Arts and Social Sciences, Irving K. Barber Faculty of (Okanagan)
Psychology, Department of (Okanagan)
DSpace
UBCO
Brown, Cameo
Master of Arts - MA
University of British Columbia
Lies are ubiquitous in interpersonal communication. Despite their prevalence, people are generally unsuspecting of deception in social interactions. Even when asked to evaluate veracity directly, people only perform slightly better than chance. Taken together, these findings suggest that lies are often ‘missed’ and people are easily duped. This study considers an alternative possibility; that people naturally restrict their interactions with liars (vs. truth-tellers), even in the absence of suspicion about deception. Specifically, I investigated whether people are less willing to disclose personal information to liars, relative to truth-tellers. Receivers (N = 230) watched videos of 20 senders claiming to like a target person. Half of these claims were true; the other half, deceptive. Following each video, receivers rated their willingness to share personal information in a hypothetical conversation with each sender. Consistent with my hypothesis, results demonstrated that individuals were less willing to self-disclose personal information to liars, relative to truth tellers. Additionally, a novel set of behaviors—social functional smiles—were explored as potential mediators of this effect. Although I did not find support for these behaviors as mediators, the presence and duration of social functional smiles did vary by veracity. Truth-tellers engaged in more reward smiles and fewer dominance smiles, relative to deceptive senders. Taken together, this study enhances our understanding of senders’ behavioural cues to deception and receivers’ indirect reactions to lies in conversation.
Lies, Smiles, and Self-Disclosure: An Examination of How Social Functional Smiles Mediate the Relationship Between Lying and Self-Disclosure by Cameo Brown B.A., Loyola Marymount University, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE COLLEGE OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2024 © Cameo Brown, 2024ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Lies, Smiles, and Self-Disclosure: An Examination of How Social Functional Smiles Mediate the Relationship Between Lying and Self-Disclosure submitted by Cameo Brown in partial fulfillment of the requirements of the degree of Masters of Arts . Dr. Leanne ten Brinke Supervisor Dr. Paul G. Davies, Psychology Supervisory Committee Member Dr. Lauren Human, Psychology Supervisory Committee Member Dr. Anita Chaudhuri, Creative and Critical Studies University Examiner iii Abstract Lies are ubiquitous in interpersonal communication. Despite their prevalence, people are generally unsuspecting of deception in social interactions. Even when asked to evaluate veracity directly, people only perform slightly better than chance. Taken together, these findings suggest that lies are often ‘missed’ and people are easily duped. This study considers an alternative possibility; that people naturally restrict their interactions with liars (vs. truth-tellers), even in the absence of suspicion about deception. Specifically, I investigated whether people are less willing to disclose personal information to liars, relative to truth-tellers. Receivers (N = 230) watched videos of 20 senders claiming to like a target person. Half of these claims were true; the other half, deceptive. Following each video, receivers rated their willingness to share personal information in a hypothetical conversation with each sender. Consistent with my hypothesis, results demonstrated that individuals were less willing to self-disclose personal information to liars, relative to truth tellers. Additionally, a novel set of behaviors—social functional smiles—were explored as potential mediators of this effect. Although I did not find support for these behaviors as mediators, the presence and duration of social functional smiles did vary by veracity. Truth-tellers engaged in more reward smiles and fewer dominance smiles, relative to deceptive senders. Taken together, this study enhances our understanding of senders’ behavioural cues to deception and receivers’ indirect reactions to lies in conversation. iv Lay Summary Lying is a common experience in everyday interactions. Current research suggests that people are often fooled. However, this study considers an alternative possibility; that individuals intuitively restrict their interactions with liars (vs. truth-tellers), even if they don’t suspect dishonesty. This study investigated whether people are less willing to self-disclose personal information to liars, relative to truth-tellers. Receivers watched 20 videos of senders honestly or dishonestly speaking about social relationships, and then rated their willingness to share personal information in a hypothetical conversation with each sender. Results demonstrated that individuals reported being less willing to self-disclose to dishonest (vs. honest) individuals. Although senders engaged in different smiling behaviors when telling truths and lies, this behavior did not explain the self-disclosure effect. This study furthers our understanding of behavioural cues to deception and the social consequences of lies in conversation. v Preface Ethics approval for this research project was granted by The University of British Columbia’s Behavioral Research Ethics Board (H22-01475). To date, the research included within this thesis has not been published. I helped with the development and design of this project, in collaboration with Dr. Leanne ten Brinke and Sam Sprigings. I was responsible for the development of the nonverbal behavior coding scheme, data analysis, and interpretation of the findings as presented here. Generative AI was not used in any aspect of this thesis. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ............................................................................................................................................ v Table of Contents ......................................................................................................................... vi List of Tables .............................................................................................................................. viii List of Figures ............................................................................................................................... ix Acknowledgements ....................................................................................................................... x Dedication .................................................................................................................................... xii Chapter 1. Introduction ............................................................................................................... 1 1.1 IMPORTANCE OF TRUST IN BUILDING RELATIONSHIPS ........................................................... 1 1.2 INDIRECT DECEPTION DETECTION .......................................................................................... 3 1.3 BEHAVIOURAL CUES TO DECEPTION ...................................................................................... 5 1.4 SMILES AS A CUE TO DECEPTION: TWO THEORETICAL FRAMEWORKS ................................... 6 1.4.1 The Emotional ‘Read Out’ Framework .......................................................................... 6 1.4.2 Intentional Social Signal Approach ................................................................................ 8 1.5 SOCIAL FUNCTIONING OF SMILING ......................................................................................... 9 1.5.1 Social Functional Smiles and Deception ...................................................................... 12 1.6 SMILES IN CONVERSATION ................................................................................................... 13 1.7 THE CURRENT STUDY .......................................................................................................... 13 Chapter 2. Method ...................................................................................................................... 16 2.1 PARTICIPANTS ...................................................................................................................... 16 2.2 MATERIALS AND MEASURES ................................................................................................ 17 2.3 PROCEDURE .......................................................................................................................... 19 Chapter 3. Results ....................................................................................................................... 21 3.1 ANALYSES ............................................................................................................................ 21 3.1.1 H1: Veracity effects on Self-Disclosure........................................................................ 21 3.1.2 H2, H3 & H4: Veracity is Associated with Distinct Smiling Behaviour ...................... 23 3.1.3 H5, H6 & H7: Social Functional Smiles do not Mediate Veracity and Self-Disclosure ............................................................................................................................................... 24 Chapter 4. Discussion ................................................................................................................. 30 4.1 SELF-DISCLOSURE AS A MEASURE OF INDIRECT DECEPTION DETECTION ............................ 30 4.2 SOCIAL FUNCTIONAL SMILES AS BEHAVIOURAL CUES TO DECEPTION ................................. 31 4.3 SOCIAL FUNCTIONAL SMILES ARE NOT PREDICTORS OF SELF-DISCLOSURE ......................... 32 vii 4.4 LIMITATION AND FUTURE DIRECTIONS ................................................................................ 33 4.5 THEORETICAL AND PRACTICAL IMPLICATIONS ..................................................................... 37 Chapter 5. Conclusion ................................................................................................................ 39 References .................................................................................................................................... 40 viii List of Tables Table 1: The Effect of Sender Veracity on Mean Self-Disclosure Ratings ............................ 22 ix List of Figures Figure 1: Examples of the Three Types of Social Functional Smiles ....................................... 9 Figure 2: Example of the Hypothesized Mediation Model for Reward Smiles ..................... 14 Figure 3: Example of the Hypothesized Mediation Model for Affiliation Smiles ................. 15 Figure 4: Example of the Hypothesized Mediation Model for Dominance Smiles ............... 15 Figure 5: A Bar Graph Depicting the Effect of Veracity on Ratings of Self-Disclosure..….22 Figure 6: Results of the Frequency of Reward Smiles Mediation Model .............................. 25 Figure 7: Results of the Frequency of Affiliation Smiles Mediation Model .......................... 26 Figure 8: Results of the Frequency of Dominance Smiles Mediation Model ........................ 26 Figure 9: Results of the Duration of Reward Smiles Mediation Model ................................. 27 Figure 10: Results of the Duration of Affiliation Smiles Mediation Model ........................... 28 Figure 11: Results of the Duration of Dominance Smiles Mediation Model ......................... 28 x Acknowledgements I am deeply grateful for the unwavering support and guidance I have received throughout my journey in completing this Masters thesis. I extend my heartfelt appreciation to everyone who has contributed to this endeavor. First and foremost, I am indebted to my supervisor, Dr. Leanne ten Brinke, for her exceptional mentorship and belief in my potential. Dr. ten Brinke took a chance on me and tirelessly provided the opportunities and resources necessary for my academic growth. Her support went beyond the academic realm; she became like family to me, and I am truly thankful for her guidance. I am also grateful to my committee members, Dr. Paul Davies and Lauren Human. Additionally, I would be remiss if I did not acknowledge Dr. Tareq Yousef and Dr. Cynthia Mattheison, for their invaluable feedback and encouragement. Despite knowing little about me initially, they demonstrated faith in my abilities and provided indispensable support throughout this process. Their contributions have been instrumental to my academic development and well-being. I want to give a special thanks to Diana Gareth and Mataija Henderson, two undergraduate psychology students, for their assistance in refining my co-supervisory skills, which proved vital to my personal and academic advancement. I am fortunate to have been part of the Truth and Trust Lab, where engaging discussions and relentless curiosity fostered my academic creativity and growth. I am deeply appreciative of the collaborative environment and stimulating conversations that enriched my research experience. To my family and friends back home, I owe an immeasurable debt of gratitude. Your unwavering faith in me, even during moments of self-doubt, has been my anchor. Your constant xi support and encouragement have fueled my determination to succeed, and I am profoundly thankful for your love and belief in me. Your collective influence has shaped my academic pursuits and continuously reminded me of my passion for learning. To everyone mentioned and those not mentioned, your contributions have been invaluable, and I am deeply appreciative of your role in this achievement. Thank you. xii Dedication To my family and friends from home, and to Dr. Leanne ten Brinke and her husband, These past two years have been the most challenging of my life. I've encountered obstacles I never anticipated and faced trials I never imagined. Yet, through it all, it has been your unwavering support, love, and belief in me that have sustained me. To my family and friends from home, your faith in me has been my rock. Your encouragement and belief in my abilities, even when I doubted myself, have been a constant source of strength. To Dr. ten Brinke and her husband, your guidance and support have been invaluable. You have not only been mentors but also pillars of strength, showing me that with courage, love, and authenticity, I can overcome any challenge. A simple thank you will never suffice to express the depth of my gratitude. But I hope this dedication serves as a small token of my appreciation for all you have done for me. From the bottom of my heart, thank you.LIES, SMILES, AND SELF-DISCLOSURE 1 Lies, Smiles, and Self-Disclosure: An Examination of How Social Functional Smiles Mediate the Relationship Between Lying and Self-Disclosure Chapter 1. Introduction Lies are commonplace. People admit to telling a lie in one-third of phone calls and a quarter of face-to-face conversations (Hancock et al., 2004). Despite their prevalence in day-to-day social interactions, people are generally unsuspecting and assume the information they receive from others is honest (Vrij, 2008). Even when alerted to the possibility of deception, people perform only slightly above chance in discriminating liars from truth-tellers (Levine et al., 1999; Bond & DePaulo, 2006). Further, direct judgments of veracity are commonly truth biased; people are more likely to judge others as honest, than deceptive (Levine, 2014). Accordingly, the current literature on deception suggests that people are often duped and oblivious to their naivety. However, this study aims to investigate an alternative possibility; that people identify behavioural cues to deception, even if unaware of their origin, and respond by distancing themselves from dishonest actors. 1.1 Importance of Trust in Building Relationships Humans are social beings and instinctively seek out social connections and interpersonal relationships (Levine et al., 1999). These relationships fulfill our fundamental need for belonging and include many psycho-social benefits (e.g., Cohen, 2004). One of the ways in which we build and maintain our relationships is through conversations in which we share personal information (Yeomans et al., 2022). Self-disclosure is the rate at which one individual shares information with another and is integral when building new relationships (Aron et al., 1997). Self-disclosure both requires and engenders trust—the willingness to be vulnerable to another person by sharing personal, intimate details of your life (Wheeless, 1977). Thus, conversations provide a place for 2 trust to be built and interpersonal closeness to form. However, conversations also provide a venue for trust to be violated. People report telling an average of 1.6 lies per day (DePaulo et al., 1996), and when lies are uncovered, trust between parties can be damaged to the extent that relationships dissolve (Scott, 2012). In this way, discovering deception is likely to reduce–or completely end–the sharing of sensitive personal information. However, research suggests that many instances of deception are never detected. In studies where a sender’s veracity is not directly called into question, less than 5% of receivers report even considering the possibility of deception (Gunderson et al., 2021). Vrij (2008) described this phenomenon as the ‘ostrich effect’, suggesting that people have their heads ‘in the sand’ when it comes to detecting deception. People’s propensity to presume honesty is thought to be so prevalent because it is adaptive (Levine, 2014). More often than not this assumption is accurate. It is therefore cognitively easy and socially harmonious to assume that others are truthful. However, this bias also results in poor deception detection accuracy. For example, in the aforementioned study, where only 5% of receivers reported suspicion, 100% of receivers had observed multiple instances of deception (Gunderson et al., 2021). In other words, 95% of people seemed oblivious to the fact that they had observed several high-stakes lies. Even when receivers are directly alerted to the possibility of deception and asked to discriminate liars from truth-tellers, they perform only slightly better than chance (Levine et al., 1999; Bond & DePaulo, 2006). Further, this above-chance accuracy is driven by accuracy in detecting truths (61%), not lies (47%; Bond & DePaulo, 2006). Below-chance lie detection occurs even in previously-established relationships (i.e., motivated inaccuracy; Levine et al., 2018). Specifically, McCornack & Parks (1986) found that the further developed a relationship is, the more confident a receiver is in detecting the sender’s lies, resulting in greater truth bias 3 (69%) and lower lie detection accuracy. Taken together, research suggests that people rarely consider the possibility of deception unless actively alerted to it, and close relationships can exacerbate this tendency. However, it is not clear that direct detection of deception is necessary to avoid the negative interpersonal consequences of deception. It is possible that people indirectly avoid liars (relative to truth-tellers) without ever considering others’ veracity directly. 1.2 Indirect Deception Detection Although most deception detection research has focused on the ‘direct’ judgments described above, a smaller body of research suggests that other judgments can discriminate liars from truth-tellers. Indirect deception detection is a method where receivers rate a sender on some behavioral cue or global impression other than veracity (e.g., "appears friendly"; Sporer & Ulatowska, 2021). In the first study of its kind, Vrij et al. (2001) found that police officers could distinguish between truth and lies using indirect measures. In this study, 39 police officers watched 28 videos of nursing students discussing information concerning the theft of a patient's purse in a hospital. Half of these videos were truthful; the other half were deceptive. After each video, receivers assigned to the direct detection condition were asked, "Is this person lying?" or, in the indirect condition, asked, "Does this person have to think hard?". Findings in this study suggest that when asking receivers directly to evaluate a sender's veracity, receivers attended to cues that were not valid indicators of deception and, unsurprisingly, their direct judgments did not exceed chance accuracy. However, when using the indirect method, receivers paid attention to cues that were actually indicative of deceit (i.e., increases in response latency) and rated liars as thinking harder than truth-tellers. A meta-analysis by Bond et al. (2014) found that indirect measures of perceived cooperativeness, thinking hard, indifference, and impressions of the 4 senders' vocal and verbal cues, outperformed accuracy by direct measures (see also Street & Richardson, 2015). Although much of the research on indirect deception detection to date has focused on evaluations of the sender’s behavior, more recent work suggests that measuring receiver’s social and emotional responses may also reveal indirect discrimination of liars and truth-tellers. For example, ten Brinke et al. (2023) found that people reported feeling more attracted to truth-tellers versus liars, even when unaware of the possibility of deception. Additionally, Gunderson et al. (2021) found that receivers reported feeling more sympathy and offered more monetary support to people expressing genuine versus deceptive expressions of sadness. Together, these findings suggest that evaluations of and responses to liars differ from truth-tellers–and often in ways that create relative interpersonal distance from deceivers (ten Brinke et al., 2016). These diverse effects are presumed to be linked to behavioral cues to deception and the receiver's sensitivity to verbal and nonverbal behavior, even if the receiver does not accurately identify deception as its source. As social beings, people are constantly assessing and adjusting their own behavior based on the behavior of others (e.g., Carney, Colvin, & Hall, 2007). How a receiver responds and reacts is influenced by the social norms that can be activated by the sender's behavior (Reno, Cialdini, & Kallgren, 1993). For example, receivers have predictable inferential, affective, and behavioral responses to sender’s emotional expressions (Van Kleef, 2009). And, if emotional expressions differ across liars and truth-tellers, they may result in distinct responses from receivers. Indeed, in Gunderson et al.’s (2021) study of responses to genuine versus falsified expressions of sadness, a Brunswikian lens model revealed that furrowed eyebrows, in particular, elicited sympathy and monetary support–and that this facial action appeared less often in false (vs. genuine) expressions. In short, even if people are not 5 suspicious of deception, individuals still react to the behaviour of others; because those behaviours can vary by veracity, people can have different reactions to liars vs. truth-tellers. 1.3 Behavioural Cues to Deception Which behaviours differentiate liars from truth-tellers? This question has been a focus of study for decades, and there are multiple theories of deceptive behavior (Zuckerman et al., 1981). Specifically, the cognitive load theory suggests that lying requires more cognitive resources than telling the truth (Buller & Burgoon, 1996). The cognitive complexity of lying is associated with behavioural cues such as increased response latency, speech hesitations, and pupil dilation (Zuckerman et al., 1981). The theory of emotional arousal suggests that telling the truth and lying are associated with different autonomic responses (Zuckerman et al.,1981). This theory purports that deception gives rise to increased arousal and will be revealed by increased pupil dilation, blink rate, speech errors, and hesitations (Zuckerman et al., 1981). Further, the emotional arousal theory suggests that deception activates specific emotions, including guilt about engaging in deception or anxiety about being detected (Zuckerman et al., 1981). These negative affective states are expected to be associated with an increase in negative emotional expressions, negatively-valenced word use, and an increase in indirectness or withdrawal (Zuckerman et al., 1981). Relatedly, the theory of psychological distancing suggests that liars will try to distance themselves from their deceptive message. For example, liars use fewer first-person pronouns, more tentative language, and more group references than individuals telling the truth (Ebesu & Miller, 1994; ten Brinke & Porter, 2012). Despite considerable theoretical and empirical work on the topic, DePaulo et al.'s (2003) seminal meta-analysis suggests that the relationship between individual behavioural cues and deception are generally small and unreliable (DePaulo et al., 2003; see also Hartwig & Bond, 6 2014; Luke, 2019). Indeed, there is wide agreement in the field that there is no single cue to differentiate between truth-tellers and liars. In other words, a ‘Pinnochio's nose’ does not exist. However, some research suggests that verbal cues may be more reliable than non-verbal cues (Bond et al., 2014) or that clusters of non-verbal behaviors–considered together–may be better predictors of veracity than any one cue alone (Matsumoto & Hwang, 2020; see also Hartwig & Bond, 2014). Specifically, Matsumoto & Hwang (2020) found that liars in a mock interrogation about a theft produced more facial expressions of fear and disgust, shrugs, fidgeting/grooming, body swaying, and rigid posture. In contrast, truth-tellers produced more facial expressions of happiness and surprise, longer response latencies, greater pitch ranges, longer durations, and unfilled pauses. In addition to considering clusters of behavior, the challenge of defining nonverbal behavior may contribute to why some non-verbal cues seem to be theoretically related to deception but are empirically unreliable (Virj et al., 2019). 1.4 Smiles as a Cue to Deception: Two Theoretical Frameworks 1.4.1 The Emotional ‘Read Out’ Framework One widely explored non-verbal cue to deception is smiling (Duchenne, 1862/1990; Ekman et al., 1982/1985/1988; Gunnery & Hall, 2014; Martin et al., 2017/2021). Ekman and colleagues pioneering research on the relationship between smiling and deception is predicated on the assumption that facial expressions are a readout of the internal emotion experienced by the sender. For example, previous literature supports the understanding that smiling indicates felt emotion, like 'happiness' or 'joy' (Niedenthal et al., 2010). In the 1800's, French anatomist Guillaume Duchenne began mapping the muscles involved in smiling using electrical stimulation, identifying contraction of the zygomatic major and the orbicularis oculi as essential components of the expression of enjoyment and happiness (Duchenne, 1862/1990). Paul Ekman 7 and colleagues (1982) provided initial experimental evidence that the muscle movements identified by Duchenne were indeed indicative of a 'genuine' smile or felt happiness. Specifically, Ekman et al. (1988) found that student nurses engaged in fewer Duchenne smiles when lying. Student nurses' faces were video-recorded while watching either a non-stressful nature film (honest condition) or a film on burns and amputations (deceptive condition). While watching both films, nurses had to convince the interviewer that they were watching a pleasant film. When lying, nurses were more likely to engage in smiles that lacked the Duchenne marker or produce masking smiles that involved muscular activity associated with negative emotions. For example, masking smiles were betrayed by the presumed leakage of felt emotion: either a raised upper lip, suggesting an underlying feeling of disgust, or lowered lip corners, indicating a trace of sadness. Since Ekman's (1982) seminal work on this topic, the field has operated on the proposition that a Duchenne smile, including both zygomaticus major and orbicularis oculi activation, reflects felt happiness or enjoyment. In contrast, a smile was presumed to be ‘false’ if it was missing activation around the eyes because the orbicularis oculi muscle, in particular, was purported to be outside of voluntary control. In other words, the emotional ‘read out’ approach focuses on the sender’s emotional experience and finding ways in which felt expressions are distinct from those produced in the absence of corresponding emotion. Despite the intuitive appeal of this approach, more recent research suggests that the relationship between smile morphology and feelings of happiness is less clear-cut than previously thought. In 2014, Gunnery and Hall showed that a sizeable minority of their senders could deliberately produce a Duchenne smile. In their study, senders tasted a pleasant and unpleasant-tasting juice and had to express that they both enjoyed and disliked those drinks to the 8 experimenter. The goal was to persuade the experimenter to try or not try the juice (Gunnery & Hall, 2014). Some senders were able to produce Duchenne smiles when convincing the experimenter that the unpleasant-tasting juice was good. In other words, in the absence of enjoyment, a substantial proportion of people could voluntarily activate the orbicularis oculi to produce a Duchenne smile. Interestingly, Gunnery and Hall (2014) also found that people who can produce a Duchenne smile deliberately are more persuasive than those who cannot. These findings suggest that smiling serves a social function–an idea that was not new but has contributed to shifting perspectives on the meaning of 'Duchenne' versus 'non-Duchenne' smiles. 1.4.2 Intentional Social Signal Approach W. John Smith and Alan Fridlund laid the groundwork for understanding the relationship between social signals and emotions in non-verbal behavior research. Smith's (1977) view of communication for humans and non-human animals centered on two critical arguments. The first is that senders produce beneficial evolutionary signals influencing the receiver's behavior. Secondly, receivers gather and assess this information based on the signal and context of the signals. Although Smith had not addressed emotions or facial expressions, specifically, he paved the way for communication between humans to be considered from an evolutionary perspective. This concept was then applied to emotion and facial expression research by Fridlund. Rejecting the idea that facial expressions reflect emotional states, Fridlund (1994) argued that facial expressions serve specific social motives and have meaning only in context. More recently, Neidenthal and colleagues (2010) have proposed the Simulation of Smiles (SIMS) model, which provides a theoretical framework for understanding how emotions can be expressed by a displayer advantageously in a social environment, providing a social functional account of smiling. 9 1.5 Social Functioning of Smiling Niedenthal et al. (2010) proposed three smile types, each serving a distinct social function: reward, affiliation, and dominance (see Figure 1 for an example of the three types of smiles proposed by the SIMS model). Each of these smiles has specific action units (i.e., independent movements of the face) that are produced in accordance with an individual’s need in social interactions. Figure 1 Examples of the Three Types of Social Functional Smiles Note. This figure depicts the three types of smiles proposed by the SIMS model. Reward (left), affiliation (center), and dominance (right) smiles. From “Evidence for Distinct Facial Signals of Reward, Affiliation, and Dominance from Both Perception and Production Tasks” by Martin et al., 2021, https://link.springer.com/. Copyright 2021 The Society for Affective Science. Reward Smiles. The social function of a reward smile is to reinforce desired behavior (Niendenthal et al., 2010; Martin et al., 2017). They are readouts of happy, positive internal states and reinforce the behaviors that elicit them in the first place. These smiles are consistent with Darwin’s account of the interaction between a mother and her infant. In his 1872 book, Darwin said, 10 ‘The movements of expression in the face [ ...] serve as the first means of communication between the mother and her infant; she smiles approval, and thus encourages her child on the right path.’ Reward smiles are predicated on the notion that these smiles induce positive affect in the receiver and can thus be used to reinforce behavior that gave rise to the smile (Martin et al., 2017). Reward smiles are characterized morphologically by the lifting of the eyebrows as seen in a surprised expression (AU1, AU2), lifting of the cheeks and “crow’s feet” around the eyes (AU6), dimples in the cheeks (AU14), and both corners of the lips upturned sharply (AU13, AU12; Martin et al., 2021; Rychowlska et al., 2017). Affiliation Smiles. The function of affiliation smiles is to form and maintain social bonds (Niendenthal et al., 2010; Martin et al., 2017). Affiliative smiles are used to assure others that the sender has no intention of harm or threat. For example, when passing by a stranger on the street, a person might use an affiliative smile to signal friendliness. They do not necessarily signal any underlying positive emotion by the sender, or elicit any from the receiver, but serve a communication function nonetheless. Affiliation smiles are characterized morphologically by slightly upturned corners of the lips (AU12) and the bottom and top lips pressed together slightly (AU24; Martin et al., 2021; Rychowlska et al., 2017). Dominance Smiles. Finally, dominance smiles function to negotiate social hierarchies (Niendenthal et al., 2010; Martin et al., 2017). These smiles reflect social status, control, and assert dominance. Senders may use dominance smiles to instantiate, destabilize, or maintain asymmetric hierarchical relations between the receiver and sender (Martin et al., 2017). For example, players on a winning soccer team might flash the losing team a smile that resembles a sneer to reflect their social status. Dominance smiles are characterized morphologically by lifting 11 the eyelids (AU5), cheeks and appearance of the“crow’s feet” around the eyes (AU6), wrinkling of the nose and upper lip movement as in disgust (AU9, AU10), and the upturn of one corner of the lips as in a sneer (asymmetrical AU12L/R; Martin et al., 2021; Rychowlska et al., 2017). Recent research by Martin et al. (2021) provided evidence for how people think and behave in response to reward, affiliation, and dominance smiles. In Study 1, receivers viewed a video corresponding to each type of smile and responded by describing a situation in which they believed the type of smile expression would occur. Based on a thematic response coding, receivers generally reported reward smiles as happy, affiliation smiles as fake/pretending, and dominance smiles as contemptuous and flirtatious. The thematic response coding illuminated individuals' distinct mental representations of the types of smiles. In Study 2, researchers demonstrated that the three types of smiles have different social functions by having receivers engage in an economic game requiring trust and reporting how they would behaviorally respond to partners’ displaying each type of smile. Findings suggest that receivers trusted individuals displaying reward smiles the most, followed by affiliation, with dominance smiles eliciting the least trust. Observing these different smile types also results in distinct physiological responses in receivers. In a study by Martin et al. (2017), male receivers (N = 90) in a virtual interaction addressed three topics about themselves in front of a male evaluator. After each response to a topic, receivers were privy to evaluator's (pre-recorded) facial expression, consisting of one of the three types of smiles, followed by a second neutral expression video. Receivers’ heart rate was monitored using an electrocardiograph before, during, and after responding to each topic, and stress reactivity was measured via salivary cortisol gathered at seven different time points throughout the experiment. Findings suggest that dominance smiles (compared to reward and 12 affiliation smiles) were associated with receivers’ increased heart rate and salivary cortisol, mimicking the influence of negative feedback. In contrast, the trajectory of physiological reactivity in response to reward and affiliation smiles was consistent with responses to displays of friendlessness and positive social evaluation. 1.5.1 Social Functional Smiles and Deception To date, research on smiles in the context of deception has focused on Duchenne and non-Duchenne morphologies, with the assumption that they represent genuine and false displays of happiness, respectively. However, it is clear that smiling is a far more complicated social behavior that can be used both deliberately–to advance communicative goals–and spontaneously–to express an emotional state. Using this more nuanced understanding of smiles advanced by Neidenthal and colleagues, I can posit novel hypotheses about how reward, affiliation, and dominance smiles may be used to communicate during truths and lies. In this thesis, I used the Miami University Deception Detection Database to examine the expression of smiles (reward, affiliation, dominance) when targets describe people that they know and like positively (i.e., truth), or people they know and dislike positively (i.e., lie). Previous research suggests that people exhibit an illusion of transparency–assuming that their internal states are obvious to others (Gilovich et al., 1998). This leads truth-tellers to act relatively naturally–assuming that their honesty will be apparent to others–and leads liars to monitor and modulate their behavior, in an effort to obscure their dishonesty (Buller & Burgoon, 1996; DePaulo et al.,2003). Accordingly, I expect that truth-tellers, describing a person that they genuinely like, will express more reward smiles than liars. By contrast, I expect liars to be concerned with appearing non-threatening and harmless, deliberately expressing more affiliation smiles than truth-tellers in an attempt to achieve that communicative goal. Finally, I suggest that 13 duping delight–a sense of accomplishment at successfully deceiving others (Ekman, 1992)–may be expressed in the form of dominance smiles. Specifically, I predict that liars will engage in more dominance smiles relative to truth-tellers. 1.6 Smiles in Conversation Thus far, I have identified a set of hypotheses concerning the expression of smiles in genuine versus deceptive communication. The impact of these smiles on receiver responses is considerably simpler to theorize, as the SIMS model focuses on the social functions of these facial signals. Martin et al. (2017; 2021) had receivers rate their response to each of these smile types, finding that reward and affiliation smiles consistently elicited high ratings for positive feelings, social connection, displays of friendliness, positive social evaluation, and–critically–trustworthiness (Martin et al., 2018; 2021). In contrast, dominance smiles elicited high ratings for superiority, generally low ratings of positive feelings and social connectedness, and the lowest level of trustworthiness. Although the effect of these smile types on conversational dynamics have yet to be examined, effects on expression trustworthiness and social connection suggest that smiles are likely to impact the degree to which a receiver will disclose personal information. Specifically, I predict that reward and affiliation smiles by senders will be met with increased self-disclosure from receivers, and that dominance smiles will be associated with decreased self-disclosure. 1.7 The Current Study Although current literature on lie detection suggests that people are poor lie detectors and often duped, this study examines an alternative possibility that receivers avoid liars without directly considering the possibility of deception. I argue that people may report increased willingness to self-disclose personal details to truth-tellers relative to liars (H1) and that this 14 effect will be mediated by differences in senders’ smiling behavior across veracity (see Figures 2, 3, and 4 for mediation hypotheses). By combining theories of deceptive behavior with the SIMS model of smiling, I predict that liars will use more affiliation (H2) and dominance smiles (H3) and fewer reward smiles than truth-tellers (H4). Additionally, I predict that these expressions will mediate the relationship between veracity and self-disclosure. Specifically, I hypothesize that reward (H5) and affiliation smiles (H6) will be associated with increased self-disclosure from a conversation partner, and that dominance smiles will be associated with decreased self-disclosure (H7). Support for my hypotheses would advance our understanding of how undiscovered deception affects social interactions, and would suggest that people are more responsive to cues to deception than previously considered. Figure 2 Example of the Hypothesized Mediation Model for Reward Smiles Note. This figure depicts the hypothesized within-subject mediation model where truth-tellers will use more reward smiles and reward smiles will be associated with increased self-disclosure. 15 Figure 3 Example of the Hypothesized Mediation Model for Affiliation Smiles Note. This figure depicts the hypothesized mediation model that liars will use affiliation smiles and affiliation smiles will be associated with increased self-disclosure. Figure 4 Example of the Hypothesized Mediation Model for Dominance Smiles Note. This figure depicts the hypothesized mediation model that liars will use dominance smiles and dominance smiles will be associated with reduced self-disclosure.LIES, SMILES, AND SELF-DISCLOSURE 16 Chapter 2. Method 2.1 Participants Undergraduate students (N = 261) were recruited from the University of British Columbia Psychology SONA system. Receivers watched videos of both liars and truth-tellers, and rated their willingness to disclose personal information to each individual. A priori power analyses suggest that this would be a sufficient sample size to detect a small (d = .20) within-subjects difference, setting 1- β at .80 and α at .05. All students at least 18 years of age were eligible for participation. All students received course credit for SONA as compensation for participating. Participants who did not complete the study were presumed to have revoked consent; their data was deleted and not included in analyses. Participants (N= 30) with missing data or uncompleted ratings were subsequently assessed and excluded from analyses as deemed necessary. One participant revoked their consent; consequently, their data was excluded from the analysis. Among the participants, N = 230 (186 women, 37 men, and 7 non-binary individuals) provided complete data. The mean age of the participants was 21.47 years (SD = 5.03), with ages ranging from 18 to 58 years old. Race was included as a demographics question, and 60.5% of participants self-identified as White, 9.2% as Chinese, 8.4% as South Asian, 3.8% as Southeast Asian, 3.1% as Indigenous, 3.1% as Latin American, 1.1% as Black, 1.1% as Filipino, 1.1% as Korean, 1.5% as West Asian, 0.8% as Arab, 0.8% as Japanese, and 1.1% selected choice not listed here. However, this demographic was not included in my analysis due to the study not being powered to address questions related to race. 17 2.2 Materials and Measures Stimuli. Receivers watched a randomized counterbalanced subset of 20 videos from the Miami University Deception Detection Database (MU3D; Lloyd et al., 2019). The MU3D database includes 320 video recordings of 80 senders (20 Black females, 20 Black males, 20 White females, and 20 White males) speaking honestly and dishonestly about their social relationships. Each sender created four different videos (a positive truth, a negative truth, a positive lie, and a negative lie). To reduce potential variation associated with sender race and message valence, I used only positive videos in which White individuals describe someone they claim to like. Half of the videos include true statements (i.e., the sender likes the person in their life that they are claiming to like), while the other half are deceptive (i.e., the sender does not actually like the person in their life that they are claiming to like). Accordingly, my stimuli include 80 videos: 20 White females and 20 White males, each providing one positive truth and one positive lie. Each receiver watched one of four randomized counterbalanced subsets of 20 videos, including five truthful White males, five truthful White females, five deceptive White males, and five deceptive White female senders. Subsets were created such that each sender only appeared only once. Coding. To establish intercoder reliability, two coders independently analyzed all 80 video files. The intraclass correlation coefficient (ICC) was calculated for each type of smile to determine the consistency of the relative frequency and duration of the smiles across coders (Koo & Li, 2016). ICC was chosen as the index of reliability because it reflects both degree of correlation and agreement between measurements. Specifically, I used a two-way mixed-effect model with multiple raters/measurements and absolute agreement to estimate ICCs for frequency and consistency agreement for duration. After independently coding a smaller subset including 18 30 video files, I assessed the initial intercoder reliability. Initially, this subset of videos revealed a low intercoder reliability; so we discussed and revised the coding scheme for clarification. Based on the 95% confidence interval of the ICC estimates, values between 0.75 and 0.9, and greater than 0.9 are indicative of good and excellent reliability, respectively (Koo & Li, 2016). We were able to establish acceptable interrater reliability using Intraclass Correlation Coefficients (ICCs) for both frequency and duration measures. The ICC estimates for duration measures yielded values of interrater reliability ranging from 0.718 to 0.802, indicating moderate to good reliability. Additionally, for frequency measures, interrater reliability ranged from 0.78 to 0.94, representing good to excellent reliability. Presence of Smile. Changes in smiling behaviors were coded to identify the presence of a smile. This data provided information about the frequency and durations of smiling behaviors. A change in smiling behavior was defined as the emergence of a smile, a return to a neutral face, an expression of a different emotion, or a transition to a different type of smile. If a sender smiled continuously, the presence of a new smile was only coded if they stopped smiling before starting again or transitioned to a different smile type. Otherwise, a continuous smile was coded as a single smile. The total number of smiles and the duration of each smile were coded using ELAN–software that allows coders to annotate nonverbal behavior in recorded videos (ELAN, Version 6.4, 2022). The dependent variables of interest were frequency and duration of each type of smile, as defined by the SIMs model (Niedenthal et al., 2010). Type of Smile. Coders identified each smile occurrence as one of the three types of smiles defined by Niedenthal et al. (2010): reward, affiliation, or dominance. To code a reward smile, coders had to identify sharp and distinct symmetrical movement in the corners of the lips (AU12, AU13), upwards movement in the eyebrows (AU1, AU2), and significant movement in 19 the cheeks and around the eyes (AU6). To code an affiliation smile, coders had to identify a soft symmetrical pull in the corners of the lips (AU12) and the pressing of the bottom and upper lip together (AU24). To code a dominance smile, coders had to identify nose scrunching and upper lip movement (AU9, AU10), upper eyelid lifting (AU5), and a distinct asymmetrical movement in one corner of the lips (AU12L/R). If there were smiles that did not meet any of these criteria, i.e., a smile where only AU12 was present, the smile was not coded. 2.3 Procedure Interested students signed up for this study on SONA. There, they were provided with a link to the Qualtrics survey. The first page of the survey was a consent form, providing information about the study. Participants had to indicate that they agree to study procedures and consent to participate in order to proceed. Receivers were randomly assigned to watch one subset of 20 videos from the Miami University Deception Detection Database (MU3D; Lloyd et al., 2019), in which senders claim to like a target person. Half the videos include truthful senders (i.e., they genuinely like the person they are claiming to like), while the other half includes deceptive senders (i.e., the sender does not actually like the person they are claiming to like). Critically, receivers were not made aware of the possibility of deception in the videos. After watching each video, receivers rated how open they would be in answering six personal questions in a hypothetical conversation with each sender. The questions originated from Aron et al. (1997) to generate interpersonal closeness in a lab setting. Ratings were made on a scale from 1 (not at all open) to 7 (very open). Questions are as follows: 1. What would constitute a "perfect" day for you? 20 2. If you could change anything about the way you were raised, what would it be? 3. Is there something that you've dreamed of doing for a long time? Why haven't you done it? 4. What is your most treasured memory? 5. If you were going to become a close friend with your partner, please share what would be important for him or her to know. 6. Share with your partner an embarrassing moment in your life. Following these self-disclosure ratings, receivers were asked to complete general demographic questions (i.e., age, sex, gender, and race). They were also given an opportunity to report any issues that took place during the study. Finally, receivers were fully debriefed and provided credit on SONA for their participation.LIES, SMILES, AND SELF-DISCLOSURE 21 Chapter 3. Results 3.1 Analyses 3.1.1 H1: Veracity effects on Self-Disclosure Data was visually inspected and deemed to be approximately normally distributed (Field, 2013). Therefore, I proceeded with the planned analysis using a linear mixed-effect model, using the lmerTest package in R. This model allowed me to account for random effects of both sender (i.e., stimuli) and receiver (i.e., participant). I specified sender veracity as a fixed effect, and mean self-disclosure ratings as the dependent variable. In other words, the model specification was as follows: disclosure.mean ~ veracity + (1 | sender.num) + (1 | receiver.id). Veracity was coded such that 0 represented truths and 1 represented lies. Table 1 provides all coefficients. Consistent with H1, there was a significant effect of veracity (B = - 0.16, SE = 0.03, t(4330.06) = -5.18, p < .001) such that receivers reported being more willing to self-disclose to truthful senders (M = 4.06, SE = 0.09) relative to dishonest senders (M = 3.91, SE = 0.09) [see Figure 5].1 1 I conducted two additional analyses using a linear mixed-effects model that included the gender of the sender (i.e., stimuli) and receiver (i.e., participant) as fixed effects. For the first model, I specified sender veracity and sender gender as fixed effects, and mean self-disclosure ratings as the dependent variable. The model specification was as follows: disclosure.mean ~ veracity + sender.gender + (1|sender.num) + (1|receiver.id). The model for senders revealed a significant effect of gender (B = - 0.35, SE = 0.10, t(37.58) = -3.47, p < .001) such that senders identified as men (M = 3.81, SE = 0.1) were associated with a lower mean self-disclosure than senders identified as women (M = 4.16, SE = 0.1). Similarly, for receivers, the model specification was as follows: disclosure.mean ~ veracity + receiver.gender + (1|sender.num) + (1|receiver.id). The model for receivers did not reveal a significant effect of gender (B = - 0.11, SE = 0.19, t(219.89) = 0.59, p = 0.56), indicating that receivers identified as men (M = 4.10, SE = 0.18) did not significantly differ in mean self-disclosure compared to receivers identified as women (M = 3.99, SE = 0.09). Critically, the main effect of veracity on self-disclosure remained significant in both models (ps < 0.001). This indicates that participants consistently disclosed less in response to deceptive senders, regardless of gender. Future research should examine potential interactions between sender, receiver, and veracity, as this study was not powered to do so. 22 Table 1 The Effect of Sender Veracity on Mean Self-Disclosure Ratings Note. This table demonstrates the coefficients for the Beta estimates, standard error, confidence intervals, F-statistic, and the p-value for the fixed effect and outcome variables. Specifically, the intercept (4.06) represents the mean disclosure when veracity is 0 (ie., truthful senders). The negative coefficient for veracity (- 0.16) indicates a significant decrease in mean disclosure for lies compared to truth. Additionally, it provides Beta estimates for the random effects. Figure 5 A Bar Graph Depicting the Effect of Veracity on Ratings of Self-Disclosure 23 Note. This figure depicts receivers' mean self-disclosure ratings for truthful and deceptive senders. The error bar represents the 95% confidence interval. 3.1.2 H2, H3 & H4: Veracity is Associated with Distinct Smiling Behaviour A series of paired samples t-tests were performed to evaluate whether there was a difference between the frequency of the three social functional smiles expressed by senders in their positive truth and positive lie videos. Consistent with H2, the results indicated that the frequency of reward smiles in the lie condition (M = 0 .81, SD = 1.18) was significantly lower than the frequency of reward smiles in the truth condition (M = 1.25, SD = 1.47), t(39) = 2.38, p = .022, d = 0.38, 95%CI [0.05, 0.70]. For affiliation smiles [H3], the results indicated that there was no significant difference between the frequency in the truth condition (M = 2.53, SD = 1.90) and in the lie condition (M = 2.66, SD = 1.89). t(39)= - 0.45, p = .66, d = - 0.07, 95%CI [ -0. 381, 0.239]. Finally, and consistent with H4, the results indicated that the frequency of dominance smiles in the lie condition (M = 2.23, SD = 1.81) was significantly higher than the frequency of 24 dominance smiles in the truth condition (M = 0.86, SD = 0.97), t(39) = - 4.68, p < .001, d = - 0.74, 95%CI [- 1.09, - 0.39]. Similarly, a series of paired sample t-tests were performed on the coded duration of each social functional smile type across truth and lie conditions. The pattern of results was similar to those found when considering frequency. Inconsistent with H2, the duration of reward smiles in the lie condition (M = 252.75, SD = 515.82) indicated there was no significant difference with the duration of reward smiles in the truth condition (M = 411.65, SD = 573.59), t(39) = 1.82, p = .077, d = 0.287, 95%CI [- 0.03, 0.60]. For affiliation smiles [H3], the results indicated that there was no significant difference between the duration in the truth condition (M = 128.66, SD = 165.56) and in the lie condition (M = 137.11, SD = 148.67). t(39)= - 0.28, p = .78, d = - 0.05, 95%CI [ -0. 36, 0.27]. Finally, and consistent with H4, the results indicated that the duration of dominance smiles in the lie condition (M = 476.38, SD = 476.08) was significantly higher than the duration of dominance smiles in the truth condition (M = 117.54, SD = 173.62), t(39) = - 5.28, p < .001, d = - 0.84, 95%CI [- 1.19, - 0.47]. 3.1.3 H5, H6 & H7: Social Functional Smiles do not Mediate Veracity and Self-Disclosure To test if the frequency of the three types of social functional smiles (reward, affiliation, and dominance) mediated the relationship between veracity and self-disclosure (H5, H6, and H7), three separate mediation models using Montoya & Hayes’ (2017) Macro MEMORE in SPSS were used to estimate the indirect effects of each smile type. Contrary to expectations, none of the proposed indirect effects were significant. Inconsistent with H5 and H6, reward smiles (indirect effect = 0.03, 95%CI [- 0.04, 0.13]) and affiliation smiles (indirect effect = 0.005, 95%CI [ - 0.03, 0.04]) did not mediate the relationship between veracity and ratings of 25 self-disclosure. Similarly, dominance smiles (indirect effect = -0.01, 95%CI [-0.16, 0.13]) did not mediate the relationship between veracity and ratings of self-disclosure [H7]. A full report of the a-path, b-path, direct, total, and indirect effects for frequency can be found in Figures 6-8. The same statistical approach was taken to test whether the duration of each smile time mediated the relationship between veracity and self-disclosure. As above, none of these indirect effects were statistically significant. Specifically, the duration of reward smiles (indirect effect = .02, 95% CI [-0.03, 010]), affiliation smiles (indirect effect = .00, 95% CI [-0.03, 0.05]), and dominance smiles (indirect effect = -0.03, 95% CI [-0.27, 0.16]) did not mediate the relationship between veracity and ratings of self-disclosure. Additionally, the a-path, b-path, direct, total, and indirect effects for duration can be found in Figure 9-11. Figure 6 Results of the Frequency of Reward Smiles Mediation Model 26 Note. This figure depicts the mediation model for the frequency of reward smiles in the data. The a-path is significant (p = .02), while the b-path (p = .38), direct effect (p = .16), total effect (p = .06), and indirect effect (- 0.03, CI [-0.13, .03]) are non-significant. Figure 7 Results of the Frequency of Affiliation Smiles Mediation Model Note. This figure depicts the mediation model for the frequency of affiliation smiles in the data. The a-path (p = .65), b-path (p = .42), direct effect (p =.07), total effect (p = .06), and indirect effects (- 0.005, CI [-0.04, .03]) are all non-significant. Figure 8 Results of the Frequency of Dominance Smiles Mediation Model 27 Note. This figure depicts the mediation model for the frequency of dominance smiles in the data. The a-path is highly significant (p < 0.000), while the b-path (p = .94), direct effect (p =.15), total effect (p = .06), and indirect effect (0.01, CI [- 0.13, .17]) are all non-significant. Figure 9 Results of the Duration of Reward Smiles Mediation Model 28 Note. This figure depicts the mediation model for the duration of reward smiles in the data. The a-path (p = .08), b-path (p = .34), direct effect (p = .12), total effect (p =.06), and indirect effects (- 0.02, CI [- 0.10, .03]) are all non-significant. Figure 10 Results of the Duration of Affiliation Smiles Mediation Model Note. This figure depicts the mediation model for the duration of affiliation smiles in the data. The a-path (p = .78), b-path (p = .81), direct effect (p = .06), total effect (p = .06), and indirect effects (.0009, CI [-0.05, .03]) are all non-significant. Figure 11 Results of the Duration of Dominance Smiles Mediation Model 29 Note. This figure depicts the mediation model for the frequency of dominance smiles in the data. The a-path is highly significant (p < 0.000), while the b-path (p = .79), direct effect (p = .19), total effect (p = .06), and indirect effect (0.03, CI [- 0.16, .27]) are all non-significant. LIES, SMILES, AND SELF-DISCLOSURE 30 Chapter 4. Discussion A large body of literature has examined the direct evaluation of lies (vs. truths) and the behavioral cues that occur alongside genuine and deceptive messages. Seemingly, research suggests that people are often duped as they default to the assumption that others are being truthful, even when alerted to the possibility of deception. This research takes a different approach, focusing on receivers’ indirect reaction to lies; rather than focusing on direct evaluations. I examined whether receivers— naive to the fact that veracity was manipulated —would respond differently to messages by liars versus truth-tellers. Specifically, I examined if sender veracity would affect receiver self-disclosure in a hypothetical conversation. Additionally, I expand on previous research on smiling behavior that has coded Duchenne and non-Duchenne smiles by adopting a novel coding scheme which discriminates three types of social functional smiles (i.e., reward, affiliation, and dominance smiles). I hypothesized that these smiles would mediate the relationship between veracity and self-disclosure. 4.1 Self-Disclosure as a Measure of Indirect Deception Detection My first hypothesis that receivers would report increased willingness to self-disclose personal information to truth-tellers relative to liars was supported. These findings align with previous research that finds that measuring an individuals’ social and emotional responses may indirectly discriminate liars from truth-tellers. Despite the naivety of potential deception, receivers responded differently to deceptive versus honest senders. This is also consistent with past research that suggested this difference often leads to responses that create interpersonal distance from dishonest actors (Gunderson, 2021;2023 & ten Brinke et al., 2016). These findings are also consistent with recent research that has examined the effect of veracity on the development of interpersonal closeness in text-based dyadic conversations. In a 31 recent study by Springings et al., (2023) researchers randomly assigned senders to a truth or lie condition and to converse with a receiver who was unaware of the potential of deception. Deceptive senders experienced less closeness and interpersonal connection than genuine senders. Notably, this same pattern was present for receivers paired with deceptive (vs. genuine) senders. Although this research did not examine characteristics of the actual conversation, these findings – paired with the results of this thesis – suggest that decreased willingness to disclose personal information by receivers may have been part of the mechanism explaining poor relationship development in the deceptive condition. More generally, these findings suggest that something about the effect of veracity on sender behavior may lead to receiver responses that create interpersonal distance from dishonest actors. 4.2 Social Functional Smiles as Behavioural Cues to Deception To determine what behaviors might mediate the relationship between sender veracity and receiver willingness to disclose, I coded sender behavior for the presence of social functional smiles. Consistent with my hypotheses, people used reward smiles more frequently when telling the truth than when lying. In contrast, people expressed more and longer dominance smiles when lying versus telling the truth. However, contrary to my hypothesis, affiliation smiles did not differ in frequency nor duration across truths and lies. These findings suggest that veracity is significantly associated with reward and dominance smiles, but not affiliation smiles in the context of telling positive truths and lies about interpersonal relationships. These findings are broadly consistent with the theoretical framework proposed by the SIMS model of smiling. People may enjoy talking about people they genuinely like and the expression of reward smiles may serve to reinforce the pleasurable social interactions that they have with this person. In contrast, lying about liking someone may allow the liar to assert superiority and control over the 32 situation and physically express the rush of satisfaction at their ability to manipulate the situation through a dominant smile. Additionally, a dominance smile may serve as an attempt to appear confident, mask true feelings, and make a lie more convincing. Contrary to my expectations, affiliation smiles were expressed equally by truth-tellers and liars. I expected liars to more frequently use affiliation smiles in a deliberate attempt to appear non-threatening and harmless. The lack of statistically significant effects may be explained by the possibility that truth-tellers may naturally exhibit affiliative smiles to convey their goodwill towards others. Thus, liars and truth tellers may use affiliative smiles to meet their shared goal of communicating about positive social bonds. Indeed, affiliation smiles were expressed nominally more often than either dominance and reward smiles – in both truth and lie conditions. 4.3 Social Functional Smiles are not Predictors of Self-Disclosure Finally, I expected that each type of social functional smile would mediate the relationship between sender veracity and receiver self-disclosure. However, the data did not support these relationships. In other words, neither the frequency nor duration of reward, affiliation, and dominance smiles appeared to explain the relationship between sender veracity and observers’ ratings of hypothetical self-disclosure. Specifically, mediation models revealed that smiling behavior was unrelated to receiver self-disclosure. Although there is a small body of literature that suggests that smiling is associated with greater self-disclosure (see Miller et al., 1983; Purvis et al., 1984; Norton & Montgamery, 1982); some research suggests that other factors such as greater perceived sender disclosure, greater perceived partner similarity, personality traits (i.e, propensity to trust and extraversion), and emotional states are other factors that can influence self-disclosure (Norton et al., 2007, Ignatius & Kokkonen, 2007, Sprecher et 33 al., 2012). Perhaps, smiling behaviour may be associated with receiver self-disclosure, but any effects were overwhelmed by the influence of these other factors not measured in the current study. 4.4 Limitation and Future Directions While this study provides novel insights for the deception detection literature, it is not without limitations. One significant limitation is that the stimuli is composed of only White senders and positively valenced messages. The MU3D database also includes negative valenced videos consisting of senders genuinely or deceptively describing someone in their life they did not like, as well as videos of Black male and female senders. The current study was limited to White senders and positive valenced videos to control for potential variation associated with race and message valence; however, these may be important moderators of this effect that deserve future research. People adjust and assess their own behavior in response to others, so we expect that receivers may respond differently based on the valence of statements (e.g., Carney, Colvin, & Hall, 2007). Indeed, previous research has found that lies and truths are evaluated differently on several indirect measures of veracity (i.e., anxiety; see Stel and van Dijik, 2018 for an exhaustive list of indirect measures) when negative, but not positive, emotions were expressed. Additionally, Porter and ten Brinke (2008) found that it is more common for individuals to betray some involuntary leakage of emotions during a negative emotional display. The nature of describing someone you like, whether lying or telling the truth, may be conducive to the expression of more (feigned or felt) positive emotions than negative emotions. In contrast, describing someone you do not like, whether lying or telling the truth, may not reveal any significant smiling behaviour at all. This suggests that negatively valenced videos may reveal 34 more discriminatory behavior for truthful and dishonest senders than the positively valenced videos. In future research, including the negative valenced MU3D database videos would allow us to investigate how message valence may moderate the relationship between veracity and self-disclosure. Based on previous findings, I might expect a valence by veracity interaction such that differences in self-disclosure would be greater for negative than positive messages. Previous research also suggests that results of the study might differ if it was extended to include Black senders. Findings from Lloyd et al. (2017), demonstrate that there are systematic race-based effects in deception judgements. Specifically, when asked to directly evaluate the veracity of senders, White receivers were quicker to initially gaze at the lie response button for Black senders than White senders (Lloyd et al., 2017). Despite this initial gaze, White receivers in the study exhibited a stronger truth bias for Black (vs. White) senders when asked to directly evaluate their veracity (see also O’Connor et al., 2023). It would be interesting to examine whether this exaggerated truth bias would also be observed in more indirect evaluations. Perhaps not. While previous research suggest that Black individuals on average are more likely to self-disclose to their in-group members (Shelton et al., 2010), receivers may be less attuned to subtle behavioral cues in cross-race interactions (Holoien et al., 2015; Shelton et al., 2023; Lloyd & Hugenberg, 2021). Accordingly, we might expect an interaction between race of the sender, receiver, and veracity in the form of an in-group advantage when it comes to indirect lie detection accuracy. The current study did not consider the role of culture; however, previous literature provides important insights into how different cultural contexts might influence social functional smiles. The framework suggests that the challenges and opportunities of human social living are universal. While the underlying functions of each type of smile exist across cultures, Martin et al. 35 (2017) suggest the frequency and urgency with which they are expressed can vary significantly. This variation is due to cross-cultural differences in beliefs about smiles, as these beliefs shape how smiles are displayed, perceived, and interpreted in various social contexts. Specifically, Martin et al. (2017) emphasized the need to understand specific cultural nuances, such as when and how smiles should be displayed, the perceptual biases that influence how smiles are decoded, and the social norms governing smile behavior. Previous research also highlights that cultural differences significantly affect the functions and interpretations of smiles. For instance, Rychlowska et al. (2015) investigated how the importance of the social functional subtypes of smiles varies as a function of historical heterogeneity (for a more detailed description of the methods; see Rychlowska et al., 2015). Their findings suggest that more heterogeneous and diverse countries such as the United States and New Zealand, are likely to associate affiliative smiles with social bonding than individuals in homogenous cultures, such as Japan or Indonesia. While, individuals in homogenous cultures associate the dominance function of smiles for negotiating hierarchies. Given these cultural variations, the findings of this study may not be universally applicable. The observed relationship between social functional smiling, self-disclosure, and deception may differ across cultural contexts, depending on how smiles are used to reinforce behavior, form and maintain social bonds, or manage status. Further exploration may reveal important cross-cultural differences or similarities that more accurately reflect the relationship between veracity, social functional smiling, and self-disclosure. Another limitation of the current study is its hypothetical nature. After each video, receivers were asked to rate how open they would be in answering six prompts that are used to generate interpersonal closeness in a hypothetical conversation with the sender from the video. 36 Future research should explore the current study in face to face communication by utilizing an in-person dyadic interaction. Unfortunately, we do not know exactly how well people can predict their disclosure rates. There is, however, considerable evidence that people aren’t not always accurate predictors of their future behavior or responses. For example, the affective forecasting literature suggests that people commonly mis-predict how enjoyable it will be to talk to strangers, and tell the truth (Pilin, 2021). Accordingly, I am unsure of how well these current findings will translate to actual conversations. Future research replicating the present study within a face-to-face dyadic context is needed before making definitive conclusions. The sample characteristics may also pose a limitation on the generalizability of the data. Participants were undergraduate UBC Okanagan students enrolled in a Psychology course. Although this is consistent with comparable literature, samples of undergraduate students limits the generalizability. The sample consisted mostly of young female participants, which is not representative of the general population. Previous research has indicated that sex may moderate self-disclosure rates. Specifically, female adolescents tend to disclose more personal information than male adolescents (Finkenauer et al., 2002; Soenens et al., 2006). Future research endeavors should aim to incorporate samples with a more balanced representation of sex and gender demographics. Additionally, investigating demographic differences in self-disclosure behaviors may enhance our understanding of veracity’s effect on self-disclosure rates. The results from two additional linear mixed-effects analyses, which included gender (for both senders and receivers) as a fixed effect, revealed that the main effects of veracity and self-disclosure remained statistically significant. These findings suggest that individual differences might offer a more nuanced understanding of the relationship between lying, smiling, and self-disclosure. Future research should further explore how individual differences in both senders 37 (e.g., extraversion) and receivers (e.g., emotional intelligence) may moderate these dynamics. For instance, research by Riggio (1987) indicates that extraverted senders, regardless of whether they are lying or telling the truth, tend to be perceived as more believable compared to their less extraverted counterparts. Future research should explore if high-extraversion senders elicit a higher willingness to self-disclose from receivers, irrespective of the veracity of their statements. Investigating how sender extraversion influences self-disclosure in the context of deception could provide valuable insights into how personality traits affect interpersonal communication. On the receiver's side, emotional intelligence (EI) may play a role in moderating the relationship between lying and self-disclosure. Research suggests that individuals with higher EI are better equipped to discern emotional cues and manage interpersonal interactions (Mayer, Salovey, & Caruso, 2008). However, the relationship between EI and deception accuracy is more complex. For example, Baker et al. (2013) found that high EI was associated with overconfidence and greater sympathy toward deceptive targets, which may enhance gullibility. Stewart et al. (2019) extends this by finding more successful lie detectors tend to be better at perceiving emotions and reasoning about others’ motivations, beliefs, and intentions. Further, they argue that while high EI participants may develop considerable sympathy for liars— negatively impacting their ability to accurately categorize deceit— successful lie detection also relies on cognitive theory of mind (ToM) skills, which involve explicit, detached, effortful reasoning when making veracity judgements. Thus, future research should explore the effect of individual differences in receivers EI on self-disclosure ratings across levels of veracity. 4.5 Theoretical and Practical Implications The present study advances the examination of deception by shifting the focus from direct lie detection to the indirect effects of deception on social interactions. This novel approach 38 underscores how exposure to deception can undermine trust and diminish or halt the exchange of personal information (Gordon & Miller, 2000; McCornack & Levine, 1990). The findings indicate that individuals are less likely to engage in self-disclosure with liars compared to truthful individuals. This observation suggests that there may be an inherent mechanism for self-protection against deception, which is essential for maintaining social relationships and personal well-being. Recognizing that individuals might naturally restrict self-disclosure in the presence of deception has practical implications for enhancing trust and communication in both personal and professional contexts. In group therapy settings, facilitators can leverage these insights to better manage group dynamics, while counselors can apply this understanding to navigate conflict resolution more effectively and foster healthier interactions. Additionally, the study offers novel behavioral insights by elucidating the correlation between reward and dominance smiles and truthfulness. These findings hold potential value in legal and forensic contexts, where a nuanced understanding of nonverbal cues to deception may enhance witness evaluation and interrogation techniques.LIES, SMILES, AND SELF-DISCLOSURE 39 Chapter 5. Conclusion Individuals are more willing to self-disclose to truth-tellers than liars, even when they do not suspect deceit. These findings suggest that, even in the absence of suspicion, people may wisely elect to keep interpersonal distance from liars, relative to truth-tellers. Although the presence and duration of social functional smiles did not mediate this relationship, reward smiles were more frequent in truthful versus deceptive messages, while dominance smiles showed the opposite relationship. Future research should investigate other potential cues (verbal and nonverbal) that may explain the relationship between veracity and self-disclosure. This will be important in understanding the mechanisms underlying accurate indirect deception detection, and because self-disclosure has downstream consequences for liking, conversations, and relationship formation. Overall, the current study advances our understanding of how undiscovered deception affects social interactions. 40 References Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of other in the self scale and the structure of interpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596-612. Aron, A., Melinat, E., Aron, E. N., Vallone, R. D., & Bator, R. J. (1997). The experimental generation of interpersonal closeness. Personality and Social Psychology Bulletin, 23(4), 363–377. Baker, A., ten Brinke, L. and Porter, S. (2013), Will get fooled again: Emotionally intelligent people are easily duped by high-stakes deceivers. Legal and Criminological Psychology, 18: 300-313. https://doi.org/10.1111/j.2044-8333.2012.02054.x Bogaard, Glynis, & Ewout H. Meijer. (2022). No evidence that instructions to ignore nonverbal cues improve deception detection accuracy. Applied Cognitive Psychology, 36(3), 636–47. https://doi.org/10.1002/acp.3950. Bond, C. F., & DePaulo, B. M. (2006). Accuracy of deception judgments. Personality and Social Psychology Review, 10(3), 214–234. Bond, C., Levine, T., & Hartwig, M. (2014) New findings in non-verbal lie detection. In Detecting Deception: Current Challenges and Cognitive Approaches, 37–58, https://doi.org/10.1002/9781118510001.ch2. Buller, D. B., & Burgoon, J. K. (1996). Interpersonal deception theory. Communication Theory, 6(3),203-242. Cohen, S. (2004). Social relationships and health. American Psychologist, 59(8), 676-684. Darwin, C. R. (1872). The expression of the emotions in man and animals. London: John Murray. 1st edition. 41 DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996). Lying in everyday life. Journal of Personality and Social Psychology, 70(5), 979–995. DePaulo, B. M., & Kashy, D. A. (1998). Everyday lies in close and casual relationships. Journal of Personality and Social Psychology, 74(1), 63–79. https://doi.org/10.1037/0022-3514.74.1.63 DePaulo, B. M., Lindsay, J. J., Malone, B. E., Muhlenbruck, L., Charlton, K., & Cooper, H. (2003). Cues to deception. Psychological Bulletin, 129(1), 74–118. Derlega, V. J., Winstead, B. A., & Greene, K. (2008). Self-disclosure and starting a close relationship. In S. Sprecher, A. Wenzel, & J. Harvey (Eds.), Handbook of relationship initiation (pp. 153–174). Psychology Press. Duchenne de Boulogne (1990). Mécanisme de la Physionomie Humaine (Cuthbertson RA, Ed & trans.). New York: Cambridge University Press. (Original work published 1862) Ebesu, Amy S., & Miller, M. S. (1994). Verbal and nonverbal behaviors as a function of deception type. Journal of Language and Social Psychology, 13(4), 418–42. https://doi.org/10.1177/0261927X94134004. Ekman, P., Friesen, W. V., and O'Sullivan, M. (1988). Smiles when lying. Journal of Personality and Social Psychology, 54, 414–20 Ekman P. (1985). Telling lies: Clues to deceit in the marketplace, politics and marriage. New York: W. W. Norton Ekman, P., & Friesen, W V (1982). Felt, false, and miserable smiles. Journal of Nonverbal Behavior, 6, 238-252. ELAN (Version 6.4) [Computer software]. (2022). Nijmegen: Max Planck institute for psycholinguistics, The Language Archive. Retrieved from https://archive.mpi.nl/tla/elan 42 Fletcher, G. J. O., Simpson, J. A., Thomas, G., & Giles, L. (1999). Ideals in intimate relationships. Journal of Personality and Social Psychology, 76(1), 72–89. Finkenauer, C., Engels, R.C.M.E. & Meeus, W. Keeping secrets from parents: Advantages and disadvantages of secrecy in adolescence. Journal of Youth and Adolescence 31, 123–136 (2002). https://doi.org/10.1023/A:1014069926507 Fridlund, A. J. (1994). Human facial expression: An evolutionary view. Academic Press. Gilovich, T., Savitsky, K., & Medvec, V. H. (1998). The illusion of transparency: Biased assessments of others' ability to read one's emotional states. Journal of Personality and Social Psychology, 75, 332–346. doi: 10.1037/0022-3514.75.2.332 Gordon, A. K., & Miller, A. G. (2000). Perspective differences in the construal of lies: Is deception in the eye of the beholder? Personality and Social Psychology Bulletin, 26(1), 46–55. https://doi.org/10.1177/0146167200261005 Grandey, A. A., Fisk, G. M., Mattila, A. S., Jansen, K. J., & Sideman, L. A. (2005). Is “service with a smile” enough? Authenticity of positive displays during service encounters. Organizational Behavior and Human Decision Processes, 96, 38–55. Gunderson, C., Baker, A., Pence, A., & ten Brinke, L. (2021, in press). Interpersonal consequences of deceptive sadness. Personality and Social Psychology Bulletin. Gunnery, S. D., & Hall, J. A. (2014). The Duchenne smile and persuasion. Journal of Nonverbal Behavior, 38(2), 181–194. https://doi.org/10.1007/s10919-014-0177-1 Gunderson CA & Ten Brinke L. (2022). The connection between deception detection and financial exploitation of older (vs. young) adults. J Appl Gerontol. 41(4), 940-944. doi: 10.1177/07334648211049716. Hancock, J. T., Curry, L. E., Goorha, S & Woodworth, M. (2008). On lying and being lied to: A 43 linguistic analysis of deception in computer-mediated communication, Discourse Processes, 45(1), 1 -23 Hancock, J. T., Thom-Santelli, J., & Ritchie, T. (2004). Deception and design: The impact of communication technology on lying behavior. Proceedings, Conference on Computer Human Interaction, 6, 129–134. Hartwig, M., & Bond Jr, C. F. (2014). Lie detection from multiple cues: A meta‐analysis. Applied Cognitive Psychology, 28(5), 661-676. Hauch, V., Blandón-Gitlin, I., Masip, J., & Sporer, S. L. (2015). Are computers effective lie detectors? A meta-analysis of linguistic cues to deception. Personality and Social Psychology Review, 19(4), 307-342. Hayes, Andrew F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures. 1(1) ,77–89. https://doi.org/10.1080/19312450709336664. Hennig-Thurau, T., Groth, M., Paul, M., & Gremler, D. D. (2006). Are all smiles created equal? How emotional contagion and emotional labor affect service relationships. Journal of Marketing, 70, 58–73. Holoien, D. S., Bergsieker, H. B., Shelton, J. N., & Alegre, J. M. (2015). Do you really understand? Achieving accuracy in interracial relationships. Journal of Personality and Social Psychology, 108(1), 76–92. https://doi.org/10.1037/pspi0000003 Ignatius, E., & Kokkonen, M. (2007). Factors contributing to verbal self-disclosure. Nordic Psychology, 59(4), 362–391. https://doi.org/10.1027/1901-2276.59.4.362 Koo, T. K., & Li, M. Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation 44 Coefficients for Reliability Research. Journal of chiropractic medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012 Krumhuber, E., Manstead, A. S. R., Cosker, D., Marshall, D., & Rosin, P. L. (2007). Facial dynamics as indicators of trustworthiness and cooperative behavior. Emotion, 7, 730–735. Landis, C. (1924). Studies of Emotional Reactions. II. General Behavior and Facial Expression. Journal of Comparative Psychology, 4, 447-510. Levine, T. R. (2018). Ecological validity and deception detection research design. Communication Methods and Measures, 12(1), 45-54. Levine, T. R. (2014). Truth-default theory (TDT) a theory of human deception and deception detection. Journal of Language and Social Psychology, 33(4), 378-392. Levine, T.R., Park, H.S., & McCornack, S. A. (1999) Accuracy in detecting truths and lies: Documenting the “veracity effect”, Communication Monographs, 66(2), 125-144, DOI: 10.1080/03637759909376468 Lloyd, E. P., & Hugenberg, K. (2021). Beyond bias: response bias and interpersonal (in)sensitivity as a contributors to race disparities. European Review of Social Psychology, 32(2), 201–234. https://doi.org/10.1080/10463283.2020.1820699 Lloyd, E. P., Deska, J. C., Hugenberg, K., McConnell, A. R., Humphrey, B. T., & Kunstman, J. W. (2019). Miami University deception detection database. Behavior Research Methods, 51(1), 429–439. Lloyd, E. P., Hugenberg, K., McConnell, A. R., Kunstman, J. W., & Deska, J. C. (2017). Black and white lies: Race-based biases in deception judgments. Psychological Science, 28(8), 1125-1136. https://doi.org/10.1177/0956797617705399 45 Luke, T. J. (2019). Lessons from Pinocchio: Cues to deception may be highly exaggerated. Perspectives in Psychological Science, 14(4), 646–671. https://doi.org/10.1177/1745691619838258 Mandelbaum, J. (2014), Exploring the illusion of transparency when lying and truth-telling: The impact of age, self-consciousness, and framing. CUNY Academic Works.https://academicworks.cuny.edu/gc_etds/505 Martin, J., Rychlowska, M., Wood, A., & Niedenthal, P. (2017). Smiles as multipurpose social signals. Trends in Cognitive Sciences, 21(11), 864–877.https://doi.org/10.1016/j.tics.2017.08.007 Martin, J.D., Abercrombie, H. C., Gilboa-Schechtman, E. & Niedenthal P. N. (2018). Functionally distinct smiles elicit different physiological responses in an evaluative context. Scientific Reports, 8(1), 3558. https://doi.org/10.1038/s41598-018-21536-1. Martin, J. D., Wood, A., Cox W. T. L., Sievert, S., Nowak, R., Gilboa-Schechtman, E., Zhao, F., Witkower, Z., Langbehn, A. T., & Niedenthal, P. M. (2021). Evidence for distinct facial signals of reward, affiliation, and dominance from both perception and production tasks. Affective Science, 2(1), 14–30. https://doi.org/10.1007/s42761-020-00024-8. Martin, J. (Researcher). 2021. Examples of smile stimuli. Reward (left), affiliation (center), and dominance (right) smiles. [Digital Image]. https://link.springer.com/article/10.1007/s42761-020-00024-8 Matsumoto, D & Hyisung C. H. (2020). Clusters of nonverbal behavior differentiate truths and lies about future malicious intent in checkpoint screening interviews. Psychiatry, Psychology, and Law, 28(4), 463–78. https://doi.org/10.1080/13218719.2020.1794999. 46 Mayer, J. D., Salovey, P., & Caruso, D. R. (2008). Emotional intelligence: New ability or eclectic traits? American Psychologist, 63(6), 503-517. https://doi.org/10.1037/0003-066X.63.6.503 McCornack, S. A., & Levine, T. R. (1990). When lies are uncovered: Emotional and relational outcomes of discovered deception. Communication Monographs, 57(2), 119–138. https://doi.org/10.1080/03637759009376190 McCornack, S. A., and Parks, M. R. (1986). Deception detection and relationship development: the other side of trust. Communication Yearbook , 9, 377–389. doi: 10.1080/23808985.1986.11678616 Miller, L. C., Berg, J. H., & Archer, R. L. (1983). Openers: Individuals who elicit intimate self-disclosure, Journal of Personality and Social Psychology, 44, 1234–1244. Montoya, A. K., & Hayes, A. F. (2017). Two condition within-participant statistical mediation analysis: A path-analytic framework. Psychological Methods, 22, 6-27. Niedenthal, P. M., Mermillod, M., Maringer, M., & Hess, U. (2010). The simulation of smiles (SIMS) model: Embodied simulation and the meaning of facial expression. Behavioral and Brain Sciences, 33, 417-433.http://dx.doi.org/10.1017/S0140525X10000865 Norton, R., & Montgomery, B. M. (1982). Style, content, and target components of openness. Communication Research, 9, 399–431. Norton M. I., Frost J. H., Ariely D. (2007). Less is more: The lure of ambiguity, or why familiarity breeds contempt. Journal of Personality and Social Psychology, 92, 97–105. O’Connor, A. M., Hall, W., & Campbell, K. L. (2023). Rating the honesty of white and 47 black children via implicit and explicit measures: Implications for child victims in the criminal justice system. Child Maltreatment, 28(3), 450-461. https://doi.org/10.1177/10775595231173363 Pilin, M. A. (2021). The past of predicting the future: A review of the multidisciplinary history of affective forecasting. History of the Human Sciences, 34(3-4), 290-306. https://doi.org/10.1177/0952695120976330 Purvis, J. A., Dabbs, J., & Hopper, C. (1984). The “opener”: Skilled user of facial expression and speech pattern. Personality and Social Psychology Bulletin, 10, 61–66. Reno, R. R., Cialdini, R. B., &; Kallgren, C. A. (1993). The transsituational influence of social norms. Journal of Personality and Social Psychology, 64(1), 104-112. Riggio, R. E., Widaman, K. F., Tucker, J. S., & Salinas, C. (1991). Beauty is more than skin deep: Components of attractiveness. Basic and Applied Social Psychology, 12(4), 423-439. Riggio, R. E., Tucker, J., & Throckmorton, B. (1987). Social skills and deception ability. Personality and Social Psychology Bulletin, 13(4), 568-577. Rychlowska, M., Miyamoto, Y., Matsumoto, D., Hess, U., Gilboa-Schechtman, E., Kamble, S., Muluk, H., Masuda, T., & Niedenthal, P. M. (2015). Heterogeneity of long-history migration explains cultural differences in reports of emotional expressivity and the functions of smiles. Proceedings of the National Academy of Sciences, 112(19), E2429–E2436. https://doi.org/10.1073/pnas.1413661112 Rychlowska, M., Jack, R. E., Garrod, O. G. B., Schyns, P. G., Martin, J. D., & Niedenthal, P. M. (2017). Functional Smiles: Tools for Love, Sympathy, and War. Psychological Science, 28(9), 1259–70. https://doi.org/10.1177/0956797617706082. 48 Scott, Susie. (2012). Intimate deception in everyday life. In Studies in Symbolic Interaction, edited by Norman K. Denzin. Emerald Group Publishing Limited, 251–79. https://doi.org/10.1108/S0163-2396(2012)0000039011. Shelton, J. N., Trail, T. E., West, T. V., & Bergsieker, H. B. (2010). From strangers to friends: The interpersonal process model of intimacy in developing interracial friendships. Journal of Social and Personal Relationships, 27(1), 71–90. https://doi.org/10.1177/0265407509346422 Shelton, J. N., Turetsky, K. M., & Park, Y. (2023). Responsiveness in interracial interactions. Current opinion in psychology, 53, 101653. https://doi.org/10.1016/j.copsyc.2023.101653 Smith, W. J. (1977). The behavior of communicating: An ethological approach. Cambridge, MA: Harvard University Press. Soenens, B., Vansteenkiste, M., Luyckx, K., & Goossens, L. (2006). Parenting and adolescent problem behavior: an integrated model with adolescent self-disclosure and perceived parental knowledge as intervening variables. Developmental psychology, 42(2), 305–318. https://doi.org/10.1037/0012-1649.42.2.305 Sprecher, S., Treger, S., & Wondra, J. D. (2013). Effects of self-disclosure role on liking, closeness, and other impressions in get-acquainted interactions. Journal of Social and Personal Relationships, 30(4), 497-514. https://doi.org/10.1177/0265407512459033 Street, C. N. H., & Richardson, D. C. (2015). The focal account: Indirect lie detection need not access unconscious, implicit knowledge. Journal of Experimental Psychology: Applied, 21(4), 342–355. https://doi.org/10.1037/xap0000058 Stewart, S. L. K., Wright, C., & Atherton, C. (2019). Deception Detection and Truth Detection 49 Are Dependent on Different Cognitive and Emotional Traits: An Investigation of Emotional Intelligence, Theory of Mind, and Attention. Personality and Social Psychology Bulletin, 45(5), 794-807. https://doi.org/10.1177/0146167218796795 Tait, Sophie E., & Jeske, D. (2015). Hello stranger! Trust and self-disclosure effects on online information sharing. International Journal of Cyber Behavior, Psychology and Learning, 5(1). https://doi.org/10.4018/ijcbpl.2015010104. ten Brinke, L., Vohs, K. D., & Carney, D. R. (2016). Can ordinary people detect deception after all?. Trends in Cognitive Sciences, 20(8), 579-588. ten Brinke, L. & Porter, S. (2012). Cry me a river: Identifying the behavioral consequences of extremely high-stakes interpersonal deception. Law and Human Behavior, 36, 469–77. https://doi.org/10.1037/h0093929. Tyler, M. J., Feldman, R. S., & Reichert, A. (2006). The price of deceptive behavior: Disliking and lying to people who lie to us. Journal of Experimental Social Psychology 42(1), 69–77. https://doi.org/10.1016/j.jesp.2005.02.003. Van Kleef, G. A. (2009). How emotions regulate social life. Current Directions in Psychological Science, 18(3), 184–188. Vacharkulksemsuk, T., Reit, E., Khambatta, P., Eastwick, P. W., Finkel, E. J., & Carney, D. R. (2016). Dominant, open nonverbal displays are attractive at zero-acquaintance. Proceedings of the National Academy of Sciences, 113(15), 4009-4014. Vrij, A., Hartwig, M., & Anders Granhag, P. (2019). Reading lies: Nonverbal communication and deception. Annual Review of Psychology , 70(1), 295–317. https://doi.org/10.1146/annurev-psych-010418-103135. Vrij, A. (2008). Detecting lies and deceit: Pitfalls and opportunities (2nd ed.). John Wiley & 50 Sons Ltd. Vrij, A., Edward, K., & Bull, R. (2001). Police officers’ ability to detect deceit: The benefit of indirect deception detection measures. Legal and Criminological Psychology, 6(2), 185–196. Vrij, A., Edward, K., Roberts, K.P., Bull, R. (2000). Detecting deceit via analysis of verbal and nonverbal behavior. Journal of Nonverbal Behavior, 24, 239–263. https://doi.org/10.1023/A:1006610329284 Wheeless, L. R., & Grotz, J. (1977). The measurement of trust and its relationship to self-disclosure. Human Communication Research, 3(3), 250–257. https://doi.org/10.1111/j.1468-2958.1977.tb00523.x Yeomans, M., Schweitzer, M. E., & Brooks, A. W. (2022). The conversational circumplex: Identifying, prioritizing, and pursuing informational and relational motives in Conversation. Current Opinion in Psychology, 44, 293–302. https://doi.org/10.1016/j.copsyc.2021.10.001. Zuckerman M, DePaulo BM, & Rosenthal R. (1981). Verbal and nonverbal communication of deception. Social Psychology, 14,1–59
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Ten Brinke, Leanne Marie
Lies, smiles, and self-disclosure : an examination of how social functional smiles mediate the relationship between lying and self-disclosure
Text