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,

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& 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

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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

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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

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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

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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.

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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

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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).

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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

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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

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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

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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

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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).

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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

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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

Bibliography

Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: the medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7(4), 268–277. http://doi.org/10.1038/nrn1884

Babiloni, F., & Astolfi, L. (2014). Social neuroscience and hyperscanning techniques: Past, present and future. Neuroscience & Biobehavioral Reviews, 44, 76–93. http://doi.org/10.1016/j.neubiorev.2012.07.006

Bardi, L., & Brass, M. (2017). The Control of Shared Representations and Social Cognition Lara. In S. Obhi & E. Cross (Eds.), Shared Representations: Sensorimotor Foundations of Social Life (Cambridge Social Neuroscience, pp 151–170). Cambridge: Cambridge University Press. http://doi.org/10.1017/CBO9781107279353.009

Bardi, L., Desmet, C., Nijhof, A., Wiersema, J. R., & Brass, M. (2016). Brain activation for spontaneous and explicit false belief tasks overlaps: new fMRI evidence on belief processing and violation of expectation. Social Cognitive and Affective Neuroscience, 12(3) 391-400. http://doi.org/10.1093/scan/nsw143

Bardi, L., Gheza, D., & Brass, M. (2017). TPJ-M1 interaction in the control of shared representations: New insights from tDCS and TMS combined. NeuroImage, 146, 734–740. http://doi.org/10.1016/j.neuroimage.2016.10.050

Barker, A. T., Jalinous, R., & Freeston, I. L. (1985). NON-INVASIVE MAGNETIC STIMULATION OF HUMAN MOTOR CORTEX. The Lancet, 11, 1106-1107 http://doi.org/10.1016/S0140-6736(85)92413-4

Becchio, C., Cavallo, A., Begliomini, C., Sartori, L., Feltrin, G., & Castiello, U. (2012). Social grasping: From mirroring to mentalizing. NeuroImage, 61(1), 240–248. http://doi.org/10.1016/j.neuroimage.2012.03.013

Blandin, Y., Lhuisset, L., & Proteau, L. (1999). Cognitive Processes Underlying Observational Learning of Motor Skills. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 52(4), 957–979. http://doi.org/10.1080/713755856

Blanke, O., & Arzy, S. (2005). The out-of-body experience: Disturbed self-processing atthe temporo-parietal junction. Neuroscientist, 11(1), 16-24. http://doi.org/10.1177/1073858404270885

32

Brass, M., Bekkering, H., Wohlschläger, A., & Prinz, W. (2000). Compatibility between Observed and Executed Finger Movements: Comparing Symbolic, Spatial, and Imitative Cues. Brain and Cognition, 44(2), 124–143. http://doi.org/10.1006/brcg.2000.1225

Brass, M., Derrfuss, J., Matthes-von Cramon, G., & von Cramon, D. Y. (2003). Imitative response tendencies in patients with frontal brain lesions. Neuropsychology, 17(2), 265-271. http://doi.org/10.1037/0894-4105.17.2.265

Brass, M., Derrfuss, J., & von Cramon, D. Y. (2005). The inhibition of imitative and overlearned responses: a functional double dissociation. Neuropsychologia, 43(1), 89–98. http://doi.org/10.1016/j.neuropsychologia.2004.06.018

Brass, M., & Heyes, C. (2005). Imitation: is cognitive neuroscience solving the correspondence problem? Trends in Cognitive Sciences, 9(10), 489–495. http://doi.org/10.1016/j.tics.2005.08.007

Brass, M., & Muhle-Karbe, P. S. (2014). More than associations: An ideomotor perspective on mirror neurons. Behavioral and Brain Sciences, 37(02), 195–196. http://doi.org/10.1017/S0140525X13002239

Brass, M., Ruby, P., & Spengler, S. (2009). Inhibition of imitative behaviour and social cognition. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364(1528), 2359–2367. http://doi.org/10.1098/rstb.2009.0066

Brass, M., Zysset, S., & von Cramon, D. Y. (2001). The Inhibition of Imitative Response Tendencies. NeuroImage, 14(6), 1416–1423. http://doi.org/10.1006/nimg.2001.0944

Brigadoi, S., & Cooper, R. J. (2015). How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics, 2(2), 250-255. http://doi.org/10.1117/1.NPh.2.2.025005

Buccino, G., Lui, F., Canessa, N., Patteri, I., Lagravinese, G., Benuzzi, F., … Rizzolatti, G. (2004). Neural Circuits Involved in the Recognition of Actions Performed by Nonconspecifics: An fMRI Study. Journal of Cognitive Neuroscience, 16(1), 114–126. http://doi.org/10.1162/089892904322755601

Bzdok, D., Langner, R., Schilbach, L., Engemann, D. A., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2013). Segregation of the human medial prefrontal cortex in socialcognition. Frontiers in Human Neuroscience, 7. http://doi.org/10.3389/fnhum.2013.00232

Carpenter, M., Akhtar, N., & Tomasello, M. (1998). Fourteen- through 18-month-old

33

infants differentially imitate intentional and accidental actions. Infant Behavior and Development, 21(2), 315–330. http://doi.org/10.1016/S0163-6383(98)90009-1

Carpenter, W. B. (1852). On the influence of suggestion in modifying and directing muscular movement, independently of volition. Proceedings of the Royal Institution, 147–154.

Caspers, S., Zilles, K., Laird, A. R., & Eickhoff, S. B. (2010). ALE meta-analysis of action observation and imitation in the human brain. NeuroImage, 50(3), 1148–1167. http://doi.org/10.1016/j.neuroimage.2009.12.112

Catmur, C., Walsh, V., & Heyes, C. (2009). Associative sequence learning: the role of experience in the development of imitation and the mirror system. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1528), 2369–2380. http://doi.org/10.1098/rstb.2009.0048

Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception–behaviorlink and social interaction. Journal of Personality and Social Psychology, 76(6), 893–910. http://doi.org/10.1037/0022-3514.76.6.893

Chartrand, T. L., & Lakin, J. L. (2013). The Antecedents and Consequences of Human Behavioral Mimicry. Annual Review of Psychology, 64(1), 285–308. http://doi.org/10.1146/annurev-psych-113011-143754

Chartrand, T. L., & van Baaren, R. (2009). Chapter 5 Human Mimicry. Advances in Experimental Social Psychology, 41, 219-274. http://doi.org/10.1016/S0065-2601(08)00405-X

Ciaramidaro, A., Adenzato, M., Enrici, I., Erk, S., Pia, L., Bara, B. G., & Walter, H. (2007). The intentional network: How the brain reads varieties of intentions. Neuropsychologia, 45(13), 3105–3113. http://doi.org/10.1016/j.neuropsychologia.2007.05.011

Cook, J. L., & Bird, G. (2012). Atypical Social Modulation of Imitation in Autism Spectrum Conditions. Journal of Autism and Developmental Disorders, 42(6), 1045–1051. http://doi.org/10.1007/s10803-011-1341-7

Cook, R., Bird, G., Catmur, C., Press, C., & Heyes, C. (2014). Mirror neurons: From origin to function. Behavioral and Brain Sciences, 37(02), 177–192. http://doi.org/10.1017/S0140525X13000903

Craighero, L., Bello, A., Fadiga, L., & Rizzolatti, G. (2002). Hand action preparation influences the responses to hand pictures. Neuropsychologia, 40(5), 492-502. http://doi.org/10.1016/S0028-3932(01)00134-8

34

Cui, X., Bray, S., Bryant, D. M., Glover, G. H., & Reiss, A. L. (2011). A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. NeuroImage, 54(4),2808–2821. http://doi.org/10.1016/j.neuroimage.2010.10.069

Cui, X., Bryant, D. M., & Reiss, A. L. (2012). NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. NeuroImage, 59(3), 2430–2437. http://doi.org/10.1016/j.neuroimage.2011.09.003

Cutini, S., Moro, S. B., & Bisconti, S. (2012). Review: Functional near infrared optical imaging in cognitive neuroscience: An introductory review. Journal of Near Infrared Spectroscopy, 20(1), 75–92. http://doi.org/10.1255/jnirs.969

De Coster, L., Verschuere, B., Goubert, L., Tsakiris, M., & Brass, M. (2013). I suffer more from your pain when you act like me: Being imitated enhances affective responses to seeing someone else in pain. Cognitive, Affective and Behavioral Neuroscience, 13(3), 519–532. http://doi.org/10.3758/s13415-013-0168-4

De Renzi, E., Cavalleri, F., & Facchini, S. (1996). Imitation and utilisation behaviour. Journal of Neurology, Neurosurgery & Psychiatry, 61(4), 396–400. http://doi.org/10.1136/jnnp.61.4.396

Decety, J., & Lamm, C. (2007). The Role of the Right Temporoparietal Junction in Social Interaction: How Low-Level Computational Processes Contribute to Meta-Cognition. The Neuroscientist, 13(6), 580–593. http://doi.org/10.1177/1073858407304654

Decety, J., & Sommerville, J. A. (2003). Shared representations between self and other: a social cognitive neuroscience view, 7(12), 527–533. http://doi.org/10.1016/j.tics.2003.10.004

di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: a neurophysiological study. Experimental Brain Research, 91(1), 176–180. http://doi.org/10.1007/BF00230027

Dodell-Feder, D., Koster-Hale, J., Bedny, M., & Saxe, R. (2011). fMRI item analysis in a theory of mind task. NeuroImage, 55(2), 705–712. http://doi.org/10.1016/j.neuroimage.2010.12.040

Dommer, L., Jäger, N., Scholkmann, F., Wolf, M., & Holper, L. (2012). Between-brain coherence during joint n-back task performance: A two-person functional near-infrared spectroscopy study. Behavioural Brain Research, 234(2), 212–222. http://doi.org/10.1016/j.bbr.2012.06.024

Drost, U. C., Rieger, M., Brass, M., Gunter, T. C., & Prinz, W. (2005). When hearing

35

turns into playing: Movement induction by auditory stimuli in pianists. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 58(8), 1376–1389. http://doi.org/10.1080/02724980443000610

Dunbar, R. I. M. (1998). The Social Brain Hypothesis. Evolutionary Anthropology, 6(5),178–190. http://doi.org/10.1002/(SICI)1520-6505(1998)6:5<178::AID-EVAN5>3.3.CO;2-P

Elsner, B., & Hommel, B. (2001). Effect anticipation and action control. Journal of Experimental Psychology: Human Perception and Performance, 27(1), 229–240. http://doi.org/10.1037//0096-1523.27.1.229

Enrici, I., Adenzato, M., Cappa, S., Bara, B. G., & Tettamanti, M. (2011). Intention processing in communication: a common brain network for language and gestures. Journal of Cognitive Neuroscience, 23(9), 2415–31. http://doi.org/10.1162/jocn.2010.21594

Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995). Motor facilitation during action observation: a magnetic stimulation study. Journal of Neurophysiology, 73(6), 2608–2611. http://doi.org/10.1152/jn.1995.73.6.2608

Farrer, C., Franck, N., Georgieff, N., Frith, C. ., Decety, J., & Jeannerod, M. (2003). Modulating the experience of agency: a positron emission tomography study. NeuroImage, 18(2), 324–333. http://doi.org/10.1016/S1053-8119(02)00041-1

Fletcher, P. C., Happé, F., Frith, U., Baker, S. C., Dolan, R. J., Frackowiak, R. S. J., & Frith, C. D. (1995). Other minds in the brain: a functional imaging study of “theoryof mind” in story comprehension. Cognition, 57(2), 109–128. http://doi.org/10.1016/0010-0277(95)00692-R

Frith, C. D., & Frith, U. (2006). The Neural Basis of Mentalizing, Neuron, 50(4), 531–534. http://doi.org/10.1016/j.neuron.2006.05.001

Frith, U., & Frith, C. D. (2003). Development and neurophysiology of mentalizing. Philosophical Transactions of the Royal Society B: Biological Sciences, 358(1431),459–473. http://doi.org/10.1098/rstb.2002.1218

Gallagher, H. L., Happé, F., Brunswick, N., Fletcher, P. C., Frith, U., & Frith, C. D. (2000). Reading the mind in cartoons and stories: An fMRI study of “theory of mind” in verbal and nonverbal tasks. Neuropsychologia, 38(1), 11–21. http://doi.org/10.1016/S0028-3932(99)00053-6

Gallese, V. (2003). The manifold nature of interpersonal relations: the quest for a common mechanism. Philosophical Transactions of the Royal Society B:

36

Biological Sciences, 358(1431), 517–528. http://doi.org/10.1098/rstb.2002.1234

Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119(2), 593–609. http://doi.org/10.1093/brain/119.2.593

Genschow, O., van Den Bossche, S., Cracco, E., Bardi, L., Rigoni, D., & Brass, M. (2017). Mimicry and automatic imitation are not correlated. PLOS ONE, 12(9). http://doi.org/10.1371/journal.pone.0183784

Greenwald, A. G. (1970). Sensory feedback mechanisms in performance control: With special reference to the ideo-motor mechanism. Psychological Review, 77(2), 73–99. http://doi.org/10.1037/h0028689

Guionnet, S., Nadel, J., Bertasi, E., Sperduti, M., Delaveau, P., & Fossati, P. (2012). Reciprocal imitation: Toward a neural basis of social interaction. Cerebral Cortex, 22(4), 971–978. http://doi.org/10.1093/cercor/bhr177

Hari, R., & Kujala, M. V. (2009). Brain Basis of Human Social Interaction: From Concepts to Brain Imaging. Physiological Reviews, 89(2), 453–479. http://doi.org/10.1152/physrev.00041.2007.

Heyes, C. (2001). Causes and consequences of imitation. Trends in Cognitive Sciences, 5(6), 253–261. http://doi.org/10.1016/S1364-6613(00)01661-2

Heyes, C. (2010). Where do mirror neurons come from? Neuroscience & BiobehavioralReviews, 34(4), 575–583. http://doi.org/10.1016/j.neubiorev.2009.11.007

Heyes, C. (2011). Automatic imitation. Psychological Bulletin, 137(3), 463–483. http://doi.org/10.1037/a0022288

Holper, L., Scholkmann, F., & Wolf, M. (2012). Between-brain connectivity during imitation measured by fNIRS. NeuroImage, 63(1), 212–222. http://doi.org/10.1016/j.neuroimage.2012.06.028

Hommel, B., Müsseler, J., Aschersleben, G., & Prinz, W. (2001). The Theory of Event Coding (TEC): A framework for perception and action planning. Behavioral and Brain Sciences, 24(5), 849–878. http://doi.org/10.1017/S0140525X01000103

Hyde, D. C., Betancourt, A. M., & Simon, C. E. (2015). Human temporal-parietal junction spontaneously tracks others’ beliefs: A functional near-infrared spectroscopy study. Human Brain Mapping, 36(12), 4831–4846. http://doi.org/10.1002/hbm.22953

James, W. (1950). The Principles of Psychology: vol 2. New York, NY: Dover. (Originalwork published 1890)

37

Jeannerod, M. (2004). Visual and action cues contribute to the self–other distinction. Nature Neuroscience, 7(5), 422–423. http://doi.org/10.1038/nn0504-422

Jones, S. S. (2012). Human Toddlers’ Attempts to Match Two Simple Behaviors ProvideNo Evidence for an Inherited, Dedicated Imitation Mechanism. PLoS ONE, 7(12). http://doi.org/10.1371/journal.pone.0051326

Keysers, C., & Gazzola, V. (2010). Social Neuroscience: Mirror Neurons Recorded in Humans. Current Biology, 20(8), 353–354. http://doi.org/10.1016/j.cub.2010.03.013

Kilner, J. ., Paulignan, Y., & Blakemore, S. . (2003). An Interference Effect of Observed Biological Movement on Action. Current Biology, 13(6), 522–525. http://doi.org/10.1016/S0960-9822(03)00165-9

Koehler, S., Egetemeir, J., Stenneken, P., Koch, S. P., Pauli, P., Fallgatter, A. J., & Herrmann, M. J. (2012). The human execution/observation matching system investigated with a complex everyday task: A functional near-infrared spectroscopy(fNIRS) study. Neuroscience Letters, 508(2), 73–77. http://doi.org/10.1016/j.neulet.2011.12.021

Kovacs, A. M., Teglas, E., & Endress, A. D. (2010). The Social Sense: Susceptibility to Others’ Beliefs in Human Infants and Adults. Science, 330(6012), 1830–1834. http://doi.org/10.1126/science.1190792

Krall, S. C., Rottschy, C., Oberwelland, E., Bzdok, D., Fox, P. T., Eickhoff, S. B. Fink G.R., Konrad, K. (2015). The role of the right temporoparietal junction in attentionand social interaction as revealed by ALE meta-analysis. Brain Structure and Function, 220(2), 587–604. http://doi.org/10.1007/s00429-014-0803-z

Kühn, S., Müller, B. C. N., van Baaren, R. B., Wietzker, A., Dijksterhuis, A., & Brass, M. (2010). Why do I like you when you behave like me? Neural mechanisms mediating positive consequences of observing someone being imitated. Social Neuroscience, 5(4), 384–392. http://doi.org/10.1080/17470911003633750

LaBar, K. S., Gitelman, D. R., Parrish, T. B., & Mesulam, M.-M. (1999). Neuroanatomic Overlap of Working Memory and Spatial Attention Networks: A Functional MRI Comparison within Subjects. NeuroImage, 10(6), 695–704. http://doi.org/10.1006/nimg.1999.0503

Lakin, J. L., & Chartrand, T. L. (2003). Using Nonconscious Behavioral Mimicry to Create Affiliation and Rapport. Psychological Science, 14(4), 334–339. http://doi.org/10.1111/1467-9280.14481

38

Lhermitte, F., Pillon, B., & Serdaru, M. (1986). Human autonomy and the frontal lobes. Part I: Imitation and utilization behavior: A neuropsychological study of 75 patients. Annals of Neurology, 19(4), 326–334. http://doi.org/10.1002/ana.410190404

Lieberman, M. D. (2007). Social cognitive neuroscience: a review of core processes. Annual Review of Psychology, 58, 259–289. http://doi.org/10.1146/annurev.psych.58.110405.085654

Lindenberg, R., Uhlig, M., Scherfeld, D., Schlaug, G., & Seitz, R. J. (2012). Communication with emblematic gestures: Shared and distinct neural correlates of expression and reception. Human Brain Mapping, 33(4), 812–823. http://doi.org/10.1002/hbm.21258

Liu, N., Mok, C., Witt, E., Pradhan, A. H., Chen, J. E., & Reiss, A. L. (2016). NIRS-based hyperscanning reveals inter-brain neural synchronization during cooperative Jenga game with face-to-face communication. Frontiers in Human Neuroscience, 10, 1–11. http://doi.org/10.3389/fnhum.2016.00082

Lotze, R. H. (1852). Medizinische Psychologie oder Physiologie der Seele [Medical psychology or physiology of the soul]. Leipzig, Germany: Weidmannsche Buchhandlung.

Meltzoff, A. N., & Moore, M. K. (1994). Imitation, memory, and the representation of persons. Infant Behavior and Development, 17(1), 83–99. http://doi.org/10.1016/0163-6383(94)90024-8

Meltzoff, A. N., & Moore, M. K. (1977). Imitation of Facial and Manual Gestures by Human Neonates. Science, 198(4312), 75–78. http://doi.org/10.1126/science.198.4312.75

Meltzoff, A. N., & Moore, M. K. (1997). Explaining facial imitation: a theoretical model. Early Development and Parenting, 6(3–4), 179–192. http://doi.org/10.1002/(SICI)1099-0917(199709/12)6:3/4<179::AID-EDP157>3.0.CO;2-R

Mitchell, J. P. (2008). Activity in Right Temporo-Parietal Junction is Not Selective for Theory-of-Mind. Cerebral Cortex, 18(2), 262–271. http://doi.org/10.1093/cercor/bhm051

Molenberghs, P., Cunnington, R., & Mattingley, J. B. (2012). Brain regions with mirror properties: A meta-analysis of 125 human fMRI studies. Neuroscience and Biobehavioral Reviews. http://doi.org/10.1016/j.neubiorev.2011.07.004

Montague, P. (2002). Hyperscanning: Simultaneous fMRI during Linked Social

39

Interactions. NeuroImage, 16(4), 1159–1164. http://doi.org/10.1006/nimg.2002.1150

Nitsche, M. A., Cohen, L. G., Wassermann, E. M., Priori, A., Lang, N., Antal, A., … Pascual-Leone, A. (2008). Transcranial direct current stimulation: State of the art 2008. Brain Stimulation, 1(3), 206–223. http://doi.org/10.1016/j.brs.2008.06.004

Noah, J. A., Ono, Y., Nomoto, Y., Shimada, S., Tachibana, A., Zhang, X., … Hirsch, J. (2015). fMRI Validation of fNIRS Measurements During a Naturalistic Task. Journal of Visualized Experiments: JoVE, 100. http://doi.org/10.3791/52116

Ochsner, K. N., & Lieberman, M. D. (2001). The emergence of social cognitive neuroscience. American Psychologist, 56(9), 717-734. http://doi.org/10.1037/0003-066X.56.9.717

Okada, E., Firbank, M., Schweiger, M., Arridge, S. R., Cope, M., & Delpy, D. T. (1997).Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head. Applied Optics, 36(1), 21–31. http://doi.org/10.1364/AO.36.000021

Oliver, D., Tachtsidis, I., & Hamilton, A. de C. (2017). The role of parietal cortex in overimitation: a study with fNIRS. Social Neuroscience, 13(2), 1–12. http://doi.org/10.1080/17470919.2017.1285812

Pfister, R., Dignath, D., Hommel, B., & Kunde, W. (2013). It takes two to imitate: anticipation and imitation in social interaction. Psychological Science, 24(10), 2117–21. http://doi.org/10.1177/0956797613489139

Prinz, W. (1997). Perception and Action Planning. European Journal of Cognitive Psychology, 9(2), 129–154. http://doi.org/10.1080/713752551

Prinz, W. (2002). Experimental approaches to imitation. In A. Meltzoff & W. Prinz (Eds.), The Imitative Mind: Development, Evolution, and Brain Bases (Cambridge Studies in Cognitive and Perceptual Development, pp. 143–162). Cambridge: Cambridge University Press. http://doi.org/10.1017/CBO9780511489969.009

Ray, E., & Heyes, C. (2011). Imitation in infancy: The wealth of the stimulus. Developmental Science, 14(1), 92–105. http://doi.org/10.1111/j.1467-7687.2010.00961.x

Rizzolatti, G. (2005). The mirror neuron system and its function in humans. Anatomy and Embryology, 210(5-6), 419–421. http://doi.org/10.1007/s00429-005-0039-z

Rizzolatti, G., & Arbib, M. A. (1998). Language within our grasp. Trends in Neurosciences, 21(5), 188-194. http://doi.org/10.1016/S0166-2236(98)01260-0

40

Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192. http://doi.org/10.1146/annurev.neuro.27.070203.144230

Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3(2), 131–141. http://doi.org/10.1016/0926-6410(95)00038-0

Santiesteban, I., Banissy, M. J., Catmur, C., & Bird, G. (2015). Functional lateralization of temporoparietal junction - imitation inhibition, visual perspective-taking and theory of mind. European Journal of Neuroscience, 42(8), 2527–2533. http://doi.org/10.1111/ejn.13036

Santiesteban, I., Banissy, M. J. J., Catmur, C., & Bird, G. (2012). Enhancing social ability by stimulating right temporoparietal junction. Current Biology, 22(23), 2274–2277. http://doi.org/10.1016/j.cub.2012.10.018

Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people: The role of the temporo-parietal junction in “theory of mind.” NeuroImage, 19(4), 1835–1842. http://doi.org/10.1016/S1053-8119(03)00230-1

Schilbach, L., Timmermans, B., Reddy, V., Costall, A., Bente, G., Schlicht, T., & Vogeley, K. (2013). Toward a second-person neuroscience. The Behavioral and Brain Sciences, 36(4), 393–414. http://doi.org/10.1017/S0140525X12000660

Schippers, M. B., Gazzola, V., Goebel, R., & Keysers, C. (2009). Playing charades in the fMRI: Are mirror and/or mentalizing areas involved in gestural communication? PLoS ONE, 4(8). http://doi.org/10.1371/journal.pone.0006801

Scholkmann, F., Holper, L., Wolf, U., & Wolf, M. (2013). A new methodical approach in neuroscience: assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning. Frontiers in Human Neuroscience, 7. http://doi.org/10.3389/fnhum.2013.00813

Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Mata Pavia, J., Wolf, U., & Wolf, M. (2014). A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage, 85, 6–27. http://doi.org/10.1016/j.neuroimage.2013.05.004

Schultz, W., & Dickinson, A. (2000). Neuronal Coding of Prediction Errors. Annual Review of Neuroscience, 23(1), 473–500. http://doi.org/10.1146/annurev.neuro.23.1.473

Sellaro, R., Nitsche, M. A., & Colzato, L. S. (2016). The stimulated social brain: Effects

41

of transcranial direct current stimulation on social cognition. Annals of the New York Academy of Sciences, 1369(1), 218–239. http://doi.org/10.1111/nyas.13098

Shin, Y. K., Proctor, R. W., & Capaldi, E. J. (2010). A Review of Contemporary Ideomotor Theory. Psychological Bulletin, 136(6), 943–974. http://doi.org/10.1037/a0020541

Sowden, S., & Catmur, C. (2015). The role of the right temporoparietal junction in the control of imitation. Cerebral Cortex, 25(4), 1107–1113. http://doi.org/10.1093/cercor/bht306

Spengler, S., von Cramon, D. Y., & Brass, M. (2009). Was it me or was it you? How the sense of agency originates from ideomotor learning revealed by fMRI. NeuroImage, 46(1), 290–298. http://doi.org/10.1016/J.NEUROIMAGE.2009.01.047

Spengler, S., Von Cramon, D. Y., & Brass, M. (2009). Control of shared representations relies on key processes involved in mental state attribution. Human Brain Mapping, 30(11), 3704–3718. http://doi.org/10.1002/hbm.20800

Strangman, G., Boas, D. A., & Sutton, J. P. (2002). Non-invasive neuroimaging using near-infrared light. Biological Psychiatry. http://doi.org/10.1016/S0006-3223(02)01550-0

Tang, H., Mai, X., Wang, S., Zhu, C., Krueger, F., & Liu, C. (2015). Interpersonal brain synchronization in the right temporo-parietal junction during face-to-face economic exchange. Social Cognitive and Affective Neuroscience, 11(1), 23–32. http://doi.org/10.1093/scan/nsv092

Van Overwalle, F. (2009). Social cognition and the brain: A meta-analysis. Human Brain Mapping, 30(3), 829–858. http://doi.org/10.1002/hbm.20547

Van Overwalle, F., & Baetens, K. (2009). Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis. NeuroImage, 48(3), 564–584. http://doi.org/10.1016/j.neuroimage.2009.06.009

Villarreal, M. F., Fridman, E. A., & Leiguarda, R. C. (2012). The effect of the visual context in the recognition of symbolic gestures. PLoS ONE, 7(2), 9–13. http://doi.org/10.1371/journal.pone.0029644

42