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Investigating the neural basis of socialinteraction with fNIRSWord count: 10,126
Kobe Van OlmenStudent number: 01309094
Supervisor: Dr. Lara Bardi
Dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master of Science in Psychology (Clinical Psychology)
Academic year: 2017 - 2018
Preface
I would like to thank my promotor dr. Lara Bardi who gave me the opportunity to work
on this topic and was always very helpful, be it in Belgium or through Skype calls from
Italy.
I would also like to thank dr. Roma Siugzdaite who was so kind to help with parts of the
data analysis.
Furthermore, I would like to thank professor Marcel Brass for his thoughtful comments
on the design of the experiment.
My thank also goes to Arianna and Lea who helped out with parts of the data collection.
I would like to thank my friends, who not only supported me in piloting parts of the
experiment, but also kept cheering me on along the sidelines.
Lastly, this thesis wouldn’t have been possible without the unwavering support of my
family and kind Kato.
Abstract
Recently the role of the temporo-parietal junction (TPJ) has been investigated in an
increasing number of studies. It has been shown that the TPJ is involved in domain
general processes such as attention (Mitchell, 2008), sense of agency (Decety & Lamm,
2007) and self-other discrimination (Bardi, Gheza, & Brass, 2017). However, due to
limitations of equipment most brain imaging studies have been executed in restricted
environments and without actual live interaction between two persons.
Because imitation more often happens in a social context where two or more persons are
actively engaged (Guionnet et al., 2012; Schilbach et al., 2013), the aim of our current
study was to further investigate the role of the TPJ during live face-to-face interaction
with the use of functional Near-Infrared Spectrometry (fNIRS). In our experiment brain
activity from the right TPJ was recorded from participants who had to perform
emblematic gestures while being imitated by a confederate.
Our results confirm that right TPJ is involved in computing a mismatch between self-
and other-related representations during face-to-face interaction. Furthermore, our thesis
supports the use of fNIRS as a viable alternative to fMRI for investigating imitation
during social interactions.
Keywords: fNIRS, imitation, shared representations, second-person neuroscience
Table of Contents
Introduction........................................................................................................................1 Social Cognition and Imitation.......................................................................................1 Imitation and a Shared Representational System...........................................................2 Evidence of shared representations............................................................................3 Theoretical approaches of shared representations......................................................6 Shared representations and agency.............................................................................9 The control of shared representations.......................................................................10 The Role of the TPJ: Between Motor Control and Mentalizing...................................12 Second Person Social Neuroscience.............................................................................16 Study Objectives...........................................................................................................18Method.............................................................................................................................19 Participants...................................................................................................................19 Stimuli and procedure...................................................................................................19 ToM task...................................................................................................................19 Imitation task............................................................................................................20 FNIRS Measurement....................................................................................................22 FNIRS Data Preprocessing...........................................................................................25 ToM task...................................................................................................................25 Imitation task............................................................................................................25 FNIRS Data Analysis....................................................................................................26 Results...........................................................................................................................28Discussion........................................................................................................................29Conclusion.......................................................................................................................31Bibliography....................................................................................................................32
Introduction
Social Cognition and Imitation
Human social interaction comprises of a broad array of behaviours: helping,
informing, bargaining, cooperating, playing, ... (Hari & Kujala, 2009). Imitation plays a
fundamental role in this process: it is the basis for socialization, learning skills and
language learning. Furthermore, it affects social attitudes such as liking and
relationships (Gallese, 2003; Kühn et al., 2010; Lakin & Chartrand, 2003; Rizzolatti &
Arbib, 1998).
Although the importance of learning through imitation has been hypothesized to
have led to our current brain structure (Dunbar, 1998; Rizzolatti & Craighero, 2004),
only recently studies have started to investigate the neural mechanisms of imitation and
its role in social cognition (Hari & Kujala, 2009). Given the relatively novel
convergence of social psychology, cognitive science and neuroscience to come to terms
with this task (Lieberman, 2007; Ochsner & Lieberman, 2001), there currently is a wide
array of theories and findings considering the neural underpinnings of social cognition
and the role of imitation (Amodio & Frith, 2006).
In the past two decades two more or less anatomically distinct brain networks have
been proposed to underlie social cognition: the mirror-neuron system and the
mentalizing network, both emphasizing different theoretical aspects of social cognition
(Cook, Bird, Catmur, Press, & Heyes, 2014; Frith & Frith, 2006; Van Overwalle &
Baetens, 2009). The mirror-neuron system is implicated in modelling a motor first-
person point of view of other individuals (Frith & Frith, 2006; Rizzolatti & Craighero,
2004; Van Overwalle, 2009). The mentalizing network in contrast is proposed to be
involved in a third-person point of view of others and is further thought to be involved
in meta-cognition such as Theory of Mind (ToM) and inferring the intentions of others
(Van Overwalle & Baetens, 2009).
Recently, the idea has been advanced that imitation functions as a base mechanism
which allows for the development of higher-level socio-cognitive functions such as
ToM, understanding intentions and empathy (De Coster, Verschuere, Goubert, Tsakiris,
1
& Brass, 2013; Decety & Lamm, 2007; Santiesteban, Banissy, Catmur, & Bird, 2012).
In this thesis we will first take a closer look at the neural substrates of imitation:
the mirror-neuron system and the role of shared representations. Secondly, we will
consider two brain regions – namely the temporo-parietal junction (TPJ) and the
anterior medial prefrontal cortex (amPFC) – which likely function as a connection
between the mirror neuron system and the mentalizing network. Thirdly, we will discuss
a new concept of doing social cognitive neuroscience called second-person
neuroscience (Schilbach et al., 2013).
Finally, we will describe our experiment which further examines the role of the
right TPJ (rTPJ) in social interaction using functional Near Infrared-Spectrometry
(fNIRS). The relevant methodology and the implications of our finding will be
discussed.
Imitation and a Shared Representational System
In neuroscience, the idea that the observation of a certain movement activates its
corresponding motor pattern arose with the remarkable finding that there is a set of
neurons in the F5 area of a macaque monkeys brain that responds to observing an action
as well as executing the same action (di Pellegrino, Fadiga, Fogassi, Gallese, &
Rizzolatti, 1992). Four years later, these neurons were ingeniously dubbed mirror
neurons (Gallese, Fadiga, Fogassi, & Rizzolatti, 1996). Further research found similar
activation patterns within humans: an observed action activates brain areas similar to
brain areas when executing this action (Caspers, Zilles, Laird, & Eickhoff, 2010;
Keysers & Gazzola, 2010; Molenberghs, Cunnington, & Mattingley, 2012; Rizzolatti,
2005). For instance, in an fMRI experiment by Buccino et al. (2004) participants had to
look at similar actions performed by humans, monkeys or dogs. They found that only
the actions performed by humans activated areas from their own motor repertoire.
Similar actions performed by monkeys and dogs were primarily processed through the
visual system. A meta-analysis by Molenberghs et al. (2012) corroborates the idea that
there exists a system such as the mirror-neuron system with its core brain activity
2
centred around the premotor cortex, the anterior inferior parietal lobule and the inferior
frontal gyrus.
Coming from cognitive psychology, Wolfgang Prinz (1997) proposed a theory
compatible with mirror neurons. He proposed a new framework where perception and
action planning rely on a shared representational domain. His theory was motivated by
behavioural studies where the observation of an action slowed down the execution of a
similar action (Prinz, 1997). The key idea of this framework is that action and
perception are linked by bidirectional associations generated between an action and its
perceived effect. Prinz also predicted a shared neural substrate for observed and
executed actions similarly to the mirror neuron system (e.g. Cook et al., 2014; Rizzolatti
& Craighero, 2004).
In the past two decades their has been a wide array of evidence supporting the idea
of a shared representational system and a neural substrate of such a system like the
mirror neuron system (Bardi & Brass, 2017; Heyes, 2010; Rizzolatti, 2005). In the
following paragraphs we will delve deeper into the possible nature of the mirror neuron
system. We will focus on the idea of a shared representational domain, since this is a
more concrete theoretical proposal than the multitude of definitions that exist for mirror
neurons.
First, we will consider evidence from four lines of research showing that
perception and action are tightly connected: cognitive psychology, neuroscience, social
psychology and clinical neuropsychology.
Evidence of shared representations.
In cognitive psychology, a simple way to investigate the existence of shared
representations is to induce conflict on the representational level. If such a system were
to exist, observing a certain movement should interfere with executing another
movement. In a behavioural study from (Brass, Bekkering, Wohlschläger, & Prinz,
2000) participants had to perform a movement when presented with a number. When
seeing a number they had to lift their index finger and when they saw another number
3
they had to lift their middle finger; participants concurrently saw a movement that was
either the same movement or a different movement (see figure 1). Interestingly,
participants showed slower response times when watching an incongruent movement
then when watching a congruent movement (Brass et al., 2000). The effect of this
‘imitation-inhibition’ task has been named automatic imitation. People tend to
automatically imitate the motor pattern of an observed action which generates conflict
when having to perform another action (Heyes, 2011). This paradigm has been used in
several studies to investigate the functional role of shared representations (Brass,
Zysset, & von Cramon, 2001; Kilner, Paulignan, & Blakemore, 2003).
In a study by Craighero, Bello, Fadiga and Rizzolatti (2002) for example,
participants had to grasp a bar when cued with a picture depicting a congruent or
incongruent posture. As predicted, a similar interference effect was found: participants
were faster on the congruent conditions and slower on the incongruent conditions.
Other authors investigated whether shared representations can be learned. Drost,
Rieger, Brass, Gunter, and Prinz (2005) examined learned associations between action
and observation with expert piano players. They found that piano players were slower
when having to perform a note while simultaneously hearing an incongruent note.
Importantly, this interference effect was not found with participants who had no
4
Figure 1: The imitation inhibition task. Participants are asked to lift theirindex finger when shown the number 1. Left: the congruent condition. Right: the incongruent condition. Participants are slower in the incongruent trials. Adapted from “Compatibility between observed and executed finger movements: Comparing symbolic, spatial, and imitativecues.” by Brass, Bekkering, Wohlschläger, and Prinz, 2000, Brain and Cognition, 44(2), 124–143.
experience of playing the piano. Again, this finding is in line with the existence of
(learned) associations between the representation of actions (i.e piano playing) and their
related sensory events (notes). Together these findings of behavioural ‘motor-priming’
and ‘automatic imitation’ all corroborate the idea of a shared representational system
(Elsner & Hommel, 2001; Heyes, 2011).
In neuroscience, research investigating the mirror neuron system is compatible
with shared representations. As mentioned above, it has been shown that observing a
movement activates similar brain areas than when executing this movement (Gallese et
al., 1996; Keysers & Gazzola, 2010; Rizzolatti, Fadiga, Gallese, & Fogassi, 1996).
In an interesting study by (Fadiga, Fogassi, Pavesi, & Rizzolatti, 1995) motor
evoked potentials (MEPs) were recorded from participants’ hand muscles while they
had to watch a certain action. The recording of MEPs was possible by stimulating the
motor cortex with single pulse transcranial magnetic stimulation (spTMS) (Barker,
Jalinous, & Freeston, 1985). In this study, it was found that the intensity of the spTMS
needed to elicit MEPs was lower during the observation of an action than during the
observation of a control stimuli; thus, there were higher MEPs during the observation of
actions than during control stimuli. This suggests that the motor system is activated and
being prepared while seeing a certain action. This finding is in line with the idea of
shared representations between action observation and action execution.
In a third line of research, social psychology, motor priming and automatic
imitation has also been found in overt response tendencies such as the imitation of facial
expression or postures during social interactions (Chartrand & Lakin, 2013; Lakin &
Chartrand, 2003): people tend to automatically and unconsciously imitate postures. This
finding – mimicry or the ‘chameleon effect’ (Chartrand & Bargh, 1999) - has been
extensively researched in social psychology and plays an important role in social
interaction (Chartrand & van Baaren, 2009; Cook & Bird, 2012). Mimicry could be
linked to motor priming, suggesting an important role for shared representations in
social cognition (Chartrand & Bargh, 1999). However, it should be noted that the
assumed link between automatic imitation and mimicry still remains an open question.
A recent meta-analysis for example found no correlation between mimicry and
5
automatic imitation (Genschow et al., 2017), possibly due to the large methodological
differences between research on automatic imitation and mimicry.
Lastly, clinical neuropsychology has shown that patients with a prefrontal lesion
sometimes tend to unwillingly imitate behaviours of others. When patients were asked
why they made these movements they answered that they were instructed to do so
(Brass, Derrfuss, Matthes-von Cramon, & von Cramon, 2003; De Renzi, Cavalleri, &
Facchini, 1996; Lhermitte, Pillon, & Serdaru, 1986). This suggests that these patients
confuse the origin of the representation of the movement and attribute it to themselves.
Together, evidence coming from reaction time studies, neurostimulation studies,
social psychology and clinical neuropsychology all reaffirm that the observation and
execution of actions are tightly linked and that they could rely on a shared
representational system (Brass, Ruby, & Spengler, 2009).
However, both Prinz’s (1997) concept of shared representations and the idea of
mirror neurons come with a theoretical problem. When imitating a movement, the
imitator translates the sensory input to their own motor repertoire. How is this
achieved? More concrete, this poses a computational issue: how is the sensory
information – most often in a different modality and frame of reference – transformed to
a motor plan in ones own frame of reference? In the literature this problem has been
referred to as the correspondence problem (Brass & Heyes, 2005). Now we will discuss
the three main theoretical approaches which formulate different solutions for this
problem.
Theoretical approaches of shared representations.
In the recent debate about the origins of mirror neurons (see Cook et al., 2014),
three main theories return which have different implications for the correspondence
problem: the Active Intermodal Mapping account (AIM), the Associative Sequence
Learning account (ASL), and the Ideomotor account (IM) (Bardi & Brass, 2017; Heyes,
2001; Ray & Heyes, 2011). The three theories and their implications will be briefly
discussed.
6
Active Intermodal Mapping (AIM).
Given the natural ease with which imitation takes place, it has been assumed that
the correspondence problem is solved by an innate neural system (Heyes, 2001).
Meltzoff and Moore (1997) proposed that imitation is based on ‘active intermodal
mapping’. The key feature of their model is that there exists an innate supramodal
representational system that exists as a comparator between observed and executed
movements. This theory was proposed to explain the finding that babies can imitate a
range of different movements without being aware of their own movements (Meltzoff &
Moore, 1994; Meltzoff & Moore, 1977).
However, several studies contract the idea that this system is genetically
predefined (Jones, 2012); rather, evidence points to the direction that the ability to solve
the correspondence problem arises for a large part as a result of experience acquired
during development (Blandin, Lhuisset, & Proteau, 1999; Carpenter, Akhtar, &
Tomasello, 1998) and that this is dependent on the contextual richness in which a child
grows up (Ray & Heyes, 2011).
Associative Sequence Learning (ASL).
Given the conflicting evidence found against AIM, ASL outlines a theory which
proposes that the correspondence problem is not solved by an innate mechanism but by
a highly experience-dependent process (Heyes, 2001). The ASL theory of imitation
states that imitation is learned by forming bidirectional vertical links, a sort of excitatory
links, between observed actions and executions of this same action.
These associations are proposed to be formed by the concurrent observation of
both movement and motor execution during development (Catmur, Walsh, & Heyes,
2009). Furthermore, the ASL assumes that these formed links are not different from any
other kind of association (Schultz & Dickinson, 2000). In this view, associations
between sensory and motor neurons are formed when a particular movement and the
observation of this movement are correlated or contingent; or, when the observation of a
particular movement is more likely to occur with its motor counterpart than with
another counterpart. Following this theory, rich sociocultural contexts should enhance
the learning process of imitation. A recent review found substantially more evidence in
7
favour of ASL than of AIM during developmental learning of imitation (Ray & Heyes,
2011).
Ideomotor theory (IM)
The Ideomotor Theory states that actions are represented by their perceived
effects. As a consequence, every perceived stimulus representing a motor effect – and
even the idea of this effect – should activate its corresponding motor pattern
(Greenwald, 1970). This idea was already written down by other 19th century
philosophers such as Lotze (1852) and Carpenter (1852). James (1890) emphasized its
importance, as seen for example in this quote:
”[...] every representation of a movement awakens in some degree the
actual movement which is the object; and awakens it in a maximum
degree whenever it is not kept from so doing by an antagonistic
representation present simultaneously to the mind” (James 1890, p. 526)
However, ideomotor theory lost traction in the middle of the 20th century with the
advance of behavourialism (see Shin, Proctor, & Capaldi, 2010 for a review of
ideomotor theory).
Later, Anthony Greenwald (1970) revived the ideomotor theory and outlined an
elaborate scientific theory of James’ intuitive musings. Namely, he outlined a theory
where the performance of an action is influenced by anticipatory representations of its
own feedback or of feedback from the reaction to a goal to which the response leads. In
this account of ideomotor theory, associations between sensory and motor codes are
formed as a by-product of motor control (see figure 2 for a detailed account). This idea
was expanded by the idea of shared representations formulated by (Prinz, 1997) with
the added hypothesis that these representations share a common neural substrate (e.g.
the common coding approach Hommel, Müsseler, Aschersleben, & Prinz, 2001).
8
While both ASL and IM state that the observation of a movement will activate its
respective motor pattern, only IM states that these shared representations are also
involved in intentional motor control (see also Shin et al., 2010) for a review of
contemporary ideomotor theories).
If indeed mirror neurons and shared representations are linked to motor control,
this poses a new problem. Because motor responses are linked to their anticipatory
feedback, this could create self-other confusion (Jeannerod, 2004). How is it that agency
is attributed to a perceived action and an action generated by the self? We discuss this
problem – which has been called the agency problem (Bardi & Brass, 2017) - briefly in
the following paragraph and introduce the concept of self-other distinctions.
Shared representations and agency.
If motor control relies on similar representations than motor execution, why is it
that one does not automatically imitate an observed action? This problem cannot be
solved on a representational level with sensory information. Rather, the person must
resolve whether the activated representation follows from motor intent, or from visual
input (Bardi & Brass, 2017). One could also say that the agent must differentiate
between self-related and other-related representations (Jeannerod, 2004). Thus, there
seems to be a need for a mental comparator system which checks whether the observed
motor pattern is generated by oneself or triggered externally by someone else
9
Figure 2: An adaptation from Greenwald (1970). (a) A stimulus (S) is followed by a respone (R) that occurs with an effect (E) (b) The formed association between response and effect leads to an anticipation of this effect (e). (c) The learned anticipation of a certain effect offers a possibility for control of this respone. (d) Motor priming: a stimulus (Se) that is similar to the effect of the respone triggers the anticipation (e) which triggers the respone (R). Adapted from “More than associations: an ideomotor perspective on mirror neurons.” by Marcel Brass and Muhle-Karbe, 2014,Behavioral and Brain Sciences, 37(2), 195-196.
(Jeannerod, 2004; Prinz, 2002).
For example, a recent study by Pfister, Dignath, Hommel and Kunde (2013)
showed that the anticipation of being imitated (i.e. a potential sensory feedback of
others) facilitates the execution of overt motor actions. This confirms the idea that
imitation and shared representations could play an important role in the modeling of
action.
A simple solution for this problem could be that the control of shared
representations (or the distinction of self- and other related representations) relies on
general executive control mechanisms (Cook et al., 2014). In the following paragraph,
we will consider research regarding self-other distinctions. We will discuss evidence
regarding its neural correlates and show that these neural structures are also involved in
the processing of high-level socio-cognitive processes (i.e. perspective taking and
theory of mind). Finally, we will discuss evidence differentiating this process from
general executive control mechanisms.
The control of shared representations
In this quote from James’ Principle of Psychology (1890), we already read the
concept that idea of a certain movement is involved both in the execution and control of
the movement:
“Try to feel as if you were crooking your finger, whilst keeping it straight.
In a minute it will fairly tingle with the imaginary change of position; yet
it will not sensibly move, because its not really moving is also a part of
what you have in mind.” (James, 1890, p. 527)
As discussed above, the imitation-inhibition task showed that on performing a
movement while concurrently observing an incongruent movement there is an
interference effect (Brass et al., 2000): participants were slower on incongruent trials
than on congruent trials. We can interpret this finding as a conflict of distinguishing
between self and other representations. In the experiment participants had to observe a
motor representation of a hand that was not their own (‘other-representation’) and had
10
to concurrently perform their own movement (‘self-representation’). Thus, we can argue
that participants had to resolve conflict on the representational level: they had to neglect
the motor representation coming from someone else and had to focus on their own
movement (Jeannerod, 2004; Prinz, 2002).
Furthermore this mechanism of self-other distinction relies on a different control
mechanism than general executive control (Bardi et al., 2017). A fMRI experiment by
Brass, Derrfuss, & von Cramon, (2005) examined the neural correlates of the imitation-
inhibition task. People had to perform the imitation-inhibition task and a Stroop task.
Here, they showed that the imitation-inhibition task mostly relied on different brain
areas than the ones involved in general control mechanisms. Furthermore, when
contrasting congruent and incongruent trials it was found that the anterior medial
prefronal cortex (aMPFC) and the right temporo-parietal junction (rTPJ) were more
activated in incongruent than in congruent trials thus providing evidence that these brain
areas are activated during the control of shared representations.
In social cognitive neuroscience, the aMPFC and the rTPJ have been found to be
involved in agency processing (Farrer et al., 2003), perspective taking and self-
referential processing (Decety & Lamm, 2007; Van Overwalle, 2009). Furthermore, the
aMPFC and the rTPJ seem to be two of the core brain areas of the mentalizing network
(Frith & Frith, 2006; see Van Overwalle, 2009 for a meta-analysis). This has lead
researchers to assume that the processes involved in the imitation-inhibition task (i.e.
the self-other distinction) rely on similar brain areas as do mentalizing and theory of
mind tasks (‘the functional overlap hypothesis’) (Brass et al., 2009).
A disadvantage of the previously discussed between-participant fMRI studies is
that the overlapping brain activity could be explained partly due to intra-individual
differences (LaBar, Gitelman, Parrish, & Mesulam, 1999). More convincing evidence
for the hypothesis that the control of shared representations relies on the same brain
areas as the mentalizing network should come from a within-subject approach.
Spengler, Von Cramon, & Brass (2009) for example performed a conjunction analysis in
a fMRI task comparing the imitation-inhibition task with mentalizing tasks and agency
processing tasks. They found that regions of both the aFMC and the rTPJ are involved
11
in the control of shared representations as well as mentalizing. In another study found
that deficits in the imitation-inhibition task are strongly correlated with lesions of the
TPJ and the aFMC. Lesions of the TPJ were also highly correlated with deficits in
visual and mental perspective taking. Even when controlled for other executive tasks,
there was still a high correlation on the behavioural as well as the neural level,
indicating a probable functional relationship.
Further investigating the role of the aFMC and the TPJ, Brass, Ruby, and Spengler
(2009) found evidence suggesting a differential role of both brain areas. The aFMC
seemed to be more involved in managing concurrent conflict on the representational
domain and enhancing the self-intented motor representation. The rTPJ on the other
hand seemed to be more involved in the outcome of agency judgements.
Summarizing, these studies provide strong evidence for the functional overlap
hypothesis. The aFMC and the rTPJ are both involved in the control of shared
representations as well as higher-level cognitive processes such as agency processing
and mentalizing (Bardi & Brass, 2017).
In the next paragraph we will further discuss the association between the low-level
processes of self-other distinction and the higher-level cognitive processes such as
mentalizing. In this discussion we will focus on the TPJ as a potential brain area linking
low- and high-level socio-cognitive processes.
The Role of the TPJ: Between Motor Control and Mentalizing
Having considered the substrates of motor control, we will now focus on the
neural substrates of mentalizing. Mentalizing is involved in more abstract social tasks
such as inferring the intentions and goals of others (Frith & Frith, 2003) and can be
investigated through the use of stories (Fletcher et al., 1995) or by using cartoons (Frith
& Frith, 2006; Gallagher et al., 2000).
Gallagher et al. (2000) for instance found in an fMRI study that when participants
where shown cartoons where the interpretation of the cartoons tapped into theory of
12
mind uniquely their medial prefrontal cortex (mPFC) was activated (see figure 3). A
meta-analysis argued that three regions seem to be implicated in the mentalizing system:
the temporal poles, the medial prefrontal cortex (mPFC) and the posterior superior
temporal sulcus (STS; which lies below the TPJ) (Frith & Frith, 2003).
A later meta-analysis argues that it is primarily the TPJ that is a “necessary
substrate for inferring the goals of others, even if they diverge from one’s own beliefs”
(Van Overwalle, 2009, p. 846); the role of the mPFC is not confined to theory of mind
tasks but more often comes to play when the context is more complex and involves
inferring traits of others.
Interestingly, the one region that is consistently involved in a great number of
studies investigating mentalizing is the TPJ. It is involved in explicit Theory of Mind
tasks and perspective taking tasks (Amodio & Frith, 2006; Saxe & Kanwisher, 2003).
13
Figure 3: Theory of mind cartoons from The Far Side from Gary Larson. Adapted from “Reading the mind in cartoons and stories: An fMRI study of “theory of mind” in verbal and nonverbal tasks,” by Gallagher, H. L., Happé, F., Brunswick, N., Fletcher, P. C., Frith, U., & Frith, C. D. , 2000, Neuropsychologia, 38(1), 11–21.
Recently it has been shown that TPJ also facilitates the spontaneous execution of ToM
tasks (Bardi, Desmet, Nijhof, Wiersema, & Brass, 2016; Hyde, Betancourt, & Simon,
2015). Bardi et al. (2016) for example showed that the TPJ is activated during the belief
formation phase of spontaneous ToM tasks while the mPFC is activated during outcome
evaluation. Furthermore the meta-analysis by Van Overwalle (2009) shows that the TPJ
is strongly involved in inferring goals and intentions of other people independent of
modality (see figure 4).
The TPJ, is a supramodal association area located at the intersection of the
posterior end of the superior temporal sulcus (pSTS), the inferior parietal lobule
(formed by the angular gyrus and the supramarginal gyrus), and the lateral occipital
cortex. It integrates input from the posterior and lateral thalamus, as well as visual,
auditory and limbic areas. Furthermore, it has bidirectional connections to the prefrontal
cortex and the temporal lobes (Bzdok et al., 2013; Krall et al., 2015). Given its
anatomical features, the TPJ has been found to be an important brain area involved in
the processing of different aspects of self: body-related information, phenomenological
information and cognitive aspects (Blanke & Arzy, 2005).
Van Overwalle (2009) suggested that the TPJ likely acts as a connecting region
14
Figure 4: The (r)TPJ is activated by goal-directed action, theory of mind and morality. Adapted from “Understanding others’ actions and goals by mirrorand mentalizing systems: A meta-analysis,” by VanOverwalle, F., & Baetens, K. , 2009, NeuroImage, 48(3), 564–584.
between the mirror neuron system and the mentalizing system. This idea is mainly
based upon previously discussed findings which show that the TPJ is uniquely activated
when distinguishing between self-triggered and externally triggered motor
representations (Brass et al., 2005, 2009).
Recent studies using novel brain stimulation techniques provide further support for
the idea that the TPJ plays a crucial role in distinguishing between self- and other-
related motor representations (Bardi et al., 2017; Santiesteban, Banissy, Catmur, & Bird,
2012, 2015; Sowden & Catmur, 2015; see Sellaro, Nitsche, & Colzato, 2016 for a
review) In a pair of studies Santiesteban et al. (2012, 2015) performed anodal
transcranial direct current stimulation (tDCS) (see Nitsche et al., 2008, for a review of
the technique), which induces cortical excitability, on the right temporoparietal junction
(rTPJ) while participants had to do the imitation-inhibition task and a perspective taking
task. They found that while under the effect of anodal tDCS participants performed
better on the perspective taking task as well as on the imitation-inhibition task. That is,
participants showed a smaller congruency effect on the imitation-inhibition task (i.e.
they were faster on incongruent trials). Similarly Sowden and Catmur (2015), showed
that participants performed worse on the imitation-inhibition task when the right TPJ
was temporarily inhibited with TMS.
In another study, Bardi et al. (2017) combined the recording of motor evoked
potentials induced by TMS with tDCS on the primary motor cortex during task
congruent or incongruent representations of finger movement. They found that when the
TPJ is stimulated by anodal stimulation – inducing cortical excitability – the
congruency effect is reduced, thus suggesting an enhancement of the self-representation
of the finger movement in the TPJ. Concluding, while theory of mind tasks also activate
the TPJ, this activation may rather reflect a mechanism which is based upon the origin
of mentalizing: imitation (Brass et al., 2005).
Delving deeper into the functions of the TPJ: some authors suggest that the TPJ is
also involved in a neural network which processes intentions independently of modality
dubbed the intention processing network (IPN) (Ciaramidaro et al., 2007; Enrici,
Adenzato, Cappa, Bara, & Tettamanti, 2011). Evidence supporting this idea comes from
15
experiments examining which brain areas are involved in the processing of
communicative actions such as social grasping or emblematic gestures. Becchio et al.
(2012), for instance, presented participants with videos of grasping actions in an fMRI
experiment. In this video two persons grasped a cube in a cooperative or competitive
way (slow or fast) or there was only one person grasping the cube. They found that the
activation of the TPJ was stronger for grasping in a social way (where two persons were
involved). In another fMRI study it was found that, when participants were presented
with a series of gestures in a game of charades, not only the mirror-neuron system was
activated, but also the TPJ (Schippers, Gazzola, Goebel, & Keysers, 2009). This result
also adds to the idea that the TPJ may act as a link between the mirror-neuron system
(processing the motor properties of the gesture) and the mentalizing system (inferring
the intentions of the emblematic gesture). Additional evidence has shown that the
bilateral TPJ is activated during the imagined reception of emblematic gestures
(Lindenberg, Uhlig, Scherfeld, Schlaug, & Seitz, 2012). Lastly, a recent fMRI study by
Villarreal, Fridman and Leiguarda (2012) suggested that the TPJ has a specific
preference for processing gestures.
To summarize, the TPJ seems to be involved in decoding intentions and goals from
others independent of modality (e.g. be it through reading about others or seeing others
grasp or gesticulate). Thus, the TPJ probably functions as a domain-general
computational mechanisms informing higher cognitive functions such as theory of mind
and empathy with lower-level computational processes such as sense of agency and
self-other distinction (Decety & Lamm, 2007). It is interesting to note that when there is
conflict on the representational level, the TPJ shows a higher activation thus suggesting
that it might work as an on-line comparator when discriminating between self- and
other-related (motor) representations (Bardi et al., 2017; Brass et al., 2009)
Second Person Social Neuroscience
There are two important limitations to most of the previously discussed evidence.
One limitation is that, while by definition social interaction exists at least between two
16
persons (Hari & Kujala, 2009), most social neuroimaging studies involve just one
participant. Furthermore, the participant is a passive observer and cannot interact in a
meaningful way with the experiment. This way of experimenting has been called an
“isolation paradigm” (Becchio et al., 2012; Montague, 2002): participants are to observe
and think about social interaction without actually interacting with another person.
A second limitation has to do with the restricted context in which most of
neuroimaging studies occur. For instance in a scanner natural movements and face-to-
face contact is restricted. Furthermore, a lot of ‘social’ stimuli are viewed from
computer screens. Thus, it largely remains an open question as to how the mirror-neuron
system and the mentalizing system may act in a real-time social encounter between two
persons (Schilbach et al., 2013).
To address these two issues Schilbach et al. (2013) propose a new theoretical
framework in their article Toward a second-person neuroscience. Namely, to step away
from spectatorial views of cognition where the observer passively takes in the outside
world. In their proposal they argue for a more embodied look at social interaction while
emphasizing that both interaction and social engagement is important. Already some
studies have tried to address these issues put forth by Schilbach et al. (2013).
One line of research has started recording brain activity in more ecological
environments with less restricting equipment such as functional near-infrared
spectrometry (fNIRS). Functional near-infrared spectrometry is a novel non-invasive
brain imaging technique which is as portable as EEG and with a better spatial
resolution, but with a lower temporal resolution (see below for a brief discussion of the
technique). Recently there has been a growing number of studies using fNIRS to study
sub-domains of social cognition such as: imitation (Oliver, Tachtsidis, & Hamilton,
2017), the execution and observation of action (Koehler et al., 2012) and theory of mind
(Hyde et al., 2015). In a study by Oliver et al. (2017) participants had to observe a
rational or irrational way of solving a puzzle before making the same puzzle themselves.
They found a greater activation in the anterior inferior parietal lobule (aIPL) for
irrational actions (Oliver et al., 2017). These studies show that fNIRS is a viable
neuroimaging method to record participants in more ecological settings.
17
One disadvantage of fNIRS is that it can only record cortical activity but no
subcortical activity (Scholkmann et al., 2014). However, seeing that most regions
involved in the mentalizing and the mirror-neuron system are on the cortex it is a viable
method to further examine these regions of the brain.
To conclude: while there have been studies trying to overcome the limitations of
the so-called isolation paradigm, the number of studies are sparse and it remains to be
investigated how the previously discussed brain areas are involved in more ecological
social interaction (Schilbach et al., 2013).
Study Objectives
The objective of our experiment was to further investigate the role of the rTPJ in a
face-to-face interaction when not restricted in movement and environment by equipment
such as fMRI.
What if the rTPJ also functions to discriminate between self and other in a social
context? To investigate this we will execute an experiment where two participants
imitate one another with the use of emblematic gestures. We hypothesize that:
(a) given the evidence that the rTPJ has been shown to be involved with self-
other distinction (Bardi et al., 2017), there will be more activation of the rTPJ in the
counter-imitation condition than within the imitation condition;
(b) because the rTPJ is also involved in mentalizing and inferring other persons’
intentions (Saxe & Kanwisher, 2003; Van Overwalle, 2009), there will be higher
activation with meaningful social interaction than meaningless interaction.
18
Method
Participants
28 healthy undergraduate students of the University of Ghent (3 male; mean age =
19; SD = 2.4) took part in this study for exchange of course credits and signed an
informed consent in accordance with the declaration of Helsinki (1964). The study was
approved by the local ethic committee. All participants were right handed. In total six
participants had to be excluded from analysis: two due to movement artefacts or skull
thickness; two participants because they became aware of the instructions of the
confederate; one participant because of an error in the program and one participant
because she presented too many invalid gestures.
Stimuli and Procedure
The experiment took place in a dimly lit room to not let light interfere with the
fNIRS equipment. The experiment was divided in two main parts: the false belief task
and the imitation task. To ameliorate the subsequent data analysis, triggers were sent to
the fNIRS equipment at the beginning of each trial. The presentation timing, fNIRS
triggering and randomization was controlled with the use of E-prime V2.0 (Psychology
Software Tools Inc., Pittsburgh, PA, USA).
ToM task.
In the first part of the experiment the participants had to perform an adapted Dutch
version of the False Belief Localizer task as used by Dodell-Feder, Koster-Hale, Bedny
and Saxe (2011). This task is used in fMRI studies to identify brain regions (specifically
the TPJ) involved in Theory of Mind in individual participants, by contrasting false
belief stories with false photograph stories. The task has also been used with fNIRS to
identify channels of interest for the right TPJ (Hyde et al., 2015).
In every trail, the participant had 10 seconds of time to read a short story and then
19
had to answer true or false to a question about the story within 5 seconds. Between the
trials there was a period of 4-8 seconds of jitter where a fixation cross was presented1.
The task consisted of 20 short stories followed by a question presented in a random
oder.
There were two conditions: the False Belief condition and the False Photograph
condition. In the False belief condition the stories presented characters that had an
incorrect belief about the state of the world. In the False Photograph condition, the
stories described a photograph, map or sign about a misguiding or outdated state of the
world. This task lasted about 8 minutes.
Imitation task.
In the second part of the experiment participants had to present emblematic
gestures similar to the ones used by Lindenberg et al. (2012). Two types of gestures
were used: meaningless gestures (figure 5a and 5b) and meaningful gestures (Figure 5c
and 5d). Participants were instructed to present one of the gestures depending on a
coloured square they could see. Participants were seated across a confederate who they
were told was another participant. Between the participant and confederate a screen was
placed horizontally were the instructions were presented on. A barrier on the screen
presented the participant to see the instructions of the confederate.
Participants were instructed to perform gestures towards the other participant and
to pay close attention to the gesture that the other participant was performing. To ensure
that participants payed attention to these gestures, catch trials were added asking the
participant what the gesture of the confederate was.
1 The adapted version of the localizer task used in this task, had a jitter inter-
stimulus interval of 4-8 seconds. However, the original task developed by Dodell-Feder
et al. (2011) had an inter-stimulus interval of 10 seconds.
20
There were two factors (Imitation and Gesture), each with two levels. The factor
Imitation (with levels imitation and counterimitation) was manipulated on a trial by trial
basis, while the factor Gesture (with levels meaningful and meaningless) was
manipulated per block. The order of the two blocks was counterbalanced between
participants. Both blocks consisted of an event related design where the participants had
to perform one of two possible gestures toward the confederate. The confederate was
then instructed to present either the same movement (imitation) or to present different
movement (counterimitation). At the beginning of each trial, both participants were
presented with a cue instructing them to perform one of two gestures. The actual
participant was told, however, that the confederate was free to choose which gesture to
perform.
In total there were 65 randomized trials in each block, for a total of 130 trials.
Each trial lasted 15 seconds each: 5 seconds of cue presentation and gesture execution
and 9-11 seconds of rest where the participant had to look at a fixation cross. To ensure
that participants kept paying attention to the gesture of the confederate, there were 5
catch trials interspersed between the trials. In the catch trial the participant had to
choose which of the two gestures was the last one the confederate presented by pressing
on the keyboard.
21
Figure 5: The four gestures which were instructed to be performed. Gestures (a) and (b)were considered meaningless gestures; gestures (c) and (d) meaningful.
FNIRS Measurement
Functional near infrared spectrometry (fNIRS) is a relatively new method within
cognitive neuroscience (Cutini, Moro, & Bisconti, 2012). While the technique itself has
celebrated its thirtieth birthday the previous year (Jobsis, 1977) it is only in the past
decade that it has been used to examine brain functionality. Similar to functional
magnetic resonance imaging (fMRI), fNIRS measures changes in local hemodynamic
cortical activity. This change in hemodynamic activity can be used to infer neuronal
activity. This is based upon the link between neuronal activity and cerebral blood flow
(CBF) called neurovascular coupling. Following the firing of neurons the ionic
concentration gradients need to be restored and this increases the need for metabolic
supplies such as glucose and oxygen (see figure 6b). These supplies are provided via the
vascular system leading to an increase of CBF with a net increase of oxygenated
hemoglobin (HbO) and a - relatively smaller - decrease of deoxygenated hemoglobin
(HbR) (Scholkmann, Kleiser, Metz, Zimmermann, Wolf & Wolf, 2014). This increase in
blood flow after a neuronal firing is called the hemodynamic response function (HRF)
(see figure 6a) and broadly corresponds to the blood oxygen level dependent (BOLD)
signal used in fMRI (Noah et al., 2015).
22
Figure 6: (a) The hemodynamic response function (HRF) after a stimulus. (b) The physiological reactions following neural activity. Adapted from “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology.” by Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Mata Pavia, J., Wolf, U., & Wolf, M., 2014, NeuroImage, 85, 6–27.
While fMRI measures the hemodynamic response function using the paramagnetic
properties of HbR, fNIRS measures the HRF using near-infrared light. The
measurement of cerebral blood flow with near-infrared light is based upon the principle
that HbO and HbR selectively absorb light at different wavelengths because of differing
chromophores (respectively 690nm and 830nm.) Furthermore human skin tissue is
relatively transparent to near-infrared light. Because of this it is possible to transmit
near-infrared light through the skull and record the scattered photons that come back
with the use of emitters and receivers connected to optical fibers. (Scholkmann et al.,
2014). The light from one emitter to a detector follows an ellipsoid trajectory or a so-
called ‘banana-shaped’ trajectory (see figure 7). It is important to place the emitters and
receivers in an optimal distance: too close and the light will not pass through the skull;
too far and the returning signal will be too weak (Brigadoi & Cooper, 2015; Cui, Bray,
Bryant, Glover, & Reiss, 2011). This trajectory from emitter to detector is called a
channel.
As discussed above one of the main advantage of fNIRS above fMRI and PET is
the fact that it can be used in more ecological situations (i.e. is not restricted to a
scanner) and allows for a lot more movement of the head. For example, as a proof of
concept fNIRS has recently successfully been used to record brain activity during
23
Figure 7: The ellipsoid trajectory of near-infrared light from emitter to detector. Adapted from “fNIRS-based brain-computer interfaces: a review,” by Naseer, N., & Hong, K., 2015, Frontiers in Human Neuroscience, 9(January), 3.
dancing (Scholkmann et al., 2014). Another advantage of fNIRS is its relatively good
spatial resolution in comparison to EEG although still much lower than the spatial
resolution of fMRI. It must be noted that one of the main disadvantages of fNIRS is that
its signal to noise ratio (SNR) is relatively low compared to that of fMRI: the SNR
variability is correlated to the the scalp brain distance. This is mainly due to the skull
absorbing too much light (Okada et al., 1997). However it has been shown that there is a
high correlation between fNIRS and fMRI data (Cui et al., 2011; Noah et al., 2015). For
a further overview of the different uses of fNIRS within cognitive neuroscience see
Cutini, Moro and Bisconti (2012).
In our study fNIRS data was recorded by using a NIRScoutX (NIRx
Medizintechnik, Germany) continuous wave tomography imager. The NIRScoutX is
currently the device with the most emitters and detectors, making it an ideal candidate
for hyperscanning. For a review of other continuous wave fNIRS devices see (Cui et al.,
2011; Noah et al., 2015)
Participants with similar head circumference will be given the same cap size
ensuring that the channels broadly cover the same areas (See figure 8 for an example of
the cap connected to some emitters and receivers.) Seven optodes and eight receivers
24
Figure 8: NIRScoutX cap setup for both parietal cortices with emitters and detecters. In our experiment the same setup was used, but only for the right hemisphere.
(adding up to 21 channels) were placed on an area broadly covering the right parietal
and temporo-parietal cortex. Hair under the sensors was carefully brushed aside to
ensure good skin contact. Before the start of the experiment each channel was calibrated
to check if the signal was strong enough. If a channel gave a signal that was too weak
the emitter and receiver of the respective channel were checked again to ensure that
there was no hair interfering with the signal.
FNIRS Data Preprocessing
ToM task
Analysis and preprocessing of the individual time-series was done in MATLAB®
with the use of HomER2 (v2.2), a set of MATLAB® scripts developed to process
fNIRS data (PMILab, 2012). However, during the preprocessing some errors were
found in the coding of the triggers that were sent during the task. Furthermore, the data
analysis did not give the expected clear results that were found in previous fMRI
studies. This might be related to the use of a shorter inter-stimulus interval with respect
to the original task (Doddell-Feder et al., 2011). Given these two problems with the
task, data from the localizer task were not further analyzed; it was decided to focus our
analysis on the channels corresponding anatomically to the rTPJ.
Imitation task
Analysis and preprocessing of the individual time-series were done in MATLAB®
with the use of HomER2 (v2.2), a set of MATLAB® scripts developed to process
fNIRS data (PMILab, 2012). In the preprocessing stream special attention was given to
physiological noise such as heartbeat, breathing and low-frequency drift (see also:
Brigadoi & Cooper, 2015; Hyde et al., 2015; Scholkmann et al., 2014). The use of
reference channel subtraction (RCS) was considered as a way to deal with remaining
physiological noise (Brigadoi & Cooper, 2015), however due to physical constraints of
the equipment it was not possible to make a short shallow channel.
First, the raw data intensity was normalized and converted to optical density (OD).
25
Channels with low OD were discarded from analysis. Secondly, the data was high-pass
filtered at 0.01 Hz to remove slow signal drift and low-pass filtered at 0.5 Hz to remove
physiological noise from the heartbeat. After conversion to OD, filtering and pruning of
the channels, the data was converted to oxy- and deoxyhemoglobin concentrations using
the modified Beer-Lambert law (Okada et al., 1997; Strangman, Boas, & Sutton, 2002).
Signals from the same channels and same condition were block averaged with a window
of -5 to 15 seconds.
Similarly to the study done by Hyde et al. (2015), we ran a two-way ANOVA on
the averaged HbO concentrations for each block.
FNIRS Data Analysis
The setup of our optodes and channels broadly covered an area around the TPJ.
However, it was not possible to make a selection in the channels with the localizer task.
Thus, we decided to focus our analysis on the channels that were closest anatomically to
the rTPJ.
To precisely localize the channels measuring activity in the rTPJ we made an
estimation of the cortical sensitivity of each channel using a Monte Carlo photon
26
Figure 9: Cortical sensitivity of channel 15. The centre of activity of this channel (54, -50, 35) was theclosest to the MNI coordinates of the rTPJ (56, -47, 33) .
migration simulation algorithm with Atlasviewer (implemented in Homer2). We first
digitized the location of every detector and source from three participants with a
neuronavigation system (Localite) and then registered these digital points to the default
3D head model (Colin-27) in Atlasviewer. The Montreal Neurological Institute (MNI)
coordinates corresponding to the centre of the cortical sensitivity of each source-
detector channel were extracted and averaged across the three participants. Lastly, we
selected a channel with a centre of activity closest to the MNI coordinates of the rTPJ
(56, -47, 33) (Kovacs, Teglas, & Endress, 2010). The cortical sensitivity of channel 15
(54 -50 35) was in sphere less than 10mm of the MNI coordinates of the rTPJ and was
chosen as our channel of interest (see figure 9).
After the selection of our channel, we defined a window of 2 seconds around the
peak of the HRF (-1, +1 seconds). Then, we ran a 2 x 2 repeated measures analysis of
variance (ANOVA) with factors imitation (imitation vs counterimitation) and gesture
type (meaningful vs meaningless) on the total HbO concentration around the peak (see
figure 10 and 11).
27
Figure 10: The averaged hemodynamic response function of the meaningless counterimitation (in red) and imitation condition (in magenta). The green box visualises the time window we selected around the peak (-1, +1 seconds).
Results
The ANOVA of the revealed a main effect of Gesture F(1,21) = 6.51, p = 0.0186,
ηp= 0.12024; a main effect of Imitation F(1,21) = 8.77, p = 0.0074, ηp= 0.18667; and a
significant interaction of Gesture x Imitation F(1,21) = 6.13, p = 0.0219, ηp= 0.24635.
Post-hoc analysis on the significant interaction revealed that the rTPJ was significantly
more active in the counterimitation than in the imitation condition with meaningless
gestures (t(21)=2.56, p=0.018). However, this difference was not present with
meaningful gestures (t(21) = 0.33, p = .75). These results confirm the predicted higher
activation of the rTPJ in the counterimitation condition in comparison to the imitation
condition.
28
Figure 11: Averaged delta OxyHb concentrations of the four different conditions around the peak of the HRF (Meaningless Imitation, Meaningless Counterimitation, Meaningful Imitation and Meaningful Counterimitation).
Discussion
In our experiment we investigated the functional role of the rTPJ with fNIRS in
live social interactions. We found that participants showed more activation in the rTPJ
when there was conflict on the representational domain (i.e. during counterimitation).
The activation of the rTPJ was lower when there was no conflict on the representational
domain (i.e. imitation). This confirms our hypothesis that the rTPJ is also involved in
self-other discrimination in more ecological, social interactions. This finding is in line
with research suggesting that the rTPJ is not only involved in the mirror neuron system
and the mentalizing system, but functions as a gateway between both by detecting a
mismatch between self- and other-related representations informing both systems with
information of this mismatch (Bardi et al., 2017; Brass et al., 2009).
Furthermore, our analysis showed that there was more activation in the rTPJ for
meaningful gestures than for meaningless gestures. This suggests that the rTPJ is more
engaged when presented with meaningful, social stimuli and confirms our hypothesis
that the TPJ is also involved in processing social actions and intentions (Ciaramidaro et
al., 2007; Enrici et al., 2011; Spengler, von Cramon, & Brass, 2009).
We also found an interaction effect between factors gesture and imitation. In our
post-hoc analysis we saw that there was a significant difference between imitation and
counterimitation with meaningless gestures, but not with meaningful gestures. One
explanation for this finding could be that more general control processes are used during
the meaningless imitation condition, because the representations don’t mismatch and the
stimuli aren’t really social thus potentially circumventing the use of the rTPJ. Another
explanation is that the reason we didn’t find a difference in activation within the
meaningful condition is that the rTPJ is overly involved in processing the social value
of the gestures, thus masking the effect of the mismatch between the different gestures.
To better discriminate between these two possible explanations, future studies
could manipulate the valence and meaning of the gestures. Doing this, it could be
specified whether the difference in activation is explained by a pure visual difference,
also by its social valence.
29
A limitation to our study is that the experimental procedure made for relatively dry
social interactions. Participants were instructed to perform one of four gestures and
were told to observe whether the confederate imitated them or not. (Schilbach et al.,
2013) argued that social cognition is radically different when it constitutes of emotional
engagement as well as meaningful interaction. While our experiment included a level of
interaction (imitation and observing this imitation), there was not so much emotional
engagement. In future research one could invest more in emotional engagement by
making the imitation more reciprocal (Guionnet et al., 2012) or by letting participants
add more emotional valence to their gestures by for example gesticulating to express a
certain emotion.
To further advance social cognitive neuroscience, an interesting avenue is to
record the brain activity of two brains concurrently. Recently, researchers have tried to
capture the dynamics of two interacting brains (Montague, 2002). This technique, called
hyperscanning, has successfully been used with fMRI and EEG (for a review see:
Babiloni & Astolfi, 2014). However, recently fNIRS has successfully been used in a
handful of hyperscanning studies examining brain coherence during economic tasks, n-
back tasks or during a simple finger tapping task (Cui, Bryant, & Reiss, 2012; Dommer,
Jäger, Scholkmann, Wolf, & Holper, 2012; Holper, Scholkmann, & Wolf, 2012; Liu et
al., 2016; Tang et al., 2015; for a review see Scholkmann, Holper, Wolf, & Wolf, 2013)
For example, Cui et al. (2012) measured the brain activity from two people
simultaneously while they played a cooperation game side by side on the same
computer. They found that the signals from the participants right superior frontal
cortices showed greater coherence during cooperation than during competition.
Increased coherence was also associated with a better cooperation performance.
Interestingly, Cui et al. (2012) found no task-specific patterns of brain activity on the
individual level. However, they did find an increased inter-brain coherence during task
blocks. This emphasizes the idea that the simultaneous recording of two participants can
add additional information to the understanding of how brain activity acts during social
interaction.
Of the recent hyperscanning studies that used fNIRS only one study successfully
30
recorded the simultaneous activity of the rTPJ (Tang et al., 2015). In this study
participants had to play an adapted version of the ultimatum game on the computer.
There where two conditions: one were participants sat face-to-face or one where a
screen was blocking the view of the other participant. There were two main findings.
First, the activity of the rTPJ was higher when there was competition (the participant
denied a proposal in the game) than during cooperation (the other participant accepted a
proposal in the game). This suggests that the TPJ had to compute more about the other
persons’ goals. Second, the rTPJ showed more synchronization during cooperation and
even more during face-to-face interaction. Both findings lie in line with the idea of the
rTPJ as the area for inferring mental states of others (Saxe & Kanwisher, 2003).
Conclusion
The findings of our experiment are in line with a growing number of studies
supporting the concept of a shared representational system between action execution
and observation (Bardi & Brass, 2017). An observed movement activates its
corresponding motor representation (Brass & Heyes, 2005; Decety & Sommerville,
2003) which leads to response interference when concurrently performing a certain
movement (Brass et al., 2000, 2001).
Furthermore, our study is compatible with recent neuroscientific research
suggesting that the rTPJ is involved in detecting a mismatch when there is conflict
between self- and other-related representations (Bardi et al., 2017; Brass et al., 2009;
Sowden & Catmur, 2015). And it also supports the idea that the rTPJ functions as a top
down control mechanism in higher-level social cognitive processes such as
(spontaneous) ToM (Bardi et al., 2016; Hyde et al., 2015) and intention processing
(Enrici et al., 2011).
Lastly, our study shows that fNIRS is a feasible tool to investigate imitation in
ecological social interactions.
31
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