Nishimura et al. Journal of Physiological Anthropology
https://doi.org/10.1186/s40101-018-0184-8(2018) 37:24

ORIGINAL ARTICLE

Open Access

The relationship between inhibition of
automatic imitation and personal cognitive
stylesYuki Nishimura1,2* , Yuki Ikeda1,2 and Shigekazu Higuchi3

Abstract
Background: Previous studies have demonstrated the importance of the inhibition of automatic imitation in social
interactions. Additionally, cognitive traits are known to vary among individuals. According to the empathizingsystemizing (E-S) model, personality can be quantified by empathizing and systemizing drives in causal cognition.
Since inhibition of automatic imitation is strongly related to social cognition, the level of inhibition may be
explained by personal cognitive traits. Thus, the current study tested whether cognitive traits, measured based on
the E-S model, correlated with levels of automatic imitation inhibition.
Methods: The empathizing-systemizing quotient (EQ-SQ) questionnaire was used to assess cognitive traits.
Behavioral and electroencephalogram data were acquired during the imitation inhibition task. In addition toreaction time, based on signal detection theory, task sensitivity and response bias were calculated from reaction
data. As a physiological measure of automatic imitation, mu rhythm power suppression was calculated fromelectroencephalogram data. Congruency effects for reaction time and electroencephalogram measures were
calculated by subtracting congruent trials from incongruent trails.
Results: Correlation analyses between cognitive traits and task measures were conducted. There was a negative
correlation found between EQ score and the behavioral index reflecting task performance. Moreover, a negative
correlation was found between SQ score and the congruency effect on mu suppression.
Conclusions: Participants with higher EQ scored relatively lower in inhibiting their responses. Conversely, highSQ participants showed successful inhibition of mu suppression. The imitative tendency may disturb the
inhibition of response. The correlation between SQ and mu index suggests the involvement of domain-general
information processing on imitation inhibition; however, further research is required to determine this. Since different
correlations were found for behavioral and physiological measures, these measures may reflect different steps ofinformation processing for successful task execution. Through correlational analysis, a possible relation was identified
between the inhibiting process of automatic imitation and personal cognitive styles on social interactions.
Keywords: Imitation inhibition, Personal cognitive traits, Mirror neuron system, Mu rhythm suppression* Correspondence: y.nishimura.941@s.kyushu-u.ac.jp
1
Graduate School of Integrated Frontier Sciences, Kyushu University, 4-9-1
Shiobaru, Minami-ku, Fukuoka 8158540, Japan
2
Research Fellow of the Japan Society for the Promotion of Science, 4-9-1
Shiobaru, Minami-ku, Fukuoka 8158540, Japan
Full list of author information is available at the end of the article© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Background
Throughout human evolution, cooperation (and resultant society building) has been selected for ultimately increasing the species’ chances of survival. As a social
animal, it has been suggested that humans possess enhanced social cognitive abilities compared to any othercreature [1]. Of these abilities, empathy is likely one of
the most important, not only for the creation of societies, but also for the acquisition of motor skills [2–4].
Automatic imitation is one of the core functions of empathy. The neural basis of imitation is the mirror neuron
system (MNS) [5, 6]. The MNS is a brain system firstfound in monkeys, which activates during both the performance and observation of a body action [5, 7–9]. A
number of brain imaging and single-cell recoding studies
have also demonstrated the existence of an MNS inhumans [10–13]. It is thought that automatic imitationis one kind of stimulus-response compatibility that is acquired during development [14–16]. Several experiments
and meta-analyses have shown that suppression of electroencephalogram (EEG) sensory-motor mu wave possibly reflects MNS activity, as mu wave power issuppressed by both the execution and observation of anaction [17–21]. Especially, hand movements have a pronounced effect on imitative brain response measured bymu suppression [18, 22]. Moreover, spontaneous recording of fMRI and EEG have shown that mu wave reduction is one reliable measure of MNS activity [17, 21, 23].
Although automatic imitation, supported by the MNS,
is a core function of social interaction, imitation is obviously not always the most appropriate reaction in daily
life. Inhibition of imitative behavior is important for thefacilitation of smooth complementary and joint actions.
Therefore, in some cases, mirroring may be counterproductive [24–27]. For example, while handing a cup fromone person to another, or while playing catch, the receivershould inhibit his/her imitative motor action and prepare
an appropriate response. Furthermore, not only the actual
imitation but also the in-brain imitation must be inhibitedin the same manner for successful interaction. There is an
ongoing debate as to which brain network is responsible
for controlling automatic imitation. Initially, “social brain”
regions, related to theory of mind, were found to be active
during an imitation inhibition task. Brass et al. investigated the inhibitory system of imitative response tendencies and reported several brain regions involved ininhibition, including the temporal parietal junction, dorsolateral prefrontal cortex, right frontopolar cortex, right anterior parietal cortex, and precuneus [25, 28]. Morerecently, a domain-general brain network related to broad
reaction-inhibition has also been implicated in imitation
inhibition [29]. The imitation-inhibition task is designed
to evaluate the level of inhibition of imitative tendencies[28]. It has been adopted in a number of studies to test

the conditional or individual ability to inhibit the imitative
response [25, 26, 28–34].
According to Decety and Svetlova, empathy is a complex
construct consisting of both emotional and cognitive elements [2]. Furthermore, there has been an attempt to quantify human personality from causal cognition. Baron-Cohendeveloped the empathizing-systemizing (E-S) model to explain individual cognitive traits from two psychological“drives” [35]. Empathizing is a drive that identifies other’semotions or thoughts and causes an individual to react with
an appropriate emotion by analyzing psychological causal
relations. By contrast, systemizing is a drive that analyzes
factors of systems and derives basic patterns, which defy
the function of the system. The term “system” here covers
all processes, from physical law to social phenomenon,
which have any kind of law-like nature [35, 36]. The
Empathizing-Systemizing Quotient (EQ-SQ) questionnaire
was developed to measure these constructs [37, 38].
Variation between individuals is known to exist in
imitation-inhibition task performance and EQ-SQ score,both of which are thought to reflect social interactive skills.
However, no study has aimed to address the relationshipbetween an individual’s cognitive style (as assessed via the
EQ-SQ questionnaire) and adaptive imitation-inhibition
performance, despite both being closely related to social
cognition and interaction in daily life and both possibly
originating from higher order frontal brain functions. By
investigating the relationship between personal cognitive
traits assessed by the self-completion questionnaire andthe performance of inhibition of automatic imitation, we
aimed to deepen understanding of the involvement of theinhibiting process of automatic imitation in forming in individual’s traits related to social interaction.
To accomplish this, participants were asked to respondby lifting up either the index or middle finger, according to
the task indicator and hand movement stimuli presented
on the screen. Reaction time (RT), task sensitivity, and response bias were collected as behavioral measures andevent-related mu wave desynchronization (ERD) of EEG
was measured to determine imitating MNS activity and its
modulation. We used indices from signal detection theory(SDT) instead of the classical proportion of correct answers. By using SDT measures, participant’s task performance (d’) is estimated independently from their responsebias (C) [39–41]. We hypothesized that participants with ahigh EQ would show better performance on the task andsuccessful inhibition of imitative brain activity.

Methods
Participants

Twenty-six young adults participated in the study (15male, 11 female; mean age 23.2 ± 1.25 years). All participants were right-handed, as confirmed by an Edinburgh
Handedness inventory (min–max 70–100; median

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84.12). Participants were naïve as to the purpose of theexperiment and were informed before the study that
their privacy would be secured. Written informed consent was provided by the participants prior to the commencement of the experiment, and participants were
debriefed following experiment completion.
The procedure of the study was approved by the
Ethics Committee of Kyushu University. The study was
conducted according to the principles of the Declarationof Helsinki.

response by the congruent or incongruent finger. The
assignment of colors to the tasks (congruent or incongruent) was counter balanced among the participants.
For example, if the index finger was lifted in the stimuli
and the colored frame indicated an incongruent response, the correct response for the participant was lifting their middle finger.
In addition to the imitation condition described above,a spatial condition, in which one of two black-dots
moved upwards, was tested to take the spatial compatibility aspect into account. The order of the conditions
was counter balanced.
The task was divided into three blocks, including arandomization of trials, in which half were congruent
and the other half were incongruent. This was done sothat participants could not predict the next task instruction (total 300 trials per condition; 75 trials each forcongruent index finger, congruent-middle finger, incongruent index finger, and incongruent middle finger).

Equipment

The study was conducted in an acoustically and electrically sealed room located in Kyushu University. A
64-channnel EEG was recorded using an EEG amplifier(Net Amps 200, EGI) with sensor-net (HCGSN-64, EGI).
Electrode impedance was maintained to remain under
50 kΩ as suggested by the manufacturer. EEG data were
filtered in real time by the amplifier hardware, with thefilter set to 0.01 Hz high-pass and 200 Hz low-pass, anddigitized at 500 Hz. Stimuli were delivered with Presentation Ver. 20.0 (NBS Inc.) and an LCD display(E2351VR-BN, LG Electronics) refreshing at 60 Hz. Participants reaction (finger lifting) was acquired by capacitance sensor (AD00019, Bit Trade One, LTD.) connectedto the Presentation software.
Imitation-inhibition task

The task utilized in the current study was based on atask developed and used in previous research [42]. Participants were required to respond as soon as possible
according to simultaneously displayed finger movement
and instructions (congruent or incongruent; Fig. 1). The
presented stimuli consisted of a short movie of a handquickly lifting either an index or middle finger, and asurrounding colored frame (red or green) indicating a

Behavioral measurements and analysis

Reaction time (RT) was calculated for each task instruction(congruent or incongruent) of each condition (imitation or
spatial). In order to highlight the congruency-effect, ΔRT
was calculated by subtracting the mean RT of congruent
trials from the mean RT of incongruent trials for eachcondition.
Based on signal detection theory (SDT), task sensitivity
index (d’), and response bias (C) were calculated as performance indices. SDT assumes that, in an uncertain
condition, participants are active decision makers whomake difficult perceptual judgements. The SDT index,
rather than the percent of correct responses, provides
effective solutions to differentiate participants’ response
tendencies (bias) from the ability to detect and discriminate information (task sensitivity) [39–41]. We calculated

Fig. 1 Example of the stimuli for imitation and spatial conditions. Instruction (congruent or incongruent) assignment to the colors (red or green)
was counter balanced among participants. Presentation order of fingers (index or middle finger) and colors (red or green) in each condition was
randomized. For example, if red was assigned to “congruent” and green was assigned to “incongruent,” lifting index finger is the correct response
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sensitivity and bias by categorizing congruent success,congruent failure, incongruent failure, and incongruent
success as hit, miss, false alarm, and correct rejection,
respectively. A higher d’ indicates higher task performance. The response bias index (C) is zero when there isno bias at all. This variable would take a positive value
when responses are biased toward incongruent responses, and vice versa. Formulas for d’ and C are thefollowing:

across the regions of interest (ROIs) to improve the reliability of the data. The left central (LC) region, representing mu ERD, was covered by C3 and its neighboring
channels (i.e., 15, 16, 20, 21, and 22). The mid occipital(MO) region, representing alpha ERD, was covered by Oz
and its neighboring channels (i.e., 35, 37, and 39; Fig. 2).
After calculating ERDs, a congruency effect for mu andalpha ERDs was calculated in the same manner as ΔRT.

d 0 ¼ Z hit −Z false alarm
C ¼ −0:5  ½Z hit þ Z false alarm 
where, Zhit is the z-transformed hit rate [hit count/(hitcount + miss count)] and Zfalse alarm is the z transformed
false alarm rate [false alarm count/(correct rejection
count + false alarm count)].
EEG measurements and analysis

As an index of automatic imitation, mu wave
event-related desynchronization (ERD) measured aroundcentral sulcus was acquired. In addition to mu ERD,
alpha ERD possibly reflecting the attention level was calculated from the occipital sites, in order to take alphawave contamination to the mu ERD into account. All
EEG preprocessing and analysis was carried out using
EEGLAB 14.1.1b [43], which is an open source toolboxof MATLAB (MathWorks Inc.). Raw EEG data, following manual rejection of bad channels, were filtered by
FIR band-pass filter (0.5–40 Hz; transition band width
1 Hz) and epoched according to stimuli onset. Furthermore, bad epochs found in the data were automaticallyrejected according to joint probability of the data (bothsingle-channel and all-channel threshold were set to
3 S.D). Following this, all data were re-referenced to theaverage. While data were re-referenced, outer electrodes(e.g., facial electrodes) were excluded from calculations(channels 23, 55, and 61 ~ 64). Two participants were
excluded from further analysis due to an insufficientnumber of epochs. Preprocessed data were then sent toinfomax independent component analysis (ICA). Independent components representing eye-blinks oreye-movement were manually rejected based on the
topographical map, frequency spectrum, and activationsynchrony with electrooculography (EOG).
ERD was calculated by EEGLAB’s time-frequency analysis function. First, event-related spectrum perturbations were calculated by wavelet analysis, starting with
2 cycles and increasing by 0.5 cycles toward higher frequencies. Second, mu wave (8–13 Hz) power from
300 ms to 600 ms after the event onset was calculated
for each participant in decibel, according to the baseline
period, which was 200 ms to 0 ms prior to the event onset. Finally, ERDs from each channel were averaged

Statistical analyses

In order to test the relationship between the task performance and the EQ-SQ scores, Spearman’s rank correlation
was calculated. A Bonferroni correction was used to adjust
the p value for multiple tests. To highlight the effect oftask and personal cognitive traits, an additional
mixed-design analysis of variance (ANOVA) was conducted on data that showed significant correlations. The
factors included in ANOVAs were high and low groups of
EQ or SQ, and indicated tasks (congruent and incongruent). High EQ or SQ groups contained the upper 33% ofparticipants and low EQ or SQ groups consisted of thelower 33% of participants. An independent t test was conducted to test the shift of response bias. All results are reported using an α of p < 0.05. All statistical tests wereconducted using R version 3.5.1 [44].

Results
EQ-SQ score

EQ and SQ distribution of the participants are shown in
Fig. 3. Both EQ and SQ varied widely (median = 29, 29;
min = 7, 16; max = 58, 64; for EQ and SQ, respectively). No
correlation was found between these two scores (rho =0.333, n = 25, p = 0.103).

Fig. 2 Channel location of the EEG sensor net. Each dot represents 1
of 65 channels. Channels included in the ROIs are circled

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Fig. 3 Histogram of EQ and SQ score distribution. Correlation analysis found no correlation between these scores (rho = 0.333, n = 25, p = 0.103)

Behavioral measures

Figure 4 shows the distribution of ΔRT, d’, and C foreach condition. Generally, RT for the incongruent trials
was slower than that of congruent trials, similar to results reported in earlier studies [26]. Although task sensitivity (d’) was generally high, it ranged from nearly 2 to
5. A one sample t test showed that response biases (C)were shifted toward a positive value, indicating thatreactions were biased toward incongruent reactions inboth the imitation and spatial conditions [t(24) = 24.582,
p < 0.001; t(24) = 24.773, p < 0.001, respectively].
The results of a Spearman’s correlation test for the behavioral indices are shown in Table 1. In opposition to
our hypothesis, negative correlations between EQ and
ΔRT in the imitation condition and between EQ and d’in the spatial condition were found, although the pvalues did not reach statistical significance after Bonferroni correction.
A supplemental ANOVA on RT measured at imitation
condition with task (congruent and incongruent) and EQ
group (high and low) as factors revealed a significant main
effect of task [F(1, 17) = 10.605, p = 0.005, ηp2 = 0.384]
indicating prolonged RT in incongruent trials, and aninteraction of the two factors [F(1,17) = 4.785, p = 0.043,
ηp2 = 0.220]. Post hoc tests on the interaction showed asignificant simple main effect of task for the low EQ
group [F(1,9) = 14.680, p = 0.004, ηp2 = 0.620] indicating shorter RT in the congruent trials. On the other

hand, there were no RT differences in the high EQ
group [F(1,8) = 0.586, p = 0.466, ηp2 = 0.068]. As illustrated in Fig. 5, smaller ΔRT in participants withhigher EQ originated from slower RT in the congruent trials. There were no significant correlations
found between SQ and behavioral indices.

Physiological measures

Grand averaged mu and alpha ERD waveforms areshown in Fig. 6. The distribution of ΔERD measured
over the LC (mu ERD) and MO (alpha ERD) are shownin Fig. 7. Higher Δmu ERD indicates a more successful
inhibition of MNS activity during incongruent trials.
Conversely, negative values of Δmu ERD indicate greater
inhibition of MNS activity during congruent trials. All
ΔERD are distributed from positive to negative values.
The correlation analysis conducted on ΔERD revealed apositive correlation between SQ and Δmu ERD in the imitation condition (Table 1). An additional ANOVA on ERD
measured at LC in the imitation condition with task (congruent and incongruent) and SQ-group (high and low) as
factors was conducted. As a result, a significant interaction of the two factors was found [F(1,16) = 6.266, p =0.024, ηp2 = 0.281]. Post hoc analysis on the interaction
showed a significant simple main effect of task for the
low-SQ group indicating greater mu suppression in the incongruent trial [F(1,8) = 6.247, p = 0.037, ηp2 = 0.439]

Fig. 4 Distribution of ΔRT, task sensitivity (d’), and response bias (C) for each condition. ΔRT was calculated by subtracting the RT of congruent
trial from incongruent trial in order to highlight the effect of imitative congruency. Higher task sensitivity scores indicate higher task performance.
A zero value in the response bias indicates that there was no response bias observed. Since the distribution is slightly biased toward a positive
value, it is indicated that the participant’s reactions were biased toward incongruent reactions, confirmed by a one-sample t test

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Table 1 Correlation results for behavioral measures
Condition
Imitation

Spatial

Index

EQ

SQ

rho

Pcorrectedrho

Pcorrected

ΔRT− 0.43

0.06†

0.07

1.00

d’

− 0.200.68− 0.25

0.45

C

0.06

1.00

− 0.270.37

ΔRT− 0.29

0.31

0.04

1.00

d’

− 0.430.06†− 0.20

0.69

C

0.27

0.40

− 0.37

0.13

Spearman’s rank correlation testn = 24; Bonferroni corrected: † for Pcorrected < 0.1(Fig. 8). No significant correlation was found between EQ
and ΔERDs. In addition, there were no significant correlations found in the spatial condition (Table 2).

Discussion
In order to reveal the involvement of inhibition of imitation on formation of personal cognitive traits, we tested
the relationship between personal cognitive traits, andperformance on an imitation inhibition task, as measured by behavioral and physiological indices. In the imitation condition, a negative correlation was observed
between EQ and ΔRT, although p values did not reachstatistical significance following Bonferroni correction.
Moreover, a positive correlation was observed between
SQ and the congruency effect, as calculated from the
mu ERD, which is an EEG index of MNS activity. These
correlations were specific to the imitation condition,
thus the corresponding correlations in the spatial condition were not significant. In the spatial condition, which
was set to take the spatial compatibility effect into account, a trend of correlation between EQ and d’, which

Fig. 5 Reaction time at imitation condition for low and high EQ
group. Red and blue boxes represent reaction time of congruent
and incongruent trials, respectively. Shorter reaction time isinterpreted as better performance. As a result of post hoc test
following the ANOVA, a significant simple main effect of task wasfound in the low EQ group

Fig. 6 Grand average of mu and alpha ERDs measured at ROIs (LC andMO), respectively. Red lines represent imitation condition and bluelines represent spatial condition. Solid lines represent congruent trials
and dotted lines represent incongruent trials for each condition

was specific to this spatial condition, was found. No correlation was found for ΔERD.
The ΔRT of the imitation condition showed a trend ofnegative correlations with EQ after the correction ofmultiple tests. A smaller ΔRT can be interpreted as a diminished congruency effect, which can further be interpreted as higher task performance. Nevertheless, in thecurrent study, smaller ΔRTs originated from prolonged
RTs in the congruent trials in high-EQ participants,

Fig. 7 The observed congruency effect on mu and alpha ERD for eachcondition. Higher values of ΔERD indicate greater activity during theincongruent trials compared to the congruent trials and vice versa

Nishimura et al. Journal of Physiological Anthropology(2018) 37:24

Fig. 8 The mu ERD at imitation condition for low and high SQ groups.
Red and blue box represents reaction time of congruent andincongruent trial, respectively. The greater ERD (more negative value)indicates greater sensory-motor activation including automatic imitation.
The post hoc test following the ANOVA showed significant simple main
effect of task for the low SQ group, indicating greater mu ERD atincongruent trials only for the low SQ groupsuggesting that they took longer to respond in both congruent and incongruent trials. Therefore, higher empathic cognitive traits may lead to poorer imitation
inhibition performance. From the related early research,
it is known that the amount of unconscious imitation is
influenced by the amount of rapport feeling [45, 46].
Moreover, one study, which primed participants either
pro- or non-socially and compared the congruency effect,
found that pro-socially primed adults showed a larger
compatibility effect in an imitation task [47]. Later research also confirmed the effect of priming, especially inadults compared to adolescents (mean ages around 27 and13 years, respectively) [48]. As such, higher empathic traits
may disturb smooth selection (resulting in poorer reactiontime) during the imitation inhibition task. Meanwhile,
both empathic traits and higher imitation-inhibition ability
are often associated with higher social ability and/or successful interactions [5, 9, 49]. Since the correlation found
was negative, the current results suggest that a relationshipexists between empathic cognitive traits and task performance but not in a direct manner. Further study is required
to reveal the causal relationship related to this point.
Table 2 Correlation results for EEG measures
Condition

ROI

EQ

SQ

rho

Pcorrectedrho

Pcorrected

0.58

0.46

0.04 *

Imitation

LC

0.22

MO

− 0.160.88− 0.02

1.00

SpatialLC− 0.200.69− 0.111.00

MO− 0.121.00− 0.01

1.00

Spearman’s rank correlation test. ROI region of interest; LC left central; MO mid
occipital. n = 25; Bonferroni corrected: * for Pcorrected < 0.05

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The current study is the first to apply SDT to investigate the inhibition of automatic imitation. A number of
earlier studies have used ΔRT to highlight the congruency effect of behavioral measures [26, 28, 33]. Compared to reaction time, error rate is less likely to reflect
the effect. Conversely, nominal data contains important
information on participants’ performance, especiallywhen data is analyzed based on SDT [39–41]. In thecurrent study, the performance index d’ at spatial condition showed a trend of correlation, yet response bias
(i.e., a shift of decision criterion) did not. No correlations
were found between EQ-SQ scores and SDT measuresof the imitation condition. Meanwhile, we found that
their response was biased toward the incongruent responses by analyzing SDT measures. It was indicatedthat the participants were more prepared for incongruent responses, which required more completed information processing [28, 33, 50, 51]. In this study, we could
not find any evidence of a relationship between personalcognitive traits and an individual’s task sensitivity or response bias in the imitation condition. However, the useof SDT measures may still provide deeper insights forbehavioral data in future research, such as the significant
response bias found in the current study. In addition,task sensitivity in the spatial condition showed a marginal negative correlation with EQ score. This suggests
poorer task sensitivity in participants who have a higher
tendency toward empathic cognitions. Although the correlation was not hypothesized and did not reach significance, a relationship may exist between the processingof spatial compatibility generated by the stimuli we used
and the empathizing score.
In imitation inhibition tasks, behavioral measures canbe defined as the final output of information processing.
Thereby, we focused on EEG mu power suppression related to the observation of action of others. By measuring in-brain action mirroring through the widely used
mu ERD [17, 52, 53], we assessed the congruency effectat a lower level of information processing. As a result,
there was a significant positive correlation found between Δmu ERD and SQ scores. However, no correlation
between Δmu ERD and EQ was found. This suggeststhat participants with higher SQ scores were able tomodulate mu suppression during incongruent trials,
while individuals with lower SQ scores showed greater
MNS activity in incongruent, compared to congruent,
trials. Thus, task strategy may differ between individuals
with lower and higher SQ scores; however, further studyof this possibility is required. There were no correlations
found in spatial condition. This suggests that the congruency effect observed in the imitation condition was
likely originated from the process of automatic imitation
activated by the presence of biological motion in thestimuli, not from the actual finger movement.

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Notably, there were no significant correlations found for
Δalpha ERD, indicating that personal traits were only correlated with congruency effects observed in sensory motor
mu rhythms and not in the occipital alpha rhythm. Moreover, no significant correlation was found for the spatialcondition. While the required response was exactly thesame among conditions, absence of the biological motion
was the factor controlled between the two conditions.
This result implies that not the ability of handling spatialcompatibility, but rather the ability of dealing with biological incompatibility, is correlated with the personal cognitive traits measured by the EQ-SQ questionnaire.
Measures of Δmu ERD showed significant correlations with SQ scores, while behavioral measures
showed significant correlations with EQ scores. Although EQ and SQ scores are known to be moderately
correlated, each score is designed to represent an independent aspect of cognition [35, 36, 54]. In addition, inthe current study, no correlation was found between
the two scores. As was discussed earlier, if it is assumed
that a behavioral measure is the final outcome of information processing flow for the inhibition task, the mirroring activity and its inhibitory control would beplaced at relatively earlier steps. Therefore, the behavioral and physiological indices may reflect different
levels of processing for the inhibition of automatic imitation. According to the associative sequence learningmodel, automatic imitation is mediated by low-levelmechanisms [14, 55, 56]. While behavioral measures reflect the whole process, the congruency effect on ERDs
(i.e., ΔERDs) may reflect not only the level of imitation
inhibition facilitated by higher order brain function, butalso the lower level processing of observed action. In
other words, not only empathic cognitive function, butalso the systemizing aspect of the cognitive ability of social cognition plays an important role in successful task
execution. A number of studies using fMRI to determine
the brain network responsible for the inhibition of automatic imitation have been conducted [25, 26, 33, 51].
Combining these methods and personal trait data may
provide deeper insight into the topic.
Because of their simplicity and popularity in the scientific field, we used EQ-SQ scores to assess cognitive
traits. However, further study with many different
questionnaires, combined with appropriate statistics,
may provide a more robust result. Further, despite
mu rhythm suppression being a widely used measureof MNS activity, there are several concerns about its
quality. The major claim concerns contamination ofalpha wave attenuation related to attention and visual
input [21, 23]. Although simultaneous measurementof fMRI and EEG have suggested that mu rhythm is areliable index of MNS activity, additional study withdifferent methods to assess brain activity is required.

Conclusions
In conclusion, this study indicated that behavioral andphysiological measures of an imitation inhibition task
are differentially related to personal cognitive traits. The
congruency effect on the reaction time obtained fromthe imitation inhibition task was negatively correlatedwith EQ scores, and the physiological index was positively correlated with SQ scores. The application of SDT
to the imitation-inhibition paradigm provided a deeper
understanding to this field of study. Therefore, thecurrent study indicated that variation in cognition is related to the inhibition of congruency effect produced byfinger movements. In addition, it is suggested that the
behavioral indices and the ΔERD reflect different steps
of information processing. Further research with different empathizing-systemizing assessment methods andneurophysiological measures are required in the future.
Abbreviations
ANOVA: Analysis of variances; EEG: Electroencephalogram; EQ: Empathizing
quotient; ERD: Event-related desynchronization; fMRI: Functional magnetic
resonance imaging; LC: Left central; MNS: Mirror neuron system; MO: Mid
occipital; ROI: Region of interest; SDT: Signal detection theory;
SQ: Systemizing quotient
Acknowledgements
We thank Mr. Fujiwara and other laboratory members for their support.
Funding
This work was supported by the Japan Society for the Promotion of Science(16J04125).
Availability of data and materials
The datasets used and/or analyzed during the current study are available
from the corresponding author on reasonable request.
Authors’ contributions
YN conceived and designed the study, performed the experiments and thestatistical analysis, and drafted the manuscript. YI helped to carry out theexperiments and analyze the data. YI and SH participated in designing andcoordinating the experiment, and helped to draft the manuscript. All authors
have read and approved the final manuscript.
Ethics approval and consent to participate
All participants gave their written informed consent after a complete
explanation of this study and before participation. The study was performedin accordance with the guidelines of the Declaration of Helsinki and wasapproved by the ethics committee of Kyushu University.
Consent for publication
Not applicableCompeting interests
The authors declare that they have no competing interest.

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Author details
1
Graduate School of Integrated Frontier Sciences, Kyushu University, 4-9-1
Shiobaru, Minami-ku, Fukuoka 8158540, Japan. 2Research Fellow of the Japan
Society for the Promotion of Science, 4-9-1 Shiobaru, Minami-ku, Fukuoka
8158540, Japan. 3Faculty of Design, Kyushu University, 4-9-1 Shiobaru,
Minami-ku, Fukuoka 8158540, Japan.

Nishimura et al. Journal of Physiological Anthropology(2018) 37:24

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Received: 16 July 2018 Accepted: 10 October 2018

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