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Human Brain Mapping 00:00–00 (2016)
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Substrates of Metacognition on Perception and
Metacognition on Higher-order Cognition
Relate to Different Subsystems of the
Mentalizing Network
Sofie L. Valk,1†* Boris C. Bernhardt,1,2† Anne B€
ockler,1,3 Philipp Kanske,1 and
Tania Singer1
1
Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain
Sciences, Leipzig, Germany
2
Neuroimaging of Epilepsy Lab and McConnell Brain Imaging Center, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University,
Montreal, QC, Canada
3
€rzburg, Germany
Department of Psychology, Julius Maximilians University, Wu
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Abstract: Humans have the ability to reflect upon their perception, thoughts, and actions, known as
metacognition (MC). The brain basis of MC is incompletely understood, and it is debated whether MC
on different processes is subserved by common or divergent networks. We combined behavioral phenotyping with multi-modal neuroimaging to investigate whether structural substrates of individual differences in MC on higher-order cognition (MC-C) are dissociable from those underlying MC on
perceptual accuracy (MC-P). Motivated by conceptual work suggesting a link between MC and cognitive perspective taking, we furthermore tested for overlaps between MC substrates and mentalizing
networks. In a large sample of healthy adults, individual differences in MC-C and MC-P did not correlate. MRI-based cortical thickness mapping revealed a structural basis of this independence, by showing that individual differences in MC-P related to right prefrontal cortical thickness, while MC-C
scores correlated with measures in lateral prefrontal, temporo-parietal, and posterior midline regions.
Surface-based superficial white matter diffusivity analysis revealed substrates resembling those seen
for cortical thickness, confirming the divergence of both MC faculties using an independent imaging
marker. Despite their specificity, substrates of MC-C and MC-P fell clearly within networks known to
participate in mentalizing, confirmed by task-based fMRI in the same subjects, previous metaanalytical findings, and ad-hoc Neurosynth-based meta-analyses. Our integrative multi-method
approach indicates domain-specific substrates of MC; despite their divergence, these nevertheless likely
rely on component processes mediated by circuits also involved in mentalizing. Hum Brain Mapp
C 2016 Wiley Periodicals, Inc.
V
00:000–000, 2016.
Additional Supporting Information may be found in the online
version of this article.
Contract grant sponsor: European Research Council under the
European Community’s Seventh Framework Program; Contract
grant number: FP7/2007-2013/ERC Grant agreement number
205557 [EMPATHICBRAIN]; Contract grant sponsor: Canadian
Institutes of Health Research
†
These authors contributed equally to the study
*Correspondence to: Sofie Valk, Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain SciC 2016 Wiley Periodicals, Inc.
V
ences, Stephanstrasse 1a, 04103 Leipzig, Germany. E-mail:valk@
cbs.mpg.de
Received for publication 8 December 2015; Revised 15 April 2016;
Accepted 24 April 2016.
DOI: 10.1002/hbm.23247
Published online 00 Month 2016 in Wiley Online Library
(wileyonlinelibrary.com).
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Valk et al.
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Key words: metacognition; structural MRI; individual differences; cognition; theory of mind; interoceptive accuracy
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domain-general ability, and that different types of MC
may relate to mentalizing networks. Alternatively, MC on
different processes may relate to different networks, possibly those involved in the basic process itself. This would
then speak for a shared network of only MC on mentalizing/factual reasoning and mentalizing. To address this
topic at a structural-anatomical level, we carried out a
multimodal neuroimaging assessment in a large cohort of
healthy participants and contrasted substrates of individual differences in well-established markers of MC-P with a
novel marker of MC on the accuracy in a high-level cognitive task (henceforth, MC-C; [Kanske et al., 2015]. Our
advanced MRI framework assessed cortical morphology
and diffusion anisotropy of the superficial white matter
(WM) running immediately below the cortical mantle, for
the evaluation of multimethod consistency. Analyses were
spatially unconstrained (i.e., no a-priori ROIs were chosen)
to objectively evaluate whether substrates of MC-P and
MC-C would overlap or diverge. However, based on previous conceptual suggestions indicating that MC abilities
may rely on operations that also play a role in mentalizing
[Frith, 2012], we tested specifically whether substrates of
both might generally fall into subcomponents of mentalizing networks, such as the medial prefrontal, lateral temporal, temporo-parietal, and parietal midline regions [Bzdok
et al., 2012; Mar, 2011]. We therefore assessed the overlap
between our structural MRI findings and a map of taskbased fMRI activations obtained during a mentalizing task
in the same subjects from a previously published study
[Kanske et al., 2015], meta-analytical data on imaging studies on mentalizing [Bzdok et al., 2012], and ad-hoc forward
and reverse inference obtained from Neurosynth (http://
www.neurosynth.org/).
INTRODUCTION
Metacognition (MC) is the process through which individuals reflect on and control their perceptions, memories,
and actions [Metcalfe, 1994]. A key aspect of MC is the
evaluation of one’s own performance, and such metacognitive accuracy can be measured based on the fidelity of
subjects’ trial-by-trial confidence judgments with respect to
their objective task performance [Clarke et al., 1959; Fleming et al., 2012a; Galvin et al., 2003; Koriat 2007; Maniscalco and Lau, 2012]. MC has been assessed by studying
confidence ratings of signal detection task performance for
perception [Fleming et al., 2010, 2012a] and with respect to
memory accuracy [Koriat and Bjork, 2006; Oppenheimer,
2008]. It has been suggested that MC may be key to executive function [Koriat, 2007], social cognition [Frith, 2012],
and mental health [Teasdale et al., 2002].
Despite its important role in guiding behavior and cognition, fundamental aspects of MC are still incompletely
understood. Particularly, it is debated whether MC is a
domain-specific skill (i.e., MC abilities are specific to a
given domain, and are not mandatory to translate to
others) or rather domain-general (i.e., MC is a general ability, independent of the specific domain involved). Behavioral studies addressing this question, mostly focusing MC
on memory vs MC on perception (MC-P), have been rather
inconclusive, with some work showing correlations
between individual abilities across both MC faculties
[McCurdy et al., 2013], while others suggested their independence [Baird et al., 2013; Fleming et al., 2014]. Similarly, the brain basis of MC is still unknown. On the one
hand, functional and structural neuroimaging studies have
frequently localized substrates of MC in prefrontal regions
[Baird et al., 2013, 2013; Fleming et al., 2010, 2012b, 2014;
McCaig et al., 2011; Yokoyama et al., 2010], possibly suggesting some domain-generality. In an influential theory,
MC was related to mentalizing, also called Theory of
Mind (ToM) [Frith, 2012], where taking the perspective on
one’s own actions may rely on processes and (largely prefrontal) networks similar to those involved in taking the
perspective of others [Lombardo et al., 2010; Frith, 2012].
Yet, prefrontal substrates for MC have not always been
reported [McCurdy et al., 2013], and there is overall only
modest overlap across studies trying to localize MC [Baird
et al., 2013; Cabeza, 2008; Fleck et al., 2006; Henson et al.,
2000], possibly challenging domain-general accounts. Furthermore, no study to date has compared MC on perception to MC on higher-order cognition, involving high-level
factual reasoning and mentalizing.
Based on previous work, one can thus test for two competing hypotheses. The first is that MC may be a rather
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MATERIALS AND METHODS
Participants
Following an extensive advertising campaign for a
large-scale longitudinal study in 2013 (for details, see
[Singer et al., in press]), we studied 191 consecutively
enrolled healthy adults (116 women, age mean 6 SD 5 41 6
10 years, 20–55 years). Participants were recruited as two
matched subsamples from the cities of Berlin (n 5 93; 55
women, age mean 6 SD 5 43 6 8.4 years, 26–55 years) and
Leipzig (n 5 98; 61 women, age mean 6 SD 5 39.2 6 10.1
years, 20–55 years). Participants had normal-to-high IQ
(mean 6 SD 115 6 15 years, 78–152 years), an average of
18 6 3 years of education, and normal or corrected-tonormal vision. Volunteers gave written and informed
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Multimodal MRI Studies of Metacognition
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consent prior to participation. The study was approved by
the Research Ethics Committees of the University of Leipzig and the Humboldt University in Berlin.
range 5 15.9–49.0) and MC-C (% errors: mean 6
SD 5 40.6 6 12.4, range 5 12.5–68.7) were both normally
distributed.
Measuring Metacognition
MRI Acquisition
In two separate sessions, participants completed a perceptual discrimination task and a high-level mentalizing/
factual reasoning task (Figs. 1A and 2A). In both measures,
signal-detection-theory [Green and Swets, 1974] was used
to quantify individual differences in metacognitive ability
(“type II sensitivity”), here defined as the ability to accurately link confidence with performance.
MRI data from all participants, irrespective of recruitment
site, were acquired on the same 3T Siemens Magnetom
Verio (Siemens Healthcare, Erlangen, Germany) using a 32channel head coil in Leipzig. Structural images were
acquired using a T1-weighted 3D-MPRAGE sequence (176
sagittal slices, repetition time [TR] 5 2,300 ms, echo time
[TE] 5 2.98 ms, flip angle 5 78, field-of-view [FOV] 5 240 3
256 mm2, matrix 5 240 3 256, voxel size 5 1 3 1 3 1 mm3).
Diffusion-weighted images (DWI) were obtained using
twice-refocused EPI sequence (TR 5 8,900 ms, TE 5 90 ms,
flip angle 5 908, FOV 5 210 3 210 mm2, matrix 5 110 3 110,
no gap, 63 axial slices, voxel size 5 1.9 3 1.9 3 1.9 mm3, 64
diffusion directions with b 5 1,000 s mm22 along with seven
interspersed non-diffusion weighted volumes). Functional
masks were derived from a task-based fMRI paradigm
described in more detail by Kanske et al. [2015].
MC-P
A visual discrimination-task [Baird et al., 2013; Fleming
et al., 2010; Song et al., 2011] was performed at an individually determined threshold. Each trial (n 5 120) consisted
of a display of six Gabor patches, followed by a blank
screen, prior to a second display of six patches. In one of
the two displays, one randomly selected Gabor patch was
rotated using an adaptive staircase procedure. Participants
were asked to assess whether the shift occurred in the first
or second stimulus display, prior to them rating their confidence in the accuracy of their response on a trial-by-trial
basis on a Likert scale [ranging from 0 (not confident) to 6
(confident)].
MRI-based Cortical Thickness Measurements
We used FreeSurfer to generate cortical surface models
and to measure cortical thickness from T1-weigthed MRI
(Version 5.1.0; http://surfer.nmr.mgh.harvard.edu). Previous work has cross-validated FreeSurfer with histological
analysis [Cardinale et al., 2014; Rosas et al., 2002] and
manual measurements [Kuperberg et al., 2003]. Processing
steps have been outlined in detail elsewhere [Dale et al.,
1999; Fischl et al., 1999; Han et al., 2006]. Following surface
extraction, sulcal and gyral features of an individual were
warped to an average spherical representation, fsaverage5,
which allows for accurate matching of measurement locations across participants. Surfaces were visually inspected
and inaccuracies manually corrected (SLV, BCB). The current analysis employed a 20-mm full-width-at-half-maximum (FWHM) Gaussian smoothing kernel, following
previous recommendations [Lerch and Evans, 2005] and
previous studies of our group and others [Bermudez et al.,
2009; Bernhardt et al., 2010; Doyle-Thomas et al., 2013;
Lerch et al., 2005; Shaw et al., 2006, 2015; Valk et al., 2016].
Smoothing was carried out along cortical surface topology,
to minimize partial volume effects and to offer high anatomical sensitivity and specificity.
MC-C
The EmpaToM is a newly developed task measuring
empathy, compassion, mentalizing, and MC based on naturalistic video stimuli [Kanske et al., 2015]. In this 30min-long paradigm, people recount autobiographical episodes that are either emotionally negative (e.g., loss of a
loved one) or neutral (e.g., commuting to work). Multiplechoice questions with three response options after each
video assessed either mentalizing (24 questions about the
mental states of people in the video) or factual reasoning
(24 questions about the content of the story). After each
trial (total of 48 trails), participants rated their confidence
in performance accuracy after having performed the highlevel cognition questions on a trial-by-trial basis on a continuous rating scale (ranging from “not confident” to
“confident”). Agreement between confidence ratings and
actual performance was then used as MC measure.
We chose the well-established receiver operator characteristic, ROC, to quantify meta-cognitive accuracy across
both tasks using SPSS (Version 22, IBM, Armonck, NY).
ROC analyses have been applied in several neuroimaging
studies assessing MC [Baird et al., 2013; Fleming et al.,
2010; Song et al., 2011]. Please note that the set-up of the
MC-C task does not allow for separate computation of the
so-called meta d-prime [Maniscalco and Lau, 2012] metric,
an alternative index of MC. Kolmogorov–Smirnov-tests
indicated that MC-P (% errors: mean 6 SD 5 25.8 6 5.3,
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Assessment of the Superficial White Matter
DWI preprocessing
Preprocessing, based on FSL (Version 5.0; http://www.
fmrib.ox.ac.uk/fsl), involved motion correction, eddy current correction, and estimation of the diffusion tensor and
fractional anisotropy (FA), a measure of directionality of
water diffusion. A boundary-based registration [Greve and
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Fischl, 2009] aligned FA images with T1-weighted MRI by
maximizing the intensity gradient across tissue boundaries, using the surfaces that separate brain structures and
tissue types of the T1-weighted reference image, and the
tissue intensity of diffusion image.
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Ti 5b0 1b1 Sex 1b2 Age 1b3 Performance 1 b4 MC
Ti is the thickness at surface-point i. In the formula above,
we corrected for Sex and Age, given their marked effects
on brain structure [Salat et al., 2004; Sowell, et al., 2003,
2007]; the MC effects of interest were either MC-P or MCC scores. In each domain, we controlled for Performance,
given a possible relationship between task-performance
and MC [Fleming et al., 2012a; Galvin et al., 2003; Maniscalco and Lau, 2012]. To account for shared variance
between MC-P and MC-C, we also analyzed models that
controlled for MC-C score when calculating brain correlates of MC-P, and including MC-P score when calculating
brain correlates of MC-C. We also analyzed the model
including recruitment site (Berlin, Leipzig) as a covariate
to rule out possible recruitment effects, and a model
including IQ score to rule out confounds of general
intelligence.
White matter surface generation
For each participant, we generated a surface to systematically sample diffusion anisotropy of the superficial WM
immediately below the cortical mantle, similar to previous
work [Fjell et al., 2008; Kang et al., 2011, 2012]. We estimated a Laplacian deformation field running from the
cortical interface toward the ventricles, which guided subsequent placement of a surface running 2 mm below the
cortical interface. A Laplacian field guarantees point-wise
correspondence between cortical and superficial WM
surfaces, a requirement for meaningful integration of cortical thickness and WM diffusion measures. The correspondence guarantees implicit between-subject alignment of
WM parameters via the surface-registration estimated at
the level of the neocortex. In agreement with previous
findings [Kang et al., 2011, 2012], high anisotropy-values
were detected in regions proximal to the corpus callosum,
bilateral central cortices, and insula; conversely, low anisotropy was observed in occipital regions, temporoparietal regions, and medial prefrontal cortices. Similar to
thickness measures, superficial WM anisotropy data were
surface-smoothed at 20-mm FWHM.
Analysis of the superficial WM
We followed the same modeling approach as in (b) to
evaluate the relationship between individual differences in
MC and diffusion FA sampled from the superficial WM.
Multi-modal overlap analysis
Within each MC domain (i.e., MC-P, MC-C), we intersected findings from cortical thickness and superficial WM
analysis (i.e., (b) and (c)) to assess common substrates
across MRI modalities. Within intersections, we evaluated
effects of the other MC domain, as above, to test for
specificity.
Quality control and case selection
A total of 155/191 (91 women, mean 6 SD age 5 40.1 6
9.5 years; Berlin: 78, Leipzig: 77) participants had complete
metacognitive and performance measures (Supporting
Information Fig. 1). Quality controlled thickness measures
were available in all remaining 155 participants, DWI data
in 151 (Berlin: 77, Leipzig: 74).
Multi-method overlaps with mentalizing network
Correlational analyses tested for pair-wise associations
between individual differences in MC-C, MC-P, performance measures, and IQ.
We overlaid results generated by (d) with task-based
activations during ToM questions in the same subjects
from a previously published study [Kanske et al., 2015], a
previously published meta-analysis on mentalizing [Bzdok
et al., 2012], and an automated ad-hoc meta-analysis using
both forward and reverse inference masks of mentalizing
based on a total of 124 studies at the time of study in
Neurosynth (October 2015; http://www.neurosynth.org/
analyses/terms/mentalizing/). Furthermore, to test the
association of the MC-C and MC-P correlates with ROIs
based on the fMRI ToM localizer in the same subjects
[Kanske et al., 2015], we performed a post-hoc analysis
correlating thickness in the 10 largest regions of this
activation mask (cluster size > 250 mm2) with MC-P and
MC-C score.
Cortical substrates: Cortical thickness mapping
Correction for multiple comparisons
Linear models at each cortical surface-point i assessed
the relationship between thickness T and metacognitive
capacities:
Surface-based findings were adjusted using random
field theory for nonisotropic images [Worsley et al., 1999],
controlling the probability of reporting a family-wise error
Statistical Analyses
As in previous work [Bernhardt et al., 2014b; Bernhardt
et al., 2015; Valk et al., 2015], analyses were performed
using SurfStat [Worsley et al., 2009] for Matlab (Version
2013b; The Mathworks, Natick, MA).
Behavioral analysis
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to PFWE < 0.05. Post-hoc analyses within overlaps were corrected at a family-wise error level of PFWE < 0.05 using
Bonferroni-adjustment.
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lation was consistently seen when additionally controlling
for age and sex. Power analysis (G*power 3.1; [Faul et al.,
2009]) based on the observed effect size indicated that
>1600 participants would have been necessary to detect a
positive correlation, even at uncorrected thresholds (set 1beta 5 0.9, alpha 5 0.05, two-tailed). For a single intercorrelation between two behavioral variables, our study in
general had a high power to detect small-to-medium
effects (for an r 5 0.25, we would have achieved a power
of 1-beta 5 0.94 with 155 subjects at alpha 5 0.05). On the
other hand, for very small effects, such as the correlation
of r 5 20.08 between MC-C and MC-P, our large sample
was not offering sufficient power (1-beta 5 0.25).
RESULTS
Behavioral Findings
Neither MC-P nor MC-C showed significant correlations
with task accuracy in their respective domain (r 5 20.11,
P > 0.1). Importantly, although task-accuracies marginally
correlated across both domains (r 5 0.17, P < 0.04), MC-P
and MC-C did not correlate (r 5 20.08, P > 0.1). No corre-
Figure 1.
cold colors indicate cortical thickness increases/decreases in individuals with higher MC-C scores; (C) Superficial WM anisotropy
(FA) correlates with individual differences in MC-C. To correct
for multiple comparisons in B and C, findings were thresholded
at PFWE < 0.05, using random field theory for non-isotropic
images (black outlines), superimposed on trends (semi-transparent, no black outlines).
Substrates of metacognition on higher-order cognition (MC-C).
(A) i Behavioral assessment: all participants underwent the
EmpaTom-task (n 5 48 trials) [Kanske et al., 2015], where they
were asked to answer three-way multiple choice questions concerning details of 15-sec video-stories they just saw, followed by
confidence ratings; ii Distribution of task-performance (left) and
respective MC-C scores (right) (z-scored); (B) Cortical thickness correlates of individual differences in MC-C, where warm/
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Figure 2.
MC-P, metacognition on perception. (A) i Behavioral assessment:
participants underwent a perceptual discrimination task, where
they were asked whether the first or second Gabor-patch
included titled lines, followed by confidence ratings (n 5 120 trials); ii Distribution of z-scored behavioral score distribution
accuracy and respective MC-P scores (z-scored); (B) Cortical
thickness correlates of individual differences in MC-P; (C) Superficial WM anisotropy (FA) correlates with individual differences
in perceptual metacognition. Please see Figure 1 for further
details.
Individual difference analysis, thus, suggests independence between the abilities to accurately evaluate one’s own
performance during perceptual processing as opposed to
higher-level cognitive tasks.
overlapping, yet more restricted and only marginally significant positive correlations between MC-C and FA of
right middle temporal regions (PFWE < 0.07), please see
Supporting Information Table I for further details on the
clusters reported.
Considering MC-P (Fig. 2), we observed a positive correlation with thickness in right medial prefrontal and anterior cingulate cortices (PFWE < 0.01), as well as in right
lateral prefrontal regions at trend levels (PFWE < 0.09). Individuals with a more accurate assessment of their visual
perceptual sensitivity, thus, showed increased thickness in
these regions relative to those with low accuracy. Additionally, diffusion anisotropy findings in the superficial
WM converged with those at the level of cortical thickness,
by showing colocalized, albeit more restricted substrates in
medial prefrontal cortices (PFWE < 0.03). Please see
Cortical Substrates of Metacognitive Abilities
Cortical thickness analysis complemented behavioral
findings and revealed specific, non-overlapping structural
substrates of individual differences in both MC domains.
Higher MC-C scores correlated positively with thickness
increases in a large bilateral cluster encompassing lateral
frontal, superior and inferior temporal, temporo-parietal,
and posterior midline regions (PFWE < 0.05; Fig. 1). Separate assessment of WM diffusion parameters revealed
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Multimodal MRI Studies of Metacognition
Supporting Information Table II for further details on the
clusters reported. Findings were consistent when controlling for the other MC scores (i.e., MC-C score when assessing MC-P correlates, MC-P score when assessing MC-C
correlates; Supporting Information Fig. 2), when controlling for recruitment site (Supporting Information Fig. 3),
and when controlling for IQ (Supporting Information Fig.
4). Complementing the partial correlation analysis, we
assessed differences in correlations in clusters of findings
using a framework that accounts for shared variables
[Steiger, 1980]. For cortical thickness, this analysis confirmed marked interactions in mPFC (Z 5 23.3, P < 0.001)
and trends in right STG/STS (Z 5 1.74, P < 0.1). For FA, it
confirmed marked interactions in mPFC (Z 5 22.9,
P < 0.005) and STG/STS (Z 5 2.3, P < 0.02). Interactions in
right inferior temporal regions were pointing in consistent
directions, but were of lower significance (CT: Z 5 1.65,
P 5 0.1, FA: Z 5 0.9. n.s.).
DISCUSSION
To study the brain basis of meta-cognition, the current
work tested whether meta-cognitive abilities in different
domains share common or distinct structural-anatomical
substrates. We contrasted a widely studied measure of
meta-cognitive accuracy on perception, MC-P [Fleming
et al., 2010], with a novel marker of meta-cognitive accuracy on higher-level cognition, MC-C [Kanske et al., 2015].
Behavioral correlation analyses in a large sample of
healthy adults revealed no evidence for a significant association between individual differences in MC-P and MC-C;
moreover, multimodal MRI analyses indicated that individual variations in both domains related to specific and
non-overlapping substrates at the level of cortical morphology and diffusion anisotropy of the superficial WM
(MC-P: prefrontal regions, MC-C: parietal and posterior
temporal cortices). The lack of behavioral inter-correlation
together with the divergence of structural substrates
speaks against a domain-general substrate of MC. Despite
the regional divergence, however, overlaying findings
with a task-based fMRI localizer from the same sample
[Kanske et al., 2015] and meta-analytical data across previous studies demonstrated that each MC substrate clearly
fell within a different large-scale functional network
known to be involved in mentalizing. Our multi-method
structural and diffusion MRI findings, thus, suggest that
different components in this overarching network may
subserve both the reflection about self and others, allowing
for meta-cognitive processing in general as well as
introspection.
At a behavioral level, the observed lack of correlations
between MC-C and MC-P supports claims that metacognitive capacities may be domain-selective [Baird et al., 2013;
Kelemen et al., 2000; Pannu et al., 2005; Schnyer et al.,
2004]. As our findings are based on one of the largest
cohorts published in the MC literature to date, our sample
has sufficiently high power for even small-to-medium
effects. Indeed, for the observed correlations as small as
r 5 20.08, samples of 1,600 and more participants would
have been necessary to obtain even marginal significance
levels at a power of 0.9. Multimodal neuroimaging complemented behavioral results by showing non-overlapping
structural substrates of each meta-cognitive faculty.
Indeed, while MC-C ability correlated positively with
thickness of lateral and midline parietal cortices, as well as
lateral temporal and dorsolateral prefrontal regions, MC-P
related to anterior and medial prefrontal grey matter.
Superficial WM analysis provided further support for their
distinction, by highlighting effects in similar regions, albeit
of a more restricted extent. Our confidence in the validity
of our findings is high, as results were consistent across
different imaging modalities and when controlling for various possible confounds such as recruitment site and IQ.
On a histological level, macroscopic cortical thickness
changes may reflect alterations in neuronal and synaptic
markers [Giedd et al., 1999; Sowell et al., 2003], while
Multimethod Overlaps
Cortical thickness and WM diffusion anisotropy findings
(at PFWE < 0.1) overlapped within a given MC domain,
providing cross-validation across two imaging modalities
and reemphasizing divergence of MC-P and MC-C (Fig. 3,
Supporting Information Table II). Indeed, correlations of
individual differences in MC-P with cortical thickness and
FA of the superficial WM consistently intersected in
medial prefrontal regions, while cortical thickness and diffusion substrates of MC-C showed overlaps in right lateral
temporal, and superior temporal sulcus/gyrus. Post-hoc
analysis showed that mean thickness and FA in the overlap cluster relating to individual differences of MC-C did
not correlate with MC-P individual difference scores, and
vice versa, further supporting their independence.
Overlap with Mentalizing Network
Despite their regional divergence, substrates of MC-P
and MC-P were both falling into a network known to play
a role in mentalizing (Fig. 4, Supporting Information Table
III). First, overlaps were observed with task-based fMRI
activations (during a mentalizing vs control condition) carried out in the same subjects [Kanske et al., 2015], suggesting sample-specific relevance (see panel A). A subset of
ROIs (cluster size > 250 mm2) based on this functional contrast correlated with MC scores. Please see Supporting
Information Table IV. Second, intersections also overlapped with previous meta-analytical findings in the
domain of mentalizing (please see Bzdok et al. [2012];
panel B). Last, they also fell into regions highlighted by
ad-hoc meta-analytical forward/reverse inference based on
124 studies using Neurosynth (see panel C and D).
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Figure 3.
Multi-modal overlap analysis. The small brains in top panels
show cortical thickness (CT) (light blue, PFWE < 0.1) and superficial WM anisotropy (FA) (dark blue, PFWE < 0.1) substrates of
MC-C and MC-P, respectively. Overlaps of substrates across
both modalities are delineated in white. The larger surface rendering display MC-P and MC-C on the same brain, illustrating
their topographic divergence. Scatterplots are shown for multimodal overlaps within each MC domain (left two scatter plots:
FA/CT overlap for MC-P in medial prefrontal cortex; middle two
scatter plots: FA/CT overlap in inferior temporal regions; right
two scatter plots: FA/CT overlap in superior temporal regions).
MC-P/MC-C scores are marked in red/blue.
superficial WM anisotropy may relate to membrane integrity, myelination, and fiber arrangement [Concha et al.,
2010; Fjell et al., 2008; Kang et al., 2012]. Given their overlap, our findings may ultimately reflect to a more efficient
architecture of anatomical connections within and below
these regions in individuals with high meta-cognitive ability, possibly allowing for more integrated functional processing within each domain.
Motivated by conceptual accounts suggesting a link
between MC and mentalizing [Frith, 2012], we overlaid
the location of structural-anatomical substrates with metaanalytical as well as task-based fMRI findings on mentalizing. Despite them being non-overlapping, substrates of
MC-P and MC-C clearly fell into components of the men-
talizing circuitry, supporting the notion that similar brain
regions may be essential for understanding both the self
and others [Amodio and Frith, 2006; Frith, 2012; Lombardo
et al., 2010; Mitchell et al., 2006; Saxe et al., 2006; Singer
et al., 2004]. The mentalizing network has been related to
processes sub-serving simulating an alternate viewpoint to
the present, a process by which mental models are used
adaptively to imaging events beyond those that emerge
from the current setting [Buckner et al., 2008]. These findings may also relate to the substantial similarity between
the mentalizing circuitry and the default mode network
[Buckner and Vincent, 2007; Fox et al., 2005; Greicius et al.,
2003; Raichle et al., 2001]; a network active in the absence
of a specific task, and related to self-generated, internally
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Figure 4.
Relation of MC substrates the mentalizing network, derived
from three different methods. (A) Correspondence to fMRI activations derived from a mentalizing task in the same subjects
[Kanske et al., 2015]; (B) Correspondence to a previously published meta-analysis on mentalizing and social cognition [Bzdok
et al., 2012]; (C) Correspondence with Neurosynth reverse
inference map of the term “mentalizing”; (D) Correspondence
with Neurosynth forward inference map of the term
“mentalizing”. (Masks are depicted in semi-transparent white
with black delineation).
focused, and self-referential thought [Amodio and Frith,
2006; Bernhardt et al., 2014a; Botvinick et al., 2004; Christoff et al., 2009; Smallwood and Schooler, 2006]. The default
mode network has been associated with “decoupling,” a
class of cognitive processes possibly underlying both taking the cognitive perspective of others and by MC
[Andrews-Hanna et al., 2010; Buckner et al., 2008; Buckner
and Vincent, 2007; Vincent et al., 2006].
The focus on structural substrates underlying individual
differences in the current work may not have targeted all
brain regions functionally involved in MC-P and MC-C.
Future studies based on fMRI may provide further
insights, and also address dynamic and connectional properties of different network components. Nevertheless, the
specific role of prefrontal regions in MC-P may relate to
their involvement in both decoupled self-generated
thought and the evaluation of incoming sensory information [Gilbert et al., 2005; Sommer et al., 2007], possibly
mediated by pathways from and to higher visual areas
[Fleming et al., 2012b; Ramnani and Owen, 2004; Young,
1992]. This might, in turn, subserve action monitoring and
the planning of possible future actions [D’Argembeau
et al., 2007; Gusnard et al., 2001; Mitchell et al., 2005;
Northoff et al., 2006; Vogeley et al., 2004], likely requiring
accurate representations of sensory stimuli and perceptual
performance. Contrary, MC-C substrates fell more specifically into posterior midline and lateral temporal nodes of
the mentalizing and default mode networks. Posterior
nodes are heavily involved in episodic memory processing, and strongly interconnected with the medial temporal
lobe system [Andrews-Hanna et al., 2010; Buckner et al.,
2008; Buckner and Carroll, 2007]. Episodic memory
retrieval may indeed have been relevant for MC-C, as the
employed paradigm queried participants’ confidence on
given answers to questions relating to the content of
others’ autobiographical narratives. At trend level, we
found that high MC-C ability related to decreases in
mPFC. In previous literature not only increases but also
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Valk et al.
decreases in thickness have been related to expert knowledge and enhanced behavioral performance [Hyde et al.,
2007; Shaw et al., 2006]. Though beyond the scope of the
current project, future studies might follow up on this
interesting pattern of results. Also, it is worth pointing out
that mentalizing relies on multiple inferences (e.g., infer
actor’s goals; infer most likely action given a goal), which
individually will have a certain amount of associated
uncertainty, all of which would finally be integrated when
making confidence judgments. On this basis, one might
have expected a more widespread substrate involved in
MC for high-level cognition than for MC about perception,
as shown by our findings.
To conclude, our findings robustly suggest divergent
structural–anatomical substrates for MC in different
domains that nevertheless colocalize with different components of the mentalizing circuitry. Our result could inform
training studies that aim at enhancing MC, ToM, and
social cognition in general. Specifically, a crucial question
for future intervention research will be to investigate
whether improvement in MC transfers to mentalizing
skills, and vice versa.
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ACKNOWLEDGMENTS
The authors are thankful to the Department of Social Neurosciences for their support with the ReSource project.
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