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Social Neuroscience
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/psns20
Does the TPJ fit it all? Representational similarity
analysis of different forms of mentalizing
Karolina Golec-Staśkiewicz, Agnieszka Pluta, Jakub Wojciechowski, Łukasz
Okruszek, Maciej Haman, Joanna Wysocka & Tomasz Wolak
To cite this article: Karolina Golec-Staśkiewicz, Agnieszka Pluta, Jakub Wojciechowski, Łukasz
Okruszek, Maciej Haman, Joanna Wysocka & Tomasz Wolak (2022): Does the TPJ fit it all?
Representational similarity analysis of different forms of mentalizing, Social Neuroscience, DOI:
10.1080/17470919.2022.2138536
To link to this article: https://doi.org/10.1080/17470919.2022.2138536
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
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Published online: 30 Oct 2022.
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RESEARCH PAPER
Does the TPJ t it all? Representational similarity analysis of dierent forms of
mentalizing
Karolina Golec-Staśkiewicz
a
, Agnieszka Pluta
a,b
, Jakub Wojciechowski
b,c
, Łukasz Okruszek
d
, Maciej Haman
a
,
Joanna Wysocka
a
and Tomasz Wolak
a
a
Faculty of Psychology, University of Warsaw, Warsaw, Poland;
b
Bioimaging Research Center, Institute of Physiology and Pathology of Hearing,
World Hearing Center, Kajetany, Poland;
c
Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology, Warsaw, Poland;
d
Social Neuroscience Lab, Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
ABSTRACT
Mentalizing is the key socio-cognitive ability. Its heterogeneous structure may result from a variety
of forms of mental state inference, which may be based on lower-level processing of cues encoded
in the observable behavior of others, or rather involve higher-level computations aimed at under-
standing another person’s perspective. Here we aimed to investigate the representational content
of the brain regions engaged in mentalizing. To this end, 61 healthy adults took part in an fMRI
study. We explored ROI activity patterns associated with ve well-recognized ToM tasks that induce
either decoding of mental states from motion kinematics or belief-reasoning. By using multivariate
representational similarity analysis, we examined whether these examples of lower- and higher-
level forms of social inference induced common or distinct patterns of brain activity. Distinct
patterns of brain activity related to decoding of mental states from motion kinematics and belief-
reasoning were found in lTPJp and the left IFG, but not the rTPJp. This may indicate that rTPJp
supports a general mechanism for the representation of mental states. The divergent patterns of
activation in lTPJp and frontal areas likely reect dierences in the degree of involvement of
cognitive functions which support the basic mentalizing processes engaged by the two task
groups.
ARTICLE HISTORY
Received 12 May 2022
Revised 31 August 2022
Published online 30 October
2022
KEYWORDS
Mentalizing; theory of mind;
social brain; temporoparietal
junction; fMRI
Introduction
Humans are an inherently social species. In order to
navigate the social environment, we need to make
sense of others’ behavior; to do so, we think of their
actions in terms of mental states (e.g., beliefs or inten-
tions). This ability is referred to as mentalizing or Theory
of Mind (for consistency, we will use the former term
here). Due to its importance for eective social function-
ing, mentalizing is seen as the core of social cognition
and has been the subject of increasing scientic interest.
Nevertheless, the neurocognitive mechanisms underly-
ing this capacity are still a matter of debate (Happé et al.,
2017; Quesque & Rossetti, 2020; Schaafsma et al., 2015;
Warnell & Redcay, 2019).
According to the classic denitions, mentalizing
encompasses the capacity to attribute mental states to
conspecics’ in order to predict their behavior (Premack
& Woodru, 1978). Although it is often considered as
a unitary concept, the available evidence suggests that it
might have a heterogeneous structure with complemen-
tary but functionally separate modules, including var-
ious forms of mental state inference (Happé et al.,
2017; Quesque & Rossetti, 2020; Schaafsma et al., 2015;
Schurz et al., 2014; Warnell & Redcay, 2019). Indeed, we
mentalize on the basis of a variety of social cues. Others’
mental states might be either decoded from the low-
level features of observed behavior, for example motion
kinematics, or rather inferred through understanding of
another person’s perspective, such as their false beliefs.
Belief-reasoning has been considered a key manifesta-
tion of mentalizing competence (Wellman, 2018), with
false belief tasks (FBT) regarded as a litmus test (Dennett,
1978). In a classic FBT, participants must recognize that the
protagonist of a story or a cartoon holds a false belief
concerning the location of an object, taking into account
that the person does not know that the object has been
moved. The participant is then asked a direct question
about the protagonist’s mental state and belief-based
behavior (Wimmer & Perner, 1983). Various measures
have emerged to capture the processes related to belief-
reasoning (Dodell-Feder et al., 2011; Kovács et al., 2010;
Wellman, 2018). For instance, in the non-verbal (implicit)
versions of the FBT, tracking others’ mental states is spon-
taneous and uninstructed. According to the evidence,
CONTACT Karolina Golec-Staśkiewicz karolina.golec@psych.uw.edu.pl Faculty of Psychology, University of Warsaw, Warsaw, Poland
Supplemental data for this article can be accessed online at https://doi.org/10.1080/17470919.2022.2138536.
SOCIAL NEUROSCIENCE
https://doi.org/10.1080/17470919.2022.2138536
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-
nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built
upon in any way.
albeit not particularly robust, belief-tracking induced by
both implicit and explicit false-belief tasks depend on the
same neurocognitive basis (see, e.g., Bardi et al., 2017).
Decoding mental states from motion kinematics is
considered a lower-level form of mentalizing (Gobbini
et al., 2007). This has been applied in tasks that induce
automatic attribution of intentions on the basis of either
animated motion of geometric gures (Frith – Happé
Animations Test; Abell et al., 2000; White et al., 2011) or
the biological motion of actors recorded with the use of
point-light displays (PLDs; Johansson, 1973). Such mea-
sures rely on non-complex visual input and therefore are
very often used, especially in clinical groups, as ecient
indicators of mental state inference (see, e.g., Centelles
et al., 2013; Moessnang et al., 2020; Okruszek, 2018).
However, the evidence from developmental studies sug-
gests a dissociation between decoding of intentions
from motion kinematics as a lower-level of reasoning
and belief-reasoning as a higher-level of reasoning
about others’ behavior (Quesque & Rossetti, 2020;
Wellman, 2018). Already in the rst year of life, long
before passing verbal FBTs, infants can interpret others’
actions as intentional on the basis of observed actions
(Csibra et al., 2003; Phillips & Wellman, 2005).
Indeed, understanding of motion kinematics relies
on low-level processes enabling a direct interpretation
of the perceptually available visuo-spatial data, i.a.
recognition of a goal-directed movement characteris-
tics (e.g., A is waving his hand in the direction of B).
However, the results of studies on clinical groups sug-
gest that success in inferring intentions from motion
kinematics cannot be fully explained by the basic
visuospatial processing. For instance, Okruszek et al.
(2015) showed that the performance in discriminating
the specic intentions of the point-light agents in
patients with schizophrenia was not accounted by non-
social visuo-spatial skills. In fact, processing the motion
kinematics might automatically trigger the mental state
attribution (e.g., People wave their hands when they
intend to say “hello” so A might intend to greet B;
Centelles et al., 2011). In the case of belief-reasoning
such an inference requires a purposeful shift to the
others’ state of knowledge along with a greater invol-
vement of working memory and executive functions, i.
a. to identify and update others’ perspective in the
changing reality (e.g., A is waving his hand, but B didn’t
notice that; hence, B doesn’t know that A is waving;
Perner & Roessler, 2012) or inhibit one’s own perspec-
tive (I know that A waves to say hello, but it is irrelevant
to predict the behavior of B; Flynn, 2007).
Although they engage diverse cognitive functions,
these two types of social reasoning could both involve
a common process of representing the mental states of
other agents in a general fashion (e.g., A intends to greet
B; B believes that A hasn’t arrived yet). However, it has not
been settled yet whether there exists a general neuro-
cognitive mechanism which integrates the various forms
of mental state inference.
Neuroimaging methods provide useful tools to
address such issues (Happé et al., 2017; Schaafsma
et al., 2015; Warnell & Redcay, 2019). For instance, it
has been demonstrated that the bilateral temporo-
parietal junction (TPJ) and the medial prefrontal cortex
(mPFC), as the key nodes of the mentalizing network, are
consistently engaged by dierent tasks that induce
thinking about others’ mental states, independently of
the type and format of social cues embedded in the
stimuli (Gallagher & Frith, 2003; Molenberghs et al.,
2016; Saxe & Kanwisher, 2003; Schurz et al., 2014).
Therefore, under the assumption that common activa-
tions result from a common cognitive mechanism, the
bilateral TPJ and mPFC might constitute the core of the
broader mentalizing network, responsible for the gen-
eral ability to represent mental states on the basis of
dierent kinds of social information of varying complex-
ity (Molenberghs et al., 2016; Schurz et al., 2014).
However, another line of evidence suggests that various
forms of mental state inference may dierentially
engage the mentalization network, including its core,
which would rather suggest that they are linked to
specic computational mechanisms (see, e.g., Schurz
et al., 2020). Firstly, it has been shown that the roles of
the left TPJ (lTPJ) and the right TPJ (rTPJ) might be
dissociable (see, e.g., Dodell-Feder et al., 2016; Ogawa
& Kameda, 2019). Moreover, studies that focused on the
relationship between eortful higher-level and auto-
matic lower-level social cognitive processes (exemplied
by belief-reasoning and mental state decoding from
motion kinematics as discussed above) have found con-
tradictory results regarding the underlying neural
mechanisms (Frith & Frith, 2008; Spunt & Lieberman,
2014; Van Overwalle & Baetens, 2009). Finally, the studies
that have considered potential dierences between var-
ious forms of mental state inference, and could thus
provide insights into the underlying functional structure
of the mentalizing ability, have been conducted mostly
with the use of mass-univariate methods (e.g., Bardi
et al., 2017; Gobbini et al., 2007; Jacoby et al., 2016;
Thye et al., 2018). Such methods are the necessary rst
step in studying the neurocognitive mechanisms of
mentalization by providing localization data. However,
because of their reliance on the average magnitude of
responses across multiple voxels, the univariate techni-
ques provide limited data and therefore do not indicate
how dierent categories of stimuli are represented in
a given brain region (Haxby, 2012).
2K. GOLEC-STAŚKIEWICZ ET AL.
In order to ll this research gap, multivariate methods
(Mur et al., 2009) are increasingly used to indicate and
characterize the patterns of neural responses related to
thinking about others’ cognitive and aective states
(e.g., Koster-Hale et al., 2017; Tamir et al., 2016;
Thornton & Mitchell, 2018). Nevertheless, it still remains
unclear how the key mentalizing brain regions represent
the information obtained through dierent forms of
social reasoning. Better understanding of this issue
could help to determine whether higher- and lower-
level forms of mental state inference engage
a common computational mechanism. This could be
indicated by the level of similarity of multivoxel patterns
of activity related to belief-reasoning and decoding
intentions from motion kinematics. Hence, in the current
fMRI study we aim to address this issue by combining
classic whole-brain analysis and multivariate pattern
analysis-representational similarity analysis (MVPA-RSA;
Kriegeskorte et al., 2008; Popal et al., 2019). We compare
the patterns evoked by ve popular tasks which are all
used as the mentalizing measures according to the lit-
erature (e.g., Fehlbaum et al., 2022). They, however, vary
in terms of the social-cognitive processes engaged. The
rst three measures induced belief-reasoning of dier-
ent levels of complexity, ranging from spontaneous,
non-verbal belief-tracking to false-belief inference on
the basis of verbal information derived from a relatively
complex social context. The other two tasks engaged
intention recognition on the basis of social cues coded
in two types of observed actions: animated goal-
directed movement and biological motion. We hypothe-
size that both belief-reasoning and decoding of mental
states from motion kinematics rely on the common
process of representing others’ mental states. Hence,
we expect the multivoxel patterns of activity originating
from belief and motion tasks to be similar in the struc-
tures known as the core of the mentalizing network
(bilateral TPJ, mPFC). At the same time, we assume that
the patterns in the remaining parietal (inferior parietal
lobule), temporal (posterior middle temporal gyrus) and
lateral frontal (middle temporal, anterior temporal, infer-
ior temporal clusters) parts of the mentalizing network
would be dissimilar which would reect the functional
diversity of these two forms of mental state inference
(i.e., belief-reasoning and decoding of mental states
from motion kinematics).
Methods
Participants
A total of 61 right handed participants with no history of
neurological or psychiatric treatment were recruited to
participate in the study. Four subjects did not complete
the fMRI examination and were therefore excluded from
the analysis. As a result, the nal sample consisted of 57
participants (31 females; age: M = 27.07, SD = 7.52). All
participants had normal or corrected-to-normal vision,
provided written informed consent, and were nancially
compensated with 110 PLN (approximately 23 EUR). The
experimental procedure was approved by the Ethical
Committee of the Faculty of Psychology, University of
Warsaw and was carried out in accordance with the
Declaration of Helsinki.
Procedure
The total MRI procedure was divided into two sessions,
each lasting 1 hour respectively, with a compulsory
refreshment break outside the scanner (10 min). The
rst session comprised the resting-state fMRI sequence
(15 min 18 sec; not reported in the current study), the
Implicit False-Belief Task (19 min 14 sec), and the Social
Perception and Interaction Task (22 min 52 sec).
The second session consisted of the Explicit False-Belief
Task (19 min 14 sec), HCP Social Task (6 min 52 sec),
Understanding False-Belief Stories task (9 min 02 sec),
T1-weighted (6 min 52 sec), T2-weighted (8 min 37 sec;
not analyzed here) and diusor tensor imaging
sequences (14 min 54 sec; not analysed in the current
study). Apart from the BOLD response, accuracy and
response times were measured during task fMRI
sequences.
Implicit false-belief task (Implicit FBT)
This task was designed on the basis of a classic change-
of-location FBT (Wimmer & Perner, 1983). It should be
noted that it was developed for the study of neurode-
velopmental mechanisms of mentalizing, hence the
child-friendly character of the stimuli. The stimuli con-
sisted of temporally-tuned 3D animations (35 seconds
each), depicting a child (four dierent female agents),
two boxes, and four toys. After four familiarization ani-
mations, 24 animations assigned to one of three condi-
tions (8 per condition) were presented in two runs of 12
trials each. In the False Belief (FB) condition, the agent
observes the toy (moving in a self-propelled way) being
placed in the rst box and then, while the agent is
absent, the toy moves itself to the second box (reloca-
tion phase, 18–24 sec). The agent comes back and
reaches for one of the boxes in a manner consistent or
inconsistent with her belief regarding the toy’s location.
In the True Belief (TB) condition, the agent comes back
earlier to witness the toy’s relocation, and therefore has
a true belief regarding its location. In the No Belief (NB)
control condition, there is no agent throughout the
SOCIAL NEUROSCIENCE 3
whole scene. The order of FB, TB, and NB trials, the side
of the toy’s initial location (left or right), and the box into
which the agent reaches (left or right) were pseudo-
randomized. A static picture of the sun was displayed
at the beginning of the run and between trials instead of
a classic xation cross (with jitter, lasting 9000–12500
ms). The task aimed to induce spontaneous tracking of
mental states, so the subjects were only instructed to
carefully follow what was happening on the screen.
Explicit false-belief task (Explicit FBT)
The explicit version of the change-of-location False-
Belief Task was designed to be as similar as possible to
the implicit version. The only dierence was that instead
of passively watching the story, participants were expli-
citly instructed to reason about the agent’s mental states
and answer the previously recorded question (FB and TB:
“Where does the child think the toy is?”, NB: “Where was
the toy previously?”) by pressing the button correspond-
ing to the proper box (left or right). To this end, in the
explicit version the agent reaching for one of the boxes
in the 30th second was removed and the trial nished
with the static picture of the agent standing in the
middle of the room, while participants heard the
recorded question. (Figure 1). The explicit FBT was
always performed after the Implicit version, in
a separate session.
Inferring communicative intentions from biological
motion (SOPIT)
The task was based on the four types of vignettes
(Interaction, Non-Interaction, Emotions, and Scrambled
Motion) from the Social Perception and Interaction
Database, which was designed to study higher-order
processing of biological motion (Okruszek &
Chrustowicz, 2020). During the four runs, participants
were presented with 60 vignettes presented in ca. 10
second blocks of animations followed by a 3 second
response screen, followed by an inter-block-interval of
8 seconds (Rest). During the tasks, the participant was
asked to classify stimuli into one of four categories
(Interaction/Non-Interaction/Emotions/Scrambled
Motion). For the current study, the point-light vignettes
depicting the use of communicative gestures between
the two agents (e.g., waving to each other to say good-
bye, Interaction) were contrasted with Scrambled
Motion.
Inferring mental states from the social animations
(HCP soc task)
The stimuli set was adapted to Polish from the social
cognition task included in the fMRI battery for the
Human Connectome Project (Barch et al., 2013; Castelli
et al., 2000, 2002; White et al., 2011). Participants watch
10 animations (20 seconds) depicting the movement of
geometric gures. The task consisted of two fMRI runs (5
animations per run). In the Mentalizing condition, the
actions of the gures were goal-oriented and directed at
each other, which was supposed to stimulate intention
attribution processes. In the Random condition, the
actions of the gures were entirely random, so that
they should not induce any mental state inference.
Each animation was followed by a forced-choice
Question (3 seconds): the subjects were asked to
Figure 1. Schematic illustration of the events in three conditions of implicit and explicit false belief tasks (FBT).
4K. GOLEC-STAŚKIEWICZ ET AL.
indicate with a button press whether an interaction or
no interaction was depicted; this was followed by a rest
period (15 seconds).
Understanding false-belief stories (FB localizer)
We developed a Polish version of the False Belief (FB)
Localizer by Dodell-Feder and colleagues (Dodell-
Feder et al., 2011). Stimuli consisted of 20 short stor-
ies in two conditions (10 per condition). The stories
were pseudo-randomly presented in two blocks
(10 per block). In the Belief condition, the protago-
nists have an incorrect belief regarding the situation
they were involved in. In the Photo (control) condi-
tion, an outdated photograph, map, or sign misrepre-
senting the current state of the world was presented.
After reading each vignette (10 sec), participants
assessed whether a statement related to the story
was true or false (Belief question/Photo question)
with a button press (4 sec), and then a xation cross
was displayed (12 sec).
fMRI data acquisition
The study took place in Bioimaging Research Center in
XXX. All tasks were displayed in a 3T MRI scanner using
a VisualSystem HD (NordicNeurolab Inc.). MRI scanning
was performed on a 3T Siemens Prisma MRI scanner
equipped with a 64-channel phased-array RF head coil.
Functional data for all tasks were acquired using a Multi-
band (Simultaneous Multi-Slice) echo-planar-imaging
(EPI) sequence (TR = 800 ms, TE = 38 ms, ip angle =
52°, FOV = 216x216 mm, 108 × 108 mm image matrix,
72 transversal slices of 2.00 mm slice thickness, voxel
size of 2.0 x 2.0 x 2.0 mm, Slice Accel. Factor = 8, In-
Plane Accel. Factor = 1, IPAT = 8). The following numbers
of volumes were acquired for each run of the tasks:
(Implicit FBT: TA = 9.34 min, 704 volumes; Explicit FBT:
TA = 9.34 min, 704 volumes; SOPIT: TA = 5.43 min, 415
volumes, HCP Soc Task: TA = 3.26 min, 244 volumes; FB
Localizer: TA = 4.31 min, 325 volumes). Although the
Multi-band EPI sequence causes a 40% increase of the
signal-to-noise ratio, it also introduces image distortions
related to the accumulation of phase errors in k-space.
This eect was accounted for by dividing all functional
scans into even numbers of runs with the opposite
phase coding direction (Anterior-Posterior and
Posterior-Anterior). Structural images were collected
with a T1-weighted 3D MP-Rage sequence (TR = 2400
ms, TI = 1000 ms, TE = 2.74 ms, 8° ip angle, FOV =
256×256 mm, image matrix 320 × 320 mm, voxel size of
0.80 x 0.80 x 0.80 mm, 240 slices of 0.80 mm slice thick-
ness, TA = 6.52 min).
fMRI data preprocessing
Preprocessing of the fMRI data was carried out with the
use of SPM12 (SPM; WellcomeTrust Centre for
Neuroimaging, London, UK) and in-house code. The
FSL topup() tool (Andersson et al., 2003) based on the
average representation of the images in both directions
(Anterior-Posterior and Posterior-Anterior) was used to
correct the spatial distortions. The functional data were
spatially realigned to the mean image and co-registered
to the individual structural images. High-resolution
structural images were segmented and normalized to
the common MNI space with resampling to 1 mm iso-
metric voxels. The obtained transformation parameters
were applied to the functional volumes with resampling
to 2 mm isometric voxels. Spatial smoothing with
a Gaussian kernel of full-width half-maximum (FWHM)
of 6 mm was performed on the normalised functional
images. Additional high-pass lters were used for the
functional data: Explicit FBT, Implicit FBT and SOPIT (cut-
o: 256 seconds), HCP Soc Task (cuto: 512 seconds), and
FB Localizer (cuto: 128 seconds).
fMRI data analysis
Whole-brain analysis
The whole-brain analyses were performed using SPM12
and the general linear model (GLM) approach. First-level
models were created subject-wise for each task. For
Implicit and Explicit FBTs, the entire timeline of the
story was divided into consecutive events within each
condition (see Figure 1). The toy’s relocation in the FB
and TB conditions was assumed to overlap with the
phase of belief attribution to the agent (belief formation
phase; see Kovács et al., 2010). In the No Belief (NB)
control condition, there was no agent during the
whole scene as this condition should not evoke belief
attribution. Thus, although all events were added to the
GLM models as regressors, the change-of-location (6 sec)
corresponding to the belief formation phase was used as
the time window of interest in further analysis. For the
other tasks, the stimuli were also divided into phases
(SOPIT: Interaction/No-interaction/Emotions/Scrambled
motion/Rest; HCP Soc Task: Mentalizing/Random/
Question; FB Localizer: Belief/Photo/Belief question/
Photo question) which were modeled as regressors and
convolved with the canonical HRF. Furthermore, six sub-
ject-specic movement regressors calculated during the
realignment step of preprocessing were added per run
to account for head motion. Second-level random
eects analysis was performed separately for each task
on the key contrast images generated from the rst-level
models: SOPIT: Interaction > Scrambled Motion; HCP Soc
SOCIAL NEUROSCIENCE 5
Task: Mentalizing > Random; FB Localizer: Belief >
Photo). FB-change-of-location > NB-change-of-location
was the contrast of interest for the Implicit and Explicit
FBT. The rationale for this choice was as follows: 1) These
two conditions do involve tracking the sequence of
changing states of reality, but in the case of NB, this
does not require the representation of mental states.
Therefore, this should result in dierent activation pat-
terns (FB should activate the mentalization network, NB
does not); 2) FB-change-of-location and NB-change-of-
location are perceptually identical (the agent is absent)
in the belief formation phase which is not the case for
the TB condition (the agent is present); 3) using the FB-
change-of-location > NB-change-of-location contrast
makes the analysis comparable to the FB Localizer
Task, in which the condition requiring false belief under-
standing (Belief) is contrasted with the stimuli that
engage reasoning about the content of a nonsocial
representation of reality (Photo; Saxe, 2006). The results
for FB-change-of-location > TB-change-of-location and
TB-change-of-location > NB-change-of-location are pre-
sented in the Supplementary Materials (Table S4).
We report the whole-brain analysis results above
which survived the family-wise error correction thresh-
old with cluster extent-based correction (FWEc) at the p
< .05 level of signicance (cluster-forming threshold p
< .001). The Bspmview toolbox, based on the Anatomy
Toolbox, was used for automated anatomical labeling of
the results (http://www.bobspunt.com/bspmview/).
Representational similarity analysis
This method is based on the assumption that stimuli
evoking similar neural processes will produce similar
voxel activation patterns (Kriegeskorte et al., 2008). We
applied RSA to compare brain representations of dier-
ent types of mental state inference engaged by 2 groups
of tasks inducing either belief-reasoning (Implicit and
Explicit FBT, FB Localizer) or decoding of mental states
from motion kinematics (HCP Soc Task, SOPIT).
We aimed to explore how these types of reasoning
are represented in structures that previous literature has
reported as being the key mentalizing regions.
Accordingly, the analyses were performed on 18
a priori Regions of Interest (ROIs) created as spheres (8
mm radius) centered around the peak coordinates
reported by Schurz et al. in a meta-analysis of functional
neuroimaging studies on mentalizing (Schurz et al.,
2014). If the spheres with coordinates based on Schurtz
(Schurz et al., 2014) did not t precisely into the gray
matter, they were slightly shifted, which resulted in the
following ROIs (Figure 2). The coordinates are presented
in Table 1 in Supplementary Materials.
Figure 2. The localization of a priori ROIs (L – left, R – right): inferior parietal lobule (IPL), posterior temporoparietal junction (TPJp),
anterior temporoparietal junction (TPJa), posterior middle temporal gyrus (pMTG), middle temporal cluster (MiddTemp), anterior
temporal cluster (AntTemp), inferior frontal cluster (InfFront), ventral medial prefrontal cortex (vmPFC), dorsal medial prefrontal cortex
(dmPFC), pre-supplementary motor area (preSMA), precuneus.
Table 1. Summary of the behavioral results.
Task
Accuracy:
mean (standard deviation)
Mean response times:
mean (standard deviation)
Implicit FBT - -
Explicit FBT FB: 95.9% (11.8%);
TB: 98.8% (5.4%);
NB: 98.6% (7.3%)
FB: 2858.0 ms (880.2 ms);
TB = 2908.3 ms (823.6 ms);
NB = 2917.9 ms (802.2 ms)
SOPIT Interaction: 91.3% (11.6%);
Scrambled motion: 88.2% (23.2%)
Interaction: 2066.0 ms (1503.0 ms);
Scrambled motion: 2100.0 ms (1331.0 ms)
HCP Soc Task Mentalizing: 85.7% (14.4%);
Random: 95.8% (12.0%)
Mentalizing: 1256.3 ms (406.5 ms);
Random: 1104.3 ms (393.8 ms)
FB Localizer Belief: 84.7% (12.6%);
Photo: 82.6% (13.6%)
Belief: 2466.8 ms (454.9 ms);
Photo 2324.2 ms (438.6)
6K. GOLEC-STAŚKIEWICZ ET AL.
We followed the workow implemented in the RSA
toolbox (Nili et al., 2014). The activity patterns represent-
ing dierent forms of mental state reasoning were
obtained from the corresponding rst-level contrast
maps (see Whole-brain analysis). In this respect, for
each task, the dierences of beta estimates of conditions
of interest were extracted from each voxel in the ROIs.
Such task-related activities, otherwise called patterns,
were then correlated pairwise. This procedure was
repeated for all ROIs. As a result, for each subject we
acquired eighteen 5 × 5 representational similarity
matrices (RSMs) containing values of Spearman’s corre-
lation coecients between pairs of tasks. As the RSMs
are symmetrical about a diagonal of ones, for the pur-
pose of further analysis they were transformed into the
lower-triangular vector format containing only the o-
diagonal cells with unique pairwise similarities (see
Diedrichsen & Kriegeskorte, 2017).
In the next step, a model RSM was specied to con-
trast Implicit FBT, Explicit FBT, and FB Localizer with
SOPIT and HCP Soc Task (Figure 4(a)). Next, the related-
ness of individual vectorized RDMs to the model was
tested for each region of interest with the use of a one-
sided signed-rank test across the single-subject RDM to
model Spearman correlations. We report only the results
which survived Bonferroni correction for multiple com-
parisons (p < .05; Figure 4(b)).
To test the performance of the model for each ROI, we
also computed the upper and lower bounds of the noise
ceiling as implemented in the RSA toolbox (Nili et al., 2014).
Any model that exceeded the lower bounds of the noise
ceiling and had signicant RDM-model correlation was
considered as a reliable predictor of a representation in
a given ROI.
The supplementary statistical analysis and RSA graphs
were prepared using R studio (2015).
Results
Behavioral results
The analysis of accuracy and reaction times suggests
that, in general, participants performed well and paid
attention to tasks during the fMRI examination
(Table 1).
Whole-brain results
The exploratory whole-brain analysis revealed both
common and dierential activation patterns evoked
by the tasks (Figure 3). Patterns of activations for all
tasks overlapped in the temporo-parietal areas bilat-
erally, apart from the one related to the Implicit FBT,
which occurred mainly in the left hemisphere.
Implicit FBT yielded activation in the bilateral frontal,
left middle temporal, and left posterior superior
regions. For the Explicit FBT, peaks were observed
in the posterior superior and the middle temporal
areas bilaterally, the temporal poles, and the precu-
neus. The overlap between Explicit and Implicit FBT
observed only in the left temporal cortex was sur-
prisingly small relative to what could have been
expected to be revealed with the use of almost
identical stimuli. Both the SOPIT and HCP Soc Task
yielded similar activations in the right temporal
areas along the superior temporal sulcus, and in
the lateral frontal gyri. SOPIT also evoked substantial
responses in the middle cingulate cortex and
Figure 3. Results of whole-brain analysis. The figure presents active areas for the following contrasts: implicit and explicit FBT: FB-
change-of-location > NB-change-of-location; SOPIT: Interaction > scrambled motion; HCP soc task > mentalizing > random; FB
localizer: belief > photo. FWEc correction at the p < .05 level of significance, cluster-forming threshold p < .001.
SOCIAL NEUROSCIENCE 7
occipital and parietal areas. Peaks for the HCP Soc
Task were found in the insula, thalamus, and cere-
bellum. The precentral areas were activated by the
HCP Soc Task and the Explicit FBT. For the FB
Localizer, large clusters were located in the precu-
neus, the bilateral middle temporal and the right
superior temporal areas, extending to the temporal
poles, similarly to the explicit FBT patterns. FB
Localizer also revealed broad activation of medial
prefrontal areas. Additionally, for this task large clus-
ters of activation were observed in the lingual gyrus.
Peaks related to the FB Localizer were also found in
the parahippocampal gyrus and cerebellum. Finally,
an overlap analysis indicated only one common clus-
ter for all the main contrasts localized in the left
middle temporal region. Tables S2 and S3 in the
Supplementary Materials show all local maxima
separated by more than 20 mm.
Representational similarity analysis results
Visual inspection of the average RSMs per ROI revealed
the structure of representations in the left IFG and lTPJp
to be similar to the model, which assumed that the
mentalizing regions represent others’ internal states dif-
ferentially, depending on the form of mentalizing
engaged by the task (Figure 4). The model performed
above chance in three regions: lTPJp (Spearman’s Rho
=.25, p = .0018), rTPJp (Spearman’s Rho =.19, p = .0039)
and left IFG (Spearman’s Rho =.216, p = .0072). For the
results of the remaining ROIs, see Supplementary
Materials, Figure 1. The correlation values were in the
range of the noise ceiling for the left IFG (Figure 5(a)) and
the lTPJp (Figure 5(b)). This means that these activity
patterns can be explained well by our model and sug-
gests that the mental states originating from belief rea-
soning and decoding of mental states from motion
Figure 4. The model and the average representational similarity matrices (RSMs) for ROIs. Panel a depicts a planned model of similarity
of neural response patterns between the two groups of tasks (Implicite FBT, explicite FBT and FB localizer are grouped as belief-
reasoning tasks whereas SOPIT and HPC soc task as decoding of intentions from motion kinematics). Panel B shows the average
representational similarity matrices (RSMs) for the ROIs in which the model was significantly correlated with the activity patterns
(Bonferroni corrected, p < .05). Scale represents degree of similarity between categories.
Figure 5. (a-c). Significant correlations between the model and activity patterns in the ROIs (Bonferroni corrected, p < .05). The red and
blue dashed lines illustrate upper and lower bounds of the noise ceiling respectively.
8K. GOLEC-STAŚKIEWICZ ET AL.
kinematics are represented distinctively in these regions.
In the case of rTPJp, the correlation value did not reach
the limits of the noise ceiling, suggesting that the repre-
sentation in this ROI does not follow the gradient
hypothesized by the model (Figure 5(c)).
In order to conrm that the results described above
originated from the actual activity, we conducted addi-
tional conrmatory ROI analysis and tested whether the
mean beta values for the contrasts of interest are sig-
nicantly dierent from zero. The analysis revealed that
for each ROI, at least 4 out of ve tasks elicited signicant
responses. All results of this analysis are summarized in
the Supplementary Materials.
Discussion
In this work we aimed to broaden the understanding of
the functional architecture of the ability to mentalize by
investigating the representational content of the key
nodes of the mentalizing network. By conducting repre-
sentational similarity analysis (RSA) we complement the
results obtained with classical methods on the whole-
brain level. For the majority (4 out of 5) of tasks we
observed similar whole-brain patterns of response in
bilateral temporo-parietal areas. However, as a more
sensitive method the RSA revealed that, the multivoxel
patterns of activity originating from belief and action
tasks were similar in the right posterior temporo-
parietal junction (rTPJp), but not in the left posterior
temporo-parietal junction (lTPJp). This is in line with
our hypothesis that the rTPJp supports a common
mechanism of representing the mental states
(Molenberghs et al., 2016; Schurz et al., 2014). However,
this would also imply the division of roles between lTPJp
and rTPJp and suggest that even the core of the menta-
lizing network might be functionally heterogeneous.
What is more, while the patterns of response in the
lateral frontal gyri occurred specically for the motion
kinematics tasks, substantial clusters of activation in the
medial-prefrontal cortical areas (mPFC) were present on
the whole-brain level only for the belief-reasoning tasks
(False Belief Localizer and the Implicite FBT).
Accordingly, dierential patterns of activity suggestive
of the separate cognitive mechanisms related to the two
groups of tasks were revealed by the RSA in left inferior
frontal gyrus (left IFG), but not in the mPFC. Overall, our
results add to previous literature on the functional orga-
nization of the mentalizing network.
The RSA shows that the rTPJp appears to be equally
sensitive to social cues derived from actions (e.g., com-
municative intentions encoded in motion) and reasoned
based on understanding the other person’s perspective
(e.g., false beliefs). In line with what has been proposed
in a vast majority of previous studies on the neurocog-
nitive mechanisms of mentalizing, this suggests that the
rTPJp represents others’ internal states irrespective of
what is the format and complexity of source social infor-
mation (Saxe & Kanwisher, 2003). Therefore it supports
a general mechanism of mentalizing. This would con-
form to the notion of rTPJp as a key node of the social
brain (Santiesteban et al., 2012). Our data are also con-
sistent with the nexus model proposed by Carter and
Huettel (2013) that assumes that the rTPJp receives and
integrates abstract forms of social information derived
from dierent regions of the social brain, such as the
lTPJp. In this way, it might establish the representation
of social context and thus enable successful navigation
in the social world (Schuwerk et al., 2014; see also:
Schuwerk et al., 2017). This result should be interpreted
with caution, as our whole-brain and conrmatory ROI
analyses found that only 4 out of 5 tasks (excluding the
Implicit FBT) evoked signicant activation in this region.
However, we assume that representational similarity
analysis is more sensitive to how the information is
represented. For instance, a region responding more
strongly to one category might also have some sensitiv-
ity to information from another category (Haxby, 2012;
Popal et al., 2019).
The lTPJp has been considered a part of the core
mentalizing network, alongside the rTPJp, and thus
should support the general ability to reason about the
mental states (intentions, desires, beliefs), irrespective of
the format and complexity of the source of the social
information. Nevertheless, its exact role in mentalizing
has remained unclear (Molenberghs et al., 2016; Schurz
et al., 2014). Our data provide new insights into the
function of the lTPJp in the mentalizing network as we
show that it dierentiates between mental state compu-
tations derived from belief-reasoning and decoded from
motion kinematics. Previous studies have reported that
focal lesions in the area of the lTPJp are related to
selective decits in mentalizing, suggesting that the
role of the lTPJp in mental state inference is more than
subsidiary and, in fact, that it might be necessary for
successful perspective taking (Biervoye et al., 2016;
Samson et al., 2004). Based on an analysis of patterns
of errors made by patients with lesions, Biervoye et al.
speculated that the lTPJp might trigger automatic bot-
tom-up signaling of the detection of others’ perspective
and thus induce mental state inference (Biervoye et al.,
2016). This would be supported by the fact that we
observed an overlap in the lTPJp on the whole-brain
level for all tasks despite dierences in stimulus com-
plexity and perceptual features. Accordingly, based on
analysis of event-related potentials (McCleery et al.,
SOCIAL NEUROSCIENCE 9
2011) as well as dynamic causal modeling (Schuwerk
et al., 2014), it has been proposed that the lTPJ not
only signals the mere presence of others’ perspectives,
but more precisely represents the discrepancy between
one’s own and others’ perspectives (see also: Arora et al.,
2015; Perner et al., 2006; Schurz et al., 2013). Our RSA
results suggest that such representations are not general
but they rather depend on the form of mentalizing.
Whilst others’ intentions are inherent to their behavior
in tasks requiring mental state inference from motion
kinematics (e.g., A intends to greet B) and so must be
consistent with the observer’s perspective, a signicant
discrepancy can occur in the case of belief reasoning
when the protagonist’s perspective (e.g., A believes that
the toy is in box X) does not conform to reality (the toy is
in box Y) and to the observer’s knowledge (I know that
the toy is in box Y). Thus one might hypothesize that the
dierential patterns of activity in the lTPJ might reect
the level of incompatibility between one’s own and
others’ perspectives, which would be low in the case of
action observation tasks and high for belief-reasoning
tasks. Such nuanced information could be then elabo-
rated by other regions of the mentalizing network, espe-
cially rTPJp, and eventually used for constructing an up-
to-date model of the other minds.
We also demonstrated that the representations of the
internal states resulting from dierent forms of social infer-
ence are distinctive in the left IFG. The IFG is regarded as
important for processing the meaning of the basic social
cues encoded in observed behavior as a key part of an
action observation network (AON) along with the inferior
parietal lobule and superior temporal sulcus (Caspers et al.,
2010; Press et al., 2012). This could mean that the lateral
frontal patterns of response that we found on the whole
brain level specically for the decoding of mental states
from motion kinematics might reect encoding of the
semantic features of others’ behavior (Press et al., 2012). In
this context, it seems plausible that the representations of
the mental states in the left IFG derived from motion kine-
matics and belief reasoning dier in complexity and thus
might originate from separate computational mechanisms.
In the case of false belief tasks, the semantic features of
behavior (A wants to use an object B) additionally have to be
considered in a wider context of changing circumstances
(The object B has changed its location in the absence of A), in
which the meaning of behavior needs to be updated (A will
look for the object B in a wrong location). For motion kine-
matics tasks, the semantic features are inherent to per-
ceived actions and are therefore directly available.
Interestingly, the AON has been also described as the mirror
network as it responds when an individual completes an
action or when they observe another complete that action
(Rizzolatti & Craighero, 2004). Thus the left IFG might be
involved in the sensorimotor loop that takes part in the
preparation for the execution of an equivalent motor
response based on visuospatial information about the
movements of others (Molenberghs et al., 2012). Such
visuo-spatial cues are more crucial for recognizing others’
mental states in the motion kinematics than in false belief
tasks (Koul et al., 2018). In contrast, a successful perfor-
mance in the later requires greater executive and memory
control which are supported i.a. by the mPFC (Friedman &
Robbins, 2022). Thus, the dierential activation patterns
observed for the two groups of tasks in the frontal area
might reect the dierences in the level of complexity of
cognitive functions supporting the general mentalizing
processes which, in the light of our whole-brain and RSA
results, are probably subserved by the rTPJp. In fact, in
accordance with what has been suggested by Centelles
et al. (2011), we demonstrate that the mirror and mentaliz-
ing systems can be simultaneously active during motion
kinematics tasks, with the former possibly aiding the latter
in structuring the representations of others’ intentions. In
general, our data add to the existing knowledge on the role
of frontal cortices in mentalizing.
The medial prefrontal cortex (mPFC) has been consid-
ered to be responsible for higher-order, stimuli-
independent computations, as a part of the core mentaliz-
ing network besides the rTPJp (Gallagher & Frith, 2003;
Schurz et al., 2014; Schuwerk et al., 2014). In line with this
hypothesis, we found that the activity patterns within med-
ial prefrontal ROIs were not explained by our theoretical
model, which assumed distinct representations for belief-
reasoning and decoding of mental states from motion
kinematics. However, our whole-brain data do not support
this idea, as we did not register any pattern in the medial-
prefrontal areas common to belief-reasoning and decoding
of mental states from motion kinematics tasks, despite
them both requiring mental state ascription. The multi-
study investigation of Boccadoro et al. also did not nd
evidence for the activity of mPFC during mental state infer-
ence which might suggest that mPFC is not involved in the
general process of mental state representation (Boccadoro
et al., 2019). Others observed the mPFC activity only in the
outcome phase of mentalizing tasks (Bardi et al., 2017). It
has been hypothesized that the mPFC might be particularly
engaged in self-referential processing of social cues and, as
discussed above, such processes are not required to the
same extent by dierent forms of mental state reasoning.
Thus we argue that more research is needed to reveal
whether and how the mPFC plays a role in mentalizing, as
it might not be specic to reasoning about internal states.
It seems important to point out that although we
selected ve dierent social cognitive tasks for our
study, we were still unable to consider mentalizing in
all its complexity. Other forms of mental state inference
10 K. GOLEC-STAŚKIEWICZ ET AL.
might be engaged in tasks requiring, inter alia, reading
the mind in the eyes, trait judgments, strategic games,
rational actions, and moral reasoning (Schurz et al.,
2020). This might be the reason that our model only
explained the activity patterns in two out of eighteen
regions highlighted by the previous neuroimaging stu-
dies as the key nodes of the social brain engaged in
mentalizing (Schurz et al., 2014). The remaining struc-
tures might be sensitive to aspects of the mentalizing
cues not considered in the current paper; this can be
addressed by future studies. However, we believe that
by focusing on the discrepancies between belief-
reasoning and decoding of mental states from action
kinematics, we were able to look into the dierences
between high and low-level constituent processes of
the mentalizing ability and therefore explore the func-
tional organization of the mentalizing network.
Limitations
A potential limitation of this study might be a xed order of
the tasks, especially considering relatively long scanning
time. However, a post-hoc comparison of the average
accuracy levels has shown that the scanning time did not
signicantly aect subjects’ performance in the second
part of the experiment as overall there was no signicant
downward trend in accuracy (see Supplementary
Materials). The dierences between tasks might rather
result from varied levels of diculty, with the FB Localizer
as the most demanding one. Additionally, the accuracy
levels were well above chance (min. 82.6% for the FB
Localizer task) conrming that the subjects were engaged
in tasks till the end of the experiment.
Moreover, in the current study we used canonical
tasks, widely used in research on mentalizing, albeit
based on relatively complex stimuli and a limited num-
ber of items per stimulus category. This results in certain
constraints being placed on the use of the full potential
of representational similarity analysis (see for example:
Dimsdale-Zucker & Ranganath, 2019). Our results should
be the starting point for future studies that could benet
from unifying the methodology and analyzing the repre-
sentational similarity of various forms of mental state
inference at the level of single items.
Conclusions
Our study informs the ongoing debate concerning the
neurocognitive basis of mental state inference and pro-
vides new insights into the representational content of
the mentalizing network. We provide evidence that the
structure of such representations in lTPJ and left IFG
depends on the form of mental state inference, as
exemplied by the patterns related to belief-reasoning
and decoding of intentions from motion kinematics,
while the rTPJp seems to support a general mechanism
of representing the mental states. In the light of these
results, we propose that the rTPJp subserves the functional
core of mentalizing. With the involvement of low (e.g.,
visuo-spatial processing, mirror system) and high-level
(e.g., working memory, executive functions) cognitive func-
tions in varying proportions, depending on the type of
available social cues, it creates a high-level stimulus-
independent representations of social context, in which
others’ mental states are the important constituents.
Acknowledgements
We would like to thank all the participants who agreed to take
part in this study.
Disclosure statement
No potential conict of interest was reported by the author(s).
Funding
This work was supported by The Polish National Science Center
under OPUS Grant [2017/25/B/HS6/01624] to AP and by the
Faculty of Psychology, University of Warsaw, from the funds
awarded by the Ministry of Science and Higher Education in
the form of a subsidy for the maintenance and development of
research potential in 2022 (501-D125-01-1250000 zlec.
5011000189).
ORCID
Karolina Golec-Staśkiewicz http://orcid.org/0000-0002-
4651-685X
Data availability statement
The datasets generated for this study are available upon
request from the corresponding author.
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