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The capacity to deceive others is a complex mental skill that requires the ability to suppress truthful information. The polygraph is widely used in countries such as the USA to detect deception. However, little is known about the effects of emotional processes (such as the fear of being found guilty despite being innocent) on the physiological responses that are used to detect lies. The aim of this study was to investigate the time course and neural correlates of untruthful behavior by analyzing electrocortical indexes in response to visually presented neutral and affective questions. Affective questions included sexual, shameful or disgusting topics. A total of 296 questions that were inherently true or false were presented to 25 subjects while ERPs were recorded from 128 scalp sites. Subjects were asked to lie on half of the questions and to answer truthfully on the remaining half. Behavioral and ERP responses indicated an increased need for executive control functions, namely working memory, inhibition and task switching processes, during deceptive responses. Deceptive responses also elicited a more negative N400 over the prefrontal areas and a smaller late positivity (LP 550-750 ms) over the prefrontal and frontal areas. However, a reduction in LP amplitude was also elicited by truthful affective responses. The failure to observe a difference in LP responses across conditions likely results from emotional interference. A swLORETA inverse solution was computed on the N400 amplitude (300-400 ms) for the dishonest - honest contrast. These results showed the activation of the superior, medial, middle and inferior frontal gyri (BA9, 11, 47) and the anterior cingulate cortex during deceptive responses. Our results conclude that the N400 amplitude is a reliable neural marker of deception.
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Can You Catch a Liar? How Negative Emotions Affect
Brain Responses when Lying or Telling the Truth
Alice Mado Proverbio*, Maria Elide Vanutelli, Roberta Adorni
Department of Psychology, University of Milano-Bicocca, Milan, Italy
The capacity to deceive others is a complex mental skill that requires the ability to suppress truthful information. The
polygraph is widely used in countries such as the USA to detect deception. However, little is known about the effects of
emotional processes (such as the fear of being found guilty despite being innocent) on the physiological responses that are
used to detect lies. The aim of this study was to investigate the time course and neural correlates of untruthful behavior by
analyzing electrocortical indexes in response to visually presented neutral and affective questions. Affective questions
included sexual, shameful or disgusting topics. A total of 296 questions that were inherently true or false were presented to
25 subjects while ERPs were recorded from 128 scalp sites. Subjects were asked to lie on half of the questions and to answer
truthfully on the remaining half. Behavioral and ERP responses indicated an increased need for executive control functions,
namely working memory, inhibition and task switching processes, during deceptive responses. Deceptive responses also
elicited a more negative N400 over the prefrontal areas and a smaller late positivity (LP 550–750 ms) over the prefrontal and
frontal areas. However, a reduction in LP amplitude was also elicited by truthful affective responses. The failure to observe
a difference in LP responses across conditions likely results from emotional interference. A swLORETA inverse solution was
computed on the N400 amplitude (300–400 ms) for the dishonest – honest contrast. These results showed the activation of
the superior, medial, middle and inferior frontal gyri (BA9, 11, 47) and the anterior cingulate cortex during deceptive
responses. Our results conclude that the N400 amplitude is a reliable neural marker of deception.
Citation: Proverbio AM, Vanutelli ME, Adorni R (2013) Can You Catch a Liar? How Negative Emotions Affect Brain Responses when Lying or Telling the Truth. PLoS
ONE 8(3): e59383. doi:10.1371/journal.pone.0059383
Editor: Francesco Di Russo, University of Rome, Italy
Received December 21, 2012; Accepted February 14, 2013; Published March 25, 2013
Copyright: ß2013 Proverbio et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by a FAR2011 grant from the University of Milano-Bicocca to AMP. AR was supported in part by ‘‘Dote ricercatori’’:
FSE,Fondo Sociale Europeo (European Social Fund), Regione Lombardia. The funders had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail:
When we lie (i.e., deliberately utter a falsehood with the
intention to deceive), our brain arousal level is increased because
of a catecholaminic response that is triggered by the Autonomic
Nervous System. This system is also responsible for other body
changes that can be detected easily by lie detector tests, including
voice modulation, which can be detected via ‘‘voice stress analyzers’’
[1]; pupil mydriasis; increases in respiratory and cardiac frequen-
cy; and skin conductance changes (electrodermal response).
However, these physiological indexes reflect an emotional
perturbation rather than the cognitive act of lying. Therefore,
these indexes cannot be used reliably to identify deception if an
innocent suspect experiences these physiological changes due to
The polygraph is widely used in countries such as the USA to
detect deception. However, little is known about the effect of
negative emotional processes (e.g., fear) on the physiological
responses that are used to detect lies. Therefore, the aim of this
study was to investigate the time course and neural correlates of
untruthful behavior by analyzing electrocortical indexes in re-
sponse to visually presented neutral and affective questions.
Several neuroimaging studies have revealed the crucial roles of
the prefrontal and inferior frontal cortices [2] as well as the
anterior cingulate cortex in the monitoring of conflict [3], the
inhibition of competing responses [4], working memory [5] and
the regulation of arousal [6]. These processes are all necessary to
improvise false responses. For example, in an fMRI study by Ganis
et al. [7], subjects were asked to lie about memorable autobio-
graphic experiences. These lies were associated with the activation
of the anterior region of the bilateral middle frontal gyrus (BA10)
and the anterior cingulate cortex (BA32). In another study by
Nun˜ez et al. [8], subjects were asked to either lie or tell the truth
about either a personal experience or shared semantic informa-
tion. This was associated with an increased activation of the
anterior cingulate cortex, the caudate and thalamic nuclei, and the
dorsolateral prefrontal cortex (DLPFC). A more recent fMRI
study by Lee et al. [9] investigated the interaction between
answering untruthfully and the affective valence of the subject of
the lie. Based on an initial rating of the affective value of IAPS
images (The International Affective Picture System), the 40 images
that were judged as the most positive and the 40 images that were
judged as the most negative by each participant were selected as
stimuli during their experiment. Subsequently, participants were
instructed to either lie or tell the truth about how they rated each
picture. Lying was associated with an increase in the BOLD signal
in the medial and superior frontal gyri (BA9), the left DLPFC
(BA46), the cingulate cortex, the bilateral insula (BA47, 48), and
the left precentral gyrus (BA9), as well as other brain regions. A
clear valence-related effect on deception was observed in several
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brain regions, including the lateral prefrontal and inferior parietal
cortex. However, activity in these regions has also been reported in
fMRI studies on deception that used neutral stimuli. Therefore,
the specific role of emotion in modulating brain activity during
lying was not assessed conclusively in this study. A more specific
effect of emotional state on deception was found in a PET study by
Abe et al. [10] in which participants were asked to respond
verbally with a single word to 48 questions that related to
autobiographical semantic information. Deception increased the
activation of the left ventromedial prefrontal cortex (VMPFC,
BA11), the right medial temporal gyrus (BA38), the right inferior
temporal gyrus (BA20/38) and the left amygdala, as well as other
brain regions. The activation of the amygdala is compatible with
its role in the perception and expression of fear sensation [11]. The
role of the dorsolateral prefrontal cortex (DLPFC) was targeted in
a Transcranial Direct Current Stimulation (tDCS) study by Priori
et al. [12] in which subjects were asked about the possession of
selected image cards. This study showed that stimulation of the
DLPFC significantly disrupted the ability to lie compared with
telling the truth.
These studies all required the execution of a response that was
incompatible with the truth, which stimulates the activation of
frontal and prefrontal cortical regions. A meta-analysis [13] of the
results from 12 functional MRI and PET studies identified the
activated regions that were common across all the studies during
the act of lying. Working memory, which is associated with
enhanced activity in the DLPFC, is important for maintaining
a representation of the truth. This area is also involved in
planning, problem solving, action implementation and inhibition,
manipulation of information and control of emotions [8,14,15].
The effectiveness of a lie requires the intervention of control
processes to efficiently resolve the conflict between a tendency to
respond sincerely and the need to produce a mendacious response
while inhibiting undesirable responses. According to neuroimaging
data, the area predominantly involved in this function is the
anterior cingulate cortex (ACC).
One problem with available neurometabolic studies is that these
studies require subjects to lie about specific sensory material (such
as pictures [16] or word lists [15]) during brain scans. Therefore,
the activation data and BOLD signals represent the neural
mechanisms that underlie not only the ability to lie but also the
processing of the objects or sensory information that are presented
(e.g., images, pictures, words). These latter brain processes are
independent of deception [17]. The lack of temporal resolution in
PET and fMRI techniques prevents the discrimination between
the timing of the perceptual and cognitive processing of presented
material and the timing of the decision making and planning and
execution of untruthful vs. truthful responses.
This problem can be addressed using electrophysiological
techniques such as Event-Related Potentials (ERPs). Because of
their high temporal resolution, ERPs can provide data on a time
scale of ms, which is the time course of the neural processing that is
involved in deception processes (e.g., [15,18–19].
In a recent ERP study [20], ERPs were recorded while
participants responded either truthfully or untruthfully about their
preferences for celebrities, food, sports or animals. The ERP
results showed an increase in negativity over the fronto-central
areas between 400 and 700 ms during untruthful responses. The
Principal Component Analysis (PCA) for the lie - honesty activity
difference identified two main dipoles in the medial frontal gyrus
and middle temporal gyrus. The authors hypothesized that these
areas might reflect conflict detection and control processes during
the processing of false answers. In another study by Dong et al.
[21], a group of students assessed the attractiveness of the
individuals in 200 photographs (100 women and 100 men) using
a multiple choice questionnaire. Based on the questionnaire
results, 80 photographs that were rated as attractive and un-
attractive were selected as stimuli for the ERP recording. Subjects
were asked to either lie or answer honestly about the attractiveness
of the individual in each picture. The ERP data showed an
increase in the LPC (Late Positive Component) between 300 and
500 ms during truthful responses compared with mendacious
responses and an enhanced negativity between 500 and 1000 ms
for untruthful responses. Hu et al. [22] asked subjects to respond
truthfully or untruthfully about autobiographical information,
such as name, date and city of birth, that could belong to either
themselves or a hypothetical stranger. Deception was associated
with an increased negativity (at the level of the parietal-occipital
N1 and the frontal-central N2) and a decreased frontal-central P3
positivity. The effect of emotional factors on deception was not
investigated in these ERP studies.
The aim of the present study was to investigate the time course
and neural correlates of untruthful responses to 296 visually
presented neutral and affective questions by analyzing electro-
cortical indexes that were recorded from 128 scalp sites in 25
volunteers. Subjects were asked to lie for half of the questions and
to answer truthfully for the remaining questions.
We wished to disentangle the effect of the cognitive act of lying
(controlled by the central nervous system) from the effect of the
emotional and physiological activation intrinsic to lying, which is
triggered by the autonomic nervous system (as well as the limbic
brain, including the amygdala), by identifying reliable neural
electrophysiological markers of lying and emotional states. We also
wished to distinguish the brain activation related to the processing
of the questions from the act of lying (or being truthful). To
accomplish this latter goal, EEG was recorded in a continuous
modality, and brain activity was time-locked to the response
prompt that followed each question rather than to the question
Based on the previous ERP literature, we expected to find an
increased N400 to untruthful responses and an increased P3/late
positivity to truthful responses. In addition, we investigated the
effect of emotion on the two conditions (to simulate the stressful
conditions under which a suspect takes a lie detector test) by asking
neutral vs. embarrassing, shameful or disgusting questions. Since
the effect of emotion in lying had not been previously investigated,
specifically with ERPs, we had no a priori hypothesis about the
possible component to be affected by it. We aimed at elucidating
precisely to which extent an emotional state (very likely affecting
more the healthy than the psychopathic brain) was able to mask or
alter the neural marker of deception, as for example the Late
Positivity described in Dong et al.’s [21] study, or the P3 from Hu
et al’s [22] study.
Materials and Methods
Twenty-five university students (12 males and 13 females)
volunteered for this experiment. The females ranged in age from
20 to 26 years (mean age = 23.15 years, SD = 1.63) and had a high
level of education (15.31 years in school, SD = 2.06). The males
ranged in age from 24 to 29 years (mean age =24.83 years,
SD = 1.85) with the same level of education as the females (15.25
years in school, SD = 2.14). All participants had normal or
corrected-to-normal vision with right eye dominance. All partic-
ipants were right-handed as assessed by the Edinburgh Inventory,
and none had any left-handed relatives. Experiments were
conducted with the understanding and written consent of each
Can You Catch a Liar? Electrocortical Markers
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participant according to the Declaration of Helsinki (BMJ 1991;
302: 1194) with approval from the Ethical Committee of the
Italian National Research Council (CNR) and in compliance with
APA ethical standards for the treatment of human volunteers
(1992, American Psychological Association). All participants
received academic credit for their participation. Data from 2
men and 2 women were subsequently discarded because of
excessive eye movements and electroencephalogram (EEG)
The stimuli were selected from an initial set of 320 sentences
that were evaluated by a group of 20 judges (10 men and 10
women) for their affective content on a 3-point scale (not at all,
somewhat emotional, extremely emotional). The sentences were
posed in the form of questions relative to inherently true or false
facts. For example, a few questions that were used in the study
include the following: ,,Is Washington, D.C. the capital of the
United States?.. (True sentence) Neutral. ,,Does the eel live
in the desert?.. (False sentence) Neutral. ,,Have you ever
tortured a child to death?.. (False sentence) Emotional.
,,Have you ever put your fingers in your nose? (True
sentence).. Emotional. Neutral sentences evaluated by more
than 40% of judges as emotional were discarded from the initial
set of stimuli. Likewise, emotional sentences evaluated by more
than 40% of judges as neutral were discarded. The final set of
stimuli included 296 neutral and affective questions that were
typed in Arial narrow size 10 font and balanced for topic, type of
information (semantic or autobiographical knowledge), affective
value (within the true and false categories), and length (min =26.6
letters, 5.16 words; max =32.3 letters, 5.57 words; mean = 29.2
letters, 5.35 words) across all categories (74 true neutral questions;
74 true affective questions; 74 false neutral questions; 74 false
affective questions). Sentences were presented at a visual angle of
approximately 3u249(min = 1u309, max = 6u) in length and 1u289
(min = 309; max = 1u459) in height.
Each sentence was presented for 1400 ms in one or two short
lines around the fixation point. Following an inter-stimulus
interval (ISI) that ranged from 500 to 600 ms, a red cross (1 cm
in size, 0.5 degree of visual angle) appeared at the center of the
visual field for 2 seconds to prompt the motor response. The EEG
was synchronized to the onset of the response prompt.
Task and Procedure
Participants were seated comfortably in a dark and acoustically
and electrically shielded test area in front of a high-resolution
computer screen located 114 cm from their eyes. Participants were
instructed to gaze at the center of the screen at a small red circle
that served as the fixation point and to avoid any eye or body
movements during the recording session.
The task consisted of responding to questions as quickly and
accurately as possible by pressing a response key with the index or
middle finger (yes or no, respectively) according to the specific
instructions (lie vs. answer truthfully). The two hands were
alternated during the recording session. The order of the hand
and task conditions was counterbalanced across subjects. At the
beginning of each session, subjects were told what the task
requirement was (lying or telling the truth) and which hand would
be used to make responses. For each condition, 2 stimuli sequences
(or runs) were presented one for each response hand, separated by
a short pause. Overall the experimental session comprised the
presentation of 8 runs. The experimental session was preceded by
a training session that included two conditions: lie or answer
truthfully, for each of the two hands (i.e. 4 short stimuli sequences).
EEG Recording and Analysis
EEG data were recorded continuously from 128 scalp sites at
a sampling rate of 512 Hz using the EEProbe recording system
(Advanced Neuro Technology (ANT) Enschede, The Nether-
Horizontal and vertical eye movements were also recorded
using the linked ears as the reference lead. The EEG and
electrooculogram (EOG) were amplified with a half-amplitude
band pass of 0.016–100 Hz. Electrode impedance was maintained
below 5 kV. EEG epochs were synchronized with the onset of the
stimulus presentation. Computerized artifact rejection was per-
formed to discard epochs in which eye movements, blinks,
excessive muscle potentials or amplifier blocking occurred. The
artifact rejection criterion was a peak-to-peak amplitude that
exceeded 50 mV, which resulted in a rejection rate of ,5%.
Evoked-response potentials (ERPs) from 100 ms before (2100 ms)
to 1000 ms after stimulus onset were averaged. ERP components
(including the site and latency to reach maximum amplitude) were
identified and measured with respect to the average baseline
voltage over the interval from 2100 to 0 ms.
The amplitudes of the N400 component, which reached its
maximum amplitude between 300 and 400 ms, and the prefrontal
late positivity (LP), which reached its maximum amplitude
between 550 and 750 ms, were measured at anterior frontal
(AF3, AF4, AFp3h, AFp4h) and prefrontal and frontocentral sites
(AFF5h h, AFF6h, FFC3h, FFC4h), respectively.
Topographical voltage maps of the ERPs were generated by
plotting color-coded isopotentials that were obtained by in-
terpolating voltage values between scalp electrodes at specific
latencies. A multifactorial repeated-measures analysis of variance
(ANOVA) was applied to the ERP data. The factors included
condition (deception, truthfulness), emotional content (emotional,
neutral), question intrinsic veracity (true, false), electrode (accord-
ing to the ERP component of interest) and hemisphere (left, right).
Multiple post-hoc mean comparisons were performed using the
Tukey test. Reaction times (RTs) that exceeded the mean value
62 standard deviations were discarded, which resulted in
a rejection rate of 5%. Error rate percentages were converted to
arcsin values. Both RTs and error percentages were subjected to
separate multifactorial repeated-measures ANOVAs with 3
within-subject factors: condition (deception, truthfulness), emo-
tional content (emotional, neutral), and question intrinsic veracity
(true, false).
Behavioral Results
Accuracy data. The analysis revealed a main effect of
condition (F1, 24 = 104.07, p,0.00001) in which subjects com-
mitted more errors when they had to lie compared with when they
had to answer truthfully (deception: 12.37%, SE = 0.63 and
truthfulness: 6.16%, SE = 0.44). The analysis also revealed
a significant effect of the question veracity (F1, 24 = 5.07,
p,0.05) and post-hoc comparisons showed that the subjects
committed more errors when the question was inherently true
(10.18%, SE = 0.57) compared with when it was inherently false
(8.34%, SE = 0.65), as displayed in Fig. 1. This effect also
depended on the significant interaction between emotional content
and question veracity (F1, 24 = 3.18, p,0.0005). Post-hoc
comparisons showed that subjects committed more errors
(p,0.05) when responding to emotional true sentences (11.53%,
SE = 0.7) compared with neutral true sentences (8.84%, SE = 0.8);
however, subjects committed more errors (p,0.05) on neutral false
sentences (9.67%, SE = 0.8) compared with emotional false
Can You Catch a Liar? Electrocortical Markers
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sentences (7.02%, SE = 0.83). Moreover, subjects committed more
errors on emotional questions (p,0.0005) when they were true
(11.52%, SE = 0.7) compared with when they were false (7.02%,
SE = 0.83).
Response times (RTs). The ANOVA revealed a significant
effect of condition (F1, 24 = 5.87, p,0.05) with faster responses in
the truthfulness compared with the deception condition (tell the
truth = 522 ms, SE = 28.79; lie = 548 ms, SE = 23.23). The emo-
tional content of the questions also affected the RTs (F1, 24 = 6.8,
p,0.05) with faster responses to neutral (527 ms, SE = 26)
compared with emotional (543.35 ms, SE = 25.54) questions.
Furthermore, significant effects of question veracity (F1,
24 = 16.98, p,0.0005) and the interaction of veracity and
condition (F1, 24 = 4.83, p,0.05) were found. Post-hoc compar-
isons showed that responses were significantly slower (p,0.0005)
when subjects were lying about a true (561.4 ms, SE = 23.29)
compared with a false (524.34 ms, SE = 28.11) question. No
significant difference was found between RTs to false questions in
the two conditions (as displayed in Figure 2).
Electrophysiological Results
The ERPs recorded at anterior and posterior scalp sites in the
two conditions (lie, tell the truth) are shown in Fig. 3. The two
conditions differ in the amplitude of both the N400 and LP
responses over the prefrontal sites. There was no difference
between the two conditions observed at posterior sites, which
suggests that the linguistic, perceptual and sensory nature of the
questions that were posed to the subjects were identical in both
N400 response. The ANOVA performed on the N400
amplitudes revealed a significant effect of hemisphere
(F1,20 = 5.25, p,0.05) with a stronger activation of the left
compared with the right hemisphere (LH: 21mV, SE = 0.3,
RH = 20.84 mV, SE = 0.3). The largest activity was observed over
the left prefrontal site, as shown by the interaction of the factors
electrode and hemisphere (F1,20 = 12.56, p,0.005).
The ANOVA also revealed a significant effect of condition (lie,
tell the truth) (F1,20 = 6.95; p,0.05) in which lying was associated
with a larger N400 amplitude compared with the truthful
condition (lie: 21.27 mV, SE = 0.33; tell the truth: 20.57 mV,
SE = 0.33). Figure 4 shows the scalp distributions of the N400
component, which was larger at central locations, compared with
task-related modulation, which was larger at prefrontal sites (see
Fig. 3).
To identify the neural bases of this effect, a swLORETA inverse
solution (Fig. 5, Top) was applied to the ERP responses that were
recorded in the lying condition between 300–400 ms post-
stimulus. Table 1 reports the electromagnetic dipoles that
generated the surface voltage of the N400 component. The
inverse solution showed that the strongest generators of the N400
component were located bilaterally within the fusiform gyrus
(BA37/19) and the right cingulate cortex (BA30 and 31). A
swLORETA inverse solution was also applied to the ERPs
recorded in the tell the truth condition (Fig. 5, Middle) and showed
that the strongest neural generators were also located over the left
and right fusiform gyrus (BA20/37) but involved less recruitment
from the cingulate cortex (see the dipole magnitude in Table 1).
To better highlight the differences between the lie and tell the
truth conditions, difference waves (for each EEG channel) were
computed between the two conditions (lie – telling the truth). A
swLORETA inverse solution was applied to the difference waves
in the 300–400 ms time window. The results (Fig. 5, bottom)
showed that untruthful responses were associated with stronger
activity in the left and right anterior brain regions (including
BA47, 9, 11, and the cingulate cortex), especially the left middle
frontal gyrus (BA47, see Table 2 for a list of dipoles).
Analysis also showed a significant effect of question veracity
(F1,20 = 5.05, p,0.05) in which the N400 amplitude was larger
during false (21.17 mV, SE = 0.29) compared with true
(20.67 mV, SE = 0.35) questions.
Furthermore, the ANOVA showed a significant interaction
between affective value and hemisphere (F1,20 = 6.49, p,0.05).
Post-hoc mean comparisons showed stronger activity over the left
Figure 1. Arc sin transformed percentage of errors committed
in responding to affective or neutral true or false questions.
The data show how challenging was to tell the truth about an
emotional question related to sex, disgusting matter or shameful
behavior, while it was much easier to deny a false statement.
doi:10.1371/journal.pone.0059383.g001 Figure 2. Response times recorded in response to inherently
true or false statements, as a function of experimental
condition (lie, or tell the truth). The data show how it was much
difficult to deny a truthful than false information.
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Figure 3. Grand-average ERPs recorded at 128 scalp sites over the left and right hemisphere during the ‘‘lie’’ and ‘‘tell the truth’’
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(21.06 mV, SE = 0.33) compared with the right (20.8 mV,
SE = 0.33) hemisphere for responses to neutral questions and no
hemispheric asymmetry for responses to affective questions.
Late positivity (LP). LP deflection reached its maximum
amplitude between 550 and 750 ms over central sites (as displayed
in Fig. 6). LP amplitude was quantified for task-related modulation
that was observed at anterior frontal and frontal sites (AFF5h,
AFF6h, FFC3h, FFC4h). Fig. 7 shows the combined effect of the
condition and the affective value of the question at these locations.
The ANOVA revealed a significant effect of the condition
(F1,20 = 7.43; p,0.05) in which truthful responses elicited a larger
LP compared with untruthful responses (1.92 mV, SE = 0.33 and
1.36 mV, SE = 0.23, respectively). Furthermore, emotional content
was also significant (F1,20 = 8.53, p,0.01) such that larger LP
were observed in response to neutral compared with emotional
questions (1.96 mV, SE = 0.32 and 1.33 mV, SE = 0.32, respec-
tively). The post-hoc comparison of the significant three-way
interaction of condition, emotional content and hemisphere
Figure 4. Isocolour topographic maps (top left view) of brain voltage recorded in between 340–390 ms of latency (N400 peak)
during the lie (Bottom) vs. tell the truth condition (Top) as a function of question affective value (Left = affective; Right = neutral).
Mendacious responses were characterized by an increase in negativity at this stage of processing.
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(F1,20 = 4.47, p,0.05) revealed that LP responses were larger
during truthful compared with untruthful responses only when the
affective content was neutral (especially over the right hemisphere).
However, there was no difference in the response to emotional
questions between the two conditions in either hemisphere, as
displayed in Fig. 6 (LP mean amplitudes: Truthful neutral:
RH = 2.29 mV, SE = 0.3; LH = 2.45 mV, SE = 0.37. Truthful
emotional: RH = 1.26 mV, SE = 0.4; LH = 1.7 mV, SE = 0.42.
Untruthful neutral: RH = 1.28 mV, SE = 0.25; LH = 1.8 mV,
SE = 0.3. Untruthful emotional: RH = 1 mV, SE = 1.27;
LH = 1.37 mV, SE = 0.36).
Figure 5. swLORETA inverse solution performed on ERP responses recorded in the two conditions and on the difference waves
(‘‘lie’’ minus ‘‘tell the truth’’ condition) in the time window of 300–400 corresponding to the N400 component. Red arrows indicate
electromagnetic dipoles. Coronal, axial and sagittal views are represented (from the left). L = left hemisphere, R = right hemisphere, A = anterior,
P = posterior.
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The similarity of the LP responses to affective questions in both
conditions (lying vs. telling the truth) can be observed in the scalp
topographical distributions of LP voltage depicted in Fig. 6. The
failure to observe a difference in LP responses across conditions
likely results from emotional interference.
The analysis of behavioral responses showed an increase in the
time needed to respond to untruthful compared with truthful
responses [8,9,20–22]. This increase in response time is also
known as the ‘‘lie’’ effect [9]. The analysis also showed a decrease
in accuracy due to both response conflict and the need to suppress
truthful information. Lying requires a greater mental load due to
the need to inhibit the honest response, which is more automatic,
and prevaricating. Consequently, the need for ‘‘extra’’ cognitive
resources during lying triggers the activation of control-related
brain regions such as the frontal and prefrontal cortical areas [14].
The analysis of our behavioral data also showed that responses
to inherently false questions were faster and more accurate than
responses to inherently true questions. Subjects may find it easier
to answer questions that can be immediately recognized as
incorrect, such as ‘‘Is the cob a type of fungus?’’ compared with
questions in which the truth needs to be verified, such as ‘‘Can
there be raisins in a cake?’’.
In addition, our analysis showed that responses to emotional
questions resulted in longer RTs compared with responses to
neutral questions. This finding indicates that there is a cost
associated with processing affective compared with neutral in-
formation. This finding has been supported by previous neuroi-
maging studies [11,24,25] that have shown that emotionally
valenced stimuli are prioritized during processing and are able to
interrupt or disrupt ongoing cognitive processes and divert
attention from the primary cognitive task. This property has
a clear adaptive value that enables a quick reaction to potentially
threatening stimuli [26]. For example, in a recent study [27] of the
Stroop task with facial expressions, subjects were asked to
categorize the emotions conveyed on the faces in the photographs
that were shown and to ignore the words that were presented in
the center of the face. These words could be neutral or affective
and the results demonstrated that only the affective distracters
interfered with task performance.
In the present ERP data, lying was associated with an increase
in negativity (N400) between 300 and 400 ms over the prefrontal
areas, especially in the left hemisphere. This finding likely indexes
an increased mental workload. This increase in the N400
component, which we consider a reliable neural marker of a lie,
was independent of the affective value of the question.
To identify the origin of this effect, 3 different swLORETA
inverse solutions were applied to the ERP responses recorded
between 300 and 400 ms during lying, telling the truth, and the
difference between the two conditions to differentiate the patterns
of cerebral activation. The inverse solutions showed a common
activation of the ventral stream, namely the fusiform gyrus of the
left (BA19 and BA37) and right (BA20 and 37) hemisphere
(involved in the processing of visual objects of various categories
[28], in response to the visual processing of both the sentences and
the fixation red cross in both the lying and telling the truth
conditions. Therefore, this result indicates that both conditions
triggered the same perceptual processes.
In addition, compared with the tell the truth condition, the lie
condition showed stronger activation of the posterior cingulate
cortex (BA30), which is an area that is involved in the encoding of
emotional aspects of visual information [29–32]. The swLORETA
applied to the differential activity recorded in the lie minus the tell
the truth condition showed significant activations in a series of
anterior regions, with the strongest activation observed in the left
Table 1. Tailarach coordinates (in mm) corresponding to the
intracranial generators explaining the surface voltage
recorded during response time in the lie vs. tell the truth
condition in the 300–400 ms time window, according to
swLORETA (ASA) [23], grid spacing = 5 mm, estimated SNR = 3.
(mm) Hem. Lobe Area BA
15.09 51 255 218 RH Temp FG 37
13.49 249 266 211 LH Temp FG 19
11.29 21 268 5 RH Limbic Post. Cingulate 30
10.36 11 230 35 RH limbic Cingulate gyrus 31
15.09 51 255 218 RH Temp FG 37
14.29 51 234 224 RH Temp FG 20
13.18 249 256 210 LH Temp FG 37
11.05 21 290 21 RH Occ Cuneus 18
10.38 51 21228 RH Temp MTG 21
9.33 11 230 35 RH Limbic Cingulate gyrus 31
Power RMS: Lie = 59.4; Tell the truth = 53.1 mV.
Table 2. Tailarach coordinates (in mm) corresponding to the
intracranial generators explaining the different voltage
recorded in the lie minus tell the truth conditions in the 300–
400 ms time window, according to swLORETA (ASA) [23], grid
spacing = 5 mm, estimated SNR = 3.
(mm) Hem. Lobe Area BA
31.58 249 36 23 LH Frontal Middle Frontal g. 47
24.84 41 27 211 RH Frontal Inferior Frontal g. 47
21.92 2 38 218 RH Frontal Medial Frontal g. 11
19.51 21 52 34 RH Frontal Superior
Frontal g.
16.79 219 21 65 LH Frontal Superior
Frontal g.
17.55 61 26 37 RH Frontal Precentral gyrus 6
15.11 2921228 LH Limbic Uncus 28
14.61 2 2 29 RH Limbic Anterior
20.16 61 255 218 RH Occipital Fusiform gyrus 37
20.12 61 225 216 RH Temporal Inferior Temporal
19.98 219 297 213 LH Occipital Lingual gyrus 18
14.86 259 29222 LH Temporal Inferior Temporal
22.27 11 299 2 RH Occipital Cuneus
27.11 11 273 49 RH Parietal Precuneus 7
Power RMS: 10.3 mV.
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PLOS ONE | 8 March 2013 | Volume 8 | Issue 3 | e59383
middle frontal gyrus (BA 47); the right middle, inferior and
superior frontal gyri; and the anterior cingulate cortex (BA24).
Our pattern of results is consistent with the findings by Abe et al.
[10], in which falsifying truthful responses was associated with
increased brain activity in the left dorsolateral and right anterior
prefrontal cortices. Therefore, these findings support the in-
terpretation of previous studies that the generation of untruthful
responses is related to executive function.
Prior neuroimaging studies have shown a role of the BA47 in
motor response inhibition [7], emotion regulation [33] and
cognitive and self-control [34–35]. Furthermore, the medial
orbitofrontal gyrus (BA11) has been associated with the imple-
mentation of processes that underlie control performance [36–38],
automonitoring in action regulation [39,40], and conflict detection
and control [16,19]. This area is also involved in emotion
regulation [33,41,42]. For example, a study by Ohira et al. [43] on
the voluntary suppression of emotions showed that this area is
Figure 6. Grand-average ERP waveforms recorded at left and right anterior frontal and fronto-central sites as a function of
questions affective content (dotted: affective, solid: neutral) and task conditions (blue = tell the truth; red = lie). It is visible a lack of
difference in LP responses to affective questions across the two ask conditions (lying vs. telling the truth) probably because of the emotional
interference. On the other hand, LP clearly differentiates the response on the basis of its truthfulness when no emotion is involved.
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PLOS ONE | 9 March 2013 | Volume 8 | Issue 3 | e59383
associated with the top-down control of peripheral physiological
responses that are linked to an emotional experience.
The superior frontal gyrus (BA6 and BA9), which is part of the
dorsolateral prefrontal cortex (DLPFC), has been implicated in the
formulation of mendacious responses [7–10,12,13] and is re-
sponsible for regulating and inhibiting undesired behavior. Studies
suggest that the DLPFC plays a key role in maintaining relevant
information in working memory [44,45], inhibiting irrelevant
information and responses, and trouble shooting, conflict moni-
toring and conflict solving [46,47].
The activation of the premotor cortex (BA6), which was also
found in studies by Ganis et al. [7] and Nun˜ ez et al. [8], has been
related to the need to suppress undesired behavior and prepare the
correct motor response.
Finally, the anterior cingulate cortex (BA24) plays a multifunc-
tional role in controlling and monitoring responses in the event of
a conflict between the required answer and a more automatic but
undesired answer [15,47,48]. This area is also involved in the
inhibition of such undesirable responses [4,36,49,50].
Figure 7. Topographical maps of surface volte activity recorded at the time window corresponding to LP maximum amplitude, as
a function of questions affective content (left: affective, right: neutral) and task conditions (top= tell the truth; bottom: lie).
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The N400 amplitude was larger over left compared with right
prefrontal sites, which may be related to the linguistic nature of the
stimuli that were used in this study. Some studies have shown
greater activity over the right hemisphere in tasks that involve
lying [51], but many of these studies have used pictures or
photographs rather than phrases for the experimental stimuli. In
our study, N400 was not affected by the affective valence of the
stimulus. Therefore, N400 is a reliable neural marker of lying that
is independent of the emotional circumstance. In contrast, the late
positivity between 550 and 750 ms post-stimulus at prefrontal and
frontal sites was identified as a neural marker for truthful
responses. This finding is consistent with the results of many
other ERP studies on the LP component (e.g., [21,22]) and on
P300 responses [52,53]. Johnsons et al. [18] has suggested that the
decrease in the amplitude of the LP for deceptive responses may
be due to the inhibition of truthful answers.
However, in our study we observed that the LP did not
distinguish between truthful and untruthful responses when the
question was emotional. Therefore, the use of LP as a neural
marker may not be reliable if the data are used as legal proof to
incriminate a suspect. This finding indicates that the emotional
tone of the question can modulate brain activity in relation to the
responses given in the two different conditions. This finding agrees
with Ekman & Sullivan [54] in which the authors stated that
changes from the autonomic system are not in themselves direct
measures of the lie but rather are the product of emotions. These
automatic changes in the autonomic response are related to
feelings of guilt and shame (as well as the fear of being discovered)
and should not be considered as measures of the lie itself. The
activation of the autonomic nervous system and the affective brain
(in our study BA24 and BA28, limbic cortex) may affect both the
LP amplitude and the physiological parameters of an extremely
anxious person who is being questioned for a disgraceful crime.
Therefore, the LP amplitude is not a reliable marker for deception.
However, the N400 is a reliable marker of lying that is not affected
by emotional factors.
The ERP data show the existence of a reliable neural marker of
lying in the form of an increased amplitude of the N400
component (which likely indexes conscious control processing) in
frontal and prefrontal regions of the left hemisphere between 300
and 400 ms post-stimulus. Importantly, this marker was observed
to be independent of the affective value of the question. The
neural generators underlying this effect included the prefrontal
cortex and anterior cingulate cortex. In contrast, a later LP
deflection proved to be a marker of truthfulness only for neutral
questions because emotional questions always reduced LP
amplitudes (which likely indexes an increased arousal level that
is triggered by the emotion-related autonomic response) regardless
of whether the responses were truthful or untruthful.
One possible limitation of this study is that lying or telling the
truth (although performed rather automatically and very accu-
rately by anxious participants) was not specifically reinforced or
guided by low-level emotional drives such as fear or pleasure (as
can occur in real life), but were cognitively guided (‘‘I must do as
required’’). However, the same problem holds for all ERP studies
in the literature (e.g., [10,21,22]) as well as neuroimaging studies.
Overall, we believe that the 2 neural markers that we have
discovered are sufficiently general to apply to a wider neural
mechanism of lying and involve a look-for-reward or pain-
avoidant motivation.
The authors are grateful to Mirella Manfredi and Alberto Zani for their
comments and to all of the participants for their cooperation.
Author Contributions
Conceived and designed the experiments: AMP MEV. Performed the
experiments: RA. Analyzed the data: MEV RA. Wrote the paper: AMP.
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... Tivatansakul et al. (2014) for instance, achieve an accuracy of about 86% in detecting the six basic emotions based on facial expressions. Electroencephalogram (EEG) signals also provide meaningful indications concerning the emotional conditions of individuals (Proverbio et al. 2013). Zhang et al. (2020) reported that EEG electrodes applied to the frontal lobe of a subject revealed a classification accuracy of more than 90% across different emotional states. ...
... A complex emotion of interest to the service industry is 'lying'. According to Ekman (2004), lying is a complex emotional construct that is interdependent with other emotions and itself triggers emotions in the liar and in the person being lied to (Proverbio et al. 2013). Not telling the truth comes in many different flavors: There is the straight lie, but also deception, bluffing, swindle, or the white lie, and many more. ...
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Customers' emotions play a vital role in the service industry. The better frontline personnel understand the customer, the better the service they can provide. As human emotions generate certain (unintentional) bodily reactions, such as increase in heart rate, sweating, dilation, blushing and paling, which are measurable, artificial intelligence (AI) technologies can interpret these signals. Great progress has been made in recent years to automatically detect basic emotions like joy, anger etc. Complex emotions, consisting of multiple interdependent basic emotions, are more difficult to identify. One complex emotion which is of great interest to the service industry is difficult to detect: whether a customer is telling the truth or just a story. This research presents an AI-method for capturing and sensing emotional data. With an accuracy of around 98 %, the best trained model was able to detect whether a participant of a debating challenge was arguing for or against her/his conviction, using speech analysis. The data set was collected in an experimental setting with 40 participants. The findings are applicable to a wide range of service processes and specifically useful for all customer interactions that take place via telephone. The algorithm presented can be applied in any situation where it is helpful for the agent to know whether a customer is speaking to her/his conviction. This could, for example, lead to a reduction in doubtful insurance claims, or untruthful statements in job interviews. This would not only reduce operational losses for service companies, but also encourage customers to be more truthful.
... When the specificity was Innocent-actual, the AUC was following (AUC, Confidence (Rugg & Curran, 2007), and conscious control processing (Proverbio, Vanutelli, & Adorni, 2013). ...
... Of these 118 references several primary studies had to be excluded because of the following reasons: (1) k = 10 studies investigated electrodermal parameters (e.g., skin conductance level) or cardiovascular parameters (e.g., heart rate), (2) k = 4 studies investigated EEG frequency band data or connectivity data, (3) k = 9 studies investigated exclusively other stimulus-locked ERPs (e.g., N400 amplitude) or stimulus-locked P300 at occipital sites instead of parietal sites (Gibbons, Schnürch, Wittinghofer, Armbrecht, & Stahl, 2018), (4) k = 7 studies investigated response-locked or feedback-locked ERPs (e.g., responselocked medial frontal negativity, feedback-locked P3), (5) k = 3 studies investigated dipole sources in a deception task, (6) k = 9 deception studies did not report ERP findings but discussed the overall investigation of physiological parameters in deception studies or (7) were reviews (k = 7), (8) k = 1 study was not on deception although the P3 was investigated (Spapé, Hoggan, Jaccucci, & Ravaja, 2015), (9) k = 2 studies investigated the frontal P3 (Gibbons et al., 2018;Proverbio, Note. Because too few studies investigated individual differences of deception (e.g., Leue & Beauducel, 2015;Leue et al., 2012), effects of individual differences (e.g., injustice sensitivity, trait-anxiety) on the probe-irrelevant P3 difference could not be calculated in this meta-analysis. ...
In deception tasks the parietal P3 amplitude of the event-related potential indicates either recognition of salient stimuli (larger P3 following salient information) or mental effort (smaller P3 following demanding information). This meta-analysis (k = 77) investigated population effect sizes (δ) for conceptual and methodological a-priori moderators (study design, pre-task scenario, context of deception tasks, and P3 quantification). Within-subject designs show evidence of the underlying cognitive processes, between-subject designs allow for comparisons of cognitive processes in culprits vs. innocents. Committed vs. imagined mock crime scenarios yield larger δ. Deception tasks with a legal context result in almost twice as large δ than deception tasks with social-evaluative and social-biographical contexts. Peak-to-peak P3 quantification resulted in larger δ than other quantifications. Counter-measure techniques in 3-stimulus protocols reduce the discriminability of concealed vs. truthful P3 amplitudes. Depending on stimulus knowledge, deception tasks provide evidence for the salience hypothesis and the mental effort hypothesis, respectively.
... The function of inhibition is closely linked to the neural activities of the prefrontal cortex, especially related to the activities of the left middle frontal gyrus (MFG) and the bilateral inferior frontal gyrus (IFG; Jonides et al., 1998;Aron et al., 2003;Swick et al., 2008;Marchewka et al., 2012;Sip et al., 2013). Existing studies show empirical evidence that these regions involved in inhibition could be significantly activated during different kinds of deception ( Browndyke et al., 2008;Ito et al., 2011;Marchewka et al., 2012;Proverbio et al., 2013). For instance, Marchewka et al. (2012) proved that significantly greater activation of the bilateral IFG could be observed whether lying about general information or about individual information than telling the truth. ...
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... A különböző idegrendszeri folyamatokról ld. Proverbio et al., 2013). ...
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... It revealed that deceptive response to attractive faces required a longer RT and triggered higher LPC amplitude than deceptive response to unattractive faces. Moreover, truthful and deceptive behavior concerning the evaluation of attitude [21], emotion [16,22], self-relevance [23] and interpersonal familiarity [9] were investigated, and the moderating roles of the evaluations on truthful and deceptive responses were also observed. ...
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Deceptive behavior is common in human social interactions. Researchers have been trying to uncover the cognitive process and neural basis underlying deception due to its theoretical and practical significance. We used Event-related potentials (ERPs) to investigate the neural correlates of deception when the participants completed a hazard judgment task. Pictures conveying or not conveying hazard information were presented to the participants who were then requested to discriminate the hazard content (safe or hazardous) and make a response corresponding to the cues (truthful or deceptive). Behavioral and electrophysiological data were recorded during the entire experiment. Results showed that deceptive responses, compared to truthful responses, were associated with longer reaction time (RT), lower accuracy, increased N2 and reduced late positive potential (LPP), suggesting a cognitively more demanding process to respond deceptively. The decrement in LPP correlated negatively with the increment in RT for deceptive relative to truthful responses, regardless of hazard content. In addition, hazardous information evoked larger N1 and P300 than safe information, reflecting an early processing bias and a later evaluative categorization process based on motivational significance, respectively. Finally, the interaction between honesty (truthful/deceptive) and safety (safe/hazardous) on accuracy and LPP indicated that deceptive responses towards safe information required more effort than deceptive responses towards hazardous information. Overall, these results demonstrate the neurocognitive substrates underlying deception about hazard information.
... It is therefore important to examine the role of emotion and its possible interaction with cognition in deception more closely (Dolcos, Iordan, & Dolcos, 2011;Pessoa, 2008). Such research may, for instance, compare lying about neutral and emotional stimuli in order to explore effects of stimulus valence on the RT for truth telling and lying (Lee, Lee, Raine, & Chan, 2010;Proverbio, Vanutelli, & Adorni, 2013). ...
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Lie detection techniques are frequently used, but most of them have been criticized for the lack of empirical support for their predictive validity and presumed underlying mechanisms. This situation has led to increased efforts to unravel the cognitive mechanisms underlying deception and to develop a comprehensive theory of deception. A cognitive approach to deception has reinvigorated interest in reaction time (RT) measures to differentiate lies from truths and to investigate whether lying is more cognitively demanding than truth telling. Here, we provide the results of a meta-analysis of 114 studies (n = 3307) using computerized RT paradigms to assess the cognitive cost of lying. Results revealed a large standardized RT difference, even after correction for publication bias (d = 1.049; 95% CI [0.930; 1.169]), with a large heterogeneity amongst effect sizes. Moderator analyses revealed that the RT deception effect was smaller, yet still large, in studies in which participants received instructions to avoid detection. The autobiographical Implicit Association Test produced smaller effects than the Concealed Information Test, the Sheffield Lie Test, and the Differentiation of Deception paradigm. An additional meta-analysis (17 studies, n = 348) showed that, like other deception measures, RT deception measures are susceptible to countermeasures. Whereas our meta-analysis corroborates current cognitive approaches to deception, the observed heterogeneity calls for further research on the boundary conditions of the cognitive cost of deception. RT-based measures of deception may have potential in applied settings, but countermeasures remain an important challenge.
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Lies and deceptions are a common human behavior. Lying has many determinants, including developmental, biological, social, and psychodynamic. Understanding the neural basis of human honesty and deception has enormous potential scientific and practical value. Through a review of the legal, scientific and pseudo-scientific issues surrounding deception, a greater understanding is reached of the complexity of this universal and morally loaded behavior. Are lying and deception described in the Bible? Who were the leaders who lied? The Biblical texts were examined and verses relating to deception in leaders were studied closely from a contemporary viewpoint.
Lze najít obecnou a šířeji akceptovatelnou definici lži? Můžeme obelhat sami sebe? Jak lžou děti a jak lžou dospělí? Jaké jsou verbální a neverbální projevy lhaní? Jaké jsou limity a úskalí detekce lži? Na tyto a další otázky hledá odborně podložené odpovědi ojedinělá a komplexní monografie věnovaná fenoménu lži. Publikace nahlíží problematiku lhaní v sociálním, filozofickém, ekonomickém a neuropsychologickém kontextu. Především však zpracovává lhaní jako sociální fenomén vystupující v situacích vyžadujících odborné psychologické působení - například v psychoterapii, forenzní a školněpsychologické praxi. Autorka seznamuje čtenáře s jevem lži z hlediska typologie, motivů, frekvence lhaní, ale i z hlediska vývojových, genderových i kulturních souvislostí. Ukazuje, jak se komplexní povaha lhaní projevuje v řeči, v chování i v prožívání. Závěrečná kapitola je věnována pozoruhodným situacím, ve kterých jsou lži ve skutečnosti důsledkem sugestibility, falešných vzpomínek či poruch osobnosti. Monografie jistě zaujme odborníky z vědních oblastí, jako je psychologie, pedagogika, sociologie, právo, ale přínosná bude také pro učitele, rodiče, speciální pedagogy a forenzní psychology.
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Summary Goal-directed behaviour depends on keeping relevant information in mind (working memory) and irrelevant information out of mind (behavioural inhibition or interference resolution). Prefrontal cortex is essential for working memory and for interference resolution, but it is unknown whether these two mental abilities are mediated by common or distinct prefrontal regions. To address this question, functional MRI was used to identify brain regions activated by separate manipulations of working memory load and interference within a single task (the Sternberg item recognition paradigm). Both load and interference manipulations were associated with performance decrements. Subjects were unaware of the interference manipulation. There was a high degree of overlap between the regions activated by load and interference, which included bilateral ventrolateral and dorsolateral prefrontal cortex, anterior insula, anterior cingulate and parietal cortex. Critically, no region was activated exclusively by interference. Several regions within this common network exhibited a brain–behaviour
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The neural basis of self and identity has received extensive research. However, most of these existing studies have focused on situations where the internal representation of the self is consistent with the external one. The present study used fMRI methodology to examine the neural correlates of two different types of identity conflict: identity faking and concealment. Participants were presented with a sequence of names and asked to either conceal their own identity or fake another one. The results revealed that the right insular cortex and bilaterally inferior frontal gyrus were more active for identity concealment compared to the control condition, whereas identity faking elicited a significantly larger percentage signal increase than the control condition in the right superior frontal gyrus, left calcarine, and right caudate. These results suggest that different neural systems associated with both identity processing and deception were involved in identity concealment and faking.
An evolved module for fear elicitation and fear learning with 4 characteristics is proposed. (a) The fear module is preferentially activated in aversive contexts by stimuli that are fear relevant in an evolutionary perspective. (b) Its activation to such stimuli is automatic. (c) It is relatively impenetrable to cognitive control. (d) It originates in a dedicated neural circuitry, centered on the amygdala. Evidence supporting these propositions is reviewed from conditioning studies, both in humans and in monkeys; illusory correlation studies; studies using unreportable stimuli; and studies from animal neuroscience. The fear module is assumed to mediate an emotional level of fear learning that is relatively independent and dissociable from cognitive learning of stimulus relationships.
Anterior cingulate cortex (ACC) is a part of the brain's limbic system. Classically, this region has been related to affect, on the basis of lesion studies in humans and in animals. In the late 1980s, neuroimaging research indicated that ACC was active in many studies of cognition. The findings from EEG studies of a focal area of negativity in scalp electrodes following an error response led to the idea that ACC might be the brain's error detection and correction device. In this article, these various findings are reviewed in relation to the idea that ACC is a part of a circuit involved in a form of attention that serves to regulate both cognitive and emotional processing. Neuroimaging studies showing that separate areas of ACC are involved in cognition and emotion are discussed and related to results showing that the error negativity is influenced by affect and motivation. In addition, the development of the emotional and cognitive roles of ACC are discussed, and how the success of this regulation in controlling responses might be correlated with cingulate size. Finally, some theories are considered about how the different subdivisions of ACC might interact with other cortical structures as a part of the circuits involved in the regulation of mental and emotional activity.
An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.
Previous research on the neural underpinnings of empathy has been limited to affective situations experienced in a similar way by an observer and a target individual. In daily life we also interact with people whose responses to affective stimuli can be very different from our own. How do we understand the affective states of these individuals? We used functional magnetic resonance imaging to assess how participants empathize with the feelings of patients who reacted with no pain to surgical procedures but with pain to a soft touch. Empathy for pain of these patients activated the same areas (insula, medial/anterior cingulate cortex) as empathy for persons who responded to painful stimuli in the same way as the observer. Empathy in a situation that was aversive only for the observer but neutral for the patient recruited areas involved in self-other distinction (dorsomedial prefrontal cortex) and cognitive control (right inferior frontal cortex). In addition, effective connectivity between the latter and areas implicated in affective processing was enhanced. This suggests that inferring the affective state of someone who is not like us can rely upon the same neural structures as empathy for someone who is similar to us. When strong emotional response tendencies exist though, these tendencies have to be overcome by executive functions. Our results demonstrate that the fronto-cortical attention network is crucially involved in this process, corroborating that empathy is a flexible phenomenon which involves both automatic and controlled cognitive mechanisms. Our findings have important implications for the understanding and promotion of empathy, demonstrating that regulation of one's egocentric perspective is crucial for understanding others.
Recent research from cognitive psychology and cognitive neuroscience has suggested that the control mechanisms by which people are able to regulate task performance can be dissociated into evaluative and ex-ecutive components. One process, implemented in the an-terior cingulate cortex of the brain, monitors the amount of conflict that occurs during information processing; an-other process, implemented in the dorsolateral prefrontal cortex, is involved with maintaining the requirements of the task at hand and with biasing information processing in favor of appropriate responses. In the current article, we review this theory and some of the research that has supported it, including its implication for understanding cognitive disturbances in clinical disorders such as schizo-phrenia and obsessive-compulsive disorder. We conclude by addressing several interesting possibilities for future research. Whenever one performs a task, one has to make sure that one selects the relevant information (stimuli, actions) and not get distracted by stimuli or thoughts that are irrelevant to the task. Such distraction might lead to inappropriate actions, such as errors. How the brain manages to do this is the central question in this paper: specifically, how people manage to pay more attention after they have either made an error or almost made an error. One of the key aspects of cognitive control is how flexible it is. The issue of how people monitor and correct for errors has be-come a popular topic of inquiry in cognitive research; not only can this issue offer insight into the flexible nature of control and self-monitoring, but understanding the cognitive and neural basis of these functions can possibly shed substantial light on cognitive dysfunction of self-monitoring and control in clinical disorders such as schizophrenia and obsessive-compulsive disorder (OCD).
Truth-telling (Truth) and simulated malingering (Malinger) groups were tested in a matching-to-sample procedure in which each sample three-digit number was followed by a series of nine test numbers, only one of which matched the sample. P300 was recorded during test-number presentation. Group analyses revealed differences between the P300s of the groups in unscaled amplitude, but not latency, in response to match and mismatch stimuli. P300 amplitudes at Fz, Cz, and Pz were scaled to remove possible confounding effects of amplitude in tests of the interactions of site with other variables. Significant interactions of both stimulus-type (match vs. mismatch) and group (Truth vs. Malinger) with site were obtained. Within the Malinger group, a significant interaction was obtained (scaled data) between site and response type (honest vs. dishonest). These interactions suggest that deceptive and honest responding are associated with different neurogenerator sets or different sets of P300-overlapping components. In within-individual analyses, 100% of the Truth participants and 87% of the Malinger participants were found to have larger P300 responses at Pz to match stimuli than to mismatch stimuli on the basis of intra-individual bootstrap tests. This represents an improvement in comparison with our related, previous report on a matching-to-sample test using only one test stimulus per sample.