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Review ARTICLE

Front. Psychiatry, 21 December 2018 | https://doi.org/10.3389/fpsyt.2018.00700

A Review of Approaches to Detecting Malingering in Forensic Contexts and Promising Cognitive Load-Inducing Lie Detection Techniques

Jeffrey J. Walczyk*, Nate Sewell and Meghan B. DiBenedetto
  • Psychology and Behavioral Sciences, Louisiana Tech University, Ruston, LA, United States

Malingering, the feigning of psychological or physical ailment for gain, imposes high costs on society, especially on the criminal-justice system. In this article, we review some of the costs of malingering in forensic contexts. Then the most common methods of malingering detection are reviewed, including those for feigned psychiatric and cognitive impairments. The shortcomings of each are considered. The article continues with a discussion of commonly used means for detecting deception. Although not traditionally used to uncover malingering, new, innovative methods are emphasized that attempt to induce greater cognitive load on liars than truth tellers, some informed by theoretical accounts of deception. As a type of deception, we argue that such cognitive approaches and theoretical understanding can be adapted to the detection of malingering to supplement existing methods.

The present article is partly a review of methods of detecting malingering. Previous reviews of malingering detection methods include Sartori et al. (1) as well as Sartori et al. (2). The present review adds uniquely to the literature by highlighting recent cognitive-based methods of lie detection and relevant theory potentially applicable to malingering detection.

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) defines malingering as “the intentional production of false or grossly exaggerated physical or psychological symptoms, motivated by external incentives” [(3), p. 726]. Although the concept of malingering has existed for centuries, it was not until the mid-1900's that the term “malingering” was introduced to refer to soldiers who feigned illness or disability in order to avoid military service (4). The term's usage has broadened to include other incentives, such as avoiding work, gaining financial advantage, avoiding arrest, evading criminal prosecution, mitigating sentencing, receiving medication, or gaining admission to a hospital for shelter (3, 5). Despite a clear definition, the detection of malingering is elusive. For instance, Rogers and Shuman (6) found that the use of DSM criteria results in the accurate identification of only 13.6–20.1% of actual malingerers (true positives). However, 79.9–86.4% of individuals are misclassified as malingerers (false positives) using the same criteria. The accurate detection of malingering is thus a pressing societal issue.

Negative Effects of Misclassification/Burden on the Criminal-Justice System

In addition to the problem of not identifying individuals who are malingering, there are also very serious consequences for misclassifying malingering when an individual's presentation is genuine (false positives). Labeling an individual as a malingerer can be stigmatizing, which carries negative connotations and can negatively impact individuals for the remainder of their lives (7). In addition, many clinicians avoid diagnosing malingering for fear of legal consequences. Because of the difficulty of arriving at an accurate diagnosis, these clinicians fear they will be sued and are, therefore, reluctant to apply the label (8).

In the criminal-legal realm, malingering has a negative impact on the proper execution of justice. Failure to detect malingering in cases of insanity or incompetency can delay prosecution for months or years and often results in unnecessary hospitalizations. It also provides malingerers with the opportunity to be moved from secure facilities, such as jails or prisons, to psychiatric facilities with more comfortable environments and from which escape is easier (9). Prison inmates also feign psychiatric or cognitive symptoms in order to transfer to medical centers where they can gain access to pain medication and have greater contact with female staff (10, 11). Some researchers have expressed concern regarding the inappropriate use of antipsychotic medications administered to inmates who successfully feign psychosis. In addition to being very costly, such medications can cause harmful side-effects such as dystonias, diabetes, high blood cholesterol, and metabolic syndrome (12).

In summary, despite ongoing advances in malingering detection, many individuals successfully malinger mental, cognitive, and physical disorders in order to gain financial compensation, avoid work, gain access to medications, and avoid prison. This places a large financial burden on society, negatively impacts the efficient operation of the healthcare system, and increases medical costs. The creation or discovery of new and effective malingering detection methods has the potential to significantly reduce the burden of malingering on the criminal justice system and on society generally.

Current Assessments of Malingering

Measuring Malingering Detection Accuracy

The detection of malingering is typically done using standardized assessments as this approach gives clinicians access to the most current and scientifically-based methods (13). Malingering detection accuracy is assessed by evaluating each measure's sensitivity, hit rate, positive predictive power (PPP), and negative predictive power (NPP). Sensitivity refers to the ability of a measure to accurately identify individuals who have the condition the measure is designed to detect. Specificity is the ability of a measure to identify individuals for whom the condition is not present. The hit rate is the total proportion of accurately identified cases, i.e., the true positives plus the true negatives (1416). PPP is the percentage of individuals detected as malingering who are actually malingering, while the NPP is the percentage of honest individuals (17, 18).

Psychiatric and Cognitive Malingering Detection Strategies

Rogers et al. validated 10 strategies for the detection of feigning within the domain of mental disorders (19, 20). These strategies fall into two broad categories: unlikely presentations and amplified presentations (21). Table 1 provides a description of these 10 strategies by category (unlikely or amplified). Examination of these strategies reveals a common thread. Compared to those genuinely suffering from psychiatric disorders, malingerers present symptoms and other patterns of behavior that are deviant from what is typical, are rare, or exaggerated. In other words, compared with those actually suffering from disorders, malingerers seek to create in the minds of clinicians an impression of their affliction that often will overshoot the mark by not agreeing with actual symptom patterns of genuine cases.

TABLE 1
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Table 1. Detection strategies for malingered mental disorders.

Strategies for the detection of cognitive malingering differ from those used to detect the malingering of mental disorders, as they focus more on performance accuracy, although the detection of unusual response patterns applies to both domains (2224). The two categories that classify detection strategies for cognitive malingering are excessive impairment and unexpected patterns (6, 20). Table 2 provides a description of each strategy by category. Responses detected by these strategies include performance failures on items that are typically achievable even by those with actual cognitive impairment and the detection of failure rates that are statistically unlikely. As before, a common thread across these different kinds of malingering, psychiatric or cognitive, is that malingerers seek to create false impressions in mental health professionals and often will miss the mark.

TABLE 2
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Table 2. Detection strategies for malingered cognitive impairment.

Assessments of Psychiatric Malingering

Structures Interview of Reported Symptoms (SIRS)

SIRS is a comprehensive assessment for detecting feigned mental disorders, specifically an interview-based measure that consists of 172 items. A primary strength of the SIRS is its incorporation of multiple mental disorder detection strategies, including many of those identified in Tables 1, 2. Its primary scales include: Rare Symptoms, Symptom Combinations, Improbable and Absurd Symptoms, Blatant Symptoms, Subtle Symptoms, Selectivity of Symptoms, Severity of Symptoms, and Reported vs. Observed Symptoms [RO; (21)]. Five additional scales comprise the supplemental scales, producing a total of thirteen detection strategies, resulting in a particularly robust instrument. Items on the SIRS include Detailed Inquiries regarding symptomology and their levels of severity. Repeated Inquiries assess response consistency; and General Inquiries, which are designed to probe for specific symptoms, symptom patterns and general psychological disturbances (25).

The SIRS is the most commonly used and best-validated assessment in the forensic detection of malingering (11, 21). Although some research has suggested that the SIRS has low vulnerability to coaching, it is reported to produce lower specificity estimates than those reported in the official manual and has a higher rate of classifying true patients as malingerers than indicated by previous estimates [as cited in (26)]. Finally, many settings are inadequately equipped to utilize the SIRS given that the administration is complex, and the length of the interview can take significantly longer than the administration time of 30–40 min suggested by Rogers et al. (2729).

Structured Inventory of Malingered Symptomology (SIMS)

The SIMS (30) is a paper-and-pencil screening devise for detecting malingering. Its items were drawn and revised from validity items of existent instruments and others were derived from research on attributes typical of malingerers. A 75-item scale, its subscales include psychosis, amnesic disorders, neurological impairment, affective disorders, and low intelligence. The SIMS yields a total score and subscale scores for each of the five subscales. Based on research with college students who were instructed either to malingerer or respond honestly, compared to other measures of malingering (e.g., the F and K scales of the MMPI), the SIMS total score has the highest sensitivity for detecting malingering (95.6%). Still, its validity in detecting malingering in more authentic contexts is largely unknown.

Minnesota Multiphasic Personality Inventory-2 (MMPI-2)

The MMPI-2 is a 567-item self-report measure designed to assess personality characteristics and psychopathology, although it is also used extensively outside of mental health and medical settings (3135). The MMPI-2 has several validity scales designed to evaluate the accuracy with which test takers respond to test items and to predict distorted presentations. These include scales to detect the under- or over-reporting of symptoms.

Validity scales, developed to uncover malingering on the MMPI-2, include the F Scale (Infrequency), Fb scale (Back Infrequency), Fp Scale (Infrequency-Psychopathology), FBS (Symptom Validity), and Gough's Dissimulation Scale [Ds; (19, 36)]. Although the F Scale achieved the highest effect sizes among the various validity scales in two meta-analyses (37, 38), some researchers consider it to be inadequate considering it was designed only to detect atypical responding, which may also occur as a result of confusion regarding test items, a low reading level, or pathological interpretation of personal experiences (21). Many of the items on the F and Fb scales do not accurately distinguish between feigning and honest responding, with the Fb scale demonstrating poorer performance than the F scale (36, 39). Rogers and Neumann (36) concluded that both F and Fb are flawed scales for the detection of malingering. The most effective scales can misclassify 5–15% of individuals who attempt to malinger (40, 41). Heinze (42) reported even higher rates of false positives, stating that between 12 and 55% of individuals with genuine mental disorders have been identified by the MMPI-2 as malingerers.

Millon Clinical Multiaxial Inventory MCMI-III

The MCMI-III is 175 item self-report scale (true/false items) that takes about 30 min to complete (43). With a focus on personality disorders, its 28 subscales comprise the following categories: Modifying Indices (including validity items), Clinical Personality Patterns, Severe Personality Pathology, Severe Syndrome, and Clinical Syndrome. Atypical patterns, extreme scores, or high invalidity can suggest malingering (43).

Miller Forensic Assessment of Symptoms (M-FAST)

The M-FAST (44) is a brief screening measure designed to detect malingered mental illness in forensic settings by assessing individual response styles (4547). The M-FAST contains 25 items, including 15 true or false questions, 5 Likert items, 2 yes/no questions, and 3 items designed to detect discrepancies between responses and observations (45, 47).

The M-FAST utilizes similar detection strategies as the SIRS, with four of its seven scales employing the same detection strategies (Reported vs. Observed, Extreme Symptomology, Rare Combinations, Unusual Hallucinations). It also contains three additional scales: Unusual Symptom Course, which assesses the reported speed of onset of mental illness; Negative Image, which capitalizes on the tendency of malingerers to believe that they should be viewed negatively by others; and Suggestibility, which relies on the likelihood that malingerers will endorse symptoms they believe will make them appear mentally ill (9, 4850). However, a third of the scales on the M-FAST have low internal consistency, resulting in low reliability for these scales. Vitacco et al. (49) found problems with homogeneity for the individual M-FAST scales and lower utility estimates compared to the total score. In addition, they found that the M-FAST produced an unacceptably high rate of false positives (10%) using the total scale scores.

Assessments of Cognitive Malingering

Tests of Memory Malingering (TOMM)

The TOMM is a recognition memory test that utilizes symptom validity testing (SVT), forced-choice, and floor-effect detection strategies. As a forced-choice SVT, the TOMM presents the respondent with two alternatives per test item, allowing for a 50% chance of choosing correctly. Scores falling significantly below this probability level suggest malingering (51, 52). As noted in Table 2, the floor-effect strategy involves the presentation of cognitive tasks which malingerers incorrectly believe impaired individuals are incapable of completing accurately (19). The TOMM contains 50 items and consists of two memory learning trials, with each trial followed by an assessment of recognition memory (53, 54). The respondent is initially shown a series of 50 line drawings, followed by a recognition assessment in which each drawing is presented alongside a foil. The subject is asked to identify the previously presented drawing and is given feedback regarding the correctness of the response (5456). If the respondent does not achieve a correct score during the second trial on at least 45 items, a Retention Trial is administered. Malingering should be suspected if the respondent earns a score of 45 or less on the second trial or the Retention Trial (53, 56). Some researchers have reported lower hit rates with the TOMM than with other measures. Unfortunately, high face validity enables a large number of respondents to perceive it correctly as an assessment of malingering (57).

Rey Fifteen-Item Test (FIT)

The FIT utilizes the floor effect detection strategy but without a forced-choice design (6, 19, 58, 59). The FIT presents a memory task that appears difficult but is actually easy. The individual is shown 15 different items consisting of letters, numbers, and geometric shapes for a brief period and then asked to recall and reproduce as many of the items as possible (5861). The fifteen items are presented in five rows containing three items each. The first row presents the numbers 1, 2, and 3; the second presents the roman numerals I, II, and III; the third presents a square, a triangle, and a circle; the fourth presents the letters A, B, and C (Capitalized); and the fifth presents the letters a, b, and c, all in lowercase (61). A cut-off score of nine is most commonly used (54), although some have suggested the use of lower cut-off scores of eight or less to accommodate those with true impairment (62). Schretlen et al. (63) concluded that the FIT has several limitations and that patients with genuine impairment often perform poorly on the test, while many malingerers score above the recommended cut-off score. A number of studies have shown that forced choice recognition tests are more useful in identifying cognitive malingering than the standard FIT. Clinicians should also note that the FIT does not meet the Daubert standard, which outlines criteria for the admissibility of scientific evidence in court (6466).

Word Memory Test (WMT)

The WMT is a forced-choice test of malingering. In addition to the forced-choice detection strategy, it also utilizes the following: (a) violation of learning principles, (b) floor effect, (c) symptom validity testing, and (d) the performance curve (6, 19, 57), all noted in Table 2. The learning principle it utilizes is the advantage of recognition memory performance over recall, which malingerers may not account for in their efforts to deceive. The WMT is more effective than other measures of feigning in its use of this detection strategy, yielding large effect sizes (19).

Regarding administration, the respondent is presented with 20 pairs of semantically-related words during two learning trials. Immediately following these presentations, the Immediate Recognition trial begins in which each of the 40 words is paired with a foil and the individual is asked to select the correct target word. After 30 min, the delayed recognition trial is given, and target words are paired with new foils. Four separate effort tests, designed to evaluate verbal memory, are then given, including the multiple choice, paired associates, delayed free recall, and long delayed free recall subtests. Scoring is accomplished by comparing the number of words recognized consistently across the immediate and delayed trials. A score of 82.5% or below is the cut-off (54, 55, 67). Although simulated malingerers perform worse than participants instructed to perform at their best on the WMT, coaching and the use of sophisticated simulators has resulted in less accurate detection of malingering with this instrument (67, 68). Pella et al. (59) warn that the WMT may be particularly vulnerable to coaching compared to other instruments, resulting in a high rate of false negatives.

Lie Detection

Despite advances in malingering detection technology, current methods are far from adequate, with high rates of false positives, false negatives, and a susceptibility to coaching. Perhaps the detection of malingering can be facilitated by incorporating developments from the field of lie detection given that malingering is high-stakes deception. Current methods of lie detection are reviewed, with an emphasis on innovative cognitive-based approaches.

Human Lie Detectors

Although lying is common in everyday life (69, 70), people are amazingly poor lie detectors. Individuals accurately judge lying at or slightly above chance levels but are a bit better at identifying truth telling (7175). Although one might assume that professional lie-catchers (e.g., police officers, customs officers, judges, mental health professionals) have better accuracies at detecting lies, the majority of studies show that they do not (73, 7679). Rather than having to depend on unreliable human lie detectors, we now review some prominent and emerging technologies potentially applicable to ferreting out malingering, many with minimal dependence on human lie detectors.

Arousal-Based Approaches

Control Question Technique

The polygraph is a scientific instrument that continuously records psycho-physiological arousal as assessed by pulse rate, blood pressure, respiration rate, and/or skin conductivity, which has been applied to the detection of deception. The most common questioning procedure used with it is the Control Question Technique (CQT; 79). In a typical test, a respondent is given a pretest interview for gathering information that provides the basis for control questions. Once questions are constructed, the examiner will preview them with the respondent to ensure that they are understood and will not surprise the respondent when asked later. During the examination, irrelevant questions are asked such as “What is your age?,” along with the control questions that most people tend to lie to. For example, “Have you ever stolen anything from your place of employment?” Finally, relevant questions, probing the issue central to the exam, are asked (e.g., “Did you rape … on January 7th?”). The questions usually elicit brief answers. A guilty liar, it is hypothesized, will show more arousal to relevant questions than to control questions, whereas an innocent, honest respondent will show more arousal to control questions (80). Law enforcement and federal agencies in the United States use the CQT as a screening device for hiring and retaining employees and as a tool for criminal investigations. The CQT has been used to verify victim's statements, evaluate the veracity of witnesses, and to exonerate suspects. Still, test results are largely inadmissible in US courtrooms (81).

A major criticism of polygraph-based techniques, especially the CQT, regards their generally poor validity. Specifically, the CQT produces a high rate of false positives, that is, the labeling of honest individuals as liars (8184). Researchers have also found that respondents can easily be trained to evade detection by using mental and physical distraction techniques known as countermeasures (81, 84).

Concealed Information Test (CIT)/Guilty Knowledge Test (GKT)/Concealed Knowledge Test (CKT)

Partly in response to the validity concerns with the CQT, the CIT, also known as the CKT and GKT, was proposed. It is a questioning paradigm that can be used with the polygraph to uncover the false denials of respondents by exposing whether they possess guilty knowledge or concealed information, presumably resulting from their participation in a crime or some other experience (80). During a typical CIT, the respondent is presented with multiple-choice questions, each having one relevant alternative (correct answer) and several neutral alternatives (plausible distractors). The latter should be chosen such that an innocent person could not discriminate them from the relevant alternative (80). An example of a relevant question is “How was the victim killed?,” with the response alternatives of “shot,” “stabbed,” “struck,” “strangled,” or “poisoned.” This question could be re-asked multiple times, along with other questions probing different aspects of a crime scene. The respondent need not answer. If heightened arousal occurs consistently to relevant responses, then the respondent may be concealing information as the perpetrator. The CIT assumes that innocent respondents could not have acquired guilty knowledge indirectly and that guilty respondents encoded guilty knowledge and have retained it (85).

Some validity concerns with the CQT were resolved in the CIT, including more standardization of the procedure, more appropriate control alternatives, fewer false positives, and a stronger theoretical basis (80). Also, beyond the psycho-physiological measures of the polygraph, concealed information has been uncovered with the diverse cues of response time (8690), event-related potentials (9193), and pupil dilation (94). Also, the CIT has been used to expose the simulation of amnesia (95). Still, the CIT is limited in the deception it can uncover to the false denials of those possessing concealed knowledge.

Cognitive Load-Inducing Approaches

Cognitive load refers to the demands made on the limited pools of attention and working memory resources for performing mental tasks (96, 97). Some recent, novel, and promising techniques for detecting deception, and possibly malingering, rather than viewing deception as a physiological/emotional event as does the CQT, view it as a cognitive act that generally imposes greater cognitive load on respondents than honesty does. In support, Vrij and Mann (98) reported that telling complex, high-stakes lies increased cognitive loads, with liars exerting significantly more effort to control their speech than did truth tellers. As further neurological support, brain imaging studies using fMRI (functional magnetic resonance imaging) scanners, which reveal brain activity during task performance, suggest that deception activates higher brain centers associated with cognitive demand, particularly in the frontal lobe (99, 100). If lying is more cognitively demanding than truth telling, deception should reveal itself in longer times needed to answer questions, more inconsistencies and hesitancy in answering logically interrelated questions, greater pupil dilation, more activity in the brain's prefrontal cortex, more blinking, and in other signs of heightened cognitive load.

Cognitive load-inducing lie detection techniques, only some of which can be reviewed due to their sheer number, seek to enhance the mental effort of liars compared to truth tellers, in effect, making it mentally harder to deceive than to be honest (101, 102). Once refined and validated, such techniques may accurately expose malingering in forensic settings, perhaps used in conjunction with existing methods.

Asking Surprise Questions/Soliciting Surprise Drawings

Asking surprise questions of respondents can increase cognitive load on liars. For instance, Vrij et al. (103) instructed pairs of participants to lie or tell the truth about whether they had lunch together. All pairs then prepared for an interview that followed, which included anticipating likely questions. During the interview, general and unanticipated questions were asked, some of the latter probing minor details like these: “What was the color of the shirt your partner wore?” “Who sat closest to the door?” Inconsistencies across such questions from members of each pair allowed observers to classify liars and truth tellers beyond chance, as did discrepancies across surprise pictures that members were asked to draw of the layout of the restaurant. Although researchers did not measure the cognitive loads produced by the surprise questions or drawings directly, we regard them as cognitive load-inducing because deceptive participants likely had to think more than truth tellers to guess at how their partners might respond to the questions to ensure their answers and drawings would be consistent (104).

These results are promising. Still, asking surprise, detailed-oriented questions has limitations. Once knowledge of this lie detection technique disseminates, liars may include spatial and other obscure details into their deceptive narratives in anticipation of surprise questions. Also, memory for minor details can go unnoticed by truth tellers (105). Thus, if respondents claim “I can't remember” to detail-oriented questions, they may be answering honestly. Similar concerns apply to drawing pictures. Even so, refinement of these techniques may overcome these concerns.

Having to Maintain Eye Contact

Having to maintain eye contact with another can selectively heighten cognitive load and anxiety in liars. In support, Vrij et al. (106) directed some participants to lie to interview questions while others told the truth. Some of the participants were also directed to maintain continuous eye contact with the interviewer. Interviews were videotaped and observers of the recordings were more accurate at discriminating liars from truth tellers when eye contact had to be maintained, suggesting that doing so induces higher load and anxiety in liars than in truth tellers, perhaps because eye contact is distracting to liars who need to focus their attention inwardly to construct plausible deceptions.

One likely countermeasure, as knowledge of this load-inducing technique spreads, would be to practice lying while maintaining eye contact with another, which might reduce liar-truth teller differences. Even so, combined with other techniques, it may be useful in revealing malingering in forensic contexts.

Rather than heightening cognitive load through surprise or by imposing a concurrent task (e.g., maintaining eye contact), the two techniques described next (aIAT, TARA) add to cognitive load by creating response interference in deceivers by having them respond quickly and accurately to some items intermixed with others they may want to lie to. Such techniques also allow automated lie detection, not dependent on unreliable human observers.

Autobiographical Implicit Association Test (aIAT)

Based on the Implicit Association Test of Greenwald et al. (107), the aIAT is designed to determine whether respondents possess actual autobiographical memories, for instance, of a true alibi at the time of a crime. This computerized, forced choice assessment confronts respondents with five blocks of sentences to be classified (108). In block 1, respondents classify sentences with verifiable truths as true or false. In block 2, target sentences probing specific episodic truths (guilty if true; innocence if false) about them are likewise classified. Blocks 3 and 5 are crucial. In block 3, true and guilty sentences are intermixed and classified with the same response key. In block 5, true and innocent sentences are likewise classified together. An index, D, which penalizes for incorrect responses, is largely based on subtracting block 3 response times from those of block 5. Positive D scores are expected of guilty respondents, negative D score of innocent because of the interference in guilty respondents caused by the incongruence of combining truthful and innocent responses in block 5.

The aIAT has an impressive 91% accuracy rate in identifying those possessing genuine autobiographical memories (108) and has proven effective in uncovering the malingering of whiplash (109), and unveiling phantom limb pain (110). Clearly those genuinely affected by cognitive or psychiatric impairments should have many life memories of experienced symptomology that can be probed. Still, the aIAT has some limitations. It does not allow for ascertaining the truths of answers to specific or open-ended questions (e.g., When did you first notice your memory problems?). Also, research has not adequately explored whether countermeasures, such as deliberately slowing on some blocks and speeding up on others, could reduce deception detection (108). Another limitation, the aIAT requires the possession of true identity information that can be contrasted with faked identity information. In the case of those seeking to fake their identities in the field, such information may be unavailable to examiners (111).

The Timed Antagonistic Response Alethiometer (TARA)

Like the IAT and aIAT, TARA (112) involves a multi-block classification task. This computer-administered, response time-based method of lie detection assumes that, following instructions to minimize errors, incompatible tasks take longer to execute than compatible ones. Statements are presented on a computer screen that respondents must quickly classify as true or false. At first, control statements with verifiable truths (e.g., Rocks are hard. Mozart wrote novels.) are presented. In blocks that follow, target statements probing truths specific to the individual (I am male. I am a citizen of Egypt.) are presented. When target and control statements are combined within the same block, dishonest respondents experience response interference and the longest response times, having to perform the incompatible responses of deception and truthfulness. TARA correctly classified liars and truth tellers with an accuracy of 85%.

TARA differs from the aIAT in some important ways. TARA uses two categorizations (true, false) rather than four, uses only one critical block rather than two, and identifies lying from truths based on absolute RTs in the critical block. The latter requires comparison with a matched control group, a limitation of this technique (108). Still, TARA has potential to uncover a variety of deception types, including malingering. However, like the aIAT, it does not support the verification of open-ended responses or an answer to a particular question, nor has it been applied to detect deception involving a specific issue such as participation in a crime. Also, the effects on detection accuracy of the extensive practice of deception, deliberate slowing on certain blocks, or the use of other countermeasures are unknown.

Detecting Faked Identities With Unexpected Questions and Mouse Movements

The aIAT and TARA use key press response times to uncover deception. In order to discover faked identities in a way not reliant on possessing accurate identity information about respondents, Monaro et al. (111) explored the use of computer mouse movements in responding yes/no as the cues to deception in conjunction with asking unexpected questions. Measuring mouse movements allows a much richer set of behavioral cues, such as acceleration and trajectory, not easily controlled via countermeasures. Investigators assigned participants either to rehearse their true identities or rehearse then lie based on fake identities. Expected questions (i.e., concerning rehearsed information, such as birth month) and unexpected questions (e.g., one's Zodiac sign), were asked, the latter hypothesized to be constructed impromptu under high cognitive load. Detecting an impressive 95% accurately, fakers took longer, especially in responding to unexpected questions, and had longer response trajectories, among other differences.

Asking unexpected question, [see (113)] combined with mouse movements, has much potential, for instance, in detecting faked depression (114). However, would it be effective in uncovering malingerers who have faked depression or other psychiatric disorders for years? Also, the guidelines for generating unexpected questions are unclear. For example, a truth teller, not inclined toward superstition, might lack quick access in memory to their Zodiac sign. Verifying answers to open-ended questions is not possible as well. Even so, it is interesting to consider the kinds of unexpected questions that might blindside malingerers (e.g., Does your impairment affect you when driving?) and expose them.

Time-Restricted Integrity Confirmation (TRI-Con)

Walczyk et al. (115) proposed a cognitive load-inducing technique, Time Restricted Integrity-Confirmation (TRI-Con), with potential to uncover different kinds of deception including malingering. It is based on a cognitive theory of high-stakes deception called Activation-Decision-Construction-Action Theory (ADCAT), summarized later. Like the aIAT and TARA, TRI-Con can be largely automated via computer-administration and scoring and selectively enhances the cognitive load on liars by adhering to seven guidelines during lie detection examinations (115, 116).

The guidelines are: (a) Respondents are prompted about the focus of the question set to follow (e.g., “The next 15 questions concern your activities and whereabouts at the time of the crime.”). By priming relevant episodic and semantic truths, prompts reduce respondents' need to search memory to tell the truth, making cognitive load indices clearer cues of when respondents are constructing lies. As with reviewing questions before a polygraph exam, prompting also reduces the emotional surprise accompanying blindsiding respondents with questions that probe sensitive issues. (b) Still, the specific questions are not disclosed until asked during an exam, thus surprising respondents cognitively and reducing the chance that deceptive answers were prepared and rehearsed. (c) Questions, both yes/no and open-ended, are written when possible to be unclear regarding what truths are sought until fully asked, which should reduce respondents' chance of preparing deceptive answers as questions are being asked. (d) To obtain clearer assessment of the cognitive load needed to answer completely, questions are written to be answerable, as much as possible, with one or a few words. (e) Respondents are instructed to answer as quickly as possible to discourage and expose attempts to deceive. The high cognitive load of rapid responding to surprise questions may also increase cue leakage in the form of voice pitch elevation, pupil dilation, increased blinking, and long response times because of the limited opportunity for liars to monitor and control their own behavior (75, 117, 118) and may increase accidental blurting of the truth (119). (f) Without adequate preparation, liars' deceptive accounts should be incomplete. Questions are asked and then re-asked, along with logically interrelated questions, to increase liars' cognitive load and provoke inconsistencies (120). (g) Behavioral baselines for ground-truth answers are established for all cognitive load indices for comparison with levels of these cues of answers suspected of deception. This practice controls for individual differences in behavioral base rates and improves the accuracy of lie detection (71).

Given the inaccuracy of human lie detectors (71, 72), automatable techniques of lie detection, such as TRI-Con, TARA, and the aIAT, provide auspicious alternatives. For instance, with TRI-Con questions can be recorded and asked by a computer. Using microphone-headsets, answer response times can be precisely measured to the millisecond level of precision. Connected modern eye-tracking systems can concurrently measure pupil dilation, eye movements/fixations, and blinking rate. Voice pitch elevation can be detected using the appropriate software, etc.

Following the guidelines above, studies have shown the effectiveness of TRI-Con for uncovering deceptive answers to yes/no and open-ended questions. Walczyk et al. (115) instructed adults to lie or tell the truth to questions about various aspects of their lives such as employment history and their performance on standardized tests. Using response time as the cue, discriminant analyses allowed classification of liars and truth tellers above chance. Likewise, Walczyk et al. (116) tested TRI-Con again by asking participants to lie or tell the truth about their lives and included a rehearsal condition in which participants prepared deceptive answers, a likely load-reducing countermeasure. The consistency of answers across interrelated questions was added as a cue. Liars and truth tellers were classified up to 89% accurately. Analyses also showed that the countermeasure of rehearsing deception is detectable. Also, Walczyk et al. (121) tested TRI-Con in a forensically-relevant context. “Witnesses” observed actual crime videos, then later told the truth or lied, rehearsed or unrehearsed, when interviewed about them. The cognitive cues were response time, answer consistency, eye movements, and pupil dilation. Discriminant analyses allowed classification of the three conditions 69% accurately, 33% expected by chance. Truth tellers generally had moderate response times, the fewest inconsistencies, and the most eye movements. Regarding the latter findings, liars appeared to move their eyes less to avoid visual distraction that would have heighten cognitive loads as they focused attention inwardly to construct lies. Walczyk et al. (122) observed similar results for participants who lied or told the truth concerning their participation in a mock crime. Across these studies, low rates of false positives were observed, recalling that high rates are a perennial problem with the CQT (81).

Although TRI-Con has potential for the detection of malingering, it too is susceptible to the countermeasure of rehearsal. The good news is that load-reducing techniques can be combined. TRI-Con already involves surprise questions. Respondents can be further instructed to maintain eye contact with someone present. Surprise drawing can be added after the exam to solicit non-verbal information. Other load-inducing techniques can be added. Combining several load-inducing techniques within lie detection exams and assessing several indices of cognitive load should make the detection of malingering hard to foil.

Activation-Decision-Construction-Action Theory (ADCAT)

A major criticism of the polygraph-based CQT is its lack of a valid theoretical foundation (80, 81). Similarly, most existent load-inducing techniques assume that lying is more cognitively demanding than truth telling. Our discussion of the countermeasure of rehearsing deception, however, suggests that this is not always true. No coherent theory underlies most of these techniques. TRI-Con is an exception, based on ADCAT, a theory of high-stakes deception. ADCAT, with some tweaking, might account for malingering. Such a theory, once validated, could suggest cues of when malingering has taken place and new ways of detecting it. The most recent version of ADCAT, Walczyk et al. (123), is summarized below, with an emphasis on its application to malingering.

ADCAT accounts for how individuals respond deceptively to solicitations of the truth, such as a question, under high stakes. A high stakes social context is one in which being honest with targets (those soliciting truths) would likely prove very costly to respondents in the non-attainment of goals important to them. High-stakes contexts include a perpetrator interrogated by a detective concerning an alibi or a psychiatrist assessing a sane perpetrator regarding his fitness to stand trial.

ADCAT specifies four psychological components involved in most instances of deception. Each elaborates on underlying cognitive processes. ADCAT incorporates established concepts of cognitive science, including working memory and executive functioning (123). Of central importance is Theory of Mind (ToM), which involves the inferences individuals make regarding the mental states of targets. First-order ToM inferences in deception entail the false beliefs that liars are trying to create in others (e.g., “I want this psychiatrist to believe that I cannot distinguish right from wrong.”). More abstract and cognitively demanding second-order ToM inferences concern, for instance, malingerers' guesses of what targets will expect in them if their deceit is believed (“How should I behave and what should I say to come across as legally insane?,” (124, 125). As noted, malingerers are often wrong in these guesses. Both types of inferences are heavily involved in all four components.

Activation Component

The first component of ADCAT, Activation, involves the retrieval of the truth following targets' solicitations of accurate information. For instance, a police detective might ask a perpetrator who is feigning memory loss whether she can remember even a small fragment regarding where she was when the crime occurred (123). Based on the social context and roles targets play, ToM inferences are made regarding why, for instance, the detective is seeking the information, to what use sharing the truth will be put, etc. (e.g., “This detective suspects me and wants to build a case to charge me.”). Most truths are automatically activated by a question but occasionally must be searched for in LTM if they have not been accessed in a long time or may need to be newly constructed in WM, both of which can add to the cognitive load of truth telling.

Decision Component

Typically with the truth now active in WM (126, 127), the second component, Decision, will execute. It describes how respondents choose whether and then how to deceive. With the help of ToM inferences, respondents will first evaluate what the likely overall gain/loss is of sharing the truth vis-à-vis the non-attainment of important goals such as staying out of prison or maintaining their disability income. Such evaluations are made intuitively when deception is impromptu but can be more deliberate when high-stakes truth solicitations are anticipated. These calculations involve intuitively combining estimates of the likelihoods of salient outcomes with their subjective utilities, that is, the personal value of the outcomes to respondents (128). The more negative the expected overall loss, the more likely a deception will be considered (123). In such a case, one or more context-appropriate deceptions will be evaluated in terms of their overall likely gain/cost vis-à-vis their believability and how well each helps respondents to achieve their goals. Again, first- and second-order inferences are crucial to accurately evaluate the likely impact of deceptions on targets.

The deception with the highest expect gain, if any, will be chosen, which can vary from sharing a truth with an important detail withheld (lie of omission) to a bald-faced lie (complete fabrication). The preference for respondents will be to minimize the deception needed to attain their goals (129). The decision to deceive intrinsically adds to cognitive load (115), an implication of which is that surprising respondents with questions will require them to decide impromptu whether to lie, enhancing the mental work of deception and related cognitive cues.

Construction Component

During the third stage, Construction, the specific deception chosen is elaborated as needed to go undetected and achieve respondents' other goals. The cognitive load imposed varies with the type of deception. A false denial or a lie of omission can impose minimal load whereas constructing a bald-faced lie, for instance, a false alibi for what happened at the time of the crime, can impose the greatest. Especially for the latter, second-order ToM inferences must be made to ensure that a lie is internally consistent, consistent with what targets' know or are likely to find out, and detailed enough to be believable (123). A chance to prepare deceptions in advance of delivery will make them more believable, internally consistent, etc., and allow respondents to anticipate likely questions from targets (130, 131). A relevant question for the detection of malingering during this component concerns what kinds of ToM inferences do malingers typically make to mislead mental health professionals in forensic contexts. Little research has addressed this question. Asking surprise and complex questions of respondents suspected of malingering under TRI-Con concerning lesser known actual symptoms of disorders might trip up malingerers, producing long response times and other signs of cognitive load compared to those actually afflicted.

Central to the construction component is the plausibility principle, which specifies the order of steps respondents generally will take to construct believable deceptions, especially the bald-faced variety. Respondents will (a) first attempt to modify the truth, related episodic memories, or other personally experienced memories based on second-order ToM inferences of what targets will believe (102, 123, 129, 131). Because recently accessed memories are more retrievable, they will be preferred to distant memories (132). If respondents have no such memories, for instance, because malingerers have never actually suffered from a particular mental disorder, they may (b) use schemata or scripts of what is typical within that context to provide the basis of the deception (132134). If such schemata are unavailable, again due to limited life experience or if relevant schemata are inaccessible, respondents will (c) construct deceptions using assorted information accessible from LTM as cued by the social context, which imposes the highest cognitive load. To summarize, the plausibility principle predicts that cognitive load will increase when going from a to c as the basis of a deception and lie plausibility will tend to decrease. However, the opportunity to prepare and rehearse deceptions, for example, a false presentation of being insane, in advance of delivery is a countermeasure that can lower the cognitive load experienced by liars, even below that of truth tellers (116, 135). On a positive note, the use of such rehearsal may be detectable by cognitive load indices falling below levels of truth telling (116).

Action Component

During the final component, Action, respondents deliver the lies they have prepared, or will generate impromptu, to targets. In general, they will attempt to control physical movements and appear relaxed but may self-regulate too much because of inaccurate ToM beliefs they hold about the actual behavior of truth tellers. Many liars naively implicitly assume that honest individuals are relaxed and do not experience recall failures or make other mistakes in conveying truths (72, 104). As noted, the cognitive load of delivery will decrease for well-rehearsed lies, but will increase when social contexts are unfamiliar and complex. Cognitive load will also increase when, for instance, malingerers are surprised by truth solicitations, which allows little time for lie preparation (115).

Because deception is typically chosen only when honesty blocks goal attainment, truth telling is usually more practiced and automatic (129). Especially for well-rehearsed truths, conveying corresponding deceptions can impose a cognitive load during delivery, requiring active suppression of the truth (100, 116, 136, 137). In addition to this source of cognitive load, lies told in high-stakes situations are highly motivated, which can heighten the fear of being caught as well as the cognitive load of delivery (138). ADCAT hypothesizes that impromptu liars will manage the increased load of deception by minimizing eye contact (106), reducing eye movements (122), reducing body movements, occasionally scanning the environment for lie construction hints, and implementing time-buying strategies like asking for a question to be repeated or pausing before and during delivery of the lie (123).

Applying Cognitive Load-Inducing Technique and ADCAT to Detect Malingering

Only sketched above, ADCAT advances understanding of the behavioral manifestations of deception by providing a detailed cognitive account of the processes individuals engage in as they choose deception, construct lies, and deliver them to targets (123). Professionals interested in advancing the cognitive detection of malingering are encouraged to learn more about this and other cognitive accounts of deception [see (84, 139)]. Malingering is high-stakes deception in which malingerers must actively inhibit the truth (e.g., a lack of mental illness) and decide which deceptive presentation of behavior to construct and practice. Interestingly, constructing presentations of feigned mental disorders may be more cognitively complex than constructing, for instance, alibis based on complete fabrications. Second-order ToM inferences are likely extensively made as malingerers study the kinds of symptoms typical of those afflicted with particular disorders and how the disorders are assessed. ADCAT helps clarify when cognitive load-inducing approaches for detecting malingering are likely to be effective. For instance, ADCAT recognizes the preparation and rehearsal of high-stakes deception before delivery as the most likely foil of such approaches and recommends that respondents be blindsided with memory tasks for accessing truths. These include asking unanticipated and complex questions, soliciting surprise drawings, or accessing memories for events in unusual ways like recounting an alibi in reverse-chronological order (140). In such cases, the cognitive load of deception should exceed that of honesty. Surprisingly, most researchers who have tested load-inducing approaches have not given much attention to the countermeasure of rehearsal.

The customary methods for malingering detection we reviewed rely on atypical levels or combinations of symptoms, unusual performance on cognitive tasks, or other behavioral anomalies. Sadly, their rates of false positives and false negatives tend to be high. As an alternative, we encourage those interested in detecting malingering in forensic contexts to consider combining several cognitive load-inducing approaches like those we discussed. For instance, TRI-Con automates many aspects of lie detection, involves surprise questions, and can include maintaining eye contact and other load-inducing techniques. The non-load-inducing cognitive methods of lie detection of Criteria Based Content Analysis (CBCA) and Reality Monitoring (RM) can be added as well, which assume that liars fabricate information when constructing lies (73, 141). Both attempt to differentiate memories of real experiences from fabrications by assessing for features of authentic experiences such as sensory details, the reporting of unexpected complications, thoughts or feelings experienced, contextual information, temporal details, and affective information (98, 141). Under TRI-Con, asking respondents surprise and complex questions about details like these related to their disorders, their time of onset, or how they made respondents feel might expose significantly higher cognitive loads in malingerers than in genuine patients as longer response times, elevated pitch, dilated pupils, less eye movement, or as slower and longer mouse movements (111). In conclusion, the more that malingering is understood cognitively, the more that innovative methods of lie deception detection like TARA, the aIAT, and TRI-Con can be refined to supplement existing assessments.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

1. Sartori G, Orrù G, Zangrossi A. Detection of malingering in personal injury and damage ascertainment. In: Ferrara S, Boscolo-Berto R, Viel G, editors. Personal Injury and Damage Ascertainment under Civil Law. Cham: Springer (2016).

Google Scholar

2. Sartori G, Zangrossi A, Orrù G, Monaro M. Detection of malingering in psychic damage ascertainment. In: Ferrara SD, editor. P5 Medicine and Justice. Cham: Springer (2017). p. 330–341.

Google Scholar

3. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington, DC: American Psychiatric Publishing (2013).

4. McCaffrey RJ, Weber M. A clinical approach to evaluating malingering in forensic neuropsychological evaluations. Span J Neuropsychol. (2000) 2:21–36.

5. Resnick PJ, Knoll JL. Malingered psychosis. In: Rogers R, editor. Clinical Assessment of Malingering and Deception. 3rd ed. New York, NY: Guilford (2008). p. 34–53.

PubMed Abstract

6. Rogers R, Shuman DW. Fundamentals of Forensic Practice: Mental Health and Criminal Law. New York, NY: Springer (2005). Available online at: http://link.springer.com/chapter/10.1007/0-387-25227-4_2

Google Scholar

7. Reynolds CR, Horton AM. Clinical acumen, common sense, and data-based decision making in the assessment of dissimulation during head injury litigation. In: Reynolds CR, Horton AM, editors. Detection of Malingering During Head Injury Litigation. New York, NY: Springer (2012). p. 351–69. Available online at: http://0-dx.doi.org.dewey2.library.denison.edu/10.1007/978-1-4614-0442-2

Google Scholar

8. Adetunji BA, Basil B, Mathews M, Williams A, Osinowo T, Oladinni O. Detection and management of malingering in a clinical setting. Primary Psychiatr. (2006) 13:61.

Google Scholar

9. Soliman S, Resnick PJ. Feigning in adjudicative competence evaluations. Behav Sci Law (2010) 28:614–29. doi: 10.1002/bsl.950

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Denney RL. Assessment of malingering in criminal forensic neuropsychological settings. In: Boone KB, editor. Assessment of Feigned Cognitive Impairment: A Neuropsychological Perspective. New York, NY: Guilford Press (2007). p. 428–50. Available online at: http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10201023

Google Scholar

11. Reid WH. Malingering. J Psychiatr Pract. (2000) 6:226–8. doi: 10.1097/00131746-200007000-00008

CrossRef Full Text | Google Scholar

12. McDermott BE, Sokolov G. Malingering in a correctional setting: the use of the structured interview of reported symptoms in a jail sample. Behav Sci Law (2009) 27:753–65. doi: 10.1002/bsl.892

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Heilbronner RL, Sweet JJ, Morgan JE, Larrabee GJ, Millis SR, Conference Participants. American Academy of Clinical Neuropsychology Consensus Conference Statement on the neuropsychological assessment of effort, response bias, and malingering. Clin Neuropsychol. (2009) 23:1093–129. doi: 10.1080/13854040903155063

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Kane AW. Psychology, causality, and court. In: Young G, Nicholson K, Kane AW, editors. Psychological Knowledge in Court. Boston, MA: Springer (2006). p. 13–51. Available online at: http://link.springer.com/chapter/10.1007/0-387-25610-5_2

Google Scholar

15. Larrabee GJ, Berry DTR. Diagnostic classification statistics and diagnostic validity of malingering assessment. In: Larrabee GJ, editor. Assessment of Malingered Neuropsychological Deficits. New York, NY: Oxford University Press (2007). p. 14–26.

Google Scholar

16. Millis SR, Putnam SH, Adams KM, Ricker JH. The california verbal learning test in the detection of incomplete effort in neuropsychological evaluation. Psychol Assessment (1995) 7:463. doi: 10.1037/1040-3590.7.4.463

CrossRef Full Text | Google Scholar

17. Berry DTR, Schipper LJ. Detection of feigned psychiatric symptoms during forensic neuropsychological examinations. In: Larrabee GJ, editor. Assessment of Malingered Neuropsychological Deficits. New York, NY: Oxford University Press (2007). p. 226–63.

Google Scholar

18. Rosenfeld B, Sands SA, Van Gorp WG. Have we forgotten the base rate problem? Methodological issues in the detection of distortion. Arch Clin Neuropsychol. (2000) 15:349–59. doi: 10.1093/arclin/15.4.349

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Rogers R. Structured interviews and dissimulation. In: Rogers R, editor. Clinical Assessment of Malingering and Deception. 3rd ed. New York, NY: Guilford (2008). p. 301–22.

20. Rogers R, Bender SD. Evaluation of malingering and related response styles. In: Weiner IB, Otto RK, editors. Handbook of Psychology, Vol. 11: Forensic Psychology. 2nd ed. New Jersey, NJ: John Wiley and Sons (2013). p. 517–40.

Google Scholar

21. Rogers R, Correa AA. Determinations of malingering: evolution from case-based methods to detection strategies. Psychiatr Psychol Law (2008) 15:213–23. doi: 10.1080/13218710802014501

CrossRef Full Text | Google Scholar

22. Rogers R, Gillard ND, Berry DTR, Granacher RPJ. Effectiveness of the MMPI-2-RF validity scales for feigned mental disorders and cognitive impairment: a known-groups study. J Psychopathol Behav Assessment (2011) 33:355–67. doi: 10.1007/s10862-011-9222-0

CrossRef Full Text | Google Scholar

23. Rogers R, Harrell EH, Liff CD. Feigning neuropsychological impairment: a critical review of methodological and clinical considerations. Clin Psychol Rev. (1993) 13:255–74. doi: 10.1016/0272-7358(93)90023-F

CrossRef Full Text | Google Scholar

24. Rogers R. Detection strategies for malingering and defensiveness. In: Rogers R, editor. Clinical Assessment of Malingering and Deception. 3rd ed. New York, NY: Guilford (2008). p. 14–35.

Google Scholar

25. Heilbronner RL. Structured Interview of Reported Symptoms (SIRS). In: Kreutzer JS, DeLuca J, Caplan B, editors. Encyclopedia of Clinical Neuropsychology. New York, NY: Springer (2011). p. 2417–8. Available online at: http://link.springer.com/referenceworkentry/10.1007/978-0-387-79948-3_848

Google Scholar

26. Singh J, Avasthi A, Grover S. Malingering of psychiatric disorders: a review. German J Psychiatr. (2007) 10:126–32.

Google Scholar

27. Edens JF, Poythress NG, Watkins-Clay MM. Detection of malingering in psychiatric unit and general population prison inmates: a comparison of the PAI, MS, and SIRS. J Personal Assessment (2007) 88:33–42. doi: 10.1080/00223890709336832

CrossRef Full Text | Google Scholar

28. Rogers R, Gillis JR, Bagby RM, Monteiro E. Detection of malingering on the Structured Interview of Reported Symptoms (SIRS). Psychol Assessment (1991) 3:673–7. doi: 10.1037/1040-3590.3.4.673

CrossRef Full Text | Google Scholar

29. Seron X. Lying in neuropsychology. Clin Neurophysiol. (2014) 44:389–403. doi: 10.1016/j.neucli.2014.04.002

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Smith GP, Burger GK. Detection of malingering: validation of the Structured Inventory of Malingered Symptomatology (SIMS). J Am Acad Psychiatr Law Online (1997) 25:183–9.

PubMed Abstract | Google Scholar

31. Ben-Porath YS. Interpreting the MMPI-2-RF. Minneapolis, MN: University of Minnesota Press (2012).

PubMed Abstract

32. Butcher JN, Williams CL. Personality assessment with the MMPI-2: historical roots, international adaptations, and current challenges. Appl Psychol Health Well-Being (2009) 1:105–35. doi: 10.1111/j.1758-0854.2008.01007.x

CrossRef Full Text | Google Scholar

33. Greene RL. Malingering and defensiveness on the MMPI-2. In: Rogers R, editor. Clinical Assessment of Malingering and Deception. 3rd ed. New York, NY: Guilford (2008). p. 159–81.

34. Heinze MC, Purisch AD. Beneath the mask: use of psychological tests to detect and subtype malingering in criminal defendants. J Foren Psychol Pract. (2001) 1:23. doi: 10.1300/J158v01n04_02

CrossRef Full Text | Google Scholar

35. Pope KS, Butcher JN, Seelen J. The MMPI, MMPI-2 and MMPI-A in Court: A Practical Guide for Expert Witnesses and Attorneys. 3rd ed. Washington, DC: American Psychological Association (2006).

Google Scholar

36. Rogers R, Neumann CS. Conceptual issues and explanatory models of malingering. In: Halligan PW, Bass CM, Oakley DA, editors. Malingering and Illness Deception. New York, NY: Oxford University Press (2003). p. 71–82.

Google Scholar

37. Berry DTR, Baer RA, Harris MJ. Detection of malingering on the MMPI: a meta-analysis. Clin Psychol Rev. (1991) 11:585–98. doi: 10.1016/0272-7358(91)90005-F

CrossRef Full Text | Google Scholar

38. Rogers R, Sewell KW, Goldstein AM. Explanatory models of malingering. Law Hum Behav. (1994) 18:543–52. doi: 10.1007/BF01499173

CrossRef Full Text | Google Scholar

39. Greene RL. The MMPI-2: An Interpretive Manual. Boston, MA: Allyn and Bacon (2000).

Google Scholar

40. Pelfrey WV. The relationship between malingerers' intelligence and MMPI-2 knowledge and their ability to avoid detection. Int J Offend Ther Comp Criminol. (2004) 48:649–63. doi: 10.1177/0306624X04265085

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Raine M. Helping advocates to understand the psychological diagnosis and assessment of malingering. Psychiatr Psychol Law (2009) 16:322–8. doi: 10.1080/13218710802389457

CrossRef Full Text | Google Scholar

42. Heinze MC. Developing sensitivity to distortion: utility of psychological tests in differentiating malingering and psychopathology in criminal defendants. J Foren Psychiatr Psychol. (2003) 14:151–77. doi: 10.1080/1478994031000077961

CrossRef Full Text | Google Scholar

43. Millon T, Millon C, Davis RD, Grossman S. Millon Clinical Multiaxial Inventory-III (MCMI-III): Manual. Upper Saddle River, NJ: Pearson/PsychCorp (2009).

44. Miller HA. M-FAST: Miller-Forensic Assessment of Symptoms Test Professional Manual. Odessa, FL: Psychological Assessment Resources (2001).

45. Guriel J, Yañez T, Fremouw W, Shreve-Neiger A, Ware L, Filcheck H, et al. Impact of coaching on malingered postraumatic stress symptoms on the M-FAST and the TSI. J Foren Psychol Pract. (2004) 4:37–56. doi: 10.1300/J158v04n02_02

CrossRef Full Text | Google Scholar

46. Miller HA. The Miller-Forensic Assessment of Symptoms Test (M-FAST) test generalizability and utility across race literacy, and clinical opinion. Criminal Justice Behav. (2005) 32:591–611. doi: 10.1177/0093854805278805

CrossRef Full Text | Google Scholar

47. Smith GP. Brief screening measures for the detection of feigned psychopathology. In: Rogers R, editor. Clinical Assessment of Malingering and Deception. 3rd ed. New York, NY: Guilford (2008). p. 323–39.

Google Scholar

48. Guy LS, Miller HA. Screening for malingered psychopathology in a correctional setting utility of the Miller-Forensic Assessment of Symptoms Test (M-FAST). Criminal Justice Behav. (2004) 31:695–716. doi: 10.1177/0093854804268754

CrossRef Full Text | Google Scholar

49. Vitacco MJ, Rogers R, Gabel J, Munizza J. An evaluation of malingering screens with competency to stand trial patients: a known-groups comparison. Law Hum Behav. (2007) 31:249–60. doi: 10.1007/s10979-006-9062-8

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Miller HA. Examining the use of the M-FAST with criminal defendants incompetent to stand trial. Int J Offend Ther Comp Criminol. (2004) 48:268–80. doi: 10.1177/0306624X03259167

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Rogers R. Current status of clinical methods. In: Rogers R, editor. Clinical Assessment of Malingering and Deception. 3rd ed. New York, NY: Guilford Press (2012). p. 391–410.

Google Scholar

52. Young G. Malingering, Feigning, and Response Bias in Psychiatric/Psychological Injury: Implications for Practice and Court. Vol. 56. Dordrecht: Springer (2014). Available online at: http://link.springer.com/10.1007/978-94-007-7899-3

53. Ashendorf L, Constantinou M, McCaffrey RJ. The effect of depression and anxiety on the TOMM in community-dwelling older adults. Arch Clin Neuropsychol. (2004) 19:125–30. doi: 10.1016/S0887-6177(02)00218-4

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological Assessment. 5th ed. OUP. New York, NY (2012).

PubMed Abstract

55. Grote CL, Hook JN. Forced-choice recognition tests of malingering. In: Larrabee GJ, editor. Assessment of Malingered Neuropsychological Deficits. New York, NY: Oxford University Press (2007). p. 44–79.

Google Scholar

56. Tombaugh TN. The Test of Memory Malingering (TOMM): normative data from cognitively intact and cognitively impaired individuals. Psychol Assessment (1997) 9:260–8. doi: 10.1037/1040-3590.9.3.260

CrossRef Full Text | Google Scholar

57. Tan JE, Slick DJ, Strauss E, Hultsch DF. How'd they do it? Malingering strategies on symptom validity tests Clin Neuropsychol. (2002) 16:495–505. doi: 10.1076/clin.16.4.495.13909

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Guidotti Breting LM, Sweet JJ. Freestanding cognitive symptom validity tests: use and selection in mild traumatic brain injury. In: Bush SS, Carone DA, editors. Mild Traumatic Brain Injury: Symptom Validity Assessment and Malingering. New York, NY: Springer (2013). Co. p. 145–57. Available online at: http://ezproxy.uvu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=trueanddb=e025xnaandAN=477408andsite=eds-live

Google Scholar

59. Pella RD, Hill BD, Singh AN, Hayes JS, Gouvier WD. Noncredible performance in mild traumatic brain injury. In: Reynolds CR, Horton AM, editors. Detection of Malingering During Head Injury Litigation. Boston, MA: Springer (2012). p. 121–50. Available online at: http://link.springer.com/10.1007/978-1-4614-0442-2_3

Google Scholar

60. Frederick RI. A review of Rey's strategies for detecting malingered neuropsychological impairment. J Foren Neuropsychol. (2002) 2:1–25. doi: 10.1300/j151v02n03_01

CrossRef Full Text | Google Scholar

61. Williams JM, Jones K. Factitious responding and malingered memory disorder. In: Reynolds CR, Horton AM, editors. Detection of Malingering During Head Injury Litigation. New York, NY: Springer (2012). p. 169–99. Available online at: http://0-dx.doi.org.dewey2.library.denison.edu/10.1007/978-1-4614-0442-2_5

Google Scholar

62. Arnett PA, Hammeke TA, Schwartz L. Quantitative and qualitative performance on Rey's 15-item test in neurological patients and dissimulators. Clin Neuropsychol. (1995) 9:17–26. doi: 10.1080/13854049508402052

CrossRef Full Text | Google Scholar

63. Schretlen D, Brandt J, Krafft L, Van Gorp W. Some caveats in using the Rey 15-Item Memory Test to detect malingered amnesia. Psychol Assessment (1991) 3:667–72. doi: 10.1037/1040-3590.3.4.667

CrossRef Full Text | Google Scholar

64. Strauss E, Sherman EMS, Spreen O. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary. Oxford: Oxford University Press (2006).

Google Scholar

65. Vallabhajosula B, Van Gorp W. Post-Daubert admissibility of scientific evidence on malingering of cognitive deficits. Arch Clin Neuropsychol. (2000) 15:847. doi: 10.1093/arclin/15.8.847

CrossRef Full Text | Google Scholar

66. Vickery CD, Berry DTR, Inman TH, Harris MJ, Orey SA. Detection of inadequate effort on neuropsychological testing. Arch Clin Neuropsychol. (2001) 16:45–73. doi: 10.1016/s0887-6177(99)00058-x

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Green P, Lees-Haley PR, Allen LM. The Word Memory Test and the validity of neuropsychological test scores. J Foren Neuropsychol. (2002) 2:97–124. doi: 10.1300/J151v02n03_05

CrossRef Full Text | Google Scholar

68. Dunn TM, Shear PK, Howe S, Ris MD. Detecting neuropsychological malingering: effects of coaching and information. Arch Clin Neuropsychol. (2003) 18:121–34. doi: 10.1016/S0887-6177(01)00188-3

PubMed Abstract | CrossRef Full Text | Google Scholar

69. DePaulo BM, Kashy DA. Everyday lies in close and casual relationships. J Personal Soc Psychol. (1998) 74:63–79. doi: 10.1037/0022-3514.74.1.63

PubMed Abstract | CrossRef Full Text | Google Scholar

70. DePaulo BM, Kashy DA, Kirkendol SE, Wyer MM, Epstein JA. Lying in everyday life. J Personal Soc Psychol. (1996) 70:979–95. doi: 10.1037/0022-3514.70.5.979

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Bond CF, DePaulo BM. Accuracy of deception judgments. Personal Soc Psychol Rev. (2006) 10:214–34. doi: 10.1207/s15327957pspr1003_2

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Bond CF Jr, DePaulo BM. Individual differences in judging deception: accuracy and bias. Psychol Bull. (2008) 134:477–92. doi: 10.1037/0033-2909.134.4.477

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Vrij A. Detecting Lies and Deceit: Pitfalls and Opportunities. 2nd ed. Hoboken, NJ: Wiley (2008).

Google Scholar

74. Vrij A, Mann S. Telling and detecting lies in a high-stake situation: the case of a convicted murderer. Appl Cogn Psychol. (2001) 15:187–203.

Google Scholar

75. Zuckerman M, DePaulo BM, Rosenthal R. Verbal and nonverbal communication of deception. In: Berkowitz L, editor. Advances in Experimental Social Psychology. Vol. 14. New York, NY: Academic Press (1981). p. 1–59.

76. Bull, P. (2009). Detecting deceit: current issues. Int Dev Investig Interview 12:190–206. doi: 10.1350/ijps.2010.12.1.167

CrossRef Full Text | Google Scholar

77. Ekman P, O'Sullivan M. Who can catch a liar? Am Psychol. (1991) 46:913–20.

PubMed Abstract | Google Scholar

78. Nicholson K, Martelli MF. Malingering: overview and basic concepts. In: Young G, Kane AW, Nicholson K, editors. Causality of Psychological Injury: Presenting Evidence in Court. New York, NY: Springer Science+Business Media, LLC (2007). p. 375–409.

Google Scholar

79. Vrij A, Mann S. Who killed my relative? Police officers' ability to detect real-life high-stakes lies. Psychol Crime Law (2001) 7:119–32. doi: 10.1080/10683160108401791

CrossRef Full Text | Google Scholar

80. Lykken DT. A Tremor in the Blood: Uses and Abuses of the Lie Detector. New York, NY: McGraw-Hill (1998).

Google Scholar

81. National Research Council. The Polygraph And Lie Detection. Committee to Review the Scientific Evidence on the Polygraph. Washington, DC: National Academies Press (2003). Available online at: http://site.ebrary.com/id/10032342

82. Boaz TL, Perry NW, Raney G, Fischler IS, Shuman D. Detection of guilty knowledge with event-related potentials. J Appl Psychol. (1991) 76:788–95. doi: 10.1037/0021-9010.76.6.788

CrossRef Full Text | Google Scholar

83. Ekman P. Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage. New York, NY: W.W. Norton (1992).

84. Gombos VA. The cognition of deception: the role of executive processes in producing lies. Genet Soc Gen Psychol Monogr. (2006) 132:197–214. doi: 10.3200/MONO.132.3.197-214

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Elaad E. Detection of guilty knowledge in real-life criminal investigations. J Appl Psychol. (1990) 75:521–9. doi: 10.1037/0021-9010.75.5.521

CrossRef Full Text | Google Scholar

86. Seymour TL, Fraynt BR. Time and encoding effects in the concealed knowledge test. Appl Psychophysiol Feedback (2009) 34:177–87. doi: 10.1007/s10484-009-9092-3

PubMed Abstract | CrossRef Full Text | Google Scholar

87. Seymour TL, Kerlin JR. Successful detection of verbal and visual concealed knowledge using an RT-based paradigm. Appl Cogn Psychol. (2008) 22:475–90. doi: 10.1002/acp.1375

CrossRef Full Text | Google Scholar

88. Seymour TL, Seifert CM, Shafto MG, Mosmann AL. Using response time to measures to assess “guilty knowledge.” J Appl Psychol. (2000) 85:30–7. doi: 10.1037/0021-9010.85.1.30

PubMed Abstract | CrossRef Full Text | Google Scholar

89. Varga M, Visu-Petra L, Miclea M, Buş I. The RT-based concealed information test: an overview of current research and future perspectives. Procedia Soc Behav Sci. (2014) 127:681–5. doi: 10.1016/j.sbspro.2014.03.335

CrossRef Full Text | Google Scholar

90. Verschuere B, Crombez G, Degrootte T, Rosseel Y. Detecting concealed information with reaction times: validity and comparison with the polygraph. Appl Cogn Psychol. (2010) 24:991–1002. doi: 10.1002/acp.1601

CrossRef Full Text | Google Scholar

91. Rosenfeld J. Detecting Concealed Information and Deception: Recent Developments. London: Academic Press (2018).

Google Scholar

92. Rosenfeld JP, Biroschak JR, Furedy JJ. P300-based detection of concealed autobiographical versus incidentally acquired information in target and non-target paradigms. Int J Psychophysiol. (2006) 60:251–9. doi: 10.1016/j.ijpsycho.2005.06.002

PubMed Abstract | CrossRef Full Text | Google Scholar

93. Rosenfeld JP, Cantwell B, Nasman VT, Wojdac V, Ivanova S, Mazzeri L. A modified, event-related potential-based guilty knowledge test. Int J Neurosci. (1988) 24:157–61. doi: 10.3109/00207458808985770

CrossRef Full Text | Google Scholar

94. Dionisio DP, Granholm E, Hillix WA, Perrine WF. Differentiation of deception using pupillary response as an index of cognitive processing. Psychophysiology (2001) 38:205–11. doi: 10.1111/1469-8986.3820205

CrossRef Full Text | Google Scholar

95. Allen JJ. Clinical applications of the concealed knowledge test. In: Verschuere B, Ben-Shakhar G, Meijer E, editors. Memory Detection: Theory and Application of the Concealed Information Test. Cambridge, UK: Cambridge University Press (2011). p. 231–52.

Google Scholar

96. Sweller J. Cognitive load during problem solving: effects on learning. Cogn Sci. (1988) 12:257–85. doi: 10.1016/0364-0213(88)90023-7

CrossRef Full Text | Google Scholar

97. van Merriënboer JJG, Sweller J. Cognitive load theory and complex learning: recent developments and future directions. Edu Psychol Rev. (2005) 17:147–77. doi: 10.1007/s10648-005-3951-0

CrossRef Full Text | Google Scholar

98. Vrij A, Mann S. Criteria-based content analysis: an empirical test of its underlying processes. Psychol Crime Law (2006) 12:337–49. doi: 10.1080/10683160500129007

CrossRef Full Text | Google Scholar

99. Langleben DD, Loughead JW, Bilker WB, Ruparel K, Childress AR, Busch SI, et al. Telling truth from lie in individual subjects with fast event-related fMRI. Hum Brain Mapping (2005) 26:262–72. doi: 10.1002/hbm.20191

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Mohamed FB, Faro SH, Gordon NJ, Platek SM, Ahmad H, Williams JM. Brain mapping of deception and truth telling about an ecologically valid situation: functional MR imaging and polygraph investigation—initial experience. Radiology (2006) 238:679–88. doi: 10.1148/radiol.2382050237

PubMed Abstract | CrossRef Full Text | Google Scholar

101. Vrij A, Fisher R, Mann S, Leal S. Detection of deception: cognitive load. In: Cutler B, editor. Encyclopedia of Psychology and Law. Thousand Oaks, CA: SAGE Publications (2008). p. 195–6. Available online at: http://eprints.port.ac.uk/11201/

102. Walczyk JJ, Roper KS, Seemann E, Humphrey AM. Cognitive mechanisms underlying lying to questions: response time as a cue to deception. Appl Cogn Psychol. (2003) 17:755. doi: 10.1002/acp.914

CrossRef Full Text | Google Scholar

103. Vrij A, Leal S, Granhag PA, Mann S, Fisher RP, Hillman J, et al. Outsmarting the liars: the benefit of asking unanticipated questions. Law Hum Behav. (2009) 33:159–66. doi: 10.1007/s10979-008-9143-y

PubMed Abstract | CrossRef Full Text | Google Scholar

104. DePaulo BM, Lindsay JJ, Malone BE, Muhlenbruck L, Charlton K, Harris C. Cues to deception. Psychol Bull. (2003) 129:74–112. doi: 10.1037/0033-2909.129.1.74

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Loftus EF. Memory distortions: problems solved and unsolved. In: Garry M, Hayne H, editors. Do Justice and Let the Sky Fall: Elizabeth Loftus and Her Contributions to Science, Law, and Academic Freedom. Mahwah, NJ: Lawrence Erlbaum Associates Publishers (2007). p. 1–14.

Google Scholar

106. Vrij A, Mann S, Leal S, Fisher R. “Look into my eyes”: can an instruction to maintain eye contact facilitate lie detection? Psychol Crime Law (2010) 16:327–48. doi: 10.1080/10683160902740633

CrossRef Full Text | Google Scholar

107. Greenwald AG, McGhee DE, Schwartz JKL. Measuring individual differences in implicit cognition: the implicit association test. J Personal Soc Psychol. (1998) 74:1464–80. doi: 10.1037/0022-3514.74.6.1464

PubMed Abstract | CrossRef Full Text | Google Scholar

108. Sartori G, Agosta S, Zogmaister C, Ferrara SD, Castiello U. How to accurately detect autobiographical events. Psychol Sci. (2008) 9:772–80. doi: 10.1111/j.1467-9280.2008.02156.x

CrossRef Full Text | Google Scholar

109. Sartori G, Agosta S, Gnoato F. High accuracy detection of malingered whiplash syndrome. Presentation at the International Whiplash Trauma Congress. Miami, FL (2007).

110. Ferrara SD, Ananian V, Baccino E, Boscolo–Berto R, Domenici R, Hernàndez-Cueto C, et al. A novel methodology for the objective ascertainment of psychic and existential damage. Int J Legal Med. (2016) 130:1387–99. doi: 10.1007/s00414-016-1366-8

PubMed Abstract | CrossRef Full Text | Google Scholar

111. Monaro M, Gamberini L, Sartori G. The detection of faked identity using unexpected questions and mouse dynamics. PLoS ONE (2017) 12:e0177851. doi: 10.1371/journal.pone.0177851

PubMed Abstract | CrossRef Full Text | Google Scholar

112. Gregg A. When vying reveals lying: the timed antagonistic response alethiometer. Appl Cogn Psychol. (2007) 21:621–47. doi: 10.1002/acp.1298

CrossRef Full Text | Google Scholar

113. Monaro M, Gamberini L, Zecchinato F, Sartori G. False identity detection using complex sentences. Front Psychol. (2018) 9:283. doi: 10.3389/fpsyg.2018.00283

PubMed Abstract | CrossRef Full Text | Google Scholar

114. Monaro M, Toncini A, Ferracuti S, Tessari G, Vaccaro MG, De Fazio P, et al. The detection of malingering: a new tool to identify made up depression. Front Psychiatr. (2018) 9:249. doi: 10.3389/fpsyt.2018.00249

PubMed Abstract | CrossRef Full Text | Google Scholar

115. Walczyk JJ, Schwartz JP, Clifton R, Adams B, Wei M, Zha P. Lying person-to-person about life events: a cognitive framework for lie detection. Personnel Psychol. (2005) 58:141–70. doi: 10.1111/j.1744-6570.2005.00484.x

CrossRef Full Text | Google Scholar

116. Walczyk JJ, Mahoney K, Doverspike D, Griffith-Ross D. Cognitive lie detection: response time and consistency of answers as cues to deception. J Business Psychol. (2009) 24:33–49. doi: 10.1007/s10869-009-9090-8

CrossRef Full Text | Google Scholar

117. Buller DB, Burgoon JK. Interpersonal deception theory. Commun Theory (1996) 6:203–42. doi: 10.1111/j.1468-2885.1996.tb00127.x

CrossRef Full Text | Google Scholar

118. Burgoon JK, Buller DB. Interpersonal deception theory. In: Baxter LA, Braithewaite DO, Dawn O, editors. Engaging Theories in Interpersonal Communication: Multiple Perspectives. Thousand Oaks, CA: Sage Publications (2008). p. 227-39.

Google Scholar

119. Lane JD, Wegner DM. The cognitive consequences of secrecy. J Personal Soc Psychol. (1995) 69:237–53. doi: 10.1037/0022-3514.69.2.237

CrossRef Full Text | Google Scholar

120. Granhag PA, Hartwig M. A new theoretical perspective on deception detection: on the psychology of instrumental mind-reading. Psychol Crime Law (2008)14:189–200. doi: 10.1080/10683160701645181

CrossRef Full Text | Google Scholar

121. Walczyk JJ, Griffith DA, Yates R, Visconte S, Simoneaux B, Harris LL. Lie detection by inducing cognitive load: eye movements and other cues to the false answers of “witnesses” to crimes. Criminal Justice Behav. (2012) 39:887–909. doi: 10.1177/0093854812437014

CrossRef Full Text | Google Scholar

122. Walczyk JJ, Griffith DA, Yates R, Visconte S, Simoneaux B. Eye movements and other cognitive cues to rehearsed and unrehearsed deception when interrogated about a mock crime. Appl Psychol Criminal Justice (2013) 9:1.

Google Scholar

123. Walczyk JJ, Harris LL, Duck TK, Mulay D. A social-cognitive framework for understanding serious lies: activation-decision-construction-action theory. New Ideas Psychol. (2014) 34:22–36. doi: 10.1016/j.newideapsych.2014.03.001

CrossRef Full Text | Google Scholar

124. Apperly IA, Back E, Samson D, France L. The cost of thinking about false beliefs: evidence from adults' performance on a non-inferential theory of mind task. Cognition (2008) 106:1093–108. doi: 10.1016/j.cognition.2007.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

125. Kobayakawa M, Tsuruya N, Kawamura M. Theory of mind impairment in adult-onset myotonic dystrophy type 1. Neurosci Res. (2012) 72:341–6. doi: 10.1016/j.neures.2012.01.005

PubMed Abstract | CrossRef Full Text | Google Scholar

126. Baddeley A. Exploring the central executive. Q J Exp Psychol. (1996) 49:5–28. doi: 10.1080/713755608

CrossRef Full Text | Google Scholar

127. Baddeley A. The episodic buffer: a new component of working memory? Trends Cogn Sci. (2000) 4:417–23. doi: 10.1016/S1364-6613(00)01538-2

PubMed Abstract | CrossRef Full Text | Google Scholar

128. Stanovich KE. Decision Making and Rationality in the Modern World. New York, NY: Oxford University Press (2010).

Google Scholar

129. Levine TR, Kim RK, Hamel LM. People lie for a reason: three experiments documenting the principle of veracity. Commun Res Rep. (2010) 27:271–85. doi: 10.1080/08824096.2010.496334

CrossRef Full Text | Google Scholar

130. Strömwall LA, Hartwig M, Granhag PA. To act truthfully: nonverbal behavior and strategies during a police interrogation. Psychol Crime Law (2006) 12:207–19. doi: 10.1080/10683160512331331328

CrossRef Full Text | Google Scholar

131. Strömwall LA, Willén RM. Inside criminal minds: offenders' strategies when lying. J Investig Psychol Offend Profil. (2011) 8:271–81. doi: 10.1002/jip.148

CrossRef Full Text | Google Scholar

132. Leins DA, Fisher RP, Ross SJ. Exploring liars' strategies for creating deceptive reports. Legal Criminol Psychol. (2013) 18:141–51. doi: 10.1111/j.2044-8333.2011.02041.x

CrossRef Full Text | Google Scholar

133. Sporer SL, Schwandt B. Paraverbal indicators of deception: a meta-analytic synthesis. Appl Cogn Psychol. (2006) 20:421–46. doi: 10.1002/acp.1190

CrossRef Full Text | Google Scholar

134. Sporer SL, Schwandt B. Moderators of nonverbal indicators of deception: a meta-analytic synthesis. Psychol Public Policy Law (2007) 13:1–34. doi: 10.1037/1076-8971.13.1.1

CrossRef Full Text | Google Scholar

135. O'Hair HD, Cody MJ, McLaughlin ML. Prepared lies, spontaneous lies, machiavellianism, and nonverbal communication. Hum Commun Res. (1981) 7:325–39. doi: 10.1111/j.1468-2958.1981.tb00579.x

CrossRef Full Text | Google Scholar

136. Osman M, Channon S, Fitzpatrick S. Does the truth interfere with our ability to deceive? Psychonom Bull Rev. (2009) 16:901–6. doi: 10.3758/PBR.16.5.901

PubMed Abstract | CrossRef Full Text | Google Scholar

137. Pennebaker JW, Chew CH. Behavioral inhibition and electodermal activity during deception. J Personal Soc Psychol. (1985) 49:1427–33. doi: 10.1037/0022-3514.49.5.1427

PubMed Abstract | CrossRef Full Text | Google Scholar

138. Ekman P, Frank MG. Lies that fail. In: Lewis M, Saarni C, editors. Lying and Deception in Everyday Life. New York, NY: Guilford Press (1993). p. 184–200.

Google Scholar

139. McCornack SA, Morrison K, Paik JE, Wisner AM, Zhu X. Information manipulation theory 2: a propositional theory of deceptive discourse production. J Language Soc Psychol. (2014) 33:348–77. doi: 10.1177/0261927X14534656

CrossRef Full Text | Google Scholar

140. Vrij A, Mann S, Fisher R, Leal S, Milne B, Bull R. Increasing cognitive load to facilitate lie detection: the benefit of recalling an event in reverse order. Law Hum Behav. (2008) 32:253–65. doi: 10.1007/s10979-007-9103-y

PubMed Abstract | CrossRef Full Text | Google Scholar

141. Johnson MK, Raye CL. Reality monitoring. Psychol Rev. (1981) 88:67–85. doi: 10.1037/0033-295X.88.1.67

CrossRef Full Text | Google Scholar

Keywords: malingering detection techniques, cognitive malingering detection, theory of mind, forensic psychiatry, inducing cognitive load

Citation: Walczyk JJ, Sewell N and DiBenedetto MB (2018) A Review of Approaches to Detecting Malingering in Forensic Contexts and Promising Cognitive Load-Inducing Lie Detection Techniques. Front. Psychiatry 9:700. doi: 10.3389/fpsyt.2018.00700

Received: 12 April 2018; Accepted: 03 December 2018;
Published: 21 December 2018.

Edited by:

Cristina Scarpazza, Università degli Studi di Padova, Italy

Reviewed by:

Merylin Monaro, Università degli Studi di Padova, Italy
Ilaria Zampieri, IMT School for Advanced Studies Lucca, Italy

Copyright © 2018 Walczyk, Sewell and DiBenedetto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jeffrey J. Walczyk, walczyk@latech.edu