Lie detection

Assessment of statements to reveal deceit

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. 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. 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." There is no evidence to substantiate that 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.

The cumulative research evidence suggests that machines do detect deception better than chance, but with significant error rates and that strategies used to "beat" polygraph examinations, so called countermeasures, may be effective. 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. In 1983 the U. S. Congress Office of Technology Assessment published a review of the technology

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. 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. Nevertheless, extraneous "noise" on the polygraph can come from embarrassment or anxiety and not be specific to lying. 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.

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. 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 This method shows particular promise as it is non confrontational as well as scientifically and cross culturally valid.

John Kircher, Doug Hacker, Anne Cook, Dan Woltz and David Raskin have developed 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. 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. 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 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 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. 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.

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. 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. 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 in 2007. Information obtained by publicly disclosed truth drugs has been shown to be highly unreliable, with subjects apparently freely mixing fact and fantasy. 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.