Running head: POSTURE AND DECEPTION DETECTION 1
Sitting in judgment: How body posture influences deception detection and gazing
behavior
Mircea Zloteanu* and Daniel C. Richardson
University College London
Author Note
Mircea Zloteanu, and Daniel C. Richardson, Department of Experimental Psychology,
University College London, United Kingdom. Mircea is now at the School of Social
Sciences, Humanities & Law, Teesside University, United Kingdom.
*Correspondence concerning this article should be addressed to Mircea Zloteanu,
School of Social Sciences, Humanities & Law, Teesside University, Middleborough, TS1
3BX, United Kingdom. E-mail: [email protected]
Word count: 6,057 (excluding references)
Conflict of Interest statement: The authors do not have any conflict of interest to report. POSTURE AND DECEPTION DETECTION 2
Abstract
Body postures can affect how we process and attend to information. Here, a novel effect of adopting an open or closed posture on the ability to detect deception was investigated. It was hypothesized that the posture adopted by decoders would affect their social acuity, resulting in differences in the detection of nonverbal behavior and discrimination of deceptive and truthful statements. In Study 1, adopting an open posture produced higher accuracy for detecting naturalistic lies, but no difference in the recognition of brief facial expressions, as compared to adopting a closed posture. Study 2 measured differences in gaze behavior based on posture when detecting both low and high stakes lies.
Sitting in an open posture reduced visual attention towards senders, and in particular, the attention given to their hands. The findings suggest that simply shifting posture can impact veracity judgments and the way they attend to visual information.
Keywords: facial expressions; deception detection; body postures; eye gaze; accuracy; embodiment
POSTURE AND DECEPTION DETECTION 3
1. Introduction
Changes in posture appear to have systematic and causal effects on how we see the
world and ourselves (Beigel, 1952; Neumann, Förster, & Strack, 2003). Although, it is not
yet clear whether posture has these effects by modulating attention, emotion, or some other
cognitive processes. Here, we manipulated body posture while asking people to judge if
others were lying or being honest, and assessed how their emotion perception, visual
attention, and veracity judgments were changed. In this context, our goal was to understand
how postural changes might interact with complex social judgments.
People are poor at detecting if others are lying or telling the truth, usually being only
slightly better than predicted by chance (Aamodt & Custer, 2006; Bond & DePaulo, 2006).
They are also overconfident in their judgments regardless of accuracy (Holm & Kawagoe,
2010) and tend to assume most statements people make are honest (i.e. truth-biased; Levine
et al., 1999).
The majority of past research attempting to improve deception detection accuracy has
focused either on differences between decoders (e.g., experience or training; Cleary &
Warner, 2016; Hartwig, 2011; Meissner & Kassin, 2002; Vrij, 2008) or between senders
(e.g., demeanor or physical characteristics; (Funk & Todorov, 2013; Levine et al., 2011).
Here, we raise the possibility that simply changing the decoder’s body posture might affect
their ability to perceive and interpret behavioral information from a sender.
1.1. Body Postures
Postures are representative of gross affect (Ekman & Friesen, 1967), reflecting
dimensions such as friendliness-unfriendliness (Mehrabian & Friar, 1969). Body postures can
affect how one perceives, reacts to, and interprets information (Beigel, 1952). Adopting
specific postures can influence the likelihood that certain thoughts occur (Neumann et al., POSTURE AND DECEPTION DETECTION 4
2003) and serve as a cue for how individuals view themselves and external stimuli (Bem,
1972; Bull, 1987; D’Mello, Chipman, & Graesser, 2007; Price & Harmon-Jones, 2010;
Priester, Cacioppo, & Petty, 1996; Riskind & Gotay, 1982; Stepper & Strack, 1993).
Research on the psychological and physiological effects of posture is still in its infancy, and, at times, a highly controversial topic. For instance, the literature on “power posing”—adopting expansive postures to increase feelings of power (Carney, Cuddy, & Yap,
2015)—has recently come under fire, with reexaminations reporting much weaker and more limited effects (Ranehill et al., 2015; Simmons & Simonsohn, 2017).
In the past, research has linked the posture a person adopts with how they interact with the world. For instance, adopting an upright posture has been associated with improved mood, higher self-esteem, and increased arousal (Nair, Sagar, Sollers, Consedine, &
Broadbent, 2015; Wilkes, Kydd, Sagar, & Broadbent, 2017). Posture can also affect information processing and attention. Riskind (1983) reported quicker recall for pleasant memories when participants sat in an upright posture, but quicker recall for negative events
(both in quantity and intensity) when in a slumped posture. Similar effects on cognition and memory have been found, where the posture adopted by an individual (upright or slumped) directly affected performance on valence words tasks (Michalak, Mischnat, & Teismann,
2014), and mood-recovery interventions (Veenstra, Schneider, & Koole, 2017).
Our manipulation rests on open and closed postures. Open postures are expansive physical displays adopted by individuals when feeling relaxed and willing to engage in social interaction (Wood, Saltzberg, & Goldsamt, 1990); they can produce power-related feelings and cognitions (Riskind & Gotay, 1982; Tiedens & Fragale, 2003). Typically, an open posture has individuals sitting with their arms uncrossed and legs uncrossed, in a relaxed recline. A closed posture, in contrast, typically has individuals sitting with their arms and legs crossed, in a rigid position; it is adopted when feeling threatened or dismissive of another, POSTURE AND DECEPTION DETECTION 5
signaling a lack of desire to interact. Adopting a closed posture increases one’s sensitivity to
how others evaluate you (Keltner, Gruenfeld, & Anderson, 2003), and can activate the
behavioral inhibition system (Anderson & Galinsky, 2006), resulting in reduced expressivity,
gesturing, and contact with the environment (Anderson & Berdahl, 2002; Bernstein, 1973).
These postures are argued to reflect the embodied concept of the “openness” dimension of
social interaction, lying at opposite ends of the spectrum (Mehrabian & Friar, 1969).
We propose that adopting a closed posture results in social information being ignored
or processed to a lesser extent (Schwarz & Clore, 1996). Conversely, an open posture reflects
that the individual feels relaxed, and primes them to be open to interaction, preparing them to
be more responsive and attentive to social cues (Machotka, 1965).
1.2. Facial Expressions
When judging the veracity of others, people tend to rely on nonverbal behavior to
help make their decisions (Bogaard, Meijer, Vrij, & Merckelbach, 2016; The Global
Deception Research Team [GDRT], 2006; Vrij & Semin, 1996). Several prevalent cross-
cultural beliefs exist about cues to detecting deceit, such as liars self-touch, avert their gaze,
and fidget more than truth-tellers (GDRT, 2006). Regardless of their diagnostic value (which
is usually quite poor; DePaulo et al., 2003; Hartwig & Bond, 2014), people’s beliefs
regarding such nonverbal behaviors are strong (GDRT, 2006).
As the current interest is on how postures can impact the decoding of behavioral
cues, we focused on the recognition of facial expression of emotions—specifically
microexpressions—proposed in the deception literature as a source of diagnostic information
(Ekman, 2003; Ekman & Friesen, 1969; Hurley & Frank, 2011; Porter, ten Brinke, &
Wallace, 2012). Microexpressions are involuntary full-faced expressions occurring at less
than 0.5 of a second, said to reflect the genuine emotional state of the sender (Ekman, 2003; POSTURE AND DECEPTION DETECTION 6
Ekman & Friesen, 1969; Frank & Svetieva, 2015; Porter & ten Brinke, 2008). It is argued that liars and truth-tellers experience different emotions which result in differences in the facial expressions each produces (Ekman, 2003; Ekman & Friesen, 1969; Porter & ten
Brinke, 2008; Porter et al., 2012). Thus, the assumption is that a perceptive decoder can utilize these cues to accurately determine the veracity of a sender (Ekman, 2003; Ekman,
O’Sullivan, Friesen, & Scherer, 1991).
However, the literature on the emotion-based approach to lie detection is highly contentious. While some studies indicate that, in specific scenarios, the ability to detect microexpressions is positively related to deception detection performance (Ekman &
O’Sullivan, 1991; Frank & Ekman, 1997; Matsumoto, Hwang, Skinner, & Frank, 2014;
Warren, Schertler, & Bull, 2009), more recent evaluations find no link between emotional cues and improvements in accuracy (Burgoon, 2018; Hartwig & Bond, 2014).
At present, the inclusion of a microexpression task was considered for two reasons.
First, out of all nonverbal channels, facial expressions receive preferential attention and mental processing relative to other nonverbal channels (Alpers & Gerdes, 2007; Vuilleumier,
Armony, Driver, & Dolan, 2001). If the posture manipulation has an effect of social acuity, a difference in facial expression recognition ability may be most evident. Second, while the evidence on microexpression and deception detection is mixed (especially regarding human decoders; (Shaw, Porter, & ten Brinke, 2013), people do hold strong beliefs that facial expressions are diagnostic of deceit (Ekman, 2003; GDRT, 2006). If decoders in a specific posture show a difference in veracity judgments (beneficial or otherwise), an overreliance on facial cues may explain the effect. Thus, if postures can affect people’s ability to perceive and/or interpret behavioral cues, facial expressions are an ideal candidate for measuring performance differences.
2. Study 1 POSTURE AND DECEPTION DETECTION 7
We hypothesized the posture manipulation would also affect deception detection
performance, as adopting such postures will impact decoders’ attention to and interpretation
of the behavioral information displayed by liars and truth-tellers. Differences in trait empathy
were also considered, as empathy can affect facial expression recognition (Carr &
Lutjemeier, 2005; Gery, Miljkovitch, Berthoz, & Soussignan, 2009), and deception detection
(Dimberg, Andréasson, & Thunberg, 2011; Hill & Craig, 2004; Zloteanu, 2015).
3. Method
3.1. Participants and Design
A mixed-design was used, with Posture (Open or Closed) serving as the between-
subjects factor and message Veracity (Lies and Truths) as the within-subjects factor. The
dependent variables were: deception detection accuracy, response bias, and microexpression
recognition. Individual differences in empathy were also measured.
Eighty participants (30 males, 50 females; MAge = 25.30, SD = 8.49) were recruited
through the university’s online subject pool, in return for £1 or course credit. An a priori
power analysis using G*Power 3.1 (Faul, Erdfelder, Lang, & Buchner, 2007) for an
interaction between presentation Posture (2) and Veracity (2), assuming a medium-sized
effect (Cohen’s f = 0.25), determined that a sample of this size would be sufficient for 95%
power at the .05 alpha criterion.
3.2. Stimuli and Measures
Twelve videos (6 lies and 6 truths) from the Bloomsbury Deception Set were used
(BDS; Street & Richardson, 2014; Street et al., 2011). Senders in the videos are describing
past vacations, where half of the stories are fabrications. The lies told are naturalistic, as the
aim of the senders was to deceive the person recording the video, who was not told of the
deception occurring; senders were not given any incentive to deceive beyond being asked to POSTURE AND DECEPTION DETECTION 8
help with a travel documentary. The videos contained an equal number of male and female
senders and no sender was used twice. All videos are around 30 seconds.
Facial recognition ability was assessed using the Micro Expression Training Tool
(METT; Ekman, 2002). The METT was developed to train the recognition of
microexpressions of the seven basic emotions: happiness, anger, sadness, disgust, fear,
surprise, and contempt. The test module consists of 14 color portrait photographs of facial
expressions of emotions (Japanese and Caucasians), two for each emotion. Users view a
neutral expression followed by an emotional expression for 1/25th of a second, to which they
respond with an emotion label from the list of seven displayed on the screen. The maximum
test score is 100%. The METT has been used in past studies (e.g., Endres & Laidlaw, 2009;
Frank & Ekman, 1997; Warren et al., 2009), and is based on the Brief Affect Recognition
Test which has good validity and reliability (Matsumoto et al., 2000).
Empathy was measured using the Interpersonal Reactivity Index (IRI; Davis, 1983).
The measure consists of 28 questions, 7 specific to each of the four subscales: Perspective-
taking, Fantasy, Empathic Concern, and Personal Distress. The IRI has high internal and
external validity (Davis & Franzoi, 1991), and good test-retest reliability (Bartholow, Sestir,
& Davis, 2005; Davis, 1983).
3.3. Procedure
Participants first completed the IRI. For each item, they responded using a scale with
letters from A (does not describe me well) to E (describes me very well). Afterwards,
participants were randomly allocated to either the Open or Closed posture condition. The
experiment was presented as an ergonomics study interested in posture and task engagement.
They were given verbal instructions on how to adopt the posture and shown a visual
depiction (Figure 1); the words “open” and “closed” were never used, to prevent confounds. POSTURE AND DECEPTION DETECTION 9
The open posture had participants seated with their arms uncrossed and legs uncrossed, while
the closed posture had participants sit with their arms folded at chest height and legs crossed.
Participants provided all responses verbally, ensuring they maintained their posture
throughout the experiment. First, they completed the facial recognition task. Before the task
began they were given a demonstration of the software. They then completed the deception
detection task. Participants read the related instructions and were first presented with an
example video. They then viewed the 12 videos (randomized). After each video, participants
stated their veracity judgments, replying with a number from a 7-point scale on screen
ranging from (-3) - “Very Dishonest” – 0 - “Don’t Know” – 3 - “Very Honest”. Afterwards,
they were fully debriefed.
Figure 1. Visual depictions of the open (A) and closed (B) postures.
3.4. Results
Facial Expression Recognition. An ANCOVA investigated the effect of posture on
facial expression recognition, controlling for individual differences in empathy. Contrary to
the prediction, the results showed no statistically significant effect of posture, F < 1, p = .987, POSTURE AND DECEPTION DETECTION 10
2 or empathy, F(1,77) = 1.64, p = .203, ηp = .021.
Deception Detection. Participants’ responses on the honesty scale were collapsed into three possible responses: -3 to -1 were recoded as “Lie”, 1 to 3 as “Truth”, and 0 as “Don’t
Know”. This was done as differences in response certainty do not reflect differences in accuracy (i.e. “Very Dishonest” is not more accurate than “Dishonest”; for an in-depth discussion on continuous versus dichotomous veracity scales, see Levine, 2001). These responses were compared to the veracity of each video and given a score of either “correct” or “incorrect” and summed across trials; the “Don’t Know” responses were treated as incorrect. Verifying that the posture manipulation did not influence uncertainty, an analysis of the “Don’t Know” responses in each condition was performed, which was not found to be significant, F < 1, p = .812.
The accuracy data were then processed using Signal Detection Theory (SDT; Green &
Swets, 1966), to ensure that any effects of the posture manipulation are representative of a change in discriminability and not a change in response bias. STD has been suggested to provide a better method of differentiating differences in decoder groups’ accuracy from their bias when making veracity judgments (Masip, Alonso, Garrido, & Herrero, 2009; Meissner &
Kassin, 2002). Two new dependent variables were created from the accuracy scores, A’ measuring overall accuracy independently of bias (Rae, 1976), and B” measuring participants’ response bias (Donaldson, 1992). For A’, a value of .50 indicates chance level performance. For B” <0 values represent a lie-bias, while >0 values indicate a truth-bias.
An ANCOVA was conducted to determine any difference between posture on accuracy (A’), controlling for empathy. In line with the prediction, the results revealed a
2 statistically significant effect of posture, F(1,77) = 5.37, p = .023, ηp = .065, 90% CI [.01,
.17], where participants in the Open condition (M = .66, SD = .19) outperformed those in the
Closed condition (M = .57, SD = .21). Empathy was also found to have a statistically POSTURE AND DECEPTION DETECTION 11
2 significant additive effect, F(1,77) = 5.09, p = .027, ηp = .062, 90% CI [.00, .16].
A second ANCOVA was conducted to determine if posture affected response bias
(B”), controlling for empathy. The results did not reveal an effect of posture or empathy, Fs <
1, ps > .50. Overall, decoders in both the open (M = .62, SD = .55) and closed (M = .54, SD =
.48) postures were significantly truth-biased, t(39) = 7.15, p < .001, 95% CI [.44, .79], d =
1.13, and t(39) = 7.05, p < .001, 95% CI [.39, .69], d = 1.11.
3.5. Discussion
The posture adopted by decoders had a significant effect on their ability to detect
deception but not on the accuracy of categorizing microexpressions. Specifically, decoders
adopting an open posture had higher accuracy compared to those in the closed posture. Using
a perceptual explanation, the accuracy difference may be a result of posture affecting
attention to behavioral information. However, the lack of a difference in facial recognition
scores seems to not support this explanation (cf. Hartwig & Bond, 2014). Considering a
cognitive explanation, posture may be influencing how decoders are processing information,
such as increasing resources allocated to the judgment process.
Microexpression recognition, however, was unaffected by either individual
differences in trait empathy or posture manipulation. The lack of a posture effect suggests
this manipulation does not impact the ability to classify the emotional displays of others. The
lack of a relationship between the microexpression scores and empathy may be explained by
the artificial nature of the task. Microexpressions are brief flashes of prototypical expressions
of emotions, whereas most studies employ recognition tasks using clear valence (positive-
negative) facial expressions presented for longer periods (e.g., Carr & Lutjemeier, 2005).
Furthermore, recent propositions suggest that empathy relates more to the speed of
processing facial expressions, and not how accurately they are classified (Kosonogov, Titova, POSTURE AND DECEPTION DETECTION 12
& Vorobyeva, 2015; Petrides & Furnham, 2003). It would be worthwhile to explore if recognition of more naturalistic or social-emotional displays would show an effect of posture.
4. Study 2
Study 2 explored the posture effect by considering which aspect of decoders’ judgment was affected by the manipulation. Either open posture decoders were more attentive to some behavioral cues (i.e. better detectors) or they were better at processing the information they received (i.e. better analyzers). Decoders’ gazing behavior was measured using an eye-tracker to investigate if the posture manipulation changed where decoders were looking and/or focusing on during the decoding process.
We used both high-stakes and low-stakes videos in this study, for two reasons. Stakes are the rewards to the liar for escaping detection and/or the punishment that they would receive for being caught. Stakes make controlling one’s behavior channels more difficult
(Ekman & Frank, 1993; Vrij, Harden, Terry, Edward, & Bull, 2001). And increase the likelihood of unintended behavioral cues associate with lying, both in quantity and intensity
(DePaulo, Kashy, Kirkendol, Wyer, & Epstein, 1996; Hartwig & Bond, 2011; McCornack,
1997; Porter & ten Brinke, 2010; Vrij, 2000), which can make detection easier (Frank &
Feeley, 2003; Hartwig & Bond, 2014; Porter et al., 2012). If the posture manipulation affects the attention decoders give to behavioral cues a difference in accuracy should be more pronounced for the high-stakes lies. The second benefit is uncovering the stability and generalizability of the posture effect on different lie scenarios.
It should be noted that the role of stakes in lie detection is a debated topic. The meta- analysis by DePaulo and colleagues (2003) reported an effect of motivation (a close proxy to stakes) on the detectability of deception, however, the more recent meta-analysis by Hartwig and Bond (2014) failed to replicate this finding. POSTURE AND DECEPTION DETECTION 13
It was predicted that the posture effect observed in Study 1 would be replicated and
extended to the high-stakes video. Second, for the high-stakes condition, this difference
between postures would be more pronounced. Finally, that posture would influence the
gazing behavior of decoders.
5. Methods
5.1. Participants and Design
An a priori power analysis for an interaction between Posture (2), Stakes (2), and
Veracity (2), assuming a medium-sized effect (Cohen’s f = 0.25), determined that a sample of
this size of 54 was necessary for 95% power at the .05 alpha criterion. However, due to a
technical malfunction with the eye-tracker, only 32 participants could be recorded. Thirty-
two students (6 males, 26 females; MAge = 19.88, SD = 1.98) were recruited using the
university’s online subject pool, in return for course credit. None of the participants had any
uncorrected visual impairments.
A mixed-design was employed, with Posture (Open or Closed) as the between-
subjects variable, and Veracity (Lies and Truths) and Stakes (High and Low) as the within-
subjects variables. The dependent variables were: accuracy, bias, confidence, and gazing
behavior. Given Study 1’s accuracy effect and the past literature on the relationship between
expansive postures and decision confidence (Briñol, Petty, Valle, Rucker, & Becerra, 2007;
Fischer, Fischer, Englich, Aydin, & Frey, 2011) the confidence measure was added to asses if
the manipulation also influences decoders’ self-perceived lie detection ability. Differences in
empathy were also measured.
5.2. Stimuli and Apparatus
Deception Detection Videos. For the low-stakes videos, eighteen videos (9 lies) were
selected from the BDS. They are considered to contain low-stakes lies, as the senders had no POSTURE AND DECEPTION DETECTION 14
incentive to lie and suffered no consequences if caught.
For the high-stakes videos, eighteen videos (9 lies) were selected from Levine’s
(2007) trivia game interviews. In Levine’s study, students played what they believed was a
teamwork game where they could win a cash prize. They worked with a partner, whom,
unbeknownst to them, was one of the researchers. During the trivia game, the experimenter
steps out, leaving the trivia answers unattended. The confederate attempts to convince the
participant to cheat. The videos are of the post-trivia game interviews where the participants
were asked if they had cheated. Nine videos were of individuals that had cheated, and nine
who had not cheated. All videos were on average 20s long. The videos are considered high-
stakes as senders believed they were subject to university rules, which if violated could have
severe consequences, including expulsion, and if they were successful could win the
monetary reward from the game.
Empathy. Trait empathy was assessed using the IRI.
Eye-Tracking. Eye movement was measured using a SensoMotoric Instruments
(SMI) RED500 Binocular Eye-Tracker, set to record at 120Hz. The presentation of stimuli,
order of the tasks, and recording of eye movements were controlled by the experimenter
using the SMI laptop. The videos were displayed to an external monitor (1280x960
resolution) with the eye-tracker mounted underneath. For each participant, a 10-point
calibration followed by a 4-point validation process was conducted.
5.3. Procedure
Participants first completed the IRI. They were then placed in front of the eye-tracker
and randomly assigned to a posture condition, open or closed. After ensuring the posture was
properly adopted, the calibration and validation of the eye-tracker was performed.
Participants were shown instructions regarding the first video set, either high-stakes or low- POSTURE AND DECEPTION DETECTION 15
stakes (counterbalanced, and videos were randomized within each set). For each video
participants gave two verbal responses: the veracity judgment, using the same scale as in
Study 1, and their confidence, using a 7-point scale ranging from “Not at all confident” to
“Extremely confident”. At the end, participants were debriefed.
5.4. Results
Eye-Movements. The eye-tracking data was coded for several areas of interest (AOI),
representing the body (AOI-1), the hands (AOI-2), the upper face (AOI-3), and the lower face
(AOI-4). This allowed for a more fine-grained analysis of decoders’ gazing patterns to
different nonverbal channels related to objective and stereotypical cues of deception
(DePaulo et al., 2003; GDRT, 2006). For each area, two measures from the SMI BeGaze
native software are reported: average dwell time as a percentage (AvgDT%), reflecting the
time spent looking at the specific area divided by the total looking time of all areas, and
fixation count (AvgFC), measuring the average number of times decoders fixated on each
target area.
Preliminary analysis revealed differences between the high-stakes and low-stakes
videos for both variables (stemming from the differences between the videos sets; see Figure
2), ps < .001, however, no interactions with the other factors were found and analyzing them
separately did not impact the results; therefore, Stakes was collapsed as a factor. Empathy,
also, did not show any effects on looking patterns, Fs < 1, ps > .14. POSTURE AND DECEPTION DETECTION 16
Figure 2. Eye-gaze heat maps for looking frequency of Open and Closed
posture decoders looking at the Low-Stakes and High-stakes videos.
MANOVAs were considered for analyzing the difference in the AOIs based on posture. However, as the measures were either correlated too strongly (rs > .9; multicollinearity) or not at all (rs < .2), and a number of assumptions for this analysis were violated (i.e., homogeneity of covariance, Box’s M, ps < .001; multivariate normality,
Royston’s Test, ps < .001), it was dismissed. An inspection of the skewness and kurtosis, in addition to Q-Q plots and histograms, indicated that the assumption of normality of variance was violated for several AOIs. Therefore, the appropriate dependent variables were transformed in order to obtain a normal distribution of data.
An independent samples t-test comparing total (all AOIs combined) percentage of looking times (AvgDT%) based on posture was conducted. Overall, decoders in the Open posture (M = 18.6%, SD = 4.7%) devoted less time looking at senders, than those in the
Closed posture (M = 21.8%, SD = 1.5%). To obtain normality, the analysis was conducted on POSTURE AND DECEPTION DETECTION 17 base 10 log-transformed data. This revealed a statistically significant difference based on posture, t(24.67) = 2.08, p = .048, 95% CI [.01, .39], d = 0.72 (unequal variance).
To unpack this difference, planned comparisons were conducted for each AOI.
Investigating differences in looking at the hands of senders revealed that the Open posture decoders (M = 1.8%, SD = 1.1%) looked less at this area than Closed posture decoders (M =
3.2%, SD = 1.8%), t(30) = -2.40, p = .023, 95% CI [-.77, -.06], d = 0.84 (square root- transformed data). No other differences in decoder gazing behavior between the two posture conditions were found, ts ≤ 1, ps > .05.
To understand if decoders differed in the number of times they focused on different areas, and not just the amount of time spent, analyses were conducted on the fixation count of each area. Overall, decoders in both conditions seemed to fixate an equal number of times at the senders while making their veracity judgments, t(30) = -1.01, p = .321, 95% CI [-4.45,
1.45]. Considering each AOI separately, planned t-tests revealed a marginally significant effect for the open posture decoders (M = 1.3, SD = 0.9) fixating fewer times at the hands than the closed posture decoders (M = 2.2, SD = 1.6), t(30) = -1.84, p = .076, 95% CI [-.62,
.03], d = 0.64 (sqrt-transformed data). No other significant differences emerged (ts ≤ 1, ps >
.05).
Deception Detection. The decoders’ accuracy responses were coded and analyzed using SDT. To test the effect of posture on detecting deception (A’), a repeated-measures
ANCOVA was conducted on Posture (Open or Closed) and Stakes (High and Low), while controlling for empathy. A marginal main effect of stakes was observed, F(1,29) = 4.05, p =
2 .053, ηp = .123, suggesting that High-stakes videos (M = .61, SD = .16) were easier to classify than Low-stakes videos (M = .51, SD = .21). While decoders in the Open posture (M
= .59, SD = .15) had higher accuracy than decoders in the Closed posture (M = .55, SD = .11), the difference was not significant, F < 1, p = .335. The Stakes X Posture interaction, while POSTURE AND DECEPTION DETECTION 18
also in the predicted direction—the difference between the Open and Closed posture on
accuracy being more pronounced for high-stakes videos (Mdiff = 5.15%) than low-stakes
videos (Mdiff = 3.60%)—was not significant, F < 1, p = .948. Empathy was not found to affect
the results, Fs < 1, p = .644.
Subsequently, an ANCOVA investigated the influence of posture and stakes on
response bias (B”), controlling for empathy. This did not indicate any effect of posture, F < 1,
2 p = .693, stakes, F < 1, p = .956, or interaction F(1,28) = 2.60, p = .118, ηp = .085. All
decoders were significantly truth-biased, both in the Open (M = .44, SD = .35; t(16) = 5.24, p
< .001, 95% CI [.26, .62], d = 2.62), and Closed posture (M = .36, SD = .25; t(14) = 5.56, p <
.001, 95% CI [.22, .50], d = 2.97).
Confidence. An independent samples t-test was conducted on decoders’ confidence
ratings, based on posture. The analyses did not find a significant difference, t(30) = 1.23, p =
.227, suggesting that the posture manipulation did not impact decoder’s confidence in their
veracity judgments.
5.5. Discussion
The analysis comparing looking patterns revealed an additional effect of the posture
manipulation on decoders, which may assist in understanding the mechanisms behind the
effects seen in Study 1. Decoders adopting an open posture overall looked less at senders
(both in the number of times and in duration) than decoders adopting a closed posture. This
seems to contradict the social acuity assumption—that adopting an open posture will make
decoders more attentive and/or receptive to the behaviors of others. Specifically, the data
show that decoders in the open posture looked less at the hands of senders than decoders in
the closed posture, but focused equally on all other locations (i.e. face, arms, body).
This finding seems to also go against the emotion-based lie detection literature, which POSTURE AND DECEPTION DETECTION 19 claims that astute decoders focus more on the nonverbal behavior of senders. Here, the opposite is displayed, as the decoders in the open posture focused less at the nonverbal behavior of senders. Unfortunately, it is difficult to speculate further as to the connection between gazing behavior and lie detection performance as the accuracy effect from Study 1 was not replicated (attributed to the smaller sample). Although, a tentative trend in the same direction was observed in the data.
While directionality of the posture effect cannot be investigated with the current design, two post-hoc explanations are offered for the gazing difference. Adopting an open posture may have relied less on nonverbal information, focusing attention more on other channels (e.g., paraverbal or content). Indeed, past research has shown that relying less on nonverbal cues can increase accuracy (e.g, Bond, Howard, Hutchison, & Masip, 2013; Sporer
& Schwandt, 2006). Alternatively, in keeping with the “openness to communication” dimension, closed posture decoders may be focusing more on senders’ hands in an attempt to avoid areas associated with social information and interaction (Mansell, Clark, Ehlers, &
Chen, 1999). However, this does not account for the lack of differences in looking at the face between the two posture conditions.
The data also suggested a slight difference in performance due to the type of lie decoders saw, as a marginal stakes effect on accuracy was observed. This would indicate that high-stakes videos were easier to classify than low-stakes videos. However, the data do not permit any strong inferences with the current sample. Future explorations of the posture effect should employ a larger sample size and include stimuli containing more serious
(criminal) lies to fully understand the role that the decoder’s posture can have on veracity judgments and the generalizability of the posture effects.
Finally, the results showed that posture did not affect decoders’ response bias
(replicating Study 1) or confidence in their veracity judgments. These findings are POSTURE AND DECEPTION DETECTION 20 noteworthy, as past intervention studies (e.g., lie detection training) have occasionally resulted in detrimental effects on decoders’ biases (Levine et al., 1999; Meissner & Kassin,
2002; Vrij, 2008) and confidence (Holm & Kawagoe, 2010).
6. General Discussion
Over two experiments, we investigated a novel effect of the decoder’s posture on the perceptions and judgments they made about other peoples. The data finds that the posture adopted by a decoder can impact their ability to discriminate truthful and deceptive statements (Study 1) and their gazing behavior (Study 2), without affecting response bias or confidence.
In Study 1, it was found that adopting an open posture resulted in higher discriminability compared to adopting a closed posture, an effect that was more visible with higher levels of empathy. The results also showed that this effect was not attributable to a shift in response bias brought about by the manipulation. Facial expression recognition, however, was unaffected by either empathy or posture manipulation.
Building on these results, in Study 2 the looking behavior of decoders in the two posture conditions was assessed while also expanding the decoding task to high-stakes stimuli, assessing the stability and generalizability of the posture effect on different types of lies. With respect to the judgment data, decoders adopting an open posture showed a trend towards higher accuracy than their closed posture counterparts on all stimuli. For stakes, a marginal improvement based on posture was found, where higher-stakes lies and truths were easier to classify than their lower-stakes counterparts. Adopting an open posture compared to a closed posture, also, seemingly increased the difference in detection between the two stimuli, although, this trend was non-significant.
With respect to gazing behavior, adopting an open posture resulted in less attention POSTURE AND DECEPTION DETECTION 21 being given to the nonverbal behavior of senders. This was the opposite of what was initially predicted. It was found that decoders placed in an open posture spent less time gazing at senders. Specifically, they focused less on the hands of senders. This could be because open posture decoders were faster and/or more efficient at extracting information from nonverbal signals and thus required less time looking at senders. Or it could be they relied less on nonverbal information for their veracity judgments.
The case for an information-processing account can be made by combining the results of the two studies. In Study 1, the accuracy and empathy effects can be taken as posture affecting the processing of information more than the attention paid to senders’ cues. Indeed, compatibility between posture and situation has been shown to facilitate performance, by increasing the availability of processing resources (Barsalou, Niedenthal, Barbey, & Ruppert,
2003; Förster & Strack, 1996; Riskind, 1983). In Study 2, the reduced gazing time for open posture decoders would suggest that information-processing, and not attention, is impacted by the posture effect. Perhaps, if decoders adopt an open posture, they are primed to dedicate more resources to processing social information. In this way, posture may be affecting how decoders process information from senders, and not their tendency to inspect their targets more thoroughly.
Although the lack of a control posture in the design limits any directionality interpretation, tentatively, the accuracy seen in the open posture (66% in Study 1; 59% in
Study 2) would suggest that the manipulation aided deception detection, as the closed posture accuracy (57% in Study 1; 55% in Study 2) is in line with the usual levels reported by past research (i.e. 54%; DePaulo et al., 2003; Levine, 2014). This suggests a moderate effect-sized improvement on accuracy, similar to that obtained from deception detection training (Cohen,
1969; Driskell, 2012; Richardson, 2011). This effect, if replicated, has practical implications for security, law enforcement, and clinical psychology. For example, in many countries a POSTURE AND DECEPTION DETECTION 22 police suspect cannot be forcefully interrogated using a lie detection technique (such as the cognitive lie detection method; Vrij & Granhag, 2012), however, using the current paradigm, interrogators can simply adopt an open posture to improve the odds of discriminating veracity.
The postural effect also has implications for our understanding of nonverbal behavior in social interactions and for embodied cognition. Due to the commonality of such postures in daily life, their effects on perception are important to understand. Individuals adopt these postures frequently, spontaneously, and unconsciously, which may unknowingly affect their interaction with and perception of others in social settings. The current findings provide initial evidence for a new embodied effect of postures on judgment under uncertainty (here, deception detection).
An extension of the current paradigm would be using an interactive design, as much of veracity judgments in forensic settings are done on the basis of face-to-face interactions
(Gudjonsson, 1992). In an optimistic prediction, the open posture effect may foster affiliation between sender and decoder (Lakin, Jefferis, Cheng, & Chartrand, 2003). Thus, if the decoder and sender were in a similar embodied state (i.e. exchange information accurately; especially for truth-tellers) they would activate similar cognitive and affective states
(Barsalou et al., 2003), enhancing empathy and rapport (Chartrand & Bargh, 1999; Lakin &
Chartrand, 2003; Stel, Van Baaren, & Vonk, 2008), improving communication.
7. Conclusion
At present, this is the first exploration into an effect of posture on deception detection accuracy, extending work on embodiment to include veracity judgment and eye-gazing differences. The results offer preliminary support for a novel postural effect on deception detection. The data show that the posture adopted by a decoder can produce significant POSTURE AND DECEPTION DETECTION 23 differences in gazing behavior and deception detection. The effect also indicates a relationship with empathic perception and information processing, suggesting that the posture manipulation relates to how information is processed and not to the attention given to specific behavior. The aim of this approach was to aid deception detection without introducing harmful elements or additional demand characteristics into the decoding process. From an applied setting, the posture manipulation may offer a simple-to-implement method for improving veracity judgment accuracy.
Contributions
MZ and DR were main writing contributors to the manuscript. MZ developed the research idea, with critical guidance from DR. MZ coordinated data collection and performed the analyses.
Acknowledgments
We thank Timothy R. Levine for providing the stimuli used in Study 2.
POSTURE AND DECEPTION DETECTION 24
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