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The Structure of Deception: Validation of the Lying Profile
Questionnaire
Dominique Makowski1,*, Tam Pham1, Zen J. Lau1, Adrian Raine2, & S.H. Annabel Chen1, 3, 4, *
1School of Social Sciences, Nanyang Technological University, Singapore
2Departments of Criminology, Psychiatry, and Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
3Centre for Research and Development in Learning, Nanyang Technological University, Singapore
4Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
The conceptualization of deception as a dispositional trait is under-represented in the liter-
ature. Despite scientific evidence supporting the existence of individual differences in lying,
a validated measure of dispositional deception is still lacking. This study aims to explore
the structure of dispositional deception by validating a 16-item questionnaire to characterize
individuals’ lying patterns. The final sample included 716 participants (Mean age = 25.02;
55.87% females) who were recruited via posters, flyers, and online social media platforms in
Singapore. Our findings suggested four distinct latent dimensions: frequency, ability, nega-
tivity, and contextuality. We established the convergent validity of our measure by showing
significant relationships with social desirability, malevolent traits, cognitive control deficits,
normal and pathological personality traits, as well as demographic variables such as sex, age,
and religiosity. Overall, the present study introduced a general framework to understanding
deception as a dispositional trait.
Keywords: deception, lying, questionnaire, psychopathy, personality
Word count: 7981
The scientific study of deception and lying has had a turbu-
lent history, nourishing key developments in ethics and phi-
losophy, and sometimes being (mis)used for forensic or po-
litical purposes. Although often put at the forefront of pub-
lic attention through a criminological or psychopathological
lens, the topic remains at the crossroads of different fields
even within psychology. For example, although deception is
a common phenomenon, practiced by virtually all (Kashy &
DePaulo, 1996), the emphasis has often been placed on lie
detection or pathological cases. Thus, it seems that a general
framework for understanding this phenomenon at the popu-
lation level is still lacking.
Using an integrative framework, we propose to conceptual-
ize deception as the process of achieving an inaccurate ex-
perience of a piece of information in relation to its objec-
tive qualities. This definition is neutral to the nature of the
object and its cause, i.e., not limited for instance to verbal
information (visual illusions could be interpreted as a form
Correspondence concerning this article should be addressed to
Dominique Makowski, HSS 04-18, 48 Nanyang Avenue, Singa-
pore. E-mail: dmakowski@ntu.edu.sg
of sensory deception), humans (deceptive behaviours being
documented in other species; e.g., Hirata, 1986; Waal, 2005)
nor specific purposes (e.g., being necessarily beneficial for
the deceiver, as suggested by Bond & Robinson, 1988). As
such, the study of this form of reality-bending could benefit
from being placed within a larger framework of the sense of
reality and its naturally connected neurocognitive functions
such as perception, emotions or consciousness (Makowski
et al., 2017; Makowski, 2018; Riva et al., 2007; Seth et al.,
2012).
Lying is a form of intentional deception. It includes the cre-
ation and delivery of information that is believed to be inac-
curate, with the aim of making it believed to be accurate (note
that both deception and lying are used interchangeably in the
context of this study). While lying is often studied as an
act, involving but not limited to its production or reception,
lying could also be understood and investigated as a disposi-
tional trait, i.e., as a metastable characteristic of personality.
In fact, one of the motivations supporting this perspective is
the emergence of evidence supporting the existence of inter-
individual variability in lying.
Since lying can be described as a common and potentially
universal phenomenon (Kashy & DePaulo, 1996), one naive
hypothesis could be that individual differences are less rele-
2DOMINIQUE MAKOWSKI1,*, TAM PHAM1, ZEN J. LAU1, ADRIAN RAINE2, & S.H. ANNABEL CHEN1, 3, 4, *
vant than situational factors (Aquino & Becker, 2005). How-
ever, empirical evidence has recently rebutted the assumption
of ubiquitous lying behaviors, suggesting that the extent to
which people engage in lying varies considerably from one
individual to the next (Gozna et al., 2001; Kashy & DePaulo,
1996). For instance, differences have been observed between
participants in the frequency of lying, their (perceived) abil-
ity to tell and detect lies, the emotions associated with ly-
ing (e.g., guilt) or the moral attitude toward it (Serota et al.,
2010; Serota & Levine, 2015). Importantly, these patterns
have been shown to be related to other interindividual vari-
ables Jensen et al. (2004). In spite of this evidence, most
experimental research on deception has overlooked trait-like
interindividual variability, and has instead focused on the be-
havioral and neural correlates of lying and lie detection. Crit-
ically, it is possible that the absence of such control of trait-
deception in experimental studies is related to a lack of val-
idated questionnaires made to assess individual variations in
lying.
However, using self-report questionnaires as a measure of de-
ception inherently begs the question of whether results may
be contaminated by response bias and distorted perceptions
in self assessment. Although the issue of deception assess-
ment using self-report tools being beyond the scope of this
study, past studies have demonstrated some level of support
for its suitability. Self-reported lying frequencies have been
shown to converge with respondents’ estimations of others’
lying tendencies (Serota et al., 2010), as well as real life dis-
honesty in the lab (i.e., cheating on a task, Halevy et al.,
2014a). A methodologically rigorous approach is also cru-
cial in eliminating any potential caveats. This includes ac-
counting for social desirability to adjust for each subject’s
bias (Fisher & Katz, 2000; Serota et al., 2010) and encourag-
ing accurate reporting through guaranteeing data anonymity
(Serota & Levine, 2015) and providing a clear definition of
lying in the questionnaire (Serota et al., 2010). These addi-
tional measures are closely followed in the present study and
will be elaborated on in later sections.
Indeed, while “lie scales” are common in psychometric re-
search, they often refer to social desirability measures (e.g.,
the LIE subscale in the Eysenck Personality Questionnaire,
Eysenck & Eysenck, 1975) rather than to validated ques-
tionnaires reliably measuring different aspects of deception.
Existing developments in lie scales are also often limited to
measures that are created and used in studies targeting de-
ception as a dependent or independent variable (as opposed
to an exploration of the dimensionality to trait-deception).
For instance, Azizli et al. (2016) assessed the tendency to lie
in a high-stake deception using the in-house created Propen-
sity To Lie Questionnaire. The first part of this questionnaire
included questions about the general subjective tendency to
lie, while the second part includes items pertaining to two
short scenarios describing hypothetical lying situations. The
findings reported a relationship between the tendency to lie
and antisocial tendencies (Machiavellianism, narcissism, and
psychopathy). Unfortunately, this study did not report nor-
mative data nor a factor structure analysis of the question-
naire. Similarly, El Haj et al. (2018) created a five-item scale,
adapted from the Impression Management subscale of the
Balanced Inventory of Desirable Responding (BIDR, Delroy
L. Paulhus, 1991), assessing the tendency to lie (or rather, to
impress others), and showed that people with a higher trait
tendency to lie have a higher ability to remember to whom
they have told a piece of information. More in line with
our dimensional approach to trait-deception, Zvi and Elaad
(2018) developed the Lie-Truth Ability Assessment Scale to
measure different facets related to lying, such as the ability to
tell lies, to perceive lies, to tell the truth, and also to believe
others. Although the factor structure and internal reliability
of the scale were not thoroughly tested, the study reported
a positive relationship between lying ability and narcissism.
Nonetheless, the authors emphasize the need to develop a
reliable and valid lie scale.
Using a different approach, Serota and Levine (2015) inves-
tigated how participants can be grouped into different “pro-
files” based on the number of lies they tell per day. They sug-
gested the existence of two distinct groups, everyday liars,
the majority of the general population, and prolific liars, who
possess a significantly higher tendency to lie and whose dis-
honest behaviors are often associated with serious matters.
This is in line with the studies suggesting that manipulative-
ness, sociability, anxiousness, and impression management
are features related to a stronger self-reported lying ability
(Elaad & Reizer, 2015; Gozna et al., 2001; Kashy & De-
Paulo, 1996; Panasiti et al., 2011). Unfortunately, method-
ological limitations of the study (the absence of statistically-
driven cluster analysis) underline the necessity to further in-
vestigate this interesting profile perspective.
One of the challenges for validating a lying questionnaire is
the identification of relevant related constructs to assess con-
vergent validity. Although the literature on trait-deception
described above is rather sparse, experimental and observa-
tional research investigating how deceptive behaviors relate
to other inter-individual characteristics can be used as a start-
ing point to establish specific and testable hypotheses. For
instance, a large body of research has investigated how lying
relates to normal personality traits (Gozna et al., 2001), with
evidence suggesting that extraversion is related to a higher
lying frequency (Weiss & Feldman, 2006), contrary to con-
scientiousness which tends to be related to honesty (Gillath
et al., 2010). Perceived ability to tell lies shares a positive re-
lationship with extraversion and openness and a negative re-
lationship with agreeableness (Elaad, 2018; Elaad & Reizer,
2015; Kashy & DePaulo, 1996). The hypothesized underly-
THE STRUCTURE OF DECEPTION 3
ing link is that people with higher extraversion and openness
are more likely to engage in social events, offering more op-
portunities to lie (Kashy & DePaulo, 1996), which in turn
boosts the frequency of - as well as their confidence in - ly-
ing. On the other hand, agreeable and conscientious individ-
uals are less likely to lie (and arguably more honest about
themselves), consequently describing themselves as being
less skilled at it (Gillath et al., 2010).
Another personality dimension related to deception is nar-
cissism (Zvi & Elaad, 2018), a trait commonly found in
several personality disorders and one of the facets of the
socially malevolent personality profile coined as the “dark
triad” (Delroy L. Paulhus & Williams, 2002). Given that nar-
cissists value power and are endowed with a grandiose sense
of self, their primary motive to lie is for self-gain and self-
enhancement Dike et al. (2005). Another possible mech-
anism at play could be the role of grandiosity in support-
ing self-deception in the context of negative feedback, which
would subsequently facilitate lying to others (Uziel, 2014;
Wright et al., 2015).
Naturally, deception is most commonly studied in relation-
ship with antisocial and antagonistic traits, such as psychopa-
thy (Hare & Forth, 1985), which is positively correlated with
lying frequency (Halevy et al., 2014b), with self-reported ly-
ing ability and with the tendency to lie without reason (Jona-
son et al., 2014). Neuroimaging evidence during a decep-
tion task has suggested that specific aspects of psychopathy,
namely fearlessness and coldheartedness, were associated
with lower activity in the orbitofrontal and temporal cortex,
respectively (Fullam et al., 2009). These findings emphasize
the potential role of social and emotional sensitivity and con-
trol in modulating deceptive behaviors.
More specifically, it is plausible that the relationship between
higher-order antisocial traits and deception could be sup-
ported by lower-order processes, such as self-control which
has been shown to be correlated with the “dark triad” (Jona-
son & Tost, 2010). This is in line with theoretical and empir-
ical evidence suggesting the implication of cognitive control
in lying (Abe, 2009; Debey et al., 2012; Lee et al., 2009;
Poletti et al., 2011), supporting processes such as decision-
making, taking others’ perspectives, maintaining consistency
of the fabricated story or inhibiting previously learned con-
tent or true responses. This is consistent with neuroscientific
findings underlining the role of prefrontal regions in decep-
tion (e.g., Christ et al., Oxford University Press, UK; Karim
et al., 2010) as well as with individuals reporting greater cog-
nitive effort when having to lie as compared to telling the
truth (Vrij & Semin, 1996). Nonetheless, as most of the evi-
dence presented above was gathered in experimental settings,
it remains unclear how these variables are related to disposi-
tional deception.
Thus, this study aims primarily at investigating the factor and
cluster structure of lying as a trait. To achieve these two re-
spective aims, this study explores how questions about ly-
ing map onto latent factors related to deception, and whether
groups of individuals emerge in the uncovered multidimen-
sional space of deception facets. This will be accomplished
through the validation of a short yet reliable questionnaire
on the general population, which will allow future deception
studies to account for the inter-individual variability in the
natural disposition to lie.
In line with the evidence presenting deception as a phe-
nomenon supported by a neurocognitively distributed net-
work of processes, we hypothesized the questions related to
deception to preferentially fit a multidimensional structure,
composed of different distinct latent factors. Regarding con-
vergent validity, we expect deception to be positively related
to malevolent and antisocial traits, such as psychopathy, an-
tagonism, and narcissism. Correspondingly, we also predict
a negative link with benevolent traits, such as agreeableness
or the recently defined “light triad” traits (Kaufman et al.,
2019). A relationship is also expected with normal personal-
ity dimensions, such as extraversion, openness and honesty-
humility, as well as to traits related to potential deficits of
cognitive control, such as impulsivity (Enticott et al., 2006;
Fino et al., 2014) and emotion regulation (Kohn et al., 2014;
Makowski, Sperduti, et al., 2019; Ochsner & Gross, 2005;
Sperduti et al., 2017). Notably, beyond its use as a mere
proxy of cognitive control, difficulties in emotion regulation
might impact lying in another way, as the engagement in de-
ception might be dependent on one’s ability to cope with the
emotional states related to lying - e.g., stress - and its conse-
quences - e.g., guilt or shame (Arndt et al., 2013; Carlson &
Wang, 2007).
Another aspect of the relationship between emotions and
cognitive processes (such as decision making), understudied
in the context of deception, is interoception, which refers to
one’s sensitivity to internal signals and bodily states (Füstös
et al., 2012; Garfinkel et al., 2015; Kever et al., 2015). In-
deed, decision-making research has shown that individuals
with higher interoceptive awareness are less likely to make
risky decisions (Dunn et al., 2010; Furman et al., 2013). This
is in accordance with the somatic marker hypothesis, in that
the accurate detection of physiological arousal guide the use
of such interoceptive feedback to make safe, reasoned deci-
sions (Bechara & Damasio, 2005). By extension, since lying
could be perceived as a risky behaviour that increases bodily
arousal (e.g., Turck & Miller, 1985), we expect interoception
to be related to lying behaviour (for instance, lying frequency
or ability).
4DOMINIQUE MAKOWSKI1,*, TAM PHAM1, ZEN J. LAU1, ADRIAN RAINE2, & S.H. ANNABEL CHEN1, 3, 4, *
Methods
The study plan was preregistered (https://osf.io/3kv7f). In
the spirit of open and honest science, the raw data, as well
as the entire analysis script as Supplementary Materials 1
(including details and additional analyses) can be found at
https://github.com/DominiqueMakowski/ 2020structure. An
interactive web application to compute the scores of the ques-
tionnaire is available at https://neuropsychology.shinyapps.
io/proflier.
Participants
One thousand and eleven participants from the general pop-
ulation were initially recruited via posters and flyers, as well
as online social media platforms (e.g., Facebook). Inclusion
criteria included residing in Singapore, as well as an absence
of neurological and psychiatric history. Participants were
reimbursed 5 Singapore Dollars in cash or vouchers upon
survey completion. The study was approved by the Insti-
tutional Review Board (Reference Number: IRB-2019-02-
026) of Nanyang Technological University (NTU).
Due to the presence of a monetary incentive, as much as
to the nature of the investigated construct, a strict proce-
dure was used in order to ensure and maximize data qual-
ity. 5 participants were excluded due to missing data, fol-
lowed by 141 participants with a completion time outside
the 90% percentile [< 10.95 min and > 61.94 min]. In ad-
dition to completion time (which is considered as the best
indicator of data quality, Leiner, 2013), we identified multi-
variate outliers based on a composite outlier score (see the
check_outliers function in the performance R pack-
age, D. Lüdecke et al., 2019) obtained via the joint appli-
cation of multiple outliers detection algorithms (e.g., Ma-
halanobis distance, Invariant Coordinate Selection, or Local
Outlier Factor; see D. Lüdecke et al., 2019 for the full de-
scription). This led to the exclusion of 149 participants that
were classified as outliers by more than half of the methods
used.
The final sample included 716 participants (Mean age =
25.02, SD = 7.07, range = [16.55, 73.51]; 55.87% females;
Mean education in years relative to high school completion
= 3.54, SD = 1.95, range = [-7, 10]; Mean Religious Engage-
ment = 3.82, SD = 3.09, range = [0, 10]; Mean Religiosity =
4.22, SD = 2.97, range = [0, 10]; Median monthly income per
household per capita = SGD2,000, MAD = SGD1482.60).
Procedure
Participants completed an online questionnaire created via
the Qualtrics©platform. After informed consent was ob-
tained, participants responded to items on visual analog
scales or Likert scales (adapted to screen size). Items from
9 existing questionnaires, as well as our Lying Profile Ques-
tionnaire (LIE), were included (see below). The presenta-
tion order of these 10 inventories was randomized. Note
that while items within the LIE inventory were presented in a
randomized order, all existing questionnaires were presented
following their original validation, and scores for their di-
mensions were calculated accordingly (by averaging or sum-
ming, see Supplementary Materials 1). Participants then
responded to questions about demographics at the end of the
questionnaire. A transcript of the complete survey presenta-
tion is available in Supplementary Materials 2.
Measures
Lying. Based on the theoretical literature and the existing
scales on lying, we outlined 3 general domains related to ly-
ing relevant for a questionnaire; lying frequency, ability, and
accompanying features, such as motives and reactions. From
there, we developed an initial pool of items later refined for
face validity, ambiguous wording, jargon, or poor phrasing
in focus group discussions. The final Lying Profile Ques-
tionnaire (LIE) included 44 items phrased in statements such
as “I lie more often than most people do” or “I find lying
difficult,” presented on visual analog scales with “Disagree”
and “Agree” as the two extremities. While the LIE items
required relative subjective judgments, we also included two
questions pertaining to the Absolute Frequency of lying (ask-
ing how many lies one tells per day and per week). We aver-
aged these two items into one score expressed in lies/day.
We adapted our self-report lie measures in accordance with
the methodology used by Serota et al. (2010) to encour-
age accurate reporting. Prior to recording participants’ re-
sponses, we provided a definition of lying as encompassing
the intent to deceive. This is accompanied with descriptions
of different types of lies, making sure to eliminate any in-
stances of disparagement in these presentations. Participants
are thus encouraged to consider lies of different magnitude,
content (to emphasize that half-truths are also lies), effective-
ness, intent (i.e., told for prosocial or antisocial purposes),
and consequences. The description is available in Supple-
mentary Materials 2 (p. 21).
Social Desirability. The 16-item Balanced Inventory of
Desirable Responding (BIDR-16) self-report questionnaire
(Hart et al., 2015) was included to control for related bi-
ases in responding such as over-reporting positive traits and
under-reporting lying tendencies. This scale includes 2 dis-
tinct dimensions, Self-Deceptive Enhancement (participants’
deception of themselves with a tendency towards positive
traits), and Impression Management (participants’ intention
to deceive for the sake of pleasing others).
THE STRUCTURE OF DECEPTION 5
Psychopathy. The 58-item Triarchic Psychopathy Mea-
sure self-report questionnaire (TriPM, Christopher J. Patrick,
2010) was used to assess the 3 dimensions suggested by the
triarchic model of psychopathy (Christopher J. Patrick et al.,
2009), namely Boldness (fearlessness, social dominance, and
the tendency to engage in adventure-seeking behaviors), Dis-
inhibition (the lack of behavioral restrain, manifesting as im-
pulsivity, disregard for social conventions, and aggression)
and Meanness (the unempathetic and instrumental treatment
of others).
Narcissism. The short version of the Five-Factor Narcis-
sism Inventory Sherman et al. (2015) was used to measure 9
specific traits, namely Acclaim Seeking (preoccupation with
achieving acclaim, status, and/or fame), Entitlement (expec-
tations of special and self-serving treatment), Need for Admi-
ration (excessive need for the admiration and approbation of
others), Manipulativeness (a disposition to deceptively ma-
nipulate the feelings and/or opinions of others), Lack of Em-
pathy (failure to be aware of, appreciate, or acknowledge the
feelings of others), Indifference (lack of self-consciousness
or self-doubt in response to criticism or rebuke), Thrill Seek-
ing (excessive excitement-seeking that leads to high-risk be-
havior for the sake of thrills and excitement), Distrust (mal-
adaptive low level of trust concerning the intentions and mo-
tivations of others), and Exploitativeness (a disposition for
instrumental treatment of others, i.e., to exploit or take ad-
vantages of others).
Normal Personality. The 24-item measure of the Big-Six
personality dimensions (Mini-IPIP6, Sibley et al., 2011) was
used to measure the Big Six “normal” (as opposed to patho-
logical) personality traits based on the HEXACO Personal-
ity Model (Ashton & Lee, 2009), namely Extraversion (the
tendency to engage in social behaviors such as exhibiting
leadership and sociability), Openness (the extent to which
one is open-minded in terms of imagination and curiosity),
Agreeableness (how cooperative and tolerant one is of oth-
ers, with individuals high on this trait often being perceived
as warm, forgiving, and kind), Conscientiousness (being dili-
gent, meticulous and organized during task execution), Neu-
roticism (the tendency to experience a persisting negative
emotional state), and Honesty-Humility (being honest, sin-
cere, and fair during social exchanges).
Pathological Personality. The 25-item Personality Inven-
tory for DSM-5 Brief Form (PID-5-BF) was used to assess 5
pathological personality traits (Al-Dajani et al., 2016; Hop-
wood et al., 2012), namely Negative Affect (the frequency
and intensity of negative emotional experiences such as anx-
iety, anger, and depression), Detachment (social withdrawal
and diminished affective experiences), Antagonism (manip-
ulativeness, deceitfulness, callousness and hostility) Disin-
hibition (engagement in impulsive behaviors for immedi-
ate gratification), and Psychoticism (eccentric or incongruent
behaviors and cognitions, such as hallucinations and delu-
sions).
Light Triad. The 12-item Light Triad Scale (LTS, Kauf-
man et al., 2019) was used to measure prosocial and morally
positive traits (as opposed to antisocial or antagonistic ones),
including Faith in Humanity (the belief and trust that indi-
viduals are fundamentally good in nature), Humanism (the
extent to which one places value on the dignity and worth
of others), and Kantianism (the treatment of individuals as
means to themselves rather than using them instrumentally).
Impulsivity. The 20-item Short UPPS-P Impulsive Be-
haviour Scale (Cyders et al., 2014; Whiteside & Lynam,
2001) was used to measure 5 facets of impulsivity, namely
Negative Urgency and Positive Urgency (one’s propensity to
act impulsively under negative and positive emotional states,
respectively), Lack of Perseverance (the inability to focus on
tasks of a boring or difficult nature), Lack of Premeditation
(the propensity to act without thinking), and Sensation Seek-
ing (the inclination towards partaking in novel and thrilling
experiences).
Emotion Regulation. The 18-item Difficulty in Emotions
Regulation Scale (DERS, Victor & Klonsky, 2016) was used
to measure 6 facets of emotion regulation deficits, namely
Awareness (lack of recognition and appreciation of one’s
emotions), Clarity (difficulties in giving meaning to emo-
tions), Goals (difficulties in engaging in goal-directed cog-
nition and behavior when distressed), Impulse (lack of con-
trol when distressed), Non-Acceptance (unwillingness to ac-
cept certain emotional responses), Strategies (lack of access
to strategies for feeling better when distressed).
Interoception. We used 11 items from the Multidimen-
sional Assessment of Interoceptive Awareness, Version 2
(MAIA-2, Mehling et al., 2018) to specifically measure
2 facets of interoception, namely Noticing (the conscious
awareness of bodily sensations), and Body Listening (he abil-
ity and tendency for active listening to the body for insight).
Demographic. Participants provided demographic infor-
mation related to their Education (highest academic qualifi-
cation achieved or the qualification they are currently pursu-
ing), Sex,Age, and their socio-economic status (SES) which
was operationalized as the average monthly household In-
come per capita. Additionally, two items related to religious
Faith were presented on Likert scales, pertaining to how re-
ligious the participants perceive themselves to be and how
actively engaged in religious activities they are.
Data Analysis
We started by investigating the factor structure of our ini-
tial set of items. We randomly split the study sample into
6DOMINIQUE MAKOWSKI1,*, TAM PHAM1, ZEN J. LAU1, ADRIAN RAINE2, & S.H. ANNABEL CHEN1, 3, 4, *
a training set (60%) and a test set (40%). Exploratory Fac-
tor Analysis (EFA) was carried out with the training set to
explore the scale’s underlying factor structure and Confirma-
tory Factor Analysis (CFA) was performed with the test set to
test the goodness-of-fit of the suggested factor structures. We
then examined the cluster structure using k-means clustering
and assessed the convergent validity with partial correlation
analysis.
Data processing was carried out with R (R Core Team, 2019)
and in particular the psych (Revelle, 2018) and the lavaan
(Rosseel, 2012) packages, as well as the easystats ecosys-
tem (Daniel Lüdecke et al., 2019; Makowski, Ben-Shachar,
& Lüdecke, 2019a). The raw data, as well as the full re-
producible analysis script (along with complementary results
and figures), are available in Supplementary Materials 1.
Results
Factor Structure
The 44 initial items were deemed suitable for factor analysis
(KMO = 0.93; Bartlett’s test of sphericity χ2(946) = 9653.38,
p< .001). The factor number exploration using the method
agreement procedure (see the n_factors function in the
parameters package, Makowski, Ben-Shachar, & Lüdecke,
2019b) suggested 2 optimal factor solutions: four factors,
and one latent factor, respectively accounting for 43.00% and
24.63% of variance of the dataset. Hence, we submitted the
unique one-factor and four-factor models to Confirmatory
Factor Analysis (CFA).
Table 1
CFA fit indices of the full four-factor model, one-factor model,
hypothesized model, and the short-form four-factor models (con-
taining 3, 4, and 5 loading items per dimension respectively).
Model AIC BIC (adj.) Chi2 RMSEA CFI SRMR
Four Factors: all items 57650.70 57702.29 2142.547 0.068 0.821 0.089
One Factor: all items 59691.09 59739.39 4194.940 0.109 0.527 0.122
Hypothesized: all items 58197.46 58247.40 2695.311 0.081 0.742 0.097
Four Factors: 3 items 15296.48 15312.94 117.046 0.069 0.960 0.061
Four Factors: 4 items 20309.91 20330.76 223.804 0.065 0.949 0.061
Four Factors: 5 items 25408.64 25433.89 322.178 0.056 0.949 0.061
The confirmatory factor analysis favoured the four-factor so-
lution over the one-factor. We then compared the four-factor
solution with the initial hypothetic model with which we
built the scale, which favoured the four-factor model. Fi-
nally, we compared the full four-factor model (including all
items) with short forms retaining only the 3, 4 or 5 most
loading items for each of the 4 dimensions. The 3-items
version outperformed all versions, including 5-items and 4-
items. Nonetheless, as 3-items per construct is the bare min-
imum for adequate reliability, we decided to keep the second
best performing version with 4-items per factor, which also
displayed excellent indices of fit (see Table 1).
Table 2
All initial item loadings from the Exploratory Factor Analysis
(EFA). The final item selection (the 4 most loading items of each
dimension) also has the corresponding regression coefficients from
the Confirmatory Factor Analysis (CFA) model that were used to
calculate individual scores (between brackets).
Item Label Frequency Ability Negativity Contextuality
Q4 I have a tendency to lie 0.75 [1.01] 0.17 -0.17 0.05
Q23 I find it difficult to refrain myself from lying 0.73 [0.82] 0.11 -0.15 5.67e-03
Q5 I lie more often than most people do 0.73 [0.92] 0.18 -0.24 -8.04e-03
Q1 I lie frequently 0.70 [1.00] 0.18 -0.30 0.15
Q22 I find myself lying without any reason 0.68 0.06 -0.14 -4.26e-04
Q7 I lie more than I think I should 0.67 0.05 -8.44e-03 0.11
Q6 I lie more frequently than what I expect myself to 0.65 0.10 -0.08 0.11
Q2 I lie in many situations 0.61 0.18 -0.34 0.09
Q26 I enjoy lying 0.59 0.23 -0.24 -0.03
Q8 Others lie less often than I do 0.53 0.14 -0.11 0.02
Q29 I lie whenever it’s convenient 0.49 0.18 -0.18 0.25
Q21 I have to try hard to avoid lying 0.45 -0.04 0.09 -0.08
Q31 I lie if it’s the most direct way to get what I want 0.44 0.12 -0.12 0.31
Q28 I feel satisfied when others believe my lie 0.36 0.24 -0.12 0.30
Q24 It is easy to hold back from telling lies -0.29 0.09 0.15 0.06
Q10 I can lie well 0.25 0.82 [1.05] -0.14 0.21
Q9 I am a good liar 0.30 0.75 [1.00] -0.17 0.18
Q18 It is easy for me to make up clever lies 0.25 0.73 [0.87] -0.11 0.16
Q14 It is hard for others to detect my lies 0.15 0.73 [0.77] -0.03 0.18
Q11 I am good at deceiving others 0.33 0.71 -0.11 0.14
Q13 Others can easily tell when I’m lying 0.11 -0.69 0.19 -0.06
Q12 I can lie effectively if I want to 0.13 0.67 -0.04 0.27
Q17 I find lying difficult -0.08 -0.67 0.42 -0.14
Q15 I almost never get caught lying 0.08 0.65 -0.07 0.25
Q20 I do not have to prepare much for a lie 0.22 0.58 -0.13 0.16
Q19 I find it taxing to come up with a good lie -0.06 -0.49 0.36 0.07
Q27 I feel tense whenever I have to lie -0.03 -0.49 0.44 0.04
Q16 My lies often arouse suspicion from others 0.28 -0.46 0.11 -0.13
Q41 Lying is against my principles -0.19 -0.23 0.62 [1.30] -0.19
Q34 I always avoid lying if I can -0.41 -0.05 0.57 [0.86] 0.06
Q44 It is bad to lie -0.13 -0.21 0.55 [1.07] -0.17
Q25 I feel guilty after lying -0.08 -0.30 0.54 [1.00] -0.09
Q36 I prefer to tell the truth even if it gets me into trouble -0.20 -0.12 0.46 -0.13
Q35 I would only lie if I have no other choice -0.12 0.01 0.36 0.32
Q37 I would never lie for trivial matters -0.15 -0.03 0.34 0.02
Q38 I would never lie in serious contexts -0.11 -0.10 0.31 -0.01
Q43 It is okay to lie sometimes 0.07 0.16 -0.21 0.71 [1.21]
Q33 I lie when necessary 0.08 0.18 -0.02 0.69 [1.00]
Q42 It is acceptable to lie depending on the context 0.02 0.25 -0.10 0.62 [1.03]
Q39 I would lie if something important was at stake 0.02 0.18 0.05 0.47 [0.78]
Q40 I would only lie if it is harmless -0.06 0.10 0.09 0.46
Q30 I lie when it’s easier than telling the truth 0.32 0.04 -0.08 0.38
Q32 I lie when telling the truth is too troublesome 0.32 7.61e-03 -0.15 0.38
Q3 I never tell lies 0.03 -0.13 0.26 -0.32
The final version of the LIE questionnaire assesses 4 latent
dimensions measured with 4 items each (16 items in total).
Based on the most loading items (see Table 2), we labeled
these factors Ability (representing one’s subjective ability and
ease to create and deliver believable lies), Frequency (repre-
senting one’s subjective and relative assessment of lying fre-
quency), Negativity (the unwillingness to lie related to neg-
ative internal factors such as moral values and/or emotional
reactions associated with lying) and Contextuality (the flexi-
ble and context-driven willingness to lie depending on exter-
nal factors such as stakes, necessity and alternative options).
These factors were significantly correlated together (see Fig-
ure 1), with Ability,Frequency and Contextuality showing
positive relationships, and Negativity being negatively asso-
ciated with them. We back-fitted the CFA model on the full
dataset in order extract individual factor scores.
In line with recent recommendations, we assessed the mul-
tidimensional reliability by means of omega coefficients
(Green & Yang, 2015; Watkins, 2017), suggesting in gen-
THE STRUCTURE OF DECEPTION 7
Figure 1.Confirmatory structure of the deception scale items and
the correlation between the latent factors. Red links represent neg-
ative correlations and green links represent positive correlations.
The green arrows represent the loadings of the items onto their
respective factors. The numbers correspond to the regression coef-
ficients from the Confirmatory Factor Analysis (CFA).
eral a high reliability of the 16 items (ωtotal = 0.83) as well
as for each dimension (ωAbility
total = 0.87; ωF requency
total = 0.91;
ωContextualit y
total = 0.75; ωNegativit y
total = 0.76). Importantly, the
analysis confirmed that the 4 dimensions cannot be consid-
ered as only reflecting a unique underlying general factor
(ωhierarchical = 0.36).
Cluster Structure
We investigated the presence of higher-density regions in
the four-dimensional space of the LIE factor structure. The
dataset was deemed suitable for clustering (Hopkins’ H =
0.24), and the method agreement procedure (aggregating 28
methods to estimate the optimal number of clusters; see
Supplementary Materials 1), supported the existence of 2
(8/28) or 3 (11/28) clusters. We then applied k-means clus-
tering, which revealed that grouping the participants in 2 and
3 clusters would account for 44.92% and 57.58% of the to-
tal variance of the four dimensions of the questionnaire, re-
spectively. Thus, we decided to go ahead with the latter so-
lution and assign each participant to its nearest cluster (see
Figure 2), labeling them as Average (41.86% of the sam-
ple; people that report an average lying ability, slightly lower
than average frequency, average negativity and contextual-
ity), Trickster (35.04% of the sample; people with high re-
ported lying ability, frequency, low negative experience as-
sociated with deception and above-average flexibility in its
implementation), and Virtuous (23.10% of the sample; peo-
ple with very low reported lying ability and frequency, strong
negative emotions and moral attitude associated with lying
and high rigidity in their (non-)usage of deception).
Figure 2.The distribution of the LIE dimensions (A) and the cen-
tre values of the 3 clusters of participants.
Convergent Validity
Bayesian Regressions (from which we will report the 89%
Credible Interval (CI) and the probability of direction pd,
a Bayesian equivalent of the p-value; see Makowski, Ben-
Shachar, Chen, et al., 2019), using both the LIE dimensions
and profiles were used to assess the role of demographic vari-
ables, and Gaussian Graphical Models (GGMs, Epskamp et
al., 2018), i.e., networks based on partial correlations, were
used to assess the links between the LIE dimensions and
other theoretically related constructs. As the details of the
analyses are available in Supplementary Materials 1, we
will only report in the manuscript the significant links (Bon-
ferroni corrected and p< .001 for the GGMs to control for
spurious links).
Demographics.
Sex.
8DOMINIQUE MAKOWSKI1,*, TAM PHAM1, ZEN J. LAU1, ADRIAN RAINE2, & S.H. ANNABEL CHEN1, 3, 4, *
We fitted two Bayesian logistic regressions to predict Sex
with the lying profile (offering a simpler combined and in-
tegrated perspective), and the 4 lying dimensions (to assess
the underlying driving effects independently of one another).
These revealed that men, as opposed to women, were more
likely to be Tricksters than Average (coefficient = 0.41, 89%
CI [0.13, 0.67], pd = 99.15%) as well as more likely to be
Average than Virtuous (coefficient = -0.48, 89% CI [-0.81,
-0.19], pd = 99.38%). This effect was likely to be driven by
Ability, the only dimension significantly sensitive to sex (co-
efficient = 0.14, 89% CI [0.07, 0.21], pd = 99.85%), reported
as higher by men than by women.
Age.
The Bayesian linear mixed models predicting Age were ad-
justed for Sex (entered as random factor), Income and Ed-
ucation (entered as fixed effects). Age was higher for the
Virtuous, relative to the Average profile (coefficient = 2.81,
89% CI [1.68, 4.01], pd = 100%). These differences were
likely driven by Ability which was negatively related to Age
(coefficient = -0.36, 89% CI [-0.60, -0.13], pd = 99.17%),
suggesting that younger people tend to portray themselves as
good liars.
Socio-Economical Status.
The Bayesian linear mixed models predicting Income were
adjusted for Sex (entered as random factor), Education and
Age (entered as fixed effects). Although the profiles were not
different in terms of Income,Ability was the only dimension
significantly and positively related to Income (coefficient =
189.56, 89% CI [28.94, 342.75], pd = 97.12%).
Education.
The Bayesian linear mixed models predicting Education
were adjusted for Sex (entered as random factor) and Age
(entered as fixed effects). Education was higher for the Vir-
tuous (coefficient = 0.36, 89% CI [0.08, 0.61], pd = 98.62%)
and lower for Trickster (coefficient = -0.36, 89% CI [-0.58,
-0.12], pd = 99.52%), both relative to the Average profile. No
dimension was significantly related to Education.
Faith.
Due to their strong correlation (r = 0.86, p< .001), we col-
lapsed the two religion-related items into one Faith variable.
The Bayesian linear mixed models predicting Faith were ad-
justed for religion type (entered as a random factor). Faith
was stronger for the Virtuous, relative to Average profile (co-
efficient = 0.67, 89% CI [0.34, 1.05], pd = 99.85%) and
weaker for Tricksters relative to the Average (coefficient =
-0.37, 89% CI [-0.68, -0.07], pd = 97.42%). This effect was
likely driven by the fact that all lying dimensions - except
Ability - were associated with Faith. Stronger Faith was re-
lated to higher Negativity (coefficient = 0.53, 89% CI [0.38,
0.68], pd = 99.98%), lower Contextuality (coefficient = -0.25,
89% CI [-0.37, -0.12], pd = 99.98%) and higher Frequency
(coefficient = 0.25, 89% CI [0.14, 0.36], pd = 100%).
Figure 3.The linear relationship between lying dimensions and
demographic variables. Asterisks represent effects with a signifi-
cant probability of existence (° > 95%, * > 97%, ** > 99%, *** >
99.9%).
Absolute Lying Frequency. The Bayesian linear models
predicting absolute lying frequency (in lies told per day;
adjusted for social desirability) revealed that Tricksters re-
ported a higher (coefficient = 0.51, 89% CI [0.39, 0.64], pd =
100%), and the Virtuous a lower (coefficient = -0.35, 89% CI
[-0.49, -0.22], pd = 100%) absolute lying frequency than the
Average, respectively. Specifically, the dimensional model
suggested that relative Frequency (coefficient = 0.17, 89%
CI [0.13, 0.21], pd = 100%), as measured by the question-
naire, was significantly predicting the absolute Frequency of
lies told per day (see Figure 4).
Social Desirability. The GGM network suggested that
Self-Deceptive Enhancement was positively associated with
reported lying Ability (r = 0.21, 95% CI [0.14, 0.28]) and
negatively with lying Frequency (r = -0.20, 95% CI [-0.27,
-0.13]). On the other hand, active Impression Management
was positively associated with Negativity (r = 0.12, 95% CI
[0.05, 0.19]) and negatively with Frequency (r = -0.13, 95%
CI [-0.20, -0.06]) and Contextuality (r = -0.17, 95% CI [-
0.24, -0.10]). The relationship between lying Frequency and
Contextuality also changed from positive (in the previous
analyses) to negative (r = -0.17, 95% CI [-0.23, -0.10]).
Psychopathy. To avoid contamination of the following
models by previously identified mediators, we adjusted LIE
THE STRUCTURE OF DECEPTION 9
Figure 4.The relationship between the reported absolute fre-
quency (the number of lies told per day, which distribution is
showed on the right) and the Frequency dimension of the ques-
tionnaire.
scores by regressing out social desirability, age and sex. The
GGM network investigating the relationship with psychopa-
thy suggested that lying Ability was positively associated
with Boldness (r = 0.23, 95% CI [0.16, 0.30]), that Frequency
was positively associated with Disinhibition (r = 0.23, 95%
CI [0.17, 0.30]) and that Negativity was negatively associated
with Meanness (r = -0.19, 95% CI [-0.26, -0.12]) but also
positively with Disinhibition (r = 0.15, 95% CI [0.08, 0.22]).
Narcissism. The GGM network suggested that lying was
mainly associated with three core components of narcissism.
Lying Ability was positively associated with Manipulative-
ness (r = 0.35, 95% CI [0.29, 0.43]). Negativity was neg-
atively associated with Exploitativeness (r = -0.13, 95% CI
[-0.20, -0.05]).
Normal Personality. The GGM network suggested that ly-
ing was mainly associated with two dimensions of normal
personality. Lying Ability was positively associated with
Openness (r = 0.15, 95% CI [0.08, 0.22]). Lying Negativity
was negatively associated with Honesty/Humility (r = -0.15,
95% CI [-0.22, -0.08])
Pathological Personality. The GGM network suggested
that lying was mainly associated with two dimensions of
pathological personality. Lying Ability was positively asso-
ciated with Antagonism (r = 0.19, 95% CI [0.12, 0.26]) and
Frequency was positively associated with Disinhibition (r =
0.13, 95% CI [0.05, 0.20]).
Light Triad. The GGM network suggested that lying was
independent of the Light Triad facets.
Impulsivity. The GGM network suggested that lying Fre-
quency was positively associated with Positive Urgency (r =
0.16, 95% CI [0.09, 0.23]) and that Negativity was negatively
associated with the Lack of Premeditation (r = -0.15, 95% CI
[-0.21, -0.08]).
Emotion Regulation. The GGM network suggested that
lying Frequency was positively associated with deficits of
Impulse control in emotional contexts (r = 0.13, 95% CI
[0.05, 0.19]).
Interoception. The GGM network suggested that lying
was independent of the sensitivity to bodily signals.
Figure 5.Gaussian Graphical Models (GGMs) for convergent
validity with other constructs. The relationships with the lying di-
mensions were, for all networks except the first, adjusted for social
desirability, age and sex. Red and green links represent negative
and positive correlations, respectively.
Discussion
This study aimed at investigating the structure of disposi-
tional deception and its personality correlates by validating
a lying questionnaire on a diverse sample. Using a cogni-
tive perspective, we attempted to decompose lying as a high-
level phenomenon, and explore the distinct underlying mech-
anisms that contribute to it. Our findings suggest that decep-
tion, taken as a trait, comprises of four latent dimensions,
namely ability (the reported proficiency and ease to create
and deliver believable lies), frequency (the reported tendency
to lie), negativity (the negative perception of lying related
to internal factors, such as emotions or moral values), and
contextuality (flexibility of one’s willingness to lie related to
external factors such as stakes, necessity or alternative op-
tions). Although this multi-dimensionality was shown to be
robust and reliable, the consistent residual inter-correlation
between the four dimensions is nonetheless be compatible
10 DOMINIQUE MAKOWSKI1,*, TAM PHAM1, ZEN J. LAU1, ADRIAN RAINE2, & S.H. ANNABEL CHEN1, 3, 4, *
with the existence of an underlying over-arching general fac-
tor. Such general inclination towards lying also manifests in
the existence of distinct lying profiles, i.e., clusters of spe-
cific patterns of the dimensional structure. This, in turn,
underlines the specificity and distinctiveness of lying as a
phenomenon, suggesting its conceptualization as a special
action and context that requires, triggers and recruits a spe-
cific combination of - and interaction between - a specific
set of distinct general-purpose mechanisms. In other words,
the different facets of lying share a common core component
that is specific to its object and purpose. Thus, it appears that
deception can hardly be reduced to the sum of its underly-
ing mechanisms, and that a global perspective is required to
appropriately capture this phenomenon.
Importantly, we also investigated how these facets of decep-
tion are related to other dispositional characteristics. In line
with our hypotheses, we found a significant relationship with
antisocial traits. Specifically, individuals presenting with an-
tagonistic attributes, such as meanness, boldness and manip-
ulativeness reported high deception abilities, deception fre-
quency and low negativity related to lying. Though the nega-
tive relationship between meanness and negativity was unex-
pected, a likely explanation is that individuals who score high
on antisocial traits confer less reliance on moral principles
when making the decision to lie or have less negative emo-
tional reactivity towards lying, which translates into greater
ease when lying.
We were initially expecting to find the opposite relation-
ship with markers of a benevolent nature. However, under
our stringent statistical criteria, we found no significant re-
lationship between deception and pro-social traits. This is
in line with recent studies showing a relative independence
of benevolence traits in relation to malevolent traits (Kauf-
man et al., 2019; Tortoriello & Hart, 2019), or a more com-
plex and subtle pattern of the relationship between the (seem-
ingly) opposite extremes of human nature. For instance, nar-
cissism, traditionally considered as one of the pillars of an-
tisociality, has been found to show an independent, positive
correlation to benevolent traits (Kaufman et al., 2019). Aside
from further challenging the notion of a clear and relevant di-
chotomy between so-called “dark” and “light” sides of per-
sonality, our findings suggest that a pro-social attitude and
nature can co-exist with all types and forms of dispositional
deception.
Our study also confirmed links with specific dimensions of
normal personality. In particular, lying ability was positively
related to one’s openness to experiences. This link might be
mediated by the increased tendency of exposure to complex,
new and uncertain contexts, such as social situations, that
would in turn create more opportunities for lying (e.g., for
preserving others’ impression of themselves), thus nurturing
confidence in one’s ability to lie. However, we also found
honesty-humility to be negatively associated with negative
perceptions of lying. Though this link might appear contra-
dictory initially, one possible explanation is that individuals
with strong honest-humility traits tend to approach deception
with a more neutral stance. They may circumvent any moral
judgment that typically comes with it, cooperating genuinely
with others even in situations where they have been exploited
and/or lied to. Their sincerity and honesty may drive them to
perceive deception as an objectively common phenomenon
with no intrinsic nor absolute moral value.
One striking finding of the present study is the relation-
ship between specific aspects of dispositional deception and
markers of cognitive control (more exactly, proxies of cog-
nitive control deficits). We showed that individuals with
difficulties in cognitive control tend to have a higher lying
frequency, indicating the involvement of executive functions
(e.g., inhibition and flexibility) in the controlled delivery of
lies and manipulation of reality. Importantly, this pattern was
consistently found across different measures, such as impul-
sivity, emotion regulation deficits, and disinhibited behav-
ior. Further and more direct investigations of the differential
role of executive functions in deception are necessary to cast
more direct light on the mechanisms at stake.
Interestingly, a facet of impulsivity, namely the lack of pre-
meditation was inversely related to negative perceptions of
lying. These traits implicate one’s ability to plan lies sys-
tematically and focus their attention on the the task of ly-
ing. This result suggests that the ability to consider the emo-
tional and practical implications of one’s actions plays a sig-
nificant role in deception. A greater tendency to weigh the
consequences of lying enhances perceptions of negativity to-
wards lying as an act, as lying can be cognitively and emo-
tionally overwhelming. However, while we would have ex-
pected such negative perceptions to be more pronounced in
individuals with low emotion regulation skills and high inte-
roceptive sensibility, we found no evidence in favor of this
hypothesis. Nevertheless, it is important to note that embod-
ied constructs, such as interoceptive and emotion regulation
abilities, are only partially measured via self-reported ques-
tionnaires (Barrett et al., 2004; Garfinkel et al., 2015). Thus,
we suspect that the tools used in the present study were not
sufficiently sensitive, or simply not appropriate, which pre-
vents us from drawing any definite conclusions regarding the
absence of associations between these dimensions. Future
studies should investigate the role of such embodied aspects
of cognition through more direct means.
Finally, we emphasize the importance of measuring - and
controlling for - social desirability when attempting to mea-
sure morally or socially loaded constructs, such as lying,
through self-reported questions. Our findings suggest a
strong yet subtle relationship, revealing that individuals who
perceive themselves more favorably rated themselves as bet-
THE STRUCTURE OF DECEPTION 11
ter, yet less frequent, liars. Additionally, people who tend
to consciously and actively portray themselves in a socially
desirable manner are both more likely to report stronger neg-
ativity towards lying and being less influenced by external
factors in their decision to lie. In line with this pattern, they
also report lying less frequently, which can be observed using
different types of measures, such as the absolute frequency
of lying (i.e., the number of lies told a day). It is interesting
to note that after controlling for social desirability, the re-
lationship between lying frequency and context-driven will-
ingness to lie shifted from positive to negative. Consistent
with our other results, we suggest that a common cognitive
basis might be driving this relationship. In particular, cogni-
tive control, which allows and supports the control and inhi-
bition over the tendency to lie, would also be necessary for
facilitating flexible and context-driven usage. Thus, people
who tend to lie a lot in general also use this strategy in a less
parsimonious and adaptive manner.
Importantly, we found that lying behavior is also modulated
by demographic variables. Consistent with previous findings
(Elaad, 2018), individuals who are male and have a higher
income reported a higher ability to lie. This concurs with
research showing that malevolent traits are negatively asso-
ciated with being female and income (Kaufman et al., 2019).
The age- and sex-related links with honesty, social desirabil-
ity, and boldness, as well as the relationship between income
and self-control (Duckworth, 2011; Moffitt et al., 2011), are
likely to mediate such effects.
In addition, we found that people with high religiosity tend
to perceive lying as intrinsically negative (possibly related to
more absolute or immanent moral values) and are less pres-
sured by the external context necessitating lying (suggesting
a stronger role of these moral values). However, our findings
also suggest that religiosity is positively correlated with lying
frequency. Although surprising, this effect could be mediated
by an increased honesty of religious people, as well by the
fact that the greater negativity (and therefore saliency) of lies
enhances the encoding and ease of retrieval of these events,
leading in turn to an overestimation of their frequency.
Limitations and Future Directions
It is important to note that lies are not all alike and that dif-
ferent types exist, from “harmless” white lies in daily social
interactions to more serious and high stakes attempts, for in-
stance to conceal an act of law violation. Moreover, lying is
arguably not, in most cases, the end goal but rather the means
to attain given goals, which vary extensively across the popu-
lation. Lying types and motives, although not the focus of the
present study, are potentially relevant aspects to gain a com-
prehensive understanding of this phenomenon. However, one
of the main challenges of investigating lying motives is the
validation of a robust and usable framework for the classi-
fication of lies. For instance, Weber (2017) suggested the
existence of 11 types of lies while Zvi and Elaad (2018) em-
phasized 3 motives (self-gain, altruism, and lying for no rea-
son). Their study further shows that vanity and exhibition-
ism, two facets of narcissism, were respectively predictive of
self-beneficial lies and lying for no reason. Though explor-
ing deception-related motivations is beyond the scope of the
present study, these previous findings suggest that the extent
of one’s motivation to lie (and the type of motive) might be
sensitive to personality traits, supporting its relevance within
a dispositional approach to deception. This serves as a po-
tential avenue of exploration for studies to further delineate
the mechanisms involved in deception.
Another interesting and complementary approach could fo-
cus on the reception of lies, rather than how individuals per-
ceive themselves as liars. Prior work suggests that extro-
verts are more proficient at telling lies as well as detecting
lies (Elaad & Reizer, 2015), and that frequent liars are more
likely to perceive themselves as good lie detectors (Zvi &
Elaad, 2018), underlining this issue as a promising topic for
investigation.
Though the present study did not aim at study cultural deter-
minants or inter-cultural differences in deception, we also ac-
knowledge the possible role of population-related character-
istics in influencing our results. In this context, it is important
to highlight that many psychology studies, including task and
questionnaire validations, have been criticized for their over-
representation of a biased subset of participants (referred
to as “WEIRD” samples - Western Educated Industrialised
Rich and Democratic, Henrich et al., 2010). The demograph-
ics of our Singapore sample is not only predominantly non-
western, but is diverse in terms of age, SES, ethnicity, cul-
ture and religion (Singapore being a multicultural and mul-
tiethnic society), which constitutes a unique strength of the
present study. That said, the possible sample-related speci-
ficities warrant extra caution on any claims of generalization,
as the conceptualization of lying may vary across cultural
norms. In most of deception research using WEIRD sam-
ples, where self-interest is at the core of decision-making,
lying is perceived as a strategic choice to achieve personal
goals (Rodriguez, 1996). On the other hand, members of col-
lectivistic communities are comparatively more motivated by
their social obligation to group members (e.g., lying to help
other members ‘save face’; Gudykunst et al. (1988)) (Ro-
driguez, 1996). The motivation to lie, and hence the context
in which lying occurs, is thus very much dependent on one’s
cultural identity. As we did not measure specifically culture-
dependent constructs, we cannot preclude the possibility that
a different population may give rise to differences. It is thus
critical that future studies investigate intercultural differences
and carry out a cross-cultural validation of the structure of
12 DOMINIQUE MAKOWSKI1,*, TAM PHAM1, ZEN J. LAU1, ADRIAN RAINE2, & S.H. ANNABEL CHEN1, 3, 4, *
deception.
Finally, it is crucial to corroborate questionnaire scores with
actual behavior (Serota et al., 2010) to support the validity
of such self-reported measures. However, behavioral ex-
periments have been lacking in ecological validity due to
paradigms that fail to elicit realistic lying from participants
in a way that is self-motivated and spontaneous (e.g., par-
ticipants are sometimes instructed to lie). The use of games
with monetary incentives seems to be a promising approach
to investigate whether actual lying behaviors are consistent
with self-reported measures of lying (Levine et al., 2010).
In conclusion, this study attempted to investigate the trait-
like aspect of deception. On a theoretical level, our findings
underline lying as a specific yet multi-faceted phenomenon,
related to (and modulated by) a variety of inter-individual
characteristics. On a practical level, the brief questionnaire
validated in this study will allow for including this measure
in future experiments on deception to obtain a more com-
plete and accurate picture of its behavioral and neural cor-
relates. Nevertheless, further investigation is warranted to
understand how lying behavior varies across different moti-
vating factors, contexts and implications, as well as specific
populations defined by factors like culture, pathology, and
criminality.
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