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Artículos de revistas sobre el tema "Detecting deception"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 4 de febrero de 2022

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1

Gergen, Kenneth J. "Detecting deception". Behavioral and Brain Sciences 20, n.º 1 (marzo de 1997): 114–15. http://dx.doi.org/10.1017/s0140525x97350031.

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I find three major shortcomings in Mele's account. First, verbal ambiguities suggest that the analysis is irrelevant to self-deception and/or that the traditional conception is subtly reinstated. Second, the data offer no means of establishing the superiority of the present account. Finally, as political rhetoric, Mele's proposal not only operates to disqualify others, but establishes science as their judge.

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2

Lykken, David T. "Detecting deception". Society 22, n.º 6 (septiembre de 1985): 34–39. http://dx.doi.org/10.1007/bf02695837.

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3

Wu, Jiang y Yangyang Liu. "Deception detection methods incorporating discourse network metrics in synchronous computer-mediated communication". Journal of Information Science 46, n.º 1 (22 de enero de 2019): 64–81. http://dx.doi.org/10.1177/0165551518823176.

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The prevalence of deception in computer-mediated communication and the risk of misjudgement based on deceptive information call for effective detection methods of deception. Extant models for online deception detection rely mainly on verbal behaviours of participants while largely ignoring context. Discourse behaviour analysis, which can better investigate the information in context, has been proved effective for online deception detection; nevertheless, these discourse behaviours have been analysed in isolation without referring to other behaviours in context. To achieve the ultimate goal of effective prediction of deception in synchronous computer-mediated communication, this research exploits temporal networks in uncovering the dynamics of deception behaviours, proposes novel deception detection methods using discourse network metrics as predictive features, and empirically evaluates the performances of deception detection methods incorporating three types of predictive features (non-discourse features, discourse features and discourse network metrics). The results suggest that discourse network features are more effective in detecting deception and incorporating these features with non-discourse and discourse features can significantly improve the performance of deception detection. The findings not only demonstrate the efficacy of structural features in deception detection but also offer both methodological and theoretical contributions to deception detection from the perspective of temporal network.

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4

Kuzio, Anna. "The role of sex differences in detecting deception in computer-mediated communication in English". Journal of Computer-Assisted Linguistic Research 2, n.º 1 (12 de julio de 2018): 39. http://dx.doi.org/10.4995/jclr.2018.10521.

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<p>While deception seems to be a common approach in interpersonal communication, most examination on interpersonal deception sees the sex of the interlocutor as unconnected with the capability to notice deceptive messages. This research studies the truth and deception detection capability of both male and female receivers when replying to both true and deceptive messages from both male and female speakers. The outcomes indicate that sex may be a significant variable in comprehending the interpersonal detection probabilities of truth and of lies. An interaction of variables including the speakers’ sex, receivers’ sex, and whether the message appears to be truthful or deceptive is created to relate to detection capability.</p>

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5

Murphy, Kevin R. "Detecting infrequent deception." Journal of Applied Psychology 72, n.º 4 (noviembre de 1987): 611–14. http://dx.doi.org/10.1037/0021-9010.72.4.611.

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6

Picornell, Isabel. "Analysing Deception in Written Witness Statements". Linguistic Evidence in Security, Law and Intelligence 1, n.º 1 (6 de diciembre de 2013): 41–50. http://dx.doi.org/10.5195/lesli.2013.2.

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Written witness statements are a unique source for the study of high-stakes textual deception. To date, however, there is no distinction in the way that they and other forms of verbal deception have been analysed, with written statements treated as extensions of transcribed versions of oral reports. Given the highly context-dependent nature of cues, it makes sense to take the characteristics of the medium into account when analysing for deceptive language. This study examines the characteristic features of witness narratives and proposes a new approach to search for deception cues. Narratives are treated as a progression of episodes over time, and deception as a progression of acts over time. This allows for the profiling of linguistic bundles in sequence, revealing the statements’ internal gradient, and deceivers’ choice of deceptive linguistic strategy. Study results suggest that, at least in the context of written witness statements, the weighting of individual features as deception cues is not static but depends on their interaction with other cues, and that detecting deceivers’ use of linguistic strategy is en effective vehicle for identifying deception.

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7

Grosch, James W. y John E. Sparrow. "Detecting Deception: A Classroom Demonstration". Teaching of Psychology 19, n.º 3 (octubre de 1992): 166–68. http://dx.doi.org/10.1207/s15328023top1903_10.

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Lie detection, a topic that most students of psychology find fascinating, can be demonstrated using an inexpensive, highly portable galvanic skin response (GSR) monitor. This demonstration can be used to touch on the current controversy surrounding the use of physiological measures in detecting deception and is relevant to other topics, such as emotion, perception, and industrial/organizational psychology.

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8

Tsikerdekis, Michail y Sherali Zeadally. "Detecting Online Content Deception". IT Professional 22, n.º 2 (1 de marzo de 2020): 35–44. http://dx.doi.org/10.1109/mitp.2019.2961638.

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9

Luke, Timothy J. "Lessons From Pinocchio: Cues to Deception May Be Highly Exaggerated". Perspectives on Psychological Science 14, n.º 4 (7 de junio de 2019): 646–71. http://dx.doi.org/10.1177/1745691619838258.

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Deception researchers widely acknowledge that cues to deception—observable behaviors that may differ between truthful and deceptive messages—tend to be weak. Nevertheless, several deception cues have been reported with unusually large effect sizes, and some researchers have advocated the use of such cues as tools for detecting deceit and assessing credibility in practical contexts. By examining data from empirical deception-cue research and using a series of Monte Carlo simulations, I demonstrate that many estimated effect sizes of deception cues may be greatly inflated by publication bias, small numbers of estimates, and low power. Indeed, simulations indicate the informational value of the present deception literature is quite low, such that it is not possible to determine whether any given effect is real or a false positive. I warn against the hazards of relying on potentially illusory cues to deception and offer some recommendations for improving the state of the science of deception.

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10

Levine, Timothy R. "Active Deception Detection". Policy Insights from the Behavioral and Brain Sciences 1, n.º 1 (octubre de 2014): 122–28. http://dx.doi.org/10.1177/2372732214548863.

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Actively detecting deception requires (a) gathering information for fact-checking the communication content, (b) strategically prompting deception cues, and (c) encouraging honest admissions and discouraging continued deceit. Most deception-detection research, active or otherwise, finds that people are only slightly better than chance at correctly distinguishing truth from lies. Poor accuracy stems from a lack of reliable deception cues that hold across people and situations. Consequently, basing lie detection on deception cues is prone to error. However, some approaches to active deception detection yield higher accuracy than passive observation. Not all active approaches are advantageous. Mere interaction and mere question-asking produce outcomes similar to passive observation. Evidence-based and confession-solicitation approaches can be highly effective: for example, strategic use of evidence (SUE) and the content in context approach.

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11

Owolafe, Otasowie. "Detecting Deception Using Neurofuzzy Approach". International Journal of Intelligent Computing Research 10, n.º 2 (30 de junio de 2019): 988–96. http://dx.doi.org/10.20533/ijicr.2042.4655.2019.0120.

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12

梁, 静远. "Detecting Deception through Reaction Time". Advances in Psychology 09, n.º 10 (2019): 1735–47. http://dx.doi.org/10.12677/ap.2019.910211.

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13

King, William R. y Thomas M. Dunn. "Detecting deception in field settings". Policing: An International Journal of Police Strategies & Management 33, n.º 2 (junio de 2010): 305–20. http://dx.doi.org/10.1108/13639511011044902.

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14

Armistead, Timothy W. "Detecting deception in written statements". Policing: An International Journal of Police Strategies & Management 34, n.º 4 (8 de noviembre de 2011): 588–605. http://dx.doi.org/10.1108/13639511111180225.

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15

Nortje, Alicia y Colin Tredoux. "How good are we at detecting deception? A review of current techniques and theories". South African Journal of Psychology 49, n.º 4 (17 de enero de 2019): 491–504. http://dx.doi.org/10.1177/0081246318822953.

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The task of discerning truth from untruth has long interested psychologists; however, methods for doing so accurately remain elusive. In this article, we provide an overview and evaluation of methods of detecting deception used in the laboratory and the field. We identify and discuss three broad approaches to detecting deception: measurement of non-verbal behaviour, verbal interview methods, and statement evaluation by humans and computers. Part of the problem in devising good methods for detecting deception is the absence of a sound understanding of deception in human lives. We thus consider three theories of deception – leakage, reality monitoring, and truth-default – and conclude that although promising, they do not yet provide an adequate foundation. We review 10 extant methods of detecting deception in the second part of the article, focusing at greatest length on the most widely used method in South Africa, the polygraph test of deception. Our conclusion is that non-verbal methods that work by inducing anxiety in interviewees are fundamentally flawed, and that we ought to move away from such methods. Alternate methods of detecting deception, including statement analysis, are considered, but ultimately our view is that there are currently no methods sufficiently accurate for practitioners to rely on. We suspect that a precondition for developing such measures is a coherent and validated theory.

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16

Desforges, Donna M. y Thomas C. Lee. "Detecting Deception is Not as Easy as it Looks". Teaching of Psychology 22, n.º 2 (abril de 1995): 128–30. http://dx.doi.org/10.1207/s15328023top2202_10.

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This exercise allows students to experience the difference between cues for perceived and actual deception. Students viewed classmates telling the truth and lying. Students' judgments indicated that deception detection was accurate less than 50% of the time. This discovery allowed for an in-depth discussion of deception, detection, and nonverbal communication.

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17

Lee, Chih-Chen y Robert B. Welker. "Prior Exposure to Interviewee's Truth-Telling (Baselining) and Deception-Detection Accuracy in Interviews". Behavioral Research in Accounting 23, n.º 2 (1 de noviembre de 2011): 131–46. http://dx.doi.org/10.2308/bria-50019.

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ABSTRACT Professional accounting guidance recommends that interviewers attend to deception detection in audit interviews. Prior studies suggest that interviewers are poor detectors of a stranger's deceptions. This study assesses whether behavioral baselining (acquiring familiarity with truth-telling style prior to initiating an interview of an unfamiliar interviewee), a recommended procedure for investigative interviews, improves the ability to detect the deceptions of an unfamiliar interviewee in accounting interviews. In the present study, interviewers conducted five preliminary interviews of a truth-telling interviewee prior to the focal accounting interview. This exposure to the interviewee's truth telling increased the accuracy of detecting truths as opposed to lies, suggesting that behavioral baselining of truth telling aided the detection of primarily truth telling. These findings suggest that behavioral baselining may not facilitate the auditor's objective, that is, the detection of lies. Data Availability: Confidentiality agreements with participants, written with the assistance of human subjects committees, prevent the sharing of data with others.

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18

Blair, J. Pete, Timothy R. Levine y Bob E. Vasquez. "Producing deception detection expertise". Policing: An International Journal of Police Strategies & Management 38, n.º 1 (16 de marzo de 2015): 71–85. http://dx.doi.org/10.1108/pijpsm-09-2014-0092.

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Purpose – To function effectively, police must separate lies from truth. Police, ideally, would be experts at this task, yet there is debate surrounding whether expertise in detecting deception is possible. Drawing upon literature outside of deception detection, the purpose of this paper is to explore whether subjects making deception judgments can improve their performance. Design/methodology/approach – The sample was 19 students from two graduate-level classes. Subjects viewed six sets of videos over eight weeks. The first five sets displayed individuals reporting whether they cheated on an exam. The sixth set displayed individuals reporting whether they had committed a (mock) robbery. After each video, subjects judged whether the videoed individual was truthful, and then the actual status was revealed. Findings – Subjects’ accuracy improved consistently over the first five sets of videos; from about 69 percent accuracy to about 89 percent accuracy. However, the accuracy for the sixth set of videos dropped dramatically to 46 percent. The results indicate that expertise in deception detection may be possible, but is likely to be limited in terms of generalizability. Research limitations/implications – The actual environment of police investigations is more variable than the study’s setting. Future studies should integrate inaccurate and incomplete feedback, which are realistic characteristics of investigations. Practical implications – The findings suggest that, like other areas of expertise, it may be possible to develop expertise in detecting deception through the use of deliberate practice with accurate feedback. Originality/value – This study combines three literature-substantiated criteria for developing deception detection expertise. The study environment involved regularity. Subjects were deliberately practicing, and subjects received accurate feedback.

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19

Takiguchi, Yuta y Yasuhiro Omi. "Search of cues in detecting deception". Proceedings of the Annual Convention of the Japanese Psychological Association 81 (20 de septiembre de 2017): 3C—008–3C—008. http://dx.doi.org/10.4992/pacjpa.81.0_3c-008.

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20

WU, Song, Sheng-Hua JIN, Wei CAI y Shao-Zhuan LI. "Detecting Deception by Verbal Content Cues". Advances in Psychological Science 20, n.º 3 (24 de mayo de 2013): 457–66. http://dx.doi.org/10.3724/sp.j.1042.2012.00457.

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21

Vrij, Aldert, Ronald Fisher, Samantha Mann y Sharon Leal. "Detecting deception by manipulating cognitive load". Trends in Cognitive Sciences 10, n.º 4 (abril de 2006): 141–42. http://dx.doi.org/10.1016/j.tics.2006.02.003.

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22

Sip, Kamila E., Andreas Roepstorff, William McGregor y Chris D. Frith. "Detecting deception: the scope and limits". Trends in Cognitive Sciences 12, n.º 2 (febrero de 2008): 48–53. http://dx.doi.org/10.1016/j.tics.2007.11.008.

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23

Lanyon, Richard I. "Detecting Deception: Current Models and Directions". Clinical Psychology: Science and Practice 4, n.º 4 (diciembre de 1997): 377–87. http://dx.doi.org/10.1111/j.1468-2850.1997.tb00128.x.

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24

Wang, Wei, Jeffrey Bickford, Ilona Murynets, Ramesh Subbaraman y Gokul Singaraju. "Detecting Targeted Attacks By Multilayer Deception". Journal of Cyber Security and Mobility 2, n.º 2 (2013): 175–99. http://dx.doi.org/10.13052/jcsm2245-1439.224.

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25

Da Silva, Cayla S. y Amy-May Leach. "Detecting deception in second-language speakers". Legal and Criminological Psychology 18, n.º 1 (5 de octubre de 2011): 115–27. http://dx.doi.org/10.1111/j.2044-8333.2011.02030.x.

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26

Perelman, Brandon S. "Detecting deception via eyeblink frequency modulation". PeerJ 2 (13 de febrero de 2014): e260. http://dx.doi.org/10.7717/peerj.260.

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27

Paik, Jihyun Esther y Lyn M. Van Swol. "Justifications and Questions in Detecting Deception". Group Decision and Negotiation 26, n.º 6 (12 de mayo de 2017): 1041–60. http://dx.doi.org/10.1007/s10726-017-9536-8.

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28

Whaley, Bart y Jeffrey Busby. "Detecting deception: Practice, practitioners, and theory". Trends in Organized Crime 6, n.º 1 (septiembre de 2000): 73–105. http://dx.doi.org/10.1007/s12117-000-1007-x.

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29

Wang, G. A., H. Chen, J. J. Xu y H. Atabakhsh. "Automatically detecting criminal identity deception: an adaptive detection algorithm". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 36, n.º 5 (septiembre de 2006): 988–99. http://dx.doi.org/10.1109/tsmca.2006.871799.

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30

Gamer, Matthias. "Mind Reading Using Neuroimaging". European Psychologist 19, n.º 3 (1 de enero de 2014): 172–83. http://dx.doi.org/10.1027/1016-9040/a000193.

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Traditional lie detection tools, such as the polygraph, voice stress analysis, or special interrogation techniques, rely on behavioral or psychophysiological manifestations of deception. With the advent of neuroimaging techniques, the question emerged whether it would be possible to directly identify deceit in the part of the body where it is generated: the brain. After a few promising studies, these techniques became soon commercially available and there have been attempts to use such results in the court in recent years. The current article reviews the development of neuroimaging techniques in the field of deception detection and critically discusses the potential but also the shortcomings of such methods. Unfortunately, the majority of research in this field was rather unsystematic and neglected the accumulated knowledge regarding methodological pitfalls that were extensively discussed in the scientific community in conjunction with the polygraph. Therefore, neuroimaging studies on deception largely differ with respect to the experimental paradigm (the interrogation technique), the methods for analyzing the data, and the procedures to obtain individual diagnoses. Moreover, most studies used artificial laboratory settings that differ considerably from real-life applications. As a consequence, neuroimaging techniques are not applicable for detecting deception in individual field cases at the moment. However, recent advantages such as multivariate pattern analysis might yield novel neuroimaging applications in the near future that are capable of improving established techniques for detecting deception or concealed knowledge.

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31

Miller, Claude H., Norah E. Dunbar, Matthew L. Jensen, Zachary B. Massey, Yu-Hao Lee, Spencer B. Nicholls, Chris Anderson et al. "Training Law Enforcement Officers to Identify Reliable Deception Cues With a Serious Digital Game". International Journal of Game-Based Learning 9, n.º 3 (julio de 2019): 1–22. http://dx.doi.org/10.4018/ijgbl.2019070101.

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Extant research indicates that professional law enforcement officers (LEOs) are generally no better than untrained novices at detecting deception. Moreover, traditional training methods are often less effective than no training at all at improving successful detection. Compared to the traditional training, interactive digital games can provide an immersive learning environment for deeper internalization of new information through simulated practices. VERITAS—an interactive digital game—was designed and developed to train LEOs to better detect reliable deception cues when questioning suspects and determining the veracity of their answers. The authors hypothesized that reducing players' reactance would mitigate resistance to training, motivate engagement with materials, and result in greater success at deception detection and knowledge. As hypothesized, LEOs playing VERITAS showed significant improvement in deception detection from the first to the second scenario within the game; and the low-reactance version provided the most effective training. The authors also compared various responses to the game between LEOs and a separate undergraduate student sample. Relative to students, findings show LEOs perceived VERITAS to be significantly more intrinsically motivating, engaging, and appealing as a deception detection activity.

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32

Dzindolet, Mary T. y Linda G. Pierce. "Using a Linguistic Analysis Tool to Detect Deception". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, n.º 3 (septiembre de 2005): 563–67. http://dx.doi.org/10.1177/154193120504900374.

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Detecting deception is important, yet accuracy rates remain low (e.g., DePaulo & Friedman, 1998). Pennebaker et al. (2003) suggest a linguistic analysis tool may be able to detect deception because people use a different linguistic style when telling the truth than when lying. For example, the anxiety experienced by liars may “leak” into their words. The cognitive resources devoted to the lie will be taken from the message. Newman et al. (2003) found support for these hypotheses using the Linguistic Inquiry Word Count (LIWC). Dzindolet and Pierce (2004a) found the LIWC was useful in detecting deception among participants discussing music preferences. This study expands their work to include other topics. Results from the 2 (topic: movie or television) x 2 (topic importance: high or low) x 2 (communication type: lie or truth) design indicated that linguistic analysis tools may be useful in detecting deception across a variety of topics.

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33

EDWARDS, H. BERRYMAN. "Detecting Deception on The Part of Patients". Clinical Psychiatry News 36, n.º 3 (marzo de 2008): 13. http://dx.doi.org/10.1016/s0270-6644(08)70138-3.

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34

Evans, Jacqueline R. y Stephen W. Michael. "Detecting Deception in Non-Native English Speakers". Applied Cognitive Psychology 28, n.º 2 (12 de diciembre de 2013): 226–37. http://dx.doi.org/10.1002/acp.2990.

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35

Gongola, Jennifer, Nicholas Scurich y Jodi A. Quas. "Detecting deception in children: A meta-analysis." Law and Human Behavior 41, n.º 1 (2017): 44–54. http://dx.doi.org/10.1037/lhb0000211.

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36

DePaulo, Bella M., John Tang y Julie I. Stone. "Physical Attractiveness and Skill at Detecting Deception". Personality and Social Psychology Bulletin 13, n.º 2 (junio de 1987): 177–87. http://dx.doi.org/10.1177/0146167287132004.

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37

Hill, Marilyn L. y Kenneth D. Craig. "Detecting Deception in Facial Expressions of Pain". Clinical Journal of Pain 20, n.º 6 (noviembre de 2004): 415–22. http://dx.doi.org/10.1097/00002508-200411000-00006.

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38

Hartwig, Maria, Pär Anders Granhag, Leif A. Strömwall y Aldert Vrij. "Detecting deception via strategic disclosure of evidence." Law and Human Behavior 29, n.º 4 (2005): 469–84. http://dx.doi.org/10.1007/s10979-005-5521-x.

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39

Kovalev, Artem, Elizaveta Luniakova y Galina Menshikova. "Using eye movement characteristics for detecting deception". International Journal of Psychophysiology 108 (octubre de 2016): 161. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.463.

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40

Kozel, F. Andrew, Kevin A. Johnson, Qiwen Mu, Emily L. Grenesko, Steven J. Laken y Mark S. George. "Detecting Deception Using Functional Magnetic Resonance Imaging". Biological Psychiatry 58, n.º 8 (octubre de 2005): 605–13. http://dx.doi.org/10.1016/j.biopsych.2005.07.040.

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41

Warren, Gemma, Elizabeth Schertler y Peter Bull. "Detecting Deception from Emotional and Unemotional Cues". Journal of Nonverbal Behavior 33, n.º 1 (4 de noviembre de 2008): 59–69. http://dx.doi.org/10.1007/s10919-008-0057-7.

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42

Zloteanu, Mircea, Eva G. Krumhuber y Daniel C. Richardson. "Sitting in Judgment: How Body Posture Influences Deception Detection and Gazing Behavior". Behavioral Sciences 11, n.º 6 (10 de junio de 2021): 85. http://dx.doi.org/10.3390/bs11060085.

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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 judges would affect their social acuity, resulting in differences in the detection of nonverbal behavior (i.e., microexpression recognition) and the discrimination of deceptive and truthful statements. In Study 1, adopting an open posture produced higher accuracy for detecting naturalistic lies, but no difference was observed in the recognition of brief facial expressions as compared to adopting a closed posture; trait empathy was found to have an additive effect on posture, with more empathic judges having higher deception detection scores. In Study 2, with the use of an eye-tracker, posture effects on gazing behavior when judging both low-stakes and high-stakes lies were measured. Sitting in an open posture reduced judges’ average dwell times looking at senders, and in particular, the amount and length of time they focused on their hands. The findings suggest that simply shifting posture can impact judges’ attention to visual information and veracity judgments (Mg = 0.40, 95% CI (0.03, 0.78)).

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43

Hill, Marilyn L. y Kenneth D. Craig. "Detecting deception in pain expressions: the structure of genuine and deceptive facial displays". Pain 98, n.º 1 (julio de 2002): 135–44. http://dx.doi.org/10.1016/s0304-3959(02)00037-4.

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44

Gallardo-Antolín, Ascensión y Juan M. Montero. "Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework". Applied Sciences 11, n.º 14 (11 de julio de 2021): 6393. http://dx.doi.org/10.3390/app11146393.

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The automatic detection of deceptive behaviors has recently attracted the attention of the research community due to the variety of areas where it can play a crucial role, such as security or criminology. This work is focused on the development of an automatic deception detection system based on gaze and speech features. The first contribution of our research on this topic is the use of attention Long Short-Term Memory (LSTM) networks for single-modal systems with frame-level features as input. In the second contribution, we propose a multimodal system that combines the gaze and speech modalities into the LSTM architecture using two different combination strategies: Late Fusion and Attention-Pooling Fusion. The proposed models are evaluated over the Bag-of-Lies dataset, a multimodal database recorded in real conditions. On the one hand, results show that attentional LSTM networks are able to adequately model the gaze and speech feature sequences, outperforming a reference Support Vector Machine (SVM)-based system with compact features. On the other hand, both combination strategies produce better results than the single-modal systems and the multimodal reference system, suggesting that gaze and speech modalities carry complementary information for the task of deception detection that can be effectively exploited by using LSTMs.

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45

Salles, Bruno. "Do Lie Detection Tools Really Catch Liars?" Brazilian Journal of Forensic Sciences, Medical Law and Bioethics 9, n.º 3 (29 de junio de 2020): 373–93. http://dx.doi.org/10.17063/bjfs9(3)y2020373-393.

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Lying is ubiquitous in every society. However, in forensic contexts lies must be revealed so that investigations/judgments can be fair and effective. For this reason, distinct tools (verbal and nonverbal) of lie detection were examined. CBCA and RM showed the best performance in distinguishing between truth and lie within verbal tools. Lack of empirical support made SCAN not recommended for lie detection applications. Moreover, studies have shown that people guided by BAI are less accurate in detecting lies than untrained people. Ekman’s Deception Theory (EDT) showed more effective predictions about nonverbal deception cues than BAI. However, the lack of standardization in the use of EDT predictions to detect lies can be seen as a weakness of the method. Future efforts may be aimed at developing a tool that uses both verbal and nonverbal predictions to obtain greater accuracy in detecting lies than currently available methods.

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46

Pinker, Steven. "Representations and decision rules in the theory of self-deception". Behavioral and Brain Sciences 34, n.º 1 (febrero de 2011): 35–37. http://dx.doi.org/10.1017/s0140525x1000261x.

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AbstractSelf-deception is a powerful but overapplied theory. It is adaptive only when a deception-detecting audience is in the loop, not when an inaccurate representation is invoked as an internal motivator. First, an inaccurate representation cannot be equated with self-deception, which entails two representations, one inaccurate and the other accurate. Second, any motivational advantages are best achieved with an adjustment to the decision rule on when to act, not with a systematic error in an internal representation.

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47

Otasowie, Owolafe, Alese Bonifacem y Adewale Olumide. "Towards Detecting Deception using K-Nearest Neighbour Model". International Journal of Computer Applications 181, n.º 20 (17 de octubre de 2018): 14–18. http://dx.doi.org/10.5120/ijca2018917846.

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48

Geraerts, Elke, Dragica Kozarić-Kovačić, Harald Merckelbach, Tina Peraica, Marko Jelicic y Ingrid Candel. "Detecting deception of war-related posttraumatic stress disorder". Journal of Forensic Psychiatry & Psychology 20, n.º 2 (abril de 2009): 278–85. http://dx.doi.org/10.1080/14789940802340906.

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49

Poon Teng Fatt, James. "Detecting deception through non‐verbal cues: gender differences". Equal Opportunities International 17, n.º 2 (marzo de 1998): 1–9. http://dx.doi.org/10.1108/02610159810785494.

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50

Dannenbaum, Stephen E. y Richard I. Lanyon. "The Use of Subtle Items in Detecting Deception". Journal of Personality Assessment 61, n.º 3 (diciembre de 1993): 501–10. http://dx.doi.org/10.1207/s15327752jpa6103_6.

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