Thursday, March 13, 2008

Detecting Deception in Neuropsychological Cases:


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Detecting Deception in Neuropsychological Cases:

Toward an Applied Model

Abstract

The neuropsychological literature and clinical-forensic experience in both criminal and civil contexts suggest an applied model of deception analysis. This article presents such a model in terms of a faker’s (1) target/symptoms, (2) response styles, and (3) detection strategies. The deceiver’s targets/symptoms can be broken down into distorted behavioral, affective, cognitive, psychophysiological, and somatic problems. Response styles include honest responding (from the perception of the assessee), faking bad (malingering), faking good (defensiveness), attempts at invalidation, mixed responding (faking good and bad), and a fluctuating, changing style that occurs within one evaluation session. Detection strategies involve the use of neurometric and psychometric testing, observation, clinical and structured interviews, and comparison to values such as base rates for the deceptive group to which the faker holds membership. Expert testimony that meets Daubert standards, based on such an applied model of deception analysis and report writing, clearly communicates the decision path and findings of the forensic evaluator.

Fall 2007 THE FORENSIC EXAMINER

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By Harold V. Hall, PhD; Jane S. Thompson, PhD; and Joseph G. Poirier, PhD, ABPP

Key Words: deception analysis, neuropsychological malingering, faking good, faking bad, applied model

Introduction

Faking cerebral dysfunction has an extensive and infamous history. Malingering brain damage has long been recognized in forensic settings despite an unwarranted belief that neuropsychological tests could not be malingered (Hart, 1995; Heaton, Smith, Lehman, & Vogt, 1978; Miller & Cartlidge, 1972; Ziskin & Faust, 1988). The assessment of malingered traumatic brain injury (TBI) is difficult for several reasons. To begin, in spite of considerable research, there is little support for any type of “malingering profile” on neuropsychological tests (Franzen, Iverson, & McCracken, 1990; Heubrock & Peterman, 1998; van Gorp et al., 1999). Actual malingerers rarely acknowledge their deception and, therefore, it is difficult to distinguish malingerers from honest responders for empirical comparison (Greiffenstein, Baker, & Gola, 1994; Hartwig, Granhag, Stromwall, & Vrij, 2005; Kassin, 2004; Meissner & Kassin, 2002; Vrij, 2000).

Few applied models of feigning cerebral dysfunctions have been offered in the forensic neuropsychological literature. This article incorporates Hall & Poirier’s (2001) model, which scrutinizes targets of the faker, response styles employed, and the detection strategies used to uncover feigned neuropsychological dysfunctions (see Table 1).

Targets

For the first part of the model involving targets and goals of the faker, the basic questions concern whether the individual has a motive for faking and the most likely targets of deception. Keep in mind that in deliberate deception, of which malingering is an example, DSM-IV-TR states that the individual is externally motivated and is aware of the motivation behind the deception. According to DSM-IV-TR, a factitious disorder is shown by an individual who is usually not aware of his or her own motivation behind the distorted behavior and who lacks external incentives. However, this article considers deceptive behavior to include factitious disorders because the symptoms of both are deliberately faked.

Targets can be broken down into the building blocks of personality—behavioral, somatic, sensory, affective, cognitive, and interpersonal—as discussed by Lazarus (1976, 2002, 2006). For example, a faker may display a deficiency in behavior such as mutism, or pretending to have lost the ability to speak. The selection of targets by the deceiver is limited only by the deceiver’s imagination, and targets have been found to take myriad forms within those categories listed above (Hall & Poirier, 2001; Rogers, 1997). One faker may wish to present an exaggerated pain response in an attempt to feign difficulty in ambulation, whereas another may show slowness of response or a festinating gait to convey the same problem. The evaluator should keep in mind that targets of the faker may change, but the long-range objective of the deception (for example, increased compensation) usually does not (Hall & Poirier; Hall & Pritchard, 1996).

The evaluator is urged to take into account that fear of getting caught may affect the selection of faking criteria. As described by Freedland (1982), this occurs for two reasons:

First, mild deficits are easier to fake than severe ones; therefore a mild faking criterion is less likely to be questioned by an examiner. Second, choices of criteria may be influenced by the effects they will have on the patient’s lifestyle. For example, fake unilateral deafness is seen more often than fake bilateral deafness. Bilateral deafness might merit a larger compensatory settlement, but the patient wishing to avoid later charges of fraud might have to feign total deafness indefinitely. Unilateral deafness is worth less money, but the patient is able to engage in most of his or her favorite activities without arousing suspicion.

Response Styles

Response styles are behaviors exhibited by the faker to achieve some outcome. The assessment of response styles lies at the crux of a deception analysis using the applied model. The response styles themselves provide the basis of the detection strategies employed by the evaluator presented in the next section.

With regard to the applied model, six primary response styles should be considered. The first is honest responding, where the examinee, from his or her perspective, presents non-deceptive behavior. Honest responding is not equivalent to a good performance in that some examinees may consistently err on testing tasks because of genuine deficiencies (e.g., mental retardation, florid psychosis, dementia; Rogers & Bender, 2003).

The second response style is faking bad, or malingering. This is the category in which most of the examples in this article fall, where the assessee deliberately attempts to exaggerate or fabricate a problem or denies or minimizes his or her strengths. There are many behaviors that fakers may employ in attempts to feign believable deficits on neuropsychological evaluations (Aubrey, Dobbs, & Rule, 1989; Craine, 1981, 1990; Drummond, 2004; Hall, 1985, 1990; Hall & Pritchard, 1996; Hall & Poirier, 2001):

  • Present realistic symptoms—A deceiver will employ a common sense or popular schema of what persons with brain damage are like and will select symptoms that accord with that naive view (Aubrey et al., 1989).
  • Distribute errors—To cover their targets, fakers tend to make deliberate mistakes throughout their evaluations rather than miss only difficult items. A balance is sought between appearing fully functional (missing too few items) and appearing too impaired (missing too many items). Fakers attempt to control their errors as much as possible, but, in practice, they fail to maintain a realistic percentage of errors.
  • Protest that tasks are too difficult and/or feign confusion and frustration—The faker may feign confusion, anger, or other emotions superimposed upon adequate cooperation and task compliance.
  • Perform at a crudely estimated fraction of actual ability—Speed may be deliberately decreased. The faker is generally knowledgeable of his or her true rate of responding but may decide to show a partial performance.

Table 1 presents 12 types of faking bad, the associated behavior strategy, and an example of each.

A third response style is faking good, which involves denial or downplaying real problems and/or fabricating or exaggerating existing strengths and abilities. Fourth is invalidation, which is the deliberate attempt to sabotage or render useless evaluation efforts (e.g., missing appointments, incorrect marking on computer-scored measures). A fifth response style is mixed responding, such as faking both bad and good during one evaluation session (e.g., denial of sexual problems with exaggeration of depression on the MMPI). Finally, the last response style is a fluctuating style, which is, within one session, changing how one responds to test questions or the examiner (e.g., from honesty to malingering in accordance with a plan to do so; from faking good to attempts to invalidate testing as the examinee becomes fatigued).

Detection Strategies

Detection strategies employed by the evaluator have been investigated for their effectiveness at detecting particular response patterns of the faker as well as for characteristics of standardized tests used to assess deception. As a measure of response set, most neuropsychologists employ personality tests in addition to specific tests of malingering.

The most widely used personality test is the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) (Butcher, 2006). The original MMPI has been a mainstay component of neuropsychological test batteries (Lezak, 1995), and the MMPI-2 has been demonstrated to have comparable utility to the MMPI with neurological populations (Miller & Paniak, 1995; Mittenberg, Tremont, & Rayls, 1996). The MMPI-2 provides the neuropsychologist with valuable information about the patient’s non-cognitive functioning to include behavioral, emotional, and psychiatric issues. Also particularly useful in forensic circumstances are the MMPI-2 scales designed to indicate response bias. Lamb, Berry, Wetter, and Baer (1994) noted that the MMPI-2 is vulnerable to simulated closed head injury; MMPI-2 findings alone, however, should not be relied upon as the sole indicator of malingering of a head injury.

Detection strategies to measure specific areas of neuropsychological faking outside the domain of personality fall into several categories (Craine, 1990; Freedland & Craine, personal communication, 1981; Hall, Shooter, Craine, & Paulsen, 1991). These include assessing lack of neurological fit, retesting or comparison strategies, assessing certain test characteristics, and searching for departures from expected levels of accuracy on forced-choice tests.

Lack of Neurological Fit

Reported history, presenting symptoms, or responses on neuropsychological tests or on individual test items must make sense compared to what is known about the functional neurological systems involved. Otherwise, a lack of neurological fit exists. For single items or symptoms, does the assessee present signs that do not make sense neurologically to a supposedly involved hemisphere (such as glove amnesia or hemiparesis ipsilateral)? On multidimensional tests, does the assessee produce a pattern of scores (profile) that is consistent with known neuropsychological syndromes?

Retesting or Comparison Strategies

There are three types of resting or comparison strategies.

Easy versus difficult versions of similar tests. The faker may not understand that a second testing may be easier or more difficult than the first. Thus, fakers may perform similarly on the two versions whereas non-fakers would perform differently.

The Dot Counting Test (DCT) illustrates this method. Cards A, B, and C (consisting of massed dots) are more difficult than their counterparts, Cards, D, E, and F (consisting of clusters of dots), even though the two sets have the same number of dots to count. Administration of the two sets of the Dot Counting Test may yield such inconsistent results that only a conscious attempt to control performance can explain them. In a meta-analytic review of selected malingering detection procedures, Vickery, Berry, Inman, Harris, and Orey (2001) found that on the DCT, malingerers scored .75 standard deviations below honest responders. In analyzing 32 studies of commonly researched neuropsychological malingering tests, Vickery et al. found that the Digit Memory Test (DMT) and the Portland Digit Recognition Test (PDRT) could effectively discriminate honest responders from fakers, separating the two groups by approximately 2 standard deviations.

Another test with built-in easy-versus-difficult items is the Auditory Discrimination Test (ADT) (Language Research Association, 1958). Initial administration of the ADT involves informing the assessee that words will be read, two at a time, and that the task is to say whether the two words are the same or different (e.g., tub - tub, lack - lack, web - wed, leg - led, chap - chap). A second form presents same or different word pairs of similar difficulty and number (N = 40) as the first form (e.g., gear - beer, cad - cab, bug - bud). In terms of threshold values, the faker may not realize that normals can miss many of the “same” items (<> 4) before the performance appears suspicious. Comparison of the hit rates for same versus different items may detect a suspicious asymmetry in the types of items missed.

Caution should be exercised, however, in deciding that failures of easy items within a test are more characteristic of deliberate distortion than of genuine responding. Mittenberg, Hammeke, and Rao (1989) examined the distribution of intratest scatter among brain-damaged and normal subjects on the vocabulary, comprehension, and similarities subtests of the Wechsler Adult Intelligence Scale - Revised (WAIS-R). Their results suggested high cut-off scores (e.g., more than six failed items interpolated among passed items) for distinguishing brain-damaged subjects from normal subjects. If intratest scatter is to be used as an index of faking bad as well as an index of brain damage, then the cut-off scores for distinguishing actual brain damage from malingered brain damage would have to be even higher than those for distinguishing brain damaged from normal subjects.

In another study, Mittenberg, Theroux-Fichera, Zielinski, and Heilbronner (1995) compared selected WAIS-R subtest score differences between a group of recruited simulators and a mild head injury group. A significant discriminant function was identified, and the cutoffs differentiated the two groups. The TBI group showed negligible differences between vocabulary and digit span subtest performance. The simulators performed better on vocabulary compared to digit span. The authors acknowledged the limitation of their experimental cutoff scores being used with actual clinical populations.

In a third study, from a survey of the members of the American Board of Clinical Neuropsychology (ABCN), Mittenberg, Patton, Canyock, and Condit (2002) determined the base rate for faking bad based on more than 33,500 cases involving personal injury, disability, criminal, and medical matters. Base rates for malingering did not change for geographic location or type of setting but were affected by type of referral (plaintiff vs. defense). The following rates for probable malingering were reported: personal injury, 29%; disability, 30%; criminal, 19%; medical cases, 8%; mild head injury, 39%; fibromyalgia/chronic fatigue, 35%; chronic pain, 31%; neurotoxic, 27%; and electrical injury, 22%. Mittenberg et al. (2002) stated the following:

Diagnosis was supported by multiple sources of evidence, including severity (65% of cases) or pattern (64% of cases) of cognitive impairment that was inconsistent with the condition, scores below empirical cutoffs on forced choice tests (57% of cases); discrepancies among records, self-report, and observed behavior (56%); implausible self-reported symptoms in interview (46%); implausible changes in test scores across repeated examinations (45%); and validity scales on objective personality tests (38% of cases).

Parallel testing. Repeatedly administrating the same test or a parallel form of a test should yield similar performances. The faker may not understand that a repeat of the test will be given and, therefore, may have difficulty replicating the previous performance. Faked scores in general are less stable than genuine scores.

The Peabody Picture Vocabulary Test (PPVT) as a test of receptive vocabulary is an example. Faking or malingering clients often have difficulty obtaining the same score on a parallel form even when the test is administered a short time later.

Deviations from predicted scores. The evaluator can compare predicted scores on a test with actual performance on that test. For example, regression equations have been developed to predict WAIS-R scores from scores on the Shipley-Hartford Institute of Living Scale (Weiss & Schell, 1991; Zachry, 1986), Ravens Progressive Matrices (O’Leary, Rusch, & Gudstello, 1991), and the National Adult Reading Test (Willshire, Kinsella, & Pryor, 1991). A faker’s obtained score on the WAIS-R may fall outside the confidence interval predicted from one of these three other tests.

The Shipley-Hartford and Ravens Progressive Matrices in particular are useful screening measures of intelligence because they take only a short time to administer. In addition, the Shipley-Hartford provides alternate forms that will yield information on retest performance as well as gives an estimated WAIS-R IQ. Impaired non-faking subjects should obtain WAIS-R IQ scores similar to those predicted by these two tests.

Certain Test Characteristics

Some tests expose fakers by measuring inconsistencies between similar tasks or gauging a assessee’s failure to exhibit that he or she has learned.

Inconsistencies across similar items or tasks. Within the same test, the faker may not pay attention to item similarity and, therefore, not perform in an identical fashion. A less stable performance on similar items is frequently seen in fakers, just as with parallel tests. On tests with repeated trials of the same task (e.g., finger tapping, dynamometer), intertrial variability also increases with faking. However, it should be remembered that the reliability of item-level scores is much lower than the reliability of scale-level scores. Therefore, less confidence should be placed in item-level or trial-level inconsistencies than in scale-level inconsistencies.

Failure to show learning. Fakers often do not show expected learning curves (or may perhaps even show deterioration) across repeated trials of a task. The Mirror Tracing Test (Andreas, 1960; Millard, 1985) illustrates this expectation. The subject is told to trace the path between the two solid lines of a maze while viewing the maze in a mirror. An error is counted each time the subject’s pencil touches a guideline. If the subject crosses the line, he or she must re-enter at the same point; otherwise, a re-entry counts as a second error. Faking may be suspected if the expected bilateral transfer of training (improved performance with the opposite hand after training with one hand) does not occur, if the expected improvement over trials (learning curve) is not apparent, or if the total time exceeds 5 minutes.

Departures from Expected Accuracy

Forced-choice testing and forced-choice reaction-time testing provide powerful methods of assessing deception of deficits. These tasks are all so easy that even severely impaired persons should perform satisfactorily. Departures from expected levels of performance provide a measure of a conscious attempt to manipulate performance.

Symptom Validity Testing (SVT) and Explicit Alternative Testing (EAT) both attempt to measure faked sensory and recall deficits (Grosz & Zimmerman, 1965; Hall & Shooter, 1989; Hall & Thompson, 2007; Pankratz, 1979, 1983, 1988; Pankratz, Fausti, & Peed, 1975; Theodor & Mandelcorn, 1973). EAT involves the presentation of stimuli whose perception or recognition is either affirmed or denied by the assessee. An interference period may be added if recall, rather than sensory perception, is the target of evaluation.

Almost no one should miss the presented items unless a genuine impairment exists. In the case of total impairment (e.g., total blindness or deafness), one’s performance should approximate chance responding (50% accuracy with two-choice tasks). A significant deviation from chance responding is defined as an accuracy score with a probability less than some specified level (e.g., p < .05 or p < .01) as determined by the binomial distribution; for example, the one-tailed probability of obtaining fewer than 40 correct responses in 100 trials of a two-choice task is less than 2% (p = .0176). Achieving fewer than 36 correct answers would occur by chance less than twice in a thousand tests (p = .0018).

Fakers usually assume that impaired performance requires less than 50% accuracy (Haughton, Lewsley, Wilson, & Williams, 1979; Pankratz, 1988). Persons genuinely impaired will usually guess randomly on EAT testing. Fakers do worse than chance because they intentionally suppress the correct answers on items to which they know the answers.

The Smell Identification Test (SIT) provides an illustration of forced-choice testing of faked sensory deficits. Developed by Doty and colleagues at the University of Pennsylvania (Doty, Shaman, & Dann, 1984; Doty, Shaman, & Kimmelman, 1984), the 40-item SIT provides a quantitative measure of olfactory dysfunction in less than 15 minutes. The authors note that problems with the sense of smell are frequently associated with head trauma, with anosmia found in between 7% and 8% of cases.

The SIT may be useful when the assessee is suspected of malingering with regard to his or her sense of smell such as when insurance/accident claims are filed and there appears to be no basis for the claim. Four choices of smells are presented upon release of an odorant, yielding a 25% chance of accuracy in correctly identifying the designated smell, given total anosmia (10 out of 40). Most non-faking patients will correctly identify 35 or more of the 40 odorants, with females generally outscoring males at all age levels. Zero was the modal number of correct guesses for 158 men and women instructed to fake bad in the Doty, Shaman, & Kimmelman (1984) study. Doty (personal communication, 1991) notes that under the assumption that p = 0.25, the probability of obtaining a score of zero by chance is one in 100,000; the chance of obtaining five or fewer correct on the SIT is less than 5 in 100. Those with genuine problems reflecting total loss of smell (i.e., anosmia) generally score around 10 at chance level because of essentially random responding. Patients with partial dysfunction have intermediate SIT scores. Patients with multiple sclerosis yield scores slightly above average; Parkinson’s or Alzheimer’s patients produce scores that are significantly lower than average, but that are still substantially above the expected range for random responding.

Frederick (1997) described the Validity Indicator Profile (VIP), a two-alternative forced-choice (2AFC) procedure designed to identify when the results of cognitive and neuropsychological testing may be invalid because of malingering or other problematic response styles. The instrument is comprised of 100 problems that assess nonverbal abstraction capacity and 78 word-definition problems. The VIP attempts to establish a subject’s performance as representative of the subject’s overall capacity (i.e., valid or invalid). A valid performance is classified as compliant, and an invalid performance is sub-classified as careless (low effort to respond correctly), irrelevant (low effort to respond incorrectly), or malingering (high effort to respond incorrectly). Frederick & Crosby (2000) reported a cross-validation study with 152 non-clinical subjects, 61 brain-injured subjects, 49 subjects considered to be at risk for malingering, and 100 randomly generated VIP protocols. The nonverbal and verbal subtests of the VIP demonstrated overall classification rates of 79.8% (73.5% sensitivity and 85.7% specificity) and 75.5% (67.3% sensitivity and 83.1% specificity) respectively. The VIP is another promising instrument for the detection of malingering. Its author suggested that the instrument’s fourfold classification scheme (i.e., cross classification of high to low motivation and high to low effort) reduces problems with false-positive classifications.

The Victoria Symptom Validity Test (VSVT) (Slick, Hopp, Strauss, & Thompson, 1997) is a forced-choice instrument. The VSVT was designed to address the validity of reported cognitive impairments. The VSVT is computer-administered and consists of five-digit numbers of varying difficulty. One feature of the VSVT is an administration time of 10–15 minutes, while another is its production of probability values (scores for valid, questionable, and invalid profiles). The test manual has a table of binomial probability values that is used to estimate the probability of obtaining the number of correct items out of the total number of items completed. The VSVT was found to be effective in detecting feigned memory impairment (Bauer & McCaffrey, 2006; Loring, Lee, & Meador, 2005; Slick, Hopp, Strauss, & Spellacy, 1996; Slick et al., 2003). Overall, validity and reliability data are encouraging (see review by Lees-Haley, Dunn, & Betz, 1999).

Another instrument is the widely used Rey 15-Item Memory Test (FIT) (Liff, 2004; Rey, 1964). The FIT is a screening instrument designed to identify malingered memory complaints. To the examinee, the FIT initially appears to be more difficult than it actually is. The redundancy of simple character sets makes the memory task relatively simple, such that significant memory problems are necessary to generate actual deficit performance. Twenty years of studies (for a review, see Hart, 1995), mostly consisting of cutoff score refinements and adjustments, have indicated the FIT to be vulnerable to false positive findings. The FIT is unique in its simplicity and brevity, but these same attributes render it not able to yield meaningful discriminant functions. In Vickery et al.’s (2001) study, they found that the FIT separated the two groups by .75 of a standard deviation.

Importantly, the evaluator should note that empirically designed tests of deception are likely to meet the Daubert standards for admission of scientific evidence (Hall & Poirier, 2001; Hall & Thompson, 2007).

Caveats

Neuropsychologists, firstly, should understand that their training, especially in clinical settings, poorly prepares them for deception analysis. The emerging literature discussed in this article has the typical neuropsychological practice-incorporated deception analysis. Likewise, in actual clinical practice, a multidimensional view is necessary in that genuine symptoms may be distorted and/or exaggerated in intensity, frequency, and duration (Zielinski, 1995).

Secondly, the evaluator should be wary of the traditionally accepted signs of hysteria and malingering, which actually may reflect cerebral dysfunction. One study, Gould, Miller, Goldberg, & Benson (1986), surveyed the literature and found that the majority of clients (60% to 80%) thought to be hysteric or presenting neurological problems because of secondary gain, actually suffered brain damage.

A third caveat concerns the need for multiple measures of distortion. When evaluating neuropsychological patients for forensic purposes, the authors use specific devices for detecting deception in addition to a composite neuropsychological battery and as much historical and premorbid information available.

A fourth caveat involves the array of factors that are reviewed in assessing neuropsychological symptoms. In a study of base-rate data regarding postconcussive syndrome (PCS) with a large sample (N = 1,116), neurological, psychological, and environmental variables were found to affect symptom presentation (Fox, Lees-Haley, Earnest, & Dolezal-Wood, 1995). The authors observed that neuropsychological test data must be reviewed in the context of a broad range of factors before PCS complaints are used as a basis for brain damage.

A fifth caveat notes that the effects of coaching or priming others should be carefully scrutinized. There have been forensic cases in which the adversarial attorneys made available to their clients books and journal articles on deception and distortion. The implicit aspects of coaching in these situations were impossible to demonstrate but were suspected. In these cases, ironically, the clients were detected in their faking by a battery of tests, suggesting as a testable hypothesis that even with knowledge of the literature on faking, deceptive persons can nevertheless be identified.

But this may not be true. Despite widespread use of malingering instruments, few, if any, empirical studies have been published determining the vulnerability of malingering measures to explicit coaching. Dunn, Shear, Howe, and Ris (2003) studied two commonly used measures of deception—The Computerized Assessment of Response Bias-97 and The Word Memory Test—with participants aged 18–30 years, and found that both tests could detect malingering, but neither test differentiated between na├»ve and coached participants. They also found that response times and items correct were the two best indicators of participants not giving their full effort. Borckardt et al. (2003) found similar results on the Cognitive Behavioral Driver’s Inventory for 98 student subjects. The students were divided into a coached group and an un-coached group; the coached students performed indistinguishably from the un-coached students on the measure.

In an attempt to detect malingering on the Ravens Standard Progressive Matrices, McKinzey, Prieler, and Raven (2003), found that all but 2 of 44 subjects (ages 7–17 years) could produce lower scores when asked to malinger on the test. Yet a simple rule involving missing any of three easy items (i.e., A3, A4, or B1) yielded 5% true positives and negatives and an overall hit rate of 95%.

A sixth caveat rests on the necessity of the examiner’s attempting to reconstruct the malingering experience from the perspective of the suspected faker. Alban (2003) found that subjects who were asked to malinger were unable to maintain faked responses under cognitive overload for reaction-time measures, but this finding did not hold true for other neuropsychological domains. It can be speculated that for timed tasks, fakers would have difficulty determining which temporal limitation was associated with impairment.

As a last concern, the evaluator who provides expert testimony in court should be cognizant of Daubert standards for admissibility of evidence. Relevant questions for a deception analysis may include those designed to reveal the sensitivity and specificity of the measures employed, as well as the degree of error associated with the different procedure’s steps employed (e.g., order of administration). Findings that stem from empirical studies on deception may be more helpful to the trier of fact (as well as less painful to the expert who is cross examined) than subjectively based findings or clinical judgment. The expert who opines on deception in a particular case should be prepared to share with the courts his or her own accuracy level in attempting to detect deception in forensic cases.
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This article is approved by the following for continuing education credit:

(ACFEI) The American College of Forensic Examiners International provides this continuing education credit for Diplomates.

(CFC) The American College of Forensic Examiners International provides this continuing education credit for Certified Forensic Consultants.

(NBCC) The American College of Forensic Examiners International is an NBCC Approved Continuing Education Provider (ACEP) and may offer NBCC approved clock hours for events that meet NBCC requirements. The ACEP solely is responsible for all aspects of the program. Provider #5812.

(APA) The American College of Forensic Examiners International is approved by the American Psychological Association to sponsor continuing education for psychologists. ACFEI maintains responsibility for this program and its content.

Style Behavioral Strategy Examples

THE FORENSIC EXAMINER Fall 2007

1. Verbal fabrication

2. Verbal exaggeration

3. Verbal denial

4. Verbal minimizing

5. Misattribution

6. Behavioral fractionalizing

7. Behavioral approximating

8. Behavioral infrequency

9. Behavioral disengagement

10. Impusivity

11. Perseveration

12. Randomizing

Table 1: Faking Bad Response Styles

Claiming a nonexistent problem Amplifying real problem

Disclaiming an ability

Downplaying an ability

Stating deficit due to false cause rather than true etiology

Shows crudely estimated fraction of ability

Gets a close, but not exact, answer

Sprinkles errors throughout performance on graduated scale

Shows confusion and frustration—may give up

Answering quickly, presents first thing on mind

Persists with one response mode regardless of feedback

No consistent pattern of errors

“I have ringing in my ear.”

“I’m more forgetful than usual.”

“I can’t smell anything.”

“I can walk only one block.”

Claiming developmental learning diability caused by a vehicular accident

Hand grip scores only 1/2 of ability

“6+6=13; 7×3=22”

Errors on WAIS-R Comp. and Vocab. on initial items

Claims total inability during blindfolded period of TPT testing

Poor on Arith. and Block Design compared to untimed performance

Alternates errors on WCST or Explicit Alternative Testing

Speech Perception Test errors due to deliberate inattention

Fall 2007 THE FORENSIC EXAMINER

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THE FORENSIC EXAMINER Fall 2007

There have been forensic cases in which the adversarial attorneys made available to their clients books and journal articles on deception and distortion . . . . ironically, the clients were detected in their faking by a battery of tests, suggesting a testible hypothesis that even with knowledge of the literature on faking, deceptive persons can nevertheless be identified.

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Summary of Suggestive Signs of Faking

Neuropsychological testing lends itself well to deception analysis. Fruitful areas of inquiry are plentiful within a composite battery consistent with an applied model. Tests for cerebral functioning can be combined with personality tests, clinical observation, and cross-validating sources to provide conclusions regarding faking.

The following list presents cross-validated signs suggestive of deception:

  • Failure on specific measures adapted to assess faking of cerebral impairment (e.g., illusorily difficult tests)
  • Inconsistency between clinical/test behaviors and known neuropsychological syndromes (i.e., goodness of neurological fit)
  • Failure to exhibit impaired function outside the context of evaluation
  • Skill performance changes on parallel testing
  • Anterograde memory that is better than retrograde memory
  • Approximate answers in interviews when concurrent testing reveals adequate skills
  • Neuropsychological test results consistent with statistical rules for detecting malingering
  • Similar or better performance on easy compared to difficult versions of the same test
  • Less than accurate performance on forced-choice sensory, recall, and reaction-time tests
  • No improvement where expected (e.g., absence of learning curve)
  • Test scores outside of predicted confidence intervals (e.g., actual WAIS-R Full Scale IQ outside the confidence interval predicted by the score on the Shipley)

While not definitive in themselves, these factors are suggestive enough of distorted performance to justify a more intensive investigation of the possibility of deliberate deception.

Clinically-based empirical research in this area is strongly encouraged. Much is unknown concerning the dimensions of faking, under what conditions it most likely will occur, its magnitude and direction, the role of motivation and vested interest in given outcomes by the faker and, importantly, interventions that could reduce deliberate deception while simultaneously offering a reliable and valid evaluation of the forensic client. Practically, a deception analysis could be applied to every forensic examination by integrating measures of faking good and faking bad into the battery of tests employed. Thus, all forensic professionals, not only neuropsychologists and psychologists, who use evaluation findings corrected for deception would benefit from its application. Finally, the proposed model lends itself well to research that will ultimately lead to an empirically-based theory of human deception.

Fall 2007 THE FORENSIC EXAMINER

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Earn CE Credit

To earn CE credit, complete the exam for this article on page 76 or complete the exam online at www.acfei.com (select “Online CE”).

About the Authors

Harold V. Hall, PhD, ABPS, is a forensic neuropsychologist and director of The Pacific Institute for the Study of Conflict and Aggression. He is board certified in forensic neuropsychology. He has written 10 books including Detecting Malingering and Deception, Second Edition, which was nominated for the Manfried Guttmacher Award of the American Psychiatric Association.

Jane Thompson, PhD, received her doctoral degree in quantitative psychology from the University of California at Davis in 2004. She is now on the faculty at the University of Hawaii at Hilo, specializing in teaching courses in statistics and methodology.

Joseph G. Poirier, PhD, ABPP, is a clinical psychologist with a specialty in forensic psychology. He is currently in private practice in Maryland, having worked decades for several Maryland agencies that oversee forensic assessments of criminal, juvenile, civil, and domestic matters.

Publisher Robert L. OBlock


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