Imposing cognitive load to unmask prepared lies

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1 Imposing cognitive load to unmask prepared lies A temporal pattern detection approach Valentino Zurloni, Barbara Diana, & Massimiliano Elia Human Sciences for Education Department Riccardo Massa University of Milano-Bicocca Milano, Italy valentino.zurloni@unimib.it Abstract The aim of our study is to examine whether the overall organization of behavior differs when people report truthful vs. deceptive messages within the framework of the T-pattern model. We tested the hypothesis that the differences between liars and truth tellers will be greater under high cognitive load conditions. We argue that recalling stories in reverse order will produce cognitive overloading in subjects, because their cognitive resources are already partially spent on the lying task; this should emphasize nonverbal differences between liars and truth tellers. Results support the hypothesis that discriminating behavioral patterns between truth and lie could be easier under high cognitive load condition. Moreover, they suggest that future research on deception detection may focus more on patterns of behavior rather than on individual cues. Keywords deception; nonverbal behavior; t-pattern analysis; cognitive load. I. INTRODUCTION Deception is a common, everyday occurrence in interpersonal, group, media, and public contexts. However, one of the most well-documented claims in the deception literature is that humans are poor detectors of deception. Such human fallibility is exacerbated by the speed, complexity, volume, and global reach of communication and information exchange that current information technologies can afford now, enabling the collection of massive amounts of information information that must be sorted, analyzed, and synthesized. The interactions and complex interdependencies of information systems and social systems make the problem even more difficult and challenging [1]. Although people show a statistically reliable ability to discriminate truths from lies, overall accuracy rates average 54% or only a little above chance [2]. Moreover, the average total accuracy rates of professional lie catchers (56%) is similar to that of laypersons. What is the reason for the near-chance performance of human lie detection? Do liars behave consistently with people beliefs about deceptive behavior? Three recent, comprehensive meta-analyses [3], [5] which considered above 130 studies published in English examining 158 different cues to deception, including a large variety of nonverbal, verbal, and paraverbal cues, as well as certain content features of deceptive and truthful messages, reveal that many conflicting results have been found. Even if research on this topic revealed only a few, and usually weak, relationships between nonverbal cues and deception, police training packages often include nonverbal and paraverbal behaviors as important cues to deception. Lay people and professionals alike have believed that these behaviors are particularly helpful in catching a liar [6], [7]. Although there seem to be no reliable cues, a closer look reveals that some studies have found reliable differences between liars and truth tellers under certain conditions, which may account for the overall inconsistency in findings. An explanation for the contradictory findings obtained across individual studies might be that studies differ regarding experimental method, the type of sample, or the operationalizations used to measure the nonverbal behaviors of interest. A host of moderator variables may blur the association between behavioral cues and deception. These studies reveal that a sign equivalent to Pinocchio s growing nose has not been found [5]. In fact, most of the nonverbal cues do not appear to be related to deception. However, for some cues a weak relationship with deception emerged. Why only a few and rather weak relationships between nonverbal behavior and deception have been found? Could it be that more nonverbal cues to deception exist than we are aware of, but that researchers so far have overlooked some diagnostic nonverbal cues to deception? And could it be that a combination of nonverbal cues, rather than individual cues, would reveal diagnostic indicators of deceit? Nonverbal cues to deception are typically faint and unreliable, as confirmed by the studies above mentioned. A contributing factor is that the underlying theoretical explanations for why such cues occur, like nervousness and cognitive load, also apply to truth tellers [8]. In order to explain deceptive communication, Zuckerman, DePaulo, and Rosenthal [9] pointed out that liars show signs of deceit as an outcome of experiencing emotions or cognitive load. However, those assumptions may lead to opposite behaviors. Different researches have recently examined whether liars actually do experience these processes more than truth tellers [10], [11], with controversial results. Studies in the past have focused on eliciting and amplifying emotions [12] for example by asking questions, but it is uncertain whether this procedure will necessarily raise more concern in liars than in truth tellers. Conversely, only a few efforts focused on unmasking the liars by applying a cognitive lie detection approach [12], [13]. Vrij and his colleagues [8], [12] have recently suggested that lying can be more cognitively demanding than truth telling. First, formulating the lie may be cognitively demanding. Furthermore, liars are typically less likely than truth tellers to take their credibility for granted [8]. Liars also have to suppress

2 the truth while they are fabricating, and this is also cognitively demanding. In order to discriminate more effectively between truth tellers and liars, a lie catcher could exploit the different levels of cognitive load that they experience. If liars require more cognitive resources than truth tellers, they will have fewer cognitive resources left over. Cognitive demand can be further raised by making additional requests. Liars may not be as good as truth tellers in coping with these additional requests. One way to impose cognitive load is by asking speakers to tell their stories in reverse order [8], [12]. The underlying assumption is that recalling events in reverse order will be particularly debilitating for liars whose cognitive resources have already been partially depleted by the cognitively demanding task of lying because (a) it runs counter to the natural forward-order coding of sequentially occurring events [14], [15], and (b) it disrupts reconstructing events from a schema [16]. This is analogous to the finding in the cognitiveattention literature that information processing in the primary task is slower in dual-task conditions than in single-task conditions [17]. In an experiment, half of the liars and truth tellers were requested to recall their stories in reverse order [12], whereas no instruction was given to the other half of the participants. More cues to deceit emerged in the reverse-order condition than in the control condition. Observers who watched these videotaped interviews could distinguish between truths and lies better in the reverse-order condition than in the control condition. For example, in the reverse-order experiment, 42% of the lies were correctly classified in the control condition, well below what is typically found in nonverbal lie detection research, suggesting that the lie detection task was difficult. Yet, in the experimental condition, 60% of the lies were correctly classified, more than typically found in this type of lie detection research. Since no diagnostic cue to deception occurs, it could be that a diagnostic pattern does arise when a combination of cues is taken into account [5]. Senders are able to arrange a set of different signaling systems to communicate and make their communicative intentions public, like language, the paralinguistic (or supra-segmental) system, the face and gestures system, the gaze, the proxemics and the haptic, as well as the chronemic. Although it sounds reasonable to suggest that looking at a combination of nonverbal and verbal behaviors will lead to more accurate classifications of liars and truth tellers than investigating nonverbal and verbal behaviors separately, researchers rarely investigate both types simultaneously. Vrij and his colleagues [18], [19] examined participants' nonverbal and verbal behavior and obtained the most accurate classification of liars and truth tellers when both the nonverbal and verbal behaviors were taken into account. On the basis of a combination of four nonverbal behaviors (illustrators, hesitations, latency period, and hand/finger movements) they correctly classified 70,6% of participating truth tellers and 84,6% of liars, whereas any of these behaviors separately resulted in much more disappointing findings. Ekman and colleagues found similar patterns of nonverbal behaviors during deception. Up to 80% of truths and lies could be detected when a trained observer paid attention to microfacial expressions [20]. When the tone of voice was taken into account in addition to micro-facial expressions, 86% of the truths and lies could be detected [21]. Other studies have shown that between 71% and 78% of correct classifications were made when the researchers investigated a cluster of behaviors [18], [22], [23]. In other words, more accurate truth/lie classifications can be made if a cluster of nonverbal cues is examined rather than each of these cues separately. Clustering nonverbal cues of deception brings out some issues that remain unresolved at present. First of all, which kind of behavior should be clustered? Currently, different researchers examine different clusters of behavior, and it cannot be ruled out that a cluster that is effective in pinpointing lying in one situation or group of participants is not effective in another situation or group of participants [5]. Second, is there a criterion for grouping different behaviors into the same pattern? For example, the temporal distance between one cue and another is decisive. How much time must elapse between two cues to consider them as part of the same pattern? Finally, liars sometimes deliberately attempt to appear credible to avoid detection. For example, able liars, or people who are informed about the operating method on nonverbal behaviors, can successfully employ countermeasures to conceal nonverbal cues to deception. If it is known amongst terrorists, spies and criminals which lie detection tools will be used to catch them, they may learn more about these tools and attempt to beat them. If they succeed in doing so, the tools are no longer effective. Of course, people can easily control only those patterns that are manifest and have a macroscopic nature, easily readable from the outside time by time. However, patterns in behavior are frequently hidden from the consciousness of those who perform them as well as to unaided observers [24]. As Eibl- Eibesfeldt [25] argued, behavior consists of patterns in time. Investigations of behavior deal with sequences that, in contrast to bodily characteristics, are not always visible. Order alone is not a valuable criterion to detect hidden recurrent behavior patterns because deceptive strategies are characterized by a large complexity and by a great variability in the number of behaviors occurring between the liar and his interlocutor. One approach is to include the element of time in the analysis of nonverbal deceptive behavior. Neural networks have been used frequently over the last few decades for static pattern recognition and pattern recognition in time. Discovering the real-time multi-layered and partly parallel structure of even the most common dyadic verbal and nonverbal interactions remains a formidable challenge where clues to deception may be hidden to unaided observers in anything from the tiniest of details to intricate aspects of temporal structure. A mathematical approach that may be particularly suitable for defining and discovering repeated temporal patterns in deceptive behavior is T-pattern sequential analysis. T-pattern detection was developed by Magnus S. Magnusson [24], [26], [27] to find temporal and sequential structure in behavior. The

3 algorithm is implemented in the software THEME [28]. T- pattern analysis focuses on determining whether arbitrary events e1 and e2 in a symbolic string of {ei} events sequentially occur within a specified time interval at a rate greater than that expected by chance. In this way, it detects repeated patterns of intra- or inter-individual behavior coded as events on one-dimensional discrete scales. This kind of analysis has been used in a wide variety of observational studies, including microanalysis of Drosophila courtship behavior [29], cooperative behavior between humans and dogs when constructing an object [30], patient-therapist communication in computer assisted environments [31], and analysis of soccer team play [32]. The common feature in all of the above studies is the need to identify repeated behavioral patterns that may regularly or irregularly occur within a period of observation. Since human behavior is highly organized, in this chapter, rather than concentrating on individual non-verbal cues of lies, our aim is to observe and describe the overall organization of behavior patterns which repeat over time. We have focused in verifying the presence of significant and systematic differences in the organization of nonverbal behavioral patterns when participants tell the truth or lie, both in the condition of imposed cognitive load and that of no cognitive load manipulation, in order to detect regularities in the temporal patterns of individuals belonging to the same condition. II. THE PRESENT STUDY A. Method Participants were 16 students, all females, aged from 21 to 26 (mean age ± 2.13), all native to the same geographic area. They were recruited at the faculty of Psychology from the University of Milano-Bicocca. After being recruited, all participants gave their informed consent both to audio and video recording. The present study was carried at the University of Milano- Bicocca in an audio-isolated laboratory room equipped with four cameras, set to video-record participants full-lengths and close-ups. The cameras were connected to a 2 channel quad device (split-screen technique). We asked participants to think about two autobiographical episodes. One of the stories had to be about the last time they went out to have a pizza with friends (which happens quite often in Italy); the other had to be about their last night out for a party. We selected these kind of arguments trying to maintain the arousal and the emotional involvement as low as possible. For the same reason we asked participants to focus on the descriptions of events rather than the emotional or affective aspects of the stories. Finally, subjects who didn t follow these indications were excluded from further analysis. Participants were given 20 minutes to prepare the two stories, knowing that after they would have to report both episodes to another person (a confederate), keeping in mind that one of these two episodes had to be untruthful and that the interlocutor didn t know which one. We asked half of the students to tell the pizza episode first, while the other half did the opposite. All of them were told to lie during the first episode. We controlled motivation by telling them that, if they succeeded in telling the lie (meaning, the confederate couldn t tell which episode was made up), they would receive extra credits. In the first experimental condition, the confederate asked the participant to report both episodes in chronological order. In the second condition, the confederate asked to report both episodes in reverse mode (starting from the end of the story and going back to the beginning). The time frame for the experimental task was 10 minutes for each participant (5 minutes per episode), marked by audio signals. To establish the ground truth and verify cognitive load manipulation, motivation, arousal and emotional involvement, we asked the subjects to complete a questionnaire after the experiment was finished. Later, we watched the video recordings with the participants and asked them when had they lied (veracity status). B. Data analysis The coding grid was built basing on literature review of nonverbal cues in lie detection. [3], [5], [33]. We considered body movements (head, trunk, arms, hands, legs and feet), gestures (rhythmic, iconic, metaphoric and deictic), selfcontacts, gaze and some facial micro-movements (action units from FACS- Facial Action Coding System) [34]. Two-minutes observation intervals were considered for subsequent analysis. Videos were coded on THEME coder software by two coders (both females, in accordance to the gender of the subjects) using a blind coding procedure. Each behavior occurrence was regarded as a case of an event-type that occurs at a particular point on a discrete time scale, but has no duration. The occurrences of each event-type within the selected observation period form the so called T-dataset. To assess inter-rater reliability of the T-dataset, Cohen s Kappa was calculated on 10% of the encodings. Although differing through categories, inter-coder reliability was found to be good to satisfactory (ranging from 0.70 to 0.92; p < 0.05). When disagreements were identified or the agreement was not perfect, the specific cases were discussed and agreed by both coders. Datasets were then analyzed using THEME 6 beta software. THEME detects statistically significant time patterns in sequences of behaviors. The term T-pattern stands for temporal pattern; they are based on the timing of events, relative to each other. T-pattern detection [26], [27], [35] was developed for finding temporal and sequential structure in behavior. The algorithm implemented in the software detects repeated patterns of intra- or inter-individual behavior coded as events on one-dimensional discrete scales. T-patterns are repeated occurrences of two or more event-types such that their order (allowing also for concurrence) is the same each time and the distances between them are significantly fixed as defined by a statistically defined and detected critical interval relationship. A minimal T-pattern consists of two event types. An event type is a category of observable behavior whereas an event is an instance of behavior occurring at a particular time unit without a duration [26]. The search naturally stops when no more patterns can be found and the result of such procedure is the discovery of patterns occurring more often than chance and embedded within the raw data stream in a non-intuitive

4 manner. By means of relevant options, the program allows the experimenter to set specific search parameters. Parameters set in present study were: critical interval type (free), significance (0.001), minimum occurrence (50% + 1 of the sample), burst detection (0.005), lumping factor (0.90), minimum % of samples (60), packet base type (off), types of randomizations (shuffling and rotation), number of runs per type (20). The aim of the first analysis was to observe the combined effects of the two independent variables on the organization of nonverbal behavior in each condition, identifying the most significant patterns of each. For this, we created a project for each of the 4 conditions (normal truth, normal lie, reverse truth, reverse lie). We then combined the individual datasets using the function concatenate in to a multi-sample file. It creates a single file containing all the single-sample files in the project. Thus the resulting file contains more than one sample and is called a multi-sample file. Complex T-patterns (the longest ones and those composed by the highest number of levels) were considered, since they are regarded as the most interesting, due to their potential meaning and the (low) likeliness to be detected using other definitions and algorithms [26], [28]. However, instead of ignoring less complex patterns (e.g. those composed by only two event-types), we selected only the ones composed by event-types consistent with literature on lie detection. Afterwards, a second analysis was performed in order to identify the patterns that allow us to distinguish the truth from the lie in a situation of normal cognitive load and in a situation in which cognitive load was induced. Thus we split the sample and created two projects, one for each level of the independent variable content (lie and truth). Then, we used again the function concatenate into a multi-sample file to obtain a single dataset. At last, we used the function of THEME selection - multi-sample file selection statistical. THEME selects distinctive patterns of a specific condition, the ones appearing significantly more often in selected samples than in the multi-sample file as a whole. The level of significance was set at C. Results Fig. 1 shows the number of patterns, Fig. 2 the mean of levels and length of patterns detected in the four conditions. The normal-truth condition showed patterns including iconic, rhythmic and metaphoric gestures, head movements (tilt and turn) and spontaneous smile, composed by au6 (cheek raiser) and au12 (lip corner puller) [34]. In the normal-lie condition, most patterns included gaze, head movements, rhythmic gestures. In the reverse-truth condition, most patterns included fidgeting + rhythmic gestures and spontaneous smile (au6 + au12). In the reverse-lie condition, most patterns included nod + deny and fake smile (au12 without au6). Lots of patterns were composed by one repeated event-type. This was the only condition not including any pattern showing spontaneous smile (au6 + au12). Figure 1. Number of patterns. Figure 2. Patterns levels and length In the second analysis, within the normal condition, the truth showed 20 significantly distinctive patterns, most of them not distributed among the subjects. The relevant patterns we detected included simultaneous presence of iconic and rhythmic gestures. The lie showed 35 significantly distinctive patterns, most composed by event-types belonging to different categories. The most significant patterns related to head movements, gaze and deny. We defined denying as a movement of the head from right to left or left to right in rapid sequence, corresponding to a negation gesture. To quantify the movement's speed, we first observed the recordings at normal speed, then we coded frame by frame the behavior s begins and endings. Since literature doesn t define how rapid the movement has to be in order to be considered a negation, we based our encodings on the raters agreement. Within the reverse condition the truth showed 39 significantly distinctive patterns. Most patterns (34 out of 39) contained the spontaneous smile (au6 + au12). The lie showed 19 significantly distinctive patterns. More relevant patterns showed the simultaneous presence of nod and deny and selffidgeting. D. Discussion Research on detection of deception mostly focused on identifying cues to deception, while few studies observed the sequential and temporal structure of deceptive behavior [5]. Through T-pattern analysis, our results showed repetitive temporal patterning among the different experimental conditions. As Vrij stated [5], certain behavioral patterns are associated with honesty and likeability, such as directed gaze to a

5 conversation partner, smiling, head nodding, leaning forward, direct body orientation, posture mirroring, uncrossed arms, articulate gesturing, moderate speaking rates, a lack of ums and ers, and vocal variety [36] [39]. Some people show such demeanor naturally even when they are lying (e.g., natural performers) [40]. These natural performers are likely to be good liars because their natural behavior is likely to allay suspicion. DePaulo and colleagues [3] examined around 100 different nonverbal behaviors. Significant findings emerged for 21 behaviors. The cues were ranked in terms of their effect sizes. The highest effect sizes were found in the cues that have not often been investigated, such as changes in foot movements, pupil changes, smiling. In all conditions, except for the reverse - lie one, there are patterns containing event types ascribable to the spontaneous smile [37] (au6 + au12). This behavior is associated to truthful communication in literature [37] and it could be helpful to discriminate lying and truth telling in the reverse condition. Specifically, taking the significant patterns into account, smile appears significantly present in the reverse - truth condition. In different studies it was further found that liars make fewer illustrators, fewer hand and finger movements, and fewer leg and foot movements than truth tellers. DePaulo and collegues meta-analysis [3] showed that the effect for illustrators was small, whereas the effect for hand and finger movements was somewhat more. In the normal - truth condition several patterns contain iconic and rhythmic gestures; within the reverse - lie condition the number of patterns containing gestures decreases. Globally, the number of gestures decreases (less articulate gesturing) in the reverse lie condition if we compare it to the other conditions. No patterns related to feet and/or legs movement were detected. Finally, in the reverse lie condition there are many patterns containing both nodding and denying; conversely, in the other conditions, no patterns containing these event-types were detected. If we look at the overall pattern frequencies by group, the number of occurrences, lengths and levels decreases between the normal and reverse conditions; furthermore, in the normal condition, deceptive patterns are significantly more frequent than in the reverse condition. Nonetheless, if we take a look at significant patterns, it can be seen how, within the normal condition, patterns that are significantly more present in truthful reports are not as well distributed among subjects as in the made up stories. Patterns significantly more present in the lie conditions are much more irregular and include several, different events from different categories. Conversely, very regular patterns are more significantly present in the reverse condition, both in the lie and truth conditions (most patterns contain sub-patterns with repetition of event types). Since patterns in the reverse condition are more recurrent, regular and simpler than the ones seen in the normal condition, it is easier to assign a meaning to them. Besides, being the patterns in the reverse - truth condition quite different from the ones in the reverse - lie condition, it could be easier to discriminate nonverbal behaviors between liars and truth tellers in the reverse condition, than doing it in the normal one. In conclusion, differences detected in behavior organization between the four experimental conditions, suggest that: a) manipulating cognitive load could make discriminating between lie and truth easier; b) choosing the temporal analysis of behavioral patterns, rather than focusing on single cues, could be a valid strategy to improve research in lie detection. III. CONCLUSION Although the data of the current research lends some support to our objectives and general line of argument, readers should keep in mind the limitations of our study. First, as we asked participants to tell the truth or lying about their autobiographical episodes, it is critical to address the question of whether we can believe these self-reported episodes [41]. Self-report measures of deception are commonly used [42], but often questioned based on response biases and individuals awareness of how frequently they lie. For instance, lie tellers may give socially desirable versions of their autobiographical episodes violating the experimental instructions [43]. To reduce such uncertainty, before the experimental phase we had an extensive initial briefing session with the participants in which we emphasized the importance of accuracy and conscientiousness. Moreover, after the recording of the autobiographical episodes we asked participants to tell us if they accurately followed the experimental instructions. We cannot assume that participants will always be able to retrieve the target event easily in this condition. However, during the manipulation check, we asked participants to report whether their cognitive load was increased both in truth and lie condition, in only one of the conditions, or none of them. Another aspect to keep in mind consists in the participants ability to recall the events in reverse order. A number of important factors were excluded from the current research but could be explored by similar methods in the future. We deliberately confined our studies to women whose age ranged between 21 and 26, but this methodology should be applied for studying deception detection to wider samples in terms of age range and gender. 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