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Are errors differentiable from deceptive responses when feigning memory impairment? An fMRI study
Brain and Cognition 69 (2009) 406–412
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Brain and Cognition journal homepage: www.elsevier.com/locate/b&c
Are errors differentiable from deceptive responses when feigning memory impairment? An fMRI study Tatia M.C. Lee a,b,c,*, Ricky K.C. Au a,b, Ho-Ling Liu d,e, K.H. Ting f, Chih-Mao Huang g, Chetwyn C.H. Chan f,h a
Laboratory of Neuropsychology, The University of Hong Kong, K610, Pokfulam Road, Hong Kong SAR, Hong Kong, China Laboratory of Cognitive Affective Neuroscience, The University of Hong Kong, Hong Kong, China c State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China d Department of Medical Technology, Chang Gung University, Taiwan e MRI Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan f Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong g Department of Psychology, University of Illinois at Urbana-Champaign, USA h Laboratory of Applied Cognitive Neuroscience, The Hong Kong Polytechnic University, Hong Kong b
a r t i c l e
i n f o
Article history: Accepted 10 September 2008 Available online 19 October 2008 Keywords: Deception Functional magnetic resonance imaging (fMRI) Genuine errors Lie detection Lying Memory impairment
a b s t r a c t Previous neuroimaging studies have suggested that the neural activity associated with truthful recall, with false memory, and with feigned memory impairment are different from one another. Here, we report a functional magnetic resonance imaging (fMRI) study that addressed an important but yet unanswered question: Is the neural activity associated with intentional faked responses and with errors differentiable? Using a word list learning recognition paradigm, the findings of this mixed event-related fMRI study clearly indicated that the brain activity associated with intentional faked responses was different to the activity associated with errors committed unintentionally. For intentional faked responses, significant activation was found in the ventrolateral prefrontal cortex, the posterior cingulate region, and the precuneus. However, no significant activation was observed for unintentional errors. The results suggest that deception, in terms of feigning memory impairment, is not only more cognitively demanding than making unintentional errors but also utilizes different cognitive processes. Ó 2008 Elsevier Inc. All rights reserved.
1. Introduction Deception is a ubiquitous phenomenon in society. Because of the prevalence and nature of deception, there are many legal, political, and industrial settings where society could benefit from its accurate detection. For example, feigning memory impairment may be associated with secondary gains, such as monetary rewards and attention (e.g. Vrij, 2008). Hence, verification of the validity of the subjective claims about memory impairments is important for the delivery of cost-effective interventions. In this connection, many detection methods have been developed, including both behavioral (e.g. Iverson & Franzen, 1998) and psychophysiological measures, such as a polygraph (e.g. Green, Iverson, & Allen, 1999; Ross, Krukowski, Putnam, & Adams, 2003; Teichner & Wagner, 2004). However, the validity of these indices has been the subject of much debate because of the indirect nature of measurement offered by these methods; for example, peripheral physiological arousal (e.g. Ahlmeyer, Heil, McKee, & English, 2000; Farwell & Smith, 2001). With the high spatial * Corresponding author. Fax: +852 2819 0978. E-mail address: [email protected] (T.M.C. Lee). 0278-2626/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.bandc.2008.09.002
resolution that enhances its ability to simultaneously detect brain activity in all parts of the brain, the functional magnetic resonance imaging (fMRI) technique offers a whole new avenue for examining the brain activity that directly underlie deceptive behaviors. Recently, fMRI studies describing neural correlates of various forms of deception have been conducted in laboratory settings (e.g. Abe, Suzuki, Mori, Itoh, & Fujii, 2007; Abe et al., 2006; Davatzikos et al., 2005; Gamer, Bauermann, Stoeter, & Vossel, 2007; Ganis, Kosslyn, Stose, Thompson, & Yurgelun-Todd, 2003; Kozel, Padgett, & George, 2004a; Kozel et al., 2004b, 2005; Langleben et al., 2002, 2005; Lee, 2006; Lee et al., 2002, 2005; Mohamed et al., 2006; Nunez, Casey, Egner, Hare, & Hirsch, 2005; Phan et al., 2005; Spence et al., 2001, 2004, 2008). For example, Spence et al. (2001) utilized a computer-based interrogation paradigm to probe the recent episodic memory of 10 volunteers scanned in an MRI unit. Lying was associated with longer response times and greater activity in the bilateral ventrolateral prefrontal cortices. In further studies, Spence et al. (2004) suggest that responding with a lie demands additional cognitive processing for executive control of behavior. Langleben et al. (2002) used the Guilty Knowledge Test to study deception
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and observed that deceptive responses were associated with increased activity in the frontal, anterior cingulate, and parietal regions. Davatzikos et al. (2005) used a high-dimensional nonlinear pattern classification method to discriminate between the spatial patterns of brain activity associated with lying and telling the truth. For 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. The results suggest that fMRI could play a useful role in studying deceptive behavior. Kozel et al. (2004a, 2004b) instructed healthy, right-handed adults to either tell the truth or to lie while being imaged in a 3T MRI scanner. Their findings revealed that lying was associated with significant activation in the frontal and cingulate regions. Ganis et al. (2003) challenged an implicit assumption in early lie detection research—that there was only one type of lie. Their fMRI data suggested that distinct neural networks supported the notion that there are different types of deception. A general consensus among these fMRI reports is that deception is associated with activity in the prefrontal regions. In situations that involved feigned memory impairment, activity of the bilateral frontal-anterior cingulate-parietal regions was observed (Lee, 2006; Lee et al., 2002, 2005). However, previous studies have employed simple experimental tasks in order to control for the unwanted confound introduced by errors. To understand the validity of deception detection by fMRI, it is important to verify if the brain activity associated with deception responses and with unintentional memory errors is differentiable. Research effort to differentiate the neural activity related to intentional deception and unintentional memory errors are very important but the evidence has not been sufficiently established. For a word-recognition paradigm, unintentional memory errors could stem from confusion between information that is semantically related. For example, in a Deese–Roediger–McDermott false memory paradigm (Roediger & McDermott, 1995), lures (words semantically related to the target words) could be falsely recognized. In this connection, Abe et al. (2008) have successfully demonstrated that fMRI can detect the difference in brain activity between deception and unintentional memory errors presented as false memory—false recognition of lures in a Deese–Roediger– McDermott form of false memory paradigm. However, unintentional memory errors could also be memory errors presented as false recognition of foils that are semantically unrelated to the target words, or false rejection of the target words. The question remains as to whether the neural activity associated with unintentional errors of information semantically unrelated to the targets is differentiable from that of intentionally faked responses. In order to identify the pattern of brain activities that best differentiated errors and intentionally faked responses, we designed an experimental paradigm that encouraged the commission of errors. We adopted a word list learning recognition paradigm that produced a 70% accuracy recall rate; this was confirmed by the findings of our pilot test of this learning paradigm. We defined unintentional errors as errors committed without the intention of faking a poor performance. This could be in the form of false positive recognition (recognizing non-targets as targets) and/or false negative rejection (rejecting targets as non-targets). In a mixed event-related design fMRI study, we compared the neural activities associated with truthful responses, with intentionally faked responses, and with errors. Based on the findings of previous studies, we hypothesized that prefrontal-anterior-cingulate-parietal activities would be observed during deception. Furthermore, since deception involves inhibition and other frontal executive processes to a greater degree than does the commission of errors, we hypothesized that the brain activities in the prefrontal and cingulate regions are differentiable for intentional faked responses and errors.
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2. Materials and method 2.1. Participants A total of 10 healthy Chinese male undergraduates (mean age 20.7 years, SD 2.63, range 19–25) with similar levels of education (mean 14.4 years of education, SD 1.90) participated in the study. The participants were all strongly right-handed as measured by the Lateral Dominance Test (Spreen & Strauss, 1998), had normal attention span as tested by the Digit Span Test (achieving a span of 7 digits), and had a normal mood state during the experiment as measured by a 7-interval self-report scale. No participant had a history of neurological or psychiatric illness, and they all had normal or corrected-to-normal vision according to self-reports. The researchers obtained ethical approval from the Human Research Ethics Committee for Non-clinical Faculties of The University of Hong Kong. Informed consent was obtained from the participants after they had been given an explanation of the study. 2.2. Experimental stimuli We employed a word-recognition paradigm to study the differences in brain activations involved when people: (1) accurately recognize old words and reject new words; (2) commit unintentional memory errors by falsely rejecting old words and recognizing new words; and (3) make fake incorrect responses by rejecting old words and recognizing new words. Altogether, 2 2 12 12word lists were developed for the participants to encode. Fortyeight out of 576 possible target words, together with 96 non-target words, were presented to the participants for recognition during the experiment. The target and non-target words were drawn from a pool of simple words that an average adult would encounter in everyday life. We pilot tested these words for reading level. Data from the pilot test confirmed that the reading level associated with these words was at entry secondary level of schooling in the Hong Kong education system (equivalent to Grade 7 in the US system). 2.3. Experimental design and procedures There were two experimental conditions in this study: the ‘‘Truthful condition,” requiring the participants to make their best effort and give accurate, honest responses, and the ‘‘Faking condition,” during which the participants had to intentionally fake their responses (i.e. the targets should be identified as non-targets and the non-targets as targets). To motivate the participants to fake having a memory problem, we adopted the instructions used in our previous studies (Lee et al., 2002, 2005). Each participant was presented with the following instructions prior to taking part in the experimental tasks: ‘‘You are required to feign a memory problem and deliberately do badly on the test. Imagine a scenario which envisages that a poor result will lead to an attractive sum of money as compensation for your memory problem. You should fake skillfully to avoid detection. So, your goal is to fake well—do it with skill—and avoid detection.” The encoding phase occurred during a structural scan (Gallo, Kensinger, & Schacter, 2006). At the beginning of the experiment, we presented a ‘‘Practice block” to familiarize the participants with the experimental procedures. During the practice block, the instructions were presented on the screen and the participants were given a few practice trials using some short word lists of five items (with minimal semantic association with the experimental word lists). The experiment consisted of four blocks: two for the Truthful condition and two for the Faking condition. The experimental paradigm was delivered via EPrime software version 1.1 (Psychology Software Tools Inc.). The sequence of presentation of the blocks
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was two blocks for the Truthful condition, followed by two blocks for the Faking condition. This order of presentation was counterbalanced among participants according to the Latin-square randomization. In each block of the experiment, while in the MRI scanner for structural scanning (Gallo et al., 2006), the participants were first asked to memorize the given list of target words (12 12 targets). The words were presented on the screen at the rate of 1.5 s per word. This lasted for 231 s. After learning, the brain activity associated with recognition of these learned items was monitored to address the research questions. A list of 36 words was presented, including 12 targets (randomly selected from the targets presented during the learning phase) and 24 non-targets. The participants fixated on the cross at the center of the screen for varying time intervals (either 3 or 6 or 9 s); a word (which was either a target or a non-target) then appeared on the screen for 3 s and the participants were instructed to indicate whether they had seen the presented word during the learning phase by pressing the ‘‘yes” or ‘‘no” button as quickly as they could. The word would disappear if no response was detected 3 s after the word had appeared on the screen, and the program would proceed to the next trial. This lasted for 333 s (see Fig. 1). The participants were prompted for the responses that they were required to make, either genuine or fake, at the beginning of each block. 2.4. Behavioral measures The overall accuracy rates and average reaction times of the participants during the task performance were recorded for both the truthful and faking conditions. The rate of accurate response and the average reaction times to targets and non-targets were also recorded.
2.5. Image acquisition A mixed event-related design fMRI study was performed on a 3 T MRI scanner (Signa Excite, GE Medical Systems, Milwaukee, WI) at the Buddhist Xindian Tzu Chi General Hospital, Taipei Branch, Taiwan. fMRI images were obtained with gradient echoplanar imaging (EPI), TR = 2 s, TE = 30 ms, FOV = 211 mm 211 mm, acquisition matrix = 64 64, flip angle = 90, number of slices = 30, slice thickness = 3 mm, and gap = 1 mm. Goggles were used for the presentation of stimuli; a cage and cushion were used for head stabilization. The imaging was performed only in the Recognition Phase of each block of the experiment (162 images acquired in each run, total scan time = 5 mins 24 s per block). Anatomical MRI was obtained with a three-dimensional T1weighted sequence (TR = 4.7 s, TE = 1.228 ms, FOV = 240 mm 240 mm, voxel size = 1 mm 1 mm 1 mm). 2.6. Data processing and statistical analyses The functional imaging data were preprocessed and analyzed with the Statistical Parametric Mapping (SPM2) software (Wellcome Department of Cognitive Neurology, London, UK) running under Matlab 6.5 (MathWorks, Natick, MA, USA). Time series data were realigned to the central volume. The data of individual participants were resliced into 2 mm 2 mm 2 mm isotopic voxels. The images were spatially normalized to the T1 template. The imaging data were then spatially smoothed using an 8-mm fullwidth half maximum three-dimensional Gaussian kernel. Low frequency noise was removed with a high-pass filter. Analyses were first performed based on individual time-series using an event-related model. Two types of events—(a) accurately
Fig. 1. The schematic diagram for the experiment.
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recognizing targets and rejecting non-targets (truthful accurate response; TAR), and (b) falsely rejecting targets and recognizing nontargets—were taken from the two blocks of the Truthful condition as regressors (errors). The event type (c)—faking responses: false rejection of targets and recognition of non-targets in the two blocks of the intentional faked response (IFR)—was also taken as a regressor to model that analysis. The hemodynamic response of the participants to each event was modeled by a canonical hemodynamic response function. To understand the neural activity associated with feigning memory impairment, we performed a contrast between event types TAR and IFR. To examine the differentiability of neural activity associated with errors and intentional faking, contrasts between the event types (IFR, TAR, and errors) were made. These contrasts were then entered into a second-level analysis using a one-sample t-test. Because of the limited number of participants, analyses were performed at the p < .005 level, corrected at a cluster level of 30 voxels. To further explore the relationship between behavioral responses (errors or IFR) and the neural activations at the regions showing a significant different activation identified in the IFR versus errors contrast, we performed the ROI analysis using the WFU PickAtlas (Maldjian, Laurienti, Kraft, & Burdette, 2003). We first defined the ROI by Automated Anatomical Labeling (AAL) and then calculated the percent signal change in these ROIs using the MarsBaR region of interest toolbox for SPM available on the Web at http://marsbar.sourceforge.net (Brett, Anton, Valbregue, & Poline, 2002). We then used the Student’s t-test to compare the differences in the percent signal change between errors and IFR in each of the ROIs. 3. Results 3.1. Behavioral data Due to significant head movements during the fMRI scanning, the data from 3 participants were excluded from subsequent analyses; therefore, we used behavioral and fMRI data from a total of 7 participants. A summary of rates of accurate responses in each condition and reaction times (RTs; including the means and standard deviations) of the behavioral data is given in Table 1. A repeated measures ANOVA was performed to explore the differences in RT within the different conditions as the within-subject factor (i.e. condition-level analysis). The average RT of the 7 participants in the Truthful condition was shorter than that found in the Faking condition, but the difference was not significant (F(1, 6) = 3.79, p = .1); this was probably a result of the small sample size. In fact, we have also conducted a behavioral study using
Table 1 Percentage accuracy and mean reaction time in ms (SD) for the two experimental conditions and the three trial types Condition
Mean (SD)
Accuracy (%)
Truthful Faking
1279.05 (265.75) 1422.09 (385.63)
71.02 29.37
Trial type TAR Errors IFR
1264.36 (267.63) 1346.33 (293.28) 1397.11 (367.60)
TAR, Truthful accurate response (correct responses to targets and non-targets in the Truthful condition); Errors, Unintentional memory errors committed in the Truthful condition, i.e. falsely rejecting targets and recognizing non-targets in the Truthful condition; IFR, Intentional faked incorrect responses committed by rejecting old words and recognizing new words in the Faking condition; Accuracy (%), percentage of responses showing correctly recognition of the targets and rejection of the nontargets.
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the same paradigm with a greater sample size (N = 79), and the difference was shown to be highly significant (p < .001). In the comparisons between trial types (i.e. trial-level analysis), in which a repeated measures ANOVA was also used with the trial types as the within-subject factor, the mean RT of participants was marginally but significantly longer in the IFR trials than it was in the TAR trials (F(1, 6) = 4.641, p = .075). The mean RT in the IFR trials was also marginally significantly longer than it was in the error trials (i.e. incorrect responses to targets and non-targets in the truthful condition; F(1, 6) = 4.286, p = .084). 3.2. Functional imaging data The contrast between the intentional faking response and the truthful accurate response (IFR vs. TAR) trial types was associated with significant activations in the left middle frontal region (Brodmann’s area BA 6/44), the right anterior cingulate cortex (BA 24/ 32), the right insula (BA 13), the right precuneus (BA 7), the left fusiform gyrus (BA 37), the bilateral lingual gyri (BA 17), and the right thalamus. The contrast between TAR and IFR was associated with activations in the left inferior frontal gyrus (BA 47; see Table 2). For the contrast between IFR and errors, significant activations were found in the left inferior frontal area (BA 47), the right posterior cingulate cortex (BA 23), and the left precuneus. However, no significant activation was observed for the error versus IFR contrast. Table 2 shows the detailed locations of the significant activations for all the contrasts with the MNI coordinates. As with the procedure of the ROI analysis described above, we first marked the left inferior frontal area, the right posterior cingulate area, and the left precuneus by AAL. The percent signal change in these ROIs was then calculated (see Fig. 2). A dependent sample t-test revealed that the ‘‘IFR > TAR” contrast showed a significantly higher percentage signal change than the ‘‘Error > TAR” contrast in the left inferior frontal area (t6 = 2.629, p = .039), and also in the right posterior cingulate area (t6 = 3.971, p = .007). No significant difference in percentage signal change was found in the left precuneus (t6 = 1.593, p = .162). 4. Discussion The findings of this study confirm that brain activity associated with errors of memory and with intentional faked responses are differentiable. Using a word list learning recognition paradigm, we observed intentional faked responses were associated with significant activation in the left ventrolateral prefrontal region (BA 47), the right posterior cingulate region, and the left precuneus for intentionally faked responses, relative to errors. No significant activation was observed for the contrast between errors and intentional faked responses. A significant percent signal change differentiating intentionally faked responses and errors was observed in the ventrolateral prefrontal region and the posterior cingulate region. These findings are consistent with our a priori hypothesis concerning the difference in brain activities between the intentional faking of responses and the commission errors. This study represents the first attempt to examine if the brain activity associated with unintentional memory errors of information semantically unrelated to the targets and faked responses is differentiable, and provides an affirmative verification of the different cognitive processes involved in faking and in making genuine errors during deception. The pattern of brain activity in the frontal-anterior cingulateparietal regions associated with intentionally faked responses is consistent with our apriori hypothesis concerning brain activation associated with deception. This observed pattern of brain activity is comparable with that reported in our previous study (Lee
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Table 2 Brain regions associated with significant BOLD signal increases under different contrasts Contrasts
Anatomical regions
BAa
Voxels
IFR > TAR
L Middle frontal gyrus R Anterior cingulate area R Insula R Precuneus L Fusiform gyrus L Lingual R Lingual R Thalamus L Inferior frontal gyrus None None L Inferior frontal gyrus R Posterior cingulate area L Precuneus None
6/44 24/32 13 7 37 17 17/30
48 44 74 83 51 40 406 364 137 — — 63 111 244 —
Coordinatesb x
TAR > IFR Errors > TAR TAR > Errors IFR > Errors
Errors > IFR
47/48 — — 47 23 7 —
t-values y
26 8 40 4 42 8 10 8 44 — — 34 8 12 —
z 14 10 6 66 60 68 56 22 20 — — 44 40 56 —
40 42 14 46 10 6 6 4 4 — — 6 28 46 —
5.44 9.33 4.61 16.92 7.88 5.09 15.3 10.64 6.81 — — 5.6 4.97 5.79 —
TAR, Truthful accurate response; IFR, Intentional faked responses. a BA, Brodmann’s area. b The x, y, z coordinates are in the MNI coordinates.
et al., 2002) and is also largely consistent with the findings of fMRI deception studies reported so far. The role of the ventrolateral prefrontal region (BA 47) in deceptive responses has been much discussed (e.g. Abe et al., 2006; Kozel et al., 2005; Nunez et al., 2005; Phan et al., 2005; Spence et al., 2005; Lee et al., 2005). The role this region plays in response inhibition, error checking and self-monitoring of performance (Menon, Adleman, White, Glover, & Reiss, 2001), and regulation of motivated responses, via its connection with the orbitofrontal and other cortical sub-cortical regions (Happaney, Zelazo, & Stuss, 2004), matches the experimental demand of a deceptive study. The significant activation in this region during the making of intentional faked responses, relative to committing errors, suggests that making an intentional faked response demands a higher level of inhibition and self-monitoring. Significant activation of the posterior cingulate (BA 26) could carry the functional roles of inhibiting previously learned rules as well as performing self-monitoring of random errors (Amos, 2000; Carter et al., 1998). Alternatively, it has been suggested that the activity of the posterior cingulate region serves to link incoming information with a repository of activated knowledge so as to form a coherent representation of discourse (Maguire, Frith, & Morris, 1999). When producing intentionally faked responses, the participants needed to hold the rules of the task in mind, inhibit the tendency to make an accurate response, and monitor the quality of their performance in order to be successful in pretending they had memory impairment. Furthermore, the conjoint activation of the precuneus and the posterior cingulate region could reflect the online incorporation of information into a preset mental framework, which matches with the task demand for participants who had already established a mental framework for malingering well before they entered the experimental conditions and underwent fMRI scanning. Prior research has shown that the precuneus is involved in the recollection of past events (Lundstrom, Ingvar, & Petersson, 2005), and studies have similarly argued that it plays an essential role in episodic memory retrieval, perspective-taking, and the experience of agency (Cavanna & Trimble, 2006). It also plays a particular role in the retrieval of rich episodic contextual associations (Lundstrom et al., 2005). In this context, the significant activity of the precuneus suggests that faking requires more effort from the memory system than does committing errors of memory. Within this study,
the participants had to pretend they had memory impairment by correctly recalling the true answer and then making an incorrect response. In this sense, the conditions were similar to those when lying about memory impairment in real-life situations: an individual would need to accurately recall the situation before intentional faking could take place. The higher activation of the precuneus might alternatively suggest the need for the participants to recruit parietal resources to modulate the cognitive load involved in faking. When comparing the brain activity associated with intentionally faking responses and making errors, a significant activation was observed for the former but not for the latter. This suggests that committing intentionally faked responses may be more cognitively demanding on the brain. Moreover, the different patterns of activation associated with intentionally faking responses and making errors suggest that different cognitive processes are engaged for the two conditions, which are distinguishable with fMRI. The emotional components involved in the current experimental paradigm (word list learning recognition paradigm) were minimal. Moreover, the performance of deception in the current experiment did not involve any risk (there were ‘‘low stakes,” to use Spence et al.’s (2004) term). This is hardly comparable to real-life situations, where the detection of intentional faked responses may result in serious consequences (e.g. loss of benefits as punishment, breakdown of social relationships). Future studies should consider the importance of the individual’s emotional state during the performance of deception in order to achieve a more indepth understanding of the neural mechanisms of real-life deception behaviors. 5. Conclusion The current study has successfully demonstrated that the neural activations involved in making unintentional errors and in intentionally faking responses are differentiable using fMRI. Significant activations associated with intentional faked responses were found in the left ventrolateral prefrontal region (BA 47), the right posterior cingulate region (BA 23), and the left precuneus for intentionally faked responses, relative to errors. Unlike when responses were intentionally faked, no significant activation was found when unintentional errors were committed. These findings are largely consistent with the logic that in order to achieve successful decep-
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Acknowledgments This project was supported by the Research Output Prize and the CRCG seed grant of The University of Hong Kong, and the National Natural Science Foundation of China (#30828012). References
Fig. 2. On the left is the whole brain analysis for the intentionally faked response versus error contrast at p < .005, corrected at a cluster level of 30 voxels. L = left hemisphere. R = right hemisphere. x, y, z in MNI coordinates. On the right are the plots of percent signal change. Right (R) is right. BA = Brodmann’s Area. L = left hemisphere. R = right hemisphere. x, y, z in MNI coordinates. Error bars show the standard error of means.
tion, one has to inhibit previously learned rules, correctly recall information from memory, utilize response inhibition, and monitor performance.
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Contents lists available at ScienceDirect
Brain and Cognition journal homepage: www.elsevier.com/locate/b&c
Are errors differentiable from deceptive responses when feigning memory impairment? An fMRI study Tatia M.C. Lee a,b,c,*, Ricky K.C. Au a,b, Ho-Ling Liu d,e, K.H. Ting f, Chih-Mao Huang g, Chetwyn C.H. Chan f,h a
Laboratory of Neuropsychology, The University of Hong Kong, K610, Pokfulam Road, Hong Kong SAR, Hong Kong, China Laboratory of Cognitive Affective Neuroscience, The University of Hong Kong, Hong Kong, China c State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China d Department of Medical Technology, Chang Gung University, Taiwan e MRI Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan f Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong g Department of Psychology, University of Illinois at Urbana-Champaign, USA h Laboratory of Applied Cognitive Neuroscience, The Hong Kong Polytechnic University, Hong Kong b
a r t i c l e
i n f o
Article history: Accepted 10 September 2008 Available online 19 October 2008 Keywords: Deception Functional magnetic resonance imaging (fMRI) Genuine errors Lie detection Lying Memory impairment
a b s t r a c t Previous neuroimaging studies have suggested that the neural activity associated with truthful recall, with false memory, and with feigned memory impairment are different from one another. Here, we report a functional magnetic resonance imaging (fMRI) study that addressed an important but yet unanswered question: Is the neural activity associated with intentional faked responses and with errors differentiable? Using a word list learning recognition paradigm, the findings of this mixed event-related fMRI study clearly indicated that the brain activity associated with intentional faked responses was different to the activity associated with errors committed unintentionally. For intentional faked responses, significant activation was found in the ventrolateral prefrontal cortex, the posterior cingulate region, and the precuneus. However, no significant activation was observed for unintentional errors. The results suggest that deception, in terms of feigning memory impairment, is not only more cognitively demanding than making unintentional errors but also utilizes different cognitive processes. Ó 2008 Elsevier Inc. All rights reserved.
1. Introduction Deception is a ubiquitous phenomenon in society. Because of the prevalence and nature of deception, there are many legal, political, and industrial settings where society could benefit from its accurate detection. For example, feigning memory impairment may be associated with secondary gains, such as monetary rewards and attention (e.g. Vrij, 2008). Hence, verification of the validity of the subjective claims about memory impairments is important for the delivery of cost-effective interventions. In this connection, many detection methods have been developed, including both behavioral (e.g. Iverson & Franzen, 1998) and psychophysiological measures, such as a polygraph (e.g. Green, Iverson, & Allen, 1999; Ross, Krukowski, Putnam, & Adams, 2003; Teichner & Wagner, 2004). However, the validity of these indices has been the subject of much debate because of the indirect nature of measurement offered by these methods; for example, peripheral physiological arousal (e.g. Ahlmeyer, Heil, McKee, & English, 2000; Farwell & Smith, 2001). With the high spatial * Corresponding author. Fax: +852 2819 0978. E-mail address: [email protected] (T.M.C. Lee). 0278-2626/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.bandc.2008.09.002
resolution that enhances its ability to simultaneously detect brain activity in all parts of the brain, the functional magnetic resonance imaging (fMRI) technique offers a whole new avenue for examining the brain activity that directly underlie deceptive behaviors. Recently, fMRI studies describing neural correlates of various forms of deception have been conducted in laboratory settings (e.g. Abe, Suzuki, Mori, Itoh, & Fujii, 2007; Abe et al., 2006; Davatzikos et al., 2005; Gamer, Bauermann, Stoeter, & Vossel, 2007; Ganis, Kosslyn, Stose, Thompson, & Yurgelun-Todd, 2003; Kozel, Padgett, & George, 2004a; Kozel et al., 2004b, 2005; Langleben et al., 2002, 2005; Lee, 2006; Lee et al., 2002, 2005; Mohamed et al., 2006; Nunez, Casey, Egner, Hare, & Hirsch, 2005; Phan et al., 2005; Spence et al., 2001, 2004, 2008). For example, Spence et al. (2001) utilized a computer-based interrogation paradigm to probe the recent episodic memory of 10 volunteers scanned in an MRI unit. Lying was associated with longer response times and greater activity in the bilateral ventrolateral prefrontal cortices. In further studies, Spence et al. (2004) suggest that responding with a lie demands additional cognitive processing for executive control of behavior. Langleben et al. (2002) used the Guilty Knowledge Test to study deception
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and observed that deceptive responses were associated with increased activity in the frontal, anterior cingulate, and parietal regions. Davatzikos et al. (2005) used a high-dimensional nonlinear pattern classification method to discriminate between the spatial patterns of brain activity associated with lying and telling the truth. For 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. The results suggest that fMRI could play a useful role in studying deceptive behavior. Kozel et al. (2004a, 2004b) instructed healthy, right-handed adults to either tell the truth or to lie while being imaged in a 3T MRI scanner. Their findings revealed that lying was associated with significant activation in the frontal and cingulate regions. Ganis et al. (2003) challenged an implicit assumption in early lie detection research—that there was only one type of lie. Their fMRI data suggested that distinct neural networks supported the notion that there are different types of deception. A general consensus among these fMRI reports is that deception is associated with activity in the prefrontal regions. In situations that involved feigned memory impairment, activity of the bilateral frontal-anterior cingulate-parietal regions was observed (Lee, 2006; Lee et al., 2002, 2005). However, previous studies have employed simple experimental tasks in order to control for the unwanted confound introduced by errors. To understand the validity of deception detection by fMRI, it is important to verify if the brain activity associated with deception responses and with unintentional memory errors is differentiable. Research effort to differentiate the neural activity related to intentional deception and unintentional memory errors are very important but the evidence has not been sufficiently established. For a word-recognition paradigm, unintentional memory errors could stem from confusion between information that is semantically related. For example, in a Deese–Roediger–McDermott false memory paradigm (Roediger & McDermott, 1995), lures (words semantically related to the target words) could be falsely recognized. In this connection, Abe et al. (2008) have successfully demonstrated that fMRI can detect the difference in brain activity between deception and unintentional memory errors presented as false memory—false recognition of lures in a Deese–Roediger– McDermott form of false memory paradigm. However, unintentional memory errors could also be memory errors presented as false recognition of foils that are semantically unrelated to the target words, or false rejection of the target words. The question remains as to whether the neural activity associated with unintentional errors of information semantically unrelated to the targets is differentiable from that of intentionally faked responses. In order to identify the pattern of brain activities that best differentiated errors and intentionally faked responses, we designed an experimental paradigm that encouraged the commission of errors. We adopted a word list learning recognition paradigm that produced a 70% accuracy recall rate; this was confirmed by the findings of our pilot test of this learning paradigm. We defined unintentional errors as errors committed without the intention of faking a poor performance. This could be in the form of false positive recognition (recognizing non-targets as targets) and/or false negative rejection (rejecting targets as non-targets). In a mixed event-related design fMRI study, we compared the neural activities associated with truthful responses, with intentionally faked responses, and with errors. Based on the findings of previous studies, we hypothesized that prefrontal-anterior-cingulate-parietal activities would be observed during deception. Furthermore, since deception involves inhibition and other frontal executive processes to a greater degree than does the commission of errors, we hypothesized that the brain activities in the prefrontal and cingulate regions are differentiable for intentional faked responses and errors.
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2. Materials and method 2.1. Participants A total of 10 healthy Chinese male undergraduates (mean age 20.7 years, SD 2.63, range 19–25) with similar levels of education (mean 14.4 years of education, SD 1.90) participated in the study. The participants were all strongly right-handed as measured by the Lateral Dominance Test (Spreen & Strauss, 1998), had normal attention span as tested by the Digit Span Test (achieving a span of 7 digits), and had a normal mood state during the experiment as measured by a 7-interval self-report scale. No participant had a history of neurological or psychiatric illness, and they all had normal or corrected-to-normal vision according to self-reports. The researchers obtained ethical approval from the Human Research Ethics Committee for Non-clinical Faculties of The University of Hong Kong. Informed consent was obtained from the participants after they had been given an explanation of the study. 2.2. Experimental stimuli We employed a word-recognition paradigm to study the differences in brain activations involved when people: (1) accurately recognize old words and reject new words; (2) commit unintentional memory errors by falsely rejecting old words and recognizing new words; and (3) make fake incorrect responses by rejecting old words and recognizing new words. Altogether, 2 2 12 12word lists were developed for the participants to encode. Fortyeight out of 576 possible target words, together with 96 non-target words, were presented to the participants for recognition during the experiment. The target and non-target words were drawn from a pool of simple words that an average adult would encounter in everyday life. We pilot tested these words for reading level. Data from the pilot test confirmed that the reading level associated with these words was at entry secondary level of schooling in the Hong Kong education system (equivalent to Grade 7 in the US system). 2.3. Experimental design and procedures There were two experimental conditions in this study: the ‘‘Truthful condition,” requiring the participants to make their best effort and give accurate, honest responses, and the ‘‘Faking condition,” during which the participants had to intentionally fake their responses (i.e. the targets should be identified as non-targets and the non-targets as targets). To motivate the participants to fake having a memory problem, we adopted the instructions used in our previous studies (Lee et al., 2002, 2005). Each participant was presented with the following instructions prior to taking part in the experimental tasks: ‘‘You are required to feign a memory problem and deliberately do badly on the test. Imagine a scenario which envisages that a poor result will lead to an attractive sum of money as compensation for your memory problem. You should fake skillfully to avoid detection. So, your goal is to fake well—do it with skill—and avoid detection.” The encoding phase occurred during a structural scan (Gallo, Kensinger, & Schacter, 2006). At the beginning of the experiment, we presented a ‘‘Practice block” to familiarize the participants with the experimental procedures. During the practice block, the instructions were presented on the screen and the participants were given a few practice trials using some short word lists of five items (with minimal semantic association with the experimental word lists). The experiment consisted of four blocks: two for the Truthful condition and two for the Faking condition. The experimental paradigm was delivered via EPrime software version 1.1 (Psychology Software Tools Inc.). The sequence of presentation of the blocks
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was two blocks for the Truthful condition, followed by two blocks for the Faking condition. This order of presentation was counterbalanced among participants according to the Latin-square randomization. In each block of the experiment, while in the MRI scanner for structural scanning (Gallo et al., 2006), the participants were first asked to memorize the given list of target words (12 12 targets). The words were presented on the screen at the rate of 1.5 s per word. This lasted for 231 s. After learning, the brain activity associated with recognition of these learned items was monitored to address the research questions. A list of 36 words was presented, including 12 targets (randomly selected from the targets presented during the learning phase) and 24 non-targets. The participants fixated on the cross at the center of the screen for varying time intervals (either 3 or 6 or 9 s); a word (which was either a target or a non-target) then appeared on the screen for 3 s and the participants were instructed to indicate whether they had seen the presented word during the learning phase by pressing the ‘‘yes” or ‘‘no” button as quickly as they could. The word would disappear if no response was detected 3 s after the word had appeared on the screen, and the program would proceed to the next trial. This lasted for 333 s (see Fig. 1). The participants were prompted for the responses that they were required to make, either genuine or fake, at the beginning of each block. 2.4. Behavioral measures The overall accuracy rates and average reaction times of the participants during the task performance were recorded for both the truthful and faking conditions. The rate of accurate response and the average reaction times to targets and non-targets were also recorded.
2.5. Image acquisition A mixed event-related design fMRI study was performed on a 3 T MRI scanner (Signa Excite, GE Medical Systems, Milwaukee, WI) at the Buddhist Xindian Tzu Chi General Hospital, Taipei Branch, Taiwan. fMRI images were obtained with gradient echoplanar imaging (EPI), TR = 2 s, TE = 30 ms, FOV = 211 mm 211 mm, acquisition matrix = 64 64, flip angle = 90, number of slices = 30, slice thickness = 3 mm, and gap = 1 mm. Goggles were used for the presentation of stimuli; a cage and cushion were used for head stabilization. The imaging was performed only in the Recognition Phase of each block of the experiment (162 images acquired in each run, total scan time = 5 mins 24 s per block). Anatomical MRI was obtained with a three-dimensional T1weighted sequence (TR = 4.7 s, TE = 1.228 ms, FOV = 240 mm 240 mm, voxel size = 1 mm 1 mm 1 mm). 2.6. Data processing and statistical analyses The functional imaging data were preprocessed and analyzed with the Statistical Parametric Mapping (SPM2) software (Wellcome Department of Cognitive Neurology, London, UK) running under Matlab 6.5 (MathWorks, Natick, MA, USA). Time series data were realigned to the central volume. The data of individual participants were resliced into 2 mm 2 mm 2 mm isotopic voxels. The images were spatially normalized to the T1 template. The imaging data were then spatially smoothed using an 8-mm fullwidth half maximum three-dimensional Gaussian kernel. Low frequency noise was removed with a high-pass filter. Analyses were first performed based on individual time-series using an event-related model. Two types of events—(a) accurately
Fig. 1. The schematic diagram for the experiment.
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recognizing targets and rejecting non-targets (truthful accurate response; TAR), and (b) falsely rejecting targets and recognizing nontargets—were taken from the two blocks of the Truthful condition as regressors (errors). The event type (c)—faking responses: false rejection of targets and recognition of non-targets in the two blocks of the intentional faked response (IFR)—was also taken as a regressor to model that analysis. The hemodynamic response of the participants to each event was modeled by a canonical hemodynamic response function. To understand the neural activity associated with feigning memory impairment, we performed a contrast between event types TAR and IFR. To examine the differentiability of neural activity associated with errors and intentional faking, contrasts between the event types (IFR, TAR, and errors) were made. These contrasts were then entered into a second-level analysis using a one-sample t-test. Because of the limited number of participants, analyses were performed at the p < .005 level, corrected at a cluster level of 30 voxels. To further explore the relationship between behavioral responses (errors or IFR) and the neural activations at the regions showing a significant different activation identified in the IFR versus errors contrast, we performed the ROI analysis using the WFU PickAtlas (Maldjian, Laurienti, Kraft, & Burdette, 2003). We first defined the ROI by Automated Anatomical Labeling (AAL) and then calculated the percent signal change in these ROIs using the MarsBaR region of interest toolbox for SPM available on the Web at http://marsbar.sourceforge.net (Brett, Anton, Valbregue, & Poline, 2002). We then used the Student’s t-test to compare the differences in the percent signal change between errors and IFR in each of the ROIs. 3. Results 3.1. Behavioral data Due to significant head movements during the fMRI scanning, the data from 3 participants were excluded from subsequent analyses; therefore, we used behavioral and fMRI data from a total of 7 participants. A summary of rates of accurate responses in each condition and reaction times (RTs; including the means and standard deviations) of the behavioral data is given in Table 1. A repeated measures ANOVA was performed to explore the differences in RT within the different conditions as the within-subject factor (i.e. condition-level analysis). The average RT of the 7 participants in the Truthful condition was shorter than that found in the Faking condition, but the difference was not significant (F(1, 6) = 3.79, p = .1); this was probably a result of the small sample size. In fact, we have also conducted a behavioral study using
Table 1 Percentage accuracy and mean reaction time in ms (SD) for the two experimental conditions and the three trial types Condition
Mean (SD)
Accuracy (%)
Truthful Faking
1279.05 (265.75) 1422.09 (385.63)
71.02 29.37
Trial type TAR Errors IFR
1264.36 (267.63) 1346.33 (293.28) 1397.11 (367.60)
TAR, Truthful accurate response (correct responses to targets and non-targets in the Truthful condition); Errors, Unintentional memory errors committed in the Truthful condition, i.e. falsely rejecting targets and recognizing non-targets in the Truthful condition; IFR, Intentional faked incorrect responses committed by rejecting old words and recognizing new words in the Faking condition; Accuracy (%), percentage of responses showing correctly recognition of the targets and rejection of the nontargets.
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the same paradigm with a greater sample size (N = 79), and the difference was shown to be highly significant (p < .001). In the comparisons between trial types (i.e. trial-level analysis), in which a repeated measures ANOVA was also used with the trial types as the within-subject factor, the mean RT of participants was marginally but significantly longer in the IFR trials than it was in the TAR trials (F(1, 6) = 4.641, p = .075). The mean RT in the IFR trials was also marginally significantly longer than it was in the error trials (i.e. incorrect responses to targets and non-targets in the truthful condition; F(1, 6) = 4.286, p = .084). 3.2. Functional imaging data The contrast between the intentional faking response and the truthful accurate response (IFR vs. TAR) trial types was associated with significant activations in the left middle frontal region (Brodmann’s area BA 6/44), the right anterior cingulate cortex (BA 24/ 32), the right insula (BA 13), the right precuneus (BA 7), the left fusiform gyrus (BA 37), the bilateral lingual gyri (BA 17), and the right thalamus. The contrast between TAR and IFR was associated with activations in the left inferior frontal gyrus (BA 47; see Table 2). For the contrast between IFR and errors, significant activations were found in the left inferior frontal area (BA 47), the right posterior cingulate cortex (BA 23), and the left precuneus. However, no significant activation was observed for the error versus IFR contrast. Table 2 shows the detailed locations of the significant activations for all the contrasts with the MNI coordinates. As with the procedure of the ROI analysis described above, we first marked the left inferior frontal area, the right posterior cingulate area, and the left precuneus by AAL. The percent signal change in these ROIs was then calculated (see Fig. 2). A dependent sample t-test revealed that the ‘‘IFR > TAR” contrast showed a significantly higher percentage signal change than the ‘‘Error > TAR” contrast in the left inferior frontal area (t6 = 2.629, p = .039), and also in the right posterior cingulate area (t6 = 3.971, p = .007). No significant difference in percentage signal change was found in the left precuneus (t6 = 1.593, p = .162). 4. Discussion The findings of this study confirm that brain activity associated with errors of memory and with intentional faked responses are differentiable. Using a word list learning recognition paradigm, we observed intentional faked responses were associated with significant activation in the left ventrolateral prefrontal region (BA 47), the right posterior cingulate region, and the left precuneus for intentionally faked responses, relative to errors. No significant activation was observed for the contrast between errors and intentional faked responses. A significant percent signal change differentiating intentionally faked responses and errors was observed in the ventrolateral prefrontal region and the posterior cingulate region. These findings are consistent with our a priori hypothesis concerning the difference in brain activities between the intentional faking of responses and the commission errors. This study represents the first attempt to examine if the brain activity associated with unintentional memory errors of information semantically unrelated to the targets and faked responses is differentiable, and provides an affirmative verification of the different cognitive processes involved in faking and in making genuine errors during deception. The pattern of brain activity in the frontal-anterior cingulateparietal regions associated with intentionally faked responses is consistent with our apriori hypothesis concerning brain activation associated with deception. This observed pattern of brain activity is comparable with that reported in our previous study (Lee
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Table 2 Brain regions associated with significant BOLD signal increases under different contrasts Contrasts
Anatomical regions
BAa
Voxels
IFR > TAR
L Middle frontal gyrus R Anterior cingulate area R Insula R Precuneus L Fusiform gyrus L Lingual R Lingual R Thalamus L Inferior frontal gyrus None None L Inferior frontal gyrus R Posterior cingulate area L Precuneus None
6/44 24/32 13 7 37 17 17/30
48 44 74 83 51 40 406 364 137 — — 63 111 244 —
Coordinatesb x
TAR > IFR Errors > TAR TAR > Errors IFR > Errors
Errors > IFR
47/48 — — 47 23 7 —
t-values y
26 8 40 4 42 8 10 8 44 — — 34 8 12 —
z 14 10 6 66 60 68 56 22 20 — — 44 40 56 —
40 42 14 46 10 6 6 4 4 — — 6 28 46 —
5.44 9.33 4.61 16.92 7.88 5.09 15.3 10.64 6.81 — — 5.6 4.97 5.79 —
TAR, Truthful accurate response; IFR, Intentional faked responses. a BA, Brodmann’s area. b The x, y, z coordinates are in the MNI coordinates.
et al., 2002) and is also largely consistent with the findings of fMRI deception studies reported so far. The role of the ventrolateral prefrontal region (BA 47) in deceptive responses has been much discussed (e.g. Abe et al., 2006; Kozel et al., 2005; Nunez et al., 2005; Phan et al., 2005; Spence et al., 2005; Lee et al., 2005). The role this region plays in response inhibition, error checking and self-monitoring of performance (Menon, Adleman, White, Glover, & Reiss, 2001), and regulation of motivated responses, via its connection with the orbitofrontal and other cortical sub-cortical regions (Happaney, Zelazo, & Stuss, 2004), matches the experimental demand of a deceptive study. The significant activation in this region during the making of intentional faked responses, relative to committing errors, suggests that making an intentional faked response demands a higher level of inhibition and self-monitoring. Significant activation of the posterior cingulate (BA 26) could carry the functional roles of inhibiting previously learned rules as well as performing self-monitoring of random errors (Amos, 2000; Carter et al., 1998). Alternatively, it has been suggested that the activity of the posterior cingulate region serves to link incoming information with a repository of activated knowledge so as to form a coherent representation of discourse (Maguire, Frith, & Morris, 1999). When producing intentionally faked responses, the participants needed to hold the rules of the task in mind, inhibit the tendency to make an accurate response, and monitor the quality of their performance in order to be successful in pretending they had memory impairment. Furthermore, the conjoint activation of the precuneus and the posterior cingulate region could reflect the online incorporation of information into a preset mental framework, which matches with the task demand for participants who had already established a mental framework for malingering well before they entered the experimental conditions and underwent fMRI scanning. Prior research has shown that the precuneus is involved in the recollection of past events (Lundstrom, Ingvar, & Petersson, 2005), and studies have similarly argued that it plays an essential role in episodic memory retrieval, perspective-taking, and the experience of agency (Cavanna & Trimble, 2006). It also plays a particular role in the retrieval of rich episodic contextual associations (Lundstrom et al., 2005). In this context, the significant activity of the precuneus suggests that faking requires more effort from the memory system than does committing errors of memory. Within this study,
the participants had to pretend they had memory impairment by correctly recalling the true answer and then making an incorrect response. In this sense, the conditions were similar to those when lying about memory impairment in real-life situations: an individual would need to accurately recall the situation before intentional faking could take place. The higher activation of the precuneus might alternatively suggest the need for the participants to recruit parietal resources to modulate the cognitive load involved in faking. When comparing the brain activity associated with intentionally faking responses and making errors, a significant activation was observed for the former but not for the latter. This suggests that committing intentionally faked responses may be more cognitively demanding on the brain. Moreover, the different patterns of activation associated with intentionally faking responses and making errors suggest that different cognitive processes are engaged for the two conditions, which are distinguishable with fMRI. The emotional components involved in the current experimental paradigm (word list learning recognition paradigm) were minimal. Moreover, the performance of deception in the current experiment did not involve any risk (there were ‘‘low stakes,” to use Spence et al.’s (2004) term). This is hardly comparable to real-life situations, where the detection of intentional faked responses may result in serious consequences (e.g. loss of benefits as punishment, breakdown of social relationships). Future studies should consider the importance of the individual’s emotional state during the performance of deception in order to achieve a more indepth understanding of the neural mechanisms of real-life deception behaviors. 5. Conclusion The current study has successfully demonstrated that the neural activations involved in making unintentional errors and in intentionally faking responses are differentiable using fMRI. Significant activations associated with intentional faked responses were found in the left ventrolateral prefrontal region (BA 47), the right posterior cingulate region (BA 23), and the left precuneus for intentionally faked responses, relative to errors. Unlike when responses were intentionally faked, no significant activation was found when unintentional errors were committed. These findings are largely consistent with the logic that in order to achieve successful decep-
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Acknowledgments This project was supported by the Research Output Prize and the CRCG seed grant of The University of Hong Kong, and the National Natural Science Foundation of China (#30828012). References
Fig. 2. On the left is the whole brain analysis for the intentionally faked response versus error contrast at p < .005, corrected at a cluster level of 30 voxels. L = left hemisphere. R = right hemisphere. x, y, z in MNI coordinates. On the right are the plots of percent signal change. Right (R) is right. BA = Brodmann’s Area. L = left hemisphere. R = right hemisphere. x, y, z in MNI coordinates. Error bars show the standard error of means.
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