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ORIGINAL RESEARCH
published: 17 February 2022
doi: 10.3389/fpsyg.2021.791835
Edited by:
Martin Reuter,
University of Bonn, Germany
Reviewed by:
Ivan Enrici,
University of Turin, Italy
Giorgia Ponsi,
Sapienza University of Rome, Italy
*Correspondence:
Haiyan Wu
haiyanwu@um.edu.mo
Dean Mobbs
dmobbs@caltech.edu
Specialty section:
This article was submitted to
Personality and Social Psychology,
a section of the journal
Frontiers in Psychology
Received:
09 October 2021
Accepted:
24 December 2021
Published:
17 February 2022
Citation:
Wu H, Fung BJ and Mobbs D
(2022) Mentalizing During Social
Interaction: The Development
and Validation of the Interactive
Mentalizing Questionnaire.
Front. Psychol. 12:791835.
doi: 10.3389/fpsyg.2021.791835
Mentalizing During Social
Interaction: The Development and
Validation of the Interactive
Mentalizing Questionnaire
Haiyan Wu
1,2
*
, Bowen J. Fung
2,3
and Dean Mobbs
2,3
*
1
Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, Macau SAR, China,
2
Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States,
3
Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA, United States
Studies have shown that during social interaction a shared system underlies inferring
one’s own mental state, and the mental states of others – processes often referred to
as mentalization. However, no validated assessment has been developed to measure
second order mentalization (one’s beliefs about how transparent one’s thoughts are to
others), or whether this capacity plays a significant role in social interaction. The current
work presents a interactive mentalization theory, which divides these directional and
second order aspects of mentalization, and investigates whether these constructs are
measurable, stable, and meaningful in social interactions. We developed a 20-item, self-
report interactive mentalization questionnaire (IMQ) in order to assess the different sub-
components of mentalization: self–self, self–other, and other–self mentalization (Study
1). We then tested this scale on a large, online sample, and report convergent and
discriminant validity in the form of correlations with other measures (Study 2), as well as
correlations with social deception behaviors in real online interaction with Mturk studies
(Study 3 and Study 4). These results validate the IMQ, and support the idea that these
three factors can predict mentalization in social interaction.
Keywords: mentalization, meta-cognition, theory of mind, meta-mentalizing, scale development, mind reading,
ultimatum game
INTRODUCTION
Humans have a rich capacity to infer the mental states and thoughts of others (i.e., self–other
mentalization), possess the ability to look inward to self-monitor and assess thought processes (i.e.,
self–self mentalization; i.e., metacognition), and can make inferences about how much other agents
have insight into their own thought processes (i.e., other–self mentalization). These mentalizing
processes are particularly important in navigating a variety of social environments and building
successfully relationships. Here, we provide a brief overview of these three inferential processes in
social interaction and provide some new definitions in order to clarify our approach.
Meta-cognition refers to our second order thoughts, that is, perceptions and beliefs about our
own cognitive processes (Flavell, 1979; Nelson and Narens, 1990). This includes knowledge of our
own beliefs, awareness of mental-states, and estimates of confidence in our abilities across different
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The Interactive Mentalization Questionnaire Development
domains (Veenman et al., 2006; Rouault et al., 2018). To
complement meta-cognition about one’s own cognitive processes,
inferring the cognitive states of other individuals comes to bear
in social contexts, and this has been referred to as mentalization
(Frith and Frith, 2005).
It is of note that mentalization originally referred to cognitions
about the mental states of both oneself and others (Premack
and Woodruff, 1978). Thus, meta-cognition is historically a
subcomponent of mentalization. Regardless of this historical
relationship, it has been a recurring idea that meta-cognition
is inherently a necessary aspect of inferring the mental states
of others. For example, individuals with a higher capacity for
self-reflection have been shown to have a higher capacity to
understand others (Dimaggio et al., 2008). While this evidence
suggests a common or overlapping mechanism, in this article,
we refer to and argue for the utility of separating mentalization
into two directional sub-components: self–other mentalization,
and self–self mentalization.
A third, related component of social interaction is how
much insight we think other agents have into our thoughts
and intentions, hereby referred to as meta-mentalization, or
other–self mentalization. In some respects, this can be viewed
as a combination of perspective taking (self–other mentalization)
and meta-cognition (self–self mentalization). The importance of
meta-mentalization for strategic social interaction is relatively
clear, for example in strategic decision making (Bhatt et al.,
2010), a successful interaction requires real-time updating of the
beliefs of others, and inference of how much the other player
knows about their own thoughts (Silston et al., 2018). Notably,
this meta-mentalization component can be influenced by two
fundamental sources: estimates of another agent’s mentalizing
ability, and estimates of your own ability to hide own thoughts
to others (e.g., via faked external expressions). In the context of
most social interactions, the influence of these sources ought to be
negatively correlated – the better you think you are at deception,
the less likely you think it is that someone else has true insight into
your mental states, and vice-versa. Given the literature linking
mentalization and meta-cognition, and theoretical accounts such
as simulation theory (Gordon, 1986), it is highly likely that meta-
mentalizing relies on the other two processes. That is, it in
order to interrogate how another agent perceives you, it is first
necessary to have a model of their beliefs, as well as your own.
There would be significant utility in defining a structure
for, and outlining the relationships between meta-cognition,
perspective taking, and meta-mentalization, both for clinical and
healthy populations. The first step toward this would be the
development of robust measures of these components. Indeed,
efforts to develop such measures have previously been made,
under various different theoretic views and validated on various
samples. Most of these measures are interview-based, and have
been developed with clinical applications in mind. These include
the Reflective Function Scale (RFS; Fonagy et al., 1998) and the
Parent Development Interview (Slade et al., 2004). Along similar
lines, the Reflective Functioning Questionnaire (RFQ) developed
by Fonagy et al. (2016) purports to measure mentalizing/meta-
cognition capacity in both clinical and non-clinical samples, and
was created for application in psychoanalysis and attachment
theory. The Mentalization Scale (MentS), is another recently
developed self-report measure (Dimitrijevic et al., 2018). While
the latter scale purports to capture both mentalizing and meta-
cognitive aspects, it does not address meta-mentalization. We
feel that a comprehensive account of mentalizing, with respect to
general interpersonal and social interactions, should necessarily
include meta-mentalization, and ensure that (while it may be
related to mentalizing and meta-cognition) meta-mentalization
it is a distinct, measurable construct (Wu et al., 2020).
With the increase in the number of decision making studies
involving social interaction, such as economic games (Frith and
Singer, 2008; Polezzi et al., 2008), there is a greater requirement
to measure aspects of mentalizing between interacting minds in
everyday scenarios. For example, meta-mentalization is necessary
for high level social interactions involving deception or trust, in
which people not only need to have knowledge of themselves and
knowledge of others, but also predictions of what others think
about them (McCabe et al., 2003; Bhatt et al., 2010).
In our theoretical framework, we aim to capture these
aspects of mentalization in social interaction, and thus focus
on these three components (self–other mentalization, self–
self mentalization, and meta-mentalization, or other–self
mentalization) (see
Figure 1
). We believe these constructs are
fundamentally related, but independently measurable. Given that
increasingly more studies place importance on decision making
and social interaction, our goal was to develop an interactive
mentalization questionnaire(IMQ) that would be specifically
useful for capturing the following interactive mentalization
components with three sub-scales:
(1) Mentalizing others: mentalization of other’s mental states
from the perspective of the self (IMQ_SO; self–other);
(2) Meta-cognition: assessment of self-generated mental states
from the perspective of the self (IMQ_SS; self–self );
(3) Meta-mentalization:
evaluate
mentalization
of
self-
generated mental states from the perspective of others
(IMQ_OS; other–self).
We hypothesized that these subscales would have predictive
power with respect to players’ decisions in real online social
interaction. Specifically, in light of simulation theory, we
hypothesized that IMQ_SS (our measure of meta-cognition)
would correlate with IMQ_SO (our measure of perspective
taking), as well as IMQ_OS (our measure of meta-mentalizing).
Given the previous study show mentalizing impairments in
autisms, we also predicted negative correlations between the
components in IMQ and autism spectrum quotient scores. Given
that meta-cognition has been associated with decision confidence
(Bang and Fleming, 2018), we further hypothesized that both
the IMQ_SS and IMQ_OS would be positively associated with
confidence ratings as measured in our version of the ultimatum
game. Following this hypothesis, we also predicted that relative
to those with lower meta-mentalization scores (IMQ_OS),
individuals with higher scores who suffer social rejection will
subsequently show lower happiness rating, given their higher
expectations and self-confidence in their abilities.
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The Interactive Mentalization Questionnaire Development
FIGURE 1 |
The three different components (IMQ_SS: self–self mentalization;
IMQ_SO: self–other mentalization, and IMQ_OS: other–self mentalization) in
our Interactive Mentalization Questionnaire.
STUDY 1: SCALE DEVELOPMENT
Method and Results
Participants and Procedure
All Mturk participants were recruited and provided informed
consent according to the guidelines of the Institutional Review
Board (Protocol number: 18-0790). 332 participants (38%
female) recruited through Amazon Mechanical Turk (MTurk)
(see
Table 1
). The instruction was “Please use the following scale
to indicate your agreement with each of the questions.” “1
=
very
true for me 2
=
somewhat true for me 3
=
somewhat false for me
4
=
very false for me”.
Item Generation
We generated a pool of 24 items that were intended to reflect
the mentalization of other’s mental states, one’s own mental
states, and the assessment of how transparent these mental
states are to others. The full list of 24 original items are shown
in the
Supplementary Table S1
. All items were in a Likert-
type format, with responses made on a 4-point response scale
with 1 indicating strong agreement and 4 indicating strong
TABLE 1 |
The demographic characteristic of the samples.
Characteristic
Sample 1
Sample 2
Sample 3
Sample 4
n
332
417
450
299
Age range (years)
18
∼
65
18
∼
65
18
∼
65
18
∼
65
Mean age (years)
35.36
31.56
32.64
33.17
Female (%)
37.95
31.65
37.78
42.81
n, Number of participants.
disagreement. All items, together, were coded into a web-page
formatted online survey (osf link: https://osf.io/2uarp/). Prior to
analysis, we removed two items (6 and 11), due to a high degree of
conceptual overlap with another item and a typographical error,
respectively. The removal of these items did not substantially
affect any of the analyses reported below.
A flow chart depicting the processes used to examine the
validity of the IMQ is presented in
Figure 2
.
Exploratory Factor Analysis
We used the minimum residual (MinRes) method (Harman and
Jones, 1966) for Exploratory Factor Analysis (EFA). The scree
plots suggested the possibility of either three-factor or four-factor
model (see
Supplementary Figure S2
). Given our aim to create
a three-factor questionnaire, an EFA was performed specifying a
three-factor solution. The results confirmed the factor structure.
It revealed a root mean square of residuals (RMSR) of 0.04, under
the standard 0.05 thresholds (Byrne, 1998; Diamantopoulos and
Siguaw, 2000). The Tucker Lewis Index of factoring reliability
was.89, RMSEA index was 0.06, and the Sample size adjusted
Bayesian Information Criterion (BIC) was –607.08
1
.
Item Reduction
The Kaiser-Meyer-Olkin (KMO) test (Kaiser, 1974) statistic
showed Measure of Sampling Adequacy (MSA) was 0.88,
indicating suitability for PCA. The PCA analysis identified
three factors that cumulatively explained 51.08% of the variance
of responses (component 1: 31.14%, eigenvalue
=
6.54;
component 2: 13.84%, eigenvalue
=
2.91; component 3: 6.11%,
eigenvalue
=
1.28) (see
Supplementary Figure S1
).
After reviewing the performance of each item in components,
IMQ_3 showed poorly performed with low factor loading
(overlapping factor loading: 0.38, 0.40, 0.31).
After deleting IMQ_3, we ran a second PCA and showed
lower factor loading of one item (IMQ_19). We thus deleted
the item and ran a final PCA, which did not identify low factor
loading or double loading (difference lower than 0.1 between
two factors). The following analyses were therefore based on the
remaining 20 items (see
Table 2
).
Inter-Factor Correlations
A
Pearson
correlation
analysis
demonstrated
significant
relationships between the subscales of IMQ. Specifically,
IMQ_SO
was
positively
correlated
with
both
IMQ_SS
[
r
(332)
=
0.45,
p
<
0.001] and negatively correlated with
IMQ_OS [
r
(332)
=
–0.61,
p
<
0.001], with IMQ_OS was
significantly negatively correlated IMQ_SS [
r
(332)
=
–0.25,
p
<
0.001].
Inter-Item Correlations
The average inter-item Pearson correlation was 0.49 for IMQ_OS,
0.32 for IMQ_SS, and 0.43 for IMQ_SO.
1
The alternative four-factor model EFA results showed root mean square of
residuals (RMSR) was 0.04. The Tucker Lewis Index of factoring reliability is 0.92,
RMSEA index is 0.053, BIC
=
–586.69. The fit results seem good for four-factor
model as well, with however, only two items for one of the components.
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FIGURE 2 |
The flow chart depicting the processes to develop and validate IMQ.
TABLE 2 |
The IMQ scales: items, alpha reliabilities, standard deviations, and factor loadings.
Subscales and items
Factor loadings Mean
SD
Skew
α
if deleted
IMQ_SO
I believe that I am good at telling what another person is thinking.
0
.
59
2
.
32
0
.
87
0
.
12
0
.
58
I’m confident that I can tell what others are thinking.
0
.
54
2
.
45
0
.
88
0
.
21
0
.
61
When I watch a movie, I can always guess what the character will do next.
0
.
38
2
.
38
0
.
91
0
.
19
0
.
59
Under the right conditions, I’m good at lying to make people feel better.
0
.
79
2
.
48
0
.
95
0
.
19
0
.
62
I can tell someone one opinion, while thinking the opposite.
0
.
70
2
.
4
0
.
94
0
.
17
0
.
6
Compared to my friends (On average), I am better at guessing what others think.
0
.
53
2
.
39
0
.
9
0
.
16
0
.
6
IMQ_SS (metacognition)
I have accurate insight into why I act the way I do.
0
.
69
2
.
07
0
.
87
0
.
59
0
.
52
I have accurate insight into why I think the way I do.
0
.
64
2
.
13
0
.
85
0
.
53
0
.
53
I can tell if others are teasing me.
0
.
59
2
.
08
0
.
83
0
.
36
0
.
52
When I fail, I know exactly why I failed.
0
.
56
2
.
04
0
.
8
0
.
54
0
.
51
Compared to my friends (On average), I have better insight into my own thoughts and behaviors.
0
.
64
2
.
12
0
.
89
0
.
6
0
.
53
I’m good at keeping my thoughts to myself.
0
.
58
2
.
02
0
.
77
0
.
32
0
.
51
I’m confident I’m correct when I perform a new task.
0
.
49
2
.
18
0
.
82
0
.
35
0
.
55
I have high confidence in knowing who I am.
0
.
71
1
.
96
0
.
82
0
.
61
0
.
49
IMQ_OS (meta-mentalization)
Do you believe that some STRANGERS can read YOUR mind better than others?
0
.
75
1
.
93
1
.
03
0
.
58
0
.
48
Sometimes, I think people have direct insight into what I am thinking.
0
.
69
2
.
31
1
.
04
0
.
15
0
.
58
How confident are you that others can guess what you are thinking?
0
.
71
2
.
38
1
0
.
14
0
.
59
Do you believe in telepathy?
0
.
71
2
.
21
1
.
09
0
.
29
0
.
55
Advertisers are pretty accurate at knowing my current desires.
0
.
81
2
.
45
0
.
99
0
.
03
0
.
61
I cannot lie, because people will know my intentions.
0
.
69
2
.
54
0
.
95
−
0
.
11
0
.
63
IMQ, Interactive Mentalization Questionnaire.
Summary
Study 1 evaluated the factor structure and the psychometric
properties
of
the
IMQ.
Overall,
the
PCA
and
EFA
demonstrated a factor structure consistent with our proposal,
the
subscales
showed
adequate
internal
consistencies,
and the relationships between the subscales and items
did
not
show
any
statistical
pathologies.
The
inter-
factor
and
inter-item
correlations
indicated
that
the
subscales
appear
to
appropriately
map
onto
separable
components within a more general construct. Consistent
with
our
proposed
theoretical
structure,
the
other–self
mentalization (IMQ_OS) was correlated with the self–other
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mentalization (IMQ_SO), and weakly correlated with self–self
mentalization (IMQ_SS).
STUDY 2: CONFIRMATORY FACTOR
ANALYSIS, CONVERGENT AND
DISCRIMINANT VALIDITY
The aim of Study 2 was to further confirm the factor structure of
IMQ established in Study 1.
Method
Participants and Procedure
The MTurk sample for Study 2 consisted of 417 participants
(67.8% male;
M
age
=
35.36, range 18–65 years old) (see
Table 1
).
They were paid $1.5 for their participation.
Measures for Convergence and Discrimination
Analysis
As a final item set had been established, the scales were
administered along with other measures in order to establish
convergent and discriminant validity. Measures used for this
purpose included the following scales.
Autism Spectrum Quotient
The Autism Spectrum Quotient (ASQ) is a self-report scale
designed to measure these traits (Baron-Cohen et al., 2001),
and has been well validated, cross-culturally (Baron-Cohen
et al., 2001, 2006). We expected moderately strong convergence
between the ASQ and our subscales oriented to self-awareness
(IMQ_SS), and a negative correlation with the subscale oriented
to other’s mental states (IMQ_SO).
The Levenson Self-Report Psychopathy Scale Survey
The Levenson Self-Report Psychopathy Scale Survey (LSRP)
is a scale (Levenson et al., 1995; Sellbom, 2011) to assess
primary and secondary psychopathy (Miller et al., 2008;
Wang et al., 2018), where primary psychopathy refers to
selfish, uncaring, manipulative behavior toward others; and
secondary psychopathy referring to impulsivity and other self-
defeating behaviors. As previous work indicated metacognitive
impairments and psychopathy in schizophrenia (Bo et al., 2014),
we expected a strong association between LSRP and our subscales
oriented toward the self (IMQ_SS and IMQ_OS).
Empathic Concern From Interpersonal Reactivity Index
The Interpersonal Reactivity Index (IRI) is a widely used scale
to measure individual differences in empathy, and captures four
separate aspects, including: (1) Perspective Taking (the tendency
to spontaneously adopt the psychological point of view of others);
(2) Fantasy (tendency to transpose themselves imaginatively into
the feelings and actions of fictitious characters in books, movies,
and plays); (3) Empathic Concern (EC: assesses “other-oriented”
feelings of sympathy and concern for unfortunate others), and
(4) Personal Distress (“self-oriented” feelings of personal anxiety
and unease in tense interpersonal settings) (Davis, 1983). We
used the EC to validate the IMQ subscales and expected a
strong correlation between EC and our subscale for self–other
mentalization (IMQ_SO).
Zimbardo Time Perspective Inventory
The Zimbardo Time Perspective Inventory (ZTPI) measures
individual differences in time-orientation, with five subscales
(Zimbardo and Boyd, 1999): (1) Past-Negative (a focus on events
that went wrong in the past; (2) Present-Hedonistic (living in
the moment – seeking pleasure, novelty, and sensation, and
avoiding pain); (3) Present-Fatalistic (feeling that decisions are
moot because predetermined fate plays the guiding role in
life, e.g., “what will be, will be”), (4) Past-Positive (a focus
on the “good old days,” e.g., keeping scrapbooks, collecting
photos, and looking forward to celebrating traditional holidays),
and (5) Future (simply planning for the future and trusting
that decisions will work out). We used the Future subscale
to validate the IMQ_OS and IMQ_SS, as it measures people’s
confidence about their decisions or plans for future, which ought
to be related to the meta-cognition and meta-mentalization
components (Stolarski and Witowska, 2017).
Confirmatory Factor Analysis
Dimensionality of the IMQ was evaluated using the PCA
and factoring method described in Study 1. Before proceeding
with the factor analysis, the KMO factor adequacy test
showed MSA
=
0.86.
With the ‘lavaan’ CFA function in the R (Rosseel, 2012),
we used the NLMINB optimization method, with a maximum
likelihood (ML) estimator, and 39 iterations for confirmatory
factor analysis (CFA). The fit of the model was assessed through
the following indices: (1) the Satorra Bentler scaled chi-square
(
χ
2); (2) the comparative fit index (CFI); (3) the goodness-of-fit
index (GFI); and (4) the root mean square error of approximation
(RMSEA) (Browne and Cudeck, 1992).
Results
Confirmation of Factor Structure
The CFA indicated satisfactory results with respect to a three-
factor model [GFI
=
0.915, CFI
=
0.914; Tucker-Lewis Index
(TLI)
=
0.902, RMSEA
=
0.057, and the
χ
2
(167)
=
393.044,
p
<
0.001].
Correlations With the Other Measures in Sample 2
Correlations between the IMQ subscales and the other measures
are presented in
Table 3
(n
=
417).
The ASQ score was strongly negatively correlated with
three IMQ subscales,
r
=
–0.31,
p
<
0.001 for IMQ_OS,
r
=
–0.42,
p
<
0.001 for IMQ_SS,
r
=
–0.19,
p
<
0.01 for
IMQ_SO. This pattern supports the notion that those with better
capacity in all three mentalization domains are less likely to
exhibit autism traits.
The IMQ_OS was negatively correlated with psychopathy
scores in the LSRP,
r
=
–0.57,
p
<
0.001, and IMQ_SS were
negatively correlated with psychopathy scores in the LSRP,
r
=
–0.18,
p
<
0.05. In contrast, we observed a positive correlation
between IMQ_SO and LSRP psychopathy,
r
=
0.24,
p
<
0.001.
With regard to the time perspective scale, the Past Negative
Hedonism (
r
=
–0.32,
p
<
0.001), Present Hedonism (
r
=
–0.51,
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TABLE 3 |
Correlations of IMQ subscales with other measures: Sample 2.
Scale
IMQ_OS
IMQ_SS
IMQ_SO
IMQ_SS
−
0
.
16
IMQ_SO
−
0
.
48
****
0
.
55
****
LSRP
−
0
.
57
****
−
0
.
18
*
0
.
24
****
LSRP_primary
−
0
.
53
****
−
0
.
10
0
.
27
****
LSRP_secondary
−
0
.
53
****
−
0
.
28
****
0
.
15
ZTPI
Past_negative
−
0
.
32
****
−
0
.
15
0
.
16
Future
0
.
13
0
.
31
****
0
.
08
Past_positive
−
0
.
13
0
.
24
***
0
.
13
Present_hedonism
−
0
.
51
****
0
.
06
0
.
32
****
Present_fatalism
−
0
.
58
****
−
0
.
18
*
0
.
21
**
IRI
_
EC
0
.
15
0
.
15
−
0
.
05
ASQ
−
0
.
31
****
−
0
.
42
****
−
0
.
19
**
LSRP, the Levenson Self-Report Psychopathy Scale Survey; ZTPI, Zimbardo Time
Perspective Inventory; IRI_EC, Empathic concern from Interpersonal Reactivity
Index; ASQ, Autism Spectrum Quotient.
*Corrected p
<
0.05; **Corrected p
<
0.01; ***Corrected p
<
0.001; ****Corrected
p
<
0.0001.
p
<
0.001) and Present Fatalism subscales (
r
=
–0.58,
p
<
0.001)
were negatively correlated with IMQ_OS. The Present Hedonism
(
r
=
0.32,
p
<
0.001) and Present Fatalism (
r
=
0.21,
p
<
0.001)
were all positively correlated with IMQ_SO. Moreover, the Future
subscale (
r
=
0.31,
p
<
0.001) and Past Positive (
r
=
0.24,
p
<
0.001) strongly positively correlated with IMQ_SS.
However, the EC – as measured by the IRI – did not show
significant correlation with IMQ subscales after correction.
In line with our hypotheses, these relationships imply that
our subscales capture aspects of meta-cognition (e.g., a positive
correlation with future confidence), can reflect social competence
(a negative correlation with ASQ), and yet are divergent from
others measures such as EC.
Inter-Factor Correlations
IMQ_SO was positively correlated with IMQ_SS (
r
=
0.55,
p
<
0.001), and IMQ_SO was negatively correlated with IMQ_OS
(
r
=
–0.48,
p
<
0.001), while IMQ_OS was negatively correlated
with IMQ_SS (
r
=
–0.16,
p
<
0.001).
Cronbach’s
α
The internal consistencies of the three subscales were 0.81 for
IMQ_OS, 0.83 for IMQ_SS, and 0.76 for IMQ_SO.
Inter-Item Correlations
The average inter-item Pearson correlation was 0.42 for IMQ_OS,
0.37 for IMQ_SS, and 0.35 for IMQ_SO.
Overall, the data from the CFA further validated the three-
factor model of the IMQ. Moreover, the convergent and
discriminant validity indicated that the IMQ_OS and IMQ_SO
scales are related to, but also distinct from alternative measures,
such as EC and ASQ.
In sum, Study 2 further supported our three-factor
measurement scale by replicating the results of Study 1,
while in addition providing a comparison with related measures.
STUDY 3: INTERACTIVE
MENTALIZATION QUESTIONNAIRE
SUBSCALES AND THE DECEPTION
TASK
To further validate the IMQ scale, we collected data from a task
involving mentalizing and spontaneous deception which often
occur in strategic social interactions. In our ultimatum game
paradigm (Kirk et al., 2011; Marchetti et al., 2011), one player
(the proposer) is given a sum of money and then must choose
how much to tell and offer to the other player (the responder).
The responder may accept or reject the offered amount, with
rejection leading to both players receiving nothing. In this task,
individuals require mentalization in order to form expectations
and predictions. Therefore, a successful strategy relies on a
player’s confidence about their own beliefs, the content of their
opponent’s beliefs, and their opponent’s specific beliefs about the
player’s own thoughts.
Method
Participants and Procedure
The sample consisted of 450 Mturk participants (
M
age
=
32.64,
range from 18 to 65 years old, 62.2% male). They were paid $0.5
for their participation and paid with the payoff in the game after
finished the whole task.
Given the nature of the task (see below), participants were
assigned to play the role of a “proposer” or a “responder.”
Thus, the sample was ultimately divided into 218 “proposers”
(
M
age
=
33.28,
SD
=
9.61, 61.46% male) and 232 “responders”
(
M
age
=
32.05,
SD
=
9.2, 62.06% male).
Experimental Task
Our task was based on a UG task with asymmetric information
(Vesely, 2014), previously used to examine self-interest driven
dishonesty. In this version, only the proposer knows the initial
endowment, and has an opportunity to tell the responder how
much this amount is. They can either be honest to report the true
amount, or dishonest, and report any other amount. Our version
of the task was a one-shot game (i.e., there was only one round
and participants did not switch roles, leaving no possibility of
strategic behavior based on/due to learning).
Two participants were randomly paired over internet and
assigned a role of either proposer or responder. The participants
were first shown detailed instructions about the task (
Figure 3
).
On the next page, the endowment – randomly chosen from
a range of 30–160 cents – was shown to the proposer. This
amount was not shown to the responder. On the same page,
the proposer was prompted to tell the responder how much
this initial endowment was (Notably, the larger the initial
endowment was, the more opportunity for deception in this
phase of the task.). On the subsequent page, the proposer
was asked to rate how confident they were that the proposer
would believe the amount stated as the initial endowment.
Simultaneously, the responder was asked to rate how much
they trusted that the stated amount corresponded with the
true endowment. Both of these ratings were on a five-point
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Wu et al.
The Interactive Mentalization Questionnaire Development
FIGURE 3 |
The plot of the MTurk paradigm run in sample 3.
scale. Next, the proposer was prompted to actually offer a
proportion of the initial endowment. On the penultimate
page, the responder was prompted to either accept or reject
this offer. Finally, the true initial endowment, the offered
amount, and the payoff (based on the responder’s decision)
were displayed on the screen, and both players were asked to
rate how happy they were with the outcome on a five-point
scale. The experiment was followed by questionnaires measuring
mentalization (IMQ), psychopathy (LSRP), empathy (IRI_EC),
time perspective (ZTPI), and autism traits (ASQ).
Data Analysis
To test the stability of the IMQ structure when delivered in the
context of real social interaction, we repeated CFA implemented
with the same approach as in Study 2.
In order to examine the relationship between the subscales of
the IMQ and the different behavioral measures of mentalization
taken during the task, we constructed a number of indices for
each role. For the proposer data, we first defined the
deception
index
as the total amount minus the told amount, divided by
the total amount (i.e., the fraction of the initial endowment
potentially “kept for oneself ”). Secondly, we defined
self-aware
fairness
as the offered amount as a fraction of the total amount.
Thirdly, for both proposer and responder, we defined
other-
aware fairness
as offered amount as a fraction of the told
amount. Given our interest in meta-mentalization, we were also
interested in the
proposer’s confidence
about their decision, and
the
responder’s trust
rating.
Given the hypothesized relevance of all of these indices to the
subscales of the IMQ, we generated a Pearson correlation matrix
for these measures. Furthermore, we also included alternative
questionnaires (ASQ, IRI_EC, LSRP, and ZTPI) in order to
replicate the results from Study 2, and to identify whether these
measures were also related to our behavioral indices.
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Wu et al.
The Interactive Mentalization Questionnaire Development
Lastly, hierarchical multiple regression was performed to
further investigate whether and to what degree the subscales
of the IMQ were able to capture variance in the decisions and
behavioral indices of the proposers and responders.
Results
Confirmatory Factor Analysis
Confirmatory factor analysis indicated a good fit for a three-
factor model [GFI
=
0.886, CFI was 0.89; TLI
=
0.87,
RMSEA
=
0.073, and the
χ
2 (167)
=
563.91,
p
<
0.001].
Inter-Item Correlations
The average inter-item-correlation was 0.47 for IMQ_OS, 0.42 for
IMQ_SS, and 0.40 for IMQ_SO.
Behavioral Indices
A one-sample
t
-test demonstrated that the deception
index
was
significantly greater than zero [
t
(217)
=
5.4,
p
<
0.001, Cohen’s
d
=
0.37, 95% CI
=
0.10–0.22], implying that proposers were
dishonest on average, and confirming that the context of the
task was able to drive dishonest behavior. Notably, we found
substantial individual differences in dishonesty – the mean
deception index
was 0.16, with a standard deviation of 0.4.
Another description of dishonesty was the
other-aware fairness
(
M
=
0.66,
SD
=
0.27) was significantly higher than
self-aware
fairness
(
M
=
0.55,
SD
=
0.4) [
t
(217)
=
4.52,
p
<
0.001, Cohen’s
d
=
0.60, 95% CI
=
0.05–0.17], again implying that proposers
were dishonest in general.
Proposers showed relatively high confidence (
M
=
3.70,
SD
=
0.89, where 1 was “very unconfident” and 5 was “very
confident”), and relatively high happiness about the result
(
M
=
3.53,
SD
=
1.26. where 1 was “very unhappy” and 5 was
“very happy”). For the proposers whose offers were accepted,
happiness ratings were significantly higher (
M
=
4.23,
SD
=
0.82)
relative to those whose offers were rejected (
M
=
2.03,
SD
=
1.49),
[
t
(33)
=
8.01,
p
<
0.001, Cohen’s
d
=
2.32, 95% CI
=
1.64–2.76].
A majority of responders (84%) accepted the offered amount,
with moderate ratings of trust (
M
=
3.14,
SD
=
1.21). For
the responders who accepted the offer, happiness ratings were
significantly higher (
M
=
3.77,
SD
=
1.11) relative to when offers
were rejected (
M
=
2.29,
SD
=
1.29), [
t
(47)
=
6.50,
p
<
0.001,
Cohen’s
d
=
1.01, 95% CI
=
1.01–1.93].
Interactive Mentalization Questionnaire Scores for
Different Roles/Players Conditional by Accept/Reject
Response
To examine whether IMQ scores were significantly different for
proposers and responders, conditional on whether the offers were
rejected or accepted, we performed several
t
-tests. Firstly, for
proposers, there were no significant differences in total IMQ
scores [
t
(37)
=
0.85,
p
=
0.40] as a function of outcome,
nor were there any differences between the three subscales
[IMQ_OS:
t
(38)
=
0.17,
p
=
0.86; IMQ_OS:
t
(40)
=
0.17,
p
=
0.86; IMQ_SS:
t
(39)
=
1.12,
p
=
0.27]. For responders,
total IMQ scores did not differ significantly on the basis of
rejection (
M
=
56.05,
SD
=
7.02) or acceptance [
M
=
55.46,
SD
=
6.45;
t
(48)
=
0.85,
p
=
0.63]. Neither the IMQ_SO nor
IMQ_SS subscales showed any significant difference between the
rejected offer responders (IMQ_SO:
M
=
15.88,
SD
=
3.83;
IMQ_SS:
M
=
23.43,
SD
=
4.64) vs. the accepted offer responders
[IMQ_SO:
M
=
14.78,
SD
=
3.96,
t
(50)
=
1.54,
p
=
0.13;
IMQ_SS:
M
=
23.35,
SD
=
4.95,
t
(49)
=
0.08,
p
=
0.93]. However,
IMQ_OS scores were significantly higher for responders who
rejected the offer (
M
=
17.92,
SD
=
4.20) than those who
accepted the offer [
M
=
16.15,
SD
=
3.34;
t
(47)
=
6.50,
p
=
0.007, Cohen’s
d
=
0.43, 95% CI
=
–1.69 to 1.84], suggesting
that higher meta-mentalization capacity was associated with an
increase in rejections.
Relationship Between Interactive Mentalization
Questionnaire Subscales and Behavioral Indices
The first exploratory analysis of proposers’ data indicated that
proposer’s meta-mentalization was negatively associated with
the confidence in the deception task, and the fairness level
of the allocation (
Table 4
). However, the
deception index
was
not associated with IMQ scores (
Table 4
). For the responders’
data, we observed a significant negative correlation between
IMQ_OS and the trust rating to the proposer (
r
=
–0.28,
p
<
0.01, corrected). We also found a negative correlation
between IMQ_OS score and happiness ratings (
r
=
–0.31,
p
<
0.01, corrected).
To investigate whether the questionnaire score, deception
index, confidence and outcome were associated with the outcome
happiness rating, we ran a GLM, using the
deception index
,
confidence, IMQ_SO, IMQ_OS, IMQ_SS and offer response
(accept vs. reject) as predictors for proposers’ happiness ratings.
The results showed significant effects for confidence (
β
=
0.86,
SE
=
0.32,
p
=
0.007), and offer response (
β
=
2.26,
SE
=
0.18,
p
<
0.001), but not for the IMQ subscales. Given our specific
hypothesis that higher confidence in meta-mentalizing might
interact with the response to the offer, we ran a GLM using only
IMQ_OS and offer response (accept vs. reject) as predictors, we
found that an interaction between IMQ_OS and offer response
was a significant predictor (
β
=
0.14,
SE
=
0.04,
p
<
0.001).
We further analyzed this interaction by evaluating simple slopes
(Aiken and West, 1991). When the offer was accepted, the
slope of the regression line of IMQ_OS was not significant
(
β
=
0.01,
SE
=
0.02,
t
=
0.62,
p
=
0.53), while the slope of
the regression line of IMQ_OS was significant when the offer
was rejected (
β
=
–0.12,
SE
=
0.03,
t
=
–3.54,
p
<
0.01) (see
Supplementary Figure S3
).
Correlations Between Interactive Mentalization
Questionnaire and Other Measures
Both the proposers and responders showed similar correlations
with other questionnaire measures as in Study 2. For example,
we replicated the negative correlations between IMQ_OS and
psychopathy (measured by LSRP) both for proposers (
r
=
–0.53,
p
<
0.001) and responders (
r
=
–0.56,
p
<
0.001). We also
reproduced the negative correlation between IMQ components
and autism traits (for proposers, IMQ_OS to ASQ:
r
=
–0.27,
p
<
0.05; IMQ_SS to ASQ:
r
=
–0.55,
p
<
0.001; for responders,
IMQ_SS
to
ASQ:
r
=
–0.47,
p
<
0.001; IMQ_SO to ASQ:
r
=
–0.29,
p
<
0.01), with the highest correlation with IMQ_SS.
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The Interactive Mentalization Questionnaire Development
TABLE 4 |
Correlations of IMQ subscales with other measures: Sample 3.
Index or Scale
IMQ_OS
IMQ_SS
IMQ_SO
Proposers’ data
Proposers.IMQ_SS
−
0
.
06
Proposers.IMQ_SO
−
0
.
44
****
0
.
40
****
Proposers.deception index
0
.
09
0
.
08
0
.
00
Proposers.otheraware_fairness
−
0
.
38
****
−
0
.
15
0
.
13
Proposers.selfaware_fairness
−
0
.
27
*
−
0
.
10
0
.
08
Confidence
−
0
.
25
*
0
.
03
0
.
01
Results_happy
−
0
.
06
−
0
.
03
−
0
.
05
LSRP
−
0
.
53
****
−
0
.
21
0
.
20
Proposers. LSRP_primary
−
0
.
51
****
−
0
.
19
0
.
19
Proposers. LSRP_secondary
−
0
.
50
****
−
0
.
20
0
.
19
Proposers.past_negative
−
0
.
41
****
−
0
.
08
0
.
23
Proposers.future
0
.
27
*
0
.
34
****
0
.
06
Proposers.past_positive
−
0
.
05
0
.
27
*
0
.
04
Proposers.present_hedonism
−
0
.
45
****
0
.
03
0
.
32
****
Proposers.present_fatalism
−
0
.
64
****
−
0
.
18
0
.
28
**
EC
0
.
27
*
0
.
21
−
0
.
02
AQ
−
0
.
27
*
−
0
.
55
****
−
0
.
24
Responders’ data
Responders.IMQ_SS
−
0
.
26
*
Responders.IMQ_SO
−
0
.
66
****
0
.
55
****
Responder_trust
−
0
.
28
**
−
0
.
09
0
.
15
Proposers.otheraware_fairness
−
0
.
07
0
.
02
0
.
07
Proposers.selfaware_fairness
−
0
.
08
0
.
05
0
.
06
Results_happy
−
0
.
31
***
0
.
02
0
.
18
LSRP
−
0
.
56
****
−
0
.
20
0
.
29
**
Responders.LSRP_primary
−
0
.
55
****
−
0
.
12
0
.
31
***
Responders.LSRP_secondary
−
0
.
48
****
−
0
.
29
**
0
.
19
Responders.past_negative
−
0
.
36
****
−
0
.
10
0
.
31
***
Responders.future
0
.
02
0
.
37
****
0
.
12
Responders.past_positive
−
0
.
18
0
.
27
*
0
.
27
*
Responders.present_hedonism
−
0
.
49
****
0
.
16
0
.
44
****
Present_fatalism
−
0
.
54
****
−
0
.
13
0
.
37
****
IRI_EC
0
.
14
0
.
07
−
0
.
07
ASQ
−
0
.
11
−
0
.
47
****
−
0
.
29
**
LSRP, the Levenson Self-Report Psychopathy Scale Survey; ZTPI, Zimbardo Time
Perspective Inventory; IRI_EC, Empathic concern from Interpersonal Reactivity
Index; ASQ, Autism Spectrum Quotient.
*Corrected p
<
0.05; **Corrected p
<
0.01; ***Corrected p
<
0.001; ****Corrected
p
<
0.0001.
STUDY 4: CONFIRMATION THE
VALIDITY OF THE INTERACTIVE
MENTALIZATION QUESTIONNAIRE IN
THE DECEPTION TASK
One potential criticism of Study 3 was that most offers were
accepted prior to the IMQ measurement, which may affect
the scores in the IMQ. We wanted to further to validate the
IMQ when implemented exclusively after unsuccessful social
interactions with others. Thus, in Study 4 we implemented same
online task, but this time we manipulated the task such that each
participant was assigned to the role of the proposer, and all offers
were artificially rejected. Our specific aims here were twofold:
(1) to validate the IMQ in a different social context; and (2) to
examine any possible state-dependency of the IMQ subscales.
With regard to the latter, we hypothesized that the subscales
related to mentalizing and meta-mentalizing would be relatively
state-dependent (i.e., sensitive to the social environment), while
the subscale related to self-awareness would be relatively stable.
Methods
Participants and Procedure
Two hundred and twenty nine participants (
M
age
=
32.64, range
from 18 to 65 years old, 62.2% male) were again recruited through
MTurk, were paid $1.5 for their participation.
Experimental Task
The task in Study 4 was almost identical to that of Study 3,
with the exception that all players were assigned to the role of
the proposer, and all offers were ultimately rejected in order to
replicate the IMQ results after unsuccessful social interaction.
We showed participants the same instructions as in Study
3, in order to make the players believe they were interacting
with another player. As in Study 3, the proposer was given an
endowment (from 30 to 160 cents), prompted to report the
endowment to the other player, asked to rate their confidence
(
1
=
not confident at all
to
100
=
super confident
) that their report
was believable, prompted to make an offer to the responder, and
finally rate their happiness (
1
=
not happy at all
to
100
=
super
happy
) with the outcome. The task was again followed by
questionnaires (IMQ, LSRP, IRI_EC, ZTPI, and ASQ).
Data Analysis
First, CFA was implemented with the same approach as
in Study 2.
As in Study 3, we constructed behavioral indices (naturally
only for the proposer role), and generated a Pearson correlation
matrix between these indices, the subscales of the IMQ, and the
alternative questionnaire measures.
Regression analysis was performed to investigate whether and
to what degree the subscales of the IMQ were able to capture
variance in the decisions and behavioral indices of the proposers.
To explore the possible effect of previous social interaction
context on the IMQ subscales, we compared IMQ scores from
Study 4 with the scores from Study 3, conditioned on accepted
offers. The distribution plots of the IMQ sub scores are shown in
Supplementary Figure S5
.
Results
Confirmatory Factor Analysis
Confirmatory factor analysis indicated a good fit for a three-
factor model [GFI
=
0.879, CFI
=
0.872; TLI
=
0.854,
RMSEA
=
0.07, and the
χ
2(167)
=
426.27,
p
<
0.001].
Behavioral Indices
Consistent with the result of Study 3, proposers were in general
dishonest in reporting the endowment [
t
(298)
=
13.96,
p
<
0.001,
Cohen’s
d
=
0.81, 95% CI
=
0.18–0.24].
Furthermore, other-aware fairness (
M
=
0.64,
SD
=
0.35)
was significantly higher than self-aware fairness (
M
=
0.48,
SD
=
0.24), [
t
(298)
=
10.01,
p
<
0.001, Cohen’s
d
=
0.58, 95%
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February 2022 | Volume 12 | Article 791835
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Time: 16:30
# 10
Wu et al.
The Interactive Mentalization Questionnaire Development
TABLE 5 |
Correlations between questionnaires and behavioral index in Sample 4.
Scale
IMQ_OS
IMQ_SS
IMQ_SO
Proposers’ data
IMQ_SS
0
.
02
IMQ_SO
−
0
.
34
****
0
.
50
****
Total IMQ
0
.
51
****
0
.
76
****
0
.
51
****
Deception index
−
0
.
08
0
.
01
0
.
13
Otheraware_fairness
−
0
.
37
****
−
0
.
06
0
.
15
Selfaware_fairness
−
0
.
34
****
−
0
.
07
0
.
07
Confidence
−
0
.
21
*
0
.
03
0
.
12
Results_happy
−
0
.
51
****
−
0
.
06
0
.
19
LSRP
LSRP_primary
−
0
.
49
****
−
0
.
07
0
.
31
****
LSRP_secondary
−
0
.
59
****
−
0
.
24
**
0
.
19
***
ZTPI
Past_negative
−
0
.
39
****
−
0
.
12
0
.
21
*
Future
0
.
22
*
0
.
36
****
0
.
06
Past_positive
−
0
.
07
0
.
27
****
0
.
17
**
Present_hedonism
−
0
.
48
****
0
.
02
0
.
38
****
Present_fatalism
−
0
.
63
****
−
0
.
10
0
.
26
***
IRI_EC
0
.
06
0
.
13
0
.
02
ASQ
−
0
.
34
****
−
0
.
45
****
−
0
.
26
**
LSRP, the Levenson Self-Report Psychopathy Scale Survey; ZTPI, Zimbardo Time
Perspective Inventory; IRI_EC, Empathic concern from Interpersonal Reactivity
Index; ASQ, Autism Spectrum Quotient.
*Corrected p
<
0.05; **Corrected p
<
0.01; ***Corrected p
<
0.001; ****Corrected
p
<
0.0001.
CI
=
0.13–0.19]. Out of the 1–100 rating slide, the proposers
showed relative high confidence (very unconfident 1–100 very
confident),
M
=
69.14,
SD
=
24.60, and unhappy about the being
rejected result,
M
=
30.63,
SD
=
36.54.
Interactive Mentalization Questionnaire Scores After
Successful and Unsuccessful Interaction
There were no significant differences in the scores for either the
IMQ_SO nor IMQ_SS subscales between Study3 and Study 4
(both
p
>
0.12). However, we did find a significant difference
of the meta-mentalization component between the two studies
for the IMQ_OS [
t
(399)
=
–2.86,
p
=
0.004, Cohen’s
d
=
–0.25,
95% CI
=
– 1.92 to –0.36). That is, the proposers who had their
offer accepted in Study 3 (
M
=
16.19,
SD
=
4.25) exhibited
lower IMQ_OS than the proposers who were rejected in Study
4 (
M
=
17.33,
SD
=
4.31), suggesting some influence of context
on this measure.
Relationship Between Interactive Mentalization
Questionnaire Subscales and Behavioral Indices
We first wished to confirm our hypothesis that IMQ_OS should
be correlated with the proposer’s confidence in the interaction.
As in Study 3, the results indicated that proposers’ meta-
mentalization was negatively associated with confidence ratings
(
r
=
–0.21, corrected
p
<
0.01), other-aware fairness ratings
(
r
=
–0.37, corrected
p
<
0.01), and self-aware fairness ratings
(
r
=
–0.34, corrected
p
<
0.01) (
Table 5
).
In a GLM using the
deception index
, confidence, IMQ_SO,
IMQ_OS, and IMQ_SS as predictors for proposers’ happiness
ratings, the results revealed a significant effect of the confidence
ratings (
β
=
0.98,
SE
=
0.42,
p
<
0.001), but not for the
IMQ subscales (IMQ_SO:
β
=
–0.04,
SE
=
0.02,
p
=
0.21,
IMQ_OS:
β
=
0.17,
SE
=
0.09,
p
=
0.06, IMQ_SS:
β
=
0.003,
SE
=
0.02,
p
=
0.87). In the GLM, we also found a significant
interaction effect between confidence and IMQ_OS (
β
=
–0.05,
SE
=
0.02,
p
<
0.001). It indicated when the confidence was
high, the slope of the regression line of IMQ_OS was significant
(
β
=
–0.06,
SE
=
0.03,
t
=
–2.13,
p
=
0.03), while the slope
of the regression line of IMQ_OS was not significant when the
confidence was medium (
β
=
–0.02,
SE
=
0.02,
t
=
–0.81,
p
=
0.42) or low (
β
=
–0.03,
SE
=
0.03,
t
=
0.86,
p
=
0.39) (see
Supplementary Figure S4
).
Correlations Between Interactive Mentalization
Questionnaire and Other Measures
Both the proposers and responders showed similar correlations
with other questionnaire measures as in Study 2 and Study 3
(
Table 5
). For example, we replicated the negative correlations
between IMQ_OS, IMQ_SS and psychopathy measured by LSRP
(IMQ_OS and LSRP primary:
r
=
–0.49
p
<
0.001, IMQ_OS
and LSRP secondary:
r
=
–0.59,
p
<
0.001, IMQ_SS and
LSRP secondary:
r
=
–0.24,
p
<
0.01, IMQ_SO and LSRP
primary:
r
=
0.31,
p
<
0.001, IMQ_SO and LSRP secondary:
r
=
0.19,
p
<
0.001). Again, IMQ components were negatively
correlated with autism traits, with highest correlation with
IMQ_SS (IMQ_OS and ASQ:
r
=
–0.34,
p
<
0.001; IMQ_SS
and ASQ:
r
=
–0.45,
p
<
0.001; IMQ_SO and ASQ:
r
=
–0.26,
p
<
0.01).
DISCUSSION
General Discussion and Conclusions
Our aim was to develop and validate a new, brief self-
report measure to assess individual differences in three
psychological components of interactive mentalizing. These
include measures that reflect: (i) the capacity to infer the
mental states and thoughts of others (IMQ_SO), (ii) the
ability to look inward to monitor and assess one’s own
thought processes (e.g., IMQ_SS), and (iii) beliefs about the
transparency of one’s own thoughts to others (IMQ_OS). To
achieve this aim, this work was structured in four major
parts. First, we developed sets of questionnaire items that
reflected the three kinds of psychological components that
should be theoretically linked, and explored the structure
scale of the scale (
Figure 1
). We next used an independent
sample to confirm the questionnaire structure and correlated the
subscale scores with theoretically related alternative measures.
We then used the subscales to assess behavioral decisions
in a social interaction game, as well as to assess individual
confidence ratings – ecologically valid social measures. Finally,
we used the subscales to replicate these results in the
context of unsuccessful mentalizing context. Taken together,
these studies provide initial support for the structure of the
proposed IMQ scale, and indicate a reliable measurement of
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Wu et al.
The Interactive Mentalization Questionnaire Development
individual differences in mentalization processes oriented to
oneself and to others.
Structure and Hierarchy in Interactive
Mentalization Questionnaire
In our model of IMQ, the three components are related
to, but also independent from each other. With respect to
the correlations among three components, the results indicate
that the measure of IMQ_SS (self–self mentalization/meta-
cognition) was negatively correlated with the IMQ_OS (other–
self mentalization/meta-mentalization), but positively correlated
with IMQ_SO (self–other mentalization).
The positive correlation between the IMQ_SS and the
IMQ_SO subscales is consistent with simulation theory (Harris,
1992; Carruthers, 1996), which hypothesizes that people rely on
meta-cognitive processes to model the mental states of others.
Support for this theory comes from the proposed role of “mirror
neurons,” which are involved both in self-generated processes,
as well as during the observation of the same actions generated
by others (Gallese and Goldman, 1998). The positive correlation
between IMQ_SS and IMQ_SO fits the notion that people rely on
similar meta-level thinking for the inference of both our own and
other’s abilities, beliefs, and emotions.
Across our different samples, we consistently observed that
the IMQ_OS and IMQ_SO subscales were negatively correlated.
While our original hypotheses proposed a relationship between
these two subscales and reported confidence independently,
we did not anticipate a direct relationship between them.
One possible explanation, however, is that some individuals
overestimate their own abilities relative to others (Taylor and
Brown, 1988; Kruger and Dunning, 1999). This would lead
to them expressing better self-assessment of perspective taking
(IMQ_SO) as well as better self-assessment of their ability
to hide their thoughts from others (note that IMQ_OS is
reverse scored, such that higher scores indicate less transparency
of one’s own thoughts to others). Interestingly, while both
of these subscales ought to contribute to higher social
competence, such an overestimation of one’s own abilities
has been shown to be detrimental for social interaction
(i.e., “tooting one’s own horn”; Colvin et al., 1995). It is
difficult to provide much support for this interpretation
without non-self-report assessments of social competence with
which to contrast to our self-report measure. One more
speculative possibility is that there is a shared and limited
resource for meta-cognition, such that those who think
more about others think less about themselves and vice-
versa. However, we know of no current evidence that would
support this viewpoint.
While an ideal measure of these components of social
cognition ought to be consistent, we also acknowledge that
individuals dynamically learn and adjust their beliefs about
themselves and others over time. This should be particularly
apparent in ongoing social interaction, or under different
social contexts. While we did not observe state-based changes
in our measures,
per se
, we did observe a gross change
in the IMQ_OS subscale under different social contexts. In
Study 3, IMQ_OS scores were higher after a cohesive social
interaction (offer acceptance) than in Study 4, after social
rejection. Given that the IMQ_OS subscale theoretically reflects
how well other individuals can infer one’s own beliefs and
motivations, it makes sense that this should be affected
after an unsuccessful social interaction. An interesting further
question is whether behavioral changes, such as differences in
expression, might occur as a result of discrepancies in other-
self mentalization. Further studies measuring body language
(facial expressions, gestures, speech patterns) may be able
to address this.
Correlations With Other Measures
With regard to the relatively rich literature on the relationships
between mentalization and other traits, we wanted to ensure
our scale captured some of these existing relationships, while
accounting for enough new variation to be valuable on its
own. Across three studies, we observed that IMQ_SO was
positively correlated with psychopathy, while IMQ_OS and
IMQ_SS were both negatively correlated with psychopathy.
These results are not entirely consistent with some previous
studies that failed to find a relationship between psychopathy
and theory of mind (Richell et al., 2003; Del Gaizo and
Falkenbach, 2008), or literature demonstrating a negative
association between psychopathy and mentalization (Choi-Kain
and Gunderson, 2008; Bateman et al., 2013). However, it is
important to note that our study focused on typical individuals
with and trait-psychopathy, rather than clinically determined
psychopaths. Notably, our results partially consistent with
other findings showing different components of psychopathy
show different relationships with mentalization, such that
antisocial psychopaths are associated with lower mentalization
ability, while interpersonal psychopaths are associated with
higher mentalization ability (Sandvik et al., 2014). In another
study, within a non-clinical sample, psychopathy was shown
to be negatively correlated with overall accuracy in an
emotion expression test (Ali and Chamorro-Premuzic, 2010;
Vonk et al., 2015).
Consistent with literature pointing to impairments of
meta-cognition and mentalization in individuals with autistic
traits (Baron-Cohen et al., 1986; Zalla et al., 2015), and
our hypotheses, we observed negative correlations between
IMQ components and autistic traits across our studies. More
specifically, our results showed that autistic traits are most
strongly correlated with the IMQ_SS component, and most
weakly correlated with the IMQ_SO. The latter result in
particular is consistent with findings indicating that individuals
high in autism traits are unable to recognize their own emotions
and find it difficult to identify their own thoughts (Baron-
Cohen, 1997). One interesting avenue for future research is
to identify whether our IMQ subscales can provide a more
tailored fingerprint of autistic traits and symptoms, particularly
during development. For example, it may be possible that the
different IMQ components may map onto different symptoms,
and different degrees of dysfunctional behavior and social
functioning, and this could provide a valuable method for
psychiatric classification.
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The Interactive Mentalization Questionnaire Development
We hypothesized that as it relates to decision confidence
about future plans, the future component of the ZTPI
would be associated with IMQ_SS and IMQ_OS – measures
that require an estimate of confidence or ability. Across
our studies, our results showed that future perspective was
strongly positively correlated with IMQ_SS and weakly positively
correlated with IMQ_OS, but that there was no association
with IMQ_SO. This generally supports our hypothesis that
decision confidence should be related to self-assessments
of ability. In relation to the overconfidence interpretation
of the negative correlation between IMQ_SO and IMQ_OS
above, there is some literature that reports that increases
in construal (psychological distance) increase self-idealization
(Kivetz and Tyler, 2007). Interestingly, these increases in
construal, which can be predictions about future actions or
outcomes, enhance not only self-idealization and overconfidence,
but also overconfidence in the abilities of others (Griffin et al.,
1990; Vallone et al., 1990). This contrasts with the negative
correlation between IMQ_SO and IMQ_OS, which seems to
show that given this level of construal, individuals may still
preference their own abilities above those of others. To our
knowledge this has not been directly shown, but is supported
by our findings.
Correlations With Behaviors
With respect to the proposers in Study 3, our results showed
that the IMQ_OS subscale was negatively correlated with self-
reported fairness and confidence ratings when proposers were
informing the responder about the total amount on offer. While
the relationship with confidence ratings makes sense with respect
to how the individuals feel about their own strategic deception
abilities, the relationship with self-reported fairness appears to
imply that these individuals felt some superiority over their
opponents. That is, individuals were more likely to believe that
the portioned rewards were fairer if they thought that their
opponents had poor insight into their own decisions.
As for the responder data, their trust of the proposer was
negatively correlated with their IMQ_OS score, and positively
correlated with their IMQ_SO score. These latter results seem
to indicate a tradeoff where individuals who rated the proposer’s
capacity for insight as inferior were also less likely to trust them,
while if they rated their own mentalization capacity as higher,
they were more likely to trust them. A simple explanation for the
first relationship is that expectations of ability are generalized,
so that if individuals think that another agent has poor ability
to have insight into their own mental states, they also have
poor social abilities in general – including trustworthiness. One
possible interpretation for the second relationship is provided by
a social projection account, whereby individuals use beliefs about
how they would react in the same situation in order to identify
with, and place trust in the decisions of others. Thus, people’s
expectations about the trustworthiness of others are correlated
with estimates of their own tendency to trust others (Thielmann
and Hilbig, 2014). One good example of this is in the Trust Game:
one player (the investor) decides how much money out of an
initial endowment to send to another player (the trustee). The
sent amount is then tripled, and the trustee decides how much of
the money received to send back to the investor). A study using
this paradigm found that selfish investors with good mindreading
skills were less likely to display trust, and invested less, than those
with worse mindreading skills (Derks et al., 2015). Overall, these
results demonstrate that our measures are an appropriate tool to
capture aspects of behavior in real social interactions.
Notably, we did not find the direct correlation between
deception index and proposers’ IMQ subscales scores, but we
did find deception-related results with IMQ subscale scores. As
in our task, we found dishonesty for most participants, and we
tried to not only capture the deception index for the proposers,
but also to ask the proposer and responder to rate their feelings
of confidence and happiness about the results. First, since most of
the participants lied in the task, we observed a significant negative
correlation between IMQ_OS and the trust rating to the proposer.
Further, our results showed IMQ_OS scores were significantly
higher for responders who rejected the offer than those who
accepted the offer, suggesting that higher meta-mentalization
capacity was associated with an increase in rejections. It may
mean that people with higher meta-mentalization score can
recognize deception more and reject the more. As for the
proposer’s data, we found proposer’s meta-mentalization was
negatively associated with the confidence in the deception task,
and the fairness level of the allocation. This correlation between
IMQ OS subscales scores and confidence in the deception task,
may indicate that people lied (lower fairness level) but with lower
confidence in the deception. In summary, our results provide
evidence between IMQ subscale scores and deception from other
aspects (trust, rejection decisions, deception confidence) but not
the deception index directly.
Broader Issues and Future Directions
There are several limitations to our studies. One concern is the
ecological validity of our online deception task, i.e., difference
between online testing and lab testing. Deception can be induced
by different motivations, in both MTurk and lab settings (Greene
and Paxton, 2009; Wu et al., 2009; Suri et al., 2011; Fischbacher
and Föllmi-Heusi, 2013; Cui et al., 2018). Participants in Study
3 and Study 4 acted deceitfully toward other online players with
monetary incentivization. We note that many morally relevant
decisions may be different when they are interacting with real
people in the lab (Levitt and List, 2007). While our results from
this task fit our theoretical hypotheses, it remains to be tested
if these extend to other, face-to-face interactive scenarios. It is
also difficult to provide support for a clear interpretation of
the negative correlation between IMQ_OS and IMQ_SO, while
future studies, perhaps using computational modeling, may give
more insights on this topic.
A further line of enquiry focuses on the implications of
mentalization for different kinds of populations. One example
is an investigation of typical or atypical development in meta-
cognition and its impact on different aspects of social functions
throughout the lifespan. Similarly, it would be of great interest
to test social decision making and mentalization processes in
subclinical and clinical samples (Sharp and Venta, 2012; Specht
et al., 2016).
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