Individual differences in self-reported lie detection abilities

Mélanie Fernandes; Domicele Jonauskaite; Frédéric Tomas; Eric Laurent; Christine Mohr

doi:10.1371/journal.pone.0285124

. 2023 May 24;18(5):e0285124. doi: 10.1371/journal.pone.0285124

Individual differences in self-reported lie detection abilities

Mélanie Fernandes ^1,^*, Domicele Jonauskaite ^1,², Frédéric Tomas ³, Eric Laurent ⁴, Christine Mohr ¹

Editor: Peter Karl Jonason⁵

PMCID: PMC10208523 PMID: 37224102

Abstract

Previous literature on lie detection abilities bears an interesting paradox. On the group level, people detect others’ lies at guessing level. However, when asked to evaluate their own abilities, people report being able to detect lies (i.e., self-reported lie detection). Understanding this paradox is important because decisions which rely on credibility assessment and deception detection can have serious implications (e.g., trust in others, legal issues). In two online studies, we tested whether individual differences account for variance in self-reported lie detection abilities. We assessed personality traits (Big-Six personality traits, Dark Triad), empathy, emotional intelligence, cultural values, trust level, social desirability, and belief in one’s own lie detection abilities. In both studies, mean self-reported lie detection abilities were above chance level. Then, lower out-group trust and higher social desirability levels predicted higher self-reported lie detection abilities. These results suggest that social trust and norms shape our beliefs about our own lie detection abilities.

Introduction

Evolutionary approaches highlight that living in social groups is key to being successful in our environments [1]. Living in groups comes not only with advantages though. Firstly, we must assume that other group members are trustworthy and bare no threat, so that we can engage in effective communication. Without trust, we would constantly experience problematic relationships within and between social groups. Interpersonal trust ensures individuals’ well-being, the achievement of their own aims, and the longevity of social units such as families, communities, and institutions [2–4]. Yet, being excessively trustworthy also bears risks, because others might take advantage for their own benefits [4].

In an ideal world, one would always know who to trust and who not to trust, who and what to believe and not to believe. This ideal world, however, does not exist, because deception occurs daily [5, 6], on average, at least once or twice a day [7–9]. When people lie, they make completely or partially untrue statements with the intent to deceive, and about 80% of the times, people succeed with lies remaining undetected [7]. Yet, not everybody lies to the same extent with more recent studies showing that a few prolific liars are telling the majority of lies [6, 10, 11]. Then, there is not one type of lies. There are many: lies can be about feelings, opinions, achievements, failures, to name a few examples. We may lie because honesty would hamper social or material goals [7], for financial or emotional gains, to avoid embarrassment, or to protect the feelings of others [7, 12–14].

At this point, we wish to highlight a striking paradox between actual (objective) and self-reported (subjective) lie detection abilities. Experimental studies showed that objectively, people perform around chance level when trying to spot somebody else lying [15, 16]. Subjectively, however, people report being able to detect lies successfully [13, 17, 18]. This paradox might originate from the so-called truth-default state, meaning, one assumes that others are honest. Indeed, most everyday situations inspire trust, or at least, do not merit questioning. Such a truth-default state is desirable because trust facilitates cooperation and effective communication [19, 20]. Clare and Levine [19] argued that individuals would only deviate from this truth-default state subsequent to a trigger events, such as suspicious indicators or motives, dishonest behavior, information from third parties, or inconsistencies in statements or known facts.

Overall, we can assume that lying is omnipresent, but that most lies remain undetected, because people see few reasons to distrust each other. Consequently, individuals encounter few opportunities to study and learn about others’ successful lies. Thus, we face an imbalance between i) the number of undetected lies by oneself, ii) the number of undetected lies by others, and iii) the number of a few occasions when lies were detected. This imbalance may leave individuals with an inflated estimation of their own lie detection abilities, once a lie had been detected [17]. Moreover, this inflated estimation might be further supported by people’s tendency to think positively about themselves: being easily deceived would not match this positive self-view [21].

When trying to understand this inflated estimation, we noted a surprising void of published studies reporting on reasons or correlates of self-reported lie detection abilities [22–25]. The few available studies focused on the Dark Triad personality traits–Machiavellianism, narcissism, and psychopathy,–because these traits share malevolent behavioral tendencies such as self-promotion, manipulation (including lying), and lack of empathy [21–25]. Wissing and Reinhard [22] showed that higher psychopathy and higher overall scores on Dark Triad personality were associated with higher self-reported confidence in lie detection abilities. Then, Zvi and Elaad [23] showed that higher levels of narcissism were associated with higher production of lies, which in turn was associated with higher self-reported lie detection abilities in comparison to others [23]. The recent study by Elaad [25] supported the association between higher narcissism and higher self-reported lie detection abilities in comparison to others. Interestingly, self-reported confidence in one’s own lie detection abilities was unrelated to individuals’ actual lie detection abilities [26]. Also, variance in Dark Triad traits did not explain variance in actual lie detection abilities either [26].

Results on these few studies were not conclusive. Thus, we designed a new study on individual differences on self-reported lie detection abilities. In addition to the Dark Triad personality traits, we included further measures that had been studied in the context of lie detection abilities and lie production before, though again showing inconclusive results. To this end, we selected a series of different measures, namely gender, social cognition measures, cultural differences, general personality traits, and social desirability.

For gender, studies showed no differences between men and women in both lie detection and lie production [27–29]. For social cognition, we considered empathy and emotional intelligence. For instance, Duran et al. [30] found that women with lower as compared to higher levels of empathy performed better at lie detection. Former studies would have predicted the opposite, though [31, 32]. Others argued that higher levels of empathy should link to impaired lie detection performances [33, 34]. In respect to emotional intelligence, higher trait levels might facilitate lie detection [35, 36]. Actual studies, however, showed that higher emotional intelligence, particularly emotionality, yielded impaired lie detection abilities [37].

For cultural differences, the need for lie detection might differ as a function of where you live on the globe, because levels of trust, deception, and corruption vary cross-culturally [4, 9, 38]. For instance, in case of lower national trust levels, people might be more suspicious towards others, having a lower truth-default state [20]. Maybe, they perform above chance in lie detection, or they just think that they do, once the odd lie had been detected. Moreover, individuals from collectivistic societies (prioritizing harmony among group members) seem more inclined to lie for other people’s benefits than those from individualistic societies [39].

To account for individual differences, we performed two consecutive online studies to investigate self-reported lie detection abilities. In Study 1, participants completed self-report measures on the Dark Triad [40], empathy [41], cultural values [42], and national trust levels [43]. Participants also indicated whether they were able to detect lies (yes/no), and at which level of accuracy (0–25%, 25–50%, 50–75%, 75–100%). Around 80% of the sample answered yes, with little variance in the accuracy level ratings. Thus, we made several changes to Study 2. We again asked participants to complete some of the same questionnaires (empathy, cultural values, national trust levels), but exchanged the 12-item Dirty Dozen [40] with the psychometrically superior Dark Triad personality traits questionnaire [44]. Additionally, we assessed participants’ general personality traits such as Agreeableness, Openness to Experience, Extraversion, Conscientiousness, Honesty and Resiliency [45], due to formerly described relationships with Dark Triad traits [46, 47]. We also assessed emotional intelligence [48] and socially desirable responding [49]. Crucially, participants again completed the yes/no question regarding their self-reported lie detection abilities. This time they rated their estimated accuracy on a scale from 0 (unable to detect lies) to 100 (perfectly able to detect lies). Moreover, to account for previous studies on the subject (see also [18, 23–25, 50, 51], we also asked participants to judge their lie detection abilities in comparison to others on a scale ranging from 0 (much worse than others) to 100 (much better than others).

Across these two studies, we investigated whether we might observe enhanced self-reported lie detection abilities as a function of i) Dark Triad personality scores (i.e., narcissism, psychopathy, and Machiavellianism), ii) lower empathy scores, iii) lower emotional intelligence scores, and iv) cultural values. Regarding cultural values, we expected to find differences between Hofstede’s cultural values (i.e., collectivism) and self-reported lie detection abilities. Finally, we expected self-reported lie detection abilities to be positively associated with social desirability, in line with people’s tendency to think and show themselves in a positive way [21].

Study 1

Method

Participants

We recruited 764 participants (105 males). After excluding incomplete data and selecting participants between 18 and 31 years old (i.e., the majority), our final sample consisted in 487 participants (99 males) with a mean age of 21.50 years (SD_age = 6.57 years; range = 18–31 years). Of these, 418 (95 males) were undergraduate students at the University of Lausanne, Switzerland, who received course credit for their participation. The remaining 69 participants (4 males) were recruited at the University of Franche-Comté, France. All participants were native French speakers. These studies were conducted in accordance with the principles expressed in the Declaration of Helsinki and received approval from the Research Ethics Commission of the Institute of Psychology, University of Lausanne (C_SSP_052021_00001).

We had used the statistical power analysis tool G*Power [52] to estimate the minimum sample size of 473 for a two-tailed binary logistic regression with a small effect size (odds ratio of 1.68) (see[53]), the H0 probability for Y = 1 of .45, α of 0.05, power (1−β) of 0.80.

Material

Self-report questionnaires

All our questionnaires demonstrated good or acceptable internal consistency (Cronbach’s alpha values between .510 and .817) (see Table 1).

Table 1. Description of the self-report questionnaires.

Construct and Scale	Subscales (items)	Response mode	Mean (SD)	Cronbach’s α
Dark personality traits (Dark Triad) The French-Canadian Dirty Dozen	psychopathy (4) narcissism (4) Machiavellianism (4)	1 (strongly disagree) to 5 (strongly agree)	2.02 (0.33) 2.78 (0.46) 2.41 (0.49)	.596 .780 .747
Cultural dimensions Individual Cultural Values Scale	power distance (5) uncertainty avoidance (5) collectivism (6) masculinity (4) long-term orientation (6)	1 (strongly disagree) to 7 (strongly agree) 1 (extremely unimportant to me) to 7 (extremely important to me)	1.84 (0.34) 5.12 (0.32) 3.27 (0.47) 2.14 (0.37) 5.35 (0.68)	.723 .817 .779 .660 .678
Trust level The World Value Survey 5	In group trust level (3) Out group trust level (3)	1 (not at all) to 4 (completely)	3.11 (0.85) 2.64 (0.66)	.510 .710
Empathy Interpersonal Reactivity Index (IRI)	Empathy score (28)	0 (does not describe me well) to 4 (describes me very well)	3.77 (0.79)	.814

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Description of the self-report questionnaires, their subscales, and response mode. In addition, this table shows means, standard deviations (SD) and Cronbach’s alpha values.

Dark Triad Dirty Dozen inventory [54] (French-Canadian version from Savard et al

[40]). This short 12-item scale assesses psychopathy, narcissism and Machiavellianism. Participants indicated their agreement with each statement on a 5-point Likert scale (see Table 1). Narcissism items assess the search for admiration and attention, the importance of status and expectations of special favours from others (e.g., “I tend to want others to admire me”). Machiavellianism items assess the use of deception, manipulation, flattery, and exploitation of others to achieve own goals (e.g., “I tend to manipulate others to get my way”). Psychopathy items assess the lack of remorse, insensitivity, being cynical, and being unconcerned by the morality of own actions (e.g., “I tend to lack remorse”). We averaged the scores of the four items of each subscale to obtain the total score on each subscale. Higher scores indicate higher expressions of narcissistic, Machiavellian, or psychopathic traits.

Individual Cultural Values Scale [55] (French version from Zheng [42])

This 26-item scale is based on Hofstede’s cultural dimensions model [56, 57] assessing the five cultural dimensions of i) power distance, ii) uncertainty avoidance, iii) collectivism vs individualism iv) masculinity, and v) long-term vs. short-term orientation (see Table 1 for details on the scale and sub-scales). Power distance refers to the degree to which less powerful members of institutions and organizations expect and accept that power is unequally distributed. Higher scores indicate a stronger acceptance of this unequal power distribution. Uncertainty avoidance refers to the degree to which societies avoid uncertainty, are anxious, feel threatened by unknown situations, prefer clear procedures, and respect rules. Higher scores indicate a stronger uncertainty avoidance. Collectivism vs. individualism describes the degree to which collectivism (group cohesion and interests) dominates over individualism (one’s own and close kins’ interests). Lower scores indicate a bias towards collectivism, and higher scores–towards individualism. Masculinity describes the degree to which male vs. female role patterns dominate in a society, with higher scores favouring the male pattern. Long-term vs. short term orientation refers to cultures that make long-term or short-term plans, respectively. The scale considers prudence, thrift, persistence, perseverance, and willingness to sacrifice the present to favour future successes. Higher scores indicate a preference for long-term planning.

Participants expressed their agreement with each statement on a 7-point Likert scale, and the scores were averaged for each subscale (see Table 1).

The World Value Survey 5 [43]

This 6-item questionnaire determines in-group and out-group trust levels on a 4-point Likert scale (see Table 1). For the in-group trust level, participants rate their trust of family members, neighbours, and people they know personally. For out-group trust level, participants rate their trust of people they meet for the first time, from another religion, and another nationality. For the two subscales, the Likert scale scores were averaged (Table 1).

Interpersonal Reactivity Index (IRI [58]; French version from Gilet et al

[41]). The 28-item IRI questionnaire evaluates the following aspects of empathy: i) feelings of compassion and concern for unfortunate others, ii) the ability to consider the perspective of others, iii) participants’ tendencies to transpose themselves imaginatively or to identify with fictional characters, and iv) self-oriented feelings of personal anxiety and distress in difficult interpersonal situations. Participants rated their agreement with items on a 5-point Likert scale. Of the 28 items, eight were reversely coded. We calculated an average empathy score so that higher scores indicated a natural capacity to share and understand the affective state of others [59] (see Table 1).

Self-reported lie detection abilities

We asked participants to indicate their self-reported lie detection abilities. In other words, we asked whether they were able to recognize somebody lying. Participants answered on a binary yes/no scale. Afterwards, participants indicated their self-reported lie detection abilities on a 4-point Likert-type scale. They had to rate how accurate they think they were at lie detection, choosing from four options: accurate 0–25% of the time (1), 25–50% (2), 50–75% (3), or 75–100% of the time (4).

Procedure

We used the LimeSurvey platform to prepare and run our online survey. We distributed the online link to potential volunteers. In case of interest, they could complete the survey at their own convenience. On the first two pages, we provided, respectively, written study information and ethical information, such as the right to withdraw from the study at any time, and data confidentiality (Fig 1A). We also stated that we treat participants’ continuation as informed consent. Next, participants provided socio-demographic information regarding their age, gender, nationality, and field of studies (Fig 1B). Then, they completed the self-reported questionnaires in the following order: The FR-C Dark Triad Dirty Dozen, the Cultural Values Scale, the Interpersonal Reactivity Index, and their level of trust based on the World Value Survey 5 (Fig 1C-Study 1 and Table 1). Afterwards, they indicated their self-reported lie detection abilities (Fig 1D-Study 1). Finally, they were fully debriefed and thanked for their participation. The survey took about 20 minutes to complete.

Design and statistical analysis

To test for the role of individual differences in self-reported lie detection abilities, we considered the self-reported lie detection abilities score as a dependent measure, which was measured on a four-point scale (0–25%; 25–50%; 50–75%; and 75–100%). The histogram showed an uneven distribution. Of the 487 participants, 198 rated their abilities between 25–50% of accuracy and 237 between 50–75%, while only 19 participants self-reported 0–25% of accuracy, and only 33 indicate 75–100% of accuracy. Consequently, we created a dichotomous variable consisting in self-reported lie detection abilities that are below chance level (i.e. scores between 0–25% and 25–50% of accuracy) and above chance level (i.e. scores between 50–75% and 75–100% of accuracy).

To test our study question, we conducted a binary logistic regression analysis on this dependent dichotomous variable (below vs. above chance level) using the following 12 continuous predictor variables: psychopathy, narcissism, Machiavellianism, power distance, uncertainty avoidance, collectivism, masculinity, long-term orientation, empathy, ingroup trust level, outgroup trust level, and gender (see also S1 Table). We presented the correlations between all the predictor variables as supporting information (see S2 Table).

Results

Overall, most participants (81.52%) reported being able to detect lies (yes/no answer) and many participants (55.44%) self-reported that their lie detection abilities ranged between 50% and 100% (i.e. above chance level self-reported lie detection abilities). The likelihood ratio test showed that the overall binary logistic regression model on self-reported lie detection abilities was not significant; LR(12) = 656.23, p =. 360, AIC = 682.23, _pseudoR² = .025 (Cox & Snell), .034 (Nagelkerke) (see S1 Table).

Study 2

Method

Participants

We used the statistical power analysis tool G*Power [52] to estimate our sample size. We determined a minimum sample size of 231 for a linear multiple regression test with a small effect size of 0.1, 21 predictors, α of 0.05, and power (1−β) of 0.80.

We recruited a new sample of French speaking undergraduate psychology students (N = 700, 90 males) at the University of Lausanne. After excluding incomplete data and matching participants’ age to those of Study 1, we were left with 386 participants (72 males; M_age = 20.21, SD_age = 2.22, range = 18 to 31 years). One academic year separated the data collection for Study 1 and Study 2. All participants received course credit for their participation.

Material

Self-report questionnaires. Short Dark Triad (SD3) [62] (French version from Gamache et al. [44])

This 27-item version measures psychopathy, narcissism and Machiavellianism using a 5-point Likert scale. We used this questionnaire, because the one we had used in Study 1 [54] showed weaker psychometric properties [44, 63].

Big-Six questionnaire (29QB6) [45] (The French translation was done through translation and back translation, the report on the formal validation is currently prepared for publication. In the meantime, the translation can be retrieved here [64]. The 29QB6 assesses personality along six dimensions: i) Extraversion, ii) Conscientiousness, iii) Honesty/Propriety, iv) Resiliency vs. Internalizing Negative Emotionality, v) Agreeableness, and vi) Originality/Talent. Higher Extraversion scores indicate that participants score higher on traits like talkativeness, sociability, assertiveness., gregariousness, and positive emotionality measures. Conscientiousness items refer to being organized, purposeful, and self-controlled. Honesty/Propriety items measure ethical behavior, integrity and deceit, instrumental use of others as well as aspects related to negative valence. Resiliency items capture the ability to internalize negative emotionality as a reverse of Neuroticism. Lower scores on resiliency resemble higher scores on neuroticism [45] Agreeableness items measure kindness and even temper. Originality/Talent items measure perceived talents and abilities, intellectual and aesthetic interest. They also include positive valence content. The 29 items are rated on a 6-point Likert scale, with 14 being reversely coded (see Table 2). Higher scores indicate higher level of expression of each personality trait (see Table 2).

Table 2. Description of the self-report questionnaires.

Construct and Scale	Subscales (items)	Response mode	Mean (SD)	Normative values^* Mean (SD)	Cronbach’s alpha
Dark personality traits (Dark Triad) The French version of the Short Dark Triad	psychopathy (9) narcissism (9) Machiavellianism (9)	1 (strongly disagree) to 5 (strongly agree)	2.11 (0.42) 2.63 (0.59) 2.85 (0.79)	1.93 (1.01) 2.73 (0.75) 2.67 (0.99)	.727 .707 .672
Cultural dimensions Cultural Values Scale	power distance (5) uncertainty avoidance (5) collectivism (6) masculinity (4) long-term orientation (6)	1 (strongly disagree) to 7 (strongly agree) 1 (extremely unimportant to me) to 7 (extremely important to me)	1.77 (0.27) 5.15 (0.29) 3.21 (0.47) 5.39 (0.71) 2.09 (0.31)	2.15 (ns) 5.28 (ns) 3.70 (ns) 5.55 (ns) 2.83 (ns)	.723 .808 .817 .667 .718
Trust level The World Value Survey 5	in group trust level (3) out group trust level (3)	1 (not at all) to 4 (completely)	3.04 (0.90) 2.58 (0.72)	3.08 (ns) 1.72 (ns)	.456 .708
Empathy Interpersonal reactivity index	General Empathy score by adding: empathic concern (7) perspective taking (7) fantasy (7) personal distress (7)	0 (does not describe me well) to 4 (describes me very well)	3.65 (0.84) 4.10 (0.78) 3.65 (0.79) 3.86 (1.16 2.96 (1.11)	4.53 (1.06) 5.38 (0.88) 5.02 (0.96) 4.25 (1.26) 3.47 (1.13)	.786 .535 .500 .801 .800
Social Desirability The Balanced Inventory of Desirable Responding	General score of social desirability (21)	1 (completely wrong) to 7(completely true)	.333 (0.14)	.364 (0.19)	.506
Emotional intelligence TMMS	emotional attention (12) emotional clarity (10) emotional repair (6)	0 (strongly disagree) to 6 (strongly agree)	3.73 (0.40) 3.02 (0.38) 3.29 (0.37)	3.32 (0.60) 3.63 (0.69) 3.59 (0.76)
Personality traits QB6	Extraversion (5) Conscientiousness (5) Honesty/Propriety (5) Resiliency (4) Agreeableness (5) Originality/Talent(5)	1 (does not describe me well) to 6(describes me very well)	4.36 (0.90) 3.86 (1.04) 4.05 (0.89) 2.65 (0.38) 3.72 (1.01) 4.18 (0.71)		.690 .764 .670 .805 .750 .607

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Note. Description of the self-reported questionnaires, their subscales, and response mode. In addition, this table shows means, standard deviations (SD), normative values and Cronbach’s alpha.

*References for the normative values: Dark Triad (N = 405 French-Canadian participants, M_age = 31.01 years, SD_age = 11.97, age range from 18 to76 years; [44]); cultural values (N = 223 French students, 104 males, age ranging from 18 to 35 years; [42]); trust level (N = 1089 adults from fifty societies [67]); empathy (N = 322 French participants, M_age = 49.5 years, SD_age = 21.1, age ranging from 18 to 89 years; [41]); social desirability (N = 1159 French-Canadian participants, 567 males, age ranging from 17 to 67 years [51]); emotional intelligence (N = 824 French undergraduate students, 368 males, M_age = 20.7 years, SD = 2.1; [48]).

Trait Meta Mood Scale [65] (French version from Maria et al

[48]). This 30-item questionnaire measures emotional intelligence by assessing the ability to monitor and manage one’s own emotions. This scale consists in three subscales: i) emotional attention–the extent to which individuals attend to and value their feelings; ii) emotional clarity–the extent to which they feel clear about their feelings; and iii) emotional repair–the extent to which they use positive thinking to repair their negative mood. Participants rated each item on a 5-point Likert scale. Higher scores indicated higher levels of emotional attention, emotional clarity, and emotional repair (see Table 2).

Balanced Inventory of Desirable Responding [66] (French version from D’Amours-Raymond [49])

This 21-item questionnaire measures socially desirable responding along two dimensions: i) self-deceptive positivity–the tendency to give self-reports believed to have a positivity bias, and ii) impression management–deliberate effort at deception. We calculated the total score by summing all the responses on the 7-point Likert scale. We followed authors’ recommendations and coded presence of social desirability (yes) when participants scored between 6 and 7, and absence of social desirability (no) for values below 5 (see Table 2).

Self-reported lie detection abilities

We asked participants to indicate if they were able to recognize somebody lying (yes/no answer) and rate their self-reported lie detection abilities on a continuous scale ranging from 0 (unable to detect lies) to 100 (perfectly able to detect lies). Afterwards, we asked participants to report their lie detection abilities in comparison to others from 0 (much worse than others) to 100 (much better than others) (see also [23–25, 51]).

Procedure

The procedure was comparable to Study 1 in recruitment and programming, as well as in the sequence of events (see Fig 1). Participants completed the Cultural Values Scale, the Interpersonal Reactivity Index, their level of trust, the Dark Triad, the Balanced Inventory of Desirable Responding, the Trait and Meta Mood Scale, and the Big-Six questionnaires (Fig 1C-Study 2). Afterwards, participants self-reported their lie detection abilities (Fig 1D-Study 2). The entire study took about 20 minutes to complete.

Statistical analysis

We performed two multiple regression analyses to test whether our individual difference measures predicted i) participants’ self-reported lie detection abilities score (0–100), and ii) participants’ self-reported lie detection abilities in comparison to others (see Table 3). We present results of the correlations between all predictor variables in supporting information (see S3 Table).

Table 3. Results of the two multiple regression analysis on the continuous 22 predictor variables for self-reported lie detection abilities and self-reported lie detection abilities in comparison to others.

	Self-reported lie detection abilities			Self-reported lie detection abilities in comparison to others
Predictors	β (SD)	95% CI	p value	β (SD)	95% CI	p value
Machiavellianism	-0.22 (1.95)	[-4.06, 3.6]	.908	.027 (1.50)	[-2.93, 3.0]	.986
Psychopathy	3.18 (2.05)	[-0.85, 7.2]	.122	3.62 (1.58)	[0.51, 6.7]	.023*
Narcissism	1.97 (2.32)	[-2.58, 6.5]	.395	1.089 (1.79)	[-2.44, 4.6]	.546
Power distance	-2.19 (1.42)	[-4.98, 0.6]	.123	-.155 (1.10)	[-2.31, 2.0]	.888
Uncertainty avoidance	-1.13 (1.18)	[-3.46, 1.2]	.338	-.675 (.91)	[-2.47, 1.1]	.460
Collectivism	-0.27 (0.89)	[-2.03, 1.5]	.761	-1.13 (.69)	[-2.48, 0.2]	.104
Masculinity	0.34 (0.84)	[-1.31, 2.0]	.689	-.886 (.65)	[-2.16, 0.4]	.173
Long-term orientation	-0.99 (1.54)	[-4.02, 2.0]	.518	-1.83 (1.19)	[-4.16, 0.5]	.125
General Empathy	0.35 (0.45)	[-0.54, 1.2]	.438	.471 (.35)	[-0.22, 1.2]	.178
In group trust level	-3.04 (2.12)	[-7.21, 1.1]	.152	-.864 (1.64)	[-4.08, 2.4]	.598
Out group trust level	-3.34 (1.50)	[-6.30, -0.4]	.026*	-1.42 (1.16)	[-3.70, 0.9]	.220
Honesty/Propriety	-1.84 (1.20)	[-4.20, 0.5]	.126	-1.52 (.93)	[-3.34, 0.3]	.103
Resiliency	-0.19 (1.24)	[-2.63, 2.3]	.877	.091 (.96)	[-1.80, 2.0]	.925
Extraversion	-0.96 (1.23)	[-3.38, 1.5]	.437	-1.02 (.95)	[-2.88, 0.9]	.285
Agreeableness	-1.36 (1.00)	[-3.33, 0.6]	.177	-0.95 (.78)	[-2.47, 0.6]	.223
Conscientiousness	1.04 (1.01)	[-0.94, 3.0]	.302	.938 (.78)	[-0.59, 2.5]	.229
Originality/Talent	2.47 (1.57)	[-0.61, 5.5]	.116	3.98 (1.21)	[1.61, 6.4]	.001^***
Emotional repair	-0.542 (1.21)	[-2.91, 1.8]	.653	.538 (.93)	[-1.29, 2.4]	.563
Emotional attention	-1.22 (1.71)	[-4.58, 2.1]	.476	-2.06 (1.32)	[-4.65, 0.5]	.119
Emotional clarity	0.51 (1.38)	[-2.21, 3.2]	.712	-.486 (1.07)	[-2.59, 1.6]	.650
Social desirability	0.67 (0.33)	[0.01, 1.3]	.046*	.585 (.26)	[0.08, 1.1]	.023*
Gender (male)	-1.80 (2.52)	[-6.76, 3.2]	.475	2.31 (1.95)	[-1.51, 6.1]	.235

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Note. The table displays standardized coefficients (β), standard errors, p-values associated with each predictor of the regression predicting the self-reported lie detection abilities and the self-reported lie detection abilities in comparison to others.

***p < .001

**p < .01

*p < .05

Results

Most participants (76.94%) reported being able to detect lies (yes/no answer). Many participants (62.69%) reported that their abilities to detect lies was above chance level (i.e., higher than 50% of accuracy). However, when asked to rate their abilities in comparison to others, only 20.46% of the participants reported being better than others at detecting lies. Most participants (59.59%) estimated their abilities to be below those of others. However, a Pearson correlation on these two self-report measures was significant, r(386) = .683, p < .001 (see S3 Table), higher self-reported lie detection abilities correlated with higher self-reported lie detection abilities in comparison to others.

The overall multiple regression model on self-reported lie detection abilities was significant, F(363, 22) = 2.49, p < .001, R_adj² = .078. Results indicated that the model explained 7.8% of the variance (see Table 3). As shown in Table 3, lower out-group trust levels and higher social desirability predicted higher self-reported lie detection abilities. The remaining predictor variables were not significant (see Table 3).

The multiple linear regression analysis on self-reported lie detection abilities in comparison to others was significant, F(363, 22) = 3.57, p < .001, R_adj² = .128. The model explained 12.8% of the variance (see Table 3). Again, enhanced social desirability significantly predicted enhanced self-reported lie detection abilities in comparison to others (see Table 3). We also found that higher originality and psychopathy predicted higher self-reported lie detection abilities in comparison to others (see Table 3). The remaining predictor variables were not significant (see Table 3).

Discussion

The literature on lie detection demonstrates a little understood paradox. Subjectively, many people self-report being above average in lie detection abilities [21, 23–25, 50], while objectively, at least on a group level, people perform around chance level [15, 16]. In two successive online studies, we investigated if and how individual differences can predict self-reported lie detection abilities. In Study 1, we measured Dark Triad personality traits, empathy, cultural values, and trust levels. In Study 2, we additionally accounted for conventional personality traits, emotional intelligence, and social desirability. As outcome measures, participants indicated whether they could detect lies (dichotomous yes/no answer), and to what extent. In Study 1, we coded extent as above versus below chance level, and in Study 2 as scores on a 100-point rating scale. In Study 2, we also asked participants to rate their self-reported lie detection abilities in comparison to others (see also [23–25, 51]).

In both studies, over 75% of our participants indicated being able to detect lies. When asked about extent, most participants rated their accuracy above chance level (i.e., being 50% of the time correct). Thus, we replicated that individuals on the group level consider themselves able to detect lies (e.g., [21, 23–25, 50]), which is well above the performance one could expect from actual lie detection tasks [15, 16]. When asked to rate their abilities in comparison to others, we found, however, that only about 20% of our participants indicated yes, they judged themselves to be superior to others at detecting lies. For extent, the numbers were comparatively low too; participants indicated being only 40% of the time better than others at detecting lies.

Our primary study goal was to investigate individual differences in self-reported lie detection abilities. Contrary to our expectations, we found few systematic relationships. In Study 1, the overall model included 12 predictor variables and was not significant. In Study 2, the overall model included 22 predictor variables and the model was significant. We observed enhanced self-reported lie detection abilities with i) decreasing out-group trust level scores, and ii) increasing social desirability scores. Neither gender, personality traits (i.e., Dark Triad, Big-Six personality traits), social cognition measures (i.e., empathy and emotional intelligence), nor cultural values predicted self-reported lie detection abilities. Interestingly, our individual difference measures were better predictors of self-reported lie detection abilities in comparison to others. Namely, enhanced psychopathy, enhanced Originality/Talent, and enhanced social desirability predicted enhanced self-reported lie detection abilities in comparison to others. The findings on psychopathy and Originality/Talent (i.e., Openness) have been reported previously [22, 51].

Given our unexpected results, we decided to focus on three major observations. First, very few of our individual difference measures predicted self-reported lie detection abilities. We discuss in detail results on the Dark triad personality traits, because we had the strongest predictions for these traits [21–25]. Second, we replicated two previous results, the one on psychopathy [22] and the one Originality/Talent [51]. Yet, these replications were observed when looking at self-reported lie detection abilities in comparison to others. Third, we observed that increased social desirability scores linked to enhanced self-reported lie detection abilities in general as well as in comparison to others.

When designing the studies, we selected individual difference measures that have been mentioned in the lying literature before [20, 22, 23, 25, 33, 34, 37, 39]. Most obvious was the Dark Triad, because of what it stands for, malevolent behavioral tendencies such as self-promotion, manipulation (including lying), and lack of empathy [21–25]. Moreover, the Dark Triad had been previously linked to self-reported lie detection abilities [22–25]. We found that higher psychopathy scores were associated with higher self-reported lie detection abilities, but only when participants were asked to indicate their abilities in comparison to others. Wissing and Reinhard [22] found a similar relationship, but for higher confidence in self-reported lie detection performances. Thus, we found similar results despite studies using different response modes, suggesting that response mode does not matter. Against this suggestion, we did not replicate that higher levels of narcissism were associated with a higher self-reported lie detection abilities in comparison to others [23–25]. Likewise, we did not find that higher levels of narcissism were associated with higher self-reported lie detection abilities.

It is possible that differences between Dark Triad studies are due to differences in populations and measurements. For populations, this possibility is unlikely, because all relevant Dark Triad studies tested undergraduate university students from Western countries. In our case, we tested French speakers, largely in Switzerland, once 487 (99 males) participants and once 386 participants (72 males). Another study tested 207 participants (83 males) from Germany [22], and still other studies tested, respectively, 125 males [23], and 100 undergraduate students (15 males) [25] from Israel. These populations were comparable in gender composition and age, several being biased towards females and others towards males, with the mean population age not exceeding 30 years old.

For measurements, studies differed. We used different Dark Triad questionnaires in Study 1 and Study 2, both being shortened versions [40, 44, 54, 62]. In Study 1, we had used the 12-items Dirty Dozen questionnaire [40, 54], and did not replicate previous findings [22–25]. As questionnaire brevity comes with costs, such as reduced construct validity [63], we used the longer and psychometrically superior 27-items Short Dark Triad questionnaire in Study 2 [44, 62]. However, results were comparable for Study 1 and Study 2. One possible explanation could be questionnaire length, because relationships with narcissism scores were found for the 40-item Narcissistic Personality Inventory [23–25, 68, 69]. Yet, length cannot explain it all, because relationships between elevated psychopathy scores, overall Dark Triad scores, and confidence in lie detection abilities had been found using an even shorter 9-item scale [22, 70]. As of now, we have insufficient evidence to argue that divergent findings were due to populations or measurements.

Overall, we found hardly any individual difference measures, significantly predicting self-reported lie detection abilities. The situation was not really different when participants rated their lie detection abilities in comparison to others. We found that social desirability scores predicted enhanced ratings of both self-reported lie detection measures. Thus, self-reported lie detection abilities, whether in general or in comparison to others, tap into something that is akin to an attitude or a social value. Maybe, this latter proposition might also explain why lower levels of out-group trust predicted higher self-reported lie detection abilities. In this regard, self-reported lie detection abilities might reflect people’s truth-default state [20] which implies that we assume that others are trustworthy. In addition, indicating that one is able to detect lies reflects on people’s favorable self-view [21, 71, 72]. In other words, it is better for one’s self-esteem to think that one is not easily deceived.

Study limitations, challenges and future directions

We worked on self-reported lie detection abilities without assessing people’s actual lie detection performance. Notwithstanding, on a group level, we assume that our populations’ actual lie detection performances would mirror results of previous studies showing that lie detection performance is around chance level [15, 16]. Future studies should assess both actual and self-reported lie detection abilities in a within-subjects design.

As with most studies in psychology, we tested undergraduate psychology students. They are not representative of the general population, whether within or between cultures (see e.g., [38, 73, 74]. Therefore, the generalizability of our results remains questionable. To encompass this limitation, we are currently running further studies testing alternative populations such as particular groups of professionals who have a relatively enhanced probability encountering deception (i.e., police officers, teacher, and insurers). We will report these results in the future. Moreover, further studies could consider age, because studies showed that lie detection abilities decreased with age [75–78] and lies were spotted more easily in older than younger people [78].

Worth noting, we did not replicate that our participants reported being above average in self-reported lie detection abilities, i.e., being in average better when comparing oneself to others (e.g., [21, 23–25, 50]). This observation reflects the better-than-average effect [21, 79]. On the contrary, only 20% of our participants thought they were better than others. When asked about extent, only 40% of them indicated being better than others at detecting lies. We could have expected this better-than-average effect, because it seems more pronounced for young people, and for dimensions that lack external verification [72]. These conditions both apply to our study. Another observation, we might have found comparable results with previous studies using self-reported lie detection abilities in comparison to others, if we would have highlighted the middle point of the rating scale, just like the other studied did. Previous studies indicated 50 as the point at which participants rated themselves as good as others [23–25]. In contrast, we asked participants to use the scale from 0 (much worse than others) to 100 (much better than others). Therefore, the average is not explicitly presented to the participants.

As a final critical point, some of the self-report questionnaires lacked validity [63, 80, 81]. Returning to the Dark Triad, these self-report questionnaires seemed to extract the uniqueness between narcissism, psychopathy, and Machiavellianism [82]. Several studies, however, reported an overlap between the three constructs and claimed they were not independent [81, 83]. Kajonius et al. [80] even argued that the Dirty Dozen questionnaire assesses only two constructs, namely, narcissism and an anti-social trait (i.e., Machiavellianism and psychopathy combined). Consistent with this, Persson et al. [84] showed that Machiavellianism and psychopathy, measured by the Short Dark Triad, were similar concepts. Therefore, future studies investigating similar questions to ours should include different measures, such as the Narcissistic Personality Inventory [69], because the latter resulted in comparable findings between studies [23–25]. Regarding social desirability, several studies emphasized the lack of validity of the BIDR (Balanced Inventory of Desirable Responding) [85, 86]. To address this limitation, further studies are needed to measure social desirability as a trait, using validated measures of related dimensions such as self-control (e.g. [87]).

Conclusion

In two online studies, we tested self-reported lie detection abilities. On the group level, we replicated that people reported being above chance in lie detection. For individual difference measures, we observed that participants had higher lie detection abilities with increasing social desirability and decreasing out-group trust levels, respectively. Unlike our predictions, all other predictor variables were not significant, including the Dark triad and conventional personality traits, empathy, emotional intelligence, and cultural values. When participants rated their lie detection abilities in comparison to others, we did not find the better-than-average effect. Yet, we replicated that enhanced psychopathy and Originality/Talent predicted the latter measure. Our results indicated that it was socially desirable to trust others and to believe being able to detect lies. Future studies should test whether self-reported and actual lie detection abilities are associated and in which way, as well as assess these relationships in different professional groups, different age groups, and in populations beyond the frequently researched Western societies.

Supporting information

S1 Table. Results of the binary logistic regression analysis on the continuous 12 predictor variables for self-reported lie detection abilities (below vs above chance level).

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Click here for additional data file.^{(92.8KB, tif)}

S2 Table. Pearson’s correlations between individual characteristics and self-reported lie detection abilities for Study 1.

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Click here for additional data file.^{(163.3KB, tif)}

S3 Table. Pearson’s correlations between individual characteristics and self-reported lie detection abilities for Study 2.

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Click here for additional data file.^{(207.6KB, tif)}

Data Availability

All the data files of the individual differences in self-reported lie detection abilities' study are available from the Open Science Framework database (DOI: 10.17605/OSF.IO/6NRA5).

Funding Statement

We thank the Swiss National Science Foundation for making this work possible (P500PS_202956, 100014_182138).

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PLoS One. doi: 10.1371/journal.pone.0285124.r001

Decision Letter 0

Peter Karl Jonason

21 Nov 2022

PONE-D-22-29409Individual differences in self-reported lie detection abilities.PLOS ONE

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Reviewer #3: Partly

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Reviewer #1: No

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Reviewer #3: Yes

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5. Review Comments to the Author

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Reviewer #1: The present research used a correlational design to search for variables that may explain the gap between above-average self-assessed lie-detection ability and the average performance of lie detection. They used a large sample in two studies to again show that the bias exists and that people overestimate their lie-detecting ability. The perceived lie-detection ability was compared with different self-report scales, and in most cases, negative results were obtained. The outcomes are essential to enrich our knowledge and guide the exploration of the bias in additional directions. Furthermore, the perceived lie-detection ability is vital in face-to-face communication and may guide behavior. Undoubtedly, the topic deserves more empirical attention, and any addition to our limited knowledge in this domain is welcome.

Nevertheless, I have some comments that can and should be addressed in a revision.

Any ability is defined on a continuous scale. The dichotomous yes/no response is meaningless and redundant. People may succeed more or less in lie detection. No one is perfect, and no one is 100% inaccurate. Further, the dichotomization triggers odd results, such as 9 participants who answered that they were not able to detect lies and at the same time indicated that they succeeded in about 50-75% of their lie detection attempts. Furthermore, 4 participants who answered yes to the yes/no lie detection ability question indicated success in only 0-25% of their attempts (Table 2). The total frequency line (bottom) demonstrates the overestimated lie-detection bias, and the dichotomization adds nothing in this respect.

The 0 to 100% scale used in Study 2 is more sensitive than the scale used in Study 1. Therefore, why not benefit from the advantage and use parametric statistics? Instead, the authors dichotomized the scale to use a non-parametric chi-square analysis (line 362, on). Note that the sample consisted of 386 participants, which calls for parametric statistics. Further, the contradiction between answering no to the absolute lie detection ability and receiving an above-chance level score when reporting the lie detection ability persists. Therefore, the manipulation check in Table 5 is meaningless. In addition, only the bottom line in Table 6, which shows the lie-detection bias, is relevant. In sum, I suggest removing the absolute yes/no lie-detection ability test.

Line 476: In study 2, we also asked them (the participants) to report their ability in comparison to others. Unfortunately, the results were not reported here. The authors indicated that these results would be reported elsewhere. My question is, why? This is an essential addition to the present study, and the comparison between answering with and without reference to others is interesting and important (to my knowledge, the comparison to other people results in lower absolute scores).

Minor points:

Line 55: The current view about frequent lying is that not many people lie frequently, and most people reported not lying in the previous 24 h. (Daiku et al. 2021; Halevi et al. 2014; Serota and Levine 2015).

Daiku, Y., Serota, K. B., Levine, T. R. (2021). A few prolific liars in Japan:

Replication and the effects of Dark Triad personality traits. PLoS ONE 16(4):

e0249815. https://doi.org/10.1371/journal.pone.0249815

Halevy, R., Shalvi, S., and Verschuere, B. (2014). Being honest about dishonesty:

Correlating self-reports and actual lying. Human Communication Research, 40,

54-72.

Serota, K. B., & Levine, T. R. (2015). A few prolific liars: Variation in the prevalence of lying. Journal of Language and

Social Psychology, 34(2), 138-157.

Line 91: … self-reported confidence in own lie detection abilities was unrelated to individuals' actual lie detection abilities… add a reference (for example, Elaad and Gonen-Gal, 2022).

Elaad, E. & Gonen-Gal, Y. (2022). Face-to-face lying: Effects of gender and motivation

To deceive. Frontiers in Psychology: Forensic and Legal Psychology, 13: Article

820923. doi: 10.3389/fpsyg.2022.820923.

Line 214: Relative self-reported lie detection ability. Relative to what? To others? to the average person? The authors relate to the extent or degree of the perceived lie detection ability not to relativeness. This should be corrected.

Lines 230-231, 297: How were the questionnaires completed? Individually or in groups? If in groups, how big were the groups? Please be specific.

Line 236: Separate detect and changes.

Line 282: Replace Is with It.

Line 361: Why is the power set at 0.80?

Line 371: Change relative to relatively sensitive (The 0 to 100 scale is more sensitive than the categorial scale used in study 1).

Line 377: The scale ranges from 0 to 100. Remove the percent sign (100%).

Line 488: Elaad and Reizer (2015) used the Big Five, which is a different scale than the Big Six that was used in the present study.

Line 424: Not adding actual lie-detection performance is indeed a limitation. The current trend is to compare lie-detection ability scale scores with actual behavior (See Elaad and Gonen-Gal, 2022).

Reviewer #2: This paper examined individual differences (primarily of personality) on self-reported lie detection ability. The Introduction makes a solid argument for the need for the study. Individual difference mostly had few significant or strong relationships with self-reported lie detection ability. This is interesting as some have argued that they should. I do not consider the lack of strong or significant effects to be a problem, they are what they are.

The paper reports 2 studies that appear to have been properly executed with samples of a good size and well-selected measures. Data are reported in a way that address the questions of the studies, although I’d like to see tables showing the intercorrelation of all predictor variables in the supplementary material to aid in the interpretation of the regressions.

The introduction is clear and readable. I have no suggested changes

Study 1

Method

Participant numbers don’t add up or there may be an error. It is stated in the Participants section that 525 were recruited. In the Data preparation section, it is stated that 239 were excluded, leaving 487 = these figures don’t match.

Additionally, it is stated in the Participants that there were 487 participants (95 men) from one university, and this is given as the final sample size in the Data preparation section – is this a coincidence or an error in copying numbers?

Results

I can’t find how gender was coded, thus the mean and any direction of relationships cannot be interpreted by a reader.

Study 2

Method

The participants (700) minus exclusions (234) does not add to the total final sample (386). There was an age exclusion but the numbers are not stated.

Results

Why is gender omitted in the Study 2 regression analysis?

Discussion

One issue to be aware of it that a recent meta-analysis of social desirability scales suggests they are of no value (Lanz et al, 2022). Although social desirability is one of only 2 significant predictors in Study 2, it is worth asking the extent to which this is truly meaningful given the new analysis of the relevant measures.

Lanz, L., Thielmann, I., & Gerpott, F. H. (2022). Are social desirability scales desirable? A meta‐analytic test of the validity of social desirability scales in the context of prosocial behavior. Journal of Personality, 90(2), 203-221.

Reviewer #3: Verse 122. This research information should be in the method part, not in the theoretical introduction.

Verse 150 . Why is the sample of respondents (although numerous) exclusively students? After all, it is known that this is a very specific group when it comes to self -report research .

From verse 157 . Why such a long description of individual scales and tools when you can include all the most important information in a table. A lot of information is duplicated.

Table 1 : Cronbach 's alpha on the " psychopathy " scale is too low for analysis. Similarly, too low " Cronbach 's alpha " is on the in group trust level scale .

From verse 218 . Why is there so much information about the procedure and participants in the text itself? There is a diagram illustrating the procedure at the end, and the necessary information can be included in a table. This takes up a lot of space.

From verse 243 . Maybe I'm repeating myself, but why such a long description again when it can be included in a few sentences or a clear table?

Table 3 Should scales with such low reliability be included in the analysis ( Psychopathy and in - group trust)?

From verse 292. Another test of students, and in psychology at that. Is this group representative in the self report?

From verse 299 . Again, why are they introducing so many new research tools plus including the ones they used in the previous study. Do they want to measure everything in this article?

Table 4 . Again, we have quite poor reliability on several scales: in trust group , empathic concerns , perspective taking , general score of social desirability .

From verse 338 . Do you really need to re-detail what is contained in the table?

Verse 406 . Isn't 21 self -report variables too much for one article?

Verse 415 . They put in so many variables and only 13 percent of the variance is explained? It's probably a very small number. Maybe a bad theory?

Verse 442 . Of the 21 variables, only 2 are significant predictors .

Verse 447 . Since such a small number of significant variables was unexpected, perhaps it was better to start the theories earlier instead of coming to such conclusions only now after a huge amount of work.

Verse 483-485 . I don't understand this line of reasoning. On what theoretical basis is such a conclusion?

Verse 529 . Instead of a larger sample of respondents, maybe it's better not to study only psychology students?

Overall, there is a lot of chaos in this article and it lacks a cohesive structure. The authors tackle an interesting problem, but they are actually lost in the large amount of research and variables introduced that don't really explain the phenomenon of detecting lies in other people. After reading this article, it is basically unknown what these individual differences in the title would consist of.

The article is too long-winded. It seems like it could be half as long. It contains numerous repetitions and doubles. In the theoretical part, the authors briefly cite a lot of variables and studies related to lie detection, the description of which, instead of explaining this phenomenon, only complicates its understanding. In addition, in the theoretical part, they mix theory with the method that should be in the next part. Again, there is the problem of text systematization and structure

In the methodological part, the selection of samples is puzzling. Maybe students (including psychology) are not necessarily a representative group for such a common phenomenon as lying and its detection. Then there is the issue of a large number of questionnaires and variables for one article.

The article contains numerous mental abbreviations that are not always understandable to the reader. The structure of sentences, which is sometimes too complicated, does not help to understand the article.

In conclusion, it seems that the authors put a lot of work into this article, but it is too vague for the reader.

The manuscript deals with an interesting topic, but in the manuscript too often the names can refer to "lie detection" which the authors have not studied. "lie detection" is not possible to examine by self-report. It can be assumed that when the authors wrote about "lie detection", they did not mean it specifically. It should be made clear in every part of the manuscript that the theory and the study itself are not about "lie detection", because using the methods presented in the manuscript variable cannot be evaluated. It will also be advisable to reduce the number of variables to only those that have theoretical justification. The revised text will certainly be very interesting.

**********

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Reviewer #1: No

Reviewer #2: Yes: Guy J. Curtis

Reviewer #3: No

**********

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PLoS One. 2023 May 24;18(5):e0285124. doi: 10.1371/journal.pone.0285124.r002

Author response to Decision Letter 0

29 Mar 2023

Dear Editor, and editorial team, Dear Dr. Jonason,

We are delighted by your invitation to revise and resubmit our manuscript entitled “Individual differences in self-reported lie detection abilities” to PLoS ONE. We would like to thank you and the three reviewers for the detailed comments. We believe that these have greatly helped us strengthen the quality of our manuscript.

Please, find below our specific responses to the reviewers’ comments in bold. Changes in the text of the revised manuscript are highlighted in italic. We have incorporated almost all suggestions. In case we decided against implementation, we explain why in this response letter.

We hope that these changes to the manuscript will make it suitable for publication in PLoS ONE and we look forward to hearing from you at your earliest convenience.

Best regards,

Mélanie Fernandes*, Domicele Jonauskaite, Frédéric Tomas, Eric Laurent, and Christine Mohr

*corresponding author

Reviewer #1:

The present research used a correlational design to search for variables that may explain the gap between above-average self-assessed lie-detection ability and the average performance of lie detection. They used a large sample in two studies to again show that the bias exists and that people overestimate their lie-detecting ability. The perceived lie-detection ability was compared with different self-report scales, and in most cases, negative results were obtained. The outcomes are essential to enrich our knowledge and guide the exploration of the bias in additional directions. Furthermore, the perceived lie-detection ability is vital in face-to-face communication and may guide behavior. Undoubtedly, the topic deserves more empirical attention, and any addition to our limited knowledge in this domain is welcome.

Nevertheless, I have some comments that can and should be addressed in a revision.

R1.1. Any ability is defined on a continuous scale. The dichotomous yes/no response is meaningless and redundant. People may succeed more or less in lie detection. No one is perfect, and no one is 100% inaccurate. Further, the dichotomization triggers odd results, such as 9 participants who answered that they were not able to detect lies and at the same time indicated that they succeeded in about 50-75% of their lie detection attempts. Furthermore, 4 participants who answered yes to the yes/no lie detection ability question indicated success in only 0-25% of their attempts (Table 2). The total frequency line (bottom) demonstrates the overestimated lie-detection bias, and the dichotomization adds nothing in this respect.

Reply: The reviewer is right, the analysis of the dichotomous yes/no scale does not add any real value to the study but might create confusion. Therefore, in the revised manuscript, we merely present descriptive statistics on the yes/no answers. On page 13, we now write:

“Overall, most participants (81.52%) reported being able to detect lies (yes/no answer) and many participants (55.44%) self-reported that their lie detection abilities ranged between 50% and 100% (i.e. above chance level self-reported lie detection abilities).”

Then, we also removed the Chi-Square tests of independence and the binary logistic regression analysis with 12 continuous predictor variables and yes/no answers as an outcome variable. We now computed a binary logistic regression analysis with these same 12 predictor variables and the self-reported lie detection ability score as an outcome variable (see page 12 of the revised manuscript):

“To test our study question, we conducted a binary logistic regression analysis on this dependent dichotomous variable (below vs. above chance level) using the following 12 continuous predictor variables: psychopathy, narcissism, Machiavellianism, power distance, uncertainty avoidance, collectivism, masculinity, long-term orientation, empathy, ingroup trust level, outgroup trust level, and gender (see also S1 Table). We presented the correlations between all the predictor variables as supporting information (see S2 Table).”

R1.2. The 0 to 100% scale used in Study 2 is more sensitive than the scale used in Study 1. Therefore, why not benefit from the advantage and use parametric statistics? Instead, the authors dichotomized the scale to use a non-parametric chi-square analysis (line 362, on). Note that the sample consisted of 386 participants, which calls for parametric statistics. Further, the contradiction between answering no to the absolute lie detection ability and receiving an above-chance level score when reporting the lie detection ability persists. Therefore, the manipulation check in Table 5 is meaningless. In addition, only the bottom line in Table 6, which shows the lie-detection bias, is relevant. In sum, I suggest removing the absolute yes/no lie-detection ability test.

Reply: We deleted the analyses on the yes/no scale from both Studies 1 and 2 (see also our reply to the previous comment R1.1). We followed this reviewer’s suggestion to work with the continuous self-reported lie detection score and adopted parametric statistics. In the revised version of the manuscript, on page 18, we changed the statistical analysis section:

“We performed two multiple regression analyses to test whether our individual differences measures predicted i) participants’ self-reported lie detection abilities score (0-100), and ii) participants’ self-reported lie detection abilities in comparison to others (see Table 3). We present results of the correlations between all predictor variables in supporting information (see S3 Table).”

R1.3. Line 476: In study 2, we also asked them (the participants) to report their ability in comparison to others. Unfortunately, the results were not reported here. The authors indicated that these results would be reported elsewhere. My question is, why? This is an essential addition to the present study, and the comparison between answering with and without reference to others is interesting and important (to my knowledge, the comparison to other people results in lower absolute scores).

Reply: Following this comment, we reconsidered. We were interested in individuals’ belief biases, thus, what individuals think about their own lie detection abilities. Subsequent to the results of Study 1, we decided to additionally ask about individuals’ biases when compared to others, because this had been done in previous studies (Elaad, 2009; Zvi & Elaad, 2018; Elaad & Reizer, 2015, Elaad et al., 2020; Elaad, 2022). But we agree, we should give this measure more space in our analysis of Study 2. This decision had implications well beyond the method and result section, being also more extensively considered in the discussion.

In the statistical analysis section and in the result section (see page 18), we now respectively write:

[…] “However, when asked to rate their abilities in comparison to others, only 20.46% of the participants reported being better than others at detecting lies. Most participants (59.59%) estimated their abilities to be below those of others. However, a Pearson correlation on these two self-report measures was significant, r(386) = .683, p < .001 (see S3 Table), higher self-reported lie detection abilities correlated with higher self-reported lie detection abilities in comparison to others.”

We also added on page 20 (results section):

“The multiple linear regression analysis on self-reported lie detection abilities in comparison to others was significant, F(363, 22) = 3.57, p <.001, Radj2 = .128. The model explained 12.8 % of the variance (see Table 3). Again, enhanced social desirability significantly predicted enhanced self-reported lie detection abilities in comparison to others (see Table3). We also found that higher originality and psychopathy predicted higher self-reported lie detection abilities in comparison to others (see Table 3). The remaining predictor variables were not significant (see Table 3).”

On page 22 (discussion section), we now write:

” When asked to rate their abilities in comparison to others, we found, however, that only about 20% of our participants indicated that yes, they judged themselves superior to others at detecting lies. For extent, the numbers were comparatively low too; participants indicated being only 40 % of the time better than others at detecting lies.”

And also added on page 22 (discussion section):

“Interestingly, our individual difference measures were better predictors of self-reported lie detection abilities in comparison to others. Namely, enhanced psychopathy, enhanced Originality/Talent, and enhanced social desirability predicted enhanced self-reported lie detection abilities in comparison to others. The findings on psychopathy and Originality/Talent (i.e., Openness) have been reported previously [22, 51].”

Reference [22, 51] of the revised manuscript are:

22. Wissing BG, Reinhard MA. The Dark Triad and the PID-5 Maladaptive Personality Traits: Accuracy, confidence and response bias in judgments of veracity. Front Psychol. 2017 Sep 21;8:1549. doi: 10.3389/fpsyg.2017.01549.

51. Elaad E, Reizer A. Personality correlates of the self-assessed abilities to tell and detect lies, tell truths, and believe others. J Individ Differ. 2015;36(3):163-69. doi: 10.1027/1614-0001/a000168.

Minor points:

R1.4.Line 55: The current view about frequent lying is that not many people lie frequently, and most people reported not lying in the previous 24 h. (Daiku et al. 2021; Halevi et al. 2014; Serota and Levine 2015).

Daiku, Y., Serota, K. B., Levine, T. R. (2021). A few prolific liars in Japan:

Replication and the effects of Dark Triad personality traits. PLoS ONE 16(4):

e0249815. https://doi.org/10.1371/journal.pone.0249815

Halevy, R., Shalvi, S., and Verschuere, B. (2014). Being honest about dishonesty:

Correlating self-reports and actual lying. Human Communication Research, 40,

54-72.

Serota, K. B., & Levine, T. R. (2015). A few prolific liars: Variation in the prevalence of lying. Journal of Language and

Social Psychology, 34(2), 138-157.

Reply: We appreciate these literature suggestions and added the references in the revised version of the manuscript as proposed, see page 3:

“Yet, not everybody lies to the same extent with more recent studies showing that a few prolific liars are telling the majority of lies [6, 10, 11].”

References [6,10,11] of the revised manuscript corresponding to:

6. Serota KB, Levine TR. A few prolific liars: variation in the prevalence of lying. J Lang Soc Psychol. 2014 Apr 04;34(2):138-57. doi: 10.1177/0261927X14528804.

10. Daiku Y, Serota KB, Levine TR. A few prolific liars in Japan: replication and the effects of Dark Triad personality traits. PLoS One. 2021 Apr 15;16(4):e0249815. doi: 10.1371/journal.pone.0249815.

11. Halevy R, Shalvi S, Verschuere B. Being honest about dishonesty: correlating self-reports and actual lying. Hum Commun Res. 2014 Jan 01;40(1):54-72. doi: 10.1111/hcre.12019.

R1.5.Line 91: … self-reported confidence in own lie detection abilities was unrelated to individuals' actual lie detection abilities… add a reference (for example, Elaad and Gonen-Gal, 2022).

Elaad, E. & Gonen-Gal, Y. (2022). Face-to-face lying: Effects of gender and motivation to deceive. Frontiers in Psychology: Forensic and Legal Psychology, 13: Article820923. doi: 10.3389/fpsyg.2022.820923.

Reply: Idem to R1.4. We added the references in the revised version of the manuscript, see page 5:

“Interestingly, self-reported confidence in one’s own lie detection abilities was unrelated to individuals’ actual lie detection abilities [26]. Also, variance in Dark Triad traits did not explain variance in actual lie detection abilities either [26].”

Reference [26] is the suggested publication:

26. Elaad E, Gonen-Gal Y. Face-to-Face Lying: Gender and Motivation to Deceive. Front Psychol. 2022;13:820923. Epub 2022/04/09. doi: 10.3389/fpsyg.2022.820923. PubMed PMID: 35391990; PubMed Central PMCID: PMCPMC8982912.

R1.6.Line 214: Relative self-reported lie detection ability. Relative to what? To others? to the average person? The authors relate to the extent or degree of the perceived lie detection ability not to relativeness. This should be corrected.

Reply: We determined self-reported lie detection abilities in two ways: the absolute (yes/no) measure and the relative (0 to 100 scale) measure. We agree, the use of the term “relative” was not ideal in the earlier version of the manuscript. Having followed this reviewer’s suggestion (R1.1), we removed the absolute dichotomous variable (see our reply to R1.1). Therefore, the term “relative” became obsolete, we do not use it anymore in the revised version of the manuscript.

R1.7.Lines 230-231, 297: How were the questionnaires completed? Individually or in groups? If in groups, how big were the groups? Please be specific.

Reply: The questionnaires were completed individually, which we explain on page 11 of the revised manuscript:

“We used the LimeSurvey platform to prepare and run our online survey. We distributed the online link to potential volunteers. In case of interest, they could complete the survey at their own convenience. On the first two pages, we provided, respectively, written study information and ethical information, such as the right to withdraw from the study at any time, and data confidentiality (Fig 1A). We also stated that we treat participants’ continuation as informed consent. Next, participants provided socio-demographic information regarding their age, gender, nationality, and field of studies (Fig 1B). Then, they completed the self-reported questionnaires in the following order: The FR-C Dark Triad Dirty Dozen, the Cultural Values Scale, the IRI, and their level of trust based on the World Value Survey 5 (Fig 1C-Study 1 and Table 1). Afterwards, they indicated their self-reported lie detection abilities (Fig 1D-Study 1). Finally, they were fully debriefed and thanked for their participation. The survey took about 20 minutes to complete.“

R1.8. Line 236: Separate detect and changes.

Reply: We corrected this typo on page 12 of the revised manuscript:

“The flow diagram depicts parts of the survey that i) were comparable to Study 1 and Study 2 (A, B), ii) were complemented by other questionnaires in Study 2 as compared to Study 1 (C), and iii) used different rating scales for self-reported lie detection abilities (D).”

R.1.9. Line 282: Replace Is with It.

Reply: This section has been removed (see response to comment R1.1).

R1.10. Line 361: Why is the power set at 0.80?

Reply: The statistical power of a significant test is the probability of rejecting the null hypothesis, that is, avoiding a Type II error. According to Cohen (1988), setting the power at .80 implies a good balance between limiting the risk of Type II error and making the experiment feasible in terms of the sample size. A value below .80 would imply a great risk of failing to reject the null hypothesis (i.e., Type II error), and setting the value above .80 would demand larger sample sizes, which risk being difficult to recruit. Beyond this fixed value, we could also remind us that power increases with an increase in sample size. We tested 386 participants, 155 more than the minimum recommended number of 231 participants. We mention the sample size calculation on page 13 of the revised manuscript:

“We used the statistical power analysis tool G*Power [52] to estimate our sample size. We determined a minimum sample size of 231 for a linear multiple regression test with a small effect size of 0.1, 21 predictors, α of 0.05, and power (1−β) of 0.80.”

Reference [52] of the revised manuscript corresponding to :

52. Faul F, Erdfelder E, Buchner A, Lang A-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods. 2009 Nov;41(4):1149-60. doi: 10.3758/BRM.41.4.1149.

R1.11. Line 371: Change relative to relatively sensitive (The 0 to 100 scale is more sensitive than the categorial scale used in study 1).

Reply: The sentence in question has been changed, because we deleted the distinction between “absolute” and “relative” abilities (see our response R1.6).

R1.12. Line 377: The scale ranges from 0 to 100. Remove the percent sign (100%).

Reply: We removed the percent sign.

R1.13. Line 488: Elaad and Reizer (2015) used the Big Five, which is a different scale than the Big Six that was used in the present study.

Reply: The reviewer is correct. Questionnaire selection is a challenge in a multi-lingual country (Switzerland). We were interested in using a questionnaire which would be suitable for cross-cultural research, not only because of the four national languages of Switzerland (German, French, Italian, and Romanch), but also to easily implement it in the cross-cultural study we are currently preparing. Due to this situation and vision, we chose the Big Six questionnaire, because it has been validated in 26 nations accounting for cultural differences in how people talk and describe personality (see Thalmayer & Saucier, 2014; Saucier, 2009). We also had translations to French readily available. We would like to add that much of the content of the Big-Five factors is covered by the Big Six factors. The Bix Six questionnaire additionally measure the factor for Honesty/Humility, which has been shown to be important outside the Anglo-Saxon populations (Thalmayer & Saucier, 2014). Big Six also has the factor named Resiliency, which should be interpreted as the reverse scale of Neuroticism (Saucier, 2009). Thus, studies are relevant to us whether using the Big-Five or the Big Six questionnaire.

R1.14. Line 424: Not adding actual lie-detection performance is indeed a limitation. The current trend is to compare lie-detection ability scale scores with actual behavior (See Elaad and Gonen-Gal, 2022).

Reply: We totally agree with the reviewer that the study would have been stronger if we had measured actual lie-detection abilities. We had planned and prepared to do so, but COVID hit our laboratory study right at the beginning of data collection. Thus, as many others, we continued to run our studies online. Due to the local circumstances, we first worked with questionnaires, while also preparing means to test for actual lie detection performance. While running the here presented studies, we also created video material on lie detection in French. Thus, in parallel, we collected data on self-reported lie detection abilities and lie detection performance in students, comparing them to data gathered from various professional groups (e.g., policemen, teachers). Currently, we are treating these data to present them in a subsequent manuscript. Thus, the current manuscript represents the first empirical work realized by Melanie Fernandes for her doctoral thesis on self-reported lie detection abilities. In the current manuscript, we wanted to learn about individual differences measures that might be relevant for future studies we hoped to run under more controlled laboratory conditions. And indeed, we are currently preparing two more manuscripts in which we report on self-reported lie detection abilities and cues to lie detection as well as performances. We thank this reviewer for the recent (2022) reference, which will help us in our reasoning for all our ongoing studies. For the current manuscript, we also included this citation in our introduction, see response to comment R1.5.

Reviewer #2:

This paper examined individual differences (primarily of personality) on self-reported lie detection ability. The Introduction makes a solid argument for the need for the study. Individual difference mostly had few significant or strong relationships with self-reported lie detection ability. This is interesting as some have argued that they should. I do not consider the lack of strong or significant effects to be a problem, they are what they are.

R2.1.The paper reports 2 studies that appear to have been properly executed with samples of a good size and well-selected measures. Data are reported in a way that address the questions of the studies, although I’d like to see tables showing the intercorrelation of all predictor variables in the supplementary material to aid in the interpretation of the regressions.

Reply: We thank Dr. Curtis for the positive feedback. We agree that it makes sense to report the intercorrelations of all predictor variables. They can be found in the supporting information section in S2 Table and S3 Table.

R2.2. The introduction is clear and readable. I have no suggested changes

Reply: We thank Dr. Curtis for this positive feedback.

R2.3.Study 1

Method

Reply: This is an important observation for which we are grateful. We realized that the participant section needs further work to improve coherence and clarity. Accordingly, we now write on page 7 of the revised manuscript:

“We recruited 764 participants (105 males). After excluding incomplete data and selecting participants between 18 and 31 years old (i.e., the majority), our final sample consisted in 487 participants (99 males) with a mean age of 21.50 years (SDage = 6.57 years; range = 18-31 years). Of these, 418 (95 males) were undergraduate students at the University of Lausanne, Switzerland, who received course credit for their participation. The remaining 69 participants (4 males) were recruited at the University of Franche-Comté, France. All participants were native French speakers.”

R2.4.Results

I can’t find how gender was coded, thus the mean and any direction of relationships cannot be interpreted by a reader.

Reply: In the revised version, these results have been deleted. Yet, Dr. Curtis (see R2.6 below) also proposed a gender comparison, which we added to the binary logistic regression analysis (see S1 Table, on page 34 of the revised manuscript). Accordingly, we adapted the design and statistical analysis section (see page 12):

“To test our study question, we conducted a binary logistic regression analysis on this dependent dichotomous variable (below vs. above chance level) using the following 12 continuous predictor variables: psychopathy, narcissism, Machiavellianism, power distance, uncertainty avoidance, collectivism, masculinity, long-term orientation, empathy, ingroup trust level, outgroup trust level, and gender (see also S1Table). We presented the correlations between all the predictor variables as supporting information (see S2 Table).”

R2.5.Study 2

Method

The participants (700) minus exclusions (234) does not add to the total final sample (386). There was an age exclusion, but the numbers are not stated.

Reply: We added the relevant information to this section (see revised manuscript, page 13)

“We recruited a new sample of French speaking undergraduate psychology students (N = 700, 90 males) at the University of Lausanne. After excluding incomplete data and matching participants’ age to those of Study 1, we were left with 386 participants (72 males; Mage = 20.21, SDage = 2.22, range = 18 to 31 years). One academic year separated the data collection for Study 1 and Study 2. All participants received course credit for their participation.”

R2.6.Results

Why is gender omitted in the Study 2 regression analysis?

Reply: We have no strong reason to assume gender differences. Yet, we agree, we have no strong reason to assume that there are none. Accordingly, we now included gender and updated the results section of the revised manuscript accordingly (see Table 3 pages 19-20).

R2.7.Discussion

Reply: We thank Dr. Curtis for informing us that our measure of social desirability (i.e. The Balenced Inventory of Desirable responding (BIDR), Paulhus, 1991) has limitations including validity. It seems that this scale neither measures socially desirable traits nor response biases (Holden & Passey, 2010; Lanz et al., 2021). We included this information on page 26:

” Regarding social desirability, several studies emphasized the lack of validity of the BIDR (Balenced Inventory of Desirable Responding) [85, 86]. To address this limitation, further studies are needed to measure social desirability as a trait, using validated measures of related dimensions such as self-control (e.g.[87])”

References [85, 86] and [87] of the revised manuscript were:

85. Holden RR, Passey J. Socially desirable responding in personality assessment: Not necessarily faking and not necessarily substance. Pers Individ Dif. 2010 Oct;49(5):446-50. doi: 10.1016/j.paid.2010.04.015.

86. Lanz L, Thielmann I, Gerpott FH. Are social desirability scales desirable? A meta-analytic test of the validity of social desirability scales in the context of prosocial behavior. J Pers. 2022 Apr;90(2):203-21. doi: 10.1111/jopy.12662.

87. Tangney JP, Baumeister RF, Boone AL. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers. 2004 Apr;72(2):271-324. doi: 10.1111/j.0022-3506.2004.00263.x.

Reviewer #3:

R3.1. Verse 122. This research information should be in the method part, not in the theoretical introduction.

Reply: We are not totally sure what exactly the reviewer wants us to do, because Verse 122 “… explain variance in self-reported lie detection abilities. In study 1, participants completed self-reported …”relate to a particular line or the paragraph more widely. This verse and the following lines explain details of our study, and some actual results from Study 1. When it comes to the details of Study 1 (method), we are not too sure what the reviewer wishes us to do, because we are used to quickly explain what we have done in any given study at the end of an introduction. We need to do so in order to formulate our hypotheses. If we do not convey what we have done, we cannot formulate what we expect the study to show. When it comes to the first results of Study 1, we felt we need to report this finding here in order to explain Study 2, because the outcome of Study 1 had informed Study 2. If there is anything we did not capture by reading the current comment, we would appreciate if they could be more specific about what should go to the method section.

R3.2. Verse 150. Why is the sample of respondents (although numerous) exclusively students? After all, it is known that this is a very specific group when it comes to self-report research.

Reply: The reviewer is absolutely right that studies in psychology should not exclusively focus on student populations. In 2010, Heinrich and colleagues introduced the term WEIRD populations, describing their criticism of testing predominantly students in Western, Educated, Industrialised, Rich and Democratic societies. By December 2022, their article has been cited 2,624 times. Now, does this study mean we should stop testing WEIRD populations? We are not sure who would and should have the final word to decide. What is certain, and very much in line with this referee’s comment, it should not be “exclusively” students as the world is more varied than that. A sample of students is a specific sub-group of the general population, and by inference not (necessarily) representative of the general population. It is for that very reason we run additional studies testing other populations. Currently, we are starting to treat data from different professional groups (police, education, insurance), couples, and prepare a cross-cultural study. The current study had been launched before the Covid pandemic and the lockdown hit countries worldwide. We had already prepared a follow-up study in the laboratory to also test our current participants’ actual lying detection ability (see also our reply to R1.14). Yet, we had to adapt to the sanitary situation, and so we had to drop the second part. In any case, we are now advancing in the treatment of the data from professional groups, and we hope to soon report these results in subsequent peer-reviewed contributions.

R3.3. From verse 157. Why such a long description of individual scales and tools when you can include all the most important information in a table. A lot of information is duplicated.

Reply: The reviewer is right we should avoid duplicate information. Accordingly, we read carefully over the manuscript and made adjustments in this and other sections.

For the section this reviewer is referring too, we aimed for a reasonable balance between details and redundancies while respecting a potentially wide readership. For PLOS One, we expect that some readers have a solid background in self-report questionnaires, and others are novices. Thus, for the latter, without detailed description, the nature of the questionnaire is difficult to grasp. They should not have to go to the original publications to know how such scales are constructed, but get all relevant information from reading the current manuscript. This would also include the sub-dimensions. It is also worth noting that we conducted two different studies in which we sometimes measured the same dimensions using two different questionnaires (e.g., Dark Triad). Thus, a detailed description helps the reader understand differences between studies and also resultant scores.

R3.4.Table 1 : Cronbach 's alpha on the " psychopathy " scale is too low for analysis. Similarly, too low " Cronbach 's alpha " is on the in group trust level scale.

Reply: This comment is very welcome, because it forced us to think about this question, namely when low is “too low”. We went on a little journey to get reminded what these values actually imply. Does “too low” reliability also mean “too low” validity? Should we exclude the sub-scale scores all together?

In our literature search, we found that low reliability does not necessarily impact predictive validity. Indeed, Cronbach’s alpha is a measure of internal consistency, but is of limited utility for homogeneity assessment (Clark & Watson, 1995). Calculating Cronbach’s alpha as an index of reliability allows us to determine the extent to which all the items of a scale measure the same construct.

In more practical terms, Cronbach’s alpha is dependent on two parameters i) the number of tested items and ii) the average intercorrelations among them (Cronbach, 1951). Therefore, increasing the number of items will inevitably enhance internal consistency, while potentially decreasing validity due to the redundancy and added noise, leading to suboptimal construct assessment (see “the attenuation paradox” Loevinger, 1954). In our case, we assessed psychopathy using a validated self-report questionnaire (i.e., The French-Canadian Dirty Dozen; Zeng, 2013). The same was true for trust level (Inglehart et al., 2014). We agree that the reliability of these subscales was on the lower end (respectively, a Cronbach’s alpha of .596 for psychopathy and .510 for in group trust level) in our study. Yet, these alpha values fall within ranges of other published studies (e.g. Thalmayer & Saucier, 2011) and likely show that they measure related, yet not identical constructs. Worth noting, the psychopathy questionnaire we used in Study 2 had a higher Cronbach’s alpha than in Study 1 (.596 vs. 727), suggesting that the internal consistency was higher

R3.5. From verse 218 . Why is there so much information about the procedure and participants in the text itself? There is a diagram illustrating the procedure at the end, and the necessary information can be included in a table. This takes up a lot of space.

Reply: This comment echoes this reviewer’s comment R3.3. To avoid us repeating the same reply, we consider it best to refer back to a former reply (R.3.3). In addition, we also looked at the text to consider what we could delete in order to omit redundant information. Following the current reviewer’s comments as well as those of the other two referees, we have re-written the introduction and discussion alltogether, shortening and hopefully clarifying our reasoning and thoughts.

R3.6.From verse 243 . Maybe I'm repeating myself, but why such a long description again when it can be included in a few sentences or a clear table?

Reply: Regarding the detailed descriptions, we invite the reviewer to read our previous response to R3.3.

R3.7.Table 3 Should scales with such low reliability be included in the analysis (Psychopathy and in - group trust)?

Reply: Please see our reply to this reviewer’s comment R3.4.

R3.8.From verse 292. Another test of students, and in psychology at that. Is this group representative in the self report?

Reply: We can understand that this issue comes up. We have touched upon the question of whether our sample is representative in our response to comment R3.2. In addition, we wish to add some words on the representativeness in comparison to “normative” values. Several of our scales were developed by testing undergraduate students. For example, to develop the cultural value scale (Zheng, 2013), the author tested 223 French students (104 males) from 18 to 35 years old. For the emotional intelligence scale, Maria and colleagues (2016) tested 824 French undergraduate students (368 males), with a mean age of 20.7 years (standard deviation of 2.1). Regarding the Short Dark Triad normative values (see Gamache et al., 2018), data were collected in a sample of 405 French-Canadian participants (with a mean age of 31.01, standard deviation of 11.97, age range 18-76 years). The latter sample was not exclusively composed of students, but the data were collected via institutional email from two universities. Only three out of our 22 predictors variables were tested in adult samples that were not student populations. For Empathy, Gilet and colleagues (2012) tested 322 French participants, with a mean age of 49.5 years old (standard deviation of 21.1, age rang 18-89 years). For social desirability, normative data were obtained from 1,159 French-Canadian participants (567 males) between 17 and 67 years old. For trust level, Delhey and colleagues (2012) tested 1,089 adults from 50 societies without providing age information. We conclude that most of our measurements are representative for our student population.

R3.9.From verse 299 . Again, why are they introducing so many new research tools plus including the ones they used in the previous study. Do they want to measure everything in this article?

Reply: Reading this comment, we thought that this reviewer might experience some “desperation” given the number of possible individual differences measures we have been considering. And true, there are many possible ones to look at, due to a lack of research so far. Before COVID, we had launched Study 1, because we wanted to know about individual differences that might be worth pursuing when testing selected professional groups. Then COVID hit, we had our first results, and realized that some measures might have better psychometric alternatives. We presented the results of Study 1 to our peers and were advised to add a social desirability scale. Most important to us was the repetition of the study changing the self-reported lie detection abilities scale. We agree, we considered many variables, and obviously, given so many non-significant results, one might be tempted to only report a few of them. Yet, this would seem totally wrong given the widely discussed replication crises (Shrout & Rodgers, 2018). We feel very committed to report everything we have done. Therefore, we are both transparent and informative to peers who might consider the same individual difference measures. As Dr. Curtis, reviewer 2, stated “the results are what they are”.

R3.10.Table 4 . Again, we have quite poor reliability on several scales: in trust group, empathic concerns , perspective taking , general score of social desirability .

Reply: Please see our response to comment R3.4.

R3.11.From verse 338. Do you really need to re-detail what is contained in the table?

Reply: Please see our response to comment R3.3 above.

R3.12.Verse 406. Isn't 21 self -report variables too much for one article?

Reply: Please see our response to comment R3.9 above.

R.3.13.Verse 415. They put in so many variables and only 13 percent of the variance is explained? It's probably a very small number. Maybe a bad theory?

Reply: This reviewer is coming back with very similar remarks. We are uncertain what else we can explain. Sure, the reviewer and other potential readers can question our study. Yet, we wish to reiterate that there is very little research on this self-report bias. There is no solid theory yet. We need to describe a phenomenon first, and we are dedicated to this effort. This self-report bias could have real impact on human decision making (being convinced that somebody is lying, because one believes being able to spot lying). Once we have described this self-report bias and its correlates, we feel ready to also consider theories. This approach might appear to some unscientific, and also to some who teach scientific methods. However, recent voices highlight the need to allocate more time and efforts in the description of any type of phenomena, before theories are presented, and mechanisms are investigated (Scheel et al., 2021). Regarding the explained variance of the model, to encompass the fact that adding more variables increases the amount of explained variance, we rely on the adjusted R-squared which has been adjusted for the number of the predictors in the model and does not simply increase with more predictors (unlike unadjusted R-squared).

R3.14. Verse 442. Of the 21 variables, only 2 are significant predictors.

Reply: We are not sure what the reviewer wishes us to comment on, or change. Thus, we consider it possible that they feel that we report on many variables that are not significant, which they mentioned also in other comments (see also our reply to comment R3.9 and R3.13). Thus, we wish the referee to read our response there, else, we would to receive more specific information what we should do.

R3.15.Verse 447. Since such a small number of significant variables was unexpected, perhaps it was better to start the theories earlier instead of coming to such conclusions only now after a huge amount of work.

Reply: Please see our response to comment R3.13.

R3.16.Verse 483-485. I don't understand this line of reasoning. On what theoretical basis is such a conclusion?

Reply: Most of the studies on self-reported lie detection abilities and the Dark Triad found significant relationships (e.g. Wissing & Reinhard, 2017; Zvi & Elaad, 2018;Elaad et al., 2020; Elaad 2022). We did not. Thus, we detailed why these differences might have emerged between studies. As announced for Study 2, we added a second measure of self-reported lie detection abilities, one that compares own abilities to other persons’ abilities. We had been interested in what people think in principle about their abilities, not in comparison to others. Thus, in Study 2, we assessed both self-reported measures. In line with reviewer 1 (see our response to comment R1.3), we now report results on both measures. We also discuss the results in the revised manuscript on page 22-24

The verse 483-485 refers to results reported in Wissing & Reinhard (2017), namely a significant link between psychopathy and self-reported lie detection abilities using the added measurement in Study 2 (self-reported lie detection abilities in comparison to others). The way these authors assessed self-reported lie detection abilities differs from the measure we used in Study 1 and again in Study 2 (i.e., being able to detect lies in general).

R3.17.Verse 529. Instead of a larger sample of respondents, maybe it's better not to study only psychology students?

Reply: see our reply to comment R3.2 and R3.8.

R3.18.Overall, there is a lot of chaos in this article and it lacks a cohesive structure. The authors tackle an interesting problem, but they are actually lost in the large amount of research and variables introduced that don't really explain the phenomenon of detecting lies in other people. After reading this article, it is basically unknown what these individual differences in the title would consist of.

Reply: see reply to comments R3.3, R3.9. We can only add here that we read and reworked the manuscript having comments of three referees in mind.

R3.19.Again, there is the problem of text systematization and structure

Reply: Please see our reply to comments R3.2, R3.3, R3.8 and R3.9.

R3.20.The article contains numerous mental abbreviations that are not always understandable to the reader.

Reply: The mental abbreviations used in our article are always preceded by both the spelled-out version and the short form. Following the reviewer’s comment, we decided to remove all the abbreviations and corrected the manuscript accordingly.

R3.21.The structure of sentences, which is sometimes too complicated, does not help to understand the article. In conclusion, it seems that the authors put a lot of work into this article, but it is too vague for the reader.

Reply: We hope that our extensive re-write improved the simplicity of the manuscript.

R3.22.The manuscript deals with an interesting topic, but in the manuscript too often the names can refer to "lie detection" which the authors have not studied. "lie detection" is not possible to examine by self-report.

Reply: We have done a careful reading through the manuscript keeping this comment in mind. We have been careful that we always stated when studies looked at lie detection (and did so methodologically) or tested the belief that one is able to detect lying (no actual lie detection was assessed). We were also careful in our phrasing for the very few studies that looked at self-reported lie detection abilities and the actual assessment of lie detection abilities.

R3.23.It will also be advisable to reduce the number of variables to only those that have theoretical justification. The revised text will certainly be very interesting.

Reply: From the previous comments by this reviewer, we understand that they would prefer us to exclude information and data we have collected. Yet, this would be unacceptable to us, given the extensive discussion on replication problems, publication biases, data spotting, data polishing, that has brought a dark shadow on many empirical research fields, not only on psychology. Zero results should be welcomed, and all the collected data and measures should be reported. In this regard, we cannot follow this suggestion.

References not mentioned in the manuscript:

Clark LA, & Watson DB. (1995). Constructing validity: basic issues in objective scale development. Psychological Assessment, 7(3), 309-319.

Cohen, J. (1988). Statistical power analysis for the behavioral. Sciences. Hillsdale (NJ): Lawrence Erlbaum Associates, 18, 74.

Cohen, J. (1992). Statistical power analysis. Current directions in psychological science, 1(3), 98-101.

Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334 (1951). https://doi.org/10.1007/BF02310555

Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not WEIRD. Nature, 466(7302), 29-29.

Holden RR, Passey J. (2010). Socially desirable responding in personality assessment: Not necessarily faking and not necessarily substance. Personality and Individual Differences, 49(5): 446-450.

Loevinger J. (1954). The attenutation paradox in test theory. Psychological Bulletin, 51(5), 493-504.

Saucier, G. (2009). Recurrent personality dimensions in inclusive lexical studies: Indications for a Big Six structure. Journal of personality, 77(5), 1577-1614.

Scheel, A. M., Tiokhin, L., & Isager, P. M. (2021). Why Hypothesis Testers Should Spend Less Time Testing Hypotheses. Perspectives on Psychological Science, 16(4), 744-755.

Shrout, P.E., & Rodgers, J.L. (2018). Psychology, science, and knowledge construction: Broadening perspectives from the replication crisis. Annual review of psychology, 69, 487-510.

Thalmayer, A. G., & Saucier, G. (2014). The questionnaire big six in 26 nations: Developing cross–culturally applicable big six, big five and big two inventories. European Journal of Personality, 28(5), 482-496.

Thalmayer, A. G., Saucier, G., & Eigenhuis, A. (2011). Comparative validity of brief to medium-length Big Five and Big Six Personality Questionnaires. Psychological assessment, 23(4), 995.

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PLoS One. doi: 10.1371/journal.pone.0285124.r003

Decision Letter 1

Peter Karl Jonason

16 Apr 2023

Individual differences in self-reported lie detection abilities.

PONE-D-22-29409R1

Dear Dr. Fernandes,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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PLoS One. doi: 10.1371/journal.pone.0285124.r004

Acceptance letter

Peter Karl Jonason

27 Apr 2023

PONE-D-22-29409R1

Individual differences in self-reported lie detection abilities.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Results of the binary logistic regression analysis on the continuous 12 predictor variables for self-reported lie detection abilities (below vs above chance level).

(TIF)

Click here for additional data file.^{(92.8KB, tif)}

S2 Table. Pearson’s correlations between individual characteristics and self-reported lie detection abilities for Study 1.

(TIF)

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S3 Table. Pearson’s correlations between individual characteristics and self-reported lie detection abilities for Study 2.

(TIF)

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Attachment

Submitted filename: Response to Reviewers.pdf

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Data Availability Statement

All the data files of the individual differences in self-reported lie detection abilities' study are available from the Open Science Framework database (DOI: 10.17605/OSF.IO/6NRA5).

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PERMALINK

Individual differences in self-reported lie detection abilities

Mélanie Fernandes

Domicele Jonauskaite

Frédéric Tomas

Eric Laurent

Christine Mohr

Roles

Abstract

Introduction

Study 1

Method

Participants

Material

Self-report questionnaires

Table 1. Description of the self-report questionnaires.

Dark Triad Dirty Dozen inventory [54] (French-Canadian version from Savard et al

Individual Cultural Values Scale [55] (French version from Zheng [42])

The World Value Survey 5 [43]

Interpersonal Reactivity Index (IRI [58]; French version from Gilet et al

Other questionnaires and measures

Fig 1. Flow diagram of events.

Self-reported lie detection abilities

Procedure

Design and statistical analysis

Results

Study 2

Method

Participants

Material

Self-report questionnaires. Short Dark Triad (SD3) [62] (French version from Gamache et al. [44])

Table 2. Description of the self-report questionnaires.

Trait Meta Mood Scale [65] (French version from Maria et al

Balanced Inventory of Desirable Responding [66] (French version from D’Amours-Raymond [49])

Other questionnaires

Self-reported lie detection abilities

Procedure

Statistical analysis

Table 3. Results of the two multiple regression analysis on the continuous 22 predictor variables for self-reported lie detection abilities and self-reported lie detection abilities in comparison to others.

Results

Discussion

Study limitations, challenges and future directions

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Peter Karl Jonason

Roles

Author response to Decision Letter 0

Decision Letter 1

Peter Karl Jonason

Roles

Acceptance letter

Peter Karl Jonason

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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