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Open Access

Peer-reviewed

Research Article

Fast & furious: Rejecting the hypothesis that secondary psychopathy improves reaction time-based concealed information detection

Abstract

Deception, a complex aspect of human behavior, is inherently difficult to detect directly. A valid alternative involves memory detection, particularly through methods such as the Reaction-Time based Concealed Information Test (RT-CIT). The RT-CIT assesses whether an individual possesses specific knowledge by presenting various probe (familiar) items amidst irrelevant (unfamiliar) items. The task-required "unfamiliar" response to probes may induce a response conflict. Resolving this conflict, by inhibiting the automatic "familiar" response, takes time and slows probe RTs–a phenomenon known as the RT-CIT effect. Notably, secondary psychopathy is characterized by disinhibition and impulsivity, traits which may hinder the ability to effectively manage experienced conflict. Therefore, we hypothesized that secondary psychopathy would be associated with an elevated RT-CIT effect. To investigate this hypothesized relation, we conducted a pre-registered study (n = 86, student sample), employing a novel CIT paradigm that incorporates no-go trials to assess response inhibition capacity. Psychopathic traits were measured using the Levenson Self-Report Psychopathy (LSRP) scale, while the Barratt Impulsiveness Scale (BIS-11) assessed impulsivity. The novel CIT paradigm revealed impressive detection efficiency. However, contrary to our expectations, we observed no significant correlation between the RT-CIT effect and secondary psychopathic traits (BF01 = 6.98). This cautiously suggests that while secondary psychopathic tendencies do not improve RT-CIT validity, they also do not compromise it. Although future investigations should explore more diverse contexts and populations, this tentative finding is reassuring and underscores the robustness of the CIT paradigm.

Citation: Mizrahi I, klein Selle N (2024) Fast & furious: Rejecting the hypothesis that secondary psychopathy improves reaction time-based concealed information detection. PLoS ONE 19(10): e0311948. https://doi.org/10.1371/journal.pone.0311948

Editor: Vilfredo De Pascalis, Sapienza University of Rome: Universita degli Studi di Roma La Sapienza, ITALY

Received: April 17, 2024; Accepted: September 28, 2024; Published: October 15, 2024

Copyright: © 2024 Mizrahi, klein Selle. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data, analysis scripts and results were published at OSF: https://osf.io/s5mrn/.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors (we) have declared that no competing interests exist.

Introduction

Lying is an intrinsic feature of human behavior [1]. We all lie and we have all been lied to [2–4]. When people are asked to discriminate between truth and lie based on their perceptions, they correctly notice lies in about 47% of cases and classify truths as nondeceptive in about 61% of cases–which is close to chance level [5, 6]. Hence, it’s not surprising that throughout history humans have sought for techniques and methods that can distinguish between truth and lie [2, 7, 8]. In ancient Israel, for instance, a woman accused of adultery was considered guilty if her belly swelled after drinking "bitter water" [7]. In ancient China, those accused of fraud had to hold dry rice in their mouths–if the rice stayed dry, they were deemed guilty [9].

These historical methods, though lacking scientific validation, hint at a connection between physiological changes and deception. Building upon this understanding, psychophysiological methods for lie detection, popularly known as "polygraphs", emerged in the early twentieth century [10]. Such lie detection tools generally rely on physiological reactivity [11–13]. Importantly, the difference between the various lie detection methods lies in the adopted paradigm and the way its questions are formulated [see 12]. The classical, and probably most influential method, is the Control Question Test [CQT; 14]. This test assumes that guilty examinees will show stronger physiological responses to relevant, e.g., crime-related, questions, whereas innocents will show stronger physiological responses to control questions [15].

However, in real police investigations, both guilty (liars) and innocent (truth tellers) suspects may quickly identify the relevant questions and become emotionally aroused by them [11]. As a result, both types of subjects (guilty and innocent) may show enhanced physiological responses to the relevant questions, making accurate classification difficult [11, 16–19]. Consequently, it is not surprising that the scientific community has criticized this method for being biased against the innocent in addition to lacking a theoretical basis [15–17, 20]. Indeed, many criminal investigations have been hindered by the unreliable results of the CQT. For instance, consider the infamous Green River Killer case, which began in 1982 with the discovery of five bodies in the Green River, Washington. In this case, Melvin Foster, a taxi driver, failed a CQT despite his innocence. It wasn’t until 2001 that DNA evidence implicated Gary Ridgway, who was ultimately convicted of 49 murders. Remarkably, Ridgway passed a CQT in 1984 [19, 21–24].

Lykken (1959) was one of the first to question the existence of specific deception reactions and, hence, he developed the Guilty Knowledge Test [GKT; 25]. Today, the GKT is called the Concealed Information Test [CIT; 25] and is considered a well-validated diagnostic test that aims to detect concealed knowledge [11]. In this test, examinees are faced with several multiple-choice questions, each followed by one probe (e.g., crime-related) item and several irrelevant alternatives, which are similar to the probe [26]. For instance, in the Green River Killer case, the body of the first victim, Wendy Lee Coffield, was pulled from the river with a pair of blue jeans knotted around her neck [21, 22, 24]. An appropriate CIT question could have been: "What article of clothing was tied around the victim’s neck?" (a) black sweater; (b) purple shirt; (c) blue jeans; (d) red scarf; (e) green jacket. Importantly, knowledgeable suspects recognize the significant probes, leading to differential physiological and behavioral responses. Unknowledgeable suspects, on the other hand, cannot distinguish between probe and irrelevant items and respond uniformly to all items [12, 13, 27, 28]. Interestingly, while CIT researchers traditionally relied on autonomic physiological measures like heart rate, skin conductance, and brain responses, recent studies have incorporated behavioral measures such as reaction time [29, 30].

The RT-based CIT is designed according to the 3-stimulus protocol and includes, in addition to probe and irrelevant items, a third item type known as the “target stimulus” [31, 32]. These targets ensure stimulus-processing as they require a unique response [33, 34]. Specifically, participants are typically asked to judge the stimuli on familiarity and are instructed to press buttons with the captions "familiar" (for targets) versus "unfamiliar” [for probe and irrelevant items 35, 36]. The task-required "unfamiliar" response to probe items is presumed to create a response conflict [37, 38]. Such response conflict may be resolved by inhibiting the automatic “familiar” response, which requires time [39–41]. Hence, response conflict has been theorized to underlie the longer RTs for probe versus irrelevant items–i.e., the RT-CIT effect [28, 42, 43].

Several studies provide direct support for the role of response conflict. Suchotzki et al. (2018), for instance, reasoned that since conflict arises when one denies familiarity with the known probe items, conflict should be stronger when one relies more heavily on familiarity. To explore this hypothesis, the authors manipulated familiarity-based responding by: (1) increasing the number of different targets (4 instead of 2 newly learned targets); and (2) using more familiar targets (2 personally relevant instead of 2 newly learned targets). Both manipulations increased the RT-CIT effect, supporting the response conflict account. Moreover, Suchotzki et al. (2015) instructed participants to admit knowledge of half the probes and deny knowledge of the remaining half. Their findings showed that overt deception, which generates response conflict, was essential for both the RT-CIT effect and the activation of the right inferior frontal gyrus, a brain region associated with inhibition [44, 45]. Interestingly, a recent study has provided support for the crucial role of conflict, however, also suggests that additional factors such as orientation to significant information contribute to the RT-CIT effect [46].

Beyond theoretical considerations, meta-analytic research [29] has demonstrated that the RT-CIT is a highly valid method for detecting concealed information. Nevertheless, it remains to be assessed how the RT-CIT is affected by different personality traits, such as the constellation of traits associated with psychopathy [47, 48]. This is especially relevant considering that psychopathic individuals constitute a significant proportion of the incarcerated population, with prevalence ranging from 20% to 30% [49, 50]. Notably, classical dual-factor models of psychopathy distinguish between primary and secondary variants [51–54]. Secondary psychopathy, which is characterized by disinhibition and impulsivity, holds particular relevance in the context of the RT-CIT [55–59]. Specifically, a diminished ability to inhibit responses and manage response conflict should lead to an elevated RT-CIT effect.

Only a few studies have examined the influence of psychopathy on the CIT and found a significant CIT effect for psychopaths, which did not differ from that of non-psychopaths. However, these studies relied on physiological responses rather than RT [60–62]. RT serves as a behavioral measure and is assumed to reflect a different cognitive mechanism. Specifically, while the autonomic CIT effects have been tied to either orienting or arousal inhibition [see 63–66], the RT-CIT effect has primarily been associated with response conflict [28, 46]. As outlined above, efficient conflict resolution requires adept inhibition capacities, which may be compromised by secondary psychopathic tendencies [55, 57, 59]. Therefore, the objective of the present study was to examine whether the RT-based CIT is sensitive to secondary psychopathic traits in a student sample. To get a fuller comprehension of this relationship, we used a novel CIT protocol which features no-go trials to assess disinhibition (see Method).

Method

This study was approved by the Ethics Review Board of the Criminology department of Bar-Ilan University (BIU; January 26th, 2023; see Ethics Review Board approval on https://osf.io/s5mrn/) and was performed in accordance with the relevant guidelines and regulations. The methods of this study, including sample size determination and exclusion criteria, were pre-registered on: https://osf.io/hz58u.

Participants

A total of 100 BIU students (79% female) were recruited through BIU’s online research portal (i.e., SONA). Participants’ average age was 23.88 years (SD = 2.3, range = 20–37). All participants signed an informed consent form. At the end of the experiment, each participant received one credit point. All data of fourteen participants were excluded: thirteen participants were excluded because they made more than 50% errors to either target, probe or irrelevant items, and one participant was excluded because s/he did not complete the entire CIT (< 336 trials). Accordingly, the final sample included 86 participants (81.4% female, average age = 23.83, SD = 2.3, range = 20–37).

As indicated in the pre-registration, we stopped data collection when we reached N = 100, since the Bayes Factor (BF) provided substantial evidence for the null hypothesis (i.e., BF01 > 5; there is no linear association between the RT-CIT effect and secondary psychopathic traits).

Materials

The present study included (1) the Levenson’s Self-Report Psychopathy (LSRP) scale, which provided the psychopathy scores; (2) a Go/No-go RT-CIT, which provided the RT-CIT effect as well as a behavioral measure of response inhibition (i.e., the no-go error rate; as explained below); and (3) the Barratt Impulsiveness Scale (BIS-11), which provided the impulsivity scores.

LSRP.

Psychopathic traits within our student sample were assessed using the LSRP [67]. The LSRP contains a total of 26 items, rated on a four-point Likert scale from “disagree strongly” to “agree strongly", resulting in a total score range from 26 to 104. Developed specifically for non-forensic populations, the LSRP distinguishes between primary and secondary psychopathy, aligning with the original Psychopathy Checklist–Revised (PCL-R) factors [67–71]. The primary psychopathy subscale (16-items; range: 16–64) evaluates interpersonal and affective features of psychopathy, while the secondary psychopathy subscale (10-items; range: 10–40) assesses impulsivity and antisocial lifestyle [60, 67, 72].

The overall scale’s reliability typically falls within the range of 0.59 to 0.87; for the primary subscale Cronbach’s alpha ranges from 0.74 to 0.86, and for the secondary subscale, it ranges from 0.61 to 0.71 [67, 72–75]. In the current study, Cronbach’s alpha values were 0.79 for the overall LSRP, 0.8 for the primary subscale, and 0.63 for the secondary subscale. This study used a Hebrew translated version of the LSRP [76].

Go/No-go RT-CIT.

The Go/No-go task is widely used in psychology as a measure of inhibition and impulsivity [77]. Therefore, the present experiment integrated this task within the RT-CIT–i.e., this study relied on a Go/No-go RT-CIT with both go and no-go trials. The regular CIT items–probes, irrelevants and targets–played the role of ’go’ items, to which participants had to respond by pressing a button. Specifically, a “unfamiliar” button for probes and irrelevants, but a “familiar” button for targets (as is common in the RT-CIT). When seeing the no-go items, participants were asked not to respond. Importantly, these no-go items were used to measure participants’ capacity for response inhibition, which is assumed to be compromised in secondary psychopathy [78, 79].

BIS-11.

In addition to measuring response inhibition capacity with the novel no-go trials, we assessed impulsivity using the Barratt Impulsiveness Scale [BIS-11; 80]. The BIS-11 is a self-report questionnaire which contains a total of 30 items that are rated on a four-point Likert scale ranging from “rarely/never” to “almost always" [81]. Cronbach’s alpha for the BIS-11 typically falls within the range of 0.69 to 0.83 [80, 82, 83]. In the current study, Cronbach’s alpha was 0.84. This study used a Hebrew translated version of the BIS-11 [84].

Procedure

The experiment was built in PsychoPy [85] and performed online in ’Pavlovia’ (see script on https://osf.io/s5mrn/). Participants received a link to the experiment through SONA (i.e., BIU’s online research portal). Importantly, once participants finished the experiment, SONA prevented them from performing the experiment again. The experiment contained three main stages: (1) the LSRP questionnaire, (2) the RT-CIT, and (3) the BIS-11 and subjective ratings. Before starting the experiment, participants read and approved an informed consent form (by pressing a button).

Stage 1.

The LSRP questionnaire was completed after signing the informed consent form. All items (a total of 26) were presented one by one, and participants were asked to rate their agreement for each item, on a scale from 1 (“disagree strongly”) to 4 (“agree strongly").

Stage 2.

Before starting the actual CIT, participants were presented with two item-lists, one of last names, and one of first names (female names for women and male names for men). Each list contained 16 items (i.e., names). Participants were asked to mark a maximum of 12 names, from each list, that have a special meaning for them. The irrelevant items (for the CIT) were chosen randomly from the words that were not marked.

Then, participants were explained about the upcoming CIT and motivated to conceal their autobiographical items (i.e., the probe items). To increase motivation, participants read a short paragraph which states that the upcoming task is difficult, and that only highly intelligent people with a strong willpower can successfully conceal. In addition, to become familiar with the no-go items, Tiger and Zebra, participants read a short paragraph about these items (i.e., Two animals with spectacularly beautiful stripes patterns are the Tiger (part of the Felidae family) with black-orange stripes, and of course, the Zebra (part of the Equidae family) with black-and-white stripes). Similarly, to become familiar with the target items, Caesarea and Milan, participants read a short paragraph about these cities (i.e., Who has not heard about the city of Milan, which is located in northern Italy and known for its great wealth? And of course, there is no one who does not know the city of Caesarea that was established 2000 years ago by the Roman Empire!). Thus, the CIT items were divided into three semantic categories, names for probes and irrelevants, cities for targets, and animals for no-go items. In total, there were 14 distinct items: 2 probes (participants first name and participants last name), 8 irrelevants (4 other first names and 4 other last names), 2 targets (Caesarea and Milan), and 2 no-go items (Tiger and Zebra).

The RT-CIT was operated according to the multiple-probes-protocol (MPP), which means that all 14 items were intermixed in each block of the CIT (there were 4 blocks in total). Per block, each item was presented 6 times, and thus, each block contained 84 items (14 x 6 = 84). The entire experiment contained 336 items (84 items x 4 blocks = 336). The order of items’ presentation was determined randomly, with the following restriction: two consecutive presentations of the same item were not allowed. All stimuli were displayed in a serial manner, in the middle of the screen, for 1500ms. Between each two items, a symbol of a plus was presented; this inter stimulus interval (ISI) was either 250ms, 500ms, or 750ms [similar to 28, 46, 86, 87]. On top of the items, participants also saw the question: "Is this word familiar to you"? Participants were requested to respond using one of two buttons: unfamiliar (i.e., “I”) for probes and irrelevant items, familiar (i.e., “E”) for targets [34, 88]. In addition, when seeing a no-go item, participants were requested not to respond. During ’go’ trials only, two feedback messages in the form of red words could briefly appear above the item for 200ms: (1) "WRONG" if participants pressed the wrong button, and (2) "TOO SLOW", if 800ms passed since the item appeared and no button was pressed [similar to 28, 38, 46, 86, 87, 89–91]. For a visual presentation, please see Fig 1.

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Fig 1. Experimental procedure of the novel Go/No-go RT-CIT.

On “go” trials, which included irrelevant, probe and target items, participants had to respond by pressing a button. During “no-go” trials, participants were instructed not to respond.

https://doi.org/10.1371/journal.pone.0311948.g001

Importantly, the actual RT-CIT was also preceded by three successive practice phases that familiarized participants with the test procedure. These practice phases were repeated until certain criteria were met (as detailed below). In the first practice phase, which included solely “go” trials, items remained on the screen until one of the two available buttons (“E” or “I”) was pressed. If participants pressed the wrong button, they received "WRONG" feedback. In the second practice phase, which included both “go” and “no-go” trials, items remained on the screen until a button was pressed or until 1500ms had elapsed. Similar to the first practice phase, participants received "WRONG" feedback in case of an incorrect response. In the last practice phase, participants also received "TOO SLOW" feedback if they failed to press any button within 800ms during “go” trials. Please note that participants were able to advance through each phase if they met the following three criteria: (1) a maximum of 50% errors (i.e., incorrect button presses), (2) a maximum of 20% of RTs falling under 150ms, and (3) a mean reaction time that did not exceed 800ms. If participants did not meet these criteria, they received feedback about their performance (i.e., "Sorry, you failed this practice phase. Please repeat the training") and had to perform the practice phase again (up to a maximum of two attempts).

Stage 3.

After the CIT, participants were asked to complete the BIS-11 questionnaire. All items (a total of 30) were presented one by one, and participants were asked to rate their agreement for each item, on a scale from 1 (“rarely/never”) to 4 (“almost always"). Finally, after the BIS-11, participants were asked to complete four parts to summarize their experience in the experiment. First, they were asked to rate the significance level of the 2 probes, 2 targets, 8 irrelevants, and 2 no-go items on a scale from 1 ("not significant at all") to 9 ("extremely significant"). These ratings were obtained to examine (and ensure) that the selected probes were more significant than the irrelevant items. Second, participants were asked to rate how motivated they were to succeed in the test, on a scale from 1 ("not motivated at all") to 10 ("very motivated"). Third, participants were asked to rate how impulsive they think they were during the CIT, on a scale from 1 ("not impulsive at all") to 10 ("very impulsive"). Fourth, participants were presented with a list of countermeasures, and were asked to mark the options they used. If they didn’t use any countermeasures, they could mark the option "No countermeasures were used". At the end of the experiment, participants were thanked for their participation and granted their credit points.

Outliers and exclusions.

Single items were excluded according to the following criteria: (1) Each button press under 150ms; (2) Each button press above 800ms; (3) Each error of pressing the wrong button.

Moreover, the data of an entire participant were excluded when: (1) The participant made at least 50% errors (in go trials of the CIT) to any of the 3 stimulus types (probe, irrelevant, target); (2) The participant did not complete the entire CIT (< 336 trials). Accordingly, all data of fourteen participants were excluded (see Participants).

Results

All data were pre-processed using Matlab R2022b (The MathWorks, Natick, MA). Thereafter, data analyses were performed using JASP statistical program [92, version 0.17.2.]. The analysis plan was pre-registered on: https://osf.io/hz58u, and the data along with analysis scripts can be accessed at: https://osf.io/s5mrn/.

Subjective ratings

Prior to testing the main hypothesis (i.e., correlation between the RT-CIT effect and secondary psychopathic traits), we analyzed the subjective ratings which were obtained after the CIT (these analyses were not pre-registered). First, we analyzed (1) participants self-reported motivation to conceal their identity during the CIT, and (2) participants self-reported impulsivity during the CIT (in both cases, scale ranged from 1–10). Both the motivation to conceal (M = 7.71, SD = 2.2) and experienced impulsivity (M = 6.22, SD = 2.16) were high.

Second, we analyzed the self-reported significance of probe and irrelevant items (scale ranged from 1–9). As expected, the significance of probes (M = 8.58, SD = 1.21) was higher than the significance of irrelevants (M = 1.94, SD = 1.1); t(85) = 36.01, p < .001, d = 3.88, BF10 = 9.440 × 10+49.

Third, we analyzed the reported countermeasures: 9% of participants reported that they tried to distract themselves; 14% reported that they tried to answer faster to the probe items (i.e., their own name); 1% reported that they tried to answer more slowly to probes; 2% reported that they tried to answer without looking at the screen; and 70% reported that they did not use any countermeasures.

Main analyses

For the main analysis, we computed for each participant the RT-CIT effect, which is defined as the mean RT of probes minus the mean RT of irrelevants. As we relied on a modified RT-CIT with ‘no-go’ trials, we first compared the mean RT-CIT effect across participants (i.e., 55 ms; see also Table 1) to 0. A statistically significant difference was observed, t(85) = 15.7, p < .001, d = 1.69 (95% CI = [1.36, 2.02]), which was very strongly supported by the BF10 = 9.527×10+23.

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Table 1. Preregistered measurement variables.

https://doi.org/10.1371/journal.pone.0311948.t001

To test the main hypothesis, we correlated the individual RT-CIT effects with the secondary LSRP scores. Contrary to the research hypothesis, no significant correlation was observed: r = 0.04, p = 0.725, BF₀₁ = 6.98 (see Table 2).

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Table 2. Correlations (r) and BFs between the RT-CIT effect, total LSRP score, primary LSRP score, secondary LSRP score, BIS-11 score and No-go error rate.

All values below the diagonal are correlations (r), while all values above the diagonal are BFs.

https://doi.org/10.1371/journal.pone.0311948.t002

This result suggests that there is no linear association between the RT-CIT effect and secondary psychopathy (the null hypothesis is ~7 times more likely than the alternative hypothesis). Please note that similar results are obtained when including the data of the fourteen excluded participants: r = 0.03, p = 0.74, BF₀₁ = 7.5. Moreover, as can be seen in Fig 2, support for the null hypothesis increased as data accumulated.

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Fig 2. JASP output displaying a sequential analysis, showing the evidential flow of the null hypothesis (H0) versus the alternative hypothesis (H1), as data accumulates.

https://doi.org/10.1371/journal.pone.0311948.g002

To further examine the relationship between the RT-CIT effect, psychopathy, inhibition and impulsivity, we also correlated the RT-CIT effect with the total LSRP score, primary LSRP score, No-go error rate, and the BIS-11 score. Consistent with the main results reported above, which support the null hypothesis, no significant correlations were found with the RT-CIT effect (see Tables 1 and 2).

Notably, in a non-preregistered exploratory analysis, we performed a Bayesian Analysis of Covariance with Primary LSRP, Secondary LSRP, BIS-11, No-go errors, and Gender as predictors, and the RT-CIT effect as dependent variable. Using the BFInclusion metric, we compared all models including a particular predictor to those without the predictor [see 93]. The Inclusion BF for Secondary LSRP was 0.134 (note that similar values were observed for other predictors). This analysis further supports our main conclusion: there is no discernible linear relationship between secondary psychopathic traits and the RT-CIT effect (full results are available on the OSF at https://osf.io/s5mrn/).

ROC analysis

As we relied on a novel CIT protocol, the area under the ROC curve (AUC) was calculated to measure the detection efficiency of classifying participants as unknowledgeable (naïve) or knowledgeable based on their individual probe-irrelevant score (i.e., the dCIT). The dCIT is computed by subtracting the mean RT of irrelevants from the mean RT of probes and dividing this difference by the standard deviation of irrelevant RTs [91]. As there were no naïve participants in the current experiment, their data were simulated. This simulation procedure is based on the assumption that naïve participants, in contrast to knowledgeable ones, cannot distinguish between probe and irrelevant items and therefore there is no reason to expect that probes would elicit systematic differential RTs. Thus, for naïve participants, the expected mean value of dCIT is 0. The standard deviation of dCIT was estimated using the following formula: , where N is the total sample size and δ is the true effect size in the population, which is 0 in this case [e.g., 94]. Further, it was assumed that the data of individual naïve participants are distributed normally. Hence, a simulated dataset was created by taking n random samples from the normal distribution (with a mean of 0 and a SD computed as explained above). This simulation procedure, as well as the computation of the AUC, were repeated 1000 times using a bootstrapping procedure. These 1000 bootstrapped AUCs were then used to compute the mean AUC and its 95% confidence interval (CI). Accordingly, the mean AUC of our novel Go/No-go CIT was 0.92 (95% CI = [0.91, 0.94]), exceeding the average area (0.82) reported in the review paper by Meijer et al. (2016).

In sum, the novel CIT paradigm demonstrated impressive detection efficiency. However, contrary to our expectations, we observed no significant correlation between the RT-CIT effect and secondary psychopathic traits (BF01 = 6.98). This finding is further corroborated by the absence of significant correlations between the RT-CIT effect and both impulsivity (as measured by the BIS-11; BF₀₁ = 3.14) and response inhibition capacity (assessed by the no-go error rate; BF₀₁ = 3.08).

Discussion

The present study examined the relation between the RT-CIT effect and secondary psychopathy in a student sample. The RT-CIT effect has been suggested to be largely driven by response conflict [28, 42, 46, 81]. Specifically, the need to classify familiar probes as “unfamiliar” induces a conflict. This conflict may be resolved by inhibiting the automatic “familiar” response, a process that consumes time and consequently slows down RT. Hence, it was hypothesized that individuals with higher secondary psychopathic traits, marked by impulsivity and impaired inhibition capacity, would produce larger RT-CIT effects compared to individuals with lower levels of secondary psychopathic traits.

Secondary psychopathic traits were measured using the LSRP questionnaire and correlated with the RT-CIT effect. Notably, both the mean score and reliability of the different LSRP scales were consistent with other reports in the literature [95–97]. Moreover, the mean RT-CIT effect was large and significantly different from 0 (Cohen’s d = 1.69; BF10 = 9.527×10+23). However, contrary to our hypothesis, no significant correlation between secondary psychopathy and the CIT effect was observed, as supported by the Bayesian analysis that revealed substantial evidence for the null hypothesis (BF01 = 6.98).

These findings are in line with those of Verschuere and in ´t Hout (2016), who examined the cognitive cost of lying among psychopaths using a Sheffield lie test (which measures deception, not concealed information). Similar to the present study, no significant correlation was found between psychopathy and the RT effect (RTLIE minus RTTRUTH). Moreover, the current findings are in accordance with findings of CIT studies that used physiological measures and revealed no effect of psychopathy on the CIT [60, 62].

To delve deeper into our primary research question, we included two additional measures: impulsivity and response inhibition capacity. Impulsivity was assessed using the BIS-11 questionnaire, and although we found a significant correlation between impulsivity and secondary psychopathy, no significant correlation was observed between impulsivity and the RT-CIT effect [consistent with 81]. It is noteworthy that self-reports and behavioral measures (like the RT-CIT) typically yield weak correlations [97–101). Hence, to measure response inhibition capacity, we integrated a Go/No-go task within the CIT. However, consistent with our main findings, response inhibition capacity (as indicated by the no-go error rate) did not correlate with secondary psychopathy or the RT-CIT effect (please see Table 2).

Thus, the present study suggests that secondary psychopathy does not influence the RT-CIT effect. This conclusion should, however, be approached with caution for two primary reasons. Firstly, while our hypothesis was built on the premise that secondary psychopathy is marked by impulsivity and impaired response inhibition capacity, our measures of inhibition and secondary psychopathy did not correlate. This may be due to our non-forensic student sample. While studies utilizing non-forensic samples have generally shown no correlation between psychopathy and response inhibition capacity, studies involving forensic samples have demonstrated such a correlation [e.g., 102 vs. 103]. Secondly, our inhibition and CIT effect measures did not correlate. Although the integration of the Go/No-go task within the RT-CIT is unique to our study, few previous CIT studies have used “secondary response inhibition tasks”. For example, Ambach et al. (2008) included the Go/No-go task alongside the CIT (with different stimuli for each task, unlike the present study) and Suchotzki et al. (2019) introduced a Stroop task after the CIT. Both studies showed similar results to the present one–no significant correlation between response inhibition capacity and the RT-CIT effect. Ultimately, this raises the question of whether response conflict is the only mechanism underlying the RT-CIT.

Accordingly, as indicated previously, a recent study of klein Selle et al. (2023) has provided support for the idea that additional factors may contribute to the RT-CIT effect. These authors compared a conflict condition (where the response buttons emphasized familiarity) with a no conflict condition (where the response buttons emphasized categorical membership). Although conflict strengthened the RT-CIT effect, the effect was significant even in the no conflict condition. Therefore, it was suggested that conflict theory alone is not a sufficient account of the RT-CIT effect and that other mechanisms such as orientation may play a role. The orienting response entails reflexive behavioral and physiological responses to changes in the environment [104–106]. This response is primarily modulated by two key factors: the novelty of the stimulus and its perceived significance [70, 107, 108]. In the context of the CIT, probe items are both significant and novel (i.e., presented less frequently) for knowledgeable individuals. Hence, these items should elicit an enhanced orienting response. Such enhanced responses [103, 105] to significant probe items [see 63–66] may briefly interrupt ongoing behavior and consequently lengthen RTs. This notion is supported by a limited number of CIT studies. For instance, Lukács et al. (2019) categorized stimuli into three salience levels [forename, birthday, and favorite animal, from highest to lowest; 109] and found a larger RT-CIT effect for more significant items [91, 110]. Suchotzki et al. (2015) manipulated the proportion of probe versus irrelevant items and found a stronger RT-CIT effect for more novel probes [42].

Interestingly, when comparing our RT-CIT effect to that of a classical CIT study [46], which used a similar design, stimuli and was also performed online, a significant difference was observed. Specifically, the RT-CIT effect of our novel Go/No-go CIT, Cohen’s d = 1.69 (95% CI = [1.36, 2.02]), was significantly larger than that of the classical CIT study, Cohen’s d = 1.24 (95% CI [0.90; 1.57]). Although the BF (BF10 = 1.64) provides only weak evidence for this difference, a Bayesian sequential analysis showed increasing evidence for the alternative hypothesis as data accumulates (suggesting that more data should be obtained). Similarly, the Cohen’s d (1.69, 95% CI = [1.36, 2.02]) observed in the present study is higher than the mean Cohen’s d (1.30, 95% CI [1.06; 1.54]) reported in the meta-analysis of Suchotzki et al. (2017). Moreover, the current AUC value (0.92), which indicates detection efficiency of knowledgeable and unknowledgeable individuals, exceeds the mean AUC value (0.82) reported in the review paper by Meijer et al. (2016). Together this suggests that the additional “no-go” trials in our novel Go/No-go CIT may have increased CIT detection efficiency.

The observed increase in CIT detection efficiency may be the result of heightened cognitive load, a factor previously shown to enhance the RT-CIT effect [111–115]. For example, Visu-Petra et al. (2013) compared three CIT conditions: a classical RT-CIT, a RT-CIT with a concurrent memory task, and a RT-CIT with a concurrent set-shifting task. In line with the idea that additional cognitive load increases CIT detection efficiency, the RT-CIT effect was higher in the conditions that included an additional task (as evidenced by a larger increase in probe RTs than irrelevant RTs). Similarly, the no-go trials of our Go/No-go RT-CIT likely raised cognitive load, thereby reducing the capacity for inhibitory control and conflict resolution. Moreover, the additional no-go items may have also (1) made it harder to correctly respond to the different types of stimuli, thereby increasing conflict, and (2) diminished the relative frequency of probes, thereby amplifying the orienting response. As both conflict and orienting have been suggested to underlie the RT-CIT effect [see 46], it can explain how our modified format increased detection efficiency. Future investigations should aim to directly compare this novel format with a classical RT-CIT.

Additionally, while we strictly adhered to our preregistered protocol, future studies should aim to address several methodological limitations of the present study. First, as previously mentioned, the use of a non-forensic student sample may have influenced our findings. Therefore, investigating how more diverse samples could yield different results is essential. Moreover, conducting the experiment online may have influenced the RT-CT effect and, consequently, potentially affected the observed relationship between the RT-CIT effect and secondary psychopathy. Hence, replication studies conducted in a controlled laboratory setting are crucial [see 116]. Furthermore, while the use of highly salient autobiographical details ensured a strong CIT effect, it may not reflect real-world scenarios accurately. Thus, future studies should also examine the relationship between psychopathy and CIT using less salient crime-related stimuli, for instance. Lastly, it might be more appropriate to use the Single-Probe Protocol (SPP) of the CIT, where each block detects a single piece of information pertinent to the issue under investigation. This method is often the sole feasible interviewing approach in real-life contexts [117–120].

Furthermore, we would like to suggest that future examinations of psychopathy within the CIT incorporate both RT and neural measures. Notably, psychopaths exhibit distinct neural responses during tasks assessing conflict and orientation–i.e., the mechanisms assumed to underlie the RT-CT effect [121–128]. As such, methods such as fMRI, capable of monitoring conflict-related neural activity [see 42, 129–131], and EEG, capable of examining the P300 component of the event-related potential associated with attentional orientation [e.g., 132], hold particular promise. Integrating these neuroimaging methods would not only deepen our understanding of the RT-CIT effect but also further elucidate the neurobiological underpinnings of psychopathy, thereby advancing both fields of study.

In summary, previous studies have provided scientific evidence indicating that psychopathy does not affect the physiological response-based CIT [60, 62]. The present study provides preliminary evidence that psychopathic tendencies similarly do not affect the response time-based CIT. This is reassuring, as it suggests that although such tendencies do not improve CIT detection efficiency, they do not impede it. To expand and confirm these findings, future research is crucial. This should include conceptual replication studies using more diverse participant samples, CIT stimuli, and alternative protocols such as the SPP. Moreover, given the theoretical insight that orientation, alongside conflict, may drive the RT-CIT effect, it is imperative to thoroughly investigate the underlying mechanisms of this effect. Such exploration will not only advance theory but also deepen our understanding of practical aspects, such as susceptibility to countermeasures and potential influences from different clinical conditions. Ultimately, these investigations will bolster the validity and practical application of the RT-CIT across diverse settings and populations.

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