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Empathy in Negative and Positive Interpersonal Interactions. What is the Relationship Between Central (EEG, fNIRS) and Peripheral (Autonomic) Neurophysiological Responses?

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Emotional empathy is crucial to understand how we respond to interpersonal positive or negative situations. In the present research, we aim at identifying the neural networks and the autonomic responsiveness underlying the human ability to perceive and empathize with others’ emotions when positive (cooperative) or negative (uncooperative) interactions are observed. A multimethodological approach was adopted to elucidate the reciprocal interplay of autonomic (peripheral) and central (cortical) activities in empathic behavior. Electroencephalography (EEG, frequency band analysis) and hemodynamic (functional Near-Infrared Spectroscopy, fNIRS) activity were all recorded simultaneously with systemic skin conductance response (SCR) and heart rate (HR) measurements as potential biological markers of emotional empathy. Subjects were required to empathize in interpersonal interactions. As shown by fNIRS/EEG measures, negative situations elicited increased brain responses within the right prefrontal cortex (PFC), whereas positive situations elicited greater responses within the left PFC. Therefore, a relevant lateralization effect was induced by the specific valence (mainly for negative conditions) of the emotional interactions. Also, SCR was modulated by positive/negative conditions. Finally, EEG activity (mainly low-frequency theta and delta bands) intrinsically correlated with the cortical hemodynamic responsiveness, and they both predicted autonomic activity. The integrated central and autonomic measures better elucidated the significance of empathic behavior in interpersonal interactions.
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Empathy in Negative
and Positive Interpersonal
Interactions.
What is the Relationship Between
Central (EEG, fNIRS)
and Peripheral (Autonomic)
Neurophysiological Responses?
Michela Balconi 1,2 and Maria Elide Vanutelli 1,2
1 Research Unit in Aective and Social Neuroscience, Catholic University of the Sacred Heart, Milan, Italy
2 Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
interpersonal empathy,
positive/negative interac-
tions, fNIRS, brain oscilla-
tions; autonomic activity
Emotional empathy is crucial to understand how we respond to interpersonal positive or negative
situations. In the present research, we aim at identifying the neural networks and the autonomic
responsiveness underlying the human ability to perceive and empathize with others emotions
when positive (cooperative) or negative (uncooperative) interactions are observed. A multimeth-
odological approach was adopted to elucidate the reciprocal interplay of autonomic (peripheral)
and central (cortical) activities in empathic behavior. Electroencephalography (EEG, frequency
band analysis) and hemodynamic (functional Near-Infrared Spectroscopy, fNIRS) activity were all
recorded simultaneously with systemic skin conductance response (SCR) and heart rate (HR) meas-
urements as potential biological markers of emotional empathy. Subjects were required to empa-
thize in interpersonal interactions. As shown by fNIRS/EEG measures, negative situations elicited
increased brain responses within the right prefrontal cortex (PFC), whereas positive situations elic-
ited greater responses within the left PFC. Therefore, a relevant lateralization eect was induced by
the specic valence (mainly for negative conditions) of the emotional interactions. Also, SCR was
modulated by positive/negative conditions. Finally, EEG activity (mainly low-frequency theta and
delta bands) intrinsically correlated with the cortical hemodynamic responsiveness, and they both
predicted autonomic activity. The integrated central and autonomic measures better elucidated
the signicance of empathic behavior in interpersonal interactions.
Corresponding author: Michela Balconi, Department of Psychology, Catholic
University of the Sacred Heart, Largo Gemelli, 1, 20123, Milan, Italy. Phone:
+39-2-72342586; fax: +30-2-72342280. E-mail: michela.balconi@unicatt.it
E-mail: mariaelide.vanutelli@unicatt.it
AbstrAct
Keywords
DOI 10.5709/acp-0211-0
IntroductIon
e abilities to monitor and regulate emotional processes are parts
of a functional model of empathic behavior (Chauhan, Mathias, &
Critchley, 2008) which includes processes of emotional resonance. ese
are constituted by an aective response to another person, which oen
entails knowing what another person is feeling; sharing that person’s
emotional state; and, in some cases, having the intention to respond
compassionately to another persons distress (Decety & Jackson, 2006;
Hooker, Verosky, Germine, Knight, & D’Esposito, 2008; Ickes, 1997;
Preston & de Waal, 2002). Specically, the emotional behavior, in ad-
dition to the cognitive ability to share representations, constitute the
basic components of empathy (Decety & Svetlova, 2012).
However, limited previous studies explored empathy by using stim-
uli consisting of real interpersonal situations. In fact, previous research
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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mainly focalized on the emotional response to generic emotional cues
and it did not include a specic empathic task (Balconi & Bortolotti,
2014; Balconi, Grippa, & Vanutelli, 2015a; Herrmann et al., 2008), or
it explored facial expressions of emotions (Balconi & Canavesio, 2013;
Herrmann et al., 2008) or empathy in specic domains (such as em-
pathic responses to pain conditions, Avenanti, Sirigu, & Aglioti, 2010;
Rêgo et al., 2015; Wang, Wang, Hu, & Li, 2014). In addition, exiguous
research monitored analytically the eect induced by dierent types of
empathic situations—that is, the positive versus negative valence of the
situations in which the subjects were required to empathize (Balconi &
Bortolotti, 2014; Herrmann et al., 2008; Silani & Singer, 2015). In one
case, the valence eect was explored in an empathic context, although
no specic eect was found in relation to both valence of the situation
(positive or negative) and lateralization of brain activity (le or right)
considered together. In other cases (see, e.g., Tullett, Harmon-Jones,
& Inzlicht, 2012), signicant right lateralized prefrontal patterns have
been found in the case of empathy in negative circumstances. Here,
the authors suggested the mediation of feelings of sadness in the de-
velopment of the empathic mechanisms towards the suering of other,
together with the elicitation of prefrontal asymmetry. Nonetheless,
results have oen been proven to be inconsistent (see, e.g., Morelli &
Lieberman, 2013).
In the current study, we explored in an empathic context, both the
valence of the situation (positive or negative) and the lateralization of
brain activity (le or right). From a neurophysiological point of view,
it has been established that empathic responses inuence both corti-
cal activity (Brüne et al., 2012; Decety & Jackson, 2006; Rameson &
Lieberman, 2009; irioux, Mercier, Blanke, & Berthoz, 2014) and
autonomic physiological responsiveness (Balconi & Bortolotti, 2012b;
Eisenberg et al., 1989; Prguda & Neumann, 2014). Indeed, as suggested
by empathy models, the indubitable vantage of acquiring both auto-
nomic and central activities is the possibility to better elucidate the
reciprocal interplay of these two domains (Decety & Svetlova, 2012;
Preston & de Waal, 2002). e multidimensionality of the construct of
empathy makes it less compatible with single measures. However, so
far central and peripheral measures were scarcely related to each other
in empathy research (Balconi & Bortolotti, 2012b). e current study
addresses this research gap.
Concerning cortical activity correlates of empathy, previous neu-
roimaging studies on the emotional behavior in relation to empathy
have revealed a range of areas activated in response to empathic
interactions, specically, during general emotional processing, the
medial prefrontal cortex (MPFC, Seitz, Nickel, & Azari, 2006; Shamay-
Tsoory & Aharon-Peretz, 2007) and the dorsolateral prefrontal cortex
(DLPFC, Balconi & Bortolotti, 2012a; Balconi, Bortolotti, & Gonzaga,
2011; Brüne et al., 2012; Damasio, Everitt, & Bishop, 1996; Davidson,
2002; Ochsner & Gross, 2005; Rameson, Morelli, & Lieberman, 2012).
Moreover, electroencephalography (EEG) and lesion studies indicated
that the prefrontal cortex (PFC) plays a prominent role in mediat-
ing empathy-related behaviors. Specically, many studies reported a
signicant prefrontal involvement for the disruption of empathic be-
havior in the case of psychopathy (for a review see Pera-Guardiola et
al., 2016). For example, as has been found by Howard and McCullagh
(2007) in conditions involving both a categorization and a vigilance
task with aective stimuli, psychopaths showed signicantly smaller
positive Slow Wave (pSW) amplitudes than healthy controls during the
categorization task, where they were required to discriminate between
living and nonliving stimuli, thus reecting insensitivity to an aective
mismatch between neutral backgrounds and positive pictures. Also,
psychopaths showed a larger prefrontal negative event-related poten-
tial (ERP, N350), the amplitude of which positively correlated with the
behavioral markers of psychopathy. Similarly, Kiehl, Hare, McDonald,
and Brink (1999), by conducting a task comparing semantic and aec-
tive verbal information, reported greater centrofrontal negative-going
wave amplitudes in psychopaths than controls.
However, neither classical functional magnetic resonance imag-
ing (fMRI) nor EEG seem to have completely uncovered in depth the
physiological correlate of the emotional empathic experience, as both
of these methods have their shortcomings: a low temporal resolution
of fMRI and a low spatial resolution of activity below the cortical sur-
face plus an insensitivity to the hemodynamic response of the EEG.
erefore, we applied optical imaging (i.e., near-infrared spectroscopy,
NIRS) as a complementary method in the study of emotions and em-
pathy. NIRS is particularly well-suited for evaluating PFC activity,
which is among the regions involved in emotional processing (i.e., the
frontopolar cortex and the DLPFC, Doi, Nishitani, & Shinohara, 2013).
Due to its high temporal resolution, a spatial resolution exceeding that
of the EEG, and its sensitivity for hemodynamic changes, NIRS seems
well suited to study the temporally evolving representation and inte-
gration among complex, extended neural networks, of the empathic
response. e temporal resolution of NIRS is high enough for meas-
uring event-related hemodynamic responses (Elwell et al., 1993), and
combined EEG/NIRS measurements allow for the complementary
examination of neural as well as hemodynamic aspects of brain activa-
tion (Balconi et al., 2015a; Biallas, Trajkovic, Haensse, Marcar, & Wolf,
2012).
Specically, recent studies with functional NIRS (fNIRS) have
identied the PFC as a key region in the experience and regulation of
emotional responses (Brink et al., 2011; Nomura, Ogawa, & Nomura,
2010; Ogawa & Nomura, 2012). Based on this research, also a sig-
nicant lateralization eect was found, related to the positive versus
negative valence of the activating emotional context. Specically, le
PFC areas were more activated in response to positive or approach
emotions, whereas right PFC areas were more activated in response
to negative or withdrawal emotions (Balconi et al., 2015a; Balconi,
Grippa, & Vanutelli, 2015b; Tullett et al., 2012).
Concerning EEG, frequency band analysis contributed to elucidat-
ing the role of specic cortical areas, mainly with respect to the lat-
eralization eect in emotional empathy processing, too. In fact, brain
oscillations may furnish clear brain correlates of specic empathic
contexts in terms of their valence (positive or negative) and in relation
to cortical lateralization. However, the specic role of brain oscillations
in aective and empathic behavior is partially unknown (Balconi &
Lucchiari, 2006, 2008; Başar, 1999; Vanutelli & Balconi, 2015). Only
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few studies used brain oscillations to study empathy (Gutsell & Inzlicht,
2012; Moore, Gorodnitsky, & Pineda, 2012; Mu, Fan, Mao, & Han,
2008; Tullett et al., 2012). What is known from related investigations
outside empathy research proper is that, regarding the alpha frequency
band, lower-1 alpha desynchronizes in response to a warning stimulus
(Klimesch, Doppelmayr, Russegger, Pachinger, & Schwaiger, 1998).
Overall, changes in alpha power and lateralization eects related to
these changes suggested that a right frontal unbalance is associated with
negative emotions while relatively stronger le frontal activation is as-
sociated with positive emotions (Bekkedal, Rossi, & Panksepp, 2011).
An anterior asymmetry was found in alpha activity that was explained
as a correlate of changes in the aective state (Balconi, Brambilla, &
Falbo, 2009a, 2009b; Davidson, 1998; Dimberg & Petterson, 2000). In
addition, some studies showed that theta band power responds to pro-
longed visual emotional stimulation (Knyazev, 2007; Krause, Enticott,
Zangen, & Fitzgerald, 2012). erefore, the modulation of this fre-
quency band may signicantly contribute to the explanation of arousal
eects on emotional cue comprehension (Bekkedal et al., 2011). In
contrast, exiguous data concern the modulation of delta and beta band
when considering the emotional signicance of a stimulus (Karakaş,
Erzengin, & Başar, 2000). In some cases, it was shown that delta could
be a marker of novelty of the emotional cues and that it can respond to
the exigency of stimulus updating in memory (Fernández et al., 1998).
Finally, as markers of spontaneous and automatic empathic be-
havior, autonomic measures are very important for understanding the
relationship between empathy and autonomic measures. It has been
observed that dierent degrees of empathic experience may aect
autonomic psychophysiological responses (Balconi, Falbo, & Conte,
2012; Prguda & Neumann, 2014). In those cases, participants imagined
(a) a personal experience of fear or anger from their own past, (b) an
equivalent experience from another person as if it were happening to
them, or (c) a non-emotional experience from their own past (Ruby &
Decety, 2004). Autonomic dierences were found between these con-
ditions. Nevertheless, in this approach, only imagined (and not real)
empathic situations were proposed and this fact may have introduced
important variations in the subjective responses.
Systemic blood pressure (BP), heart rate (HR), and skin conduct-
ance response (SCR) were considered as potential biological markers
of emotions in empathic behavior, and recorded simultaneously with
EEG and NIRS (Tupak et al., 2014). Among the other dependent
variables, SCR provides a useful measure of limbic function (Furmark,
Fischer, Wik, Larsson, & Fredrikson, 1997; Lang, Davis, & Öhman,
2000). It is also a signicant measure of arousal modulations, as has
been demonstrated previously (Balconi et al., 2009a; Balconi & Pozzoli,
2008; Bradley & Lang, 2000).
Also several NIRS studies underlined the association between PFC
activation and autonomic responses to emotional stimulation (see,
e.g., Tanida, Katsuyama, & Sakatani, 2007). Likewise, during view-
ing of trauma-related video clips, increased hemodynamic activity
(oxy-hemoglobin, O2Hb) has been found to be positively correlated
with heart rate change (Matsuo et al., 2003). Furthermore, Moghimi,
Kushki, Guerguerian, and Chau (2012) have linked the steepness of the
O2Hb peak to subjectively reported arousal levels, which is a widely
accepted indicator of autonomic system activation (Matsuo et al., 2003;
Roos, Robertson, Lochner, Vythilingum, & Stein, 2011). Moreover,
a signicant correlation between ventromedial prefrontal cortex
(vmPFC) activation and SCR was found, based on stimulus content
(its threatening value, Tupak et al., 2014).
ese previous studies supported the view that the prefrontal ar-
eas regulate autonomic reactions or somatic markers associated with
emotional conditions. However, such research lacked a detailed and
integrated analysis of all three levels (hemodynamic, electrophysi-
ological, and autonomic) involved in emotional processing during
empathic interactions. Only one previous study directly compared he-
modynamic, EEG, and autonomic measures, but, rstly, it focused on
generic emotional cues and, secondly, it was not on empathy (Balconi
et al., 2015a). In contrast, the present research rst of all clearly focused
on empathic behavior by asking participants “to put themselves in the
shoes of another person and try to feel what this person is feeling” (as
reported in the procedural instructions). Secondly, the participants
were required to observe situations where subjects performed spe-
cic interactions and not simply a generic emotional display (such as
emotional pictures or faces). erefore, in comparison with previous
studies, a highly empathic task was included.
In conclusion, in light of current knowledge on empathy, we
propose an integration of cortical (EEG and fNIRS) measures with
autonomic psychophysiological measures, as they have been shown
to indicate the presence of emotional tuning between subjects. In the
present study, EEG (frequency band analysis), systemic SCR and HR
were all recorded simultaneously with fNIRS measurements as poten-
tial biological markers of emotional responses to empathic situations
during a natural and interpersonal situation in which positive versus
negative contexts were represented.
A consistent prefrontal activation was expected, as indicated by
a hemodynamic modulation and brain oscillations. Both fNIRS and
brain oscillations were supposed to elicit a signicant PFC response
to emotional interpersonal conditions. Specically, as a correlate of
an empathic response, we rstly expected a higher synchronous brain
activity in low-frequency bands (delta and theta) and, in contrast, a
desynchronization of the alpha band. Moreover, based on valence and
lateralization eects of emotions (Balconi & Mazza, 2010; Russell,
2003), a signicant and consistent higher prefrontal le activation
was expected for positive emotional interactions, whereas a consistent
higher prefrontal right activation was expected in response to negative
interactions. Secondly, we expected that electrodermal activity (SCR)
and HR could be signicant measures of implicit reactivity to emo-
tional cues; they should consistently vary with emotional valence, with
larger responses (increased SCR and HR) elicited in either negative or
positive or both emotional conditions compared to neutral situations
(Balconi et al., 2009a; Bradley & Lang, 2000; Lang, Greenwald, Bradley,
& Hamm, 1993).
irdly, we expected a high coherence between the three measures
(fNIRS, EEG, and autonomic variations). Signicant correlations
were hypothesized based on situational and interpersonal signicance
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(valence) of the empathic context. Indeed, we expected a relevant
modulation within the le and right PFC for hemodynamic activity in
concomitance with electrophysiological and autonomic response.
MaterIals and Methods
Subjects
Twenty-two subjects, 12 females and 10 males (Mage = 24.5 years; SD
= 3.53; age range from 20 to 33 years) participated in the experiment.
All subjects were right-handed (Edinburgh Handedness Inventory,
Oldeld, 1971), with normal or corrected-to-normal visual acuity.
Exclusion criteria were neurological or psychiatric pathologies of the
subjects or their close family members. Specically, they did not show
decits related to depression (Beck Depression Inventory II, BDI, Beck,
Steer, & Brown, 1996) and to anxiety (State-Trait Anxiety Inventory,
STAI, Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1970): Exclusion
criterion of the BDI Inventory was 19 points or lower (M = 8.95; SD =
0.46; score range from 2 to 12 points); for the STAI 39 points or lower (M
= 28.45; SD = 1.03; score range from 27 to 45 points). No payment was
provided for participation. Participants gave informed written consent
and the research was approved by the Ethical Committee institution
where the work was carried out. e experiment was conducted in ac-
cordance with the Declaration of Helsinki, and all the procedures were
carried out with adequate understanding by the subjects. e Research
Consent Form was submitted before participation in the study.
Stimuli
Subjects were required to view aective images depicting real inter-
personal situations which represented two people who interacted in
a common and familiar situation (e.g., at home, in a workplace, or
on a journey). Colored images (16 cm in width and 10 cm in height)
representing positive, negative, and neutral interactions were selected.
Twenty-four pictures were used for each type of interaction. Positive
interactions represented positive and emotionally comfortable situa-
tions (such as a handshake between two people); negative interactions
represented negative and emotionally uncomfortable situations (such
as a quarrel between two people); neutral pictures represented interac-
tions without a specic emotional valence (such as two people sitting
on a couch, see Figure 1). All images were similar in their perceptual
features (i.e., their luminance, complexity, i.e., number of details in the
scene, and characters’ genders: half of the actors were male and half
were female).
In order to validate the image dataset, a pre-experimental procedure
was adopted. Each depicted scene was evaluated by four judges on
valence and arousal dimensions, using the Self-Assessment Manikin
Scale (SAM) with a ve-point Likert scale (Bradley & Lang, 1994,
2007). Separately for each condition (positive, negative, and neutral),
ratings were averaged across all images presented. As shown by sta-
tistical analysis (two distinct repeated-measures analyses of variance
[ANOVAs] applied to valence and arousal), images rstly diered in
terms of valence (positive: M = 4.56, SD = 0.34; negative: M = 1.33,
SD = 0.26; neutral: M = 2.75, SD = 0.37)—positive interactions were
more positively rated than the other two categories, negative interac-
tions were more negatively rated than the other two categories, neutral
images were rated to be of intermediate valence between the other two
categories (for all signicant contrast comparisons, p ≤ .01). Secondly,
with respect to arousal, the positive and negative interactions (posi-
tive: M = 4.23, SD = 0.24; negative: M = 4.72, SD = 0.25; neutral: M =
1.77, SD = 0.31) were rated as more arousing than the neutral inter-
actions (for all signicant contrast comparisons, p.01). In contrast,
no signicant dierences were revealed between positive and negative
interactions (p = .32).
Procedure
Subjects were seated in a dimly lit room, facing an LCD computer moni-
tor that was placed at about 50 cm from the subject. e stimuli were
presented using E-Prime 2.0 soware (Psychology Soware Tools, Inc)
running on a laptop PC with a 15 in. screen (Acer TravelMate 250P).
Images were presented in a random order in the center of the screen for
6 s, with an inter-stimulus interval of 8 s (see Figure 2).
Participants were required to view each stimulus during fNIRS/
EEG measures recording, and they were asked to attend to the inter-
personal situations during the entire time of exposition, focusing on
the emotional conditions which characterized the represented human
actors. Moreover, they were required to empathize with the two per-
sons interacting with each other (“Try to put yourself into the shoes of
the persons and to experience their feelings in this situation”). In order
to facilitate empathizing with the depicted actors, the two actors were
of about the same age as the experimental subjects.
Before scene presentations, a 2 min resting period was registered at
the beginning of the experiment. Next, a familiarization phase followed,
in which subjects saw and evaluated a set of images (one of each emo-
Figure 1.
Some examples of positive, neutral, and negative interactions.
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tional category), dierent from the images used in the experimental
phase. Aer the experimental phase, subjects were required to rate the
pictures with the SAM on valence and arousal dimensions. As shown
by statistical analysis (two repeated-measures ANOVAs for the valence
and arousal measures), images diered in terms of valence (with more
positive evaluations of positive than negative and neutral interactions,
with more negative evaluations of negative than positive and neutral
interactions, and with intermediate evaluations of neutral compared to
positive and neutral interactions) and arousal (with signicant dier-
ences between positive and neutral interactions, and between negative
and neutral interactions, showing a higher arousal rating for positive
and negative interactions). For all paired comparisons signicance was
assumed for an alpha level of .01 or lower.
A specic questionnaire was used in order to assess the subjects
self-rating on key aspects of the subjective evaluation of the empathic
task. e questionnaire was used in a de-brieng post-experimental
section (a ve-point Likert scale for each item, from low to high). e
aspects examined included the degree of experienced empathy (“How
much did you put yourself into the shoes of the actors and felt what
they felt in the depicted situation?”), personal emotional involvement
(“How much did you feel emotionally involved in the situation?”),
semantic attribution of the situation (positive, negative, and neutral,
“How did you classify the interpersonal situation?”), and emotional
signicance (high or low, “Did you perceive an emotional signicance
of the situation?”). All subjects experienced a high sense of empathy
(M = 4.11, SD = 0.26), were emotionally engaged in the task (M = 4.23,
SD = 0.34), and were able to attribute a coherent emotional value to the
pictures (for coherent semantic attribution of valence: M = 4.09, SD =
0.32; for emotional signicance: M = 4.88, SD = 0.45).
EEG: Frequency Band Analysis
A 16-channel portable EEG-System (V-AMP, Brain Products) was
used for data acquisition. An NIRS-EEG compatible ElectroCap with
Ag/AgCl electrodes was used to record EEG from active scalp sites
referred to earlobe (10/5 system of electrode placement). EEG activ-
ity was recorded from the following positions: AFF3, AFF4, Fz, AFp1,
AFp2, C3, C4, Cz, P3, P4, Pz, T7, T8, O1, and O2 (for examples, see
Figure 3). e cap was xed with a chin strap to prevent shiing during
the task. Additionally, one EOG electrode was placed on the lower side
of the le eye.
Data preprocessing has been conducted with BrainVision Analyzer
2 (Brainproducts). e data were recorded using a sampling rate of
500 Hz, with a notch lter of 50 Hz. e impedance of recording elec-
trodes was monitored for each subject prior to data collection, and it
was always kept below 5 kΩ (rejected epochs 4%). Blinks were also
visually monitored. Ocular artefacts (eye movements and blinks) were
corrected using an eye-movement correction algorithm that employs
a regression analysis in combination with artefact averaging. Aer
EOG correction and visual inspection, only artefact-free trials (not less
than 22) were considered. To obtain a signal proportional to the power
of the EEG frequency band, the ltered signal samples were squared
and successively log-transformed (Pfurtscheller, 1992). Successively,
the data were epoched, using a time window of 1 s and an average
absolute power value was calculated for each electrode and condi-
tion. Artefact-free data have been used to compute power spectra for
relevant EEG frequency bands by the Fast Fourier transform method
(with a Hamming window of a length of 10%) that was used to obtain
estimates of spectral power (μV2) in 1 Hz wide frequency bins for each
electrode site. Spectral power values were averaged across all epochs
and were then transformed to power density values for dierent fre-
quency bands. An average of the pre-experimental absolute power (2
min) was used to determine the individual power without stimulation.
From this reference power value, individual power changes during
stimulus viewing were determined as the relative stimulus-related
decreases or increases. Digital EEG data (from all 15 active channels)
Figure 2.
Experimental setting with fNIRS, EEG, and autonomic mea-
sures.
Figure 3.
Locations of the prefrontal measurement channels of EEG
and fNIRS. For fNIRS, emitter-detector distance was 30
mm for contiguous optodes and near-infrared light of two
wavelengths (760 and 850 nm) were used. NIRS optodes
were attached to the subject’s head using a NIRS-EEG com-
patible cup, with respect to the international 10/5 system.
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soware (Biopac Systems Inc) according to the manufacturer guide-
lines. ECG was converted to HR in number of beats per minute. e
signal was low-pass ltered at 35 Hz and highpass ltered at 0.05 Hz for
motor and ocular artefacts. For SCR, before attaching the electrodes,
the skin was cleaned with alcohol and slightly abraded. e electrodes
for SCR were attached to the distal phalanges of the rst and second
nger of the le hand. SCL was recorded using two Ag/AgCl electrodes
and an isotonic gel. e signal was low-pass ltered at 10 Hz for mo-
tor, ocular, and biological artefacts. Ocular artefacts were then checked
with a visual inspection to eventually eliminate specic elements. Trials
with artefacts (2%) were excluded from the analysis. SCR elicited by
each stimulus was registered continuously with a constant voltage. It
was dened as the largest increase in conductance during emotional
image presentation, with a cut-o of at least 0.3 μS in amplitude with
respect to baseline (pre-stimulus) mean values. Baseline values were
scored during the 2 min prior to task onset.
results
e following set of analyses was performed on the data with SPSS
soware for Windows (version 18): A rst set of repeated-measures
ANOVAs was applied to each frequency band, a second set of analyses
was applied to hemodynamic d values, and a third set of ANOVAs was
applied to autonomic (HR, SCR) measures. Finally, stepwise multiple
regression and correlational analyses (Pearson correlations) were
applied to compare the three levels (band oscillations, d values, and
autonomic measures). Bonferroni correction was inserted for multiple
comparisons.
EEG Frequency Band Analysis
Frequency band data were entered into three-ways repeated-measures
ANOVAs, with independent variables of Lateralization (two sides: le
channels and right channels), Valence (3), and Localization (three sites:
frontal, AFF3/AFF4 and AFp1/AFp2; temporo-central, C3/C4 and T7/
T8; and parietal, P3/P4). Type I errors associated with inhomogene-
ity of variances were controlled by decreasing the degrees of freedom
using Greenhouse-Geiser epsilon. Post hoc comparisons were suc-
cessively applied to the data (contrast analyses for repeated-measures
ANOVA).
As shown by ANOVA, delta was modulated by valence, F(2, 42) =
6.16, p = .001, η2 = .27, and Lateralization × Valence, F(2, 42) = 7.23,
p = .001, η2 = .29. No other main eect or interaction was statistically
signicant. Delta increased for negative and positive relative to neutral
stimuli. Moreover, it increased for negative more than for positive in-
teractions (for all comparisons, p ≤ .001). In addition, concerning the
simple eects for the two-way interaction, signicant dierences were
observed between positive and negative interactions, with increased
delta within the right hemisphere for negative, F(2, 42) = 5.79, p = .001,
η2 = .24, and within the le hemisphere for positive, F(2, 42) = 6.54, p =
.001, η2 = .26, interactions (see Figure 4).
For theta, the ANOVA revealed a signicant main eect of valence,
F(1, 13) = 6.56, p = .001, η2 = .33, and a signicant Lateralization ×
were band-pass ltered in the following frequency bands: delta (0-3),
theta (4-7), alpha (8-12), and beta (13-20). During data reduction, a
bandpass lter was applied in the 0.01-50 Hz frequency band.
fNIRS
fNIRS measurements were conducted with the NIRScout System
(NIRx Medical Technologies, LLC) using a six-channel array of optodes
(four light sources/emitters and four detectors) covering the prefron-
tal area. Emitters were placed at AF3-AF4 and F5-F6 while detectors
were placed at AFF1-AFF2 and F3-F4 (see Figure 3). Emitter-detector
distance was 30 mm for contiguous optodes and a near-infrared light
of two wavelengths (760 and 850 nm) was used. NIRS optodes were
attached to the subject’s head using a NIRS-EEG compatible cup, with
respect to the international 10/5 system.
With NIRStar Acquisition Soware (NIRx Medical Technologies
LLC), changes in the concentration of O2Hb and deoxygenated he-
moglobin (HHb) were recorded from a 2 min starting baseline. Signals
obtained from the six NIRS channels were measured with a sampling
rate of 6.25 Hz and analyzed and transformed according to their wave-
length and location, resulting in values for the changes in the concen-
tration of O2Hb and HHb for each channel. Haemoglobin quantity is
scaled in mM*mm, implying that all concentration changes depended
on the path length of the NIR light in the brain.
With Nirslab Soware (v2014.05; NIRx Medical Technologies
LLC) the raw data of O2Hb and HHb from individual channels were
digitally band-pass ltered at 0.010.3 Hz. Successively, the mean con-
centration of each channel within a subject was calculated by averag-
ing data across the trials for 6 s from trial onset. Based on the mean
concentrations in the time series, we calculated the eect size in every
condition for each channel within a subject. e eect sizes (Cohen’s
d) were calculated as the dierences of the means of the baseline and
trial divided by the SD of the baseline, d = (M1M2)/SD1. Accordingly,
M1 and M2 are the mean concentration values during the baseline and
trial, and SD1 the SD of the baseline. e mean concentration value
of the 2 s immediately before the trial was used as a baseline. en,
the eect sizes obtained from the six channels were averaged in order
to increase the signal-to-noise ratio. Although the raw data of NIRS
were originally relative values and could not be averaged directly across
subjects or channels, normalized data, such as the eect sizes, could be
averaged regardless of the units of measurement (Matsuda & Hiraki,
2006; Schroeter, Zysset, Kruggel, & Von Cramon, 2003; Shimada &
Hiraki, 2006). In fact, the eect size is not aected by dierential path-
length factor (DPF, Schroeter et al., 2003). Instead of a block design, a
continuous trial design was used in the present research.
Autonomic Measures
Biopac MP 150 system (Biopac Systems Inc) was used to record the
autonomic activity. Electrocardiography (ECG) was recorded continu-
ously in lead 1 from two electrodes attached to the lower wrist, with
the positive pole on the le arm and the negative pole on the right arm.
One more reference electrode was placed over the le ankle. e ECG
signal was sampled at 1,000 Hz with the Biopac Acknowledge 3.7.1
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Valence interaction, F(2, 42) = 7.76, p = .001, η2 = .29. No other eect
or interaction was statistically signicant. eta increased in response
to negative relative to positive stimuli. Concerning the two-way in-
teraction, signicant dierences were observed between positive and
negative interactions, with increased delta within the right hemisphere
for negative, F(2, 42) = 6.09, p = .001, η2 = .26, and within the le hemi-
sphere for positive, F(2, 42) = 6.43, p = .001, η2 = .26, interactions (see
Figure 4).
Concerning the alpha band, the valence eect was statistically
signicant, F(2, 42) = 7.15, p = .001, η2 = .30. A generally decreased
alpha (increased brain activity) was observed for positive and negative
interactions. Finally, concerning beta, no signicant eects were found
(see Figure 4).
fNIRS
e statistical analysis was applied to d—the dependent measure for
O2Hb and HHb-concentrations. e analysis of HHb did not reveal
any signicant eects, and for this reason we report results for O2Hb
values only. e lack of any signicant eect for HHb may be due to
the increase in O2Hb that is larger than the decrease in HHb (Wolf
et al., 2002). D was subjected to a repeated-measures ANOVA, with
Lateralization (2) and Valence (3) as independent variables. e data
were averaged over le (Channel 1: AF3F3; Channel 2: AF3AFF1;
Channel 3: F5F3) and right (Channel 4: AF4F4; Channel 5: AF4
AFF2; Channel 6: F6–F4) channels.
As shown by the ANOVA, the eect of valence, F(2, 42) = 9.13, p
< .001, η2 = .41, and a Lateralization × Valence interaction, F(2, 42) =
8.13, p < .001, η2 = .32, were signicant. No other eect or interaction
was statistically signicant. As shown by paired comparisons, nega-
tive and positive stimuli revealed increased d values in comparison to
neutral interactions, F(1, 21) = 6.70, p < .001, η2 = .31, and F(1, 21) =
6.62, p < .001, η2 = .31, respectively. Moreover, negative interactions
showed higher d values for negative than positive interactions, F(1,
21) = 7.50, p < .001, η2 = .32. Regarding the interaction eect, positive
stimuli showed an increased brain activity within the le compared to
the right hemisphere, F(1, 21) = 8.03, p < .001, η2 = .34, whereas nega-
tive stimuli showed an increased activity within the right compared to
the le hemisphere, F(1, 21) = 8.88, p < .001, η2 = .35 (see Figure 5). In
contrast, no signicant dierences were found for neutral interactions
between le and right side, F(1, 21) = 1.16, p = .32, η2 = .16.
Autonomic Measures
HR and SCR measures were analyzed with two separate repeated-
measures ANOVAs, both with Valence (3) as an independent factor.
For SCR, the valence main eect was signicant, F(2, 41) = 8.88, p <
.001, η2 = .32: Negative stimuli induced an increased SCR relative to
positive and neutral conditions, F(1, 22) = 8.11, p < .001, η2 = .31, and
F(1, 22) = 6.90, p < 0.001, η2 = .28, respectively. Moreover, the positive
condition showed increased SCR values compared to neutral, F(1, 22)
= 7.13, p < .001, η2 = .30. For HR, no eect or interaction was sig-
Figure 4.
Frequency band power in response to valence and lateralization (M and SD reported; asterisks mark statistical signicance,
with p ≤ .05).
Figure 5.
Hemodynamic states (O2Hb relative concentrations) as a
function of size and valence (obtained with Nirslab Soft-
ware, Data viewer section, Map tool). In response to nega-
tive stimuli, the concentration of O2Hb was higher for the
right than the left side. Moreover, the concentration of
O2Hb was higher in response to negative more than posi-
tive stimuli within the right side.
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nicant (see Figure 6). No other eect or interaction was statistically
signicant.
Correlational Analyses
Pearsons correlation analyses (across-subject correlations) were carried
out on each frequency band power and d values. Correlations were cal-
culated separately for each valence (positive/negative/neutral interac-
tions) within the le and right prefrontal area. Extensive analyses were
also applied to all the EEG and prefrontal fNIRS channels. However,
since no signicant eect was found in the posterior EEG channels, for
the nal analysis, we opted to compare the EEG and fNIRS data only
for the prefrontal area.
ere was a signicant positive correlation between d and theta (r
= .491, p < .02, Variance Ination factor [VIF] = .460) and between
d and delta (r = .513, p < .01, VIF = .458) bands in response to nega-
tive stimuli within the right hemisphere. Moreover, signicant positive
correlations between d and theta (r = .561, p < .01, VIF = .511) and
between d and delta (r = .544, p < .01, VIF = .493) bands in response to
positive stimuli were observed within the le hemisphere. Finally, the
alpha band showed a negative correlation with d (r = -.511, p < .01, VIF
= .469) within the right hemisphere in response to negative stimuli.
at is, cortical activation (alpha decreasing) was revealed within the
right hemisphere for negative interactions (see Figure 7). No other cor-
relations were statistically signicant.
Regression Analysis
Two stepwise multiple regression analyses were performed for positive
and negative interactions. Predictor variables were Hemodynamic (d
Figure 6.
Mean values for SCR (up) and HR (down), with a signicant
eect shown for SCR based on positive versus negative va-
lence. (M and SD reported. Asterisks mark statistical signi-
cance, with p ≤ .05.)
Figure 7.
Scatter plots of correlational analyses between hemo-
dynamic and EEG measures as a function of valence and
lateralization. Each diamond corresponds to a single par-
ticipant.
d values
d values
d values
d values
d values
d values
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values) and EEG measurements, while the predicted variable was the
Autonomic Modulation (separately for SCR and HR). In Table 1, we re-
port the cumulative multiple correlations between predictors and pre-
dicted variables (R), cumulative proportion of explained variance (R2),
and the regression weights (β) for the regression equation at each step
of the multivariate analysis. As shown in Table 1, delta, theta, and alpha
frequency bands and d values predicted the SCR variations. Increased
delta and theta and decreased alpha, as well as increased d were related
to increased SCR in case of positive and negative conditions. Similarly,
increased delta and theta and decreased alpha band powers, as well
increased d were related to increased HR in response to positive and
negative interactions.
dIscussIon
e present research elucidated some main points to better com-
prehend the empathic response to interpersonal interactions. Our
multilevel analysis, which included three measures (hemodynamic,
electrophysiological, and autonomic), allowed us to investigate and
support the signicant key role of some specic brain areas—that is,
the PFC, and some autonomic responses in empathic emotional be-
havior. Firstly, we found that the PFC was mainly recruited when the
subjects empathized with actors in positive or negative interactions.
Secondly, a lateralization eect was also revealed, as shown by both
hemodynamic and brain oscillation modulations. irdly, the present
data supported a signicant valence eect, with increased PFC re-
sponses in the case of positive and negative interactions. Autonomic
activity (SCR) was similarly responsive to the valence of the interac-
tions, even if indistinctively for positive and negative pictures. Finally,
a systematic combined modulation was detected for fNIRS and EEG
measures, where both have a signicant predictive role for autonomic
(SCR and HR) activity, since, in the regression, both fNIRS and EEG
were predictors of SCR and HR.
e rst eect we observed was related to the PFC, which was shown
to be responsive to empathic situations where an emotional behavior
is involved. O2Hb increased within the PFC. ese results were in line
with other results. For example, some recent studies revealed a signi-
cant contribution of the right DLPFC in response to positive and nega-
tive emotional faces. Along similar lines, right prefrontal stimulation
(high frequency repetitive transcranial magnetic stimulation, rTMS)
resulted in impaired disengagement from angry faces, with a signi-
cant DLPFC eect on attentional processing of emotional information
(De Raedt et al., 2010). It was also found that, when activated, the le
DLPFC improved processing related to positive emotions and reduced
negative emotional processing (Baeken et al., 2010). Neuroimaging
studies have provided support for a functionally interactive network
tAble 1.
Stepwise Multiple Regressions
a)
positive negative
Predictor d values delta theta alpha beta d values delta theta alpha beta
Model 1 2 3 4 5 1 2 3 4 5
SCR
R0.37 0.58 0.77 0.91 0.94 0.38 0.52 0.74 0.96 0.99
R20.13 0.33 0.59 0.82 0.85 0.14 0.26 0.48 0.85 0.89
β 0.34 0.27 0.27 −0.31 0.25 0.20 0.23 0.32 −0.30 0.20
std error 0.18 0.20 0.22 0.18 0.23 0.15 0.20 0.11 0.21 0.26
t2.12*1.84*1.63*1.75* 0.98 2.35* 1.87*1.99*2.01* 0.75
b)
positive negative
Predictor d values delta theta alpha beta d values delta theta alpha beta
Model 1 2 3 4 5 1 2 3 4 5
HR
R0.39 0.58 0.72 0.95 0.98 0.27 0.50 0.74 0.96 0.99
R20.15 0.26 0.50 0.86 0.89 0.09 0.25 0.49 0.86 0.88
β 0.29 0.22 0.31 −0.22 0.18 0.23 0.21 0.16 −0.28 0.27
std error 0.25 0.20 0.26 0.20 0.21 0.20 0.25 0.24 0.20 0.17
t1.98*1.83*1.78*1.98* 0.77 2.01*1.94*2.04*2.08* 0.83
Note. (a) d, delta, theta, alpha and beta as predictor variables, SCR as predicted variable, and (b) d, delta, theta, alpha and beta as predictor variables, HR as
predicted variable
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of cortico-limbic pathways that play a central role in the top-down
regulation of emotions. Indeed, a large number of studies suggested
that the PFC activates emotion regulation by inhibiting the amygdala
(Siegle, ompson, Carter, Steinhauer, & ase, 2007).
Results from previous fMRI studies indicated that the PFC is not
only involved in emotion induction but also in emotion regulation.
Moreover, by investigating the neural correlates of emotion regulation
processes, NIRS studies underlined the role of the PFC. For example,
the instruction to decrease the eect of negative stimuli by reinterpret-
ing the displayed situation led to an increased PFC activation and a
reduced activation of the amygdala (Banks, Eddy, Angstadt, Nathan,
& Luan Phan, 2007; Eippert et al., 2007; Kalisch et al., 2005; Lévesque
et al., 2003; Ochsner, Bunge, Gross, & Gabrieli, 2002; Ochsner et al.,
2004; Phan et al., 2005). Herrmann et al. (2002) used NIRS to compare
general emotional cue processing with processing of more specic
facial patterns. ey found increased medial PFC activity during an
emotion induction paradigm which generated emotions by instructing
participants to try to feel like a person whose facial expression was dis-
played. In accordance with these results, the instruction to remember
emotional events leads to an increase of activation in the prefrontal
brain areas (Ohtani, Matsuo, Kasai, Kato, & Kato, 2005). Furthermore,
patients with post-traumatic stress disorder show increased PFC acti-
vation to disorder-related stimuli (Matsuo et al., 2003). In some cases,
the social eect of emotional face processing was considered (Nomura
et al., 2010). e study of Nomura et al. (2010) employed face stimuli
and perspective-taking, and NIRS was used to show the individual dif-
ferences in empathy that underlie the perspective taking function and
the role of the right ventrolateral PFC.
Although all these studies indicated an involvement of the PFC, for
the rst time in the present research, the empathic emotional response
to interactional aective contexts was monitored. In addition, negative
versus positive situations were systematically evaluated. Indeed, we
revealed that emotional valence aected both hemodynamic activity
and brain oscillations, with a more relevant impact for the negative
interactions. In addition, this cortical activity was shown to be later-
alized within the right hemisphere in response to negative situations
and within the le hemisphere in response to positive stimuli. is
result clearly supports the view of a lateralization eect in empathic
responses to contexts of dierent valences when an empathic task was
administered.
Some specic brain oscillations (mainly delta and theta modula-
tion) conrmed this lateralized activation eect of stimulus valence:
Low-frequency oscillations were mainly synchronized within the right
and le side in response to negative and positive emotional interac-
tions, respectively. e increased values of delta and theta that we
found in response to positive and mainly negative interactions may
support the hypothesis that delta plays a main role in regulating the
attentional behavior in the case of salient stimuli. In line with this hy-
pothesis, in previous studies, delta band was related to the relevance of
the material being processed and to the degree of attention involved in
visual stimuli processing (Balconi & Pozzoli, 2005; Keil et al., 2001).
erefore, in our case, brain responses to negative interactions could
suggest that subjects could have perceived them as the most relevant
emotional context, since they have a potentially threatening value.
It should be noted that in the present research we did not nd a
signicant and specic eect for higher frequency bands (beta). is is
in contrast with previous research (Balconi & Pozzoli, 2009). ese dif-
ferent results may be due to the adoption of dierent methodological
approaches (e.g., task dierences) and to dierent range limits used for
the computation of the oscillations.
A similar prole was observed for O2Hb measure, and the present
results thus conrmed the homogeneity of the emotional empathic
behavior in response to interpersonal situations by considering the
hemodynamic level of analysis. ese results were also supported by
a consistent cortical lateralization eect for O2Hb, in combination
with a specic prefrontal eect. A general le/right positive/negative
distinction was observed in the subjects. at is, the subjects showed
a distinct cortical lateralized response based on the emotional valence
of the interactions: more le localized for positive situations; more
right localized for negative situations. Indeed, increased brain activity
was found to be based on stimulus valence. It has to be noted though
that, compared with some previous research on neuroimaging and
NIRS (Herrmann et al., 2008; Hoshi, 2009), we found that valence was
relevant for hemispheric lateralization during processing of emotional
cues. However, some dierences were found based on valence (with
increased activation for negative situations), as previously shown by
EEG analysis. To account for the dierences, we may assume that
the most salient contexts to be processed are related to negative in-
terpersonal interactions. Due to this higher degree of salience, higher
cortical activation could have been evoked by more negative interac-
tions. In general, it might be concluded that the fNIRS/EEG measures
showed a broad sensitivity to the motivational signicance of social
interactions, varying as a function of the degree of negativity/positivity
attributed to the emotional situations. A general right/negative asso-
ciation was observed in the subjects, and it was mainly supported by
the right hemisphere—that is, negative, aversive interactions showed a
more consistent lateralized brain activation when compared to other
emotional situations (i.e., positive situations). Previous research under-
lined that human emotions are organized by two cortically lateralized
systems: the appetitive and defensive motivation systems, presum-
ably evolved from primitive approach and withdrawal tendencies
(Balconi & Bortolotti, 2014; Davidson, 1995; Davidson, Ekman, Saron,
Senulis, & Friesen, 1990; Dickinson & Dearing, 1978; Lang, Bradley, &
Cuthbert, 1990, 1997, 1998).
In line with this theory, emotional activation fundamentally varies
in centrally organized appetitive and aversive motivational systems that
have evolved to mediate a wide range of adaptive behaviors that are
necessary for an organism to survive (Bradley & Lang, 2007; Davidson
et al., 1990; Lang et al., 1990). Most pleasant aects are held to be as-
sociated with the appetitive motivation system; unpleasant aects
with defensive motivation (Cacioppo & Berntson, 1994). Specically,
aversive conditions were considered highly relevant for the survival
since they include a threatening value (Fanselow, 1994; Russell, 1980).
Also the autonomic behavior was related to empathic behavior, with
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an increased psychophysiological activity (higher SCR) for both posi-
tive and negative interactions. is response was attributed to general
emotional involvement and to the ability to respond physiologically to
the emotions displayed by other people in an interpersonal positive or
negative situation. Indeed, as suggested by recent models of empathic
behavior, a complex network of central and peripheral circuits supports
the phylogenetic developments of a specic empathy-related response
to conspecics’ emotional signs. e multiple elements of the empathic
response are continuously modied during the social interactions and
are contextually embedded (Decety & Svetlova, 2012). e relation be-
tween more central processes (mediated by PFC) and more peripheral
processes (mediated by the autonomic system) conrmed the close re-
lation existing between high order mechanisms (evolutionarily recent)
and the autonomic responsiveness (evolutionarily ancient). It was also
underlined that behaviors specically supported by arousal evolved
earlier than the mechanisms supported by more complex cognitive
processes (Decety & Svetlova, 2012). Moreover, it should be noted that
relevant models of empathic behavior have pointed out that the emo-
tional states related to empathy and the underlying neural mechanisms
are similar in all mammals (Panksepp, 1998).
e present results are also consistent with previously reported
negative, empathy-related responses to unpleasant situations (Brown,
Bradley, & Lang, 2006). Conictual (negative) and cooperative (posi-
tive) situations were shown to be more powerful in eliciting empathic
responses, presumably emotionally involving and signicant, compared
with neutral interpersonal conditions. In particular, the non-coopera-
tive condition was negatively connoted, highly empathy-inducing, and
able to produce a clear “negative” consonant autonomic reactivity.
Moreover, positive and negative situations showed a relation be-
tween empathic emotional, psychophysiological, and central (both
hemodynamic and EEG) measures. Indeed, it should be emphasized
that important connections were found in the correlational analysis
between hemodynamic and cortical EEG and the regression analyses
between these two measures and the responses at the autonomic level.
Firstly, the joined EEG-NIRS analysis revealed signicant linear as-
sociations between the hemodynamic values and brain oscillations.
e signicant positive relation between NIRS and EEG measures may
suggest, on the one hand, a general direct relation between these two
measures and PFC activation since they synchronously varied within
the prefrontal areas based on a situations valence. On the other hand,
the positive relation may support the connection between these two
brain measures in response to empathic situations. More generally, the
simultaneous registration of EEG and NIRS was found to be useful for
studies on empathic behavior. A general link between electrophysi-
ological eects and the regional hemodynamic changes was suggested
based on present and past evidences (Balconi et al., 2015a; Herrmann
et al., 2008; Schneider et al., 2014).
To summarize, the signicant correlations between EEG and NIRS
measures within PFC may suggest that a specic cortical prefrontal
area supports empathic responsiveness. Indeed, whereas in band os-
cillations only a lateralization eect was found, the intrinsic relation
between PFC activity observed in the EEG (mainly the low-frequency
band) and the hemodynamic modulation may suggest the existence of
a coherent prefrontal network for empathy. However, future research
should explore the prefrontal localization of the EEG in more depth,
also investigating potential cortical generator (e.g., with a LORETA
approach) to dene the reciprocal contribution by oscillations and
hemodynamic measures.
Secondly, regression analyses revealed that brain oscillations and
hemodynamic variations might have aected autonomic responses by
the subjects. at is, the PFC activity as marked by O2Hb increases and
synchronous cortical activity (mainly for low-frequency bands) were
signicant factors, able to explain autonomic response modulation
since subjects modied their autonomic parameters as a function of
EEG/O2Hb changes in an empathic behavioral task. Specically, in-
creased SCR/HR was predicted by frequency band and hemodynamic
activity in response to negative and positive interactions.
To summarize, the direct relation between EEG and O2Hb, shown
by correlational values, and the regression analysis, conrmed the in-
terconnections between the three levels of processing (hemodynamic,
electrophysiological, and autonomic). Indeed, the two analyses allowed
respectively evidencing the direct relationship between the two inde-
pendent measures (correlation analysis) and their consistent inuence
on autonomic responses (regression analysis).
In the end, some limitations of this study and future suggestions for
improved research should be considered. Firstly, future research should
take into account the dierent roles that emotional and cognitive em-
pathy might have in interpersonal interactions. Secondly, the deeper
relations connecting central (both hemodynamic and EEG) and pe-
ripheral measures should be explored, considering the temporal course
of their modulations in response to empathic situations. irdly, the
inter-subjective dierences related to some personality components
(such as empathy as a trait) should be explored as a stable construct
able to explain neurophysiological dierences. Indeed, possible struc-
tural components could have modulated the central and peripheral
responses based on subjective empathic and emotional responsiveness
to positive and negative situations even in the present study.
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... fNRIS allows the analysis of changes in regional cortical activation, and this area is related to emotions (Doi et al., 2013;Balconi & Vanutelli, 2016;Balconi & Vanutelli, 2017). Its use in emotion processing studies is emphasized by some authors (Doi et al., 2013;Balconi & Molteni, 2015;Balconi, Vanutelli, 2017;Gruber et al., 2020). Balconi and Molteni (2015) state that the activation of several cortical areas has already been verified, in the PFC, in the sensory areas and in the visual cortex in the face of emotional processing, and emphasize the research of emotions, especially in studies involving the dynamic pattern, such as facial expressions or auditory stimulation. ...
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... Westman et al. (2013) demonstrated that empathy was associated with crossover of positive affect, but not negative affect, when stimulated by the presentation of an affective story script. Neuroimaging studies showed that neural processing of empathy might differ across the positive and negative effects of others (Balconi and Vanutelli, 2017). Moreover, positive mood states are reported to play an important role in the prevention of depressive mood (Fredrickson et al., 2008;Gruber et al., 2013;Santos et al., 2013). ...
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While empathy is considered a critical determinant of the quality of medical care, growing evidence suggests it may be associated with both one’s own positive and negative moods among healthcare professionals. Meanwhile, sense of coherence (SOC) plays an essential role in the improvement of both psychological and physical health. Reportedly, individual SOC reaches full stability after around age 30. The aim of this study was first to evaluate the mediatory role of SOC on the association between empathy and individual moods among 114 healthcare professionals in a general hospital, and then to examine the moderating effect of age on this association. Participants completed a range of self-report demographic questionnaires, Empathy Process Scale (EPS), the 13-item Antonovsky’s SOC, and Profile of Mood States (POMS). Findings showed that SOC mediated the relations between empathy (EPS) and both POMS-Vigor (POMS-V: self-vigor mood) and POMS-Depression (POMS-D: self-depression mood). Notably, moderated mediation analysis revealed that there was a significant interaction (age × SOC) on self-vigor mood (POMS-V) in healthcare professionals. The indirect effect of empathy (EPS) on self-vigor mood (POMS-V) through SOC was significant at over mean age “32.83.” Although there was no significant interaction with age regarding the indirect effect of empathy (EPS) on self-depression mood (POMS-D), in the sub-category level analysis of empathy (EPS), we found a significant interaction item [age × empathy for other’s negative affect (EPS-N)] on SOC. This indirect effect was also significant at over mean age “32.83.” Taken, together, the current study highlighted the significant mediator of SOC on that empathy amplifies self-vigor mood and attenuates self-depression mood as a protective factor among the Japanese healthcare professionals. Some components of these processes may depend on the moderating role of age, indicating that we may need to consider the SOC development with age for more effective empathy performance interventions among healthcare professionals.
... For contiguous optodes, the emitter-detector distance was preserved at 30 mm, and it employed a near-infrared light with two wavelengths (760 and 850 nm). Using this arrangement of the optodes, it was possible to acquire a total of six channels: Ch1 (AF3-F3), Ch2 (AF3-AFF1h), Ch3 (F5-F3), corresponding to the left PFC, and Ch4 (AF4-F4), Ch5 (AF4-AFF2h), Ch6 (F6-F4) consistent with the right PFC [16,58] (Figure 2). The locations of the sources and detectors, as well as the area between them, were associated with the best underlying functional region and the most suitable Brodmann area. ...
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Currently, there is little understanding of how interoceptive attentiveness (IA) affects brain responses during synchronized cognitive or motor tasks. This pilot study explored the effect of explicit IA manipulation on hemodynamic correlates of simple cognitive tasks implying linguistic or motor synchronization. Eighteen healthy participants completed two linguistic and motor synchronization tasks during explicit IA and control conditions while oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin variations were recorded by functional Near-Infrared Spectroscopy (fNIRS). The findings suggested that the brain regions associated with sustained attention, such as the right prefrontal cortex (PFC), were more involved when an explicit focus on the breath was induced during the cognitive linguistic task requiring synchronization with a partner, as indicated by increased O2Hb. Interestingly, this effect was not significant for the motor task. In conclusion, for the first time, this pilot research found increased activity in neuroanatomical regions that promote sustained attention, attention reorientation, and synchronization when a joint task is carried out and the person is focusing on their physiological body reactions. Moreover, the results suggested that the benefits of conscious concentration on physiological interoceptive correlates while executing a task demanding synchronization, particularly verbal alignment, may be related to the right PFC.
... When participants were in the same room, increased delta activity was observed in a region centered between the left insula and the left inferior frontal gyrus. Although not exclusively, EEG delta activity was detected in frontal areas during emotion processing (Knyazev, Slobodskoj-Plusnin, & Bocharov, 2009) and the empathic observation of human interactions (Balconi & Vanutelli, 2017). The insula, already described for task effects in cluster 9, also plays an important role in empathic processes (Fan, Duncan, de Greck, & Northoff, 2011) and has often been noted in studies of social pain (Laneri et al., 2017;Masten, Morelli, & Eisenberger, 2011). ...
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The COVID-19 pandemic has prompted the production of a vast amount of COVID-19-themed brand commercials, in an attempt to exploit the salience of the topic to reach more effectively the consumers. However, the literature has produced conflicting findings of the effectiveness of negative emotional contents in advertisings. The present study aims at exploring the effect of COVID-19-related contents on the hemodynamic brain correlates of the consumer approach or avoidance motivation. Twenty Italian participants were randomly assigned to two different groups that watched COVID-19-related or non-COVID-19-related commercials. The hemodynamic response [oxygenated (O2Hb) and deoxygenated hemoglobin modulations] within the left and right prefrontal cortices (PFC) was monitored with Functional Near-Infrared Spectroscopy (fNIRS) while brand commercials were presented, as the prefrontal lateralization was shown to be indicative of the attitude toward the brand and of the approach-avoidance motivation. First, the findings showed that the COVID-19-related contents were able to prompt emotional processing within the PFC to a higher extent compared to contents non-related to COVID-19. Moreover, the single-channel analysis revealed increased O2Hb activity of the left dorsolateral PFC compared to the left pars triangularis Broca’s area in the group of participants that watched the COVID-19-related commercials, suggesting that the commercials may have driven participants to dedicate more attention toward the processing of the emotional components compared to the semantic meaning conveyed by the ad. To conclude, despite expressing unpleasant emotions, commercials referring to the highly emotional pandemic experience may benefit the advertising efficacy, increasing the capability to reach customers.
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School classrooms worldwide consist of a diversity of cultures. Teachers in these classrooms play key roles in the preparation of students not only in terms of their academic and career readiness, but also in their understanding of how to socially navigate communities. In remote areas where communities rely on their culture and social norms to guide behaviours to sustain a flourishing culture and community, non-academic life skills become part of the focus of teaching. This paper shares a portion of research investigating cultural self-perceptions of both Canadian Indigenous primary school students and their teachers. These Indigenous educators in remote communities reveal culture’s important role in teaching that impacts children’s social development. The data provides a perspective rarely investigated that shares the practices of these Indigenous teachers. Their cultural and societal expectations lead to encouraging other teachers in various global contexts to reflect on their own teaching practices, cultural identities, and non-academic life skill pedagogies.
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Individuals with psychopathy present deficits in the recognition of facial emotional expressions. However, the nature and extent of these alterations are not fully understood. Furthermore, available data on the functional neural correlates of emotional face recognition deficits in adult psychopaths have provided mixed results. In this context, emotional face morphing tasks may be suitable for clarifying mild and emotion-specific impairments in psychopaths. Likewise, studies exploring corresponding anatomical correlates may be useful for disentangling available neurofunctional evidence based on the alleged neurodevelopmental roots of psychopathic traits. We used Voxel-Based Morphometry and a morphed emotional face expression recognition task to evaluate the relationship between regional gray matter (GM) volumes and facial emotion recognition deficits in male psychopaths. In comparison to male healthy controls, psychopaths showed deficits in the recognition of sad, happy and fear emotional expressions. In subsequent brain imaging analyses psychopaths with better recognition of facial emotional expressions showed higher volume in the prefrontal cortex (orbitofrontal, inferior frontal and dorsomedial prefrontal cortices), somatosensory cortex, anterior insula, cingulate cortex and the posterior lobe of the cerebellum. Amygdala and temporal lobe volumes contributed to better emotional face recognition in controls only. These findings provide evidence suggesting that variability in brain morphometry plays a role in accounting for psychopaths' impaired ability to recognize emotional face expressions, and may have implications for comprehensively characterizing the empathy and social cognition dysfunctions typically observed in this population of subjects.
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Evaluative processes refer to the operations by which organisms discriminate threatening from nurturant environments. Low activation of positive and negative evaluative processes by a stimulus reflects neutrality, whereas high activation of such processes reflects maximal conflict. Attitudes, an important class of manifestations of evaluative processes, have traditionally been conceptualized as falling along a bipolar dimension, and the positive and negative evaluative processes underlying attitudes have been conceptualized as being reciprocally activated, making the bipolar rating scale the measure of choice. Research is reviewed suggesting that this bipolar dimension is insufficient to portray comprehensively positive and negative evaluative processes and that the question is not whether such processes are reciprocally activated but under what conditions they are reciprocally, nonreciprocally, or independently activated. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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At the heart of emotion, mood, and any other emotionally charged event are states experienced as simply feeling good or bad, energized or enervated. These states - called core affect - influence reflexes, perception, cognition, and behavior and are influenced by many causes internal and external, but people have no direct access to these causal connections. Core affect can therefore be experienced as free-floating (mood) or can be attributed to some cause (and thereby begin an emotional episode). These basic processes spawn a broad framework that includes perception of the core-affect-altering properties of stimuli, motives, empathy, emotional meta-experience, and affect versus emotion regulation; it accounts for prototypical emotional episodes, such as fear and anger, as core affect attributed to something plus various nonemotional processes.
Chapter
The unstructured dyadic interaction paradigm (UDIP) measures empathic accuracy as the extent to which a perceiver accurately infers a target person's thoughts or feelings from a video recording of their spontaneous interaction together. To measure empathic accuracy using this paradigm, an experimenter escorts two participants into an observation room that is equipped with a concealed wireless microphone and video camera and asks them to take a seat on a couch. The experimenter then “discovers” a reason for having to run a quick errand and leaves the participants alone together. The dyad members’ unstructured interaction is then video-recorded for a predetermined duration. At the end of the observation and recording period, the participants are told that they have been filmed for the purpose of studying their naturally occurring interaction behavior.With their consent, the participants then individually view their entire interaction and are instructed to stop the recording each time they recall having a thought/feeling and to record those thoughts/feelings on a standard form. The participants are then asked to view the recording a second time in order to infer the specific thoughts/feelings that their interaction partner reported having had at each of that interaction partner's recording stops. Each perceiver's total accuracy points is then calculated to measure the degree to which the content of each of the perceiver's empathic inferences matches the content of the corresponding thought or feeling that the target person actually reported.
Chapter
In this article I discuss a hypothesis, known as the somatic marker hypothesis, which I believe is relevant to the understanding of processes of human reasoning and decision making. The ventromedial sector of the prefrontal cortices is critical to the operations postulated here, but the hypothesis does not necessarily apply to prefrontal cortex as a whole and should not be seen as an attempt to unify frontal lobe functions under a single mechanism. The key idea in the hypothesis is that 'marker' signals influence the processes of response to stimuli, at multiple levels of operation, some of which occur overtly (consciously, 'in mind') and some of which occur covertly (non-consciously, in a non-minded manner). The marker signals arise in bioregulatory processes, including those which express themselves in emotions and feelings, but are not necessarily confined to those alone. This is the reason why the markers are termed somatic: they relate to body-state structure and regulation even when they do not arise in the body proper but rather in the brain's representation of the body. Examples of the covert action of 'marker' signals are the undeliberated inhibition of a response learned previously; the introduction of a bias in the selection of an aversive or appetitive mode of behaviour, or in the otherwise deliberate evaluation of varied option-outcome scenarios. Examples of overt action include the conscious 'qualifying' of certain option-outcome scenarios as dangerous or advantageous. The hypothesis rejects attempts to limit human reasoning and decision making to mechanisms relying, in an exclusive and unrelated manner, on either conditioning alone or cognition alone.