2010b).
In the following figures, statistical results provided by
the mass univariate analysis are shown by means of raster
diagrams. Average t-test results in specific time-windows are also
summarized by means of scalp topographic maps.
PSD Non-Linear Time-Frequency Analysis
In order to account for non-linear dynamics of the EEG
elicited during the continuous perception of the VEs, a method
was developed inspired by time-delay embedding procedures
(Stam, 2005, 2006). The application of non-linear dynamics to
electroencephalography, often referred to with the term “chaos
theory” (Elbert et al., 1994), paved the way to a new perspective
for the study of normal and disturbed brain function (Stam,
2003). In fact, non-linear dynamics studies of the EEG are applied
in wide research domains ranging from resting to active mental
states (Aftanas and Golocheikine, 2002; Tirsch et al., 2004, see
Stam, 2005 for a review). The wide use of non-linear analysis of
the EEG is justified by the fact that levels of synchronization of
functional sources are not constant over time, but show peculiar
fluctuations which have a scale-free character (Stam and de
Bruin, 2004).
In this analysis, the PSD time-frequency bins were averaged
according to a changing time-window depending on the values
of its autocorrelation function. Specifically, in x[n] = PSDb,ch [n],
PSD is the time series of spectral values defined for the frequency
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X
RESULTS
Behavioral Results
In Figure 3 there are several boxplots showing the z-score
distributions of the judgments related to the adopted behavioral
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EEG Correlates of Architectural VEs Perception
FIGURE 3 | Z-score boxplots of behavioral judgments among VEs. Each panel contains three boxplots related to the distribution of behavioral scores for VE1
(left), VE2 (center), and VE3 (right). On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the
most extreme data points. Red crosses indicate outliers of the distribution. (A) Presence, (B) Familiarity, (C) Novelty, (D) Comfort, (E) Pleasantness, (F) Arousal, (G)
Two-dimensional rendering of the three VEs designed and used for the stimulation.
dimensions. These graphs show that the VE judged highly
familiar was the one characterized by modern furniture, while
novelty dimension returned highest scores for the one with
cutting-edge furniture. Instead, scores related to pleasantness
and arousal revealed that subjects assigned the highest scores
to interiors with objects in general, indistinctly of the kind of
furniture.
The degree of correlation between perceptual dimensions is
shown through the computation of the Pearson’s coefficients
(Table 2). In particular, judgments of Novelty were positively
correlated with Pleasantness (R = 0.68, p < 0.01) and
negatively with Familiarity (R = −0.52, p < 0.01). Judgments
of Pleasantness were also positively correlated with Arousal (R =
0.63, p < 0.01). These results illustrate that the measured
perceptual dimensions are characterized by a certain degree of
correlation, which was investigated through cerebral data.
TABLE 2 | Pearson’s correlation coefficients among perceptual
dimensions.
Familiarity Novelty Comfort Pleasantness Arousal Presence
Familiarity
Novelty
Comfort
Pleasantness
*−0.52
0.08
−0.06
−0.07
0.11
1
0.27
**0.68
0.31
0.18
1
0.43
1
0.19
0.39
**0.63
0.21
Arousal
1
Presence
0.12
1
*p < 0.05, **p < 0.01, Bonferroni corrected for multiple comparisons.
univariate analysis. First, the z-scores related to the Presence
dimension were used in order to (i) identify the autonomic
and EEG correlates of presence and (ii) to investigate possible
time windows showing most activation during the whole 4 min
perception of the VEs. To this aim, HR, SCL and PSDw related
to positive z-scores of Presence were grouped in the High
Presence dataset (19 observations) while the HR and PSDw
related to negative z-scores formed the Low Presence dataset (13
observations).
PSD Time-Frequency Pattern and
Autonomic Variables of Presence
The z-scores computed for all behavioral dimensions were used
to pool the whole PSDw dataset into groups for the mass
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PSD Time-Frequency Patterns of
Perceptual Dimensions
The HR waveforms were z-scored and averaged across
subjects, as visible in Figure 4A. Here it is possible to appreciate
that the largest difference between the two groups is within
the time window tm = [60, 180] s comprising minutes 2 and
3, i.e., the central part of the experience in the VE. This
result is highlighted in Figure 4B, where there are two boxplots
showing the average z-score distributions, calculated on the
interval tm , for both High and Low Presence groups. The
Student’s t-test resulted in a significant increase of HR during
the observation of VEs perceived with high presence (t = 2.908,
p = 0.007).
Analysis performed on the SCL returned no significant
difference (t = −1.170, p = 0.252).
The PSDw datasets were contrasted by computing
multiple t-test (p < 0.05, FDR corrected). Such calculation
was performed separately for each frequency band of
interest. In Figure 5 are represented the results of the
mass univariate analysis in the theta band that was used
to inspect cerebral areas involved in assessing the level of
presence (Sanchez-Vives and Slater, 2005; Slobounov et al.,
2015).
In Figure 5A the statistically significant increase of activation
related to High Presence condition is highlighted in red,
while the color blue represents Low Presence. This result
shows that VEs perceived with high presence elicited a
larger amount of theta power across frontal (Fp1, Fp2,
Fz) and left temporal (T7) scalp sites, which was mainly
found during the central part of the VE experience. This
evidence is summarized in Figure 5B, which shows an average
topographic map of t-values computed within the time window
tm . Again, red color highlights an increase of theta activity
across frontal and left temporal locations (tmean > 2). The
analysis of alpha and mu band revealed a few seconds of
significant activations at frontal and central sites (Figures 5B,C)
which, on average, did not result in a sustained activity
(Figures 5E,F).
A similar analysis was performed to contrast the perceptual
dimensions of Familiarity, Comfort, Pleasantness, Arousal, and
Novelty.
The perceptual z-scores of Familiarity were divided into
positive and negative scores to compare the PSDw between
High Familiarity condition (z-scores > 0, 17 observations)
with Low Familiarity condition (z-scores < 0, 18 observations),
respectively. The results of these comparisons are shown in
Figure 6 in which raster diagrams depict the significant increase
of spectral activity in the different bands of interest, second by
second, as investigated by the mass univariate analysis. In this
case, in the middle time window tm (i.e., the central part of the
VE perception as highlighted by the dotted black box) there is an
increase of theta activity at electrodes Fz and Pz, related to High
Familiarity condition (Figure 6A). This pattern of activation is
also visible through a topographic map which shows mainly the
frontal rhythm with tmean > 2 (Figure 6D). The raster diagrams
of the alpha (Figures 6B,E) and mu (Figures 6C,F) bands do not
provide any particularly sustained activation in the Familiarity
condition.
The mass univariate analysis was then performed for the
perceptual dimension of Comfort. As described above, in this
condition the perceptual z-scores of Comfort were used to
divide the PSDw dataset into two groups: High Comfort (zscores > 0, 19 observations) and Low Comfort (z-scores < 0,
13 observations) undergoing the multiple t-tests. The results are
shown in Figure 7, which depicts the raster diagrams for the
bands of interest (Figures 7A–C), as well as their corresponding
average topographic maps (Figures 7D–F). An increase of theta
activity across the frontal midline (Fz and Fp1) associated to
the High Comfort condition is visible from the raster diagram
and the average scalp map within the time-window tm . Although
the alpha band shows significant de-synchronization for the
same condition across frontal sites, the average map computed
FIGURE 4 | Results from the Heart Rate analysis. (A) HR waveforms for the High Presence (red) and Low Presence (blue) conditions. Vertical grid highlights the
time window tm , the central part of experience, where the highest difference was found. (B) Boxplots showing the z-score distributions of High and Low Presence
conditions computed in the time interval tm . On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend
to the most extreme data points. Difference is significant with t = 2.908 and p = 0.007.
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FIGURE 5 | Time-frequency patterns of PSD for Presence. (A–C) Raster diagram showing in red (blue) significant increase (decrease) of t-values for the EEG
activity related to the perception of VEs with High Presence (Low Presence) in theta (A), alpha (B), and mu (C) bands. Gray color indicates no statistically significant
difference (p < 0.05, FDR corrected). The dotted black box indicates the time window with most of activations. (D–F) Scalp topographic map of average t-values for
theta (D), alpha (E), and mu (F) bands computed in the time window tm of VE experience. Black labels indicate the scalp sites which show tmean > 2.
the ones regarding Pleasantness, as visible in Figure 9. In fact, the
Pearson’s coefficients were calculated between the distributions
of average t-values within the time window tm summarizing the
spectral activity of these two dimensions. The results show that
the average PSDw of Arousal and Pleasantness are positively
correlated in all bands (theta: R = 0.93, p < 0.01; alpha: R =
0.94, p < 0.01; mu: R = 0.90, p < 0.01. Bonferroni corrected).
In line with this result, we report another positive correlation
between the spectral values related to the perceptual dimensions
of Pleasantness and Novelty specifically in theta (R = 0.76,
p < 0.01) and alpha (R = 0.68, p < 0.01) bands, even though the
average topographical t maps of Novelty did not reveal average
values of |tmean |> 2 (Figure 10).
within the interval tm does not reveal any locations with |tmean |>
2. Instead, the analysis computed in the mu band returned
significant de-synchronization across left central (C3) and frontal
(F3) electrodes, as visible by the scalp map presenting |tmean |> 2.
By using the perceptual z-scores of Pleasantness, the
PSDw dataset was divided into two groups associated with
High Pleasantness (z-scores > 0, 22 observations) and Low
Pleasantness (z-scores < 0, 13 observations) respectively. The
results of the mass univariate analysis are reported in Figure 8,
Figures 8A–C show the raster diagrams of the mass univariate
analysis, while Figures 8D–F depict the average topographic
map of t-values within the time window tm of VEs perception.
Figure 8A emphasizes a wide increase of theta activity across
occipito-parietal (P7, Pz, P4, P8, O1, O2) and frontal (F3,
Fp2, F8) areas for the High Pleasantness condition. Instead,
Figures 8B,C show the de-synchronization of the alpha band
related to left frontal (F3) and parietal areas (P3, Pz) as well
as a wide suppression of the mu rhythm across parietal (P3,
Pz, P4) and left central (C3, Cz) regions. For the Pleasantness
condition, cerebral activations exceeded the central part of the VE
experience and seemed to accompany the subject until the end of
the VEs experience. These results are also visible in the average
topographic maps (Figures 8D,E).
The same mass univariate analysis and average topographic
maps were performed for the perceptual dimensions of Arousal
and Novelty. The results for Arousal are highly correlated with
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DISCUSSION
In this study, the electroencephalographic activation
underpinning the perception of VEs with different architectural
appearances were investigated testing the hypothesis that
variations in the virtually presented interiors could activate
different cerebral circuits involved in mechanisms of
embodiment. For this purpose, the EEG and autonomic
activity were recorded during the visual exploration of three
VEs. Each environment was designed with the intention to elicit
different opinions on the perceptual dimensions of Pleasantness,
Novelty, Familiarity, Comfort, and Arousal. Concretely, each
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FIGURE 6 | Time-frequency patterns of PSD for Familiarity. (A–C) Raster diagram showing in red (blue) significant increase (decrease) of t-values for the EEG activity
related to the perception of VEs with High Familiarity (Low Familiarity) in theta (A), alpha (B), and mu (C) bands. Gray color indicates no statistically significant difference
(p < 0.05, FDR corrected). The dotted black box indicates the time window related to the middle part of VE experience. (D–F) Scalp topographic maps of average
t-values for the theta (D), alpha (E), and mu (F) bands computed in the time window tm of VE experience. Black labels indicate the scalp sites which show |tmean |> 2.
due to its capacity to represent real-life events and situations
in highly controlled computer-generated environments (Tarr
and Warren, 2002; Sanchez-Vives and Slater, 2005; Bohil et al.,
2011; Dombeck and Reiser, 2012). The experience of IVR can
elicit the illusory sensation of being physically present in the
VE, this sensation is defined as sense of presence (Slater and
Wilbur, 1997; Witmer and Singer, 1998; Diemer et al., 2015).
The sense of presence leads to behavioral and neurophysiological
reactions corresponding to real-life experience (Slater, 1999;
Sanchez-Vives and Slater, 2005; Parsons and Rizzo, 2008). The
intensity of these reactions depends on the number and range
of the participant’s sensory and motor channels connected
to the virtual environment through different technological
devices (Slater, 1999). For this purpose the VEs were recreated
using a CAVE system which provides a high degree of
sensorial immersion in the virtual world (Cruz-Neira et al.,
1993).
The data show that the designed VEs were able to induce
various degrees of presence, as revealed by the adopted
questionnaire (Sanchez-Vives and Slater, 2005; Parsons and
Rizzo, 2008; Figure 3A). Evidence led us to assess the cerebral
pattern and autonomic activation underpinning the sense
of presence and, accordingly, to consider the time interval
characterized by the increase of frontal midline theta activity as
the most significant for the tested architectural experience.
perceptual dimension was investigated by contrasting the
cerebral activity related to the visual exploration of VEs with
higher scores against the one of the VEs with lower scores. In
order to select the most relevant time window of exposition, the
neurophysiological data regarding the dimension of Presence
were analyzed, highlighting the cerebral areas involved during
this phenomenon and its specific temporal interval of interest.
Specifically, VEs judged with higher Presence scores revealed
the involvement of frontal midline theta power. Similar
activations were also found during the perception of familiar
and comfortable VEs. Statistical comparisons related to the
perceptual dimension of Pleasantness returned a complex pattern
of activation in the analyzed bands. In particular, the theta band
was characterized by a spread enhancement of activity across
occipito-parietal and frontal networks, whereas the alpha band
returned a de-synchronization of left parietal and frontal sites.
Finally, the perception of both highly pleasant and comfortable
VEs showed a de-synchronization of the mu rhythm mostly
located in the left hemisphere. A detailed discussion of the
aforementioned results is presented in the following sections.
Sense of Presence and Sensorimotor
Integration
IVR is a powerful tool for the investigation of the complex human
behaviors during the natural interaction with the external world
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FIGURE 7 | Time-frequency patterns of PSD for Comfort. (A–C) Raster diagram showing in red (blue) significant increase (decrease) of t-values for the EEG
activity related to the perception of VEs with High Comfort (Low Comfort) in theta (A), alpha (B), and mu (C) bands. Gray color indicates no statistically significant
difference (p < 0.05, FDR corrected). The dotted black box indicates the time window related to the middle part of VE experience. (D–F) Scalp topographic maps of
average t-values for the theta (D), alpha (E), and mu (F) bands computed in the time window tm of VE experience. Black labels indicate the scalp sites which show
|tmean |> 2.
Other studies also report significant correlations between
presence and emotion (Baños et al., 2008), suggesting that a
higher sense of presence could favor perception of emotional
states. Similarly, physiological studies on the autonomous system
report an increase of heart rate as a quantitative measure of
Presence (Meehan et al., 2005), an observation which was also
found in the present study. Based on the EEG data analysis,
the cerebral areas which were mostly involved in the evaluation
of presence were the frontal and orbitofrontal areas as well
as the left temporal region. Specifically, an increase of theta
activity was reported across these sites during the perception
of VEs rated with High Presence scores. Similar results were
also reported by two recent works on spatial navigation tasks
using virtual reality. In particular, Slobounov and colleagues
illustrate that during the state of presence in immersive 3D
scenario subjects showed an enhancement of frontal midline
theta (FM-theta) correlated with the success rate in a spatial
navigation task, especially during the route encoding. This theta
activity was considered to be a reflection of action monitoring,
cognitive control and learning (Slobounov et al., 2015). Similarly,
Kober and Neuper (2011) report an increase of FM-theta during
the processing of familiar spatial cues. Both studies support
the sensorimotor integration hypothesis which assigns to theta
oscillations the role of coordinating the sensory information with
a motor plan to direct wayfinding behavior toward known goal
locations (Bland and Oddie, 2001; Caplan et al., 2003). Other
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studies also report a correlation of FM-theta with hippocampal
theta activity during spatial navigation tasks in VEs (Caplan et al.,
2003; Ekstrom et al., 2005), although the connection between
theta cortical activity and hippocampus is still questioned
(Mitchell et al., 2008).
Fronto-central theta rhythms are also increased during
meditation and states of internalized attention (Aftanas and
Golocheikine, 2001; Baijal and Srinivasan, 2009). Furthermore,
theta band power increases with task demands and could be
related to orienting (Dietl et al., 1999), memory (Klimesch, 1999),
and affective processing (Sammler et al., 2007; Vecchiato et al.,
2011a). This cerebral feature also appears during the state of
concentration in mental and meditative tasks reflecting focused
attentional processing and could be correlated with autonomous
activity (Kubota et al., 2001). Therefore, the sense of presence
could elicit mechanisms underlying sensorimotor integration
as well as cerebral networks regulating focused attention, as
reported in this study.
In addition, FM-theta was not only elicited during the
experience of presence but also during the visuospatial
exploration of more familiar, comfortable and pleasant
environments. These findings may reflect recruitment of theta
oscillations in focused attention, memory and positive emotional
experience mechanisms associated with the exploration of
VEs. Therefore, the recognition of familiar features in the
environment, as well as the perception of comfort, could
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FIGURE 8 | Time-frequency patterns of PSD for Pleasantness. (A–C) Raster diagram showing in red (blue) significant increase (decrease) of t-values for the
theta activity related to the perception of VEs with High Pleasantness (Low Pleasantness) in theta (A), alpha (B), and mu (C) bands. Gray color indicates no statistically
significant difference (p < 0.05, FDR corrected). The dotted black box indicates the time window related to the middle part of VE experience. (D–F) Scalp topographic
maps of average t-values for the theta (D), alpha (E), and mu (F) bands computed in the time window tm of VE experience. Black labels indicate the scalp sites which
show |tmean |> 2.
activate those cerebral circuits involved in internalized attention,
relaxation and hence favor sensorimotor integration in space.
Overall, these results show that the discussed cerebral
activations are common to the dimensions of Presence,
Familiarity and Comfort, all showing an increase of theta frontal
activity. However, an additional region was characterized by
Presence only, which is the left temporal site. In their review,
Jäncke and colleagues report the activation of a wide frontoparietal network when participants reported a strong sense
of presence in a virtual roller coaster scenario (Jäncke et al.,
2009). Interestingly, these authors reported a difference between
patterns of effective connectivity in adolescents and children.
They attributed this finding to the prefrontal cortex which is
not fully matured in children. Also, children are able to engage
multi-sensory integration areas such as the temporo-parietal
junction (TPJ) which is known to be a key area for studying
self-location, i.e., the ability to place and experience oneself
in the physical space (Aglioti and Candidi, 2011). TPJ is a
cerebral region processing the body space proprioception and
bodily awareness integrating signals coming from the body. This
cortical area is involved in mental tasks where self-relocation
is required, such as transcending body-related sensorimotor
experiences usually occurring during meditative states (Urgesi
et al., 2010). In particular, Ionta and colleagues reveal that
multisensory integration in TPJ reflects the feeling of being an
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entity localized at a position in space, allowing the perception of
the world from this perspective (Ionta et al., 2011).
An additional interpretation for this finding could be to
connect the activation of the left temporal lobe with functions
of visuomotor coordination and motor representation. Tankus
and Fried (Tankus and Fried, 2012) discovered two neural
populations in the human temporal-lobe activated differently
during a motor and a visuomotor task, respectively. The second
group of neurons, connected with the parahippocampal gyrus,
had already been demonstrated to respond to visual motion (Sato
and Nakamura, 2003) as well as to the observation of paintings
reproducing landscapes (Kawabata and Zeki, 2004; Yue et al.,
2007). It is known that images depicting environments activate
the parahippocampal place area (PPA), an association area
which codes place-related information and mediates contextual
association with the environment (Epstein and Kanwisher, 1998;
Bar, 2004). In fact, this area might not have been elicited in the
above mentioned virtual roller coaster experiment because it was
made of non-static movement scenes (Jäncke et al., 2009). Hence,
these results seem to support the view that PPA could play a role
in the cerebral circuit of presence.
Aesthetic Experience
The pattern of cerebral activations related to High Pleasantness
returned increased EEG power in all the investigated frequency
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FIGURE 9 | Time-frequency patterns of PSD for Arousal. (A–C) Raster diagram showing in red (blue) significant increase (decrease) of t-values for the theta
activity related to the perception of VEs with High Arousal (Low Arousal) in theta (A), alpha (B), and mu (C) bands. Gray color indicates no statistically significant
difference (p < 0.05, FDR corrected). The dotted black box indicates the time window related to the middle part of VE experience. (D–F) Scalp topographic maps of
average t-values for the theta (D), alpha (E), and mu (F) bands computed in the time window tm of VE experience. Black labels indicate the scalp sites which
show |tmean |> 2.
bands. Specifically, the perception of High Pleasantness
environments activated not only mechanisms related to action
planning but also to the sensory visual areas and frontal regions.
According to a recent theoretical and experimental framework,
aesthetic experience may involve the interaction of a neural
systems’ triad formed by the sensory-motor, the emotionevaluation and the meaning-knowledge circuits (Chatterjee
and Vartanian, 2014). Therefore, this perspective unifies both
the sensory (e.g., pleasantness is positively correlated with the
activation of sensory areas; Zeki, 1999) and the conceptual
(e.g., pleasantness is positively correlated with the activation of
frontal regions mediating concepts; Cela-Conde et al., 2004)
hypotheses. In this regard, a recent study conducted by Thakral
and colleagues show the activation of both visual and frontal
cortices thus supporting sensory and conceptual hypotheses of
aesthetic experience (Thakral et al., 2012). More specifically,
these authors disentangle the response of the visual areas elicited
by the observation of motion pictures from the frontal activity
elicited by the observation of pleasant images. Their results
provide evidence that motion experience is associated with
activity in motion processing regions, while the experience of
pleasantness is associated with the anterior prefrontal cortex.
However, these two processes seem to interact in the same
window of activation to engender the aesthetic experience
(Lorteije et al., 2006).
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Similarly, the results obtained in this study show an increase
in the theta band in occipital, frontal and orbito-frontal
regions during the perception of highly pleasant VEs. Figure 3
illustrates that the furnished virtual rooms account for the
“High Pleasantness” condition while the empty virtual room
was evaluated as “Low Pleasantness.” Accordingly, it might
be reasonable to argue that the activity of the cerebral areas
accounting for the amount of potential motion is generated
by the possibility to interact and move around the objects
located inside the virtual rooms. The activation of occipital
areas had already been shown through the comparison of
pictures with implied motion against the ones with non-implied
motion (Lorteije et al., 2006). Other paradigms investigating the
multisensory perception of objects in motion (Senkowski et al.,
2007) and the processing of coherent meaningful objects (Vanni
et al., 1997) have achieved the same results.
At the same time, the activity of the frontal lobe during
perception of pleasantness is caused by the conceptual content
of the stimulation as already reported in research papers (CelaConde et al., 2004; Ishizu and Zeki, 2011), meta-analysis (Kühn
and Gallinat, 2012), and review (Chatterjee and Vartanian, 2014).
The activity of the parietal cortex, as indicated by the present
results, could reflect the integration of multisensory information
from different sensory modalities to form a coherent multimodal
representation of space, which is coded in a body-centered
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FIGURE 10 | Time-frequency patterns of PSD for Novelty. (A–C) Raster diagram showing in red (blue) significant increase (decrease) of t-values for the theta
activity related to the perception of VEs with High Novelty (Low Novelty) in theta (A), alpha (B), and mu (C) bands. Gray color indicates no statistically significant
difference (p < 0.05, FDR corrected). The dotted black box indicates the time window related to the middle part of VE experience. (D–F) Scalp topographic maps of
average t-values for the theta (D), alpha (E), and mu (F) bands computed in the time window tm of VE experience. Black labels indicate the scalp sites which show
|tmean |> 2.
reference frame (evidence already reported in a VR study,
Jäncke et al., 2009). The integration of multisensory cues
around the body in the peripersonal space serves to map the
position of objects in the surrounding environment in terms
of one’s own body. In addition, visual targets elicit a motor
schema for potential action that maps the position of objects
in the surrounding environment, irrespective of whether the
corresponding action is actually executed (Jeannerod et al., 1995;
Rizzolatti et al., 1997a,b). In their virtual roller coaster scenario,
Jäncke et al. (2009) argue that VEs trigger motor schemas
mapping the visual objects in terms of real motor space as well
as a corresponding plan for potential action.
Another study reports that the activity of the parietal cortex
could be also modulated by the object’s size (Tarantino et al.,
2014), while other authors discuss that parietal areas are involved
in integrating information about three-dimensional objects, such
as the object size and the grasp-relevant dimension (Monaco
et al., 2014). In addition, Salmi et al. (2014) illustrate that the
parietal activity related to goal-directed actions could also depend
on a behavioral priority accounting for percepts, thoughts and
emotions during the observation of natural scenes.
In this regard, the presented results show a theta increase
across the right parietal cortex during the perception of pleasant
VEs. In agreement with the aforementioned literature, these
findings could offer the interpretation that the perceived pleasant
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VEs may favor the triggering of motor schemas related to
potential actions planning.
The perception of pleasant VEs also returned a significant
activation of the alpha band in left-central parietal and frontal
areas, a fact which might underlie an increase in visuospatial
processing. In fact, similar findings were already reported in a
study performed with fMRI, which tested the level of pleasantness
during the observation of spaces with varying architectural
features (ceiling height and openness/enclosure), illustrating the
activation of left precuneus and left middle gyrus (Vartanian
et al., 2015). These two structures have an important role in
visuospatial processing (Kravitz et al., 2011). Other authors argue
that this lateralized activation of the left hemisphere could be
due to the processing of categorical spatial relations and not
to the processing of coordinate spatial relations (Amorapanth
et al., 2009). Categorical spatial relations are involved in tasks
that do not require a precise location. On the other hand,
coordinate spatial relations require precise metrical information
about distances among objects (Kosslyn, 1987).
Additional findings report that the left hemisphere is more
involved in processing spatial relations, while the activity of
the right parietal lobe relates to coordinate spatial relations
(Baciu et al., 1999; Suegami and Laeng, 2013). Interestingly, CelaConde et al. (2009) show that in women there is a lateralized
activity in the left parietal areas during the observation of stimuli
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rated as beautiful. Asymmetrically, men show an increase of
parietal activity in the right hemisphere. Taking an evolutionary
perspective, they discuss that women rely more on categorical
strategies when processing objects more than men do. Due to
the lack of statistical power, evidence supporting this gender
differentiation cannot be provided in this study and should be
further investigated.
However, since the visual exploration of VEs did not require
the processing of specific distance among objects, it is reasonable
to think that subjects mostly activated categorical spatial
processing during the perception of pleasant environments. This
kind of processing could be related to the fact that people were
considering the quality of the space as a whole. Vartanian et al.
(2015) report a positive linear correlation between the activation
of the left precuneus and the scores of pleasantness. Similarly
the presented results show a potential role of the left precuneus
in the perception of pleasant VEs, an area which also facilitates
visuospatial exploration. This finding could be also interpreted
according to the biophilic hypothesis (Kellert and Wilson, 1995;
McVay et al., 1995), which suggests that the activations of
areas for visuospatial exploration could support more general
human preferences for appreciating spatial properties that are
evolutionary beneficial.
produced those works. Moreover, the activity in premotor and
motor cortices has been observed in other tasks involving spatial
cognition (Rizzolatti and Craighero, 2004). In this study, the
perception of VEs rated as pleasant and comfortable could
involve spatial cognitive processes, increased somatosensory
perception and the planning and execution of movements. In this
sense, subjects felt free to “live” those spaces which triggered the
embodied mechanism (Freedberg and Gallese, 2007; Cela-Conde
et al., 2009).
Similarly, object perception provides an example of
embodiment which resides in the action domain. Specifically,
the observation of manipulable objects triggers the same
motor resources typically employed during the planning and
execution of actions targeting the same objects (Gallese and
Sinigaglia, 2013). Hence, the motor system can also be engaged
in the absence of active action execution. Recent studies show
that observing manipulable objects can lead to mu rhythm
suppression within 300 ms after stimulus presentation, possibly
reflecting the automatic access to object-associated actions
(Proverbio, 2012; Rüther et al., 2014). In particular, Proverbio
shows that this activation is evident when comparing the
observation of manipulable objects (tools) to the observation
of non-manipulable objects, mostly in the band between 10
and 12 Hz (Proverbio, 2012). In a similar fashion, Rüther and
colleagues report a significant suppression of the mu rhythm
when observing familiar tools instead of new ones, although
in a band ranging in lower frequencies (i.e., from 8 to 10 Hz;
Rüther et al., 2014). Accordingly, during the perception of
VEs, the modulation of the mu rhythm seems to depend
not only on the simple observation of specific objects in the
environment but also on the perception of the environment as
a whole. In fact, the analysis regarding Pleasantness essentially
compares the VEs with objects against VEs without objects
(Figure 3E). On the other hand, when analyzing Comfort,
the High Comfort condition had both VEs with and without
objects, making the comparison with the Low Comfort condition
object-balanced (Figure 3D). In other words, both the High (z
> 0) and Low (z < 0) Comfort datasets comprise the perception
of VEs with and without objects. Therefore, these results
show the suppression of the mu rhythm in the conditions of
High Pleasantness and High Comfort, which could reflect the
possibility to interact with the objects located in the VEs.
Because the objects were perceived in a specific functional
configuration instead of being observed independently, this
neurophysiological mechanism could have a role in regulating
potential actions which influence the pleasantness and comfort
of the environment as a whole. These results are in line with
other studies performed on object affordances, showing that the
functional identity of graspable objects influences the extent to
which they are associated with motor representations (CreemRegehr and Lee, 2005; Proverbio, 2012). Similarly, the object
familiarity could enhance the activation of action representations
and motor plans (Rüther et al., 2014). Such findings reveal that
the view of a tool automatically activates appropriate motoric
properties, including its affordance and the representation of
the associated motor interaction. In addition, behavioral and
brain stimulation studies have also shown that the affordability is
Embodiment and Affordance
The data indicate a certain degree of similarity in the pattern
returned by the mu band pertaining to the perceptual
dimensions of Pleasantness and Comfort. In fact, both
dimensions highlighted a de-synchronization of such rhythm
during the perception of VEs with High Comfort and High
Pleasantness scores. Along with several studies showing a
primary importance of cognition for art response (Cinzia and
Vittorio, 2009; Chatterjee and Vartanian, 2014), recent theories
and experimental works propose that the activation of embodied
mechanisms play a key role in the aesthetic experience of
works of art and that these mechanisms could also account for
the perception of architectural spaces (Freedberg and Gallese,
2007; Umilta’ et al., 2012; Sbriscia-Fioretti et al., 2013). In
summary, the motor system is activated due to an automatic
empathic relationship established between the artwork and the
observer—a phenomenon which could be triggered by the work’s
representational content and by artist’s creative gestures (Cinzia
and Vittorio, 2009). Umilta’ et al. (2012) report the suppression
of the mu rhythm, recorded around electrodes C3 and C4, during
the observation of the cuts on the canvases by Lucio Fontana.
Similarly, Sbriscia-Fioretti et al. (2013) illustrate that observing
the brushstrokes by Franz Kline engages motor areas along
with the occipital circuits related to vision, as well as the frontal
and orbitofrontal regions processing reward and judgment,
respectively. In both studies, the authors contrasted the EEG
activity gathered from the perception of the original works
against the activity elicited by the observation of computergenerated reproductions displaying the same patterns of lines
and stripes but without the original dynamic components (i.e.,
artist’s gestures). Thus, the described cerebral activations are
engendered by the automatic comprehension of those dynamic
components and the recognition of the motor actions that
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to the details of the environment they are experiencing. Due to
the correlational design of this work, all the points mentioned
above will be addressed with additional control conditions to
improve the setup of further experiments.
context dependent and that spatial constraints affect one’s reuse
of his/her own action representations (Costantini et al., 2010,
2011; Cardellicchio et al., 2011).
Finally, the analysis of Familiarity and Novelty returned no
significant results in the mu band, probably because the EEG
activity in both the “high” and “low” groups accounted for the
perception of objects with the same function (i.e., affordance)
but located in different VEs, and with different design. This
can be observed in the distribution of the related behavioral zscores in Figures 3B,C. However, further investigation is needed
in order to explore how the perception of affordances depends on
the architectural context. This would elucidate the relationship
between embodied mechanisms and the specific features of
architectural environments.
CONCLUSIONS
The present findings aim to provide new insights for studying
the impact of architecture on human brain. These results
revealed that perception of familiar and comfortable real-like
VEs engender the activation of those cerebral circuits elicited by
the sense of presence which facilitate sensorimotor integration.
Similarly, the perception of pleasant environments involves areas
devoted to visuospatial processing, suggesting the importance
of a fronto-parietal network in aesthetic perception of places.
These cerebral areas could be considered as evolutionary
beneficial. Finally, a common suppression of the mu rhythm
over the left motor areas was reported, characterizing highly
pleasant and comfortable environments. These results are in
agreement with the embodied simulation theory, which plays
a fundamental role in object perception and possibly in
the environment perception as a whole. Overall, this study
shows the involvement of motor and cognitive processes
for the evaluation of architectural environments. Further
research is needed for in-depth investigation of the role of
embodiment, affordances and perceptual processes underpinning
the appreciation of architectural environments. This knowledge
will provide neurophysiological findings to improve the design
of buildings and help to create environments that satisfy man’s
demands.
Limitations
Due to the explorative nature of the present study, several
limitations should be taken into account when considering
the final results. First, the tested VEs were designed with the
aim to induce different levels of Familiarity, Novelty, Comfort,
Pleasantness, and Arousal. Therefore, the corresponding main
cerebral activations were investigated regardless of the specific
features of the spaces represented (e.g., VE1 vs. VE2). The
aim was to test a simple IVR setup and at the same time to
effectively retrieve neurophysiological correlates of environment
perception. These results could be useful for shaping architectural
hypotheses in future studies. Secondly, the electromiographic
activity of the subjects enrolled in the data recording was not
controlled, still they were asked to seat in the CAVE without
moving their legs, arms and hands. During the data collection,
the experimenter monitored the behavior of all subjects during
the task and did not report any movement of their limbs. Hence,
the activity of the motor areas could have been caused only
by cerebral processes which are not related with movement
execution. Also, the gaze was not controlled using eye-tracking
measurements and therefore the objects and locations of the
VEs that were the most looked at by the participants could not
be defined. Instead, the aim was to retrieve neurophysiological
correlates of a generalized perception—that is, of architectural
space as a whole and not of the specific visual targets in the
environment. Finally, from the architectural point of view, the
setup required the participants to explore the VEs with a certain
degree of attention. Conversely, in everyday life, people usually
do not focus on architectural features but rather live the space in a
habitual and automatic manner, without paying special attention
ACKNOWLEDGMENTS
This research was supported by the grant PRIN2012 related
to the mental workload estimation funded by the Ministero
dell’Istruzione dell’Università e della ricerca to FB, and by the
grant code C26N149PK8 with the title “Neurophysiological tools
to investigate the cognitive and emotional engagement during
the experience of artworks and architectonical environments in
laboratory setup, virtual reality CAVE system and real sites”
funded by Sapienza University of Rome to GV. We would like
to thank Prof. Salvatore Maria Aglioti for his generous and
kind availability in supporting this research, and Irene Conti for
helping to improve the English editing of a previous version of
the manuscript.
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Conflict of Interest Statement: 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 © 2015 Vecchiato, Tieri, Jelic, De Matteis, Maglione and Babiloni. This
is an open-access article distributed under the terms of the Creative Commons
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is permitted, provided the original author(s) or licensor are credited and that the
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December 2015 | Volume 6 | Article 1944