Microexpressions Are Not the Best Way to Catch a Liar

Burgoon, Judee K.

doi:10.3389/fpsyg.2018.01672

Cited by 44 publications

(37 citation statements)

References 45 publications

(57 reference statements)

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“…The results found no effect of receiving training-real or bogus-on veracity judgments compared to no training. This conforms with the literature suggesting that having decoders focus on emotional information is not an optimal strategy (Burgoon, 2018;Hartwig & Bond, 2014). While accuracy was not improved, the lack of a reduction in accuracy EMOTION RECOGNITION AND DECEPTION DETECTION 23 resulting from training is also noteworthy, as past training interventions have also been shown to produce negative outcomes (Levine, 2014).…”

Section: Discussionsupporting

confidence: 86%

“…Currently, in contrast to the criticisms of the use of emotional cues for detecting deception (see Burgoon, 2018;Vrij, 2008), we focused on how decoders' emotion recognition ability and senders' emotions impact on veracity judgments. We aim to demonstrate that emotions should not be overlooked in deception research, as they are important in understanding how social interactions unfold and how people make veracity judgments.…”

Section: Emotion Recognition and Deception Detectionmentioning

confidence: 99%

“…Microexpressions are full-faced expressions occurring at <0.5 of a second, resulting from failed attempts to mask or suppress one's true emotions (Ekman, 2003a;Frank & Svetieva, 2015). The reliance on microexpression for cues to detecting deception is a highly controversial stance (see Burgoon, 2018). While these have been documented in both laboratory (e.g., Ekman & Friesen, 1969) and real-world scenarios (e.g., Porter & ten Brinke, 2008), research has not found a consistent relationship with deception detection.…”

Section: Facial Expression Of Emotionsmentioning

confidence: 99%

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Emotion recognition and deception detection

Zloteanu¹

2019

Preprint

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People hold strong beliefs regarding the role of emotional cues in detecting deception. While research on the diagnostic value of such cues has been mixed, their influence on human veracity judgments should not be ignored. Here, we address the relationship between emotional information and veracity judgments. In Study 1, the role of emotion recognition in the process of detecting naturalistic lies was investigated. Decoders’ accuracy was compared based on differences in trait empathy and their ability to recognize microexpressions and subtle expressions. Accuracy was found to be unrelated to facial cue recognition but negatively related to empathy. In Study 2, we manipulated decoders’ emotion recognition ability and the type of lies they saw: experiential or affective. Decoders either received emotion recognition training, bogus training, or no training. In all scenarios, training was not found to impact on accuracy. Experiential lies were easier to detect than affective lies, but, affective emotional lies were easier to detect than affective unemotional lies. The findings suggest that emotion recognition has a complex relationship with veracity judgments.

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Section: Discussionsupporting

confidence: 86%

Section: Emotion Recognition and Deception Detectionmentioning

confidence: 99%

Section: Facial Expression Of Emotionsmentioning

confidence: 99%

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Emotion recognition and deception detection

Zloteanu¹

2019

Preprint

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“…We appreciate that verbal veracity tools (those which encourage the interviewee to say more, allowing for their responses to be scrutinised) is not necessarily the most viable method under the circumstances. One method proposed where AI or automation may be useful is by using The Rigidity Effect (RE [Burgoon, 2018]). The RE postulates that when an individual is faced with deception under high stake situations, there is an initial freeze response (see Jansen, Nguyen, Karpitskiy, Mettenleiter, & Loewy, 1995 for an overview of autonomic responses to threat).…”

Section: Current Alternatives To Iborderctrlmentioning

confidence: 99%

Airport artificial intelligence can detect deception: or am i lying?

Jupe¹,

Keatley

2019

Secur J

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Since the 9/11 terrorist attacks, research has enveloped numerous areas within the psychological sciences as a means to increase the ability to spot potential threats. While airports took to heightened security protocols, many academics looked deeper into ways of detecting deception within international airport settings. Various verbal and nonverbal systems were intensely scrutinised under the empirical magnifying glass with the aim of creating security environments that are better able to detect potential threats. However, in 2018, a €4.5m grant from the European Union's Horizon 2020 research and innovation programme, number 700626, was awarded to further in vivo test the use of computational methods to detect deception from facial cues. The system is deemed a non-invasive psychological profiling system and stems from that of a system called 'Silent Talker' (Rothwell, Bandar, O'Shea, & McLean, 2006). The 'iBorderCtrl' AI system uses a variety of 'at home' pre-registration systems and real-time 'at the airport' automatic deception detection systems. Some of the critical methods used in automated deception detection is that of microexpressions. In this opinion article, we argue that considering the state of the psychological sciences current understanding of micro-expressions and their associations with deception, such in vivo testing is naïve and misinformed. We consider the lack of empirical research that supports the use of micro-expressions in the detection of deception and question the current understanding of the validity of specific cues to deception. With such unclear definitive and reliable cues to deception, we question the validity of using artificial intelligence that includes cues to deception, which have no current empirical support.

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“…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 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).…”

Section: Facial Expressionsmentioning

confidence: 98%

Sitting in judgment: How body posture influences deception detection and gazing behavior

Zloteanu¹,

Krumhuber²,

Richardson³

2019

Preprint

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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.

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Microexpressions Are Not the Best Way to Catch a Liar

Cited by 44 publications

References 45 publications

Emotion recognition and deception detection

Emotion recognition and deception detection

Airport artificial intelligence can detect deception: or am i lying?

Sitting in judgment: How body posture influences deception detection and gazing behavior

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