Natural Translating Locomotion Modulates Cortical Activity at Action Observation

Abstract

The present study verified if the translational component of locomotion modulated cortical activity recorded at action observation. Previous studies focusing on visual processing of biological motion mainly presented point light walker that were fixed on a spot, thus removing the net translation toward a goal that yet remains a critical feature of locomotor behavior. We hypothesized that if biological motion recognition relies on the transformation of seeing in doing and its expected sensory consequences, a significant effect of translation compared to centered displays on sensorimotor cortical activity is expected. To this aim, we explored whether EEG activity in the theta (4–8 Hz), alpha (8–12 Hz), beta 1 (14–20 Hz) and beta 2 (20–32 Hz) frequency bands exhibited selectivity as participants viewed four types of stimuli: a centered walker, a centered scrambled, a translating walker and a translating scrambled. We found higher theta synchronizations for observed stimulus with familiar shape. Higher power decreases in the beta 1 and beta 2 bands, indicating a stronger motor resonance was elicited by translating compared to centered stimuli. Finally, beta bands modulation in Superior Parietal areas showed that the translational component of locomotion induced greater motor resonance than human shape. Using a Multinomial Logistic Regression classifier we found that Dorsal-Parietal and Inferior-Frontal regions of interest (ROIs), constituting the core of action-observation system, were the only areas capable to discriminate all the four conditions, as reflected by beta activities. Our findings suggest that the embodiment elicited by an observed scenario is strongly mediated by horizontal body displacement.

Full text

ORIGINAL RESEARCH
published: 07 November 2017
doi: 10.3389/fnsys.2017.00083
Natural Translating Locomotion
Modulates Cortical Activity at Action
Observation
Thierry Pozzo 1,2*, Alberto Inuggi 3,Alejo Keuroghlanian 3,Stefano Panzeri 4,
Ghislain Saunier5and Claudio Campus6
1Centro di Neurofisiologia Traslazionale, Istituto Italiano di Tecnologia, Ferrara, Italy, 2INSERM-U1093, CAPS, Campus
Universitaire, Dijon, France, 3Unit of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy,
4Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems, University of Trento, Istituto Italiano di
Tecnologia, Rovereto, Italy, 5Laboratorio de Cognição Motora, Departamento de Anatomia, Universidade Federal do Pará,
Belém, Brasil, 6U-VIP Unit for Visually Impaired People, Istituto Italiano di Tecnologia, Genova, Italy
Edited by:
Mikhail Lebedev,
Duke University, United States
Reviewed by:
Yury Ivanenko,
Fondazione Santa Lucia (IRCCS),
Italy
Guy Cheron,
Free University of Brussels, Belgium
Stephan de la Rosa,
Max Planck Society (MPG), Germany
*Correspondence:
Thierry Pozzo
thierry.pozzo@u-bourgogne.fr
Received: 11 July 2017
Accepted: 18 October 2017
Published: 07 November 2017
Citation:
Pozzo T, Inuggi A, Keuroghlanian A,
Panzeri S, Saunier G and Campus C
(2017) Natural Translating
Locomotion Modulates Cortical
Activity at Action Observation.
Front. Syst. Neurosci. 11:83.
doi: 10.3389/fnsys.2017.00083
The present study verified if the translational component of locomotion modulated
cortical activity recorded at action observation. Previous studies focusing on visual
processing of biological motion mainly presented point light walker that were fixed
on a spot, thus removing the net translation toward a goal that yet remains a critical
feature of locomotor behavior. We hypothesized that if biological motion recognition
relies on the transformation of seeing in doing and its expected sensory consequences,
a significant effect of translation compared to centered displays on sensorimotor cortical
activity is expected. To this aim, we explored whether EEG activity in the theta (4–8 Hz),
alpha (8–12 Hz), beta 1 (14–20 Hz) and beta 2 (20–32 Hz) frequency bands exhibited
selectivity as participants viewed four types of stimuli: a centered walker, a centered
scrambled, a translating walker and a translating scrambled. We found higher theta
synchronizations for observed stimulus with familiar shape. Higher power decreases in
the beta 1 and beta 2 bands, indicating a stronger motor resonance was elicited by
translating compared to centered stimuli. Finally, beta bands modulation in Superior
Parietal areas showed that the translational component of locomotion induced greater
motor resonance than human shape. Using a Multinomial Logistic Regression classifier
we found that Dorsal-Parietal and Inferior-Frontal regions of interest (ROIs), constituting
the core of action-observation system, were the only areas capable to discriminate
all the four conditions, as reflected by beta activities. Our findings suggest that the
embodiment elicited by an observed scenario is strongly mediated by horizontal body
displacement.
Keywords: locomotion, action perception, motor resonance, EEG, translation, body shape
INTRODUCTION
Human locomotion is possible thanks to central pattern generators allowing a reciprocal activation
of flexors and extensors muscle (Grillner and Wallen, 1985). However, cyclical locomotor
skill became a decisive step in species evolution when displacement started to be oriented and
goal directed. Beside limbs oscillation, backward or forward displacements to avoid a predator or
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
to reach a prey also include a variety of related cognitive
processes. Instance of this is spatial navigation toward a goal and
the ability to integrate body translation over time that originates
from visual flow and vestibular input. Thus locomotion, as a
teleokinetic behavior (Hess, 1943) is much more than central
pattern generator activation producing limb oscillation as when
walking on the spot. Real locomotion can thus be described as a
goal-oriented action displacing the whole body from one initial
position toward a distant spatial goal. In fact, at the perceptual
level, a walker on the spot corresponds to an erratic walker
without goal, as someone can do for fun in the reverse direction
of an airport treadmill, that is a rather atypical visual stimulus.
Despite artificiality of walking without net body translation,
forward locomotion seems not a crucial variable in visual
processing of biological motion. For example, when a set
of point lights (PL) located on the joints of an invisible
walker on a treadmill is displayed on a screen the observer
reliably distinguishes a human in locomotion in contrast with
PL configurations that do not respect normal body metric
(Johansson, 1973; Blake and Shiffrar, 2007 for a review). This
contrasts with the impossibility to ‘‘read’’ the actions of other
species in one’s vicinity when displacement is absent.
Besides this a priori considerable visual relevance of body
movements for successful interactions with conspecifics and
to interpret action of others living beings, a prominent idea
regarding motion recognition is related to observer’s motor
competencies (Viviani and Stucchi, 1992; Calvo-Merino et al.,
2006; Cannon et al., 2014; Quandt and Marshall, 2014;
Meirovitch et al., 2015). According to this, the visual input
of an observed action would be mapped on to the observers’
own motor repertoire (Gallese et al., 1996; Rizzolatti et al.,
2001; Rizzolatti and Craighero, 2004). More specifically, when
one observes a living being in motion cortical sensorimotor
activity has been proposed to reflect the transformation of
perceptual representations to executable actions (Hari et al.,
1998; Pavlova and Sokolov, 2003; Pavlova et al., 2003; Oberman
et al., 2005; Pineda, 2005; Hirai et al., 2009; Perry and
Bentin, 2009). Particularly, it was observed that the visual
perception of human action led to an alteration of EEG/MEG
activity characterized by a desynchronization of alpha and beta
rhythms, which reflects the increase of neural activity within
sensorimotor cortices (Pineda, 2005; Pavlidou et al., 2014;
Cevallos et al., 2015). Therefore, if horizontal body displacement
is a key component of locomotion and if visual perception of
kinematic features is tuned by motor representations (Viviani
and Stucchi, 1992; Pozzo et al., 2006; Saunier et al., 2008),
one may assume different motor cortical activities when
displaying real locomotion compared to a walker with no net
translation.
Traditional protocols, although interesting for examining
biological motion recognition processes, presented a strong
limitation to address this question. Indeed, most of studies either
manipulated the exposure duration of the animation (Thornton,
1998; Poom and Olsson, 2002), embedded the PL in an array of
dynamic noise dots (Cutting et al., 1988; Bertenthal and Pinto,
1994; Ikeda et al., 2005; Hiris, 2007) inverted PL (Sumi, 1984)
or compared a translating PL with the translation of an object
at constant velocity (Peuskens et al., 2005). Although systematic
investigation of the role of such ecologically valid component
of action is lacking, several experiments however reported that
extrinsic motion makes PL displays more natural and easily
recognizable (Johansson, 1973; Proffitt et al., 1984; Pavlova and
Sokolov, 2003; Thurman and Lu, 2013).
The current study tests the hypothesis that motor
experience related to natural translating walking modulated
the sensorimotor alpha and beta rhythms. If motion
recognition relies on the transformation of seeing in doing
and its expected sensory consequences, a significant effect of
translation compared to centered displays on sensorimotor
cortical activity is expected. We thus collected EEG from
participants during the observation of different locomotor
patterns, manipulating the gestalt (a scrambled displays that
consists of the same amount of absolute motion but lacks
a body structure vs. a coherent global body configuration)
and the motor/kinematic (walking on a treadmill with no
net translation vs. natural translating locomotion) of the
display. More specifically, we aimed at verifying that translated
scrambled display (that modifies only the body structure but
keeps the biological kinematic) would produce sensorimotor
spectral perturbations in EEG signal and that the translation
cue on its own can link the visual input with the action
system.
MATERIALS AND METHODS
Subjects
Thirteen right-handed volunteers (7 females, 6 males, mean
age: 27, standard deviation: 3.5), with normal or corrected
to normal vision, took part in this study. All participants
provided written informed consent before the experiment began.
The experimental protocol was approved by the local ethical
committee ASL-3 (‘‘Azienda Sanitaria Locale’’, local health unit),
Genoa, and was in agreement with the Helsinki Declaration of
1975, as revised in 1983.
Experimental Protocol
Participants were presented with point-light animations (PLAs)
built from motion capture data. We used a VICON Motion
Capture System with 10 cameras to record the movements, at
100 Hz sampling frequency, of an actor walking naturally (length
of recording: 4.5 s). The actor had 13 passive infrared reflective
markers placed at the main joints and other landmarks following
the VICON Plugin Gait Full Body Template (Eltoukhy et al.,
2012): LBHD (left back of the head, roughly in a horizontal
plane of the front head markers), LSHO (acromio-clavicular
joint), LELB (left outer elbow, lateral epicondyle approximating
elbow joint axis), RELB (right outer elbow), LWRA (left wrist
bar thumb side), RWRB (right wrist bar pinkie side), LPSI
(over the left posterior superior iliac spine), LKNE (lateral
epicondyle of the left knee), RKNE (lateral epicondyle of the
right knee), LANK (left outer ankle, lateral malleolus along
an imaginary line that passes through the transmalleolar axis),
RANK (right outer ankle), LTOE (left toe, over the second
metatarsal head, on the mid-foot side of the equinus break
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
between fore-foot and mid-foot), RTOE (right toe). These data
were processed through Matlab scripts (Mathworks Inc., Natick,
MA, USA) to build the stimuli displayed using functions from
the Psychophysics Toolbox (Brainard, 1997) on an LCD monitor,
with a refresh rate of 60 Hz. Point-lights were white against a
black background.
Four types of stimuli were created: a centered walker, a
centered scrambled walker, a translating walker and a translating
scrambled walker (see Figure 1), which will be now on referred to
as cw, cs, tw and ts, respectively. During the centered conditions
the stimulus stayed with its barycenter in the middle of the screen
(visual angle approximately between 0 and 9 degrees); during
the translating conditions the stimulus moved its barycenter
from the middle always to the right of the screen (visual angle
approximately between 0 and 18 degrees). Each PLA was 1 s long
because of the visual processing duration of such stimuli that
occurs within a temporal window of 1000 ms after the display
onset (see Hirai et al., 2003; Jokisch et al., 2005; Krakowski et al.,
2011; Saunier et al., 2013). In order to avoid a possible bias
in the results due to the initial starting positions of the point-
lights, each PLA had 10 different starting positions obtained
by shifting the animation by steps of six frames (see Hirai
et al., 2003). The cw animation was built by translating all
the dots of the tw animation by the opposite of the vector
defining their center of mass with respect to the center of
the screen, at each frame: the cw animation looked like a
person walking on a treadmill. The cs animation was built
by changing randomly the initial positions of the dots in the
cw animation but keeping their velocity vectors unchanged;
the dots’ trajectories were constrained to remain inside the
vertically-oriented rectangle in which the cw animation was
inscribed. This constrain did not affect the velocity vectors of
dots in cs and ts conditions. The ts animation was built by
changing randomly the initial positions of the tw animation
in an analogous way. Therefore, each stimulus was obtained
by combining two factors with two levels each: shape (either
walker or scrambled) and translation (that could be present or
absent).
During the experiment, participants were sitting comfortably
in a darkened room, in front of the screen where PLAs were
displayed, approximately 60 cm apart. The experiment was
organized in 10 blocks. Each block consisted of 48 PLAs
(12 of each of the four types) presented in pseudo-random
order. In order to avoid possible expectation effects due to
extremely regular timing in the presentation of the stimuli,
the inter-stimulus interval (ISI) varied randomly in length
between 2 and 4 s (uniform distribution). In each block, a
random number of animations (between 2–4) changed color
from white to green during 250 ms. This change of color
occurred in a randomly determined period of the animations.
Hereafter, these changing-color stimuli will be referred to as odd
stimuli. Once an odd stimulus was presented, after a random
number of stimuli a question was presented on the screen
asking the participant which was the last animation among
the four types that changed color. Since the question arose
randomly, it was possible that between two odd stimuli no
question was presented. Therefore, the number of questions
was variable across blocks, and was less than or equal to the
number of odd stimuli presented in the respective block (one in
case of two odd stimuli or four in case of four stimuli, in
one block). EEG traces corresponding to odd stimuli were
discarded from analysis. Participants gave their answers through
a keyboard, by pressing a key number between 1, 2, 3 and
4, corresponding to cw, tw, cs and ts, respectively. The four
types of stimuli were clearly identifiable, and each participant
learned the correspondence between key numbers and stimuli
during a training session before starting the recording session.
The training consisted first in displaying the stimuli and the
associated number: participants were allowed to replay it as
many times as they needed to learn the correspondence between
numbers and animations. Then, subjects were tested in a short
experimental block (12 animations, with three odd stimuli,
and two questions) to verify they learnt the correspondence
and understood the task. Participants were allowed to replay
this second step, or to go back to the first part in case they
did not learn the correspondence between PLAs and numbers.
Participants did not receive feedback on their performance
during the actual experiment, while they did during the training
session instead.
Electrophysiological Data Recording
The electroencephalogram (EEG) was recorded from 62 Ag/AgCl
active electrodes (actiCAP, Brain Products, Munchen, Germany)
placed on the scalp, mounted on a cap according to the
international 10-20 system. The EEG was amplified with two
BrainAmp MR plus amplifiers (Brain Products), digitized at
1000 Hz. The recordings were referenced to electrode FCz.
Impedances of all electrodes were kept below 15 kOhms.
Data Pre-Processing
Raw EEG signals were band-pass filtered between 0.16 and
45 Hz through a Butterworth filter as implemented in
Brain Vision Analyzer software (Brainproducts). Data were
down sampled to 250 Hz and then imported into EEGLAB
software (Delorme and Makeig, 2004) for further analyses. A
visually inspected artifact removal was performed based on
the topographical and spectral distribution and on the time
series of the independent component calculated with the ICA
algorithm implemented by EEGLab. After artifact cleaning
the signal, the percentage of removed events was 7 ±1%
(mean ±SD) for the cs, 6 ±4% for cw, 6 ±3% for cs and
7±2% for tw, therefore, for each condition, the number of
removed events was on average lower than 5% and with a
small variability among all subjects. Data were re-referenced
to the common average reference (CAR) and epochs from
−400 (as the best compromise to get at the same time a
long enough time window and a clean baseline) to 1000 ms
with respect to stimulus presentation (time = 0) were then
extracted.
Evaluating ERSP
For each epoch, Fast Fourier Transform (FFT) was applied to
partially overlapping time segments: each segment was 256 ms
long (64 time points) and each shifting step was 8 ms. A 16 points
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
FIGURE 1 | Experimental protocol and stimuli displays. Upper part shows the four categories of stimuli. Body joint trajectories (in white) are depicted on black screen
for illustrative purposes but were not visible during the actual experiment. From left to right (upper horizontal green arrow), stimuli shape change from a scrambled
structure to a real walker body structure. From top to bottom (left lateral yellow arrow), stimuli keep the same shape but change from a centered to a translated
display. Abbreviations: cs and cw for centered scrambled and walker respectively; ts and tw for translating scrambled and walker. The trajectories of the markers
during the whole point light animation (PLA) are shown. The lower part sums up the experimental protocol. During each of the 10 experimental blocks of 48 trials (in
parenthesis), every condition was randomly presented 12 times (upper part, in parenthesis) for 1 s. During each trial, subjects were told to anchor their gaze on the
red cross displayed in the middle of the screen.
zero-padding and a Hanning-window tapering were employed,
respectively to increase smoothness in ERSP estimation and
to limit edge effects. To get a clean estimation of baseline
activity, the period between −200 ms and −10 ms was adopted.
Event related spectral perturbations (ERSPs) were calculated
as event-related power variations (in dB) compared to the
specified baseline (Makeig, 1993). ERSPs were then mediated
across epochs for each condition, considering times from
200 ms before the stimulus to 850 ms after the stimulus (8 ms
resolution) and frequencies from 4 Hz to 32 Hz (approximately
0.5 Hz resolution). Then, based on previous literature, the
EEG spectral perturbations were separately evaluated in the
theta (4–8 Hz), alpha (8–12 Hz), beta 1 (14–20 Hz) and
beta 2 (20–32 Hz) frequency bands. For each subject and
condition we used EEGLab to verify that inter trial coherency
was significant, therefore spectral modulations were consistent
among trials.
Statistical Analysis
Statistical analyses were performed using the R software
(R Core Team, 2017). We considered the average ERSP for three
regions of interest (ROIs), namely Ventral (PO7, PO8, P7, P8),
Dorsal-Parietal (P1, P2, Pz) and Inferior-Frontal (F7, F8, FT7,
FT8, F5, F6). These regions have been selected because they are
involved in biological motion perception: Ventral ROI mainly
for shape encoding (Grossman and Blake, 2002; Hirai et al.,
2003; Krakowski et al., 2011; Saunier et al., 2013), Parietal for
decoding and integration of shape and kinematic features (Saygin
et al., 2012), while Inferior Frontal reflecting motor resonance
mechanism (Cochin et al., 1999; Saygin, 2007).
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
Spectral modulations of cortical activity are possible neural
correlates related to motion perception. Particularly, the
theta band is selective to shape discrimination whilst the
alpha and beta bands are involved in matching the visual
input to motor repertoire (i.e., here referred to ‘‘motor
resonance’’). For instance, the shape of the observed
stimulus affects theta frequency band (Urgen et al., 2013).
Otherwise, alpha band is affected by both movement perception
(Capotosto et al., 2009; Zumer et al., 2014) and execution
(Cochin et al., 1999). Finally, the motor characteristics
of the observed stimulus specifically affect beta bands
(Meirovitch et al., 2015). Indeed, beta 1 is involved in the
long-range synchronization of distant areas composing a
visuo-motor network, in addition to the matching of perceived
stimuli onto the motor repertoire (Engel and Fries, 2010;
Kopell et al., 2011). Further, Beta 2 is involved in the
process of action selection and monitoring (Botvinick et al.,
1999).
Comparing ERSP between Conditions
We tested if visual stimuli elicited different neural activities
measured through EEG spectral perturbations. For each subject,
ROI and frequency band, we extracted the extreme value,
i.e., the maximum or the minimum ERSP, for each ROI and
band considered: increased mental activity is generally reflected
by a power increase (synchronization) in the theta band and
by a power decrease (desynchronization) in the alpha and
beta bands. Therefore for theta we selected the maximum,
while for other bands the minimum (for further details
about the procedure of extreme selection, see Supplementary
Statistical Analysis). To establish an order in intensity and
timing of different spectral perturbations, we applied repeated
measures analysis of variance (RM-ANOVA), investigating the
effects of ROI (Ventral, Dorsal-Parietal and Inferior-Frontal)
shape (walker, scrambled), translation (centered, translating),
as well as their interaction on the extreme of ERSP and on
their latencies. Post hoc tests were conducted using paired
two-tailed t-tests and P<0.05 after Bonferroni correction (to
reduce the risk of false positives, we considered all pairwise
comparisons between three ROIs and four conditions and
thus resulting in a correction factor of 18) were retained
as significant. Considering the number of discarded events
among subjects in each condition, standard deviations resulted
quite low (ranging from 1% to 4% depending on condition),
indicating that discarded events were quite uniformly distributed
among all subjects. Therefore, in the performed RM-ANOVA
post hoc we used one averaged value for each subject,
frequency band and condition, without any specific weighting
procedure.
ANOVAs were used to establish if a difference existed among
the magnitudes or the latencies of corresponding to the extreme
values. Then, post hoc comparisons were used to establish which
latencies (amplitudes) were lower/larger, i.e., to establish an
order (or hierarchy) between different values. For example, if the
extreme value was larger in one scalp area than in another, we
inferred that intensity of the activation (estimated as the extreme
value) was larger in the first area; if the latency of the extreme
value in one area was lower than in another area, we inferred that
the timing of the first area was earlier.
Predicting Visual Stimuli Using ERSP in Frequency
Bands
To draw more reliable functional conclusions, we applied
a multinomial logistic regression model (Press and Wilson,
1978) which allows to overcome EEG a-specificity by filtering
significant results provided by classical ANOVA followed
by post hoc comparisons through a greater constraint. We
thus searched not only for significant differences between
conditions, but for any feature that allows predicting, based on
EEG spectral features, the structure (walker or scramble) and
kinematic (with or without extrinsic movement) characteristics
of the visual input. Moreover, this allows establishing the
different weight of body shape (walker or scrambled) and
kinematic (centered or in translation) in eliciting motor
resonance.
Traditional statistical analyses rely only on raw comparisons
between distributions across all trials and subjects of different
groups or conditions, merely based on means and standard
deviations: describing properties of a sample using these two
parameters can however lead to miss information related to
subject specificity. To overcome this limitation, we used a
Multinomial Logistic Regression model that better takes into
account inter subject variability (going beyond group level
comparisons and allowing to predict for each single subject
the probability of observing a certain kind of stimulus given
recorded EEG activity in different frequency bands) and does
not assume a normally distributed sample. Moreover, we aimed
to use the model as a predictor of the visual stimuli from
the single subject measured ERSPs. In this way, we went
beyond statistical comparisons between ERSPs and built a
classification model that can decode information extracted
from EEG giving the probability of an observed stimulus
as a continuous function of the measured ERSP. We used
multinomial logistic regressions (MLRs), mainly for two reasons:
first, they are more generalizable, robust and provide better or
comparable performance than linear classification models (Press
and Wilson, 1978). Second, they allow not only distinguishing
different conditions as classical clustering based approaches, but
also allow testing the contribution of each band in decoding
visual stimuli. For consistency with ANOVAs and post hoc
comparisons, the model was fitted considering stimuli as
dependent variable and ERSP values of all subjects as predictor,
considering one value for each subject, ROI and frequency
band.
The first step (Figure 2A) consisted in extracting the model
Mo(using a multinomial logistic regression function) from the
original values V0(ERSP data). Then we calculated its predictive
ability and discriminative performance using Somers’ D index
(Do) that ranges from 0 to 1 (Somers, 1962). Predictive ability of
the model refers here to the inverse process that starts from the
ERSP of each subject and comes back to the corresponding visual
stimulus. Small D values indicate random predictions unable to
discriminate visual conditions; high D values indicate perfect
predictions and condition identification.
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
FIGURE 2 | Schema of the applied procedure for fitting and validating the models. See text for further details. (A) Calculate the coefficients and initial estimation of
Somers’ D for original model fitted to the original ERSP data. (B) For 10,000 bootstraps of original data: calculate coefficients of model fitted to bootstrapped data,
apply it to original data and original to boostrapped data, computing the difference between respective Somers’ D, i.e., Harrell’s optimism. (C) Average Harrell’s
optimism over all bootstraps. (D) Obtain a corrected estimation of Somers’ D by subtracting Harrell’s optimism from the initial estimation.
As shown in Figure 2B, the reproducibility of discriminative
performance was verified by a validation based on bootstrap
technique (Harrell, 2015), which is based on re-estimation of
model parameters on data which were repeatedly randomly
sampled with replacement by the original data set and which
was found to provide better results than classical cross-validation
techniques (Steyerberg et al., 2001). Specifically, we assessed
the robustness of the predictive ability of the model with a
different set of data Vbobtained by bootstrapping original data
(Vofrom which we extracted the model Mb(Figure 2B, left
part). Then we calculated the D index obtained by crossing the
models and the data from which they were extracted: on one
side we applied Moto Vbobtaining Dbo(Figure 2B, middle
part); on the other side we applied Mbto Voobtaining Dob. At
last, as a measure of the reproducibility of model’s performance
(Figure 2B, right part), we calculated the difference between
the two D indices Dboand D0b, i.e., the Harrel’s Optimism
OH(Harrell, 2015). We repeated this step 10,000 times, thus
getting a distribution of OH, i.e., of the differences between Dbo
and Dob.
We then (Figure 2C) calculated the average of this
distribution thus obtaining the Optimism OHwhich is an index
describing the bias present in the initial estimation of the model’s
performance: the closer is the average Optimism to 0, the more
reproducible is the performance of the model.
In the last step (Figure 2D) we subtracted OHfrom the
initial estimation of model’s performance Do, thus obtaining a
corrected and unbiased performance estimation Dc(for further
details, see Supplementary Statistical Analysis and Harrell, 2015).
We checked the potential value of ERSP in decoding body
shape (cw and tw vs. cs and ts) or translation (tw and ts vs.
cw and cs). Moreover, we investigated the respective weight
of shape and translation in classifying all visual stimuli at the
same time.
To sum up, using multinomial logistic regression model
allowed us overcoming the limitations of classical group statistics
(Press and Wilson, 1978). On one hand the models selected the
most robust effects among those showed by classical ANOVA
followed by post hoc comparisons, specifically highlighting the
peculiar role of cortical areas in the integration of stimuli’
form and motion. On the other hand, the models made it
possible to infer from ERSP in different EEG frequency bands
the probability of observing a certain kind of stimulus not
only at a group, but also at the single subject level. Therefore
having stronger (de)synchronizations in specific bands, for
each single subject, increased the probability of observing a
specific stimulus. At last, the selected class of models allowed
us to establish an order between the considered experimental
conditions, providing the strength of the ‘‘jump’’ from each
condition to another one.
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
RESULTS
As shown in Figure 3 (see also Supplementary Figure S1),
all the visual stimuli produced in all the considered ROIs an
early event related synchronization (ERS) and a later Event
Related Desynchronization (ERD). ERS spanned approximately
from around 100 ms and 400 ms after the stimulus onset
and mostly involved the theta band (4–8 Hz). ERD spanned
approximately from 200 ms to the end of the considered epoch
and mostly involved alpha (8–12 Hz), beta 1 (14–20 Hz) and beta
2 (20–32 Hz) frequency bands.
General Trend of ERSP for Shape and
Translation Effects
Based on ANOVA and post hoc comparisons (see Table 1 and
Supplementary Figures S2–S4), theta and beta 1 were modulated
by shape depending on cortical regions (see green rows in
Table 1, Supplementary Figure S2), while alpha and beta bands by
translation (see red rows in Table 1, Supplementary Figure S3).
For what concerns the shape effect, as shown on top-left of
Figure 4, in Ventral ROI, theta showed higher synchronizations
for walker (cw and tw) than for scrambled (cs and ts) shape
(t(12)= 12.33, P= 0.0000005): this was found also when
separately comparing centered walker with centered scrambled
(left, t(12)= 8.73, P= 0.00002) and translating walker with
translating scrambled (right, and t(12)= 11.91, P= 0.0000006).
A similar pattern was found in Inferior-Frontal (top-right
Figure 4,t(12)= 19.42, P= 0.000000002), also when separately
comparing cw with cs (left, t(12)= 11.28, P= 0.000001) and tw
with ts (right, t(12)= 19.56, P= 0.000000002). Conversely, in
Dorsal-Parietal (bottom-left Figure 4), walker produced deeper
beta 1 desynchronizations than scrambled shape (t(12)= 12.33,
P= 0.0000005), also when separately comparing cw with
cs (left, t(12)=−20.02, P= 0.000000001) and tw with ts
(right, t(12)=−25.16, P= 0.0000000001). A similar result was
found in Inferior-Frontal (bottom-right Figure 4, for walker
vs. scrambled t(12)= 32.22 P= 0.000000000006; left for cw
vs. cs(12)=−21.63, P= 0.0000000007; right for tw vs. ts
t(12)=−19.36, P= 0.000000003). For what concerns translation
effect, as shown on bottom-left Figure 4, in Dorsal-Parietal
beta 1 showed deeper desynchronizations for translating than
for centered stimuli (t(12)=−34.07, P= 0.0000000000008,
for ts vs. cs t(12)=−34.71, P= 0.000000000003 and for tw
vs. cw t(12)=−22.49, P= 0.0000000004). A similar result
was found in Inferior-Frontal (for translating vs. centered
t(12)=−38.07, P= 0.0000000000008, for ts vs. cs t(12)=−38.33,
P= 0.0000000000008 and for tw vs. cw t(12)=−16.50,
P= 0.00000002). Translating stimuli produced also deeper
alpha desynchronization in Dorsal-Parietal t(12)=−5.65,
P= 0.001 (See Supplementary Figure S3). Interestingly, both
Dorsal-Parietal (see bottom-left Figure 4,t(12)=−7.32,
P= 0.0001) and Inferior-Frontal beta 1 (see bottom-right
Figure 4,t(12)=−6.89, P= 0.0002) showed a deeper
desynchronization for translating scrambled than for centered
walker.
Comparing the Latencies of Extreme ERSP
Values
For all bands, the latencies of extreme ERSP values followed, as
expected, a caudal-to-rostral order: extreme values occurred in
Ventral, then in Dorsal-Parietal and finally in Inferior-Frontal.
The average peak latencies for different ROIs spanned from
110 ms to 544 ms for theta, from 119 ms to 551 ms for alpha,
from 142 ms to 574 ms for beta 1 and from 154 ms to 586 ms for
beta 2 (see also Supplementary Figures S5, S6).
ERSPs in Specific Frequency Bands Can
Predict the Observed Visual Stimuli
Figures 5–7show separately probability to perceive a particular
visual stimulus (changing in shape and/or in kinematic) as
a function of ERSP pattern, for theta, alpha, beta 1 and
beta 2 bands. The probability of observing a stimulus with
a scrambled shape increases for low theta synchronization in
FIGURE 3 | Spectrograms obtained from superior parietal ROI (SP) for the four visual stimuli. From left to right, cs and cw for centered scrambled and walker; ts and
tw for translating scrambled and walker). Event related spectral perturbations (ERSP) are expressed at different times (x-axis) and frequencies (y-axis) as in dB,
i.e., Log(Power(t)/Power during baseline). Blue indicates event related desynchronizations (ERD), i.e., power decreases; green indicates null variations, while red
indicates event related synchronizations (ERS), i.e., power increases. SP area was selected for representative purposes: in all the ROIs and conditions we found a
similar pattern with an initial ERSs, mainly involving low frequencies, followed by an extended ERD mainly involving high frequencies (see also Supplementary Results).
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
TABLE 1 | Results of ANOVAs on event related spectral perturbations (ERSP) extreme values.
Effect Band F p Ges
theta 12.30 0.0002 0.13
roi (2,24) alpha 88.70 0.000000000008 0.56
beta1 719.10 0.0000000000000000000004 0.94
beta2 310.32 0.000000000000000007 0.85
theta 133.00 0.00000007 0.23
shape (1,12) alpha 0.16 0.7 0.00025
beta1 498.95 0.00000000004 0.42
beta2 2.53 0.1 0.0022
theta 0.59 0.5 0.0023
translation (1,12) alpha 7.60 0.02 0.023
beta1 431.61 0.00000000009 0.57
beta2 0.27 0.6 0.00068
theta 124.00 0.0000000000002 0.15
roi∗shape (2,24) alpha 0.01 1 0.0000069
beta1 232.24 0.0000000000000002 0.30
beta2 1.46 0.3 0.0015
theta 1.06 0.4 0.0014
roi∗translation (2,24) alpha 5.38 0.01 0.0063
beta1 314.99 0.000000000000000006 0.39
beta2 4.70 0.02 0.0087
theta 0.02 0.9 0.000037
shape∗translation (1,12) alpha 0.51 0.5 0.00081
beta1 2.85 0.1 0.0063
beta2 0.80 0.4 0.0020
theta 0.47 0.6 0.00016
roi∗shape∗translation (2,24) alpha 11.60 0.0003 0.0043
beta1 4.45 0.02 0.0054
beta2 3.44 0.04 0.0021
Columns represent respectively: experimental effect, frequency band, F value p values and generalized eta squared index. Degrees of freedom are in parentheses.
Significant effects (P <0.05) are in bold. In green bands are shown which more responding to shape, in red bands are shown which more responding to translation effect,
while in blue bands are shown which responding to their interaction.
Ventral and Inferior-Frontal (Figure 5, first row, red area)
and progressively decreases for greater theta synchronization.
Conversely, the probability of observing a coherent body
structure (walker, in blue) increases with theta synchronization.
Interestingly, theta better discriminates shape in Inferior-Frontal
(χ2
(1)= 45.82, P= 0.00000000001) than in Ventral (χ2
(1)= 22.86,
P= 0.00005) showing a smaller overlap between scrambled
(red area) and walker (blue area). Considering now both
Dorsal-Parietal (χ2
(1)= 16.88, P= 0.00004) and Inferior-Frontal
(χ2
(1)= 22.66, P= 0.000002) ROIs (see third row of Figure 5),
the probability of observing a scrambled shape is highest for low
beta 1 desynchronization and progressively decreases for greater
beta 1 desynchronization. Conversely, the probability to see a
coherent body walker increases with beta 1 desynchronization.
Instead, alpha (second row of Figure 5) and beta 2 (fourth
row of Figure 5) show similar probability for scrambled and
walker shapes for all ERSP values (mostly overlapped red and
blue areas), therefore unable to discriminate scrambled from
walker.
Concerning translation effect (see Figure 6) only beta 1
discriminates translating from centered stimuli (see third row
of Figure 6): deeper beta 1 desynchronizations in Dorsal-
Parietal (χ2
(1)= 42.12, P= 0.00000000009) and Inferior-Frontal
(χ2
(1)= 37.14, P= 0.000000001) decrease the probability of a
centered stimulus and increase the probability of a translating
stimulus. Noticeably, translation was even better discriminated
than shape, as indicated by less overlapped areas in Figure 6
compared to Figure 5.
Figure 7 shows the probability of each observed stimulus
with respect to ERSP pattern and sums up all the previous
observations: beta 1 is the only band discriminating both shape
and translation. Specifically, as shown by third row of Figure 7,
for Dorsal-Parietal (χ2
(1)= 92.79, P<0.0000000000000002)
and Inferior-Frontal (χ2
(1)= 92.19, P<0.0000000000000002)
ROIs, the probability of observing a centered scrambled
is highest for lowest beta 1 desynchronization. Increasing
beta 1 desynchronization increases the probability to
see a centered walker, then a translating scrambled and
finally a translating walker. Interestingly, a greater beta
1 desynchronization is necessary to predict an observed
translating scrambled (blue area), while a centered walker (green
area) can be predicted with a lower beta 1 desynchronization.
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
FIGURE 4 | Effect of the four visual stimuli on the four regions of interest (ROIs) considered for both theta and beta bands. Each subplot represents a ROI. On x-axis
are visual conditions (cs, cw, ts, tw, same abbreviation than in Figure 1); on y-axis are ERSP in dB. Bars correspond to means and standard errors. Horizontal lines
correspond to significant differences (p<0.05). Top: in Ventral (top left), theta is affected by the main effect of shape, synchronizing more for walker than for
scrambled shape in Ventral and in Inferior-Frontal. Bottom: both in Dorsal-Parietal (left) and in Inferior-Frontal (right) beta 1 is affected by shape and translation at the
same time, progressively desynchronizing in centered scrambled, centered walker, translating scrambled and translating walker.
For detailed results of the models, see Supplementary
Tables S1–S3.
Possible Effects of Ocular Movements
In this study, we controlled for possible bias due to potential
eye movements induced by the translation animation. Subjects
were told to anchor their gaze on the red cross; however,
since half of the stimuli translated along the screen, eye
movements could occur during the observation of translating
stimuli, creating spurious effects on measured cortical activity.
Therefore, we linearly detrended ERSP data using the mean
magnitude of eye movements evaluated as the time course of
the absolute value of voltages at electrode AF7 referenced to
AF8 (hEye = abs(AF7 – AF8)). These electrodes have been
previously considered among the most significant forehead
electrodes to detect eye movements (Belkacem et al., 2014)
and used to detect eye artifacts during a covert horizontal
tracking task (Makin et al., 2012). Their position, near the
left and right eye respectively, make their amplitudes deflect
coherently with horizontal eye movements, due to the pointing
direction of the corneo-retinal dipoles of the eyes, similarly
to the signal provided by an horizontal electro-oculogram
(Croft and Barry, 2000). Importantly, results were statistically
unaffected by this correction. Moreover, especially for beta
bands, we found differences between conditions with the same
level of translation (i.e., cw vs. cs and tw vs. ts). Finally,
when considering the mean amplitude and the 95% confident
interval (CI) of ocular moments in all considered conditions (see
Supplementary Figure S7 in Supplementary Material), the CI
overlapped indicating the lack of significant difference between
conditions.
DISCUSSION
The aim of the present study was to examine ERSPs during
the perception of different kind of locomotor stimuli. We
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
FIGURE 5 | Model prediction on stimulus shape (scamble vs. walker) based on ERSP. Rows and columns of subplots correspond respectively to different frequency
bands and ROIs. In each subplot, on x-axis are ERSP in dB; on y-axis are probabilities of observing a scrambled (s) or a walker (w). Stars correspond to significant
shape discrimination (p<0.05). Theta band in Ventral and Inferior-Frontal, as well as beta 1 in Dorso-Parietal and in Inferior-Frontal predict the shape of the visual
stimuli: increasing theta synchronizations and beta1 desynchronization decrease the probability of observing a scrambled stimulus (blue area) and increase the
probability of observing walker stimulus (red area). To obtain the probability of observing a kind of visual stimulus we applied the Predict function of the rms package
of R. Specifically, given the coefficients estimated for the Multinomial Logistic Regression model and a vector representing putative ERSPs, the function calculated for
each value of ERSP (xcoordinate) the corresponding probability (ycoordinate).
wanted to evaluate how the visual perception of different
locomotor patterns, changing in gestalt and kinematic,
modulates cortical activity, from EEG signal recorded in
several areas.
More precisely, we made the specific hypothesis that
forward translation affects EEG activity in the theta
(4–8 Hz), alpha (8–12 Hz), beta 1 (14–20 Hz) and beta 2
(20–32 Hz) frequency bands. In support to the recorded data
a multinomial logistic regressions models (MLRs) showed
that the two variables tested (shape and translation) were
encoded in different ways. Theta ERS increased with the
more familiar shape (i.e., a walker instead of a scrambled)
within Ventral and Inferior-Frontal ROIs, while translation
induced significant attenuation in the power of beta (1 and
2) oscillations in Dorsal-Parietal and Inferior-Frontal
ROIs. Mainly, Dorsal-Parietal and Inferior-Frontal ROIs,
constituting the core of action-observation system, were the
only areas capable to discriminate all the four conditions, as
reflected by beta activities. The different effects of shape and
kinematics variables are successively discussed in the following
sections.
Shape Effect
The result that the Inferior-Frontal theta band was particularly
sensitive to body structure adds to the classical result showing
a main activity located in the temporal area when manipulating
shape factor. Consistent activation recurrently recorded in
the superior temporal gyrus when viewing a coherent gestalt
(CW) compared to scrambled animations (CS) or inverted
walker (Hirai et al., 2003; Jokisch et al., 2005; Peuskens
et al., 2005) supports a privileged role of the STS in body
form encoding. Here, we demonstrate for the first time
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
FIGURE 6 | Model prediction on stimulus kinematic (centered vs. translated) based on ERSP. Rows and columns of subplots correspond respectively to different
frequency bands and ROIs. In each subplot, on x-axis are ERSP in dB; on y-axis are probabilities of observing a centered (c) or a translated (t) stimulus. Stars
correspond to significant models (p<0.05). Beta 1 in Dorso-Parietal and in Inferior-Frontal predicts the kinematic of the stimuli: increasing beta 1 desynchronization
decrease the probability of observing a centered stimulus (green area) and increase the probability of observing a translating stimulus (violet area).
that theta synchronization in ventral and frontal cortices
is able to discriminate a human figure from a random
structure. Further, the model predicted a better discrimination
of the walker from the scrambled stimuli in frontal area,
an unexpected result regarding previous investigations that
mainly attributed to STS a role in shape recognition (Hirai
et al., 2003; Jokisch et al., 2005; Peuskens et al., 2005).
This outcome supports the idea of a significant role of
motor regions in the specific theta band for body figure
encoding.
Because centered scrambled stimulus does not contain
gestalt-like patterns, one may have predicted the lack of
sensorimotor activity for scrambled display. Instead, the
desynchronization of beta band, even if reduced, was still
present for scrambled stimuli. Nevertheless, a CS stimulus
still displays PLD motion compatible with human kinematic,
especially the dot located on the lower limbs. Precisely, the
centered scrambled preserved local motions compatible with
motor representation, namely the 2/3 power law (Lacquaniti
et al., 1983), also present during treadmill locomotion
(Ivanenko et al., 2002). For instance a cloud of dots moving
along elliptical trajectories strictly according to this motor
rule is enough to activate dorsal premotor and supplementary
motor areas (Dayan et al., 2007; Meirovitch et al., 2015).
Similarly, the present local oscillations produce by central
pattern generator can generate ERD modulation during motion
observation. A recent study performed with patient with lesion
to the form visual pathways also agrees with the idea that form
cues are not critical for biological motion perception (Gilaie-
Dotan et al., 2015) and that observer can still discriminate
locomotion direction or identify living being from spatially
scrambled displays that contain solely local biological motion
cues (Sumi, 1984; Pavlova, 1989; Chang and Troje, 2008).
Nonetheless, beta 1 band suppression in fronto-parietal areas
was stronger when the stimulus displayed a coherent body
structure compared to the scrambled version suggesting that
human body geometry improves the sensorimotor integration of
the visual input.
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
FIGURE 7 | Model prediction on both stimulus shape and kinematic based on ERSP. Same legends than as Figures 5,6. Beta1 in Dorso-Parietal and in
Inferior-Frontal predicts both shape and kinematic of the visual stimulus: increasing beta 1 desynchronization predicts the observed stimulus following this order: cs
(corresponding to lowest desynchronization), cw, ts and then tw (corresponding to highest desynchronization). Abbreviations: cs and cw for centered scrambled and
walker respectively; ts and tw for translating scrambled and walker.
Translation Effect
Surprisingly we did not find any translation effect on alpha
oscillations, a frequency band classically considered as a
correlate of the Action-Perception network activity. Indeed, Mu
suppression refers to an attenuation in the alpha frequency
range (8–13 Hz) recorded over sensorimotor cortex both during
action execution and action observation (Cochin et al., 1999;
Babiloni et al., 2002; Pineda, 2005; Hari, 2006; Orgs et al., 2008;
Perry and Bentin, 2009). A recent investigation by Kraskov
et al. (2014) recorded mirror neurons in area F5 of macaque
monkeys while they observed a reach-to grasp action may
however explain such inconsistency. These authors found that
the local field potential activity in F5 neurons recorded in the
beta-frequency range (15–23 Hz) was attenuated during action
observation. More precisely the power in the 15–23 Hz beta
range recorded in area F5 was significantly attenuated in the first
300 ms after movement onset. Moreover, Urgen et al. (2013)
found alpha band suppression for both human and robot action
observation. All together, these experimental evidences indicate
a lack of sensitivity of alpha band for human action perception,
at least for early sensory stages of action visual processing.
In support to this possibility, a recent investigation performed
during observation of human gait suggested that early alpha ERS
contributes to a general clearance of noise or distracting event
in order to selectively update relevant incoming information
and increase involvement of cognitive resources (Zarka et al.,
2014).
Comparison between centered and translated displays showed
greater beta 1 suppression in the dorso-parietal and inferior-
frontal ROIs for translating compared to centered stimuli. This
confirms that sensorimotor areas are precociously involved in
differentiating between movement kinematics (Press et al., 2011;
Di Dio et al., 2013; Meirovitch et al., 2015). This also suggests
that the translating scrambled (a sort of ‘‘blob in motion’’
as reported a posteriori by the participants) brings significant
sensorimotor information in the motor resonance process,
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
probably because local intrinsic movements are interpreted as
the cause of extrinsic translating motion (see Thurman and Lu,
2013).
We found that beta band suppression was more pronounced
toward medial and posterior locations (centro-parietal
locations) than in central or fronto-central electrodes.
Previous investigation showed sensorimotor suppression
during movement preparation and execution (Pfurtscheller
et al., 1997), motor imagery (Pfurtscheller et al., 2006) or action
observation (Muthukumaraswamy and Johnson, 2004; Ulloa
and Pineda, 2007) in a somatotopic way. For instance hand
movement is accompanied by a central-lateral alpha rhythm
suppression, whereas feet movements by centro-medial alpha
rhythm suppression (Pfurtscheller et al., 1997). The present
stronger beta rhythms suppression along medial locations,
i.e., those most likely overlaying somatosensory areas of the feet
and legs (Penfield and Rasmussen, 1950) corroborates the link
between perception and action systems.
It has been recurrently proposed that sensorimotor ERD
reflects the transformation of perceptual representations to
executable actions (Pineda, 2005). The finding that beta ERD
responses in expert dancers viewing dance movements are
stronger than in non dancers observing similar movements
(Orgs et al., 2008) also agrees with the idea of a role of the
motor system in visual perception of action. According to this,
the present significant effect of translation on the recorded
sensorimotor ERD indicates that body translation, in contrast to
treadmill walking, facilitates the matching between visual input
and motor representation. Accordingly, when walking on the
spot optical flow is missing. Moreover, locomotor reactions in
response to the passive displacement of the base of support is
not compatible with integration of the relative motion of the
head, the torso and the eyes, that is crucial to build sensorimotor
(Berthoz et al., 1992; Grasso et al., 1998; Pham et al., 2011)
and navigational components of voluntary locomotion (Plank
et al., 2010; Gramann et al., 2011; Chiu et al., 2012). The
artificial sensory context created by the treadmill locomotion
could degrade the matching between visual input incongruent
with stored representation.
Further, even if the observation of a walker without definite
goal (as walking on the spot) might activate the motor cortex
(Saunier et al., 2013), the coupling between perception and action
system is enhanced when a goal is present (Umiltà et al., 2001;
Rizzolatti and Craighero, 2004). However, inference on walker’s
goal is easier if the observer can make sensorimotor predictions
about the current state of the actor-environment system on
the basis of previous sensory input generated during natural
body translation. A simple illustration of this is the difficulty
one has to perform covert artificial tasks, as locomotion on a
treadmill compared to real forward locomotion, or when sensory
feedback information is lacking (see Courtine and Pozzo, 2004).
Even if locomotion on the spot may elicit a vivid impression
of translational motion (Pavlova et al., 2002; Saygin et al.,
2010; Viviani et al., 2011), the translating visual stimuli would
assign more easily a spatial goal to the perceived motion and
thus would facilitate the recall of specific kinematic details.
Accordingly, Thurman and Lu (2013) found that introducing
extrinsic translational motion congruent with the direction
implied by the intrinsic movements increased the perceived
animacy of spatially scrambled walkers. These authors proposed
that extrinsic motion would convey a clearer impression of
directionality and intentional behavior in the moving agents.
At last, because cyclical locomotor behavior of most
vertebrates relies on similar neural networks (Grillner and
Zangger, 1979; Lacquaniti et al., 1999; Dominici et al., 2011)
and because successful social interactions and survival of species
rely on efficient visual processing of biological motion, one
may predict that forward displacement will represent a prior
knowledge and ‘‘a strong attractor’’ for both human and
animal visual system. A recent behavioral study showing human
newborns preference for the translated locomotion supports the
existence of a privileged neural imprint constraining the visual
perception for horizontal displacements (Bidet-Ildei et al., 2014).
One possible hypothesis to explain the present impact of the
translational component on sensorimotor ERD would be that
artificial ‘‘on the spot treadmill locomotion’’ would mainly rely
on visual decoding mechanism. The occipital cortex and the STS
would ensure the visual recognition of cyclical displacements of
each body part controlled by central pattern generators similar
in several species (Orlovskil et al., 1999) whereas the horizontal
displacement would be necessary to motor resonance, that a
congruent body structure associated to biological kinematic
would improve.
CONCLUSION
Our results suggest that body translation prevails compare to
pictorial information in the process mapping the visual input
onto stored representations of movements. Nevertheless, the
exact manner by which perception of locomotion matches
the motor system remains elusive and requires further
neurophysiological investigations. Indeed, one central question
is how motor resonance initially identified for hand movement
is activated for cyclical lower limb movement mainly encoded in
the spinal cord.
AUTHOR CONTRIBUTIONS
TP and AK conceived and designed the experiments. AK
performed the experiments. CC, AI and SP analyzed the data.
TP, CC, AI, GS and SP wrote the article. TP supervised the whole
project.
FUNDING
CAPES-COFECUB (project no. 819-14) supported this work.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnsys.20
17.00083/full#supplementary-material
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Pozzo et al. Cortical Correlates of a Translating Point-Light Walker
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