Jump to content

Main menu Navigation ●Main page ●Contents ●Current events ●Random article ●About Wikipedia ●Contact us ●Donate Contribute ●Help ●Learn to edit ●Community portal ●Recent changes ●Upload file

●Create account ●Log in ●Create account ● Log in Pages for logged out editors learn more ●Contributions ●Talk

Editing Lie detection

●Article ●Talk ●Read ●Edit ●View history Tools Actions ●Read ●Edit ●View history General ●What links here ●Related changes ●Upload file ●Special pages ●Page information ●Get shortened URL ●Download QR code ●Wikidata item Appearance

You are not logged in. Your IP address will be publicly visible if you make any edits. If you log inorcreate an account, your edits will be attributed to a username, among other benefits.

Content that violates any copyrights will be deleted. Encyclopedic content must be verifiable through citations to reliable sources.

You are about to undo an edit. Please check the comparison below to verify that this is what you want to do, then publish the changes below to finish undoing the edit.

If you are undoing an edit that is not vandalism, explain the reason in the edit summary. Do not use the default message only.

{{Short description|Assessment of statements to reveal deceit}}
{{Lead rewrite|date=February 2022|reason=there is a mismatch between the article body and the lead about accuracy}}

'''Lie detection''' is an assessment of a verbal statement with the goal to reveal a possible intentional deceit. Lie detection may refer to a cognitive process of detecting deception by evaluating message content as well as non-verbal cues.<ref>{{cite book|first1=Pär Anders|last1=Granhag|first2=Aldert|last2=Vrij|first3=Bruno|last3=Verschuere|title=Detecting Deception: Current Challenges and Cognitive Approaches (Wiley Series in Psychology of Crime, Policing and Law)|year=2015|pages=205|publisher=Wiley-Blackwell|location=Hoboken, NJ|isbn=978-1118509661}}</ref> It also may refer to questioning techniques used along with technology that record physiological functions to ascertain truth and falsehood in response. The latter is commonly used by law enforcement in the United States, but rarely in other countries because it is based on [[pseudoscience]].

There are a wide variety of technologies available for this purpose.<ref name="Parliament">{{cite web|title=Detecting Deception|url=http://www.parliament.uk/briefing-papers/POST-PN-375|publisher=Parliamentary Office of Science and Technology (UK)|access-date=26 April 2012}}</ref> The most common and long used measure is the [[polygraph]]. A comprehensive 2003 review by the [[National Academy of Sciences]] of existing research concluded that there was "little basis for the expectation that a polygraph test could have extremely high accuracy."<ref name="Polygraph and Lie Detection 2003">{{Cite book|url=https://www.nap.edu/catalog/10420/the-polygraph-and-lie-detection|title=The Polygraph and Lie Detection|publisher=National Research Council|year=2003| doi=10.17226/10420 |isbn=978-0-309-26392-4|language=en}}</ref>{{rp|2,212}} There is no evidence to substantiate that [[Nonverbal communication|non-verbal]] lie detection, such as by looking at body language, is an effective way to detect lies, even if it is widely used by law enforcement.<ref>{{Cite journal|last1=Vrij|first1=Aldert|last2=Hartwig|first2=Maria|last3=Granhag|first3=Pär Anders|date=2019|title=Reading Lies: Nonverbal Communication and Deception|journal=Annual Review of Psychology|volume=70|issue=1|pages=295–317|doi=10.1146/annurev-psych-010418-103135|pmid=30609913| s2cid=58562467 |issn=0066-4308| url=https://researchportal.port.ac.uk/portal/en/publications/reading-lies-nonverbal-communication-and-deception(3d64cf66-725e-4cb5-ae5a-5619417b1452).html |doi-access=free}}</ref><ref name="Seigel">{{cite journal |last1=Seigel |first1=Jessica |title=The truth about lying |journal=Knowable Magazine |date=25 March 2021 |doi=10.1146/knowable-032421-1 |url=https://knowablemagazine.org/article/mind/2021/the-truth-about-lying |access-date=8 December 2021 |doi-access=free}}</ref>

==General accuracy and limitations of assessment==

The cumulative research evidence suggests that machines do detect deception better than chance, but with significant error rates<ref name=USCongress>{{cite journal|title=Scientific Validity of Polygraph Testing: A Research Review and Evaluation{{snd}}A Technical Memorandum|journal=Washington, DC: U.S. Congress, Office of Technology Assessment|date=November 1983|issue=OTA-TM-H-15|url=http://ota.fas.org/reports/8320.pdf|access-date=3 September 2016}}</ref> and that strategies used to "beat" polygraph examinations, so-called countermeasures, may be effective.<ref>[http://www.psychologymatters.org/polygraphs.html The Truth About Lie Detectors]. [[American Psychological Association]].</ref> Despite unreliability, results are admissible in court in some countries, such as Japan. Lie detector results are very rarely admitted in evidence in the US courts.<ref>{{cite web|url=https://www.usatoday.com/news/nation/2002-09-09-lie_x.htm|title=Telling the truth about lie detectors|work=usatoday.com}}</ref>

In 1983 the U.S. Congress Office of Technology Assessment published a review of the technology<ref name=USCongress/> and found:
:"...there is at present only limited scientific evidence for establishing the validity of polygraph testing. Even where the evidence seems to indicate that polygraph testing detects deceptive subjects better than chance, significant error rates are possible, and examiner and examinee differences and the use of countermeasures may further affect validity."<ref>{{Skeptoid|id=4422|number=422|date=8 July 2014|title=Lie Detection|access-date=22 June 2017}}</ref>

In the 2007 peer-reviewed academic article "Charlatanry in forensic speech science", the authors reviewed 50 years of lie detector research and came to the conclusion that there is no scientific evidence supporting that voice analysis lie detectors actually work.<ref>{{cite journal | last1 = Eriksson | first1 = A. | last2 = Lacerda | first2 = F. | year = 2007 | title = Charlantry in forensic speech science: A problem to be taken seriously | url = http://equinoxjournals.com/ojs/index.php/IJSLL/article/viewArticle/3775 | journal = International Journal of Speech, Language and the Law | volume = 14 | issue = 2 | doi=10.1558/ijsll.2007.14.2.169}}</ref> Lie detector manufacturer Nemesysco threatened to sue the academic publisher for libel resulting in removal of the article from online databases. In a letter to the publisher, Nemesysco's lawyers wrote that the authors of the article could be sued for defamation if they wrote on the subject again.<ref>[http://www.su.se/english/about/news_and_events/scientists_threatened_with_legal_action All lies? Scientists threatened with legal action over lie detector article] {{Webarchive|url=https://web.archive.org/web/20090413003343/http://www.su.se/english/about/news_and_events/scientists_threatened_with_legal_action |date=2009-04-13 }}. [[Stockholm University]].</ref><ref>[http://www.thelocal.se/17188/20090127/ Threats over Swedish lie detector research] {{webarchive|url=https://web.archive.org/web/20101207025139/http://www.thelocal.se/17188/20090127 |date=2010-12-07 }}. ''[[The Local]]''. January 27, 2009.</ref><ref>Online at: {{cite journal|author1=Anders Eriksson|author2=Francisco Lacerda|title=Charlatanry in forensic speech science: A problem to be taken seriously|journal=International Journal of Speech, Language and the Law|date=14 February 2007|volume=14.2 2007|page=169|url=https://www.cs.columbia.edu/~julia/papers/eriksson&lacerda07.pdf|access-date=3 September 2016|archive-url=https://web.archive.org/web/20160802124557/http://www.cs.columbia.edu/~julia/papers/eriksson%26lacerda07.pdf|archive-date=2 August 2016|url-status=dead}}</ref>

Nevertheless, extraneous "noise" on the polygraph can come from embarrassment or anxiety and not be specific to lying.<ref name=Preston /> When subjects are aware of the assessment their resulting emotional response, especially anxiety, can impact the data. Additionally, [[psychological disorders]] can cause problems with data as certain disorders can lead a person to make a statement they believe to be truth but is actually a fabrication. As well as with all testing, the examiner can cause biases within the test with their interaction with the subject and interpretation of the data.<ref name=Parliament />

==History==

===20th century===
The study of physiological methods for deception tests measuring emotional disturbances began in the early 1900s. [[Vittorio Benussi]] was the first to work on practical deception tests based on physiological changes. He detected changes in inspiration-expiration ratio—findings confirmed by N.E. Burtt. Burtt conducted studies that emphasized the changes in quantitative systolic blood-pressure. [[William Moulton Marston]] studied blood-pressure and noted increase in systolic blood pressure of 10&nbsp;mm Hg or over indicated guilt through using the tycos [[sphygmomanometer]], with which he reported 90–100% accuracy. His studies used students and actual court cases. Then in 1913 W.M. Marston determined systolic blood-pressure by oscillatory methods and his findings cite definite changes in blood pressure during the deception of criminal suspects. In 1921, [[John Augustus Larson]] criticized Marston's intermittent blood pressure method because emotional changes were so brief they could be lost. To adjust for this he modified the Erlanger [[sphygmograph]] to give a continuous blood pressure and pulse curve and used it to study 4,000 criminals.<ref name=Keeler>{{cite journal|last=Keeler|first=Leonarde|title=A Method for Detecting Deception|journal=American Journal of Police Science|date=January 1930|volume=1|issue=1|pages=38–51|doi=10.2307/1147254|jstor=1147254}}</ref> In the 1990s, a team of scientists, Stanley Abrams, Jean M. Verdier and Oleg Maltsev developed a new methodology contributing six coefficients that positively affect the accuracy of the lie detector analysis results.<ref>{{cite web |title=Historical Perspective: Some Polygraph Techniques |url=https://westernmorning.news/2018/10/03/historical-perspective-some-polygraph-techniques/ |website=Western Morning News |access-date=26 August 2020 |date=3 October 2018}}</ref>

===21st century===
Two meta-analyses conducted by 2004 found an association between lying and increased pupil size and compressed lips. Liars may stay still more, use fewer hand gestures, and make less eye contact. Liars may take more time to answer questions but on the other hand, if they have had time to prepare, they may answer more quickly than people telling the truth would, and talk less, and repeat phrases more. They do not appear to be more fidgety, blink more, or have a less-relaxed posture.<ref name="Adelson">{{cite journal|last=Adelson|first=Rachel|date=July 2004|title=Detecting Deception|url=http://www.apa.org/monitor/julaug04/detecting.aspx|journal=Monitor on Psychology|volume=37|issue=7|page=70|access-date=26 April 2012}}</ref><ref>{{cite journal |last1=Bond CF |first1=Jr |last2=DePaulo |first2=BM |title=Accuracy of deception judgments. |journal=Personality and Social Psychology Review |date=2006 |volume=10 |issue=3 |pages=214–234 |doi=10.1207/s15327957pspr1003_2 |pmid=16859438|s2cid=15248114 }}</ref><ref>{{cite book |last1=DePaulo |first1=Bella M. |last2=Morris |first2=Wendy L. |editor1-last=Granhag |editor1-first=Pär Anders |editor2-last=Strömwall |editor2-first=Leif A. |title=The detection of deception in forensic contexts |date=2004 |publisher=Cambridge University Press |location=Cambridge |isbn=978-0521541572 |edition=Transferred to digital print. |chapter=Chapter 2: Discerning lies from truth: behavioural cues to deception and the indirect pathway of intuition |url-access=registration |url=https://archive.org/details/detectionofdecep0000unse }}</ref>

[[Paul Ekman]] has used the [[Facial Action Coding System]] (FACS) and "when combined with voice and speech measures, [it] reaches detection accuracy rates of up to 90 percent." However, there is currently no evidence to support such a claim. It is currently being automated for use in law enforcement and is still being improved to increase accuracy. His studies use micro-expressions, which last less than one-fifth of a second, and "may leak emotions someone wants to conceal, such as anger or guilt." However, "signs of emotion aren't necessarily signs of guilt. An innocent person may be apprehensive and appear guilty," Ekman reminds us.
With regard to his studies, lies about emotions at the moment have the biggest payoff from face and voice cues while lies about beliefs and actions, such as crimes use cues from gestures and words are added. Ekman and his associates have validated many signs of deception, but do not publish all of them so as not to educate criminals<ref name=Adelson />

[[James Pennebaker]] uses the method of [[Linguistic Inquiry]] and Word Count (LIWC), published by [[Lawrence Erlbaum]], to conduct an analysis of written content. He claims it has accuracy in predicting lying. Pennebaker cites his method as "significantly more effective than human judges in correctly identifying deceptive or truthful writing samples"; there is a 67% accuracy rate with his method, while trained people have 52% accuracy. There were five experimental procedures used in this study. Study 1–3 asked participants to speak, hand write or type a true or false statement about abortion. The participants were randomly assigned to tell a true or false statement. Study 4 focused on feelings about friends and study 5 had the students involved in a mock crime and asked to lie. Human judges were asked to rate the truthfulness of the 400 communications dealing with abortion. The judges read or watched the statement and gave it a yes or no answer about if this statement was false or not. LIWC correctly classified 67% of the abortion communications and the judges correctly classified 52%. His studies have identified that deception carries three primary written markers. The first is fewer first-person pronouns such as 'I', 'me', 'my', 'mine', and 'myself' (singular), as well as 'we', 'us', 'our', and 'ourselves' (plural). Those lying "avoid statements of ownership, distance themselves from their stories and avoid taking responsibility for their behavior" while also using more [[negative emotion]] words such as "hate, worthless and sad." Second, they use "few exclusionary words such as except, but or nor" when "distinguish[ing] what they did from what they did not do."<ref name=Adelson />

More recently evidence has been provided by the work of CA Morgan III and GA Hazlett that a computer analysis of cognitive interview derived speech content (i.e. response length and unique word count) provides a method for detecting deception that is both demonstrably better than professional judgments of professionals and useful at distinguishing between genuine and false adult claims of exposure to highly stressful, potentially traumatic events.<ref>{{cite journal|title=Efficacy of forensic statement analysis in distinguishing truthful from deceptive eyewitness accounts of highly stressful events|author=Morgan CA 3rd |journal= Journal of Forensic Sciences|display-authors=etal|pmid=21854383|doi=10.1111/j.1556-4029.2011.01896.x|volume=56|issue=5 |year=2011|pages=1227–1234|s2cid=25246267 }}</ref> This method shows particular promise as it is non confrontational as well as scientifically and cross culturally valid.

==Questioning and testing techniques==
{{see also|Polygraph#Testing procedure}}
There are typically three types of questions used in polygraph testing or voice stress analysis testing:

Irrelevant questions establish a baseline to compare other answers by asking simple questions with clear true and false answers.

Comparison questions have an indirect relationship to the event or circumstance, and they are designed to encourage the subject to lie.

Relevant questions are compared against comparison questions (which should represent false answers) and irrelevant questions (which should represent true answers).  They are about whatever is particularly in question.

The ''control question test'' (CQT) uses control questions, with known answers, to serve as a physiological baseline in order to compare them with questions relevant to a particular incident. The control question should have a greater physiological response if truth was told and a lesser physiological response for lying.<ref name=Preston>{{cite journal|last=Preston|first=Elizabeth|title=Detecting Deception|journal=Observer|date=July 2002|volume=15|issue=6|url=http://www.psychologicalscience.org/index.php/uncategorized/detecting-deception.html|access-date=26 April 2012}}</ref> The ''guilty knowledge test'' (GKT) is a multiple-choice format in which answer choices or one correct answer and additional incorrect answers are read and the physiological response is recorded. The controls are the incorrect alternative answers. The greater physiological response should be to the correct answer.<ref name=Preston /> Its point is to determine if the subject has knowledge about a particular event.<ref name=Parliament/>

In addition to the test skewing towards not finding people innocent, there are also issues where some offenders might have a greater physiological response to the control question than to the specific question, making it difficult to determine guilt using this method even when people are not using specific techniques to try and trick the test.<ref name="Iacono 1295–1308">{{Cite journal|last=Iacono|first=William G.|date=October 2008|title=Effective Policing: Understanding How Polygraph Tests Work and Are Used|url=http://journals.sagepub.com/doi/10.1177/0093854808321529|journal=Criminal Justice and Behavior|language=en|volume=35|issue=10|pages=1295–1308|doi=10.1177/0093854808321529| s2cid=143302792 |issn=0093-8548}}</ref> Although the issues with the CQT false-positive and false-negative rates are discussed above, there are also methodological issues with how proponents of the CQT determine the accuracy of the test.<ref name="Iacono 1295–1308"/> Due to the fact that the accuracy of the CQT is often determined through whether an individual who is given the test provides the police a confession to a crime after the test is administered, this means that cases where someone was cleared of charges after taking a polygraph or, in a worse-case scenario, gives a false confession when they are actually innocent are not taken into account when it comes to determining the accuracy of the test.<ref name="Iacono 1295–1308"/> Another issue is that, due to how the CQT is administered and how the lie-detection process works, only people who are determined to be deceptive are further interrogated for a confession.<ref name="Iacono 1295–1308"/> This means that the polygraph outcome and the confession are not independent of one another, making it very difficult to use confessions as the sole determiner of the accuracy of the test.<ref name="Iacono 1295–1308"/> These methodological problems provide false evidence that supports the continued use of this test, despite the many flaws that the test possesses.<ref name="Iacono 1295–1308"/> While it could be said that including this test as a police tool is useful because it might sometimes provide accurate information, the probability of it causing undue hardship to people who are actually innocent, and wasting time in the process, makes this a very unreliable method for law enforcement officers to use.<ref name="Iacono 1295–1308"/>

Both are considered to be biased against those that are innocent, because the guilty who fear the consequences of being found out can be more motivated to cheat on the test. Various techniques (which can be found online) can teach individuals how to change the results of the tests, including curling the toes, and biting the tongue. [[Mental calculation|Mental arithmetic]] was found to be ineffective by at least one study, especially in students counting backward by seven. A study has found that in the guilty knowledge test subjects can focusing on the alternative answers and make themselves look innocent.<ref name=Preston/>

==Polygraph==
{{main|Polygraph}}

Lie detection commonly involves the [[polygraph]],<ref name=":0" /> and is used to test both styles of deception. It detects [[autonomic nervous system|autonomic]] reactions,<ref name="Adelson" /> such as micro-expressions, breathing rate, skin conductivity, and [[heart rate]].<ref>[http://www.bbc.co.uk/science/humanbody/mind/articles/psychology/psychology_6.shtml Forensic Psychology]. BBC Science & Nature.</ref> Micro-expressions are the brief and incomplete nonverbal changes in expression while the rest show an activation of the nervous system.<ref name=":0" /> These changes in body functions are not easily controlled by the conscious mind. They also may consider [[respiration rate]], [[blood pressure]], capillary dilation, and muscular movement. While taking a polygraph test the subject wears a blood pressure device to measure blood pressure fluctuations. Respiration is measured by wearing pneumographs around the chest, and finally electrodes are placed on the subject's fingers to measure skin conductivity. To determine truth it is assumed the subject will show more signs of fear when answering the control questions, known to the examiner, compared with the relevant questions, where the answers are not known. Polygraphs focus more on the exams predictive value of guilt by comparing the responses of the participant to control questions, irrelevant questions, and relevant questions to gauge arousal, which is then interpreted as a display of fear and deception is assumed.<ref name=":0" /> If a person is showing a deception there will be changes in the autonomic arousal responses to the relevant questions. Results are considered inconclusive if there is no fluctuation in any of the questions.<ref>{{cite web|title=The Truth About Lie Detectors (aka Polygraph Tests)| url=http://www.apa.org/research/action/polygraph.aspx|website=apa.org|access-date=4 September 2016}}</ref>

These measures are supposed to indicate a short-term [[stress response]] which can be from lying or significance to the subject. The problem becomes that they are also associated with mental effort and [[emotional state]], so they can be influenced by fear, anger, and surprise for example. This technique may also be used with CQT and GKT.<ref name=Parliament />

United States government agencies, such as the [[United States Department of Defense|Department of Defense]], [[United States Department of Homeland Security|Homeland Security]], [[U.S. Customs and Border Protection|Customs and Border Protection]], and even the [[United States Department of Energy|Department of Energy]] currently use polygraphs. They are regularly used by these agencies to screen employees.<ref name=Eye-Tracking>{{cite web|title=Education psychologists use eye0tracking method for detecting lies|url=http://www.news-medical.net/news/20100712/Educational-psychologists-use-eye-tracking-method-for-detecting-lies.aspx|publisher=psychologialscience.org|access-date=26 April 2012|date=2010-07-12}}</ref>

Critics claim that "lie detection" by use of [[polygraphy]] has no scientific validity because it is not a scientific procedure.<ref>[http://antipolygraph.org/lie-behind-the-lie-detector.pdf The Lie Behind the Lie Detector by George W. Maschke and Gino J. Scalabrini]</ref> People have found ways to try and cheat the system, such as taking sedatives to reduce anxiety; using antiperspirant to prevent sweating; and positioning pins or biting parts of the mouth after each question to demonstrate a constant physiological response.<ref>{{cite web|title=Polygraph Testing|url=https://epic.org/privacy/polygraph/|website=epic.org |publisher=Electronic Privacy Information Center|access-date=23 September 2016}}</ref> As technology and research have developed many have moved away from polygraphing because of the drawbacks of this style of detection. Supporters of polygraphing claim it has a 70% accuracy rate, 16% better than lie detection in the general population.<ref name=":1">{{Cite journal|doi = 10.3389/fnhum.2013.00594|title = Prospects of functional magnetic resonance imaging as lie detector|year = 2013|last1 = Rusconi|first1 = Elena|last2 = Mitchener-Nissen|first2 = Timothy|journal = Frontiers in Human Neuroscience|volume = 7|page = 594|pmid = 24065912|pmc = 3781577| doi-access=free }}</ref> Someone who has failed the test is more likely to confess than someone who has passed, contributing to [[polygraph examiner]]s not learning about mistakes they have made and thus improving.<ref name=Preston />

==Voice stress analysis==
[[Voice stress analysis]] (also called voice risk analysis) uses computers to compare [[pitch (music)|pitch]], [[frequency]], intensity and micro tremors. In this way voice analysis "detect[s] minute variations in the voice thought to signal lying." It can even be used covertly over the phone, and has been used by banking and insurance companies as well as the government of the United Kingdom. Customers are assessed for truth in certain situations by banks and insurance companies where computers are used to record responses. Software then compares control questions to relevant questions assessed for deception. However, its reliability has been debated by peer-reviewed journals.<ref name=Parliament /> "When a person lies, an involuntary interference of the nerves causes the vocal cords to produce a distorted sound wave, namely a frequency level which is different from the one produced by the same person when telling the truth."<ref>S. Manes. "Lie Detector. (Lie Detector Software Truster)." Forbes, Oct. 5, 1998</ref>

Several studies published in peer reviewed journals showed VSA to perform at chance level when it comes to detecting deception. Horvath, McCloughan, Weatherman, and Slowik, (2013),<ref>Horvath, F., McCloughan, J., Weatherman, D., & Slowik, S. (2013). "The Accuracy of auditors' and layered voice Analysis (LVA) Operators' Judgments of Truth and Deception During Police Questioning". ''Journal of Forensic Sciences'', 58, 385–392.</ref> for example, tested VSA on the recordings of interrogation of 74 suspects. Eighteen of these suspects later confessed, making the deception the most likely ground truth. With 48% accurate classification, VSA performed at chance level. Several other studies showed similar results (Damphousse, 2008; Harnsberger, Hollien, Martin, & Hollien, 2009).<ref>{{cite journal | last1 = Damphousse | year = 2008 | title = Voice stress analysis: Only 15 percent of lies about drug use detected in field test | url =https://www.nij.gov/journals/259/pages/voice-stress-analysis.aspx | journal = NIJ Journal | volume = 259 | pages = 8–12 }}</ref><ref>{{cite journal | last1 = Harnsberger | first1 = J. D. | last2 = Hollien | first2 = H. | last3 = Martin | first3 = C. A. | last4 = Hollien | first4 = K. A. | year = 2009 | title = Stress and Deception in Speech: Evaluating Layered Voice Analysis | journal = Journal of Forensic Sciences | volume = 54 | issue = 3| pages = 642–650 | doi=10.1111/j.1556-4029.2009.01026.x| pmid = 19432740 | s2cid = 205767994 }}</ref><ref>{{cite journal | last1 = Hollien | first1 = H. | last2 = Harnsberger | first2 = J. D. | last3 = Martin | first3 = C. A. | last4 = Hollien | first4 = K. A. | year = 2008 | title = Evaluation of the NITV CVSA | journal = Journal of Forensic Sciences | volume = 53 | issue = 1| pages = 183–193 | doi=10.1111/j.1556-4029.2007.00596.x| pmid = 18279255 | s2cid = 205767046 }}</ref> In 2003, the National Research Council concluded "Overall, this research and the few controlled tests conducted over the past decade offer little or no scientific basis for the use of the computer voice stress analyser or similar voice measurement instruments."<ref name="Polygraph and Lie Detection 2003"/>{{RP|168}}

==Non-verbal behavior==
People often evaluate lies based on non-verbal behavior, but are quick to place too much merit in misleading indicators, such as: avoidance of eye contact, increased pauses between statements, and excessive movements originating from the hands or feet.<ref>{{Cite journal|last1=Akehurst|last2=Koehnken|title=Lay persons' and police officers' beliefs regarding deceptive behavior.|journal=Applied Cognitive Psychology|volume=10|issue=6|pages=461–471|doi=10.1002/(sici)1099-0720(199612)10:6<461::aid-acp413>3.3.co;2-u|year=1996}}</ref>
Devices such as the [[Silent Talker Lie Detector]] monitor large numbers of [[microexpression]]s over time slots and encodes them into large vectors which are classified as showing truthful or deceptive behavior by artificial intelligence or statistical classifiers.<ref>{{cite journal | last1 = Rothwell | first1 = J. | last2 = Bandar | first2 = Z. | last3 = O'Shea | first3 = J. | last4 = McLean | first4 = D. | year = 2006| title = Silent talker: a new computer-based system for the analysis of facial cues to deception | journal = Journal of Applied Cognitive Psychology | volume = 20 | issue = 6| pages = 757–777 | doi = 10.1002/acp.1204 | doi-access = free }}</ref><ref>{{cite journal | last1 = Rothwell | first1 = J. McLean | last2 = Bandar | first2 = J. | last3 = O'Shea | first3 = Z. | s2cid = 8968052 | year = 2006 | title = Charting the behavioural state of a person using a Backpropagation Neural Network | journal = Journal of Neural Computing and Applications | volume = 16| issue = 4–5| pages = 327–339| doi = 10.1007/s00521-006-0055-9 }}</ref>

Dr. Alan Hirsch, from the department of Neurology and Psychiatry at the Rush Presbyterian-St. Luke's Medical Center in Chicago, explained the "Pinocchio Syndrome" or "Pinocchio effect" as: blood rushes to the nose when people lie. This extra blood may make the nose itchy. As a result, people who stretch the truth tend to either scratch their nose or touch it more often.<ref>{{cite web| url=https://www.pressreader.com/canada/the-telegram-st-johns/20100726/281689726095936| title=Can 'Pinocchio Syndrome' solve the niqab dilemma?| author=Dr. Game W. Gifford Jones| date=26 July 2010| accessdate=12 August 2021| publisher=[[The Telegram]]| via=[[pressreader]]}}</ref>

===Eye-tracking===
John Kircher, Doug Hacker, Anne Cook, Dan Woltz and David Raskin have developed [[Eye tracking|eye-tracking technology]] at the [[University of Utah]] that they consider a polygraph alternative. This is not an emotional reaction like the polygraph and other methods but rather a cognitive reaction. This technology measures pupil dilation, response time, reading and rereading time, and errors. Data is recorded while subjects answer true or false questions on a computer.<ref name=Eye-Tracking />

They have found that more effort is required by lying than giving the truth and thus their aim is to find indications of hard work. Individuals not telling the truth might, for instance, have dilated pupils while also taking longer to answer the question.<ref name=Eye-Tracking />

Eye-tracking claims to offer several benefits over the polygraph: lower cost, 1/5th of the time to conduct, subjects do not need to be "hooked up" to anything, and it does not require qualified polygraph examiners to give the test.<ref name=Eye-Tracking /> The technology has not been subject to peer review.

==Brain observations==
[[Cognitive chronometry]], or the measurement of the time taken to perform mental operations, can be used to distinguish lying from truth-telling. One recent instrument using cognitive chronometry for this purpose is the [[timed antagonistic response alethiometer]], or TARA.

[[Brain-reading]] uses [[fMRI]] and the multiple voxels activated in the brain evoked by a stimulus to determine what the brain has detected, and so whether it is familiar.

[[Functional near-infrared spectroscopy]] (fNIRS) also detects oxygen and activity in the brain like the fMRI, but instead it looks at blood oxygen levels. It is advantageous to the fMRI because it is portable, however its image resolution is of lower quality than the fMRI.<ref name=Parliament />

As there are different styles of lying, a spontaneous or artificial deception is constructed based on a mixture of information already stored in semantic and [[episodic memory]].<ref name=":0">{{Cite journal|doi = 10.1093/cercor/13.8.830|title = Neural Correlates of Different Types of Deception: An fMRI Investigation|year = 2003|last1 = Ganis|first1 = G.|last2 = Kosslyn|first2 = S. M.|last3 = Stose|first3 = S.|last4 = Thompson|first4 = W. L.|last5 = Yurgelun-Todd|first5 = D. A.|journal = Cerebral Cortex|volume = 13|issue = 8|pages = 830–836|pmid = 12853369|doi-access = free}}</ref> It is isolated and easier to generate because it lacks cross-checking into the larger picture. This style contrasts memorized lies that aren't as rich in detail but are retrieved from memory.<ref name=":0" /> They often fit into an actual scenario to make recall easier.

===Functional Transcranial Doppler (fTCD)===
Recent developments that permit non-invasive monitoring using [[Transcranial Doppler#Functional Transcranial Doppler (fTCD)|functional transcranial Doppler (fTCD)]] technique showed that successful problem-solving employs a discrete knowledge strategy (DKS) that selects neural pathways represented in one hemisphere, while unsuccessful outcome implicates a non-discrete knowledge strategy (nDKS).<ref>{{cite journal | last1 = Njemanze | first1 = P. C. | s2cid = 12440401 | year = 2005 | title = Cerebral lateralization and general intelligence: Gender differences in a transcranial Doppler study | journal = Brain and Language | volume = 92 | issue = 3| pages = 234–239 | doi=10.1016/j.bandl.2004.06.104 | pmid=15721956| citeseerx = 10.1.1.532.5734 }}</ref> A polygraphic test could be viewed as a working memory task. This suggests that the DKS model may have a correlate in mnemonic operations. In other words, the DKS model may have a discrete knowledge base (DKB) of essential components needed for task resolution, while for nDKS, DKB is absent and, hence, a "global" or bi-hemispheric search occurs. Based on the latter premise, a 'lie detector' system was designed as described in {{US Patent|6390979|United States Patent No. 6,390,979}}. A pattern of blood-flow-velocity changes is obtained in response to questions that include correct and incorrect answers. The wrong answer will elicit bi-hemispheric activation, from correct answer that activates unilateral response. Cognitive polygraphy based on this system is devoid of any subjective control of mental processes and, hence, high reliability and specificity; however, this is yet to be tested in forensic practice. See also cognitive [[biometrics]].

===Event-related potentials (ERP)===
{{main|Event-related potentials}}
[[Event-related potentials]] assess recognition, and therefore may or may not be effective in assessing deception. In ERP studies P3 amplitude waves are assessed, with these waves being large when an item is recognized.<ref name=Preston />
However, P100 amplitudes have been observed to have significant correlation to trustworthiness ratings, the importance of which will be discussed in the EEG section. This, along with other studies leads some to purport that because ERP studies rely on quick perceptual processes they "are integral to the detection of deception."<ref name=Heussen />

===Electroencephalography (EEG)===
{{main|Electroencephalography}}
[[Electroencephalography]], or EEG, measures brain activity through electrodes attached to the scalp of a subject. The object is to identify the recognition of meaningful data through this activity. Images or objects are shown to the subject while questioning techniques are implemented to determine recognition. This can include crime scene images, for example.<ref name=Parliament />

Perceived trustworthiness is interpreted by the individual from looking at a face, and this decreases when someone is lying. Such observations are "too subtle to be explicitly processed by observers, but [do] affect implicit cognitive and affective processes." These results, in a study by Heussen, Binkofski, and Jolij, were obtained through a study with an [[N400 (neuroscience)|N400]] paradigm including two conditions within the experiment: truthful faces and lying faces. Faces flashed for 100ms and then the participants rated them. However, the limitations of this study would be that it only had 15 participants and the [[mean]] age was 24.<ref name=Heussen>{{cite journal|last=Heussen|first=Yana|author2=Ferdinand Binkofski |author3=Jacob Jolij |title=The semantics of the lying face – An EEG Study|journal=International Journal of Psychophysiology|date=September 2010|volume=77|issue=3|page=206|doi=10.1016/j.ijpsycho.2010.06.006}}</ref>

Machine learning algorithms applied to EEG data have also been used to decode whether a subject believed or disbelieved a statement reaching ~90% accuracy. This work was an extension to work by Sam Harris and colleagues and further demonstrated that belief preceded disbelief in time, suggesting that the brain may initially accept statements as valid descriptions of the world (belief) prior to rejecting this notion (disbelief). Understanding how the brain assesses the veracity of a descriptive statement may be an important step in building neuroimaging based lie detection methods.<ref name=Douglas>{{cite journal|last=Douglas|first=Pamela K |author2=Edward Lau|author3=Ariana Anderson| author4=Wesley Kerr| author5=Austin Head| author6=Margalit Wollner| author7=Daniel Moyer| author8=Michael Durnhofer| author9=Wei Li| author10=Jen Bramen| author11=Mark S Cohen| title=Single trial decoding of belief decision making from EEG and fMRI data using independent components features|journal=Frontiers in Human Neuroscience|date= 2013|volume=7|page=392|doi=10.3389/fnhum.2013.00392|pmid=23914164 |pmc=3728485 |doi-access=free}}</ref>

===Functional magnetic resonance imaging (fMRI)===
{{main|fMRI lie detection}}

Functional magnetic resonance imaging looks to the [[central nervous system]] to compare time and topography of activity in the brain for lie detection. While a polygraph detects changes in activity in the [[peripheral nervous system]], fMRI has the potential to catch the lie at the 'source'.

fMRIs use electromagnets to create pulse sequences in the cells of the brain. The fMRI scanner then detects the different pulses and fields that are used to distinguish tissue structures and the distinction between layers of the brain, matter type, and the ability to see growths. The functional component allows researchers to see activation in the brain over time and assess efficiency and connectivity by comparing blood use in the brain, which allows for the identification of which portions of the brain are using more oxygen, and thus being used during a specific task.<ref name="Simpson2008">{{cite journal|vauthors=Simpson JR|year=2008|title=Functional MRI lie detection: too good to be true?|journal=J. Am. Acad. Psychiatry Law|volume=36|issue=4|pages=491–498|pmid=19092066}}</ref> 
FMRI data have been examined through the lens of machine learning algorithms to decode whether subjects believed or disbelieved statements, ranging from mathematical, semantic to religious belief statements.<ref name="Douglas2011">{{cite journal|last=Douglas |first=PK|year=2011|title=Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. |journal=NeuroImage|volume=56|issue=2|pages=544–553 |doi=10.1016/j.neuroimage.2010.11.002|pmid=21073969|pmc=3099263}}</ref>

Historically, fMRI lie detector tests have not been allowed into evidence in legal proceedings, the most famous attempt being Harvey Nathan's insurance fraud case<ref name=":2">{{Cite journal|pmid = 19092068|year = 2008|last1 = Langleben|first1 = D. D.|last2 = Dattilio|first2 = F. M.|title = Commentary: The future of forensic functional brain imaging|journal = The Journal of the American Academy of Psychiatry and the Law|volume = 36|issue = 4|pages = 502–4}}</ref> in 2007.<ref name=":1" /> The lack of legal support has not stopped companies like No Lie MRI and CEPHOS from offering private fMRI scans to test deception.
While fMRI studies on deception have claimed detection accuracy as high as 90% many have problems with implementing this style of detection. Only yes or no answers can be used which allows for flexibility<ref name=":1" /> in the truth and style of lying. Some people are unable to take one such as those with medical conditions, claustrophobia, or implants.<ref name=":1" />

==Drugs==
{{main|Truth serum}}
[[Truth drug]]s such as [[sodium thiopental]], [[ethanol]], and [[cannabis]] (historically speaking) are used for the purposes of obtaining accurate information from an unwilling subject.<ref>{{cite journal | last1 = Kapoor | first1 = P. | last2 = Chugh | first2 = Y. | last3 = Kapoor | first3 = A. K. | last4 = Sinha | first4 = U. S. | year = 2008 | title = Torture, terrorist and truth serum | url = http://www.indianjournals.com/ijor.aspx?target=ijor:ijmtlm&volume=10&issue=2&article=016 | journal = International Journal of Medical Toxicology & Legal Medicine | volume = 10 | issue = 2 }}</ref> Information obtained by publicly disclosed truth drugs has been shown to be highly unreliable, with subjects apparently freely mixing fact and fantasy.<ref>{{cite journal | last1 = Rowell | first1 = Jr | year = 1952 | title = Admissibility of Evidence Obtained by Scientific Devices and Analyses | journal = Florida Law Review | volume = 5 | issue = 5 }}</ref> Much of the claimed effect relies on the belief of the subjects that they cannot tell a lie while under the influence of the drug.

== See also ==
* [[Ecological fallacy]]
* [[Forensics in antiquity#Lie detection|Forensics in antiquity]]
* [[Thought identification]]
* [[Voice risk analysis]]
* [[Deception]]

== References ==
{{reflist}}

[[Category:Lie detection| ]]

Edit summary (Briefly describe your changes)

By publishing changes, you agree to the Terms of Use, and you irrevocably agree to release your contribution under the CC BY-SA 4.0 License and the GFDL. You agree that a hyperlink or URL is sufficient attribution under the Creative Commons license.

Cancel Editing help (opens in new window)

Copy and paste: – — ° ′ ″ ≈ ≠ ≤ ≥ ± − × ÷ ← → · § Cite your sources: <ref></ref>

{{}} {{{}}} | [] [[]] [[Category:]] #REDIRECT [[]]   <s></s> <code></code> <pre></pre> <blockquote></blockquote> <ref></ref> <ref name="" /> {{Reflist}} <references /> <includeonly></includeonly> <noinclude></noinclude> {{DEFAULTSORT:}} <nowiki></nowiki>

Symbols: ~ | ¡ ¿ † ‡ ↔ ↑ ↓ • ¶ # ∞ ‹› «» ¤ ₳ ฿ ₵ ¢ ₡ ₢ $ ₫ ₯ € ₠ ₣ ƒ ₴ ₭ ₤ ℳ ₥ ₦ № ₧ ₰ £ ៛ ₨ ₪ ৳ ₮ ₩ ¥ ♠ ♣ ♥ ♦ 𝄫 ♭ ♮ ♯ 𝄪 © ® ™
Latin: A a Á á À à Â â Ä ä Ǎ ǎ Ă ă Ā ā Ã ã Å å Ą ą Æ æ Ǣ ǣ B b C c Ć ć Ċ ċ Ĉ ĉ Č č Ç ç D d Ď ď Đ đ Ḍ ḍ Ð ð E e É é È è Ė ė Ê ê Ë ë Ě ě Ĕ ĕ Ē ē Ẽ ẽ Ę ę Ẹ ẹ Ɛ ɛ Ǝ ǝ Ə ə F f G g Ġ ġ Ĝ ĝ Ğ ğ Ģ ģ H h Ĥ ĥ Ħ ħ Ḥ ḥ I i İ ı Í í Ì ì Î î Ï ï Ǐ ǐ Ĭ ĭ Ī ī Ĩ ĩ Į į Ị ị J j Ĵ ĵ K k Ķ ķ L l Ĺ ĺ Ŀ ŀ Ľ ľ Ļ ļ Ł ł Ḷ ḷ Ḹ ḹ M m Ṃ ṃ N n Ń ń Ň ň Ñ ñ Ņ ņ Ṇ ṇ Ŋ ŋ O o Ó ó Ò ò Ô ô Ö ö Ǒ ǒ Ŏ ŏ Ō ō Õ õ Ǫ ǫ Ọ ọ Ő ő Ø ø Œ œ Ɔ ɔ P p Q q R r Ŕ ŕ Ř ř Ŗ ŗ Ṛ ṛ Ṝ ṝ S s Ś ś Ŝ ŝ Š š Ş ş Ș ș Ṣ ṣ ß T t Ť ť Ţ ţ Ț ț Ṭ ṭ Þ þ U u Ú ú Ù ù Û û Ü ü Ǔ ǔ Ŭ ŭ Ū ū Ũ ũ Ů ů Ų ų Ụ ụ Ű ű Ǘ ǘ Ǜ ǜ Ǚ ǚ Ǖ ǖ V v W w Ŵ ŵ X x Y y Ý ý Ŷ ŷ Ÿ ÿ Ỹ ỹ Ȳ ȳ Z z Ź ź Ż ż Ž ž ß Ð ð Þ þ Ŋ ŋ Ə ə
Greek: Ά ά Έ έ Ή ή Ί ί Ό ό Ύ ύ Ώ ώ Α α Β β Γ γ Δ δ Ε ε Ζ ζ Η η Θ θ Ι ι Κ κ Λ λ Μ μ Ν ν Ξ ξ Ο ο Π π Ρ ρ Σ σ ς Τ τ Υ υ Φ φ Χ χ Ψ ψ Ω ω {{Polytonic|}}
Cyrillic: А а Б б В в Г г Ґ ґ Ѓ ѓ Д д Ђ ђ Е е Ё ё Є є Ж ж З з Ѕ ѕ И и І і Ї ї Й й Ј ј К к Ќ ќ Л л Љ љ М м Н н Њ њ О о П п Р р С с Т т Ћ ћ У у Ў ў Ф ф Х х Ц ц Ч ч Џ џ Ш ш Щ щ Ъ ъ Ы ы Ь ь Э э Ю ю Я я ́
IPA: t̪ d̪ ʈ ɖ ɟ ɡ ɢ ʡ ʔ ɸ β θ ð ʃ ʒ ɕ ʑ ʂ ʐ ç ʝ ɣ χ ʁ ħ ʕ ʜ ʢ ɦ ɱ ɳ ɲ ŋ ɴ ʋ ɹ ɻ ɰ ʙ ⱱ ʀ ɾ ɽ ɫ ɬ ɮ ɺ ɭ ʎ ʟ ɥ ʍ ɧ ʼ ɓ ɗ ʄ ɠ ʛ ʘ ǀ ǃ ǂ ǁ ɨ ʉ ɯ ɪ ʏ ʊ ø ɘ ɵ ɤ ə ɚ ɛ œ ɜ ɝ ɞ ʌ ɔ æ ɐ ɶ ɑ ɒ ʰ ʱ ʷ ʲ ˠ ˤ ⁿ ˡ ˈ ˌ ː ˑ ̪ {{IPA|}}

Wikidata entities used in this page

lie detection: Title, Sitelink, Some statements, Miscellaneous (e.g. aliases, entity existence), Description: en

Pages transcluded onto the current version of this page (help):

Lie detection (edit)
Template:Ambox (view source) (template editor protected)
Template:Authority control (view source) (template editor protected)
Template:Cite book (view source) (protected)
Template:Cite journal (view source) (protected)
Template:Cite web (view source) (protected)
Template:Hlist/styles.css (view source) (protected)
Template:Lead rewrite (view source) (semi-protected)
Template:Main (view source) (template editor protected)
Template:Main other (view source) (protected)
Template:Navbox (view source) (template editor protected)
Template:Navbox with collapsible groups (view source) (template editor protected)
Template:Nonverbal communication (edit)
Template:Nowrap (view source) (protected)
Template:Pagetype (view source) (protected)
Template:R/superscript (view source) (template editor protected)
Template:R/where (view source) (template editor protected)
Template:RP (view source) (semi-protected)
Template:Reflist (view source) (protected)
Template:Reflist/styles.css (view source) (protected)
Template:Rp (view source) (template editor protected)
Template:SDcat (view source) (protected)
Template:See also (view source) (template editor protected)
Template:Short description (view source) (protected)
Template:Short description/lowercasecheck (view source) (protected)
Template:Skeptoid (view source) (semi-protected)
Template:Snd (view source) (template editor protected)
Template:Spaced en dash (view source) (template editor protected)
Template:Template link (view source) (protected)
Template:Terminate sentence (view source) (template editor protected)
Template:Tl (view source) (protected)
Template:US Patent (view source) (semi-protected)
Template:US patent (view source) (template editor protected)
Template:Webarchive (view source) (template editor protected)
Module:Arguments (view source) (protected)
Module:Authority control (view source) (template editor protected)
Module:Authority control/config (view source) (template editor protected)
Module:Category handler (view source) (protected)
Module:Category handler/blacklist (view source) (protected)
Module:Category handler/config (view source) (protected)
Module:Category handler/data (view source) (protected)
Module:Category handler/shared (view source) (protected)
Module:Check for unknown parameters (view source) (protected)
Module:Citation/CS1 (view source) (protected)
Module:Citation/CS1/COinS (view source) (protected)
Module:Citation/CS1/Configuration (view source) (protected)
Module:Citation/CS1/Date validation (view source) (protected)
Module:Citation/CS1/Identifiers (view source) (protected)
Module:Citation/CS1/Utilities (view source) (protected)
Module:Citation/CS1/Whitelist (view source) (protected)
Module:Citation/CS1/styles.css (view source) (protected)
Module:DecodeEncode (view source) (template editor protected)
Module:Disambiguation/templates (view source) (protected)
Module:EditAtWikidata (view source) (protected)
Module:Format link (view source) (template editor protected)
Module:GetParameters (view source) (template editor protected)
Module:Hatnote (view source) (template editor protected)
Module:Hatnote/styles.css (view source) (template editor protected)
Module:Hatnote list (view source) (template editor protected)
Module:Labelled list hatnote (view source) (template editor protected)
Module:Message box (view source) (protected)
Module:Message box/ambox.css (view source) (protected)
Module:Message box/configuration (view source) (protected)
Module:Namespace detect/config (view source) (protected)
Module:Namespace detect/data (view source) (protected)
Module:Navbar (view source) (protected)
Module:Navbar/configuration (view source) (protected)
Module:Navbar/styles.css (view source) (protected)
Module:Navbox (view source) (template editor protected)
Module:Navbox/configuration (view source) (template editor protected)
Module:Navbox/styles.css (view source) (template editor protected)
Module:Navbox with collapsible groups (view source) (template editor protected)
Module:Pagetype (view source) (protected)
Module:Pagetype/config (view source) (protected)
Module:Pagetype/disambiguation (view source) (protected)
Module:Pagetype/rfd (view source) (template editor protected)
Module:Pagetype/setindex (view source) (protected)
Module:Pagetype/softredirect (view source) (protected)
Module:Plain text (view source) (template editor protected)
Module:SDcat (view source) (protected)
Module:String (view source) (protected)
Module:String2 (view source) (template editor protected)
Module:Text (view source) (template editor protected)
Module:Unsubst (view source) (protected)
Module:Webarchive (view source) (template editor protected)
Module:Webarchive/data (view source) (template editor protected)
Module:Wikitext Parsing (view source) (protected)
Module:Yesno (view source) (protected)

This page is a member of 10 hidden categories (help):

Retrieved from "https://en.wikipedia.org/wiki/Lie_detection" ●Privacy policy ●About Wikipedia ●Disclaimers ●Contact Wikipedia ●Code of Conduct ●Developers ●Statistics ●Cookie statement ●Mobile view