The Effect of Multiple Auditors on Deception Detection in a Client Inquiry Setting

2015 ◽  
Author(s):  
Kip Holderness
Keyword(s):  
Deception Detection

Response-Specific Scalp Distributions in Deception Detection and ERP Correlates of Psychopathic Personality Traits

2004 ◽  
Vol 18 (1) ◽  
pp. 13-26 ◽  
Author(s):  
Antoinette R. Miller ◽  
J. Peter Rosenfeld
Keyword(s):  
Deception Detection ◽  
Event Related Potential ◽  
Psychopathic Personality ◽  
P300 Amplitude ◽  
Group Differences ◽  
Post Hoc Analysis ◽  
P300 Component ◽  
Psychopathic Personality Inventory ◽  
Post Hoc ◽  
First Time

Abstract University students were screened using items from the Psychopathic Personality Inventory and divided into high (n = 13) and low (n = 11) Psychopathic Personality Trait (PPT) groups. The P300 component of the event-related potential (ERP) was recorded as each group completed a two-block autobiographical oddball task, responding honestly during the first (Phone) block, in which oddball items were participants' home phone numbers, and then feigning amnesia in response to approximately 50% of items in the second (Birthday) block in which oddball items were participants' birthdates. Bootstrapping of peak-to-peak amplitudes correctly identified 100% of low PPT and 92% of high PPT participants as having intact recognition. Both groups demonstrated malingering-related P300 amplitude reduction. For the first time, P300 amplitude and topography differences were observed between honest and deceptive responses to Birthday items. No main between-group P300 effects resulted. Post-hoc analysis revealed between-group differences in a frontally located post-P300 component. Honest responses were associated with late frontal amplitudes larger than deceptive responses at frontal sites in the low PPT group only.

Spotting a liar by looking for microexpressions: Challenges and Opportunities

2019 ◽  
Author(s):  
Xunbing Shen
Keyword(s):  
Empirical Evidence ◽  
Deception Detection ◽  
Empirical Studies ◽  
High Stake ◽  
Challenges And Opportunities ◽  
Cues To Deception ◽  
The Relationship

Microexpressions do exist, and they are regarded as valid cues to deception by many researchers, furthermore, there is a lot of empirical evidence which substantiates this claim. However, some researchers don’t think the microexpression can be a way to catch a liar. The author elucidates the theories predicting that looking for microexpressions can be a way to catch a liar, and notes that some data can support for the utilization of microexpressions as a good way to detect deception. In addition, the author thinks that the mixed results in the area of investigating microexpressions and deception detection may be moderated by the stake. More empirical studies which employ high-stake lies to explore the relationship between microexpressions and deception detection are needed.

"Was It You Who Stole 500 Rubles?" - The Multimodal Deception Detection

2020 ◽  
Author(s):  
Valeriya Karpova ◽  
Polina Popenova ◽  
Nadezda Glebko ◽  
Vladimir Lyashenko ◽  
Olga Perepelkina
Keyword(s):  
Deception Detection

scholarly journals Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies

2020 ◽  
Vol 8 ◽  
pp. 199-214
Author(s):  
Xi (Leslie) Chen ◽  
Sarah Ita Levitan ◽  
Michelle Levine ◽  
Marko Mandic ◽  
Julia Hirschberg
Keyword(s):  
Deception Detection ◽  
Human Perception ◽  
Lie Detection ◽  
Challenging Problem ◽  
Cues To Deception ◽  
Lexical Cues ◽  
Insight Into ◽  
Large Corpus ◽  
Better Than

Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of deceptive and truthful interviews. We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality. With this data we built classifiers to automatically distinguish trusted from mistrusted speech, achieving an F1 of 66.1%. We next evaluated whether the strategies raters said they used to discriminate between truthful and deceptive responses were in fact useful. Our results show that, although several prosodic and lexical features were consistently perceived as trustworthy, they were not reliable cues. Also, the strategies that judges reported using in deception detection were not helpful for the task. Our work sheds light on the nature of trusted language and provides insight into the challenging problem of human deception detection.

Stereotypes of Deceptive Behaviors: a Cross-cultural Study Between China and Japan

2013 ◽  
Vol 41 (2) ◽  
pp. 335-342 ◽  
Author(s):  
Linus Chieh-Yu Yeh ◽  
Liu Xi ◽  
Zhang Jianxin
Keyword(s):  
Detailed Analysis ◽  
Cultural Study ◽  
Research Team ◽  
Deception Detection ◽  
Cross Cultural ◽  
Two Cultures ◽  
Cross Cultural Study ◽  
China And Japan ◽  
Deceptive Behaviors

We replicated and confirmed the results of the deception beliefs research conducted by The Global Deception Research Team (GDRT; 2006). We compared the deception stereotype and the perceiver cues of deception detection of people in the Chinese and Japanese cultures. Our results show that stereotypes of deceptive behaviors exist in both cultures with cross-cultural consistency. However, we also found that the deception stereotype was significantly different in these two cultures and was also different according to gender. Our findings support and validate the GDRT's findings with a deeper and more detailed analysis.

Non-invasive Deception Detection in Videos Using Machine Learning Techniques

2021 ◽  
Author(s):  
Siam Islam ◽  
Popin Saha ◽  
Touhidul Chowdhury ◽  
Asif Sorowar ◽  
Raqeebir Rab
Keyword(s):  
Machine Learning ◽  
Deception Detection ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document