Lie Detection: Still Searching for the Truth

1991 ◽  
Vol 36 (3) ◽  
pp. 221-223 ◽  
Author(s):  
William G. Iacono
Keyword(s):  
Lie Detection

Effects of Targeted Self-Coaching on Applicant Distortion of Personality Measures

2012 ◽  
Vol 11 (4) ◽  
pp. 169-175 ◽  
Author(s):  
Katherine A. Sliter ◽  
Neil D. Christiansen
Keyword(s):  
Impression Management ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Lie Detection ◽  
Personality Test ◽  
Personality Measures ◽  
Personality Tests ◽  
The Impact

The present study evaluated the impact of reading self-coaching book excerpts on success at faking a personality test. Participants (N = 207) completed an initial honest personality assessment and a subsequent assessment with faking instructions under one of the following self-coaching conditions: no coaching, chapters from a commercial book on how to fake preemployment personality scales, and personality coaching plus a chapter on avoiding lie-detection scales. Results showed that those receiving coaching materials had greater success in raising their personality scores, primarily on the traits that had been targeted in the chapters. In addition, those who read the chapter on avoiding lie-detection scales scored significantly lower on a popular impression management scale while simultaneously increasing their personality scores. Implications for the use of personality tests in personnel selection are discussed.

Statistical versus clinical lie detection reconsidered.

1984 ◽  
Vol 39 (1) ◽  
pp. 80-80 ◽  
Author(s):  
Julian J. Szucko ◽  
Benjamin Kleinmuntz
Keyword(s):  
Lie Detection

An Empirical Study of Micro-Expression Lie Detection Training

2013 ◽  
Author(s):  
Sarah Lynn Jordan ◽  
D. Brian Wallace ◽  
Saul Kassin ◽  
Maria Hartwig
Keyword(s):  
Empirical Study ◽  
Lie Detection ◽  
Micro Expression

Neural Mechanism of Deception and Its Application to Lie Detection: Evidences from fMRI Studies

2013 ◽  
Vol 21 (9) ◽  
pp. 1629-1642
Author(s):  
Qian CUI ◽  
Jun JIANG ◽  
Wenjing YANG ◽  
Qinglin ZHANG
Keyword(s):  
Lie Detection ◽  
Neural Mechanism

scholarly journals An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 560
Author(s):  
Andrea Bonci ◽  
Simone Fiori ◽  
Hiroshi Higashi ◽  
Toshihisa Tanaka ◽  
Federica Verdini
Keyword(s):  
Biomedical Engineering ◽  
Data Privacy ◽  
Lie Detection ◽  
Human Cognition ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Ethical And Legal Issues ◽  
And Performance ◽  
Brain Computer Interfacing

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

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.

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