Deceptive Intentions: Can Cues to Deception Be Measured before a Lie Is Even Stated?

Ströfer, Sabine; Noordzij, Matthijs Leendert; Ufkes, Elze Gooitzen; Giebels, Ellen

doi:10.1371/journal.pone.0125237

Cited by 15 publications

(30 citation statements)

References 68 publications

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“…That is, in the more experimental studies of Ströfer et al (2015) participants did not commit a transgression, questions were contently unrelated, and neither had personal relevance nor were embedded in an interactive interview.…”

Section: Discussionmentioning

confidence: 99%

Catching a Deceiver in the Act: Processes Underlying Deception in an Interactive Interview Setting

Ströfer

Ufkes

Noordzij

et al. 2016

Appl Psychophysiol Biofeedback

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Lying is known to evoke stress and cognitive load. Both form cues to deception and lead to an increase in sympathetic nervous system (SNS) activity. But in reality, deceivers stick to the truth most the time and only lie occasionally. The present study therefore examined in an interactive suspect interview setting, whether deceivers still have clearly diverging cognitive and emotional processes from truth tellers when only having the intention to lie incidentally. We found that deceivers who lied constantly diverge from truth tellers in SNS activity, self-reported cognitive load and stress. Across all interviews, SNS activity correlated stronger with self-reports of cognitive load than stress, which supports the cognitive load approach. Furthermore, deceivers who told the truth and lied on only one crucial question, particularly diverged in self-reported stress from truth-tellers. In terms of SNS activity and self-reported cognitive load, no differences were found. Theoretical and practical implications are discussed.

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Section: Discussionmentioning

confidence: 99%

Catching a Deceiver in the Act: Processes Underlying Deception in an Interactive Interview Setting

Ströfer

Ufkes

Noordzij

et al. 2016

Appl Psychophysiol Biofeedback

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“…Next, the time intervals (epochs) from which the EDA signal was used to extract features for associated events were determined. This process was motivated by the work of [54] that demonstrates that the cues in EDA are present before the event of interest. The segmentation procedure for various types of epochs follows.…”

Section: Data Segmentation and Annotationmentioning

confidence: 99%

“…EDA can be also used as an indicator of deceit since lying costs more mental effort than telling the truth [58] and the cognitive load activates SNS [13,38]. Previous research [36,54] found increased EDA for lying as compared to truth-telling. Moreover, EDA is an autonomic-based physiological response which makes it hard to control and therefore less susceptible to strategic manipulations [18], and is a good indicator of deceit.…”

Section: Introductionmentioning

confidence: 99%

Detecting Deception and Suspicion in Dyadic Game Interactions

Ondras

Güneş

2018

Proceedings of the 20th ACM International Conference on Multimodal Interaction

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In this paper we focus on detection of deception and suspicion from electrodermal activity (EDA) measured on left and right wrists during a dyadic game interaction. We aim to answer three research questions: (i) Is it possible to reliably distinguish deception from truth based on EDA measurements during a dyadic game interaction? (ii) Is it possible to reliably distinguish the state of suspicion from trust based on EDA measurements during a card game? (iii) What is the relative importance of EDA measured on left and right wrists? To answer our research questions we conducted a study in which 20 participants were playing the game Cheat in pairs with one EDA sensor placed on each of their wrists. Our experimental results show that EDA measures from left and right wrists provide more information for suspicion detection than for deception detection and that the person-dependent detection is more reliable than the person-independent detection. In particular, classifying the EDA signal with Support Vector Machine (SVM) yields accuracies of 52% and 57% for person-independent prediction of deception and suspicion respectively, and 63% and 76% for person-dependent prediction of deception and suspicion respectively. Also, we found that: (i) the optimal interval of informative EDA signal for deception detection is about 1 s while it is around 3.5 s for suspicion detection; (ii) the EDA signal relevant for deception/suspicion detection can be captured after around 3.0 seconds after a stimulus occurrence regardless of the stimulus type (deception/truthfulness/suspicion/trust); and that (iii) features extracted from EDA from both wrists are important for classification of both deception and suspicion. To the best of our knowledge, this is the first work that uses EDA data to automatically detect both deception and suspicion in a dyadic game interaction setting.

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“…The development of the polygraph [12] has shown that several physiological parameters can be correlated with deception, with various degrees of accuracy. We can distinguish between invasive measurement techniques, used to measure parameters such as the galvanic skin response (GSR) (or other measurements of the electro-dermal activity) [12][13] [14], heart rate and blood pressure [12] [15], respiratory rate [12] or brain activity [16] [17] and noninvasive measurement techniques, that can be used to monitor heart rate, using Fast Fourier Transform (FFT) video analysis [8] [18], respiratory rate (FFT video analysis) [19] [20], voice audio frequencies [21] [22], facial expressions [22], or eye movements and blink rates [23][24] [28].…”

Section: Deception and Its Physiological Manifestationsmentioning

confidence: 99%

First Attempts in Deception Detection in HRI by using Thermal and RGB-D cameras

Iacob¹,

Tapus²

2018

2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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A key factor of a successful Human-Robot Interaction (HRI) is the robot's ability to understand the messages transmitted by a human user, especially their nonverbal and paraverbal components. Our work focuses on the robot's ability to detect if a person is deceitful or honest, based on an evaluation of their physiological state and using only noninvasive techniques, such as thermal and RGB-D imaging. In particular, we currently study and evaluate several physiological manifestations during an experiment consisting of an interrogation conducted by a robot that follows a mockup crime, then correlate them with the truth values of the participants' answers. The first results are promising.

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Deceptive Intentions: Can Cues to Deception Be Measured before a Lie Is Even Stated?

Cited by 15 publications

References 68 publications

Catching a Deceiver in the Act: Processes Underlying Deception in an Interactive Interview Setting

Catching a Deceiver in the Act: Processes Underlying Deception in an Interactive Interview Setting

Detecting Deception and Suspicion in Dyadic Game Interactions

First Attempts in Deception Detection in HRI by using Thermal and RGB-D cameras

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