Deceptive behavior in doping related interviews: The case of Lance
Armstrong
Valentino Zurloni, Barbara Diana
*, Cesare Cavalera, Luca Argenton, Massimiliano Elia,
Fabrizia Mantovani
Centre for Studies in Communication Sciences (CESCOM), Department of Human Sciences for Education, University of Milano-Bicocca, Piazza Dell’Ateneo Nuovo, 1, 20126 Milano, Italy
a r t i c l e i n f o
Article history:Received 6 June 2013 Received in revised form 2 February 2014 Accepted 21 February 2014 Available online 12 March 2014
Keywords: Performance-enhancing drugs Deception Nonverbal communication T-pattern analysis
a b s t r a c t
Objectives: The aim of the present study is to investigate the organization of Armstrong’s nonverbal behavior in deceptive statements and in those statements in which deception is not proven. Thefinal aim of this study is to show that T-pattern methodology can be a useful tool in research about doping behavior.
Design: In this observational study we focused on Armstrong’s micro-expressions (action units, gaze movements, head movements) drawing observational material from different videos excerpts where Armstrong made doping-related statements. A baseline of Armstrong’s deceptive behavior was estab-lished by selecting three video samples from 2005 in which he fully denied ever having taken performance-enhancing drugs. They were compared to the interview conducted by Oprah Winfrey in January 2013, in which he admitted doping but denied the specific charges of bullying and corruption. Method: Our approach is based on the detection of statistically significant hierarchical sequences of behaviors in time, called T-patterns (temporal patterns). The algorithm, implemented in Theme software, determines whether apparently arbitrary events sequentially repeat, within a specified time interval, at a rate greater than that expected by chance.
Results: Data analyses allowed identifying distinctive patterns for each of the two conditions. The baseline showed a very limited number of patterns, highlighting low level of complexity and the pres-ence of stereotyped behaviors. In the Oprah video samples, the number and complexity of distinctive patterns was significantly higher, and most of them included gaze shifting behaviors.
Conclusions: T-pattern methodology might be an effective strategy to detect nonverbal features of deception, integrated with more traditional and established practice, in order to improve anti-doping measures andfight this spreading phenomenon.
Ó 2014 Elsevier Ltd. All rights reserved.
The use of prohibited performance-enhancing drugs (PEDs) represents one of the most compelling challenges the world of sport has to cope with. Given the high complexity of the phe-nomenon, the issue of doping has been deeply discussed within the scientific community from different points of view. On the one hand, there are those who emphasize the detrimental effects doping may have on the sporting culture (Houlihan, 2002;
Schneider & Butcher, 2000); on the other, there are scholars that tend to demystify the impact PEDs have on athletes and sports in general. They argue that anti-doping propaganda on the side-effects of drugs like the human Growth Hormone or EPO are often based on suppositions and deductions, instead of empirical evidence (López, 2011, 2013) and that the legalization of drugs in sports may be fairer and safer (Savulescu, Foddy, & Clayton, 2004). Even though it is difficult to estimate the actual use of PEDs among athletes and there is a lack of reliable and valid indicators of drug use (Hoberman, 2001; Kayser, Mauron, & Miah, 2007; Petróczi, Mazanov, Nepusz, Backhouse, & Naughton, 2008; Yesalis, Kopstein, & Bahrke, 2001), over the past few years a series of doping scandals have demonstrated that banned substances are still used to improve sport performance effectiveness and efficiency (Johnson, 2012).
* Corresponding author. Centre for Studies in Communication Sciences (CES-COM), via Giolli angolo via T. Mann, Università degli Studi di Milano-Bicocca, Edi-ficio U16, 20162 Milano, Italy. Tel.: þ39 02 6448 4926.
E-mail addresses: valentino.zurloni@unimib.it (V. Zurloni), barbara.diana@ unimib.it(B. Diana),cesare.cavalera@unimib.it(C. Cavalera),l.argenton@campus. unimib.it(L. Argenton),m.elia5@campus.unimib.it(M. Elia),fabrizia.mantovani@ unimib.it(F. Mantovani).
Contents lists available atScienceDirect
Psychology of Sport and Exercise
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / p s y c h s p o r t
http://dx.doi.org/10.1016/j.psychsport.2014.02.008
The scientific literature has deeply investigated this point, providing theoretical frameworks, conceptual models and concrete examples, mostly focusing on the role of social and cognitive fac-tors, like attitudes, (Alaranta et al., 2006; Gucciardi, Jalleh, & Donovan, 2010; Petróczi, 2007; Petróczi & Aidman, 2009), goals orientation and personal beliefs (Boardley & Kavussanu, 2010; Sas-Nowosielski & Swiatkowska, 2008), as well as social projection processes (Petróczi et al., 2008) and sportspersonship orientations (Barkoukis, Lazuras, Tsorbatzoudis, & Rodafinos, 2011). Less research has been done on the relationship between doping and deception.
Doping and deception
The relationship between doping and deception can be explained according to different interpretations: first, doping behavior constitutes a kind of deception, intended as a form of cheating. Secondly, doping behavior presumably leads to deceptive behavior(s), necessary in order to avoid being exposed.
Deception in research literature about sports is usually linked to cheating and refers to deliberate acts in which someone tries to gain advantage for oneself or one’s team against the opponents (Fraleigh, 2003; Loland, 2005). Thus, deception in sport can mani-fest in a wide variety of forms that can be resumed as follows: simulating, sabotaging, use of performance enhancing drugs and fixing matches (Preston & Szymanski, 2003).
Athletes who dope lie to avoid being discovered. First of all, they risk losing face and honor. Their identity is at stake, as individuals and as athletes. Moreover, they risk losing their job and all possible sponsorships, they can run into disciplinary measures and, in extreme cases, also into criminal penalties.
For all of these reasons we can affirm that athletes who dope have a high motivation to lie and that their deceptive acts are characterized by a high content (Anolli, Ciceri, & Riva, 2001). Within the framework of the Deceptive Miscommunication Theory (Anolli et al., 2001), high content deceptive acts are distinguished from the low content ones according to the cognitive processes involved; in fact, these kind of lies are characterized by high cognitive load for the deceiver.
As it happens for high-stakes lies (e.g.Vrij, 2008), the detection of high content lies has serious effects and consequences for both the deceiver and the deceived. In fact, their detection intrinsically implies a threat to the deceiver’s image and face; moreover, it can be an attack to his esteem, eliciting in him negative self-conscious emotions such as guilt, shame and embarrassment (Anolli & Zurloni, 2008; Anolli et al., 2001). On the other side, deceived competitors run the risk of being harmed or hurt in their own interest to the deceiver’s advantage. Further, fans and general public may feel betrayed and become disillusioned (Fourcroy, 2010).
High content lies are likely to happen in complicated, conflicting or face-threatening contexts like, in our case, doping behavior.
The ongoing research on detecting doping behavior has heavily focused on self-report methods, whose validity has shown to be affected by some forms of biases (Pertròczi & Haugen, 2012;
Petróczi et al., 2011). Given all the risks linked to the use of banned performance-enhancing drugs particularly in terms of sporting career, it is very difficult to obtain truthful answers, even under complete anonymity.
When asked about doping, participants may lie to hide illegal practices and behaviors they have been engaged in. As suggested by several authors, this might lead to an underestimation of the prevalence of the phenomenon (Pitsch & Emrich, 2012). Hence, other methods may be considered and integrated with self-report studies. Research in communication psychology recently provided
promising advances in the analysis of truthfulness and reliability of statements during face-to-face interviews, based on a multi-modal approach.
In particular, the analysis of doping-related deception could be supported by the identification of verbal and nonverbal cues to discriminate false from truthful statements (DePaulo et al., 2003). This information might be of value during interviews and trials, and provide some hints in order to identify critical and controversial topics that could be important to deepen.
Cues to deception
Two recent, comprehensive meta-analyses (DePaulo et al., 2003; Sporer & Schwandt, 2007) including a large variety of nonverbal, verbal, and paraverbal cues to deception, as well as certain content features of deceptive and truthful messages, reveal that many conflicting results have been found.
An explanation for the contradictoryfindings obtained across individual studies might be that a host of moderator variables may blur the association between behavioral cues and deception, such as emotions, cognitive effort, lie content and motivation (Sporer & Schwandt, 2007). For example, while deception under highly motivating conditions should be associated with a decrease of head, hand, foot and leg movements, conversely, deception under conditions of thorough preparation should be associated with an increase of head, hand, foot and leg movements (Sporer & Schwandt, 2007). So, what about a highly motivated and pre-pared liar?
Able and motivated liars should be able to successfully employ countermeasures to conceal nonverbal cues to deception and control their behavior in order to make people believe them. Research on facial expressions reveals that an important cue to deceit is the smile. In the nineteenth century, Duchenne (1862)
examined the muscle actions associated with real and false smiles, discovering that a genuine happiness expression involves not only the contraction of the zygomatic major muscle, Action Unit 12 (from FACSe Facial Action Coding System eEkman & Friesen, 1978), which upturns the mouth corners into a smile, but also the orbicularis oculi, Action Unit 6 (from FACSe Facial Action Coding SystemeEkman & Friesen, 1978), which creates crow’s feet around the eye.Ekman and Friesen (1982)later validated this observation. Conversely, a masking/false smile combines the smiling action (zygomatic major), which is part of the real smile, with traces of the muscle movements from one or another of the negative emotions (e.g. nose wrinkler from the disgust, Action Unit 9 from FACSe Facial Action Coding SystemeEkman & Friesen, 1978) (Ekman & Friesen, 1982). However, at the same time, when a person is trying to control what is showing on his face, most of his effort is focused on managing what happens in and around his mouth and lips than in the area of the eyes/lids or brows/forehead. This might be because of the role of the mouth in speech, and the consequent greater awareness a person has of what he does with that part of his face (Ekman & Friesen, 2003).
Gaze during conversation may be a function of two opposing tendencies, the tendency to monitor the other’s facial expressions and the tendency to make saccadic eye movements during thinking and speaking (Ehrlichman, 1981). During deceptive communica-tion, eye contact is mostly used to monitor the behavior of the interlocutor, to have a continuous feedback about his level of sus-picion and to change, as a result, a liar’s communication style (both verbal and non-verbal) during the production of a deceptive mes-sage. Moreover, as Vrij stated(2008), certain behavioral patterns are associated with honesty and likeability, such as gaze directed to a conversation partner (Buller & Aune, 1987; Ekman, 2009; Tickle-Degnen & Rosenthal, 1990). However, the literature focusing on
deceptive interaction emphasizes that, when deception occurs in interactive contexts, it is not a unidirectional activity. Rather, both liar and target mutually influence each other (Burgoon, Buller, Floyd, & Grandpre, 1996). According to the Interpersonal Decep-tion Theory (IDT,Buller & Burgoon, 1996), targets’ behavior may influence senders’ behavioral displays indirectly, because it may trigger behavioral adjustments (Burgoon, Buller, White, Afifi, & Buslig, 1999). For example, when exposed to negative feedback from the interlocutor, liars may realize that their performance is lacking credibility; consequently, liars may respond by making behavioral adjustments to diminish suspicions and to show“honest behavior” (Buller, Comstock, Aune, & Strzyzewski, 1989; Buller, Strzyzewski, & Comstock, 1991; Stiff & Miller, 1986). More specif-ically, liars may overcompensate their efforts to avoid common misconceptions about deceptive behavior. For example, by attempting not to avert their gaze from the interlocutor’s eyes, the liar may stare too long and too hard (Vrij, Mann, Leal & Fisher, 2010).
For all these reasons, different studies argue that reliance on a combination of behavioral channels is more effective than any single indicator (e.g. Porter & ten Brinke, 2010). Senders are able to arrange a set of different signaling systems to communicate and make their communicative intentions public, like linguistic and paralinguistic systems, face, gestures and gaze (Vrij, 2008). In fact, a diagnostic pattern might arise when a combination of cues is taken into account (Vrij, 2008). Taking in consideration only nonverbal behaviors, many studies argued that a more accurate truth/lie classifications could be made if a cluster of nonverbal cues is examined rather than each of these cues separately (Davis, Markus, Walters, Vorus, & Connors, 2005;Heilveil & Muehleman, 1981;Vrij & Mann, 2004). Patterns in behavior are frequently hidden from the consciousness of those who manifest them, as well as to unaided observers (Magnusson, 2006). As stated byEibl-Eibesfeldt (1970), “behavior consists of patterns in time. Investigations of behavior deal with sequences that, in contrast to bodily characteristics, are not always visible”. Considering only the order of events is not a valuable strategy to detect hidden recurrent behavior patterns, because deceptive acts are characterized by large complexity and great variability. One approach is to include the element of time in the analysis of nonverbal deceptive behavior. A mathematical approach that may be particularly suitable for defining and discovering repeated temporal patterns in deceptive behavior is T-pattern sequential analysis (Magnusson, 2000, 2004). Discovering the real-time multi-layered and partly parallel structure of nonverbal behavior remains a formidable challenge where cues to deception may be hidden to unaided observers in anything from the tiniest of details to intricate aspects of temporal structure.
T-pattern approach
T-pattern analysis was developed by Magnus S. Magnusson (2000, 2005, 2006)tofind temporal and sequential structure in behavior. The term T-pattern stands for temporal pattern; this approach focuses on determining whether arbitrary events sequentially occur within a specified time interval at a rate greater than that expected by chance. In this way, it detects repeated pat-terns of behavior units coded as events on one-dimensional discrete scales. This kind of analysis has been used in a wide vari-ety of observational studies (e.g. Arthur & Magnusson, 2005; Kerepesi et al., 2005) and in thefield of sport, such as analysis of soccer team play (Camerino, Chaverri, Anguera, & Jonsson, 2012), motor skill responses in body movement and dance (Castaner, Torrents, Anguera, Dinusovà, & Jonsson, 2009), effectiveness of offensive plays in basketball (Fernandez, Camerino, Anguera, & Jonsson, 2009). All of the above studies have been conducted
with the need to identify repeated behavioral patterns that may occur, regularly or irregularly, within a period of observation.
Therefore, this paper aims at presenting a preliminary investi-gation on the organization of non-verbal behavior, focusing on facial micro-movements (Ekman & Friesen, 1978), head movements and gaze direction, aiming to detect repeated T-patterns that occur in doping interviews. Specifically, we will focus on the doping-related interviews made by the former road-racing cyclist, Lance Armstrong. We will provide a descriptive analysis of the organi-zation of non-verbal patterns when Armstrong is certainly lying (baseline) and situations where the truthfulness of his statements is not sure. This will allow us to discuss how T-pattern methodology can contribute to innovative research on the recognition of deception.
Present study
Our study is based on some affirmations made in TV by Lance Edward Armstrong, an American former professional road-racing cyclist who pled guilty to doping in January 2013. Armstrong had won the Tour de France seven consecutive times between 1999 and 2005. On February 16, 2011, Armstrong announced his retirement from competitive cycling, while facing a US federal investigation into doping allegations. In June 2012, USADA charged Armstrong with having used illicit performance-enhancing drugs. On August 24, 2012 it announced a lifetime ban from competition on Arm-strong, applicable to all sports which follow the World Anti-Doping Agency code, as well as the stripping of all his seven Tour de France titles. On October 22, 2012, the Union Cycliste Internationale (UCI), the sport’s governing body, announced its decision to accept USA-DA’s findings. Armstrong chose not to appeal the decision to the Court of Arbitration for Sport, and in January 2013 he admitted doping in a television interview conducted by Oprah Winfrey, despite having made denials throughout his career.
Methods Procedure
gestures or posture, were difficult to observe due to the lack of continuity of the shooting in those areas of Lance Armstrong’s body.
We selected three different videos of Lance Armstrong. Two of them relate to the period before the admission of doping behavior, in which he denied all accusations and any involvement in the case (Oral and Videotaped deposition, November, 30th laid in Texas, 2005; Interview at CNN Larry King live, August 25th, 2005).
The third video is the interview that Oprah Winfrey (American television host, best known for her multi-award-winning talk show “The Oprah Winfrey Show”, which was the highest-rated program of its kind in history and was nationally syndicated from 1986 to 2011), conducted in a special edition of The Oprah Winfrey Show with Lance Armstrong, in January 2013. In this interview, he admitted having used PED, but denied the accusations of bullying and corruption.
In order to create a baseline of Armstrong’s deceptive behavior, we selected three video samples from 2005 in which he fully de-nied ever having taken performance-enhancing drugs. One sample was taken from his deposition, the other two from the Larry King interview (American television and radio host) for a total baseline duration of 1 min and 18 s. During the Oprah’s interview, Arm-strong gave very short answers (such as “yes/no” answers) in which he was definitely telling the truth (those were he confessed to have taken performance-enhancing drugs in the past), providing an insufficient video material to build a sincerity base-line. Therefore, “baseline” herein refers to samples in which Armstrong’s behavior was deceptive, that is, samples of past in-terviews clearly contradicted by USADA (August 24th, 2012) and UCI (October 22nd, 2012) rulings, and by Armstrong himself in the Oprah’s interview. We then decided to compare Armstrong’s baseline deceptive behavior with his behavior in three video samples from the Oprah’s interview (total duration of the excerpts: 1 min and 21 s). We selected those samples because of their relationship with particularly sensitive topics about doping, three accusations that Armstrong still strongly rejects (as things currently stands, the ground truth has not been revealed): mobbing of teammates that did not want to take banned sub-stances, a payment to USADA (United States Anti-Doping Agency), alleged to be an attempt to cover the use of PEDs (corruption charge), and the use of PEDs after 2005.
As we said before, our objective is tofind similarities or differ-ences between the organization of Armstrong’s nonverbal decep-tive behavior and the organization of his behavior in statements in which we don’t know if he was lying or not. The final aim of this study is to show that T-pattern methodology, whose effectiveness has already proved in deception detection and in otherfields (e.g.
Camerino et al., 2012; Magnusson, 2006; Zurloni, Diana, Elia, & Anolli, in press), can be a useful tool in doping behavior too. Instruments and data analysis
We used a behavior coder software to code the videos we selected frame by frame. Three different coders who had FACS (Ekman & Friesen, 1978) training (individual sessions followed by a group session) used the software to observe and code facial micro-movements in all video samples.
After coding, we verified the goodness of the data, estimating the inter-rater reliability; Cohen’s K for the presence of each behavior and AU analyzed ranged from an average of .73 to an average of.87. As suggested byLandis and Koch (1977), kappa values of above 0.60 are substantial; however, when disagreements were identified or the agreement was not perfect, the specific cases were discussed and agreed on by all coders. We analyzed datasets within the T-pattern approach, using Theme software (Magnusson, 2000,
2004). Theme detects statistically significant time patterns in se-quences of behaviors, basing on the timing of events relative to each other. It allows to detect complex and repeated temporal patterns even when a multitude of unrelated events occur in-between components of patterns, which typically makes them impossible to perceive with the naked eye.
We conducted three different analyses: the first one was executed in order to verify the common nature of the patterns contained in the baseline condition. In order to do this, we concatenated the previously coded video samples’ datasets into a multi-samplefile. The function “concatenate in to a multi-sample file” creates one file containing all the single sample files in the project. The resultingfile is analyzed as one but keeps inside the division between the samples; through the setting of a particular parameter (minimum percentage of samples), Theme allows you to launch the search for common patterns so that they have to occur in all the single samples (or in a percentage of them) in order to be detected.
By means of relevant options, the program allows the experimenter to set other specific search parameters: in this first analysis we set the significance level for pattern research at 0.05, and the minimum occurrences (the minimum number of times a pattern must be repeated to be considered by the software as a legit pattern) at 3, while the level for the minimum percentage of samples at 100%. We looked patterns occurrences, the means of their levels and of their lengths (indicating the complexity of a pattern). After doing this quantitative assessment, we looked at the actual patterns, selecting those presenting a demonstrated behavioral relationship, basing our choices on literature review. In our second analysis, we used the same software options to analyze and explore the common patterns in the Oprah interview samples. We analyzed the same data as in thefirst analysis.
A third analysis was then conducted in order to identify signif-icant patterns for both conditions and to explore common and different patterns altogether by concatenating both conditions and using the software’s function “selection e multi-sample file selec-tion e statistical”, which lets the software select patterns that appear significantly more often in our samples, weighing in the multi-samplefile as a whole. The parameters we set were identical to thefirst two analyses, except for the minimum percentage of patterns, a limit that we excluded for this analysis. After analyzing the patterns occurrences, the means of their levels and of their lengths, we looked at the multi-sample selection, which gave us as a result the patterns that were more significantly present in one condition when compared to the other by the software.
Results
In thefirst two analyses, we have identified distinctive patterns for each of the two conditions (baseline and Oprah). The baseline condition showed patterns containing head movements (such as headshakes), blinking (AU45) and pressed lips (AU24). In particular, head shaking and blinking were frequently present and distributed among the three different observation periods (Fig. 1).
In the Oprah interview, most patterns included gaze move-ments. These gaze movements consisted mostly of blinking and the repeated alternation between gaze contacts and gaze aversions, and they were both frequently present and distributed among the three observation periods (Fig. 2).
The comparison between both conditions shows that, in the baseline condition, there are fewer patterns and, in general, they all have a smaller level of complexity, both for level and length means (Figs. 3and4).
In the third analysis, we compared the two conditions through a multi-sample procedure, in order to bring out those patterns that
Fig. 1. Example of a distinctive T-pattern in the baseline condition. The left panel shows how the pattern was detected level by level; pattern length is four (one cluster contains four event types), while pattern level is three (one cluster contains three nodes). The right panel gives the occurrence times of each event type as a series of points. The lines connecting the occurrence times show how the pattern has been gradually built up. The pattern shows a repetition of head shaking (skn), blinking (AU45), and pressed lips (AU24). The pattern has 3 occurrences, and is distributed among all samples. Each sample is delimited by blue lines in the right panel. (For interpretation of the references to colour in thisfigure legend, the reader is referred to the web version of this article.)
Fig. 2. Example of a distinctive T-pattern (pattern length is four; pattern level is two) in the Oprah interview, which shows an alternation of gaze contact (cnt) and gaze aversion (avn), blinking (AU45), and gaze contact again. The pattern has 10 occurrences, and is distributed among all samples.
were significantly more present in one of the two conditions, as compared to the other.
No patterns were significantly more present in the baseline as compared to the Oprah interview analysis. In contrast, four signif-icantly distinctive patterns were detected in the Oprah interview. Those patterns were not shown in the baseline. Specifically, most different patterns included gaze movements (contact and aversion) and blinking (Fig. 5).
Discussion
Research on deception detection mostly focused on identifying cues to deception, while few studies observed the sequential and temporal structure of deceptive behavior (Vrij, 2008). Through T-pattern analysis, our results showed repetitive temporal T-patterning between the two experimental conditions.
If we look at the overall pattern frequencies by group, the number of pattern occurrences, lengths and levels increases be-tween the baseline and Oprah, we can see how the baseline shows a very limited number of patterns, characterized by a low level of complexity. This would be consistent with the literature that links the act of lying to the cognitive load phenomenon. The cognitive effort made while fabricating misleading messages would lead to a decrease in non-verbal behaviors (Zurloni et al., in press). This would produce a number of stereotyped behaviors, repeated over time (e.g. in the baseline condition, head shaking, blinking and lip pressing). Among these clues, lip pressing may indicate that liars may be tenser than truth tellers (Vrij, 2008). Moreover, lip pressing is associated to a situation in which the speaker must make an effort to say something different from what they have in mind, as in
the case lying. As a result, the pressed lips take the value of an attempt to“hold back” something, avoiding the risk of revealing clues that could expose the lie (DePaulo et al., 2003).
By contrast, in the Oprah interview, the number and complexity of distinctive patterns grows. However, patterns are almost ste-reotyped, showing a repetition of few nonverbal cues, particularly linked to eye movements (contact and aversion).
As previous discussed, gaze directed to a conversation partner is usually associated with honesty and likeability (Buller & Aune, 1987; Ekman, 2009; Tickle-Degnen & Rosenthal, 1990; Vrij, 2008); the repeated shift between gaze aversion and contact, here detected, could suggest Armstrong’s minor effort in monitoring the behavior of Oprah and in showing honest behavior. This probably happened because he did not have the need to overcompensate his behavior to avoid common misconceptions about deceptive behavior (Vrij et al., 2010).
Conclusions
In the present study, we adopted an innovative methodological approach for the analysis of nonverbal behaviors that occur when a person lies about the use of performance-enhancing drugs. The approach is based on the detection of statistically significant hier-archical sequences of behaviors called T-patterns.
We focused on the case of the seven-time winner of the Tour de France, Lance Armstrong, to identify the patterns that mark his deceptive communication. Three analyses were conducted drawing observational material from different videos excerpts where Arm-strong made doping-related statements. The first one showed a very limited number of patterns in the baseline condition of Arm-strong’s deceptive behavior, highlighting low level of complexity and the presence of stereotyped behaviors (e.g. the combination of head shaking, blinking and lip pressing). The second explored sig-nificant patterns in situations where the most controversial topics (the accusations of mobbing, a payment to USADA, and the use of PEDs after 2005) were addressed. In this case, the number and complexity of distinctive patterns was significantly higher, and most of them included gaze behavior. A third analysis identified significant patterns for both conditions and explored common and different patterns altogether.
Overall, the analyses allowed identifying distinctive patterns for each of the two conditions (baseline deceptive and Oprah). Therefore, the organization of Armstrong’s nonverbal behavior in the Oprah samples is quite different from his own nonverbal deceptive behavior. That being said, we cannot affirm with cer-tainty that, in these statements, he was sincere. More studies tell us that there is no such thing as a Pinocchio’s nose (e.g.Vrij, 2008), so
Fig. 3. Comparison of T-pattern complexities (levels and lengths) between the two conditions.
Fig. 5. Example of a significantly distinctive T-pattern (pattern length is three, pattern level is two) in the Oprah interview, as compared to the baseline condition which shows an alternation of gaze contact (cnt) and gaze aversion (avn), and blinking (AU45). The pattern has 11 occurrences in Oprah’s samples (last three columns), while only twice in the baseline condition (first three columns).
results from this study can only provide indications that more research, in addition with other tools and techniques, could explore and deepen. These results, in fact, shed new light on the important role of different methods when examining doping practices, and highlight the considerable scope for future T-pattern-based research in thisfield. Observational studies could be used to detect nonverbal deception cues, allowing researchers to overcome biases that are traditionally related to the exclusive use of self-reports (Petróczi et al., 2011).
However, it is important to note that several limitations may have affected the conclusions of the study. The most important ones are related to the use of television interviews. While T-pattern analyses require a continuous and sequential coding of observed behaviors, television shooting is generally marked by frequent alternation of camera angles with medium, long, and reverse shots as well as by different close-ups (medium and extreme). This sit-uation had two consequences on the development of the present research.
First, only the nonverbal communication systems backed by a more continuous shooting (facial micro-movements, head move-ments and gaze direction) had to be considered. It was not possible to analyze other systems, like gestures or body posture because of the frequent variations of the shooting focus. Even though a multimodal analysis was nonetheless possible, considering multi-ple body systems would have improved data quality and validity.
Second, periods where a continuous andfixed shooting was maintained and the topics of interest were discussed at the same time were relatively short. This might be considered as a minor shortcoming because of the frame-by-frame micro-level of the analysis. However, given the complexity of human behavior, a longer period of observation could result in more complete and precise analyses.
Further, there was a chronological gap between the contribu-tions analyzed: the baseline referred to the year 2005, while the interview with Oprah Winfrey was made at the beginning of 2013. There is a possibility that people might change their communication styles in such a long time. Therefore, some of the differences highlighted could be attributed to a chronological bias.
Finally, the settings where Armstrong was called to speak out were very different: the deposition was videotaped in a court and the interviews were shot in a television studio. Moreover, the role of the interlocutors (a lawyer, and two television personalities), their communicational style, and the type of interaction they establish with the other party (face-to-face in the case of the deposition and Oprah’s interview and deferred with Larry King) were different too. These may result in external variables in flu-encing Armstrong’s verbal and nonverbal behaviors. Further research has to be done to consider the impact of external variables or to control them. At the same time, it would be worthwhile to consider a broader range of cases.
Moreover, the integrated analyses of verbal and nonverbal behavior in future studies could be a promising research direction, allowing to address the issue of deceptive communication in a more comprehensive and multimodal perspective.
Nevertheless, this study provides a first exploration of this kind of approach in the investigation of doping-related commu-nication and represents a possible aid for future research and interventions on the topic of doping. Scholars and professionals have expressed growing interest in the development of anti-doping methods, procedures, and policies. The application of T-pattern analysis to detect nonverbal features of deception might be an interesting resource to be deepened and integrated with more traditional and established practice in order to fight this spreading phenomenon.
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