Deception Dynamics: Identifying Patterns of Social Coordination During

Truthful and Dishonest Conversation

A dissertation submitted to the

Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the Department of Psychology of the McMicken College of Arts and Sciences

MaryLauren Malone April 2017

M.A., University of Cincinnati, April 2014 M.A., University of Cincinnati, December 2013 B.A., Wittenberg University, May 2008

Committee Chair: M. J. Richardson, Ph.D. Committee: Michael A. Riley, Ph.D. Rachel W. Kallen, Ph.D. Richard C. Schmidt, Ph.D. Abstract

Deception and its detection are prevalent phenomena in almost all forms of social interaction. For some, lying is a relatively harmless part of maintaining relationships with friends and colleagues; for others, lie detection is a serious matter of public safety and security. However, weak theoretical and empirical support for the dominant perspective in deception research has prompted an appeal for a novel approach. As such, the current investigation presents an original experimental approach that is chiefly concerned with the contextually relevant interpersonal coordination dynamics of socially situated individuals at multiple levels of an interaction.

Motivated by the dynamical systems framework for understanding behavior, the present study was developed in consideration of specific limitations within current efforts to understand deception. First, where existing research has largely ignored the social quality of an inherently social event by focusing on individuals rather than social units, the current study treats deception as a multi-scale phenomenon that emerges, fundamentally, between interacting individuals.

Secondly, this project foregoes the traditional methodology of subjectively coding discrete, individual behaviors, focusing instead on techniques that capture the flexible, adaptive, and dynamic nature of social interaction.

A fundamental hypothesis was that the coordination dynamics of co-actors would be influenced by the deceptive nature of an interaction. If the behavioral dynamics of deception differ from those of honesty, this difference may provide a basis for lie discrimination. To test this prediction, a set of experiments were performed to assess the dynamic structure of social coordination that occurs between co-actors. During a series of deception tasks, paired individuals conversed with the aim of lying undetected or detecting deception. Dynamic social coordination patterns were then assessed with respect to the ability of co-actors to detect deception.

ii Results support the central prediction that the effect of deception on social coordination reflects corresponding differences in task performance. That is, the behavioral dynamics of truthful interactions were characterized by more robust patterns of coordination and stability than movement during deceptive interactions. Following the assumptions within a coordination dynamics framework, these results suggest that the coupled nature of an interaction is disrupted during deception, and moreover, that this disruption may provide a means through which liars can be identified. Indeed, task performance was better (i.e., more accurate, higher confidence) for interactions that embodied characteristic patterns of movement — the behavioral dynamics of high-performing pairs exhibited less stability and coordination when one of the individuals was lying, while the behavior of low performers did not. Such a relationship between lie-discrimination ability and coordination suggests that people are sensitive to information specifying deception.

These findings provide seminal evidence of an observable behavioral process that reliably differentiates truthful and deceptive interactions: social coordination dynamics. As such, the research presented here provides valuable insight into how future work should approach the concept of deception, both theoretically and methodologically, representing a point of interest not only for many different fields in the behavioral and social sciences, but also for those concerned with law enforcement, business, politics, judicial processes, and national security.

iii

Acknowledgements

I would like to acknowledge my dissertation committee, Dr. Michael Richardson, Dr. Michael

Riley, Dr. Rachel Kallen, and Dr. Richard Schmidt, for their excellent guidance in developing the project, and for their commitment to ensuring I create something to be proud of. In particular, I would like to acknowledge to Dr. Michael Richardson for his mentorship, patience, and friendship throughout my graduate studies. I would also like to acknowledge Dr. Kevin Shockley and Dr.

Sarah Cummins-Sebree for their substantial contributions to my academic and professional development.

It is also important to acknowledge my fellow graduate students who gave their time and attention to me in serving as volunteer participants in my experiments, as well as my comrades throughout the chaos and complexity of graduate school. I literally could not have done it without them.

This research was supported by National Institutes of Health grant R01GM10504

v Table of Contents Abstract ...... ii Acknowledgements ...... v Table of Contents ...... vi List of Tables and Figures ...... vii Chapter 1. Introduction ...... 1 Traditional Approaches to Deception Research ...... 2 A Dyadic Approach to Deception Research ...... 4 Information and the Social Environment ...... 6 Influential Properties of the Social Environment ...... 9 Current Investigation: Overview and Predictions ...... 11 Chapter 2. Experiment 1 ...... 16 Method ...... 17 Participants ...... 17 Materials ...... 17 Procedure ...... 21 Analysis ...... 21 Results ...... 26 Task Performance ...... 26 Intrapersonal movement dynamics ...... 28 Interpersonal coordination dynamics ...... 30 Correlations between measures of movement, performance, and dispositional characteristics ...... 33 Discussion ...... 33 Chapter 3. Experiment 2 ...... 38 Method ...... 39 Participants ...... 39 Materials ...... 39 Procedure ...... 41 Analysis ...... 41 Results ...... 41 Task Performance ...... 41 Intrapersonal movement dynamics ...... 43 Interpersonal coordination dynamics ...... 46 Correlations between measures of movement, performance, and dispositional characteristics ...... 49 Discussion ...... 49 Chapter 4. General Discussion ...... 52 References ...... 62

vi List of Tables Table 1. Lie discrimination ability in high and low performance groups as measured by composite performance score in Experiment 1 ...... 28 Table 2. Lie discrimination ability in high and low performance groups as measured by composite performance score in Experiment 2 ...... 43

List of Figures Figure 1. Experimental setup of Vision conditions and No-vision conditions...... 18 Figure 2. Range and indication of possible performance scores on a single trial...... 23 Figure 3. Truthful interactions were characterized by greater task performance and lie discrimination, as indicated by a comparison of performance scores across all conditions of information and statement type...... 27 Figure 4. Average percentage of recurrent points and average recurrent line length for the individual motor dynamics of Actors versus Perceivers in Low- and High-performing pairs in Truth and Lie conditions...... 29 Figure 5. Average proportion of diagonal line structures that characterize the movement dynamics of Low- and High-performers during Truth and Lie statements...... 30

Figure 6. Average %REC and Lmean for the interpersonal behavior of High-and Low-performing participant pairs during Truth and Lie statements...... 31 Figure 7. Average %DET for Truth and Lie statements across information conditions...... 33 Figure 8. Example of images recorded by the Kinect in standard video, and a depth-array...... 40 Figure 9. Experimental setup for video-mediated interactions...... 40 Figure 10. Truthful interactions were characterized by greater task performance and lie discrimination, as indicated by a comparison of average performance scores across all conditions of information and statement type...... 42 Figure 11. Average %REC that characterize the movement dynamics of Actors and Perceivers during Truth and Lie statements in both information conditions...... 44

Figure 12. Average Lmean and %DET for the movement dynamics of High- and Low- performing individuals during Truth and Lie statements within both information conditions...... 45 Figure 13. Average %REC for the interpersonal behavior of High- and Low-performers...... 47

Figure 14. Average Lmean and %DET for the behavioral dynamics of Low- and High- performance pairs during Truth and Lie statements in both information conditions...... 48

vii

Chapter 1

Introduction

Honesty may be the best policy, but deception is often the reality of many day-to-day interactions (Ekman, 1992). On average, lies comprise one quarter of conversational content

(DePaulo et al., 1996), and most of them go undetected (Ekman & O’Sullivan, 1991; Porter et al.,

2000). Detecting deception is an important professional skill for therapists, lawyers, health professionals, salespeople, and teachers. Recognizing dishonesty can also aid military personnel in identifying potentially dangerous individuals, help law enforcement officials solve a crime, and may assist credibility assessment investigators in maintaining public safety and security. Despite the pervasiveness of deception and the utility of its detection, current lie-discrimination techniques are based on controversial research findings that fall short on numerous reliability and validity criteria (Vrij & Fisher, 2016). As such, there are two major challenges for deception research today

(Gamer & Ambach, 2014): (1) identifying the behavioral (including sensorimotor) processes that differentiate truthful and deceptive interactions, and (2) determining how these processes are modulated by situational and personality factors.

In the wake of decades of research, it still remains unclear how the overt behavior of liars differs from the behavior of truth tellers. This lack of understanding may be due to the historic challenge of objectively capturing and quantifying the time-evolving and adaptive nature of human behavior during complex social interactions. Given recent advances in unobtrusive data collection methods and nonlinear dynamical time- and event-series analysis methods, however, it is now possible to objectively determine and evaluate the dynamics of social behavior (Richardson et al., 2014; Schmidt et al., 2012; Schmidt & Richardson, 2008). Cross-recurrence quantification analyses, in particular, provides the ability to evaluate subtle, yet important, aspects of complex

social coordination and interpersonal behavioral order (e.g., Schmidt et al., 2014; Shockley et al.,

2003). This project determined whether these methods may allow for the discrimination of differences between honest and deceptive interactions on the basis of observable motor behaviors.

The objective of the current dissertation was to establish whether and, if so, how patterns of social coordination (SC) during deceptive interaction differ from SC during truthful interaction, and how the dynamics of SC interact with dispositional characteristics. The specific aims of the present study were to: (1) determine the degree to which SC is influenced by the deceptive or non- deceptive nature of a social interaction; (2) evaluate whether an individual’s ability to detect the deceptive or truthful intent of a co-actor (i.e., discrimination ability) is related to the patterns of SC that occur between the individual and their co-actor; (3) establish the degree to which deceptive or truthful intent can be detected from changes in the structure of the kinematic information that arises during an interpersonal interaction; and (4) identify whether dispositional characteristics such as social aptitude1 and rapport relate to SC and the ability to identify deception when situated in an interaction involving SC.

Traditional Approaches to Deception Research

A central challenge in the study of deception is the identification of behavioral processes that differentiate truthful and deceptive interactions. In addressing this challenge, deception research typically focuses on either an individual’s ability to detect deception or the behaviors of the deceiver (Driskell et al., 2012; Dunbar et al., 2011; Tower et al., 2013). A perceiver-focused approach emphasizes deception detection (discrimination) accuracy and the conditions under which it occurs (e.g., O’Sullivan, 2005; O’Sullivan et al., 2009). This methodology has not proven effective, however, given that accuracy in perceiver-based studies rarely exceeds chance (Bond &

DePaulo, 2006), even when controlling for professional training (c.f. Vrij, 2008). Alternatively, an

1 Social aptitude refers collectively to measures of social understanding and personality such as social intelligence, social skill, and emotional intelligence.

actor-focused approach aims to identify particular cues or indicators associated with deceptive behavior. The polygraph is a notable example of one such methodology. However, the National

Research Council has indicated that the technique lacks significant scientific support and often produces ambiguous, if not inaccurate, results (National Research Council, 2003). Though valuable in revealing observable correlates of deception, the behavioral cues currently identified are variable across time, inconsistent across studies, and are generally too statistically weak throughout the existing literature to be regarded as reliable indicators of deception (DePaulo et al.,

2003; Sporer & Schwandt, 2007; Vrij & Fisher, 2016). One potentially hazardous result of these inconsistencies is that real-life differences between truth tellers and liars are more subtle and less clear than is stated in police interview manuals and believed by the common public (DePaulo et al., 2003; Vrij et al., 2008). In fact, there is no single behavioral cue known at present that is consistently linked with dishonesty (Bond, Levine, & Hartwig, 2015; Giolla, Granhag, & Vrij,

2015; Hartwig & Bond, 2014; Vrij, 2008).

The weak theoretical and empirical support for the dominant perspective in deception research has prompted an appeal for novel approaches (Masip & Herrero, 2015; Vrij & Granhag,

2012). The development of a successful new approach may benefit from considering two potential limitations of most current efforts to understand deception. First, by limiting the focal point of research to the behavior exhibited by a single actor or perceiver, existing research has treated deception as a solely individual-level phenomenon and has thus largely ignored the social quality of an inherently social condition (Buller & Burgoon, 1996; Elkins et al., 2014). Second, an emphasis on the subjective coding of discrete, conspicuous, pre-recorded individual behaviors leaves little consideration for the flexible, adaptive, and dynamic (i.e., time-varying) nature of social interaction (Burgoon & Qin, 2006; Cañal-Bruland, 2017); if deceptive behavior unfolds over time, many current assumptions within the deception literature may be incorrect.

A Dyadic Approach to Deception Research

In contrast to methodologies that examine isolated characteristics or abilities of individuals, a dyadic approach attempts to understand social phenomena by assessing the behaviors of interacting partners. The dyadic approach of Interpersonal Deception Theory (IDT; Buller &

Burgoon, 1996) defines lying as a socially regulated dynamic exchange between communicators, and has motivated recent investigations into how dishonesty shapes interpersonal behavior

(Burgoon et al., 2016; Casarrubea et al., 2015; Jensen et al., 2010). Studies grounded within an

IDT framework acknowledge the value of utilizing time-oriented data, but still rely on classification schemes to manually code behavior into discrete events (Burgoon et al., 2015;

Dunbar et al., 2014). The social coordination dynamics perspective (SCD; e.g., Schmidt, Carello,

& Turvey, 1990; Schmidt & O’Brien, 1997) similarly emphasizes the reciprocal influence of interacting individuals, positing that the mutual exchange of information couples co-actors and shapes the resulting, observable patterns of coordinated behavior (see Oullier & Kelso, 2009;

Schmidt & Fitzpatrick, 2016; Schmidt & Richardson, 2008 for reviews). This time-evolving mutual adaptation, known as SC or interactional synchrony, may take the form of verbal (linguistic) or nonverbal (movement) coordination (Burgoon et al., 1995). Recent research investigating the dynamics of interpersonal interactions during cooperative tasks provides evidence for the utility of integrating analyses of recurrent behavioral patterns in assessments of social perception and action

(Fusaroli & Tylén, 2016; Gorman et al., 2012; Knight, Kennedy, & McComb, 2016; Strang et al.,

2014).

It has been suggested, but never explicitly tested, that SC is disrupted during deception and this disruption may provide a means through which liars can be identified (Yu et al., 2013). Studies examining the coordination dynamics of deceptive versus truthful dyads are limited at present, though initial within-person assessments of the kinematic and linguistic behavior of liars provide a

foundation for more extensive research into the SC dynamics of lying and lie detection. For instance, the work of Burgoon and Qin (2006) verifies that verbal behavior during deception exhibits considerable variability over the course of a conversation, while text-based analyses of written language indicate differences in the dynamic patterning of deceptive versus truthful writing

(Newman et al., 2003; Zhou et al., 2004). This research not only highlights the dynamic nature of deceptive discourse, but also suggests fundamental differences in the structure of language, dependent on deceptive intent. Investigations into the intrapersonal movement dynamics of deception reveal a similar story. For example, in a study examining the stability and complexity of upper body movement dynamics within an individual, Duran and colleagues (2013) uncovered characteristic differences between the dynamical signatures of motion associated with honest and dishonest statements such that intrapersonal motor behavior during deception was less stable and more complex than during honest statements.

Taken together, these studies demonstrate that there appear to be differences in the behavioral dynamics of truthful and dishonest individuals; thus, differences in interpersonal behavioral dynamics (i.e., SC) may also exist. However, there is little, if any, research examining such potential differences. In addition to employing subjective coding techniques that likely contribute to methodological issues of reliability and sensitivity (Van Der Zee et al., 2015; Yu et al., 2015), current deception research also primarily uses pre-recorded videos of individuals as stimuli, as opposed to real time co-actors (c.f. DePaulo et al., 1997; Vrij et al., 2010). Should SC dynamics differ between truthful and deceptive interactions, this may be a channel through which people can accurately discriminate between lies and the truth. If so, it is important to address the challenges of determining the relationship between SC and task accuracy (i.e., discrimination

ability and confidence2), and how SC dynamics are modulated by dispositional qualities. These propositions, when considered with specific aims (1) and (2), prompt the following research questions:

RQ1. Do the SC dynamics of deception differ from the SC dynamics of honesty?

RQ2. Are people sensitive to changes in SC dynamics (as indicated by task accuracy)?

Information and the Social Environment

Given the proposed utility of SC measures in distinguishing between truth and lies, it is critical to understand the relationships among variables that shape social behavior. In other words, a comprehensive investigation into the behavioral dynamics that differentiate truthful and deceptive interactions should also determine the basis of that information, as well as how the observed patterns of SC are influenced by dispositional variables at the individual and interpersonal levels.

Theories of embodied and socially situated cognition argue that social interaction dynamics are driven by an attunement to information available in the physical and social environment (Marsh et al., 2006, 2009; Schmidt, Christianson, Carello, & Baron, 1994). Indeed, people are highly attuned to dynamic biological motion as it relates to socially relevant information (Blake & Shiffrar, 2007). For example, a visual appraisal of kinematic information at the level of the individual is sufficient for an observer to detect an actor’s identity (e.g., Cutting &

Kozlowski, 1977; Loula et al., 2005), gender (e.g., Kozlowski & Cutting, 1977; Mather & Murdoch,

1994), mood (e.g., Chouchourelou et al., 2006; Dittrich et al., 1996), various personality characteristics or traits (e.g., Brownlow et al. 1997; Heberlein et al. 2004; Montepare &

Zebrowitz-McArthur, 1988), vulnerability to attack (Gunns et al., 2002), hostility (Topalli, &

O’Neal, 1995). and deceptive intentions during object manipulation (Runeson & Frykholm, 1983).

2It is standard to include assessments of observers’ judgment confidence with accuracy measures in lie detection studies (Vrij & Granhag, 2012).

Importantly, Kean (2000) demonstrated that observers were able to detect cooperative versus competitive object manipulation from the interpersonal kinematics at the level of the interaction, but not at the level of the individual.

Over the past few decades, extensions of this work have examined individual person perception across various manipulations of the social stimulus array, with the aim of identifying the minimal informational support for person perception. For instance, researchers have investigated differences in person-identification accuracy between genuine (fine kinematic structure) versus diminished (gross kinematic structure) movement information (Berry et al., 1991;

Berry & Misovich, 1994; Kean, 2000), egocentric versus allocentric social perspective (Prasad &

Shiffrar, 2009; Valenti & Good, 1991), active versus passive observer response (Roca et al., 2014), and co-present versus perceptually mediated interactions (e.g., Walther, 2005). An overwhelmingly low number of these manipulations influenced the accuracy of judgments, an observation that may simply reflect the pervasive and expedient nature of kinematic information.

Conversely, it has also been proposed that these findings illustrate the low ecological validity of studies that uncouple a social unit into isolated, individual elements, and emphasize the need to develop a more representative experimental framework (Barton, 2013; Dicks et al., 2010).

Taken together, this work reveals the importance of dynamical movement information for person perception. By focusing on the passive perceptions of isolated individuals, however, the potentially rich sources of information arising from social interaction are largely ignored. An alternative approach to describing the informational basis of behavior is to instead focus on the integrated social unit and, more specifically, to consider the mutuality of interpersonal information dynamics in explaining how social interaction is structured (Marsh et al., 2006).

The dynamical systems approach provides a useful theoretical framework for experimental investigations into how coherent behavioral patterns emerge from the nonlinear interactions that

bind individual components into a collective system (see Gallagher & Appenzeller, 1999; articles therein, for a discussion). With respect to interpersonal systems in particular, a central concept within the SC dynamics literature is how individuals become reciprocally linked, and how this mutual influence, or coupling, shapes the resultant patterns of coordination (Riley et al., 2011). For instance, it has been observed that the simple act of engaging in conversation promotes coordinated patterns of language (Dale et al., 2014; Fowler et al., 2008; Fusaroli & Tylén, 2012;

Fusaroli et al., 2013), posture (Shockley et al., 2003; Stoffregen et al., 2009, 2013), and whole body movement (Schmidt et al., 2012, 2014). Previous work has demonstrated how such interpersonal coordination during social interaction can arise by means of auditory (Fusaroli et al.,

2014; Fusaroli & Tylén, 2016; Shockley et al., 2003, 2007) and visual (e.g., Lopresti-Goodman et al., 2008; Richardson et al., 2005, 2007; Schmidt et al., 1990; 1997, 2007) informational coupling. In short, the present literature seems to suggest that perceptual information is sufficient to couple interacting individuals to each other and to the social environment.

Less frequently studied has been the question of what the interpersonal dynamics of honest and deceptive behavior look like and how changes in the informational structure of this behavior affect the relation between coordination and perceptual accuracy. Beyond discrete comparisons of visual and auditory communication channels, virtual environments provide a means through which the kinematic information available during an interaction can be experimentally manipulated. Although recent meta-analyses suggest consistent accuracy rates of lie detection across media and channels (Bond & DePaulo, 2006), little appears to be known about what factors an individual may utilize to determine the honesty or deceptiveness of an interaction or the saliency of this information. Considering that the intrapersonal behavioral dynamics of liars differs from that of truth-tellers, and that informational coupling shapes the coordinated behavior of socially situated individuals, it appears reasonable to conclude that the dynamics of SC may serve

as the informational basis for detecting deception. This assertion, when considered with specific aim (3), prompts the following research question:

RQ3. To what degree is the information specifying deception susceptible to changes in the

informational structure of the environment (i.e., perceptual medium, interaction

mode, kinematic structure)?

Influential Properties of the Social Environment

Following the identification of SC dynamics as a potential behavioral process that may differentiate honest and dishonest interactions, a new challenge inherent in deception research is determining how the patterns of coordination are influenced by dispositional characteristics such as personality and rapport. It has been suggested that variables at the social-personality scale affect interactional synchrony and have the potential to constrain the dynamics of SC (Schmidt &

Fitzpatrick, 2016; Schmidt & Richardson, 2008). A closer look at this claim reveals that properties of a dyad are associated with the dynamical properties of a dyad’s coordinated behavior, as well as how the stability of coordinated movements reflects the stability of mental connectedness experienced in social interactions. For example, interpersonal coordination appears to be influenced by dispositional factors such as social aptitude and interpersonal affiliation (Hove &

Risen, 2009; Marsh et al., 2009; Miles et al., 2010, 2012). More specifically, research demonstrates that agreeableness and extroversion are positively correlated with the persistence of cooperation, drawing attention to the possibility that dispositional tendencies (i.e., personality traits) may establish the initial conditions that influence the strength of the attraction to cooperate

(Richardson et al., 2007). In addition, Schmidt and colleagues (1994) found that pairing individuals on the basis of reciprocal (as opposed to symmetrical) social skill level facilitated social coordination, and point to the embodiment of personality traits associated with leadership and social competence as a constraint on interactional synchrony. Lastly, the results of a study

examining the effect of social motive on the inclination to coordinate with others reveal that the motivation to be socially cooperative is associated with greater levels of interpersonal synchrony, and more importantly, that dispositional properties associated with cooperation have the potential to alter the dynamics of social coordination (Lumsden et al., 2012).

It is clear that interpersonal coordination is influenced by dispositional variables such as social aptitude and interpersonal affiliation. However, a large body of literature conversely demonstrates that these internal states are susceptible to the influence of interpersonal coordination. For instance, engaging in SC influences feelings of cooperation (Wiltermuth &

Heath, 2009), rapport (e.g., Hove & Risen, 2009; Miles et al., 2009), and connectedness (Miles et al, 2010). In fact, one of the most common tactics for identifying dishonesty and obtaining confessions in police investigations, FBI interviews, and terrorist interrogations is establishing rapport through interactional synchrony (Kassin et al., 2007; Turvey, 2008). Recent research conducted from the dynamical systems perspective lends support to the notion of a functional linkage between motor coordination and cognitive coordination during cooperative interaction

(Tolston, Ariyabuddhiphongs, et al., 2014), underscoring an individual’s mental state or beliefs as playing a central role in the formation of such connections (Freeman et al., 2011; Richardson &

Dale, 2005). These studies do not only highlight the relationship between measurable bodily states and complex cognitive processes, but also demonstrate that an important aspect of communication lies in the sensitivity of these processes to the states of another individual

(Richardson, Dale, & Tomlinson, 2009; Tolston et al., 2014).

The interplay between dispositional factors and SC is potentially important, especially when considering the potential applicability to professional lie detection, yet there have been very few systematic studies of the relations among interpersonal coordination, social aptitude, and rapport in a deceptive context (Dunbar et al., 2011). The majority of studies examining the effect

of individual differences in disposition on the production and detection of deception report inconclusive or inconsistent findings (DePaulo et al., 2003; Morgan, LeSage, & Kosslyn, 2009;

Riggio & Friedman, 1983; Visu-Petra, Miclea, & Visu-Petra, 2012, 2014). This gap in the literature, when considered with specific aim (4), prompts the following research question:

RQ4. Is SC (and its relation to task accuracy) influenced by dispositional characteristics

(i.e., individual differences in social aptitude) and does SC, in turn, affect the expression of

these factors (i.e., assessments of rapport)?

Current Investigation: Overview and Predictions

A central concern with the current status of deception research is its widespread focus on individual differences in lie-discrimination ability and the individual behaviors associated with appearing honest or deceptive. Though useful in demonstrating the importance of the dynamic structure of movement, the study of individual kinematics does not provide a complete view of social behavior. It is also important to note what informational properties of an interaction are used by interacting individuals to support an understanding of the interaction. In particular, while information about other individuals is revealed in the course of exploring the social environment

(Good, 2007; Shotter, 1991), it remains unclear to what degree this information is sufficient for effective social interaction across variations in environmental structure and deceptive intent.

In addition, while the studies outlined and discussed here provide valuable insight about the organization of social behavior, the problem of real-world applicability persists. For instance, much of the SC research conducted from the dynamical systems perspective has typically involved assessments of time-evolving behavioral responses, but these are commonly incidental or non-goal directed movements (e.g., rhythmic limb movement or postural coordination research). Socially contextual responses to the natural movements of real co-actors are uncommon in many experiments conducted within the dynamical systems framework (for exceptions, see Coey et al.,

2012). Moreover, although recent investigations have begun examining the interpersonal coordination dynamics of more everyday social activities (e.g., Dale et al., 2014; Fusaroli & Tylén,

2016; Schmidt et al., 2012, 2014), individuals in these studies are often motivated to cooperate to achieve a shared end goal. Thus, it remains unclear to what degree the existing literature may explain a broader range of everyday social situations during which people interact within a competitive (or not explicitly cooperative) social environment, and in particular, during deception.

In the current dissertation, I assessed the dynamic structure of SC that occurred between co-actors during a series of deception tasks in which paired individuals conversed with the aim of lying undetected (as the Actor) or detecting deception (as the Perceiver), and related those measures to the ability of co-actors to detect deception. I also manipulated the availability and structure of visual information about the interaction, such as the transformational (i.e., kinematic, motion) and structural (e.g., facial expressions, fine kinematic movement) qualities of stimulus events. Participants in the first experiment were visually separated for half of the trials, while all conversations in the second experiment were video-mediated in real time. During half of the mediated interactions, an image of the conversational partner was visualized as a two-dimensional gray-scale depth array that preserved only movement kinematics (diminished structure), and the remaining interactions were displayed in standard full-color video (genuine structure). Lastly, I evaluated the degree to which dispositional variables interacted with the dynamics of interpersonal coordination and measures of task accuracy (i.e., discrimination ability and confidence).

The social coordination dynamics framework provides a useful basis for predictions regarding performance of the conversational deception task. From this perspective, interpersonal coordination simultaneously reflects and influences the quality of the interaction. Synchrony and rapport are disrupted by emotional or behavioral distance (Giles, 2008; Levenson & Ruef, 1997),

therefore deception may introduce a tinge of complexity to an interaction and may attenuate the coordinated behavior of co-actors. If SC is influenced by the deceptive nature of an interaction, the manifestation of the effect could serve to discriminate lies from the truth. As such, the central hypothesis of the current research is that dynamical patterns of SC will differ between truthful and deceptive interactions. Should the structure of SC embody deceptive intent in a perceivable way, it may provide a basis for people to detect deception. Motivated by the results of Duran and colleagues (2013), it is specifically hypothesized that behavior during truthful interactions will be characterized by more robust patterns of coordination and stability than during deceptive interactions, and moreover, that individuals will be sensitive to such changes in SC dynamics, as indicated by lie-discrimination performance.

Recent meta-analyses suggest consistent accuracy rates of lie detection across media and channels (Aamodt & Custer, 2006; Bond & DePaulo, 2006; Hartwig & Bond, 2011). Beyond this, little appears to be known about what factors an individual may utilize to determine the honesty or deceptiveness of an interaction or the saliency of this information. An important question is whether differences in lie-discrimination ability reflect the utilization of different sources of perceptual information. Previous SC research has demonstrated that interpersonal coordination during social interaction can emerge via auditory and visual informational coupling, either in tandem or in isolation (e.g., Lopresti-Goodman et al., 2008; Richardson et al., 2005; Schmidt et al., 1990, 2007; Shockley et al., 2003, 2007). Based on the results of Schmidt and O’Brien (1997) and Richardson et al. (2007), as well as the work of Blake and Shiffrar (2007), measures of SC are expected to differ between conditions of perceptual medium (visual and/or auditory information) and kinematic structure (diminished or genuine). Regarding the effect of informational mode or quality on the proficiency of person perception, the existing literature clearly supports the notion that accuracy is preserved throughout variations in the perceptual display normally available, even

when degraded to kinematic information only (Johansson 1973, 1975; Runeson & Frykholm,

1983), and that this phenomenon extends to the detection of attributes that are fundamentally social (Gunns, Johnston, & Hudson, 2002; Johnston, 2013). Together, this work motivates an exploratory determination of the effect of coupling medium on coordination dynamics in a deceptive context. There may be reason to believe that the coupling medium will not matter, however as the first study focusing on SC dynamics and deception, the project presented here represents the first examination of this possibility.

Recent assessments of deception detection and synchrony (Dunbar et al., 2014; Jensen et al., 2013) support the claim that the incongruent cognitive states characteristic of deception may disrupt emergent behavioral and cognitive entrainment processes, as evidenced by lower rapport assessments. Considered alongside the reported effects of dispositional tendencies (i.e., personality traits, social skill) on cooperation and synchrony (Richardson et al., 2007; Schmidt et al., 1994), this research motivates the expectation that measures of SC and task performance in the present study would be related to dispositional qualities. The inconsistency within the current literature on individual differences and deception, however, dilutes this expectation. In addition, the vast majority of dynamical systems investigations into the relation between dispositional properties and coordinated behavior situate the interaction within a cooperative context, blurring expectations within the present study even further. Regardless of whether SC dynamics and task performance are influenced by the dispositional variables of social aptitude and rapport, however, the results would nevertheless aid in identifying whether such variables moderate the relationship between

SC and lie discrimination accuracy. If they do, existing research should maintain its current focus on individual differences in deception and lie-discrimination ability. If not, the results would weaken arguments claiming a reciprocal influence of social aptitude and disposition on deceptive behavior or detection accuracy, but perhaps more importantly they would indicate a shift of

research focus toward the relation between SC and discrimination performance. Moreover, this finding would prompt a critical reevaluation of the dominant approach to deception research, replacing the subjective, manual coding of static and context-independent abilities or behaviors exclusively at the level of the individual with a focus on the contextually-relevant coordination dynamics of socially-situated co-actors.

CHAPTER 2

Experiment 1

Deception is conceptually grounded in the manipulation of the inferred intentions of others—success as both a liar and a detector depends on proficient social perception (Jackson et al., 2006). A rich source of information about the social intentions of others is found in the ways that people move their bodies, as evidenced by experimental demonstrations of the functional link between bodily movement and complex cognitive processes (Richardson & Dale, 2005; Tolston et al., 2014). What remains to be clarified, however, is an account of the degree to which deceptive or truthful intent can be detected from information in the social environment and whether the structure of that information influences the patterns of SC that differentiate truthful and dishonest interactions.

The aim of Experiment 1 was to establish how patterns of social coordination differ between truthful and dishonest interactions, as well as determine the degree to which deception detection is related to dynamic patterns of coordinated behavior, manipulations of the social stimulus array, and dispositional characteristics (i.e., social aptitude and rapport). To do this, participant pairs engaged in a communication-based game that occasionally called for successful deception or lie detection, depending on the individual’s role, in two different perceptual coupling conditions. Specifically, half of the conversations were carried out in a face-to-face context that provided both visual and verbal information, while the other half were conducted through an opaque curtain that only permitted verbal interaction.

Participants took turns recounting memorable experiences, describing points of view, and discussing familiar concepts in order to allow for relatively unconstrained communication. In some instances, however, the opening speaker had previously been instructed to fabricate stories

with the aim of effectively deceiving his or her partner, who was thus tasked with discriminating between truthful and dishonest interactions.

Adhering to a social coordination dynamics perspective, the characteristic behavioral dynamics of liars observed by Duran and colleagues (2013) suggest that differences in the dynamics of interpersonal behavioral may also exist to distinguish lies from the truth. As such, the present study was designed to examine the hypothesis that measures of SC during truthful interactions are characterized by more robust patterns of entrainment and stability than during deception, and that these differences are reflected in task performance. To determine this, participant movement was recorded and displayed using a multi-station Xbox Kinect array, supplemented by a series of questionnaires for assessing personality and task accuracy data. The following methodology was implemented to test the hypothesis that time-evolving processes of coordinated behavior aid in distinguishing lying from honesty, and that these processes are shaped by the transformational, structural, and dispositional qualities of environmental stimuli.

Method

Participants

Twenty-five participant pairs (five mixed-gender, 20 matched-gender; 33 females, 17 males; 50 total individuals) were recruited through the University of Cincinnati psychology participant pool and by word of mouth. Participants either received course credit or were paid $15 for their time. All participants had normal or corrected-to-normal vision, were fluent English speakers, and were free of any motor, language, or neurological impairments. No special populations were used.

Materials

Data recording and stimulus presentation. An Xbox Kinect recorded frame-to-frame video of participants’ full-body movements at a rate of 30 Hz, as well as auditory data. The Kinect was

placed approximately 2.5 m in front of the participants at a height of about 1 m. An opaque black curtain (2 m × 2.5 m) separated participants in No-vision conditions (see Figure 1).

A A B

Figure 1. Experimental setup of (A) Vision conditions and (B) No-vision conditions.

Statement lists. A list of adaptable statements, or prompts, was generated in order to ensure a regular presentation of honest and deceptive conversation topics that were uniformly plausible, while also providing the consistency necessary for reliable data collection and analysis.

From this statement list, each participant was instructed to select five Truth statements and five Lie statements (see Appendix A). A prompt provided participants with an incomplete statement for which they were to fill in the blank (e.g., “Not many people know this, but I really enjoy ____”).

Participants were informed that a Truth must be a factual statement about them and should accurately describe something they have done, something that has happened to them, or a firm belief that they have. Conversely, Lie statements were to necessarily have had no relation to the truth. Participants also rated each of their chosen statements with respect to how personally invested they were in the topic and how well they thought they could convince their partner that it was true. The experimenter then reviewed the chosen statements with each participant individually and indicated a random order in which participants were instructed to make their statements.

Tasks. Explaining how social interaction is structured requires a focus on the integrated social unit and mutuality of interpersonal information dynamics. With respect to one of the central motivations of the current research, an explanation of the socially contextual responses to the natural movements of real co-actors necessitates a shift from incidental, non-goal directed, overtly synchronous movements toward a broader range of less constrained everyday activities in which coordinated behavior arises between two co-present socially-situated individuals, even in the absence of explicit cooperation. Therefore, in addition to employing the fundamentally interpersonal act of communication within the experimental task, the present study also utilized a series of ‘icebreaker’ tasks that functioned to enrich the adaptability and complexity of participants’ social environment.

During the icebreaking tasks, participants were instructed to take part in a series of conversational teamwork tasks, such as a timed photo scavenger hunt and a name-matching game, in order to become familiar with each other and the task environment. These tasks did not involve motor or linguistic coordination because the present study examined how patterns of SC influenced social behavior and perception.

During the experimental tasks, individuals participated as either an Actor or as a Perceiver.

The objective of the Actor was to successfully deceive their partner, the Perceiver, when making false statements about themselves. The Perceiver’s objective was to detect lies when listening to their Actor partner’s statements. Within a single one-minute trial, the participant in the role of

Actor began by sharing one of his or her chosen statements. Participants were permitted to paraphrase the statement if they preferred, but were limited to a single sentence. The participant in the role of Perceiver was then allotted the remainder of the trial to ask the Actor questions about the statement in an attempt to discern if it was true or a lie based on the Actor’s responses.

Perceivers were required to ask at least three questions about the statement, but did not have to

take up the entirety of the trial. After each trial, the Perceiver manually recorded his or her guess so as not to provide the Actor with feedback. Both participants also individually recorded a confidence rating at the end of each trial: the Perceiver rated the confidence of their guess, and the

Actor rated their confidence that Perceiver believed the statement to be true.

Assessment of dispositional characteristics. In order to determine how patterns of coordination are influenced by dispositional properties of personality and rapport, shortened versions of four self-report surveys were compiled into a single assessment to measure social aptitude: (1) the Autism Spectrum Quotient Scale (AQ), which evaluates the degree to which an adult with normal intelligence exhibits traits associated with autism; (2) the Trait Emotional

Intelligence Questionnaire Short Form (TEIQue-SF), which is designed to measure 15 trait emotional intelligence facets; (3) the Eysenck Personality Questionnaire-Revised Short Scale (EPQ-

R SS), which measures the personality dimensions and associated sub-traits of extraversion/introversion, neuroticism/stability, and psychoticism/socialization orientation; and (4) the Self-Monitoring Scale (SMS), which evaluates how well and how motivated people are at regulating public expressiveness to fit the requirements of a social situation. In addition, a rapport questionnaire was implemented after all experimental tasks had been completed to assess the degree to which paired individuals perceived one another as a social unit. Each participant was provided a 25-item questionnaire assessing their perception of rapport with the other individual in their pair, modeled after the work of Tickle-Degnen and colleagues (Puccinelli & Tickle-Degnen,

2004; Tickle-Degnen & Rosenthal, 1990). The questionnaire answers provided information about interpersonal acquaintance level, group unity, mutual attentiveness, feelings of coordination, task enjoyment, and hypothetical willingness to work together again. This assessment also included information about participants’ acquaintance level prior to the experiment.

Procedure

Participants were first given 15 minutes to individually complete the social aptitude assessment. Once both individuals completed the assessment, participants took part in the icebreaker tasks, lasting approximately 10 minutes including instruction, followed by the completion of the statement lists. Throughout the sixteen total experimental trials, each participant made four Truth statements and four Lie statements, preceded by two practice trials wherein each participant took on the role of Actor and Perceiver one time. To examine the effect of visual information on SC and communicative success, a curtain separated participants for half of the experimental trials. That is, participants completed Visual and Non-visual conditions. Participants alternated social roles (i.e., Actor or Perceiver) from trial to trial, and information condition (Vision or No-vision) was counterbalanced throughout the experiment in two blocks of four trials.

Statement type (i.e., Truth or Lie) was randomized within each block.

After all experimental trials were completed, participants answered the rapport questionnaire, and were prompted to review their guesses with their partner before being debriefed as to the true nature of the study.

Analysis

Task accuracy and performance. Accuracy in terms of discrimination ability was initially defined using the statistic d’ (d-prime), which measures a combination of participants’ sensitivity to deception and truth- or lie-bias (Macmillan & Creelman, 2004) by assessing the proportion of correct and incorrect responses to truthful and dishonest statements. In essence, values close to zero indicate guesses at chance level, while higher values correspond with increased sensitivity.

Though useful in direct appraisals of sensitivity to the simple presence or absence of a discrete signal, d’ may not be the most robust method for more complex assessments of cognitive performance and self-reported social awareness (Doyen et al., 2014; Timmermans & Cleeremans,

2015; Vermeiren & Cleeremans, 2012). In addition, d’ does not allow for the incorporation of confidence assessments within the accuracy measure, as is standard in lie detection studies (Vrij &

Granhag, 2012). Taken together, it is evident that a more detailed measure of performance is needed to fully evaluate the ability to detect deceptive or truthful intent as it relates to changes in the dynamic social environment.

Therefore, a novel combined accuracy and confidence score, or composite task performance score, was also generated to quantify performance criteria. More specifically, the accuracy of a trial was indicated by assigning a value of ‘+1’ to a correct response and a value of ‘-

1’ to an incorrect response. Confidence ratings were assigned incremental values between ‘0.2’ and ‘1’ (i.e., 1 = 0.2, 2 = 0.4, 3 = 0.6, 4 = 0.8, 5 = 1). The confidence value was then subtracted from the accuracy value of the same trial to generate a performance score for that trial. For example, if a Perceiver incorrectly identified a lie statement as being true with a low confidence rating of 1 (akin to a guess), the performance score of that trial would be ‘-0.2’. Alternatively, if a

Perceiver correctly identified a lie with a high confidence rating of 5 (akin to certainty), the performance score of that trial would be ‘1’. Thus, the magnitude of a score indicates the confidence strength, while the direction of a score indicates the accuracy (see Figure 2). Higher confidence in a correct response corresponds with higher task performance, and higher confidence in an incorrect response corresponds with lower task performance; responses made with low confidence, or guesses, correspond with values closer to zero. In order to maintain the structural integrity of the social system, an overall task performance score was generated for each pair, as opposed to an independent score for each individual within a pair; classifying task performance as an individual-level phenomenon uncouples a social unit into isolated, discrete

elements. Pairs were ordered by average overall task performance score, then dichotomized using the median split procedure to generate groups of High and Low performers3.

Figure 2. The range and indication of possible performance scores on a single trial.

Movement quantification. As per Schmidt and colleagues (2012), overall movement activity was calculated as the amount of pixel change between adjacent frames in the video recordings. The pixels of each frame are compared to those of the previous frame, yielding a value that quantifies successive change in the pixel distribution. These consecutive difference scores are then combined to generate a time series of an individual’s movement (see also Kupper et al., 2010;

Paxton & Dale, 2012).

Nonlinear time-series analyses. Following movement quantification, time series data were submitted to a series of nonlinear analyses to objectively quantify movement stability within an individual, as well as interpersonal coordination and coupling between individuals. Recurrence

Quantification Analysis (RQA; Webber, 1997; Webber & Zbilut, 1994; Zbilut & Webber, 1992) is commonly used to assess the structural patterns of variability exhibited in intrapersonal movement

(i.e., the patterns of motor behavior within an individual) and provides a dynamic assessment of stability. The recurrent structure of interpersonal movement coordination (i.e., how and when individuals are coordinated) was quantified using Cross-recurrence Quantification Analysis

(CRQA; Shockley et al., 2002; Zbilut, Giuliani, & Webber, 1998), an extension of RQA. Where

RQA is a univariate analysis that examines the recursive dynamics of individual movement

3 To control for the potential loss in power and the risk of spurious effects, the data was also trichotomized, generating three performance levels (high, moderate, low), and yielded the same pattern of results across all analyses.

trajectories, or self-recurrence, CRQA is a multivariate analysis that examines the time-correlated activity between two signals, or time series. Importantly, both of these methods are well-suited for the analysis of complex, dynamic, and noisy time series data.

The general technique of recurrence analysis essentially consists of two steps: calculating recurrences (i.e., the recurrence plot) and quantifying them (i.e., recurrence analysis). Correctly determining the appropriate values for input parameters is a necessary prerequisite to calculating recurrence variables and obtaining meaningful quantifications for assessing complex dynamical systems. A common pre-processing step is to reconstruct the attractor believed to underlie the observed time series using the method of delays (for a thorough review see Abarbanel, 1996;

Takens, 1981). The ideal delay parameter results in a temporal offset between sequential time points that minimizes the dependence between dimensions to reconstruct the attractor, but not so much as to generate complete independence. This was estimated using the first minimum of the average mutual information (AMI) function, which is essentially a measure of the dependence between pairs of random variables that determines when the variables are independent enough to be informative. More specifically, AMI values at a particular delay indicate how much new information is revealed about another signal using the same delay, whereby lower values correspond with increasingly independent signals until a stable linear system is uncovered, which generates no new information (Abarbanel, 1996). The embedding dimension parameter, which specifies the dimensions needed to reconstruct the attractor necessary for revealing the dynamics of the system, was estimated using the false nearest neighbors (FNN) methodology in order to expose the dynamic structure of the system without distortions. After the attractor is reconstructed, one additional parameter must be selected before recurrences can be identified and quantified.

The radius parameter determines whether states in the phase space trajectories are considered recurrent (i.e., the trajectory has repeated itself within some tolerance value). Although there is no

precise method in place for selecting a proper radius value, it is suggested that it should allow for moderate variability across different observations of %recurrence to avoid a floor effect or ceiling effect (see Webber & Zbilut, 1994; Zbilut & Webber, 1992). These preliminary steps suggested an

RQA embedding dimension of six, a delay of 15, and a radius of 15% the mean distance between points, as well as a CRQA embedding dimension of six, a delay of 10, and a radius of 20% the mean distance between points. All chosen values were verified using a range of parameters that yielded qualitatively similar patterns of results.

Once the parameter values were selected, they were implemented in the analyses to quantify the number and nature of recurrent points in RQA and CRQA recurrence plots. RQA thus grants quantitative indexes of how strongly patterned the behavior of a system is, and how repeated patterns are structured. CRQA analogously quantifies the strength and form of the shared dynamics of two systems. Three measures were extracted from the recurrence plots for further analysis. The %recurrence (%REC) quantification is the probability of finding a recurrent point, or the percentage of points registered on the plot, and indicates the degree to which the system(s) tend(s) to visit similar states. More specifically, %REC values provided by RQA reflect patterns of repetition within a signal, while CRQA values assess the overall ‘amount’ of coordination between two independent signals which provides a function characterization of coupling. The determinism

(%DET) variable measures the proportion of recurrent points forming diagonal line structures. This measure, in turn, indicates the repetition of strings of data points (as opposed to individual data points) and denotes periods when a system moves through a series of identical states over time. If a system is characterized by a purely random underlying process, prior states are independent of future states, producing few diagonal lines. In contrast, knowledge of prior states within a deterministic system allows for the precise prediction of future states. Randomness and determinism are thus opposite concepts; an increase in a signal’s deterministic structure signifies

dynamical patterns that are more regular, or conversely, less random. The %DET measure provided by RQA captures the degree of predictability (i.e., deterministic structure) in a time series. Similarly, %DET is informative as captured by CRQA because it represents segments in one time series that share a similar trajectory with those in another time series at a different time (i.e., quantifies the structural regularity or randomness of coordination). The average length of diagonal

line structures is measured using meanline (Lmean), which indexes how strongly patterned and

regular (i.e., stable) a signal is. For instance, high Lmean values indicate longer repeated patterns, and thus, greater structural stability in movement dynamics (obtained by RQA) or coordination dynamics (obtained by CRQA) of the observed behavior. Collectively, these measures provide the means for objectively assessing the strength and structure of behaviors that emerge and change over time, both within and between individuals.

Results

Task performance

Individuals in Experiment 1 correctly identified the deceptive nature of an interaction approximately 56% of the time, with accuracy values ranging from 31% to 83% correct. Overall task performance was consistent with chance, as indicated by the distribution of d’ values (M =

0.08, SD = 0.50) and composite performance scores (M = 0.51, SD = 0.18). As mentioned previously, several publications have appeared in recent years documenting the shortcomings of d’ as a robust method for faithful assessments of cognitive performance and self-reported social aptitude, and have argued the need for a more context-relevant, systematic method that blends subjective and objective measures of assessing perceptual judgment (Doyen et al., 2014;

Timmermans & Cleeremans, 2015; Vermeiren & Cleeremans, 2012). The current research thus utilized an aggregated performance metric as a new approach for quantifying more complex discrimination behaviors such as lie detection.

In order to examine the relation between deception and perceptual information as an influence on task performance, performance scores were submitted to a 2 × 2 repeated-measures

ANOVA comparing the within-subjects variables of statement (Truth, Lie) and information (Vision,

# No-vision). Results indicated a significant main effect of statement, F(1, 24) = 10.90, p < .05, !" =

0.31, such that individuals better identified truthful (M = 0.183, SD = 0.30) than deceptive interactions (M = 0.02, SD = 0.14), as illustrated in Figure 34.

0.35 Vision 0.30 NoVision 0.25 0.20 0.15 0.10 0.05

Performance score Performance 0.00 Truth Lie -0.05 -0.10 -0.15 Statement type

Figure 3. Truthful interactions were characterized by greater task performance and lie discrimination, as indicated by a comparison of performance scores across all conditions of information and statement type. The error bars indicate the standard error of the mean.

The distribution of task performance data suggested that certain pairs were notably better or worse than others. Using a median split analysis of composite performance scores to generate groups of High performers (M = 0.33, SD = 0.14, N = 10) and Low performers (M = 0.01, SD =

0.12, N = 15), the above two-way ANOVAs were repeated to include the performance variable as three-way ANOVAs. Measures of task performance were submitted separately to a series of 2

(statement: Truth, Lie) × 2 (information: Vision, No-vision) × 2 (performance: High, Low) mixed-

4 # Results of the analysis performed on d’ also yielded a significant main effect of statement, p < .05, !" = 0.97, indicating greater sensitivity to truthful interactions over deceptive interactions. However, as stated previously, d’ may not be the most robust method for more complex assessments of cognitive performance such as in the present study.

design ANOVAs. Results again indicated that truthful interactions elicited greater performance

# than deceptive interactions, F(1, 23) = 9.44, p < .05, !" = 0.30, as well as a significant difference

# in lie discrimination ability between groups, F(1, 23) = 26.38, p < .05, !" = 0.53. Therefore, for all subsequent analyses, the data was split into groups of High and Low performers in order to examine whether performance ability influenced SC (see Table 1).

Table 1 Lie Discrimination Ability in High (n=10) and Low (n=15) Performance Groups as Measured by Composite Performance Score

Truth Lie Performance group Vision No Vision Vision No Vision High .43(.26) .23(.21) .14(.24) .26(.11)

Low .05(.22) .13(.34) -.21(.26) .01(.32)

Total .20(.30) .17(.30) -.70(.30) .11(.28) Note. Data is reported as M(SD)

Intrapersonal movement dynamics

RQA was used to assess the structural dynamics exhibited in intrapersonal movement and language. The degree of structural organization for individual movement was significantly higher than for randomly shuffled time series, assuring that the observed data patterns were in fact due to their temporal structure, as opposed to their incidental distribution (all p < .05). Measures of RQA performed on the average pixel change data were submitted to a series of four-way ANOVAs with a between-subjects variable of performance (High, Low), and within-subjects variables of social role (Actor, Perceiver), statement (Truth, Lie), and information (Vision, No-vision)5.

Results from the analysis of %REC data revealed a significant main effect of role, F(1, 23) =

# 6.55, p < .05, !" = 0.22, with the individual movement dynamics of Actors exhibiting greater

5 Four-way mixed design ANOVAs were also performed with accuracy defined at three levels (high, moderate, low), however the overall pattern of results was the same.

recurrence (M = 2.48, SD = 2.40) than the behavior of Perceivers (M = 3.69, SD = 3.59). There were no further significant effects, all p > .05.

As can be seen in Figure 4, analyses of Lmean data also yielded a significant main effect of

# role F(1, 23) = 6.63, p < .05, !" = 0.22. As was found for comparisons of %REC above, the behavioral dynamics of Perceivers was more stable (M = 76.29, SD = 51.36) than those of Actors

(M = 52.60, SD = 31.40). Analyses reveal no additional effects or interactions, all p > .05.

6.0 Actor Perceiver 5.0

4.0

3.0

2.0 %Recurrence

1.0

0.0 Low High Low High

Truth Lie 100 90 80 70 60 50 40

Meanline 30 20 10 0 Low High Low High Truth Lie Task performance level for each statement type Figure 4. Average percentage of recurrent points (%REC, top row) and average recurrent line length

(Lmean, bottom row) for the individual motor dynamics of Actors versus Perceivers in low- and high- performing pairs in Truth and Lie conditions, obtained using Recurrence Quantification Analysis (RQA). Error bars indicate the standard error of the mean.

The ANOVA performed on measures of %DET indicated a significant main effect of

# information, F(1, 23) = 5.26, p < .05, !" = 0.19, whereby the structure of individual movement was characterized by greater regularity in Vision conditions (M = 82.80, SD = 11.18) than in No- vision conditions (M = 78.64, SD = 12.85). The analysis also uncovered a significant two-way

# interaction between statement and performance, F(1, 23) = 7.65, p ≤ .05, !" = 0.25, illustrated in

Figure 5. Pairwise comparisons of statement using a within-subjects t-test for each level of performance revealed that measures of %DET were greater for true statements (M = 83.54, SD =

5.94) than lies (M = 77.54, SD = 6.88), but only for High performing pairs, t(9) = 2.41, p < .05.

There were no significant results for analyses performed on Low performers, all p > .05.

90 Vision No vision 85 80 75 70 65 Determinism 60 55 50 Truth Lie Truth Lie Low High Statement type at each level of task performance

Figure 5. Average proportion of diagonal line structures (%DET) that characterize the movement dynamics of Low- and High-performers during Truth and Lie statements, obtained using RQA. The error bars indicate the standard error of the mean.

Interpersonal coordination dynamics

The recurrent structure of participants’ movements (i.e., how and when participants were coordinated) was quantified using CRQA, which measures the degree of coupling between pairs.

The degree of structural organization for coordinated behavior was significantly higher than for shuffled time series, indicating that the data patterns observed were due to their temporal structure

rather than their incidental distribution (all p < .05). Cross-recurrence measures were submitted to three-way ANOVAs with a between-subjects variable of performance (High, Low), and within- subjects variables of statement (Truth, Lie) and information (Vision, No-vision)6.

3.5 70 Truth Lie 3.0 60 2.5 50 2.0 40

1.5 Meanline 30 %Recurrence 1.0 20 0.5 10

0.0 0 High Low High Low Task performance Task performance Figure 6. Average %REC (left) and Lmean (right) for the interpersonal motor behavior of High-and Low- performing participant pairs during Truth and Lie statements, obtained using Cross-Recurrence Quantification Analysis (CRQA). Only High-performers exhibit a behavioral discrepancy between deceptive and honest interactions. The error bars indicate the standard error of the mean. Results from analyses of %REC data indicated a significant main effect of statement, F(1,

# 23) = 12.22, p < .01, !" = 0.35, such that the overall amount of coordination was greater during

Truth statements (M = 2.92, SD = 2.21) than Lies (M = 2.14, SD = 1.75). In addition, the analysis uncovered a significant interaction between statement and performance, F(1, 23) = 5.96, p < .01,

# !" = 0.21. Pairwise comparisons of statement using a within-subjects t-test for each level of performance confirmed that measures of %REC were greater for True statements (M = 3.07, SD =

1.92) than Lies (M = 1.74, SD = 1.18), but only for High performers, t(9) = 3.42, p < .01. These results, as illustrated in Figure 6, demonstrate that pairs with higher performance scores exhibited a greater degree of global coordination when discussing true statements as opposed to lies, while deceptive content did not influence the behavior of pairs who did not perform as well.

6 Three-way mixed-design ANOVAs were also conducted with the performance variable defined at three levels (High, Moderate, and Low). The overall pattern of results was the same.

The analysis of Lmean data likewise revealed a significant main effect of statement, F(1, 23) =

# 10.10, p < .05, !" = 0.31, with more stable periods of interpersonal coordination emerging during truthful interactions (M = 48.81, SD = 20.86) than during deception (M = 36.35, SD = 17.66).

Results also indicated a significant interaction between statement and performance, F(1, 23) =

# 5.50, p < .05, !" = 0.19, consistent with the pattern of %REC results outlined above. Separate pairwise within-subjects t-test comparisons of statement for each level of performance revealed

that Lmean values were greater for truthful trials (M = 52.80, SD = 25.98) than Lies (M = 31.14, SD =

20.77), but only for High performing pairs, t(9) = 4.02, p < .01.This further suggests that truthful interactions are predictably characterized by more stable entrainment processes than deceptive interactions, and highlights the association between stereotypical patterns of coordination and task performance.

The ANOVA performed on %DET data yielded a marginally significant interaction

# between statement and information, F(1, 23) = 4.19, p = .052, !" = 0.15, depicted in Figure 7. A series of paired-sample t-tests suggested that the dynamic structure of behavior during truthful interactions in Vision conditions may have exhibited greater deterministic coordination than during deception in Vision conditions, t(24) = 2.39, p < .05, and also greater than during truthful interactions in No-vision conditions, t(24) = 2.63, p < .05. These results are similar to the significant main effect of information previously revealed in the analysis of %DET data obtained using RQA, emphasizing the generalizability of dynamical processes for describing behavior at a variety of scales. It remains inconclusive at any scale, however, to what degree the coordination dynamics associated with deception and its detection are influenced by the informational structure of the social environment. A more controlled adjustment of perceptual information may provide a more discerning evaluation of behavioral coordination, and consequently, a more powerful basis for these comparisons.

90 Truth Lie 85

Determinism 70

60 Vision No vision Perceptual information

Figure 7. Average %DET for Truth and Lie statements across both perceptual information conditions, obtained using CRQA; error bars indicate the standard error of the mean.

Correlations between measures of movement, performance, and dispositional characteristics

Correlation analyses comparing movement dynamics, task performance and dispositional factors revealed no consistent patterns. A few significant correlations emerged, however the sample size was likely too large; in this particular context of deception, where task goal and performance varies between individuals from trial to trial, the variables simply may not be relevant.

Discussion

The objective of Experiment 1 was to establish the degree to which SC is influenced by the deceptive or non-deceptive nature of a social interaction, and to evaluate whether deception detection is related to dynamic patterns of coordinated behavior, manipulations of the social stimulus array, and dispositional qualities (i.e., social aptitude and rapport). In what follows, the results are first presented for task performance, followed by a discussion of how the coordination dynamics of intrapersonal and interpersonal movement differ as a function of statement type and perceptual information.

Consistent with the vast body of previous research in lie detection accuracy (Bond &

DePaulo, 2006; Vrij, 2008), average task performance in Experiment 1 was comparable to chance

across task performance scores. Conversely, there were no consistent patterns revealed by correlation analyses comparing measures of SC, task performance and dispositional characteristics, indicating that the variables simply may not be relevant in this context. Comparisons of lie discrimination ability across statement conditions, however, revealed that task performance was greater for identifying truthful statements over lies. For both measures of task performance presented here (d’ and composite performance), scores in the upper range (above the median) were significantly higher than those in the lower range (below the median), thereby isolating the additional variable of performance for identifying the means through which the behavior of liars differs from truth-tellers. Taken together, analyses of task performance indicated that individuals were more sensitive to, and thus could more accurately and confidently identify, truthful interactions, regardless of performance level.

A central aim of the current study was to identify the behavioral processes that differentiate truthful and deceptive interactions. Based on the joint consideration of recent social entrainment research (i.e., Freeman et al., 2011; Tolston et al., 2014) and previous investigations into the behavioral dynamics of liars (Duran et al., 2013), it was expected that measures of SC would be characterized by more robust patterns of coordination and stability during truthful interactions than during deception, and moreover, that these differences would be reflected in task performance. In line with the findings of Duran and colleagues (2013), RQA performed on %DET data revealed a significant difference in the intrapersonal movement dynamics of individuals during honest and deceptive interactions. However, these effects were not influenced by role. In other words, although deceptive content predictably influenced the intrapersonal movement dynamics of individuals, these differences cannot be attributed independently to the behavior of liars or potential lie detectors, but are observed within the behavior of both co-actors. With respect to the influence of deception on the contextually relevant behavior of socially-situated dyads,

results of CRQA performed on %REC and Lmean measures indicated that patterns of SC also differed significantly as a function statement type. These results suggest that the observed effects of statement type are fundamentally grounded within the coordinated behavioral dynamics at the level of the interaction, and not only within individual patterns of movement.

The present research was further designed to evaluate whether the dynamical patterns of

SC discussed above are related to discrimination ability, or task performance. It was hypothesized that the behavior of high-performing pairs would be characterized by stereotypical movement dynamics, such as less stability and coordination in dishonest conditions, but the behavior of low performers would not. Results from RQA and CRQA analyses indicated that the observed dynamical patterns of both intrapersonal movement and interpersonal coordination varied as a function of performance level. Specifically, the movement dynamics of individuals within high- performing pairs was more recurrent and less random during truthful interactions as compared to during deception, while Low performers did not display any sensitivity to differences between truthful and dishonest interactions. As predicted, the results of CRQA analyses revealed that pairs with higher performance scores exhibited a greater degree of interpersonal coordination and more regular patterns of behavioral coupling when discussing true statements as opposed to lies. These findings further confirm that the SC dynamics of deception predictably differ from the SC dynamics of honesty and, moreover, that the interacting individuals are sensitive to these changes.

Importantly, these findings address a central challenge of deception research by identifying SC dynamics as a behavioral process that differentiates truthful and deceptive interactions. It should be noted that, in echoing the pattern of results discussed above, these findings suggest that the characteristic patterns of behavior that distinguish deception and honesty at the individual scale are also observed at the dyadic scale. In fact, these results may provide further evidence that the effects of deception on behavior may be more informative at the level of the interaction, rather

than at the disembodied level of the individual that has dominated much of the existing methodology. Therefore, a more comprehensive investigation into these effects may be required.

There is a great deal of evidence within the literature on SC dynamics supporting the claim that perceptual information is sufficient to couple interacting individuals to each other and to the social environment (i.e., Blake & Shiffrar, 2007; Richardson et al., 2005, 2007; Schmidt & O’Brien,

1997; Shockley et al., 2003, 2007), yet little attention has been given to the informational factors an individual may utilize to determine the honesty of an interaction. The present study explored the possibility that dynamical patterns of behavior would not only differ between perceptual medium conditions (visual and/or auditory information), but that these effects would be attenuated for high-performing pairs compared to low-performers, reflecting the relationship between task performance and attunement to the structure of the social stimulus array that serves as the informational basis for accurate deception detection.

The results obtained from RQA indicated that the intrapersonal movement dynamics of individuals exhibited less stability during No-vision trials. Similarly, the CRQA analysis revealed that the coordinated behavior of co-actors was marginally less regular when co-actors could not see each other. While only partially revealed via marginal significance, the pattern of results may lend support to the hypothesis that the movement dynamics of both individual and joint behavior would be influenced by the structure of the social stimulus array. However, one must note the

# large effect size of !" = 0.15 associated with the test performed, with a large effect size typically

# defined by a value of !" = 0.1379 (Cohen, 1969; Richardson, 2011). Consequently, the magnitude

of the effect size observed in this test suggests that failing to reject H0 may in fact be a Type II error.

In addition, the pattern of results did not reveal any of the anticipated differences between high- performers and low-performers. As mentioned previously, a more controlled manipulation of the informational array may provide a more objective evaluation of participant activity.

Together, the results from Experiment 1 indicate that patterns of SC are predictably altered by the deceptive content of an interaction, but only for conversational pairs that exhibit high task accuracy and confidence. In these cases, truthful conversations were characterized by more stable global coordination. The collective results also suggest that, while the individual movement dynamics of co-actors were influenced by social role, perceptual information, and statement type, the behavioral dynamics at the level of interpersonal coordination were stable across variations in the structure of social stimulus information, varying only as a function of deceptive context. When considered in tandem with the observation that task performance was also influenced exclusively by statement type, these findings appear to suggest that studying the isolated behavior of socially- situated individuals may not provide a fully informative account of the means through which lies can be separated from the truth. Experiment 2 was implemented to more closely examine the relation between SC and the kinematic information sufficient for supporting the detection of deception.

CHAPTER 3

Experiment 2

The results of Experiment 1 provide evidence that changes in the behavioral coordination dynamics of co-actors function as a means through which people can differentiate honest and deceptive interactions. More information is needed, however, to draw conclusions about the degree to which deception detection performance is susceptible to changes in the informational structure. The aim of Experiment 2 was to further determine the degree to which the kinematic structure of the social stimulus array shapes the behavioral processes that differentiate truthful and deceptive interactions. This was accomplished by manipulating the structure of visual information during a series of visually mediated deception tasks. Following the same procedure employed in

Experiment 1, participant dyads played a conversational truth-lie game in alternating roles of Actor and Perceiver. Instead of interacting face-to-face or separated visually, however, participants in

Experiment 2 were presented to one another on a television screen and were provided either a standard, full-color video (pictorial information plus full kinematic structure) or a gray-scale depth array (diminished kinematic structure only) representation of their co-actor’s movement in real- time. It was predicated that time-evolving processes of coordinated behavior would aid in distinguishing honesty from lies, and that these processes would be shaped by the structural qualities of visual information array.

Using the same materials to assess the dispositional variables of social aptitude and rapport, record participant movement, and obtain accuracy data as were implemented in

Experiment 1, it was anticipated that the same characteristic dynamical patterns differentiating truthful and dishonest interactions would arise in Experiment 2 (i.e., less movement regularity and stability during deception). It was further predicted that the information specifying deception

would be susceptible to changes in the structure of the stimulus array, as evidenced by a difference in the behavioral dynamics exhibited during genuine (kinematic and pictorial information) and diminished information (no pictorial information) conditions. By comparing the effects of informational quality on measures of SC and task performance in visually-mediated interaction modes that were otherwise similar, the present study also explored the possibility that these differences would be attenuated for pairs demonstrating high performance. In other words, the behavioral dynamics of high-performing pairs were not expected to differ as a function of informational structure, only as a function of statement type, reflecting the relation between task performance and attunement to the informational structure of the social stimulus array.

Method

Participants

Nineteen participant pairs (eight mixed-gender, 11 matched-gender; 14 males, 24 females;

38 total individuals) were recruited through the University of Cincinnati psychology participant pool, and by word of mouth. Participants either received course credit or were paid $15 for their time. All participants had normal or corrected-to-normal vision, were fluent English speakers, and were free of any motor, language, or neurological impairments. No special populations were used.

Materials

Data recording and stimulus presentation. An Xbox Kinect again recorded auditory data as well as frame-to-frame video recordings of each participant’s movements at a rate of 30 frames per second. Two televisions, angled away from each other, displayed visual stimuli for each participant as either Standard-video (genuine condition) or as a Depth-array (diminished condition). The Depth-array representations displayed the bodily movements of individuals in a three-dimensional task environment as a two-dimensional gray-scale color map (see Figure 8); farther distances are depicted as darker (black) and nearer distances are depicted as lighter (white).

A B

Figure 8. Example of images recorded by the Kinect in (A) standard video, and (B) a depth-array.

Both participants in a dyad viewed the same type of stimulus, dependent on condition. A Kinect was affixed atop each television screen at a height of about 1.5 m, placed approximately 1 m in front of each individual. An opaque curtain (2 m × 2.5 m) separated participants in both conditions (Figure 9). The social aptitude assessments, statement selection lists, and rapport questionnaires used in Experiment 1 were also used in Experiment 2, as well as the same icebreaker and experimental tasks.

Figure 9. Experimental setup for video-mediated interactions.

Procedure

Following the same procedure as Experiment 1, participants first completed the social aptitude pre-assessment and icebreaker tasks. Participants then received instructions for selecting

Truth and Lie statements, as well as an overview of the task goals. As in Experiment 1, participants alternated roles as the Actor and Perceiver. Instead of conversing face-to-face, however, the participant pairs interactions were visually mediated; individuals were presented with a full-body real-time depiction of their partner in either a depth-array display or in the form of a standard video. In place of completely eliminating visual information in half of the trials, each of the two statement types were presented to the Perceiver in the depth-array condition twice, and in the standard video condition twice, for a total of 16 one-minute trials. All trials were randomized in terms of statement type (i.e., Truth or Lie) within two counterbalanced blocks of presentation mode

(i.e., Standard video or Depth-array). Each block consisted of four Truths and four Lies for each participant, and each trial was followed by a series of questions from the Perceiver. Following the completion of all experimental trials, the rapport questionnaire was administered in the same manner as in Experiment 1. Pairs were then prompted to review their guesses with each other and were debriefed as to the true nature of the study.

Analysis

The same analysis measures and techniques implemented in Experiment 1 were used to generate measures of task performance and coordination quantification. However, the information variable levels of Vision and No-vision were replaced with Standard-video and Depth-array.

Results

Task performance

In order to examine the independent and collective effects of deception and perceptual information on task performance, composite task performance scores were submitted to a 2 × 2

repeated-measures ANOVA comparing the within-subjects variables of statement (Truth, Lie), and information (Standard-video, Depth-array). As can be seen in Figure 10, results indicated a

# significant main effect of statement, F(1, 18) = 6.14, p < .05, !" = 0.25, such that task performance was greater for truthful interactions (M = 0.19, SD = 0.36) over deceptive interactions (M = 0.01,

SD = 0.35)7.

Standard 0.35 Depth 0.30 0.25 0.20 0.15 0.10 0.05 Performance score Performance 0.00 -0.05 Truth Lie -0.10 -0.15 Statement type

Figure 10. Truthful interactions were characterized by greater task performance and lie discrimination, as indicated by a comparison of average performance scores across all conditions of information and statement type. The error bars indicate the standard error of the mean.

As in Experiment 1, the distribution of task performance data suggested that certain pairs were notably better or worse than others. Using a median split analysis of composite performance scores to generate groups of High performers (M = 0.23, SD = 0.14, N = 10) and Low performers

(M = -0.04, SD = 0.05, N = 9), the above two-way ANOVAs were repeated to include the performance variable as three-way ANOVAs. Measures of task performance were submitted separately to a series of 2 (statement: Truth, Lie) × 2 (information: Standard-video, Depth-array) × 2

(performance: High, Low) mixed-design ANOVAs. Results again indicated that truthful interactions

7 # Results of the analysis performed on d’ also yielded a significant main effect of statement, p < .05, !" = 0.96, indicating greater sensitivity to truthful interactions over deceptive interactions. However, as stated previously, d’ may not be the most robust method for assessments of more complex cognitive performance such as in the present study.

# elicited greater task accuracy than deceptive interactions, F(1, 17) = 6.38, p < .05, !" = 0.27, as well as a significant difference in lie discrimination ability between groups, F(1, 17) = 28.78, p <

# .05, !" = 0.63. Therefore, for all subsequent analyses, the data were split into groups of High and

Low performers in order to examine whether performance ability influenced SC (see Table 2).

Table 2 Lie Discrimination Ability in High (n=10 ) and Low (n=9) Performance Groups as Measured by Composite Performance Score Truth Lie Performance group Color Depth-array Color Depth-array High .31(.39) .26(.39) .26(.39) .09(.37)

Low .19(.38) -.26(.21) -.01(.22) -.07(.12) Total .25(.38) .01(.41) .13(.34) .02(.29) Note. Data is reported as M(SD)

Intrapersonal movement dynamics

A significantly greater degree of structural organization was revealed for the observed individual movement data as compared to shuffled time series, revealing that the obtained results were not an effect of incidental distribution, but were due to their temporal structure (all p < .05).

Measures of RQA performed on the average pixel change data were submitted to four-way

ANOVAs with a between-subjects variable of performance (high, low), and within-subjects variables of social role (Actor, Perceiver), statement (Truth, Lie), and information (Standard-video,

Depth-array)8.

Following the pattern of results in Experiment 1, analyses of %REC data yielded a

# marginally significant main effect of role, F(1, 17) = 4.27, p = .054, !" = 0.20, with the individual

8 Four-way mixed design ANOVAs were also performed with accuracy defined at three levels (high, moderate, low), however the overall pattern of results was the same.

movement dynamics of Actors exhibiting greater recurrence (M = 1.44, SD = 1.46) than the behavior of Perceivers (M = 2.06, SD = 1.89), illustrated in Figure 11.

3.5 Actor Perceiver 3.0

2.5

2.0

1.5

%Recurrence 1.0

0.5

0.0 Truth Lie Truth Lie Standard Depth Statement type at each level of information Figure 11. Average percentage of recurrent points (%REC) that characterize the movement dynamics of Actors and Perceivers during Truth and Lie statements in both informational structure conditions, obtained using RQA. The error bars indicate the standard error of the mean.

Results from analyses of Lmean data indicated a significant interaction between statement

# and performance, F(1, 17) = 4.83, p < .05, !" = 0.22. Pairwise comparison analyses revealed that

Lmean measures for High performers were greater during True statements (M = 45.50, SD = 39.84) than during Lies (M = 36.71, SD = 31.87), whereas Low performers exhibited the inverse pattern:

Lmean measures were instead greater for Lies (M = 41.52, SD = 27.00) rather than for True statements (M = 31.63, SD = 28.73). Results also yielded a marginally significant interaction

# between information and performance, F(1, 17) = 3.85, p = .06, !" = 0.19, with the individual movement dynamics of low performers tending to exhibit greater stability during Depth-array trials

(M = 28.93, SD = 21.33) than during Standard-video trials (M = 44.22, SD = 28.72). All results are illustrated in Figure 12.

70 Truth Lie 60

30 Meanline 20

Standard Depth Standard Depth

Low performers High performers 90 80 70 60 50 40

Determinism 30 20 10

Standard Depth Standard Depth Low performers High performers Informational structure for each performance level

Figure 12. Average Lmean (top row) and %DET (bottom row) for the movement dynamics of High- and Low-performing individuals during Truth and Lie statements within both informational structure conditions, obtained using RQA. The error bars indicate the standard error of the mean.

The ANOVA performed on the %DET data similarly revealed a significant interaction

# between information and performance, F(1, 17) = 5.04, p < .05, !" = 0.23, as well as a significant

# main effect of information F(1, 17) = 6.08, p < .05, !" = 0.26, such that the structure of intrapersonal motor behavior was more deterministic during Standard-video trials (M = 71.10, SD

= 13.57) than during Depth-array trials (M = 66.71, SD = 15.77). As can be seen in Figure 12, pairwise comparisons of information using a within-subjects t-test for each level of performance

again confirmed that measures of %DET were greater during Standard-video trials (M = 71.96, SD

= 13.07) as compared to Depth-array trials (M = 63.58, SE = 12.75), but only for Low performers, t(8) = 4.26, p < .01. There was no effect of information on the movement dynamics of high- performing individuals, following a similar pattern of results as the marginal interaction effect

indicated by analyses of the Lmean data.

Interpersonal coordination dynamics

The degree of structural organization for coordinated interpersonal movement was significantly higher than shuffled time series, assuring that the observed data patterns were in fact due to their temporal structure, as opposed to their incidental distribution (all p < .05). Cross-

recurrence measures (i.e., %REC, %DET, Lmean) were submitted to separate three-way ANOVAs with a between-subjects variable of performance (high, low), and within-subjects variables of statement (Truth, Lie) and information (Standard-video, Depth-array)9.

Results from analyses of %REC data indicated a significant interaction between information

# and performance, F(1, 17) = 4.59, p < .05, !" = 0.21. Pairwise comparisons of information for each level of performance, conducted using repeated-samples t-tests with a Bonferroni correction, revealed that measures of %REC were greater for Standard-video trials (M = 1.95, SD = 1.0) as compared to Depth-array trials (M = 1.18, SD = 0.55), but only for Low performing pairs, t(8) =

2.34, p < .05 (see Figure 13). These results indicate that the coordinated behavior of pairs with lower performance scores is influenced by changes in the structure of visual information, but that these changes do not influence the coordination dynamics of high performing pairs.

9 Three-way mixed-design ANOVAs were also performed with the performance variable defined at three levels (High, Moderate, and Low), yielding the same overall pattern of results.

3.0 Low performers High performers 2.5

2.0

1.5 %Recurrence 1.0

0.5

0.0 Standard Depth Stimulus information

Figure 13. Average %REC for the interpersonal motor behavior of High- and Low-performers, obtained using CRQA. The error bars indicate the standard error of the mean.

The AVOVA performed on Lmean data revealed a significant main effect of statement, F(1,

# 17) = 5.41, p < .05, !" = 0.24, such that the patterns of interpersonal coordination were more stable during truthful statements (M = 36.42, SD = 16.46) as compared to lies (M = 31.30, SD =

17.69). In addition, the analysis revealed a significant interaction between information and

# performance, F(1, 17) = 7.85, p < .05, !" = 0.32, echoing the pattern of results found in the %REC analyses. Repeated-samples t-tests using a Bonferroni correction were implemented as pairwise comparisons of information for each level of performance. As illustrated in Figure 14, results

indicated that measures of Lmean were greater for Standard-video trials as compared to Depth-array trials, but only for Low performers t(8) = 2.55, p < .05.

Low performers High performers Truth 50 50 Lie

40 40

30 30

20 20 Meanline

10 10

0 0 Standard Depth Standard Depth 100 100 Truth Lie 90 90 80 80 70 70 60 60

Determinism 50 50 40 40 30 30 20 20 Standard Depth Standard Depth

Stimulus information Stimulus information

Figure 14. Average Lmean (top row) and %DET (bottom row) for the coordinated behavioral dynamics of Low- (left) and High- (right) performance pairs during Truth and Lie statements in both information conditions, obtained using CRQA. The results indicate significant differences in coordination stability as a function of informational structure (top), and statement (bottom), but only for Low-performers. The error bars indicate the standard error of the mean.

Results from analyses of %DET data yielded a significant interaction between statement

# and performance, F(1, 17) = 6.45, p < .05, !" = 0.28 (see Figure 14). Pairwise comparisons using t-tests with a Bonferroni correction revealed the coordination dynamics of Low-performing pairs exhibited less regularity during truthful interactions (M = 70.76, SD = 8.90) than during deception

(M = 75.22, SD = 10.23), t(8) = -3.06, p < .05. This pattern of results is inverse to those exhibited

by previous analyses of High-performing pairs. There were no additional effects or interactions, all p > .05.

Correlations between measures of movement, performance, and dispositional characteristics

Correlation analyses comparing movement dynamics, task performance and dispositional variables revealed no consistent patterns. A few significant correlations emerged, however the sample size was likely too large; in this particular context of deception, where task goal and performance varies between individuals from trial to trial, the variables simply may not be relevant.

Discussion

The objective of Experiment 2 was to determine the degree to which the visual informational structure of the social stimulus shapes the coordination dynamics associated with deception and its detection. As opposed to comparing the time-evolving behavioral patterns of distinct perceptual modalities akin to Experiment 1, the structure of perceptual information in

Experiment 2 was modulated by manipulating the visual properties of the stimulus array during a series of visually-mediated deception tasks. The behavioral dynamics of high-performing pairs (i.e., more accurate, higher confidence) in Experiment 2 were expected to differ only as a function of statement type, and not as a function of informational structure. More generally, it was anticipated that the overall pattern of results would be agree with that Experiment 1. These expectations were supported. Average task performance was again consistent with chance. Comparisons of lie discrimination ability between statement conditions likewise revealed that task performance was higher for identifying truthful statements over lies. Similar to the findings of Experiment 1, correlation analyses of SC, task performance, and dispositional characteristics revealed no consistent pattern of results, suggesting that the variables are simply not relevant in this context.

As in Experiment 1, RQA and CRQA were used to quantify behavioral coordination and stability with the expectation that similar characteristic movement patterns would differentiate truthful and dishonest interactions in Experiment 2 (i.e., greater structural regularity and recurrence during honesty), and would emerge as a function of task performance. As predicted, the movement dynamics of individuals within High-performing pairs exhibited greater stability and less randomness during truthful interactions than during deception, while Low performers did not display any sensitivity to differences between truthful and dishonest interactions. Similarly, the SC of dyads differed significantly as a function of both statement type and task performance, such that the coordination dynamics of pairs with better task performance were characterized by more stability when interactions were truthful, and less deterministic structure during deception. In other words, the stereotypical patterns of behavior that typically differentiate truth and lies were again associated with better task performance at both the level of the individual and the level of the interaction. These findings further confirm that the SC dynamics of deception predictably differ from the SC dynamics of truth and, moreover, that people are sensitive to these changes.

Experiment 2 was designed to examine the degree to which information specifying deception is susceptible to changes in the structure of the stimulus array by comparing the effects of informational quality on patterns of coordination and task performance during visually- mediated interaction modes. The present study also explored the possibility that the expected behavioral differences between genuine and diminished information conditions would be attenuated for pairs demonstrating high discrimination accuracy and confidence. Both RQA and

CRQA analyses indicated that the structural quality of visually-mediated interactions only influenced the behavioral dynamics of Low-performing pairs, reflecting the predicted relationship between task performance and attunement to the informational structure of the social stimulus array. One possible interpretation of these findings is that High performers are implicitly more

attuned to the information specifying deception and are thus less susceptible to changes in the informational structure of the visual environment. Alternatively, it may be that improved performance is a result of greater sensitivity to the mutually adaptive coordination dynamics that distinguish lies from the truth. In any case, the circular nature of the relationship between SC and lie discrimination ability revealed in the present study highlights the reciprocal influence of interacting individuals at multiple levels of social perception as behavior unfolds over the course of an exchange.

CHAPTER 4

General Discussion

Weak theoretical and empirical support for the dominant perspective in deception research has prompted an appeal for a novel approach (Burgoon & Qin, 2006; Vrij & Granhag, 2012). The present study was developed in consideration of two particular limitations within most current efforts to understand deception. First, where existing research has largely ignored the social quality of an inherently social event by focusing on individuals rather than social units (Buller & Burgoon,

1996), the experiments presented here treat deception as a multi-scale phenomenon that emerges, fundamentally, between interacting individuals. Secondly, this project foregoes traditional methodology that emphasizes subjectively coding discrete, conspicuous individual behaviors, and focuses instead on techniques that capture the flexible, adaptive, and dynamic nature of social interaction.

The purpose of the present study was to address the current limitations of deception research from a SC dynamics perspective by completing four specific aims. The first aim was to determine the degree to which SC is influenced by the deceptive or non-deceptive nature of a social interaction. The second aim was to evaluate whether an individual’s ability to detect the deceptive or truthful intent of a co-actor (i.e., discrimination ability) is related to the patterns of SC that occur between the individual and their co-actor. The third aim was to establish the degree to which deceptive or truthful intent can be detected from the perceptual information that exists during interpersonal interaction. Lastly, the fourth aim was to identify whether dispositional characteristics, such as personality and rapport, relate to SC and the ability to identify deception when situated in a socially coordinated interaction. Each of the two experiments presented here examined how the coordination dynamics of interacting individuals were influenced by statement

type, perceptual information, and individual differences in dispositional tendencies, as well as the degree to which the time-evolving patterns of behavior corresponded with task performance. In what follows, the collective implications of both experiments are discussed with respect to the specific aims outlined above, followed by a discussion of limitations and future research.

A fundamental hypothesis of the present research was that the coordination dynamics of co-actors would be influenced by the deceptive or non-deceptive nature of the interaction. If the behavioral dynamics of deception differ from the behavioral dynamics of honesty, it would be reasonable to propose that this difference provides a basis for distinguishing lies from the truth.

Results from both experiments support the central prediction that the effect of deception on SC reflects corresponding differences in task performance, and, furthermore, illustrate the discrimination of differences between honest and deceptive interactions on the basis of observable motor behaviors. That is, the behavioral dynamics of truthful interactions were characterized by more robust patterns of coordination and stability than movement during deceptive interactions.

Following the assumptions within a coordination dynamics framework, these results suggest that the coupled nature of an interaction is disrupted during deception, and moreover, that this disruption may provide a means through which liars can be identified. Indeed, task performance was better (i.e., more accurate, higher confidence) for interactions that embodied characteristic patterns of movement — the behavioral dynamics of high-performing pairs exhibited less stability and coordination when one of the individuals was lying, while the behavior of low performers did not. Such a relationship between lie-discrimination ability and coordination suggests that people are sensitive to information specifying deception; the directionality of this relationship, however, is not as well-defined.

While it is possible that findings presented here offer evidence for the effect of SC on deception detection performance, it is concurrently possible that they demonstrate the effect of lie

discrimination ability on coordination. One suggestion, motivated by the KSD perspective on person perception, is that deception detection is an issue of sensitivity—increased attunement to the kinematics that specify deception may correspond directly with better lie discrimination ability

(Richardson & Johnson, 2005). This suggestion also fits within accounts of social cognition arguing that social cues are properties of the social environment, the detection of which requires perceptual learning and attunement (Wiltshire et al., 2015). Recent research into the kinematics of intentionality has been cited as evidence in support of this perspective, with reference to claims that differences in individual motor dynamics arise as a function of social intent (Becchio et al,

2008, 2010; Sartori, Becchio, & Castiello, 2011). As such, it may be that the relation between coordination and performance is determined by bottom-up influences; more robustly- characteristic patterns of coordination may facilitate the perceptual attunement necessary for the specification of deception or honesty.

It still remains equally plausible, however, that the inverse is true—it may alternatively be that the causal relationship between coordination and performance is determined by top-down influences. For example, individuals within low performing pairs may be pre-disposed to focus on the wrong information, attending instead to non-movement-based properties such as Ekman’s

‘micro-expressions’ (Ekman & Friesen, 1969) that have been highly disseminated throughout various avenues of popular psychology (Haggbloom et al., 2002). In line with this interpretation, research on deception detection in athletes indicates that expert players are better than novice players at detecting deceptive movement (e.g., Barton, 2013; Jackson et al., 2006; Sebanz &

Knoblich, 2009; Sebanz & Shiffrar, 2007). The results of the current study do not definitively support one causal direction to the exclusion of the other.

The present experimental findings point toward a circular causality between coordination and lie-discrimination ability. This claim is supported by a behavioral dynamics account of social

cognition, which proposes that the emergence of socially relevant information is a function of the reciprocal influence of interacting individuals (Oullier & Kelso, 2009; Schmidt & Fitzpatrick, 2016;

Schmidt & O’Brien, 1997; Schmidt & Richardson, 2008). Specifically, the mutual exchange of information at multiple levels of the interaction supports the cognitive and behavioral coupling of co-actors that, in turn, shapes the resulting patterns of coordinated behavior. In other words, SC is both cause and consequence of lie-detection ability. It is important to appreciate, however, that this issue of a circular relationship lends itself to further debate. Future research should be aimed at untangling the bidirectionality of social perception and action, and may benefit from submitting

CRQA measures to directional analyses in order to identify which social role is driving the coordination dynamics.

Adopting the view that perceptual information simultaneously guides and is generated by social interaction, the current study was designed to uncover the basis of the information specifying deception and honesty. The issue of perceptual sensitivity to changes in the stimulus array of the social environment is clouded by inconsistent results within the existing literature. As such, two experiments were utilized to examine the effects of various structural changes in visual and auditory information. Establishing the degree to which deceptive or truthful intent can be detected from movement information consequently establishes the degree to which information specifying deception and honesty is susceptible to changes in the structure of social stimuli.

Motivated by the results of previous informational coupling research (e.g., Richardson et al., 2005, 2007; Schmidt et al., 1994, 1997; Shockley et al., 2003, 2007), as well as work demonstrating a functional link between bodily movement and complex cognitive processes

(Richardson & Dale, 2005; Tolston et al., 2014), it was anticipated that differing informational conditions of perceptual medium (visual and/or auditory information) and structure (pictorial plus

kinematic or only (diminished) kinematic and form information) would elicit differences in SC generally, but not when jointly considered with the effects of statement type or task accuracy.

As expected, the results of Experiment 1 indicted that although the coordination dynamics were more stable during audiovisual conditions as opposed to exclusively auditory conditions, there was no concurrent change in accuracy. These findings suggest that informational coupling is supported in both auditory and visual conditions, and that auditory information is sufficient for lie discrimination. Experiment 2 further examined the effect of informational structure on SC and lie detection using visually mediated interactions. Results indicated that although the movement patterns of Low-performers were influenced by changes in the structure of the social stimulus array, the behavioral dynamics of High-performing pairs remained unaffected across informational conditions. In other words, the stereotypical patterns of coordinated behavior that typically differentiated truth and dishonesty were again associated with better task performance, regardless of changes in the informational structure of the social environment.

One potential interpretation of these results is that the less constrained informational conditions (i.e., Vision, Standard-video) enhanced the dynamical stability and regularity of behavior by virtue of increasing the amount of perceptual information available. This explanation is in line with that of previous visuomotor coordination research demonstrating stronger motor entrainment (i.e., more stable coordination) during instances that permit the exposure of greater amounts of movement information (e.g., Richardson et al., 2007; Roerdink et al., 2005; Shockley,

Richardson, & Dale, 2009; Varlet et al., 2015). It was not surprising that variations in the informational structure did not similarly influence lie discrimination ability, particularly in

Experiment 2, given that a central tenet of the KSD principle is illustrated by an absence of differences in person-perception accuracy between conditions of genuine and diminished stimuli

(Runeson & Frykholm, 1983).

Lastly, the current research was designed to examine the methodological validity of lie- detection research and techniques that rely on overt manipulations of rapport and personality matching. According to both the Federal Bureau of Investigation (Driskell, Blickensderfer, & Salas,

2013) and a recent US Intelligence Science Board report on investigative interviewing (Fein,

2006), rapport-building techniques are currently considered the most critical and effective element of obtaining accurate information. Thus, identifying whether dispositional properties moderate the observed relation between SC and lie discrimination accuracy subsequently establishes whether existing research should maintain its current focus on the individual differences in deception and lie-discrimination ability.

Across both experiments, correlation analyses of SC, task performance, and dispositional measures of social aptitude and rapport did not reveal any consistent patterns. These results are neither surprising nor expected, considering the irregularity of findings within the current literature on individual differences and deception, particularly in the realm of behavioral dynamics. Given that accuracy in lie detection studies rarely exceeds chance (Bond & DePaulo, 2006), even when controlling for professional training (c.f. Vrij, 2008), the present findings may merely reflect the added layer of interactional complexity presented by deceptive context. If the coupled nature of an interaction is disrupted during deception, as previously asserted, it follows that the relationship between rapport and task performance observed in cooperative environments may also be disrupted; perhaps, in this context, these variables are simply not relevant.

Irrespective of the observed influence of social aptitude on SC dynamics and task performance, these results address the aim of identifying whether dispositional characteristics relate to SC and lie discrimination accuracy. The absence of any reliable correlation patterns weakens arguments claiming a reciprocal influence of individual differences in social aptitude on deceptive behavior or detection accuracy. In fact, the experimental findings provide further

evidence for the importance of investigating social phenomena at the level of the interaction— diluting the presupposed connection between lie discrimination and dispositional factors effectually shifts the focus back to the observed relationship between SC and performance.

Collectively, this research indicates that performance at the dyadic level is a function of the real- time interactions between co-actors and the environment, as opposed to simply being the consequence of each participant’s a priori attributes and static task constraints. This view further exposes the need to critically evaluate the current state of deception research.

Despite the ubiquitous social occurrence of deception, its fundamental complexity has stifled adequate scientific inquiry. The research methods and data analyses that have been utilized in most studies may be insufficient for a rich assessment of the phenomenon. Decades of research have yet to yield a robust account of how lies can be differentiated from the truth (DePaulo et al.,

2003; Sporer & Schwandt, 2007; Vrij, 2008; Vrij et al. 2010; Zuckerman et al., 1981). Current lie- discrimination techniques are based on controversial research findings that fall short on numerous reliability and validity criteria (Vrij & Fisher, 2016). While the conventional perspective regards lying and its detection as separate and static contributions, the current investigation presents a novel experimental approach that is chiefly concerned with the contextually relevant interpersonal coordination dynamics of socially situated individuals at multiple levels of an interaction. The collective findings provide seminal evidence of an observable behavioral process that reliably differentiates truthful and deceptive interactions: social coordination dynamics.

Considering the implication of the present study that deceptive behavior is an emergent process that fluctuates over time, it is essential that subsequent investigations into deception and lie-detection are grounded within the dynamical systems framework discussed here, as many current assumptions within the deception literature may be incorrect. This dissertation builds upon the well-established phenomena of unintentional social coordination (e.g., Lopresti-Goodman et

al., 2008; Schmidt et al., 2007; Richardson et al., 2007). Previous work in this area demonstrates that socially-situated individuals spontaneously coordinate along multiple levels of an interaction, in terms of both verbal and nonverbal behavior (Chartrand & Van Baaren, 2009; Richardson, Dale,

& Shockley, 2008). Thus, future research should consider the multimodal nature of social interaction, particularly the dynamic structure of language. For instance, the work of Burgoon and

Qin (2006) verifies that verbal behavior during deception exhibits considerable variability over the course of a conversation, while text-based analyses of written language indicate differences in the dynamic patterning of deceptive versus truthful writing (Newman et al., 2003; Zhou et al., 2004).

These findings not only highlight the dynamic nature of deceptive discourse, but also suggest fundamental differences in the structure of language dependent on deceptive intent. The results of the current study provide evidence that observable motor behavior is informative for understanding social coordination and associated processes of social cognition, but analyses of the concurrent linguistic dynamics may provide supplementary, if not novel, information about the relationship between cognition and behavior during deception.

In line with the coordination dynamics perspective, the decreased strength and regularity of SC patterns observed during deception may reflect the characteristically incongruent cognitive states of deception, allowing a breakdown in the reciprocal linkage between coupled behavioral and cognitive processes. It should be noted, however, that the results of the present study do not definitively support any causal directionality in terms of the functional relationship between coordination and performance. One potential avenue of future research into the social coordination dynamics of deception should thus be aimed at unravelling the potential bidirectionality of social perception and action. Further study of this issue may particularly benefit from submitting time series data to directional analyses of recurrence measures in order to identify which social role exhibits a leading influence the coordination dynamics of high-performing

dyads. For instance, if it appears as though the Perceiver leads the emergent coordination dynamics, it may be that lie-discrimination ability guides interpersonal behavior during deception.

Alternatively, should Actors drive the time-evolving entrainment patterns, it could be suggested that behavioral coordination exerts a causal influence on task performance. If there exists some property of coordination that leads to better performance, the current findings could be applied in the development of a machine learning algorithm for distinguishing lies from the truth. Future work aimed toward establishing a more fine-grained understanding of the intricate relationship between interpersonal behavior and performance will help evaluate this notion.

A central assumption of the dissertation work presented here is that if social cognition is embodied in interpersonal interaction, the cognitive states of interacting individuals should be reflected in the emergent patterns of social coordination. There is a solid evidential basis to suggest that pro-social individuals exhibit greater interpersonal synchrony (e.g., Lumsden et al., 2012;

Miles et al., 2010), as well as the converse relationship—that synchrony engenders cooperation

(e.g., Kokal et al., 2011; Reddish et al., 2013; Valdesolo et al., 2010; Wiltermuth & Heath, 2009).

However, work addressing the link between social coordination and task performance in non- cooperative contexts and is scarce. Although it has recently been established that cooperative goals increase interpersonal synchrony and performance in joint-action object-manipulation tasks

(Allsop et al., 2016), the current study provides the first empirical demonstration of the bidirectional relationship between social coordination and task accuracy in affiliative and non- affiliative social contexts that more closely resemble everyday interactions outside of the laboratory. Importantly, the results presented here reveal that dynamical patterns of SC differ between truthful and deceptive interactions, thus providing a reliable basis to detect deception without the use of complex equipment or training. This represents a major contribution to the study of deception by offering a means through which lies can broadly be distinguished from the

truth that is uniquely unobtrusive and objective, as well as potentially unbiased; if High performers can essentially utilize kinematic information alone (as indicated by the depth array results), pictorial information could be stripped away to eliminate biasing factors such as race or skin color.

Together, the research presented here provides valuable insight into how future work should approach the concept of deception, both theoretically and methodologically.

In light of these considerations, the present findings have great potential for a wide range of applications. Deception and its detection are prevalent phenomena in almost all forms of social interaction (Camden, Motley, & Wilson, 1984; DePaulo et al., 1996; Hample, 1980; Lippard,

1988). For some, lying is a relatively harmless part of maintaining relationships with friends and colleagues; for others, lie detection is a serious matter of public safety and security. Thus, the results presented here represent a point of interest not only for many different fields of research in the behavioral and social sciences, but also for those concerned with law enforcement, business, politics, judicial processes, and national security.

References

Aamodt, M. G. & Custer, H. (2006). Who can best catch a liar? A meta-analysis of individual

differences in detecting deception. Forensic Examiner, 15, 6-11.

Abarbanel, H. D. I. (1996). Analysis of observed chaotic data. New York, NY: Springer-Verlag.

Allsop, J. S., Vaitkus, T., Marie, D., & Miles, L. K. (2016). Coordination and collective

performance: Cooperative goals boost interpersonal synchrony and task outcomes.

Frontiers in Psychology, 7:1462. doi:10.3389/fpsyg.2016.01462

Barton, H. (2013). Identifying the visual information and processes underlying expert judgments of

deceptive intent (Doctoral dissertation). Retrieved from Brunel University School of Sport

and Education PhD Theses. Available from Brunel University Research Archive (BURA):

http://bura.brunel.ac.uk/handle/2438/8755

Becchio, C., Sartori, L., Bulgheroni, M., & Castiello, U. (2008). The case of Dr. Jekyll and Mr.

Hyde: A kinematic study on social intention. Consciousness and Cognition, 17(3), 557-

564.

Becchio, C., Sartori, L., & Castiello, U. (2010). Toward you: The social side of actions. Current

Directions in Psychological Science, 19(3), 183-188.

Berry, D. S., Kean, K. J., Misovich, S. J., & Baron, R. M. (1991). Quantized displays of human

movement: A methodological alternative to the point-light display. Journal of Nonverbal

Behavior, 15, 81-97.

Berry, D. S., & Misovich, S. J. (1994). Methodological approaches to the study of social event

perception. Personality and Social Psychology Bulletin, 20, 139-152.

Blake, R., & Shiffrar, M. (2007). Perception of human motion. Annual Review of Psychology, 58,

47-73.

Bond, C. F., & DePaulo, B. M. (2006). Accuracy of deception judgments. Personality and Social

Psychology Review, 10(3), 214-234.

Bond, C. F., Levine, T. R. and Hartwig, M. (2015). New findings in non-verbal lie detection. In P.

A. Granhag, A. Vrij & B. Verschuere (Eds.), Detecting deception: Current challenges and

cognitive approaches (pp. 37-58). Chichester, UK: John Wiley & Sons, Ltd.

Brownlow, S., Dixon, A. R., Egbert, C. A., & Radcliffe, R. D. (1997). Perception of movement and

dancer characteristics from point-light displays of dance. The Psychological Record, 47(3),

411-421.

Buller, D. B., & Burgoon, J. K. (1996). Interpersonal deception theory. Communication Theory,

6(3), 203-242.

Burgoon, J. K., & Qin, T. (2006). The dynamic nature of deceptive verbal communication. Journal

of Language and Social Psychology, 25(1), 76-96.

Burgoon, J. K., Schuetzler, R., & Wilson, D. W. (2015). Kinesic patterning in deceptive and truthful

interactions. Journal of Nonverbal Behavior, 39(1), 1-24.

Burgoon, J. K., Stern, L. A., & Dillman, L. (1995). Interpersonal adaptation: Dyadic interaction

patterns. Cambridge, UK: Cambridge University Press.

Burgoon, J. K., Wilson, D., Hass, M., & Schuetzler, R. (2016). Interactive deception in group

decision-making: New insights from communication pattern analysis. Neuromethods, 111,

37-62.

Camden, C., Motley, M. T., & Wilson, A. (1984). White lies in interpersonal communication: A

taxonomy and preliminary investigation of social motivations. Western Journal of Speech

Communication, 48, 309-325.

Cañal-Bruland, R. (2017). Deception detection in action: Embodied simulation in antisocial

human interactions. Frontiers in Psychology, 8. doi:10.3389/fpsyg.2017.00166

Casarrubea, M., Jonsson, G., Faulisi, F., Sorbera, F., Di Giovanni, G., Benigno, A., . . . Magnusson,

M. (2015). T-pattern analysis for the study of temporal structure of animal and human

behavior: A comprehensive review. Journal of Neuroscience Methods, 239, 34-46.

Chartrand, T. L., & Van Baaren, R. (2009). Human mimicry. Advances in Experimental Social

Psychology, 41, 219-274.

Chouchourelou, A., Matsuka, T., Harber, K., & Shiffrar, M. (2006). The visual analysis of emotional

actions. Social Neuroscience, 1(1), 63-74.

Coey, C. A., Varlet, M., & Richardson, M. J. (2012). Coordination dynamics in a socially situated

nervous system. Frontiers in Human Neuroscience, 6, 164.

doi:10.3389/fnhum.2012.00164

Cohen, J. (1969). Statistical power analysis for the behavioral sciences. New York, NY: Academic

Press.

Cutting, J. E., & Kozlowski, L. T. (1977). Recognizing friends by their walk: Gait perception

without familiarity cues. Bulletin of the Psychonomic Society, 9(5), 353-356.

Dale, R., Fusaroli, R., Duran, N., & Richardson, D. C. (2014). The self-organization of human

interaction. Psychology of Learning and Motivation, 59, 43-95.

DePaulo, B. M., Charlton, K., Cooper, H., Lindsay, J. J., & Muhlenbruck, L. (1997). The accuracy-

confidence correlation in the detection of deception. Personality and Social Psychology

Review, 1(4), 346-357.

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., Lindsay, J., Malone, B., Muhlenbruck, L., Charlton, K., & Cooper, H. (2003). Cues

to deception. Psychological Bulletin, 129(1), 74-118.

Dicks, M., Button, C., & Davids, K. (2010). Examination of gaze behaviors under in situ and video

simulation task constraints reveals differences in information pickup for perception and

action. Attention, Perception, & Psychophysics, 72(3), 706-720.

Dittrich, W. H., Troscianko, T., Lea, S. E., & Morgan, D. (1996). Perception of emotion from

dynamic point-light displays represented in dance. Perception, 25(6), 727-738.

Doherty, M. E., & Kurz, E. M. (1996). Social judgement theory. Thinking & Reasoning, 2(2-3), 109-

140.

Doyen, S., Klein, O., Simons, D. J., & Cleeremans, A. (2014). On the other side of the mirror:

Priming in cognitive and social psychology. Social Cognition, 32, 43-63.

Driskell, T., Blickensderfer, E. L., & Salas, E. (2013). Is three a crowd? Examining rapport in

investigative interviews. Group Dynamics: Theory, Research, and Practice, 17(1), 1-13.

Driskell, J. E., Salas, E., & Driskell, T. (2012). Social indicators of deception. Human Factors: The

Journal of the Human Factors and Ergonomics Society, 54(4), 577-588.

Dunbar, N. E., Jensen, M. L., Burgoon, J. K., Robertson, B. A. K. J., Kelley, K. M., & Harvill, L.

(2011, January). A dyadic approach to the detection of deception. Paper presented at the

44th annual Hawaii International Conference on Systems Sciences, Koloa, HI. Abstract

retrieved from

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.296.1046&rep=rep1&type=pdf

Dunbar, N. E., Jensen, M. L., Tower, D. C., & Burgoon, J. K. (2014). Synchronization of nonverbal

behaviors in detecting mediated and non-mediated deception. Journal of Nonverbal

Behavior, 38(3), 355-376.

Duran, N. D., Dale, R., Kello, C. T., Street, C. N., & Richardson, D. C. (2013). Exploring the

movement dynamics of deception. Frontiers in Psychology, 4.

doi:10.3389/fpsyg.2013.00140

Ekman, P. (1992). Telling lies: Clues to deceit in the marketplace, politics, and marriage. NY: WW

Norton and Company.

Ekman, P., & Friesen, W. V. (1969). Nonverbal leakage and clues to deception. Psychiatry, 32(1),

88-106.

Ekman, P., & O’Sullivan, M. (1991). Who can catch a liar? American Psychologist, 46(9), 913-920.

Elkins, A., Zafeiriou, S., Pantic, M., & Burgoon, J. (2015). Unobtrusive deception detection. In R.

Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective

computing (pp. 503-515). New York, NY: Oxford University Press.

Fein, R. A. (Ed.). (2006). Educing information: Interrogation: Science and art: Intelligence Science

Board Phase I report. Washington, DC: United States Department of Defense, National

Defense Intelligence College.

Fowler, C. A., Richardson, M. J., Marsh, K. L., & Shockley, K. D. (2008). Language use,

coordination, and the emergence of cooperative action. In A. Fuchs & V. K. Jirsa (Eds.),

Coordination: Neural, behavioral and social dynamics (pp. 261-279). Berlin/Heidelberg,

Germany: Springer.

Freeman, J., Dale, R., & Farmer, T. (2011). Hand in motion reveals mind in motion. Frontiers in

Psychology, 2, 59. doi:10.3389/fpsyg.2011.00059

Fusaroli, R., Abney, D. H., Bahrami, B., Kello, C., & Tylén, K. (2013). Conversation, coupling and

complexity: Matching scaling laws predict performance in a joint decision task. In M.

Knauff, M. Pauen, N. Sebanz & I. Wachsmuth (Eds.), Proceedings of 35th Annual

Conference of the Cognitive Science Society. Berlin, Germany: Cognitive Science Society.

Fusaroli, R., & Tylén, K. (2012). Carving language for social coordination: A dynamical approach.

Interaction Studies, 13(1), 103-124.

Fusaroli, R., & Tylén, K. (2016). Investigating conversational dynamics: Interactive alignment,

interpersonal synergy, and collective task performance. Cognitive Science, 40(1), 145-171.

Gallagher, R., & Appenzeller, T. (1999). Beyond reductionism. Science, 284(5411), 79–79.

http://dx.doi.org/10.1126/science.284.5411.79.

Gamer, M., & Ambach, W. (2014). Deception research today. Frontiers in Psychology, 5. doi:

10.3389/fpsyg.2014.00256

Giles, H. (2008) Communication accommodation theory. In L. A. Baxter & D. O. Braithewaite

(Eds.), Engaging theories in interpersonal communication: Multiple perspectives (pp. 161-

173). Thousand Oaks, CA: Sage.

Giolla, E. M., Granhag, P. A. and Vrij, A. (2015). Discriminating between true and false intentions.

In P. A. Granhag, A. Vrij & B. Verschuere (Eds.), Detecting deception: Current challenges

and cognitive approaches (pp. 155-173). Chichester, UK: John Wiley & Sons, Ltd.

Good, J. M. (2007). The affordances for social psychology of the ecological approach to social

knowing. Theory & Psychology, 17(2), 265-295.

Gorman, J. C., Cooke, N. J., Amazeen, P. G., & Fouse, S. (2012). Measuring patterns in team

interaction sequences using a discrete recurrence approach. Human Factors, 54, 503-517.

Gunns, R. E., Johnston, L., & Hudson, S. M. (2002). Victim selection and kinematics: A point-light

investigation of vulnerability to attack. Journal of Nonverbal Behavior, 26(3), 129-158.

Haggbloom, S. J., Warnick, R., Warnick, J. E., Jones, V. K., Yarbrough, G. L., Russell, T. M., ... &

Monte, E. (2002). The 100 most eminent psychologists of the 20th century, Review of

General Psychology, 6(2), 139-15.

Hample, D. (1980). Purposes and effects of lying. Southern Speech Communication Journal, 46,

33-47.

Hartwig, M., & Bond, C. F, Jr. (2011). Why do lie-catchers fail? A lens model meta-analysis of

human lie judgments. Psychological Bulletin, 137(4), 643–659.

Hartwig, M., & Bond, C. F. (2014). Lie detection from multiple cues: A meta-analysis. Applied

Cognitive Psychology, 28(5), 661-676.

Heberlein, A. S., Adolphs, R., Tranel, D., & Damasio, H. (2004). Cortical regions for judgments of

emotions and personality traits from point-light walkers. Journal of Cognitive Neuroscience,

16(7), 1143-1158.

Hove, M. J., & Risen, J. L. (2009). It’s all in the timing: Interpersonal synchrony increases

affiliation. Social Cognition, 27, 949-960.

Jackson, R. C., Warren, A., & Abernathy, B. (2006). Anticipation skill and susceptibility to

deceptive movement. Acta Psychologica, 123, 355-371.

Jensen, M. L., Meservy, T. O., Burgoon, J. K., & Nunamaker, J. F. (2010). Automatic, multimodal

evaluation of human interaction. Group Decision and Negotiation, 19(4), 367-389.

Johnston, L. (2013). It’s the way you walk: Kinematic specification of vulnerability to attack. In K.

L. Johnson & M. Shiffrar (Eds.), People watching: Social, perceptual, and

neurophysiological studies of body perception (pp. 220-233). NY: Oxford University Press.

Kassin, S. M., Leo, R. A., Meissner, C. A., Richman, K. D., Colwell, L. H., Leach, A.-M. & La Fon,

D. (2007). Police interviewing and interrogation: A self-report survey of police practices

and beliefs. Law and Human Behavior, 31(4), 381-400.

Kean, K. J. (2000). An investigation into the interpersonal kinematics of cooperation and

competition. Dissertation Abstracts International: Section B: The Sciences and Engineering,

60(9-B), 4965.

Knight, A. P., Kennedy, D. M., & McComb, S. A. (2016). Using recurrence analysis to examine

group dynamics. Group Dynamics: Theory, Research, and Practice, 20(3), 223-241.

Kokal, I., Engel, A., Kirschner, S., & Keysers, C. (2011). Synchronized drumming enhances activity

in the caudate and facilitates prosocial commitment. PLoS ONE, 6(11), e27272.

doi:10.1371/journal.pone.0027272

Kupper, Z., Ramseyer, F., Hoffmann, H., Kalbermatten, S., & Tschacher, W. (2010). Video-based

quantification of body movement during social interaction indicates the severity of

negative symptoms in patients with schizophrenia. Schizophrenia Research, 121(1), 90-

100.

Kozlowski, L. T., & Cutting, J. E. (1977). Recognizing the sex of a walker from a dynamic point-

light display. Perception & Psychophysics, 21(6), 575-580.

Levenson, R. W., & Ruef, A. M. (1997). Physiological aspects of emotional knowledge and rapport.

In W. J. Ickes (Ed.), Empathetic accuracy (pp. 44-72). New York, NY: Guildford Press.

Lippard, P. V. (1988). “Ask me no questions, I’ll tell you no lies”: Situational exigencies for

interpersonal deception. Western Journal of Speech Communication, 52, 91-103.

Lopresti-Goodman, S. M., Richardson, M. J., Silva, P. L., & Schmidt, R. C. (2008). Period basin of

entrainment for unintentional visual coordination. Journal of Motor Behavior, 40(1), 3-10.

Loula, F., Prasad, S., Harber, K., & Shiffrar, M. (2005). Recognizing people from their movement.

Journal of Experimental Psychology: Human Perception and Performance, 31(1), 210-220.

Lumsden, J., Miles, L. K., Richardson, M. J., Smith, C. A., & Macrae, C. N. (2012). Who syncs?

Social motives and interpersonal coordination. Journal of Experimental Social Psychology,

48(3), 746-751.

Macmillan, N. A., & Creelman, C. D. (2004). Detection theory: A user's guide. Cambridge,

England: Cambridge University Press.

Marsh, K. L., Johnston, L., Richardson, M. J., & Schmidt, R. C. (2009). Toward a radically

embodied, embedded social psychology. European Journal of Social Psychology, 39(7),

1217-1225.

Marsh, K. L., Richardson, M. J., Baron, R. M., & Schmidt, R. C. (2006). Contrasting approaches to

perceiving and acting with others. Ecological Psychology, 18(1), 1-38.

Masip, J., & Herrero, C. (2015). New approaches in deception detection I. Background and

theoretical framework. Papeles del Psicólogo, 36(2), 83-95.

Mather, G., & Murdoch, L. (1994). Gender discrimination in biological motion displays based on

dynamic cues. Proceedings of the Royal Society of London. Series B: Biological Sciences,

258(1353), 273-279.

Miles, L. K., Nind, L. K., & Macrae, C. N. (2009). The rhythm of rapport: Interpersonal synchrony

and social perception. Journal of Experimental Social Psychology, 45(3), 585-589.

Miles, L. K., Griffiths, J. L., Richardson, M. J., & Macrae, C. N. (2010). Too late to coordinate:

Contextual influences on behavioral synchrony. European Journal of Social Psychology,

40(1), 52-60.

Montepare, J. M., & Zebrowitz-McArthur, L. (1988). Impressions of people created by age-related

qualities of their gaits. Journal of Personality and Social Psychology, 55(4), 547-556.

Morgan, C. J., LeSage, J. B., & Kosslyn, S. M. (2009). Types of deception revealed by individual

differences in cognitive abilities. Social Neuroscience, 4(6), 554-569.

National Research Council. (2003). The polygraph and lie detection. Committee to Review the

Scientific Evidence of the Polygraph. Division of Behavioral and Social Sciences and

Education. Washington, DC: The National Academic Press.

Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying words: Predicting

deception from linguistic styles. Personality and Social Psychology Bulletin, 29(5), 665-675.

O’Sullivan, M. (2005). Emotional intelligence and deception detection: Why most people can’t

read others, but a few can. In R. E. Riggio, & R. S. Feldman (Eds.), Applications of

nonverbal communication (pp. 215-253). Mahwah, NJ: Lawrence Erlbaum Associates.

O’Sullivan, M., Frank, M. G., Hurley, C. M., & Tiwana, J. (2009). Police lie detection accuracy:

The effect of lie scenario. Law and Human Behavior, 33, 542-543.

Oullier, O., & Kelso, J. A. (2009). Social coordination, from the perspective of coordination

dynamics. In R. A. Meyers (Ed.), Encyclopedia of complexity and systems science (pp.

8198-8213). NY: Springer.

Pickering, M. J., & Garrod, S. (2004). Toward a mechanistic psychology of dialogue. Behavioral

and Brain Sciences, 27, 169-190.

Puccinelli, N. M., & Tickle-Degnen, L. (2004). Knowing too much about others: Moderators of the

relationship between eavesdropping and rapport in social interaction. Journal of Nonverbal

Behavior, 28(4), 223-243.

Porter, S., Woodworth, M., & Birt, A. R. (2000). Truth, lies, and videotape: an investigation of the

ability of federal parole officers to detect deception. Law and Human Behavior, 24(6), 643-

658.

Prasad, S., & Shiffrar, M. (2009). Viewpoint and the recognition of people from their movements.

Journal of Experimental Psychology: Human Perception and Performance, 35(1), 39-49.

Reddish, P., Fischer, R., and Bulbulia, J. (2013). Let’s dance together: Synchrony, shared

intentionality and cooperation. PLoS ONE, 8, e71182. doi:10.1371/journal.pone.0071182

Richardson, D. C., & Dale, R. (2005). Looking to understand: The coupling between speakers' and

listeners' eye movements and its relationship to discourse comprehension. Cognitive

Science, 29(6), 1045-1060.

Richardson, D. C., Dale, R., & Kirkham. N. (2007) The art of conversation is coordination:

Common ground and the coupling of eye movements during dialogue. Psychological

Science, 18, 407-413.

Richardson, D. C., Dale, R., & Shockley, K. (2008). Synchrony and swing in conversation:

Coordination, temporal dynamics, and communication. In I. Wachsmuth, M. Lenzen, & G.

Knoblich (Eds.), Embodied Communication in Humans and Machines (pp. 75-94). New

York, NY: Oxford University Press.

Richardson, D. C., Dale, R., & Tomlinson, J. M. (2009). Conversation, gaze coordination, and

beliefs about visual context. Cognitive Science, 33(8), 1468-1482.

Richardson, J. T. (2011). Eta squared and partial eta squared as measures of effect size in

educational research. Educational Research Review, 6(2), 135-147.

Richardson, M. J., Dale, R., & Marsh, K. (2014). Complex dynamical systems in social and

personality psychology: Theory, modeling and analysis. In H. T. Reis & C. M. Judd (Eds.),

Handbook of research methods in social and personality psychology (pp. 253-282).

Cambridge, UK: Cambridge University Press.

Richardson, M. J., & Johnston, L. (2005). Person recognition from dynamic events: The kinematic

specification of individual identity in walking style. Journal of Nonverbal Behavior, 29(1),

25-44.

Richardson, M. J., Marsh, K. L., & Schmidt, R. C. (2005). Effects of visual and verbal interaction on

unintentional interpersonal coordination. Journal of Experimental Psychology: Human

Perception and Performance, 31(1), 62-79.

Riley, M. A., Richardson M. J., Shockley, K., & Ramenzoni, V. C. (2011). Interpersonal synergies.

Frontiers in Psychology, 2, 1-7.

Riggio, R. E., & Friedman, H. S. (1983). Individual differences and cues to deception. Journal of

Personality and Social Psychology, 45(4), 899-915.

Roca, A., Williams, A. M., & Ford, P. R. (2014). Capturing and testing perceptual-cognitive

expertise: A comparison of stationary and movement response methods. Behavior Research

Methods, 46(1), 173-177.

Runeson, S., & Frykholm, G. (1983). Kinematic specification of dynamics as an informational basis

for person-and-action perception: expectation, gender recognition, and deceptive

intention. Journal of Experimental Psychology: General, 112(4), 585-615.

Sartori, L., Becchio, C., & Castiello, U. (2011). Cues to intention: The role of movement

information. Cognition, 119(2), 242-252.

Schmidt, R. C., Carello, C., & Turvey, M. T. (1990). Phase transitions and critical fluctuations in

the visual coordination of rhythmic movements between people. Journal of experimental

psychology: human perception and performance, 16(2), 227-247.

Schmidt, R. C., Christianson, N., Carello, C., & Baron, R. (1994). Effects of social and physical

variables on between-person visual coordination. Ecological Psychology, 6(3), 159-183.

Schmidt, R. C., & Fitzpatrick, P. A. (2016). The origin of the ideas of interpersonal synchrony and

synergies. In P. Passos, K. Davids, & JY Chow (Eds.), Interpersonal coordination and

performance in social systems (pp. 17-31). New York, NY: Routledge.

Schmidt, R. C., Morr, S., Fitzpatrick, P., & Richardson, M. J. (2012). Measuring the dynamics of

interactional synchrony. Journal of Nonverbal Behavior, 36(4), 263-279.

Schmidt, R. C., Nie, L., Franco, A., & Richardson, M. J. (2014). Bodily synchronization underlying

joke telling. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00633

Schmidt, R. C., & O’Brien, B. (1997). Evaluating the dynamics of unintended interpersonal

coordination. Ecological Psychology, 9, 189-206.

Schmidt, R. C., & Richardson, M. J. (2008). Coordination: Neural, behavioral and social dynamics.

In A. Fuchs & V. K. Jirsa (Eds.), Dynamics of interpersonal coordination (pp. 281-308).

Berlin/Heidelberg, Germany: Springer.

Schmidt, R. C., Richardson, M. J., Arsenault, C., & Galantucci, B. (2007). Visual tracking and

entrainment to an environmental rhythm. Journal of Experimental Psychology: Human

Perception and Performance, 33(4), 860-870.

Sebanz, N., & Knoblich, G. (2009). Prediction in joint action: What, when, and where. Topics in

Cognitive Science, 1, 353-367.

Sebanz, N., & Shiffrar, M. (2007). Bodily bonds: Effects of social context on ideomotor

movements. In Rossetti, Y., Kawato, M., & Haggard, P. (Eds.), Sensorimotor foundations of

higher cognition (Attention and Performance, XXII) (pp. 267-292). Oxford, UK: Oxford

University Press.

Shockley, K., Baker, A. A., Richardson, M. J., & Fowler, C. A. (2007). Articulatory constraints on

interpersonal postural coordination. Journal of Experimental Psychology: Human

Perception & Performance, 33, 201-208.

Shockley, K., Butwill, M., Zbilut, J. P., & Webber, C. L. (2002). Cross recurrence quantification of

coupled oscillators. Physics Letters A, 305, 59-69.

Shockley, K., Richardson, D. C., & Dale, R. (2009). Conversation and coordinative structures.

Topics in Cognitive Science, 1(2), 305-319.

Shockley, K., Santana, M. V., & Fowler, C. A. (2003). Mutual interpersonal postural constraints are

involved in cooperative conversation. Journal of Experimental Psychology: Human

Perception and Performance, 29(2), 326-332.

Shotter, J. (1991). Rhetoric and the social construction on cognitivism. Theory & Psychology, 1(4),

495-513.

Sporer, S. L., & Schwandt, B. (2007). Moderators of nonverbal indicators of deception: A meta-

analytic synthesis. Psychology, Public Policy and Law, 13(1), 1-34.

Strang, A. J., Funke, G. J., Russell, S. M., Dukes, A. W., & Middendorf, M. S. (2014). Physio-

behavioral coupling in a cooperative team task: Contributors and relations. Journal of

Experimental Psychology: Human Perception and Performance, 40, 145-158.

Takens, F. (1981). Detecting strange attractors in turbulence. Lecture Notes in Mathematics, 898,

366-381.

Tickle-Degnen, L., & Rosenthal, R. (1990). The nature of rapport and its nonverbal correlates.

Psychological Inquiry, 1(4), 285-293.

Timmermans, B., & Cleeremans, A. (2015). How can we measure awareness? An overview of

current methods. In M. Overgaard (Ed.), Behavioural methods in consciousness research,

(pp. 21-46). Oxford, UK: Oxford University Press.

Tolston, M., Ariyabuddhiphongs, K., Riley, M. A., & Shockley, K. (2014). Cross-recurrence

quantification analysis of the influence of coupling constraints on interpersonal

coordination and communication. In N. Marwan, C.L. Webber, Jr., M. A. Riley, & A.

Giuliani (Eds.), Translational recurrences: From mathematical theory to real-world

applications (pp. 157-171). Cham, Switzerland: Springer.

Tolston, M. T., Shockley, K., Riley, M. A., & Richardson, M. J. (2014). Movement constraints on

interpersonal coordination and communication. Journal of Experimental Psychology:

Human Perception and Performance. Advance online publication. doi:10.1037/a0037473

Topalli, V., & O’Neal, E. C. (1995). The recognition of hostile behaviors from dynamic point light

displays. In B. Bardy, R. Bootsma, & A. Guiard (Eds.), Studies in perception and action III

(pp. 379). Mahwah, NJ: Lawrence Erlbaum Associates.

Tower, D. L., Jensen, M. L., Dunbar, N. E., & Elkins, A. C. (2013). Don't lie to me: The impact of

deception on vocalic and linguistic synchrony. In J. Brynielsson, & F. Johansson (Eds.),

Proceedings of the European intelligence and security informatics conference (EISIC)

2013 (pp. 262-265). Los Alamitos, CA: Conference Publishing Services.

Turvey, B. E. (2008). Introduction to terrorism: Understanding and interviewing terrorists. In

Criminal profiling: An introduction to behavioral evidence analysis, 3rd ed. (pp. 523-538).

San Diego, CA: Elsevier Academic Press.

Valdesolo, P., Ouyang, J., & De Steno, D. (2010). The rhythm of joint action: Synchrony promotes

cooperative ability. Journal of Experimental Psychology, 46(4), 693-695.

Valenti, S. S., & Gold, J. M. (1991). Social affordances and interaction I: Introduction. Ecological

Psychology, 3(2), 77-98.

Van Der Zee, S., Poppe, R., Taylor, P. J., & Anderson, R. (January 2015). To freeze or not to freeze:

A motion-capture approach to detecting deceit. In Proceedings of the 48th Hawaii

International Conference on System Sciences, Kauai, HI. Abstract retrieved from

https://pauljtaylor.com/wp-content/uploads/2015/09/2015-HICCS-Van-Der-Zee.pdf

Vermeiren, A., & Cleeremans, A. (2012). The validity of d’ measures. PLoS ONE, 7(2): e31595.

doi:10.1371/journal.pone.0031595

Visu-Petra, G., Miclea, M., & Visu-Petra, L. (2012). Reaction time-based detection of concealed

information in relation to individual differences in executive functioning. Applied

Cognitive Psychology, 26(3), 342-351.

Visu-Petra, G., Miclea, M., Buş, I., & Visu-Petra, L. (2014). Detecting concealed information: The

role of individual differences in executive functions and social desirability. Psychology,

Crime & Law, 20(1), 20-36.

Vrij, A. (2008). Detecting lies and deceit: Pitfalls and opportunities. Chichester, UK: John Wiley &

Sons, Ltd.

Vrij, A., & Fisher, R. P. (2016). Which lie detection tools are ready for use in the criminal justice

system? Journal of Applied Research in Memory and Cognition, 5(3), 302-307.

Vrij, A., & Granhag, P. A. (2012). Eliciting cues to deception and truth: What matters are the

questions asked. Journal of Applied Research in Memory and Cognition, 1(2), 110-117.

Vrij, A., Granhag, P. A., & Porter, S. (2010). Pitfalls and opportunities in nonverbal and verbal lie

detection. Psychological Science in the Public Interest, 11, 89-121.

Walther, J. B., Loh, T., & Granka, L. (2005). Let me count the ways: The interchange of verbal and

nonverbal cues in computer-mediated and face-to-face affinity. Journal of Language and

Social Psychology, 24(1), 36-65.

Webber, C. L., Jr. (1997). Introduction to recurrence quantification analysis (Version 4.1).

Unpublished software documentation (available with RQA Version 4.1 software).

Webber, C. L., Jr., & Zbilut, J. P. (1994). Dynamical assessment of physiological systems and states

using recurrence plot strategies. Journal of Applied Physiology, 76, 965-973.

Wiltermuth, S. S., & Heath, C. (2009). Synchrony and cooperation. Psychological Science, 20(1),

1-5.

Wiltshire, T. J., Snow, S. L., Lobato, E. J., & Fiore, S. M. (2014). Leveraging social judgment theory

to examine the relationship between social cues and signals in human-robot interactions.

In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 1336-1340.

Chicago, IL: SAGE Publications.

Wiltshire, T., Lobato, E., McConnell, D., & Fiore, S. (2015). Prospects for direct social perception:

A multi-theoretical integration to further the science of social cognition. Frontiers in

Human Neuroscience, 8, 1007. doi:10.3389/fnhum.2014.01007

Yu, X., Zhang, S., Yan, Y., Dunbar, N., Jensen, M. L., Burgoon, J. K., & Metaxas, D. N. (2013).

Automated analysis of interactional synchrony using robust facial tracking and expression

recognition. Paper presented at the 10th IEEE International Conference and Workshops on

Automatic Face and Gesture Recognition, Shanghai.

Yu, X., Zhang, S., Yan, Z., Yang, F., Huang, J., Dunbar, N. E., ... & Metaxas, D. N. (2015). Is

interactional dissynchrony a clue to deception? Insights from automated analysis of

nonverbal visual cues. IEEE Transactions on Cybernetics, 45(3), 492-506.

Zbilut, J. P., & Webber, C. L., Jr. (1992). Embeddings and delays as derived from quantification of

recurrence plots. Physics Letters A, 171, 199-203.

Zhou, L., Burgoon, J. K., Nunamaker, J. F., & Twitchell, D. P. (2004). Automating linguistics-based

cues for detecting deception in text-based asynchronous computer-mediated

communication. Group Decision and Negotiation, 13(1), 81-106.

Zuckerman, M., DePaulo, B. M., & Rosenthal, R. (1981). Verbal and nonverbal communication of

deception. In L. Berkowitz (Ed.), Advances in experimental social psychology (pp. 1-57).

New York, NY: Academic Press.

Appendix A

Truth and Lies Task: Statement List

Below is a list of prompts that you may use in the task. Select five TRUE statements (or ‘truths’) and five FALSE statements (or ‘lies’) to personalize. Then, complete the following for each of your eight statements. • In the ‘T/F’ column, mark whether the statement you selected is true (T) or false (F). The experimenter will later fill in the ‘#’ column to provide you with the order in which you will make your statements. • Beneath each statement you chose, rate your personal investment in the story (1 = low investment, 5 = high investment), as well as how well you think you can convince your partner that you are not lying, regardless of whether the statement is true or false. (1 = low ability, 5 = high ability). Your statements should have an equal likelihood of being true or false. For example: • Good statement – “I am the youngest of four children.” • Bad statement – “I have been to the moon.” *Important to remember: A TRUTH must be 100% factual and it must apply to you. A LIE must be in no way related to the truth – half-truths and exaggerations do not count as lies. For example: • “I have lived in three states” is not a full lie if you have actually lived in four. • “I own a black lab” is not a full lie if your roommate owns it and you help take care of it. • “My proudest accomplishment is running a marathon” is not a lie if you actually have ran a marathon, but aren’t necessarily proud of it.

Prompts:

# T/F One of my special talents is ______.