Exploring nonverbal deception detection

running head: NONVERBAL DECEPTION DETECTION

EXPLORING NONVERBAL DECEPTION DETECTION Taylor Hill April 2017 Supervisor: Dr. Meg Ternes Saint Mary’s University

Taylor Hill [COMPANY NAME] [Company address]

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Overview of Deception The average person tells 1.5 lies per day. This lie can be minor, such as an error in communication, white lie, exaggeration, edit, or omission (DePaulo et al., 2003). For example, relaying information that one believes to be accurate when it is not entirely accurate is not a deliberate act of deception. Ekman (1969) distinguished these small lies from deception, postulating small lies as misleading answers, half-truths, and self-editing (including distorted memories). Building on this, DePaulo et al. (2003) later defined a lie as “a deliberate attempt to mislead” (DePaulo et al., 2003, p.74). For the purpose of this paper, I will be studying lies with the latter definition. In accordance with the relevant literature, I will refer to individuals delivering truth or lies as ‘senders’, and individuals judging such statements as ‘receivers’ (Bond & DePaulo, 2008). This article will proceed as follows: I will outline the differing motives for deception, the average accuracy rates of deception detection, followed by individual differences in accuracy, after which our experiment will be presented. Deception Detection The need for deception detection research and credibility assessment research stems from an alarming rate of wrongful convictions existing in North America. Policy makers in the legal system should ideally hold a substantial understanding of the empirically-based findings of deception detection research, to ensure accurate conviction, sanctions of offenders and the like (Aamodt & Custer, 2006). Relevant literature makes apparent that deception detection is neither a skill nor a simple task, and an infallible method of detecting deception does not exist (DePaulo, Zuckerman, & Rosenthal, 1980). Receivers, whether laypersons or persons trained in relevant legal professions, typically perform around chance level in detecting deception (40-55%) (Zuckerman, Koestner, & Alton, 1984). The average person does not hold sufficient training or

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knowledge to accurately detect deception above chance level. Additionally, many individuals are subject to biases and cognitive shortcuts such as heuristics when making decisions (Levine, Park, & McCornack, 1999). Current understandings of nonverbal deception detection are based on literature of mixed findings, but the average accuracy rate of 40-55% remains a constant. Possible reasons for these inconsistent findings across the literature include: (a) differing motivations for deceiving and detecting across individuals (see DePaulo et al., 2003), (b) a truth bias (see Levine et al., 1999), (c) the evolutionary/social cost of detecting deception (see ten Brinke, Vohs, & Carney, 2016), (d) individual differences (see Bond & DePaulo, 2008), (e) the veracity effect (see Levine et al., 1999), and (d) self-presentation (see DePaulo et al., 1992). Nonverbal deception detection is based on behavioral cues which are assumed to be associated with deception, such as changes in: posture, fidgeting, facial pleasantness, and eye movement (DePaulo et al., 2003). Our study measured the accuracy rate of behavioral cues, calculated by the veracity of the statement in 180 trials and the global judgement of credibility by the researcher. The global judgement of credibility stems from the frequency of the behavioral cues based on our hypotheses. Motivations for Deception Detection Offenders vs non-offenders. This type of research is intended to apply to the specific population of legal professionals who seek to successfully assess the credibility of criminal offenders (Aamodt & Custer, 2006). Both legal professionals (non-offenders) and offenders have high motives to be successful in detecting deception and deceiving others, respectively. Offenders that have greater motivation to lie successfully are unusually skilled and have accumulated greater experience of deceiving than the remaining population (Porter, Doucette, Woodworth, Earle, & MacNeil, 2008). Offenders

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also have higher stakes in their deception situations and have more to lose than non-offenders. Fundamentally, whether an offender deceives someone successfully or fails, is more important to them than non-offenders (Porter et al., 2008). For example, successfully lying about your favourite color to a friend who assumes it is another color (non-offender) is less important than successfully lying on the witness stand about a crime you want to get away with (offender). If your friend realizes your favourite color is purple and not blue (non-offender), there is no great consequence but if you fail to lie successfully on the stand (offender), you could be convicted of a crime. The stakes are much higher for an offender, and thus their motives to lie successfully are greater than the motives of non-offenders. For legal professionals, the motive to successfully detect deception in offenders is also high (Porter et al., 2008). Research on credibility assessment can aid legal professionals to avoid wrongful conviction and ensure justified conviction, thus relevant findings are directly applicable to various forensic and social contexts involving deception. Accuracy Rates Offenders vs non-offenders. Interestingly, legal professionals such as judges and police officers perform at a comparable level of accuracy as laypersons, yet have high stakes in detecting deception (Ekman & O’Sullivan, 1991). Even individuals who have received training in detecting deception may remain at the ceiling accuracy level of 55% (Zuckerman et al., 1984). Their motives may be equally as high as offenders, but for contrasting reasons. These professionals generally have a high motive to detect deception in offenders and accurately assess their credibility. Controversially, legal professionals typically have very little training in detection deception and subscribe to popular culture’s stereotypical clues to deception (Ekman & O’Sullivan, 1991).

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Police officers may only receive a list of general, vaguely associated cues to deception that are not evidence based and grounded in scientific findings. This may be due to the striking inconsistency across the literature regarding cues to deception, the nonexistent infallible method to detect deception, and the probabilistic nature of such cues (Ekman & O’Sullivan, 1991). Thus, all legal professionals and especially police officers should receive substantial education and training in detecting deception based on empirical findings in the scientific community. The present study. Our study seeks to build on the existing literature and knowledge of nonverbal deception detection, more specifically of the accuracy rate surrounding cues associated with deception. The aim of this paper was to explore the validity and accuracy of behavioral correlates of deception that underlie current understandings of nonverbal deception detection. Based on Levine et al. (1999)’s recommendation for coding cues, the ratio of truth and lies were altered so that there were two truths for every lie. This study did not stray from the traditional experimental paradigm, and is quite similar to Vrij et al. (2004)’s experimental design. Eighty trials of fabricated stories and 160 trials of truthful memories were coded for nonverbal deception cues, to explore the validity of these behavioral cues. Based on the relevant literature, our hypothesis is tri-fold: H1: truthful stories are more likely to be judged as credible and false stories are likely to be judged as non-credible, H2: false stories are expected to include reduced illustrators, and increased blink rate, gaze aversion, and adaptors/manipulators, H3: truthful stories are expected to include increased illustrators, increased facial pleasantness, and no change in gaze aversion.

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Participants From a previous project there was a participant pool of 80 students (58 women, 22 men; M = 21.66 years), who signed up to participate and were offered course bonus points for their time. All participants were students at Saint Mary’s University and were enrolled in at least one psychology course. Each participant told two true stories and one lie, totalling 160 truth trials and 80 lie trials. One participant was hard of hearing and required a translator present to translate the other participant’s story to her. Apparatus A video camera was utilized in the previous study. In the present study, a coding sheet was designed to code each behavioral cue (see Appendix). Procedure In the previous study, time slots on the University’s SONA system were open to two participants. Once recruited, the participants ran through the study session in the dyad they were recruited in. Neither participant knew their study partner prior to participating. Following consent to the study, participants were instructed to tell two lies and one truth, in any order and without saying which were true and which were fabricated. Each participant was filmed by a near-by, separate video camera that was openly observable by all involved. Each dyad was seated beside each other facing their respective camera and given a sheet of possible stories (e.g., a memorable birthday, an embarrassing story) from which they could pick three stories to tell. The researchers allowed the participants time to think of potential stories or questions, after which filming commenced. Participants in the dyad took turns speaking and were given a chance to ask questions regarding their study partner’s story immediately after each story was told. After

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each study partner told their three stories, the video cameras were turned off and the dyad remained in the laboratory. They filled out online questionnaires regarding demographics, lie detection and deception techniques, emotional intelligence, short dark triad personality traits, and a self-monitoring scale, after which they each received a debriefing letter outlining the study. In the present study, the first author was trained in nonverbal deception detection before coding the data collected in the previous study. The project supervisor assigned relevant literature to be reviewed, as well as dozens of short videos to practice coding on, many of which were part of a television show or an online, popular culture lying game. After sufficient training and practice, the first author began coding the previous project’s data utilizing the coding sheet designed by the project supervisor. During coding, the researcher was unaware which stories were true and which were false. Following the participation of 40 dyads in the previous study, there existed 80 videos with two truths and one lie per video. Each of the 80 videos were coded for six nonverbal behavioral cues to deception, based on frequency of each cue. Due to the interest in solely nonverbal behaviors, the videos were played on mute while being coded. Based on DePaulo et al. (2003), the cues measured in the present study included: (a) illustrators (hand and arm movements to supplement and enhance verbal content) (b) manipulators/adaptors (touching or scratching one’s arms or body above the shoulder, including hair) (c) fidgeting (of the trunk – postural movements, or the arms, hands, legs, and feet) (d) blink rate (e) eye movements/gaze aversion and (f) changed facial pleasantness. Each occurrence of a cue was noted by the time and the total number of occurrences for each cue was assigned a code based on degree of frequency observed. Every story told had a category for each cue, in which time of each instance per cue was recorded. The general cue category (i.e. illustrators, blink rate) was assigned a score of 0, 1,

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or 2, based on frequency, for each of the three stories told by a participant. The code zero was assigned to a category that contained one or zero instances of the cue, the code one was assigned to a category that contained two or three instances of the cue, and the code two was assigned to a category that contained four or more instances of the cue. This coding method made the frequency of each behavioral cue per story apparent, thus allowing researchers to clearly link the frequency of each cue with the veracity of the story. Following each coding session by the first author, during which approximately 15 trials between 5 participants were coded, the coding sheets was reviewed by the project supervisor. Results Our three hypotheses were based on directional changes in the frequency of behavioral cues: (a) we expected truthful stories to be more likely judged as credible and false stories to be more likely judged as non-credible, (b) false stories were expected to include reduced illustrators, and (c) increased blink rate, gaze aversion, and adaptors/manipulators, and truthful stories were expected to include increased illustrators, increased facial pleasantness, and no change in gaze aversion. Eighty videos were coded for each of the six behavioral cues, resulting in cue frequencies for 80 fabricated stories and 160 truthful stories. Based on DePaulo et al. (2003)’s findings, each behavioral cue (illustrators, blink rate, eye movement, adaptors/manipulators, fidgeting, and changed facial expressions) was assigned a code of 0, 1, or 2, based on frequency of occurrence within each story. The frequency codes for the two truthful stories were averaged to arrive at a single code for both conditions. Cue Frequencies The most common cue exhibited when participants were telling a truthful story was the use of illustrators; nearly all (95%) of the participants used illustrators when telling a truthful

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story. The least common cue exhibited when telling a truthful story was a change in facial expression; very few participants (15%) exhibited a change in their facial expression when telling a truthful story. When participants were telling a fabricated story, the most common cue exhibited was also illustrators, nearly all of the participants (89%) exhibited illustrators for their fabricated story. The least common cue exhibited when telling a fabricated story was increased blink rate; less than a quarter (20%) of participants exhibited increased blinking when telling their fabricated story. The mean score of the behavioral cues varied for each condition (see table below). Table 1 Average Score of Cues Exhibited Between Conditions Cue Mean score: Fabricated

Truthful

1. Illustrators

1.61

1.63

2. Blink rate

.29

.25

3. Eye movement

1.91

1.6

4. Adaptors

.53

.41

5. Fidgeting

1.18

1.01

6. Facial expression

.3

.15

Six dependent t-tests were conducted to compare the average frequencies of each cue exhibited in the truthful stories and in the fabricated stories. Four of the six cues had an insignificant difference between conditions, and two cues had a significant difference. Significant differences were found in the frequency of fidgeting between the truth telling and lying conditions; t(79) = 2.072, p  0.05, as well as in the frequency of facial expression changes in truth telling and lying conditions; t(79) = 2.424, p = 0.018.

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Order of veracity. Participants’ three stories varied in order of truthful versus fabricated veracity. We found 23 of the 80 participants (28.8%) told their fabricated story first, followed by two truthful stories. About half of the participants (48.8%) told their fabricated story between their two truthful stories, and only 18 of the 80 participants (22.25%) told their fabricated story as their last story. Of the 240 stories told, the first author classified 12 (15%) of the first-order stories as fabricated, 33 (41.3%) of the second-order stories as fabricated, and 35 (43.8%) of the last stories as fabricated. 26 out of the 80 participants were correctly classified when telling a fabricated story, resulting in a 32.5% accuracy rate in detecting deception. A McNemar’s test was performed and no significant difference was found in the credibility judgement between truthful stories and fabricated stories for participants’ first truthful story, x2(1, N=80) = 0.5814, p  0.05), or their second truthful story. Another McNemar’s test was performed and no significant difference was found in the credibility judgment between truthful stories and fabricated stories for participants’ second truthful story, x2(1, N=80) = 1.613, p  0.05). Exploratory analysis. We calculated the bivariate correlation of each cue between the veracity conditions, to explore a potential relationship between exhibiting a cue when telling a truthful story and exhibiting that cue when telling the fabricated story. Three of the six cues were significantly correlated with each other; exhibiting illustrators when lying was significantly correlated with exhibiting illustrators when telling the truth (.52, p  .05), increased blink rate when lying was significantly correlated with increased blink rate when telling the truth (.48, p  .05), and

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fidgeting when lying was significantly correlated with fidgeting when telling the truth (.66, p  .05). Discussion Hypotheses Revisited Hypothesis 1. Overall, our results challenges previous research suggesting deception detection accuracy is comparable to chance level (see Bond & DePaulo, 2008; DePaulo & Bond, 2006; Vrij, 2000). Our first hypothesis was not supported, as we had expected a higher accuracy level ( 50%) compared to our observed accuracy level (32.5%) in classifying stories as credible or non-credible. We also expected a predictable trend in the frequency of cues exhibited across truthful and fabricated stories. Interestingly, we found correlations between participants’ exhibiting certain cues when telling truthful stories and exhibiting those cues when telling fabricated stories. These correlations were statistically significant in the frequency of illustrators, blink rate, and fidgeting and tells us the strong presence of a cue in a truthful story would also likely be present in a fabricated story. While devaluing the cue’s validity as a signal of deception, this finding suggests that individuals who have a tendency to exhibit a certain behavior will continue to exhibit that behavior whether they are speaking truthfully or not. Hypothesis 2. Our second hypothesis regarding specific directional changes of cues within fabricated stories was supported. We observed a higher rate of adaptors and fidgeting and the average mean score of fidgeting between conditions was statistically significant. This supports early research suggesting a correlation between these cues and lying (DePaulo et al., 1980) and more recent research supporting this correlation (DePaulo et al., 2003). We expected increased eye

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movements, which was supported although the difference was not significant. DePaulo et al. (2003) suggests eye contact is not a valid cue to deception; our results partially support this as the difference of eye contact between conditions was not significant. We also expected an increase in blink rate and our results support this, although it was nonsignificant. Overall, our second hypothesis was supported although only the cue of fidgeting was significant. Hypothesis 3. We had expected more frequent illustrators in truthful stories, based on findings by DePaulo et al. (2003). This was supported although nonsignificant, and illustrators were the most frequently exhibited cue in both conditions. These findings suggest that illustrators may be a common behavior for individuals to help tell their story, regardless of veracity of the story. We expected more facial expression changes in truthful stories, and this was the only expectation not supported by our results. We found changes in facial expression was significantly greater in fabricated stories than in truthful stories. Overall, the frequencies of each cue exhibited were strikingly similar for both truthful and fabricated stories. This provides further support for previous research suggesting that behavioral cues may not be entirely valid for detecting deception, at least in laboratory conditions and with low stakes motivation to deceit (Frank & Feeley, 2003; Hauch, Sporer, Michael, & Meissner, 2016; Kassin, 2012). We found the difference in mean score of fidgeting and of facial expression change in truthful and fabricated stories were statistically significant, thus our expectation for fidgeting to occur more frequently in fabricated stories than in truthful stories was supported although the relationship between fidgeting and lying has been summarized as unclear (DePaulo et al., 2003).

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Limitations. While our study followed the traditional paradigm for nonverbal deception detection research in a university laboratory, limitations are apparent. First, the general environment of a laboratory and the associated participant pool of students, limit the external validity of such research. This is because the participants are not criminal offenders nor suspects, which the results are intended to apply to. The extent to which deceiving and detecting deception in university students can be generalized to criminal offenders is questionable. Second, many participants wore glasses which limited our ability to detect eye movements and blinking while measuring the frequency of cues. Thus, we may have missed some instances of eye movements and blinking which would have lowered our frequencies and mean score of these cues. Finally, the participants’ ability to successfully tell a lie was based on low stakes. In reality, offenders and the like would have extremely high motivation to successfully deceive because of the high stakes situation of a courtroom or interrogation room where their future is in stake. Further research. Future research could recruit a representative sample from which behavioral cues may be more valid. Also, it would be beneficial to implement a high-stakes situation such as a monetary prize for the successful deceiver or the successful detector, as this could increase motivation to successfully deceive and detect deception. Finally, perhaps the most valid cues to deception are fidgeting and facial expression changes; future research could narrow down on previously established cues and focus solely on validating them. The six cues we measured may not be valid cues in an interview setting, as the careful observation warranted by a video would not be applicable. In real-time monitoring of the sender, the receiver would have difficulty picking up on such subtle cues from which to shape their line

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of questions. We suggest the behavioral cues utilized in this study would be most beneficial to a play-back situation, in which the receiver has the ability to analyze a video of the sender’s statement rather than in real-time. Conclusion Our results emphasize individual differences in story-telling, as those who exhibit certain behaviors while telling truthful stories will most likely exhibit these behaviors while also telling fabricated stories. We are suggesting the occurrence and frequency of behavioral cues to deception may be more attributable to the individual rather than the veracity of the statement, as some individuals may be naturally prone to exhibiting the cues that we assume to be associated with deception. While we found that fidgeting and facial expressions differ significantly between conditions, behavioral frequencies were almost identical for our remaining four cues. In legal situations such as the investigation room or court room, caution should be taken with relying on behavioral tendencies in individuals, as the literature shows how we consistently see a lack of definitive marker for deception.

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