Emotional Intelligence and the Ability to Recognise Micro Expressions

Abstract

Emotional intelligence is the ability to recognise and understand nonverbal communication from others; one such form of nonverbal communication is a micro expression. Micro expressions are extremely brief and spontaneous flashes of basic facial expressions used to convey true emotions. Participants were asked to view twenty-six morphed images of micro expressions using a computer programme; each micro expression lasted approximately 1/15th of a second. 60 participants (30 male, 30 female) were used to distinguish if there were any links between gender, emotional intelligence, positive/negative affect, subjective confidence and overall accuracy after training. Emotional intelligence was split into two groups; high emotional intelligence and low emotional intelligence. The results showed there was no connection between accuracy and subjective confidence; no links between emotional intelligence and overall accuracy; and finally there were no differences between gender and overall accuracy. The results however did illustrate that positive emotions were recognised far more frequently than the negative emotions and training enhanced overall accuracy for all participants. The implications of these findings substantiate that training does improve the ability to recognise micro expression, which may be beneficial for teaching law enforcement interview techniques as well as other clinical applications. Future research needs to investigate whether any links between emotional intelligence and the ability to recognise micro expressions exist, due to the lack of positive association in the present research, which does contradicting previous literature regarding emotional intelligence and the expression of facial affect.

Full text

Emotional Intelligence and the Ability to
Recognise Micro Expressions
by
Wroe. E. L.

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
2
Contents
Abstract Page 3
Introduction Page 4
Methodology Page 10
Results Page 13
Discussion Page 16
References Page 24
Appendices Page 32

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
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Abstract Emotional intelligence is the ability to recognise and understand nonverbal
communication from others; one such form of nonverbal communication is a micro
expression. Micro expressions are extremely brief and spontaneous flashes of basic facial
expressions used to convey true emotions. Participants were asked to view twenty-six
morphed images of micro expressions using a computer programme; each micro expression
lasted approximately 1/15th of a second. 60 participants (30 male, 30 female) were used to
distinguish if there were any links between gender, emotional intelligence, positive/negative
affect, subjective confidence and overall accuracy after training. Emotional intelligence was
split into two groups; high emotional intelligence and low emotional intelligence. The results
showed there was no connection between accuracy and subjective confidence; no links
between emotional intelligence and overall accuracy; and finally there were no differences
between gender and overall accuracy. The results however did illustrate that positive
emotions were recognised far more frequently than the negative emotions and training
enhanced overall accuracy for all participants. The implications of these findings substantiate
that training does improve the ability to recognise micro expression, which may be beneficial
for teaching law enforcement interview techniques as well as other clinical applications.
Future research needs to investigate whether any links between emotional intelligence and
the ability to recognise micro expressions exist, due to the lack of positive association in the
present research, which does contradicting previous literature regarding emotional
intelligence and the expression of facial affect.

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
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Nonverbal communication allows individuals to convey personal feelings and emotions to
others within a social environment. It also enables a certain level of social interaction within
the species. Emotions, feelings and intentions are exchanged through facial expressions,
body language and stances (Le Roux, 2002). By cognitively appraising the nonverbal
intentions of others; behavioural actions such as the avoidance of a potential threat can take
place. Scherer (1992) stressed that cognitive appraisal is a facet of emotion and he
subsequently discovered universal cognitive appraisal patterns for specific emotions of joy,
anger, fear, sadness, disgust, shame, and guilt. Contemporary research has confirmed that
once an emotion has been cognitively appraised it will then be conveyed through facial
expressions, body mannerisms and postures in order to communicate the specific
suppressed or repressed emotion to others within their proximal social circle (Smith and
Schyns, 2009; Albanese et al, 2010; App et al, 2011: Schmidt et al, 2011). This level of
nonverbal communication is often employed in human social situations to express feelings,
arousal or moods to other humans; whereas in the animal kingdom these displays may be
used to exhibit superiority of rank, or dominance over the other members of the group
(Preuschoft, 1999). Further studies of facial expressions have provided evidence that not
only humans but nonhuman primates use nonverbal communication techniques in their
social circles and social hierarchies. Plutchik (1962) found that nonhuman primates such as
monkeys and chimpanzees used similar physical facial attributes to display the six basic
emotions (happiness, sadness, fear, anger, surprise and disgust) much like those used in
human social interactions.
The study of human emotion has been explored for many decades with the most noteworthy
of social scientists being Charles Darwin, who explained that the facial expressions of
sadness, happiness, surprise, anger, fear and disgust had similarities between many
different cultures and ethnicities (Darwin, 1872). Originally Piderit (1857) created a
schematic model for the recognition of facial expressions. He found that by using geometric
images and interchangeable pencil drawings of eyes, brows and noses posed in different
combinations can produce any of the seven basic facial expressions as well as many more.
Originally creating this model to help sculptors, it was copied by Boring and Titchener (1923)
to be utilised in a laboratory environment to produce in the region of 360 different facial
expressions. This suggests that the use of images of facial expressions provide a reliable
and valid measure of recognition.
Several researchers have written about the use of facial expressions in the experience and
expression of emotions (Wundt, 1897; James, 1884, 1894; Langfeld, 1918; Davis, Senghas

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and Ochsner, 2009). Developmental evidence has shown that newborn babies will tend to
look at faces more so than other emotional stimuli (Fantz, 1961; Kagan and Lewis, 1965)
which would suggest an innate ability to recognise and focus attention on others’ facial
expressions from an extremely early age. McArthur and Baron (1983) stated that information
from facial expressions helps to maximise our social outcomes and therefore supplies a
positive purpose in social hierarchies. If an individual has an increased ability to detect the
six basic types of facial expression and implement other nonverbal communication
techniques then they may be more habituated to the emotions of others. Kret et al (2011)
also state that the ability to read facial expressions is an important source of social
information and enables social interaction. They also suggest that as humans we are
predominantly visual animals and use our eyes to analyse the world around us. This would
therefore suggest that the recognition and use of facial expressions is an essential form of
communication which works in parallel with verbal interaction within the human species. Not
only does nonverbal communication consist of overt facial expressions but it also consists of
more subtle facial expressions.
Micro expressions are diminutive, epigrammatic and spontaneous facial expressions which
are barely recognisable to the untrained eye; these minuscule facial expressions have an
approximate duration between 1/15th and 1/30th of a second; they happen so spontaneously
that they can be missed by just blinking at the wrong moment. Originally discovered by
Haggard and Isaacs (1966) and initially labelled as “micromomentary” expressions, these
expressions were classified as an ego defence mechanism (protecting the conscience from
conflict). However research by Ekman (2004) has shown that these micro expressions tend
to be missed by the general population and can usually only be noticed when slowing down
a video clip of them. Micro expressions can occur for one of two reasons, firstly; they are a
by-product of repression (the individual is unaware of what they are feeling) or secondly they
are a by-product of suppression (conscious concealment of the felt emotion). Ekman (2009)
stated that he has found no evidence of a difference in appearance of micro expressions
from either a result of suppression or repression. Typically the suppression or concealment
of emotion is found in high stakes situations; usually when the person concealing their
authentic emotion has something to lose by being discovered in a lie (Frank and Ekman,
2004; Porter and Ten-Brinke, 2010). This leakage of emotions generally happens when an
individual loses momentary focus around the suppression and the authentic emotion comes
to the surface, thus projecting their true feelings. For example in Vrij et al (2008) study they
suggest that by having a suspect recall an event in reverse order it will increase the cognitive
load (attentional focus on suppression the lie) thus leading to the production of more micro

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expressions; which would implicate that the suspect is being deceitful. Micro expression
training can be useful in deception detection and is currently in operation within law
enforcement agencies such as the police and FBI (Ekman and Friesen, 1974; Warren et al,
2009; Vrij et al, 2011). According to O’Sullivan and Ekman (2004) certain individuals within
law enforcement including pre-trained personnel were more accurate at recognising and
detecting deceptive emotions such as contempt, fear and anger rather than other positive
emotions.
Advances in technology and the ability to and record video-clips of facial expressions have
enabled a better understanding of them and subsequently facilitated several theories of
basic universal emotions (Tomkins and McCarter, 1964: Ekman, Sorenson and Friesen,
1969; Izard, 1971) which are cross-culturally similar. Eibl-Eibesfeldt (1973) made extensive
trips to remote cultures to photograph the indigenous peoples’ facial expressions and found
that there was universality to several basic emotions. This is important because it shows that
different facial expression and the ability to recognise these expressions has in fact
developed cross-culturally with a minimal level of variation. Ekman (1999) expanded on this
and stated that a model of basic emotions should be founded upon three fundamental
factors: 1) emotions are based on social learning – which is lacks cross-culturally
differences; 2) emotions are based around human predicaments; and 3) emotions are
accessible to deal with important interpersonal encounters. A theory of basic emotions would
therefore suggest that emotions not only have similarities across-cultures but are due to
increased levels of arousal in social dilemmas and personal interactions.
Ekman and Friesen (1978) later developed The Facial Action Coding System (FACS) to
accurately and objectively measure facial movements in certain facial expressions such as a
“true” or Duchenne smile (Duchenne, 1876) which involves the zygomatic major muscle (this
muscle raises the corners of the mouth, bilaterally) and the orbicularis oculi muscle (this
muscle raises the cheeks and forms crow’s feet around the eyes). Using the FACS allows for
validation and reliable coding, which has subsequently provided evidence for a universality
of the basic emotions happiness, sadness, fear, anger and disgust. FACS enables
individuals to be taught which singular or groups of facial muscles are being used in a
specific facial expression; these muscle movements are measured in action units. Action
units are calculations of the different muscles being worked at once to create the specific
facial expression. Ekman (2002) subsequently created the micro expression training tool
(METT) which included the subtle expression training tool (SETT). Both the METT and the
SETT contained images which vary in age, gender, ethnicity and speed thus enabling the

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detection process to be more complex, but not impossible. This suggests a level of
ecological validity and these tools can be used in situations to train individuals in the
recognition of deceitful facial expressions (such as in criminal investigations, police
interviews and court appearances).
Ekman (2003) posits that the most common mistakes in facial expression recognition occur
between the differentiations of the following emotions: anger/disgust, sad/fear, fear/surprise
and contempt/happiness. Dickson and Burton (2011) found in 9 - 13 year olds that there was
a distinctive misinterpretation of the facial expression for anger. This would therefore
suggest that negative emotions are recognised more readily than positive emotions as
negative emotions such as disgust and anger may have more damaging social implications
and lead to greater personal threats (Hampson et al, 2006; Wen et al, 2008). Balconi and
Mazza (2010) believe that the motivational significance of a facial expression elucidates why
there are different responses to emotional types; and suggest that there is more cortical
activation for expressions of fear, anger, sadness and disgust. Kumar and Srinivasan (2011)
suggest that spatial frequency plays a key part in the ability to differentiate between happy
and sad facial expressions. Whereas Jordan et al (2011) stated that people tend to
underestimate the presence of negative emotions and overestimate the presence of positive
ones; they also state that negative emotions such as rumination and loneliness are more
prevalent in everyday life. This would therefore advocate an increased recognition of
negative emotions over positive ones.
There are significant sex differences in the ability to recognise facial expressions, an
evolutionary perspective would suggest that females tend to have an increased level of
interpersonal ability therefore an enhanced ability to detect facial expressions and emotions
of others in relation to mate preferences. Hall and Matsumoto (1994) suggest that there is a
clear female superiority at recognising facial expressions from brief presentation. Hill and
Craig’s (2004) research into facial expressions of pain provided evidence that females tend
to rate facial expressions of pain far more accurately than males. However a study by
Rahman, Wilson and Abrahams (2004) posits that females may have a faster reaction time
and increased accuracy of recognising facial expression but refute that there is a sex
difference in overall accuracy of positive or negative facial affect. Biele and Grabowska
(2006) suggested that females were better at detecting angry and happy facial expressions
better than male participants. Similarly Hall (2010) showed that females tend to pay more
attention to the eye area of the face, which increases their ability to recognise and

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
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distinguish facial expressions. This research would therefore suggest that females have
superiority at recognising facial expressions and emotions in others.
Emotional intelligence consists of the ability to recognise facial expressions and understand
nonverbal communication. Thorndike (1920) suggested three different types of intelligence;
verbal intelligence, mechanical intelligence and social intelligence. He discovered a way of
measuring the individual differences of emotional intelligence by presenting an individual
some photographs of facial expressions. In his study he used the 100 photographs collected
by Feleky (1914) which depict several different facial expressions. Recent emotional
intelligence theories have their origins in Gardner’s (1983) model of multiple intelligences,
but more specifically, his concepts of interpersonal and intrapersonal intelligence.
Descriptions and discussions of emotional intelligence have appeared in previous
psychological writings (Greenspan, 1989; Leuner, 1966; Payne, 1986); however, it was
Salovey and Mayer (1990) who defined and constructed a working model of what is known
today as emotional intelligence. According to Gardner (1999), “interpersonal intelligence
denotes a person’s capacity to understand the intentions, motivations, and desires of other
people and, consequently, to work effectively with others” (p43). Emotional intelligence can
therefore be defined as the ability to recognise the meanings of emotions and their
relationships and the ability to use those emotions effectively for problem solving and
reasoning in everyday social situations (Mayer, Caruso and Salovey, 2000).
According to Pizzamiglia et al (1983) there may well be links between emotional intelligence
and facial expression recognition but there needs to be more validation. Hess, Blairy and
Kleck (1997) stated that the more intense the facial expression the more readily identified
therefore the more accuracy. LaPlante and Ambady (2000) stated, however, that the more
complex the facial expression the easier it was to identify accurately. Emotional intelligence
in relation to accomplishment, Shipley, Jackson and Segrest (2010) suggest that emotional
intelligence leads to increased levels of academic success, higher job performance and
increased leadership opportunities due to ability to read others emotions and use that to an
advantage. Porter et al (2011) also stated that individuals with higher levels of emotional
intelligence were better able to perceive facial expressions and would have a higher level of
accuracy in detecting and recognising facial expressions. There also seems to be a clear
gender difference in levels of emotional intelligence. Van Rooy, Alonso and Viswesuaran
(2005) found that females tend to have a higher accuracy on self-report measures of
emotional intelligence than males. Di Fabio and Palazzeschi (2008) found that women
tended to score higher on the interpersonal facet of emotional intelligence than that of the

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
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intrapersonal facet. This research would suggest that women tend to have superiority in
emotional intelligence as well as facial expression recognition.
According to Petrides and Furnham (2000) there are two types of emotional intelligence; trait
emotional intelligence, also known as emotional self-efficacy. Trait emotional intelligence
differs from that of ability emotional intelligence, also known as cognitive-emotional ability
(Petrides and Furnham, 2001). Trait emotional intelligence is measured through self-report
questionnaires; whereas ability emotional intelligence is measured through maximal
performance or performance-related tasks (Pérez, Petrides and Furnham, 2005; Cronbach,
1949; Hofstee, 2001). Trait emotional intelligence remains stable over time much like
individual personality traits. This would therefore suggest that trait emotional intelligence is a
dispositional ability rather than a situational one. Elfenbein, Marsh and Ambady (2002)
suggest that emotional intelligence is a useful component of interpersonal functioning, and
they emphasise a link between recognising facial expressions and organisational outcomes,
thus supporting the theory that being able to read facial expressions will increase sociability
and interpersonal skills. Abrahams (2004) theorises that self-control, intrapersonal skills,
resilience and social skills influence performance in an organisational structure and promote
superior social performance, so an individual with increased levels of emotional intelligence
may be more adaptable to change within a social environment. Mavroveli et al (2009)
suggest that children with high levels of emotional intelligence tend to have higher levels of
pro-social behaviour and positive peer interaction.
By investigating gender differences in relation to emotional intelligence, there seems to be
evidence to again support of a female superiority in trait emotional intelligence (see also
Baron-Cohen and Wheelwright, 2004; Van Rooy, Alonso and Viswesvaran, 2005; Biele and
Grabowska, 2006; Fabio and Palazzeschi 2008; Freudenthaler, Neubauer and Haller, 2008)
and ability emotional intelligence (McIntyre, 2010).
The capacity to recognise positive and negative emotions may also be salient in social
situations and performance. Tugade and Fredrickson (2002) suggest that positive emotions
can be used as a guide to understanding behaviour and experience; they posit that not only
do positive emotions promote social interaction but they also promote physical and
psychological health. Albeit many studies have revealed that individuals tend to detect
negative emotions more effectively than positive ones, which may be due to the threat or
social cost they may pose (see also Schofield, Coles and Gibb, 2007; Wen et al, 2008;
Mikolajczak et al, 2009).

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Levels of confidence in accuracy vary among individuals and this may be due to levels of
emotional intelligence. Latif (2004) suggests that individuals with higher levels of emotional
intelligence will tend to have higher levels of confidence in themselves and their answers to
self-reports due to higher levels of self-confidence. Abrahams (2004) also suggests that
increase levels of emotional intelligence, self-confidence and emotional honesty, delay
performance decline thus increasing confidence levels and accuracy in overall performance.
Angeli and Valanides (2004) suggest that there are no links between perceived task
performance and confidence levels; however this does not encompass emotional
intelligence as a factor. Chua et al (2006) posit that subjective confidence is related to the
cognitive process of “knowing you know the answer”. Koriat (2011) suggests that there are
positive links between subjective confidence and accuracy levels, especially when
performance accuracy is consistent. Increased self-confidence found in individuals with
higher levels of emotional intelligence may lead to higher levels of accuracy. This would
suggest a possible link between subjective confidence ratings, accuracy and emotional
intelligence.
The aim of the present research was to analyse previous findings in relation to facial
expression recognition, emotional intelligence and gender; and examine any possible links
between them. The present study therefore hypothesises the following: there will be a
distinct relationship between the level of emotional intelligence and an increased level of
accuracy in the ability to read micro-expressions. There will be a clear gender divide in the
level of accuracy with the female participants performing significantly better than the male
participants. The participants with a higher level of emotional intelligence will have increased
levels of confidence in their ability to recognise micro expressions. All of the participants will
be able to recognise negative emotions more accurately than positive ones. Finally, the
micro-expression training tool (METT) will increase the overall ability of all the participants.
Methodology
Participants
Sixty one participants (30 female and 31 males) were recruited via a convenience sample.
They were aged between 18 and 67 years old (m=30.6, SD=13.7) and were either employed
full-time (n=25), employed part-time (n=4), a full time student (n=24), retired (n=4) or
unemployed (n=3). Ethnicity was predominantly white (n=33), black (n=8), mixed (n=5) or
other (n=14). All participants spoke fluent English.

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Design
A between-subject, repeated-measures, experimental design was used. The independent
variable was the level of training. The dependent variables were the participants’ level of
emotional intelligence, accuracy at recognising micro expressions and individuals’
confidence levels.
Materials
Demographics Questionnaire
The demographics questionnaire consisted of questions relating to the participant’s age,
employment status, occupation, marital status, and ethnicity. It also included a question
relating to previous exposure to micro-expression training, so as to minimise bias.
Confidence Questionnaire
The confidence questionnaire consisted of a ten-point Likert scale (0 being not confident at
all, 10 being completely confident) for each of the seven following emotions; happiness,
sadness, anger, contempt, disgust, surprise and fear. Koriat (2008) study showed that
confidence has been confirmed as being a predictor of accuracy in general-knowledge and
perceptual judgements using the self-consistency model. Later studies by Koriat (2011) and
Koriat and Adiv (2010) suggested that confidence levels can be an accurate predictor of
social attitudes also in relation to the self-consistency model.
Trait Emotional Intelligence Questionnaire Short Form [TEIQue-SF]
The trait emotional intelligence short form questionnaire is a short version of the Trait
Emotional Intelligence Questionnaire Full Form created by Petrides and Furnham (2003).
The full version covers 15 facets; Adaptability, Assertiveness, Emotion perception (self and
others), Emotion expression, Emotion management (others, Emotion regulation,
Impulsiveness (low), Relationships, Self-esteem, Self-motivation, Social awareness, Stress
management, Trait empathy, Trait happiness, Trait optimism. However for the short version
only two items from the 15 facets were included to enable a correlation of corresponding
total facet scores. These facets are used to measure four factors; well-being, self-control,
sociability and emotionality. It also measures a global trait emotional intelligence (Petrides
and Furnham, 2001) and has foundations in the big five model of personality (Costa and
McCrae, 1989) with high correlation between trait EI and neuroticism and extroversion
(Petrides et al, 2010).

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The Trait Emotional Intelligence Questionnaire Short Form comprises of thirty items, which
are rated on a seven-point Likert scale. One being completely disagree and seven being
completely agree. 50% of the questions are negative coded and the other 50% are positively
coded. The Trait Emotional Intelligence questionnaire has shown to be reliable across
cultures (Petrides et al, 2010; Mikolajczak and Luminet 2007). The validity of this
questionnaire has also been previously tested across ages, gender, situations and cultures
(Freudenthaler et al, 2008; Mavroveli et al, 2009; Veselka et al, 2009; Cooper and Petrides,
2010; Gardner and Qualter, 2010).
Micro Expression Training Tool (METT)
The MicroExpression Training Tool (METT) was developed by Ekman (2002). The test
consists of twenty-six individual images of male and female faces (their order is randomised
for each test). Each individual face flashes at 1/15th of a second with an emotional facial
expression (happiness, sadness, fear, anger, contempt, disgust or surprise; the facial
expressions are also randomised). Participants controlled the speed at which they
progressed throughout the test. The METT has yet to be validated, however it has been
developed from the Brief Affect Recognition Test (BART) which in itself has shown good
validity and reliability (Matsumoto et al, 2000). The METT has also been used in previous
research studies (Frank and Ekman, 1997; Russell et al, 2006; Porter and ten Brinke, 2008;
Warren, Schertler and Bull, 2009).
Procedure
In the present study at the first stage participants’ were asked to complete a consent form, a
demographics questionnaire, a TEIQue questionnaire and a pre-training confidence level
questionnaire. Participants were then given verbal instructions by the researcher beforehand
so that they were equipped and competent to carry out each stage. On completion of the
pre-test questionnaires, participants’ were asked to complete the pre-training test using the
METT online software. This pre-training METT online test consists of a computer program in
which the participant is shown a micro expression once, per individual face; they can take as
long as they like to choose their answer for each facial image; upon choosing which emotion
they believe it to be they click the corresponding button on the screen. Once clicked, an
option to click next becomes available. Upon clicking next, the next individual facial image
appears and flashes a different micro expression, this process is repeated twenty-six times.
After completing the pre-training test all participants were then asked to watch the training
video clips again using the METT online software The METT training stage consists of four

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
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video clips; which are approximately one minute in length. These four video clips explain in
detail and illustrate slowed down clips of the facial movements that are used for each
corresponding emotion.
All of the participants’ were then asked to complete a post-training confidence level
questionnaire (this is a replication of the pre-training confidence questionnaire). Following
completion of the post-training confidence questionnaire, all of the participants’ were asked
to carry out the post-training test which was again using the METT online software (this is a
replication of the pre-training METT test). Finally all of the participants’ were asked to fill out
the post-test confidence level questionnaire.
Ethical Implications
All of the participants used in this research study were over the age of eighteen and gave
their written consent prior to taking part in the experiment. Participants were also told that
their answers would remain anonymous and that they could ask to be removed at any time
should they feel uncomfortable. All participants were informed that their data would be stored
securely and confidentially on the university’s password protected hard drive prior to
participation. All participants were then subjected to a debriefing session upon completion of
the experiment; in order to answer any queries involved with participation.
Results
Statistics and Analysis The TEIQue-SF “global trait” emotional intelligence scores
were analysed using descriptive statistics to establish a median split. The median was 174;
participants who scored below this were classified as low EQ and participants who scored
above this were classified as high EQ, this enabled an equal split of all participants. Data
analysis was carried out using repeated measures ANOVA to investigate statistical
differences; further analysis of the ANOVA data used a Bonferroni post-hoc comparison. A
one sample t-test was used to measure the difference between the means, when an ANOVA
was not appropriate for the data. And each of the tested seven emotions were added
together from time of testing (2 levels; pre-training and post-training) then divided by 2 to
obtain a mean average overall score for each emotion (happiness, sadness, disgust,
contempt, anger, surprise and fear).
Accuracy vs. Emotional Intelligence
Firstly the median split of 174 was assigned to all of the data to investigate the differences
between the high emotional intelligence group and low emotional intelligence group. A one-

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
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way repeated measures analysis of variance was used to investigate accuracy at time of
testing (2 levels; pre-training and post-training) for the emotional intelligence split groups.
The results showed no significant difference in accuracy for either emotional intelligence
condition at either time point (F(1, 58) = 0.265, p=.615). The high emotional intelligence group
had a mean score of 41.10 (SD = 9.65) which was higher than the low emotional intelligence
group who had a mean score of 39.90 (SD = 10.70) at the pre-training level. The high
emotional intelligence group had a mean score of 57.33 (SD = 11.23) which was higher than
the low emotional intelligence group mean score of 55.93 (SD = 12.71) at the post-training
level. Neither emotional intelligence group mean accuracy scores differed at either time point
when Bonferroni post-hoc comparisons were made (see appendix 1 for ANOVA table).
Confidence Level vs. Emotional Intelligence
A one-way repeated measures analysis of variance was used to investigate confidence at
time of testing (3 levels; pre-test, post-training and post-test) for the emotional intelligence
split groups. The results showed no statistically significant difference between confidence
and time of testing for either of the emotional intelligence split groups (F(2, 116) = 0.365, p =
.548). The high emotional intelligence group at pre-test had a mean confidence score of 6.50
(SD = 1.46) which was higher than the low emotional intelligence group who had a mean
confidence score of 6.10 (SD = 1.47), however this was not statistically significant. The high
emotional intelligence group at post-training had a mean confidence score of 6.83 (SD =
1.26) which was again higher than the low emotional intelligence group mean confidence
score of 6.97 (SD = 1.43), nonetheless this was not statistically significant. At the final time
point the high emotional intelligence group had a mean confidence score of 7.53 (SD = 1.36)
which was higher compared to the low emotional intelligence group who had a mean
confidence score of 7.27 (SD = 1.46) again this was not statistically significant. Neither
emotional intelligence group mean confidence scores differed at any of the three time points
when Bonferroni post-hoc comparisons were made (see appendix 1 for ANOVA table).
Gender vs. Accuracy
A one-way repeated measures analysis of variance was used to investigate accuracy
differences at time of testing (2 levels; pre-training and post-training) between the male and
female participants. The results showed no significant difference in accuracy for either
gender at either time point (F(1, 58) = 0.492, p = .468). The females participants pre-training
mean accuracy score was 41.73 (SD = 10.23) which was higher than the males pre-training
mean accuracy score of 39.27 (SD 10.04) however this was not statistically significant. The

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
15
77.14
55.20
45.32 50.10 48.48
57.24
66.26
0
10
20
30
40
50
60
70
80
90
Overall % Correct
Emotion
Positive vs. Negative Emotion
*
*
female participants post-training mean accuracy score was 57.20 (SD = 11.28) which again
was higher than that of the male participants post-training mean score of 56.07 (SD = 12.68)
albeit it was not statistically significant. Neither the male or female participants mean
accuracy scores differed at either time point when Bonferroni post-hoc comparisons were
made (see appendix 1 for ANOVA table).
Positive vs. Negative Emotions
A one-way repeated measures analysis of variance was used to investigate whether positive
or negative affect was recognised more frequently. The results showed a significant
difference in overall accuracy for all of the seven emotions (F(1, 6) = 9.388, p = <.0001).
However the positive emotions were more frequently recognised than the negative emotions.
Happiness showed the highest overall mean score of 77.14 with surprise having the second
highest overall mean of 66.62, whereas fear had the lowest overall mean score of 45.32
(See appendix 2 for positive vs. negative emotion overall mean table).
Graph 1 (*p= <.0001, therefore result significant at 5% level).
Micro Expression Training vs. Time
A one-sample t-test was used to investigate the mean overall scores for all participants at
time of testing (2 levels; pre-training and post-training). The results showed that the post-
training mean overall score was 56.63 (SD = 11.91) which was significantly higher (t(59) =
12.686, p = <.0001) than the pre-training mean overall score of 50.50 (SD = 10.12).

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
16
40.50
56.63
0
10
20
30
40
50
60
Pre Training Post Training
Overall Score %
Time Point
Pre & Post Training Overall Scores
Overall Score
*
Graph 2 (* p = < .0001, therefore result significant at 5% level).
Discussion
In the present study the results found that level of emotional intelligence, gender and
subjective confidence had no effect on micro expression recognition outcomes. However
there was a clear increase in micro expression recognition accuracy after training for all of
the participants. Not only did training increase overall recognition but positive affect was also
detected far more frequently than negative affect; it is ambiguous as to whether this was due
to the training or merely a by-product of social learning. These finding from this study are
interesting and intriguing and are discussed in detail below.
The current research hypothesized that individuals with a higher level of emotional
intelligence would have higher accuracy in the recognition of micro expressions. The results
show that there was no relationship between accuracy and emotional intelligence at time of
testing. This suggests that the current theory used in the testing of emotional intelligence
(Salovey and Mayer, 1990) does not include enough information regarding the ability to
recognise the subtler facial expressions, as opposed to the full aspect of facial affect. The
present findings contradict previous conclusions reported by Porter et al (2011), Shipley,
Jackson and Segrest (2010), LaPlante and Ambady (2000) and Hess, Blairy and Kleck
(1997) who suggested that individuals with a higher level of emotional intelligence were
more accurate at recognising facial expressions. However the aforementioned studies were
based on the recognition of full facial expressions whereas the present study makes use of
micro expressions or subtle expressions, this may have had an effect on recognition
outcomes. There is extensive research into the positive links between nonverbal

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
17
communication and academic performance (Halberstadt and Hall, 1980) and in particular,
facial expressions (Izard, 1971; Izard et al, 2001). There have also been links between
general nonverbal communication and workplace performance and outcomes (Rosenthal et
al, 1979; Constanzo and Philpott, 1986). Therefore this shows that people with high levels of
emotional intelligence are more likely to be socially interactive, have enhanced verbal and
nonverbal skills, as well as, superior levels of self-esteem and self-confidence. However
some of these studies have reported null results, which corroborate weaker links between
emotional intelligence, nonverbal behaviour and facial expression recognition (Hill et al,
1981; Lee et al, 1980).
Laboratory conditions were using the present study, which may have resulted in a lack of
ecological validity, thus leading to a null result. Ecological validity may play a crucial function
in recognition ability outcomes; it may be more beneficial in future replication to measure
human interaction at this level within a practical setting rather than facing a computer screen.
A study carried out by Abrahams (2004) found similar results to the present study which
suggested that there does appear to be unsubstantial links between emotional intelligence
and job performance which advocates that in reality emotional intelligence does not predict
job outcomes or performance in the work place but instead an individual’s emotional
competencies enable a positive occupational or societal progression.
It is unclear as to why the individuals who scored higher on emotional intelligence in the
current research did not have a more accurate ability to recognise facial expressions. This
may be due to the use of the TEIQue-Short Form. Perhaps if an emotional intelligence test
that included images of facial affect such as the Mayer Salovey Caruso Emotional
Intelligence Test [MSCEIT] (Mayer, Salovey and Caruso, 2002) was used it would have
enabled a better judgement of the participant’s level emotional intelligence than just a self-
report 30 item questionnaire. As Salovey and Mayer (1990) posited in their theory of
emotional intelligence that the recognition of facial affect increases an individual’s ability to
progress within the social hierarchies in life. However due to time and financial restraints it
was not possible to use this measure of emotional intelligence. Latif (2004) suggests that by
receiving training it is possible to increase the level of emotional intelligence; this could imply
that any involvement in the present study may have encouraged participants to focus more
direct attention on the micro expressions of others than would normally be applied.
Wen Li et al (2008) suggest that subliminal priming to facial expressions such as fear and
happiness may increase ability. Their study showed that participants with high levels of trait
anxiety were better able to recognise fearful faces and had activation in the larger occipital

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
18
region. This therefore suggests that any unconsciously perceived emotional information can
manipulate an individual’s social judgments by altering the early perceptual analysis.
Scherer and Scherer (2011) suggested that individuals who had an increased ability to
recognise facial affect were “higher up” within the occupational hierarchy. This again
suggests that individuals who are more able to recognise facial affect easily will tend to be
better equipped to interact on a social level. Although these links appear within the present
study to be unclear and unsubstantiated.
The present research suggested in a second hypothesis that the female participants would
have a higher level of accuracy due to their increased levels of emotional intelligence than
that of the male participants. The results show that there was no differentiation between the
male and female participants and their overall level of recognition accuracy. From previous
research it has been assumed that females would have higher levels of trait emotional
intelligence consequently increasing their ability to recognise facial expressions (Baron-
Cohen and Wheelwright, 2004; Van Rooy, Alonso and Viswesvaran, 2005; Biele and
Grabowska, 2006; Fabio and Palazzeschi 2008; Freudenthaler, Neubauer and Haller, 2008),
however other research has shown very contradictory results (Geng et al, 2011; Hopkins
and Bilimora, 2008; Castro-Schilo and Kee, 2010; Kadam et al, 2011) finding no visible links
between gender and the demonstration of emotional intelligence.
Jaušovec and Jaušovec (2008) noted that male and females tended have similar patterns of
brain activity for emotional intelligence overall; however they state that males and females
tended to compensate for their inferior problems solving skills by increasing their levels of
attention needed to complete a task (males increased for emotional tasks and females
increased for spatial tasks). From this research the current results can be interpreted as
females may have better recognition accuracy within a larger framework (i.e. looking at full
facial affect) but there seems to be no evidence to confirm that females are more accurate
are recognising micro expressions of facial affect from a trait emotional intelligence or
focused attention point of view.
By looking at the research surround the relationships between gender and facial affect
recognition, there does appear to be clear links between females and facial expression
recognition. Güntekin and Başar (2007) demonstrated that facial processing differs for males
and females, females tend to have larger occipital beta responses, thus increased brain
activity. Ino et al (2010) found that female brain activation was increased in the recognition of
female facial expressions compared to that of male brain activation to female facial
expressions. This does suggest that although there is no significant link between gender and

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
19
overall facial affect recognition, there does seem to be a significant relationship between the
recognition of female facial affect more so than male facial affect. Hoffman et al (2010)
discovered that females tended to notice more subtle displays of facial emotions than males.
This suggests that females do have the ability to notice more subtle changes in facial
expression than males; whether females use this ability however, cannot be tested within a
laboratory environment and would need practical applications to allow for measurement.
Antypa et al (2011) found that female who had experienced life events (such as abuse and
childhood trauma) tended to be more able at recognising facial affect. It is suggested that
looking at the previous research there may be a relationship between the recognition of
facial affect from the opposite sex (i.e. males recognise female facial affect more accurately
than that of their own sex, and vice versa). This may be due to evolutionary foundation
based in mating preferences. Evolutionary psychologist Buss (1989) would posit that
recognising the facial affect of the opposite sex substantially increases the possibility of
finding a mate. Future replication of this study should investigate any links between facial
affect recognition and gender by presenting images of the opposite sex to the participants as
well as images of their own sex.
By looking at the third hypothesis it was suggested that participants with a higher emotional
intelligence would have an increased level of confidence in their performance. The results
reveal that there was no positive relationship between the amount of subjective confidence
in performance outcomes and levels of emotional intelligence. Previous research by Gohm
et al (2005) discovered that individuals with an average degree of emotional intelligence
lacked the confidence in their emotional capacity and therefore did not utilize these abilities.
Warwick, Nettelbeck and Ward (2010) found that confidence did relate to emotional
intelligence however it was a distinct personality trait and converged with fluid ability.
Therefore it is suggested that confidence does not correlate with higher levels of emotional
intelligence. Carr (2000) suggests that confidence and emotional intelligence links are
potentially problematic because they show little compassion to the intrinsically cross-cultural
value-laden nature of affective communication. The present study did not measure self-
esteem, self-confidence or personality traits as variables, but it is recommended if future
research could incorporate these themes as factors to encompass a larger picture of
emotional intelligence in relation to self-confidence; because looking at previous literature it
is assumed in the present research that an individual with a greater degree of self-
confidence would be more confident in their own abilities, since they are generally more
confident in themselves.

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
20
Barchard and Hakstian (2004) suggest that confidence lies outside the realm of cognitive
ability and should be tested using maximum-performance rather than self-report methods.
Although maximum-performance appears to be a more ecologically valid method of
measuring confidence levels; these types of practical measurement techniques may be
influenced by external factors, such as physical training and the environment in which
measurement takes place. These types of confounding variables may prove difficult to
eliminate. Briñol, Petty and Rucker (2006) stated that emotional thoughts influence meta-
cognitive confidence, which suggests that once an individual has received training they may
have an inflated level of confidence immediately after completion, thus leading to a bias
within the measurement of subjective confidence.
One of the facets within the present study was the self-report measurement of subjective
confidence in the participant’s ability to recognise facial affect. The participant’s mood prior
to involvement may also have affected this recognition outcome. Previous studies have
proposed that negative mood may decrease confidence and positive mood may increase
confidence. Participants were not asked to verify their current mood state within the present
study, however this may be beneficial in future replication to investigate whether there are
any links between current mood state and subjective confidence outcomes in relation to
facial affect recognition accuracy. Lane et al (2005) study supports participant mood as a
confounding variable by finding that goal-confidence scores were significantly lower when
participants were in a depressed mood. In addition, as people view facial expressions they
tend to imitate them (Lundqvist and Dimberg, 1995) which was confirmed and noted in the
present study. Imitation can also be found to induce corresponding moods (Cappella, 1993)
which again could be a confounding variable within the present study, again demonstrating
that by imitating certain types of facial affect; may cause and altering in subjective mood and
effect recognition outcomes.
Fourthly the present research hypothesizes that all of the participants involved would be
better able to recognise negative emotions than positive emotions from expressions of facial
affect. The results showed that there was indeed a significant difference between the
recognition of positive and negative emotions; however it was the opposite of what was
originally suggested. Participants tended to notice the positive emotions of surprise and
happiness more so than the negative emotions of fear, anger, sadness, disgust and
contempt. This could be due to smiles being one of the most common positive emotions
expressed within the public domain. Genuine smiles (or Duchenne smiles) tend to be shared
amongst individuals as a sign of happiness, friendliness and politeness. This confirms that

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
21
positive emotions are more widely expressed than negative emotions which tend to be
private or hidden from public view. A recent study by Jordan et al (2011) confirms that
individuals will tend to underestimate the negative emotions of others due to them generally
being kept private or hidden within a social context; whereas positive emotions tend to be
made more public.
Hess, Philipott and Blairy (1998) suggested that individuals will mimic facial expressions to
judge the valence of a facial expression. This was replicated in the present study; although
not measured, the participants did tend to mimic the facial expressions viewed on the micro
expression training tool (METT) in order to grasp a better understanding of the emotion
viewed. Previous studies have shown that individuals with anxiety, depression or negative
moods will tend to have an attentional bias against negative emotions (Bradley et al, 2007;
Csukly et al, 2008) the age of a participant may also affect reaction times of emotional
valence recognition (Keightley et al, 2006). This suggests that older participants may have
been able to differentiate between positive and negative emotions; although it may take
them longer to do so. Positive affect leads to approach behaviour which usually
subsequently may lead to reward behaviour, hence the ability to recognise positive emotion
more so over negative emotions. The current research shows that individuals will tend to
notice positive emotions more readily than negative emotions suggesting the approach
behaviour is much more common than avoidance behaviour. Approach behaviour appears to
be superior in humans who are predominantly a social species (Darwin, 1872). Humans are
constantly searching for positive, meaningful social interactions which enable rewards and
social connectedness; and try to avoid negative emotions which lead to feeling alone,
isolated or rejected.
Nikittin and Freund (2011) state that positive expectations (are related with approach
behaviour) and ambivalent reactions (are related with avoidance behaviour) and may play a
key role in an individual’s cognitive response to happy and angry faces. Tugade and
Fredrickson (2002) state that emotional intelligence explicitly incorporates positive emotions
as an ability to improve mental and physical health as well as enhancing well-being over
time. This suggests that individuals will tend to express positive emotions more often as the
rewards and outcomes enhance everyday living in a constructive way. The present study
lends confirmation to the idea that individuals will tend to notice positive emotions in relation
to approach behaviour because of the beneficial outcomes and rewards that they may offer.
The final hypothesis suggested that all of the participant’s accuracy of recognition would
increase with the micro expression training tool (METT). The results do in fact show that

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
22
there was a significant increase in recognition accuracy after training, consequently
suggesting that individuals are able to be taught to utilize these abilities with or without a
high level of emotional intelligence. Russell et al (2008) found that training with the micro
expression training tool increased the level of accuracy by enabling individuals to focus
attention on the key areas of the face, such as the eyes and mouth. However a study by
Warren, Schertler and Bull (2009) found that the subtle expression training tool (SETT)
increased the ability to recognise facial affect and detect deception in others better than the
micro expression training tool, they also found that the participants were no better than
chance at the recognition of deceptive micro expressions after training with the METT.
Marsh et al (2010) studied facial expression recognition in schizophrenia sufferers and found
that training with the METT improved recognition ability immediately after training and for up
to 1 month after training. This suggesting that training increases ability and appears useful in
clinical settings as well, continued training with the micro expression training tool may lead to
increased social functioning for schizophrenia sufferers. Asla, de Paúl and Pérez-Albéniz
(2011) suggest that the micro expression training tool/subtle expression training tool
(Ekman, 2003) can be used to recognise parents at high-risk of child abuse due to lack of
emotion recognition. This outcome suggests that treatment programmes could be designed
from the results obtained using the micro expression training tool. It also shows that the
training tool not only has practical clinical and forensic applications but it can also be used as
a means to detect deception within a criminal investigation.
Overall the present study obtained advantageous information regarding the recognition of
positive emotions in relation to approach behaviour and the research also found that
individuals can in fact be trained to recognise micro or subtle expressions of facial affect,
which may have beneficial clinical implications; within the field of schizophrenia. The findings
may also have constructive implications within a forensic setting; for example within law
enforcement agencies.
There does however seem to be some limitations within the present study that could lead to
confounding variables; firstly by having the participants aiming their direct gaze at the
computer screen whilst focusing upon images of facial affect may not entirely encompass
ecological validity. In a real life social situation an individual does not typically stare directly
at another person’s face when interacting or communicating; it usually consists of regular
glances, therefore this may form a bias towards the direct gaze or focused attention that is
used within the present study. Secondly, the participant’s mood prior to participation may
affect the outcomes of subjective confidence ratings, future research may wish to ask

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
23
individuals to clarify their mood state prior to participation, to enable this to be investigated
as a possible variable and to minimise bias, a positive or negative priming emotion could be
used prior to participation. Thirdly, the use of the trait emotional intelligence test short form
(TEIQue-SF) may not have encompassed all facets of emotional intelligence. By using a
more comprehensive test such as the MSCEIT (Mayer, Salovey and Caruso, 2003) which
incorporates facial affect recognition, may increase the scope between high emotional
intelligence and low emotional intelligence. Finally the present research used a small
number of participants (mostly undergraduate students) and it is therefore recommended in
future replication that a much larger and broader sample could be used to facilitate a more
diverse quantity of information.
In conclusion, the findings from the present research have shown that training with the micro
expression training tool (METT) does increases an individual’s ability to recognise micro
expressions of facial affect in others. The findings also suggest that individual’s are more
likely to recognise positive emotions (such as happiness and surprise) than negative
emotions (such as anger, sadness, contempt, fear and disgust). This may be because of
approach behaviour and social interaction being associated with the expression of positive
emotions. The expression of positive emotions may lead to more encounters within social
contexts consequently leading to more rewarding outcomes. The recognition of micro
expressions of facial affect may be beneficial within a social context as well as an academic
and occupational arena and the use of the micro expression training tool may improve an
individual’s interpersonal and intrapersonal skills to facilitate advancement within social
hierarchies despite their level of emotional intelligence. The use of micro expression training
would be advantageous within clinical and forensic settings to ameliorate the detection of
schizophrenia, psychopathy, autism or other personality disorders.

Emotional Intelligence and the Ability to Recognise Micro Expressions. 0901837
24
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Appendix
Appendix 1 - Analysis of variance tables for emotional intelligence split, confidence levels
and gender.
Source of Variance
Sum of Squares
DF
Mean Square
F-Ratio
p
EQ Split
.300
1
.300
.006
.938
Error
2862.167
58
49.348
Confidence
2.311
2
1.156
1.130
.327
Error
118.622
116
1.023
Gender
13.333
1
13.333
.271
.604
Error
2849.133
58
49.123
Appendix 2 – Overall mean of positive and negative emotions table.
Emotion and Time
Mean
SD
Overall Mean
Pre Happiness Score
67.75
38.214
Post Happiness Score
86.53
24.026
77.14
Pre Sadness Score
47.63
39.457
Post Sadness Score
62.77
35.783
55.20
Pre Fear Score
37.48
39.069
Post Fear Score
53.15
37.796
45.32
Pre Disgust Score
46.18
35.665
Post Disgust Score
54.02
31.287
50.10
Pre Contempt Score
34.03
34.345
Post Contempt Score
62.93
40.322
48.48
Pre Anger Score
46.05
38.836
Post Anger Score
68.43
38.143
57.24
Pre Surprise Score
63.52
36.400
Post Surprise Score
69.00
37.534
66.62