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Personality and Individual Differences 51 (2011) 764–768

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Personality and Individual Differences

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / p a i d

Emotional intelligence and social perception

Kendra P.A. DeBusk, Elizabeth J. Austin ⇑ Department of Psychology, School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK

a r t i c l e i n f o a b s t r a c t

Article history: Received 18 March 2011 Received in revised form 22 June 2011 Accepted 24 June 2011 Available online 23 July 2011

Keywords: Emotional intelligence Social perception Cross-race Cross-cultural

0191-8869/$ - see front matter � 2011 Elsevier Ltd. A doi:10.1016/j.paid.2011.06.026

⇑ Corresponding author. E-mail address: Elizabeth.Austin@ed.ac.uk (E.J. Au

One of the key facets of emotional intelligence (EI) is the capacity of an individual to recognise emotions in others. However, this has not been tested cross-culturally, despite the body of research indicating that people are better at recognising facial affect of members of their own culture. Given the emotion recog- nition aspect of EI, it would seem that EI should be related to correctly identifying emotion in others regardless of race. In order to test this, a social perception inspection time task was carried out in which participants (41 Caucasian and 46 Far-East Asian) were required to identify the emotion on Caucasian and Far-East Asian faces that were happy, sad, or angry. Results from this study indicate that EI was not related to correctly identifying facial expressions. The results did confirm that participants are better able to recognise people of their own ethnicity, though this was only applicable to negative emotions.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Emotion perception is an important capability which impacts the ability of individuals to negotiate their social environment. There is evidence that the ability to perceive others’ emotions is af- fected by whether the target person is a member of the same racial or cultural group as the perceiver. This phenomenon is conceptu- ally linked to that of facial recognition as a function of target race/culture. In order to place the literature of cross-race and cross-culture facial emotion recognition in context, we first review the literature on cross-group face recognition.

A meta-analysis (Meissner & Brigham, 2001) indicated a robust own-race bias in memory for faces. The theoretical interpretation of this phenomenon has been based on the idea that greater expo- sure to an individual’s own racial group than to other groups al- lows them to develop greater expertise in recognising own-race faces. More detailed studies have linked this performance advan- tage to more efficient encoding and greater use of holistic process- ing when the target is an own-race face (e.g. Michel, Caldara, & Rossion, 2006; Walker & Tanaka, 2003). A complex socially-deter- mined process underlying the own-race recognition bias is indi- cated by studies showing that there is a more general in-group recognition advantage and greater holistic processing when the target stimuli are faces belonging to social groups unrelated to race, for example when same-race face stimuli are identified as being pictures of students either at the same or a different univer- sity to the participant’s, and even when using a minimal-group paradigm in which arbitrary social groups are defined and assigned

ll rights reserved.

stin).

to the participants and stimuli (Bernstein, Young, & Hugenberg, 2007). Evidence that there is also an effect of emotional state on face recognition biases comes from a study showing that the own-race bias is reduced by a positive mood induction, which is suggested to be due to positive mood enhancing one or both of holistic processing and more inclusive social categorisation (Johnson & Fredrickson, 2005).

Given the results on cross-race and cross-group biases in face recognition, it is reasonable to expect that similar effects might be found in the identification of facial expressions of emotion. Ekman’s (1968) pioneering research showed that facial expressions of emotion are similar across cultures and races, and a meta-anal- ysis by Elfenbein and Ambady (2002) confirmed that cross-group emotion recognition occurs at better than chance levels. However, there is also an in-group advantage, i.e. higher emotion recognition accuracy is found when the perceiver and target both belong to the same national, ethnic or regional group. There is also an exposure effect: cross-group emotional recognition accuracy is higher for groups which have more contact with each other, and minority group members are better at judging the emotions of majority group members than vice versa. Explanations of this effect have mainly focused on the existence of cultural differences which mod- erate the appearance of facial expressions of emotion; such differ- ences have been referred to as facial ‘‘dialects’’ (Elfenbein & Ambady, 2003). This phenomenon, together with the effects of the degree of familiarity with such expression variations (depend- ing on amount of contact with the other group), would account for the pattern of results discussed above. Such an interpretation is supported by studies in which the in-group advantage for emotion recognition has been found when the group membership of the targets cannot be determined by the perceivers (e.g. US American

K.P.A. DeBusk, E.J. Austin / Personality and Individual Differences 51 (2011) 764–768 765

Caucasian perceivers judging a target set comprising pictures of Caucasians from several cultures, Elfenbein & Ambady, 2002). It is also possible that the processing differences for in- and out- group faces found in the face recognition studies discussed earlier play a role in the recognition of facial expressions.

Related research on the recognition and attribution of specific emotions has shown that in a neutral social context smiling is attrib- uted more often to in-group than out-group members (Beaupré & Hess, 2003), whilst angry faces are more frequently categorised as belonging to an out-group member (Dunham, 2011). Response latencies for emotion recognition are also moderated by group membership, as shown in two studies by Hugenberg (2005), who found that European Americans identified happy facial expressions more rapidly than sad or angry expressions when the target face was White, but that this effect reversed if the target face was Black. A moderating effect of emotional response by target group member- ship has also been found, with emotional mimicry of fear and anger being found to be more pronounced for in-group compared to out- group targets (van der Schalk et al., 2011).

The results of studies which show that emotions play a role in the recognition, attribution and response to facial expressions of in- and out-groups provides a motivation for examining the role of individual differences in these processes. Emotional intelligence (EI) is a candidate variable to study in this context, since high scor- ers on EI would be expected to show superior emotion recognition performance, and would also be expected to be better able to over- ride biases which might lead to facial expressions being misread (for example being more capable of taking account of cultural vari- ations in emotional expressions, or of counteracting target group- related biases in the perception of positive and negative emotions). There have been no studies of the effects of EI on cross-group emo- tion recognition, but a number of studies have linked EI with better performance on emotion and social perception tasks (e.g. Austin, 2005; Petrides & Furnham, 2003). Based on the theoretical and empirical linkages between EI and emotion perception, it is reason- able to assume that high-EI individuals should be more successful at perceiving the emotions of others regardless of race. To test this hypothesis, the present study was carried out to examine how EI is related to success in a cross-racial emotional inspection time task. Two types of EI measure were included, a trait (self-report) EI test and an ability EI test which tests emotion-related problem-solving. For more detailed discussions of trait and ability EI see Petrides, Pita, and Kokkinaki (2007) and Mayer, Roberts, and Barsade (2008).

2. Pilot study

2.1. Pilot participants

The participants were post-graduate students recruited by email. There were a total of twenty participants. Sex, age, and race were not recorded for the pilot study.

2.2. Pilot Measures

2.2.1. NimStim Stimuli The photographs used for this study were part of the NimStim

face stimulus set (Tottenham et al., 2009). The pilot study was car- ried out to determine which photographs to retain for the main study from a selection of Caucasian and Asian faces from this set. The photographs utilised were two Far-East Asian females, two Caucasian females, two Caucasian males, and one Far-East Asian male. While the original intention had been to provide participants with two photographs for each gender for both races, only one Asian male photograph was available from the stimulus set.

Additional information regarding the specific origin of the Asian models was not available.

A total of 71 colour photographs were used, consisting of five fe- male Asian models, and four female Caucasian models, while for the males there was one Asian model and five Caucasian models. The photographs were shown in a non-timed power point presentation. The facial expressions in all of the photographs were shown with closed mouths, and none of the male models used had facial hair. Though the intent was to use only happy, sad, and angry expres- sions in the final experiment, the facial expressions shown on the photographs were angry, happy, sad, surprised, and disgusted in random order to provide more variety. In addition, a broader range of facial expression would help avoid social desirability in the re- sponses of the participants because they would not know which expressions were going to be used for the final study.

The participants were each given a questionnaire on which they were asked to identify the expression on each photograph. For each of the photographs, they were able to choose: happy, sad, neutral, angry, disappointed, disgusted, calm, excited, surprised, frightened, or other. The participant viewed each photograph, marked the expression given which s/he felt best described the facial expres- sion shown, then moved on to the next photograph. They were told to not return and change any answers.

2.3. Pilot results

The results of the pilot study indicated that 100% of the partici- pants agreed on the facial expressions of 11 of the photographs. Of these photographs, there were two Asian females and one Caucasian female that had 100% agreement on at least one photograph. For the males there were three Caucasian males that had full agreement on at least one photograph. The agreement response for the rest of the photographs for these models was observed. Given that the full study would involve a forced choice between happy, sad, and angry, only these facial expressions were considered at this point.

The percentage of agreement for the photographs chosen for the final study can be seen in Table 1. All of these percentages were deemed acceptable levels of agreement. They also corresponded with the initial validity study of this stimulus set (Tottenham et al., 2009).

3. Main study methods

3.1. Participants

The participants were recruited via an advertisement posted by the student Careers Service which specified the need for participants to be of either British Caucasian or Far-East Asian descent. The cate- gory of Far-East Asian was further defined in the advertisement as people from China, Japan, Vietnam, or Taiwan. All participants were paid £5 for their participation in the study. The final sample com- prised forty-one British Caucasians and forty-six Far-East Asians.

Participants were asked their age and race, then given instruc- tions on how to complete the inspection time task. They were re- quested to fill in the EI measures upon completion of the inspection time task.

3.2. Facial affect perception inspection time task

The facial affect perception inspection time task involved a total of 105 trials in which participants had to identify faces as happy, sad, or angry. The task was comparable to the ones used by Austin (2005). Each person was shown with a happy, sad, and angry facial expression. The durations for which each picture was displayed were 25 ms, 75 ms, 100 ms, 150 ms, and 200 ms, with the order

Table 1 Percentage of agreement for facial expressions used in final study.

Sex Nationality % Agreement: happy

% Agreement: sad

% Agreement: angry

Female Asian 100 82 94 Female Asian 100 94 88 Female Caucasian 100 94 100 Female Caucasian 94 94 94 Male Asian 94 59 94 Male Caucasian 100 100 71 Male Caucasian 100 94 100

Table 2 Descriptive statistics for trait and ability EI, age, and inspection time tasks total percentage correct.

N Range Mean Std. deviation

Age 87 19.00 22.91 3.63 Ability EI 86 44.00 44.99 10.92 Trait EI 87 57.00 123.07 12.59 a_f_percenta 87 90% 54% 0.21 a_f_percenth 87 50% 96% 0.08 a_f_percents 87 40% 93% 0.09 a_m_percenta 87 100% 66% 0.26 a_m_percenth 87 80% 96% 0.11 a_m_percents 87 80% 78% 0.20 c_f_percenta 87 70% 79% 0.18 c_f_percenth 87 10% 98% 0.04 c_f_percents 87 80% 75% 0.20 c_m_percenta 87 80% 72% 0.19 c_m_percenth 87 30% 97% 0.07 c_m_percents 87 70% 90% 0.13

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of presentation randomized over target, expression and duration. Participants were given a forced choice response of happy, sad, or angry for each of the faces, having to press 1 for happy, 2 for sad, and 3 for angry. These were the only keys for which a response could be recorded in order to avoid an invalid response for any of the items. The numbers corresponding with each emotion’s re- sponse were shown after each photograph to remind the partici- pant of the choices, and the screen with these options was shown until the participant input a response. The total time of the task was between 5 and 7 min depending on how quickly the participant responded to the pictures.

3.3. Trait EI

The Schutte et al. (1998) emotional intelligence scale is a 33-item self report measure of trait emotional intelligence. This scale has been validated in several studies (e.g. Chapman & Hay- slip, 2005; Saklofske, Austin, & Minski, 2003).

3.4. Ability EI

Ability EI was measured using the Test of Emotional Intelligence (TEMINT, Schmidt-Atzert & Bühner, 2002). The TEMINT is an abil- ity EI test originally written in German, and recently translated to English (Amelang & Steinmayr, 2006). It provides scenarios in which participants rate the emotions of a target person in each of 12 situations. It was specifically developed as a measure of abil- ity EI, and research indicates that its relationship to personality and cognitive intelligence are similar to those of the MSCEIT (Knapp-Rudolph, 2003; Schmidt-Atzert, 2002) and it has good con- struct and criterion validity (Blickle et al., 2009). Despite the TEMINT being a fairly new measure of ability EI, it was deemed appropriate for this particular study because of the format, which asks participants to rate the feelings of an individual in a described scenario. Given that this study required participants to identify fa- cial affect in an inspection time task, it seemed that an ability EI measure which did not call for participants to identify emotions in photographs would be an appropriate measure.

4. Results

4.1. Descriptive statistics and gender differences

Internal reliabilities for all of the scales were assessed using Cronbach’s alpha. All of the scales showed acceptable alpha levels

Table 3 Sex and group specific means and t-test results for personality, trait and ability EI.

Female Male t df S

Ability EI 44.23 (10.45)

46.73 (11.97)

�0.97 0.84 0

Trait EI 123.52 (12.34)

122 (13.25)

0.52 0.85 0

of above .70. Descriptive statistics for age, trait and ability EI, and the percentage correct score for each picture category are shown in Table 2. The mean age for the sample (N = 87) was 22.91 (SD = 3.63), with 61 females and 26 males, of whom 41 were Cau- casian and 46 were Far-East Asian. Scores on the inspection time task are given as a percentage correct score for each emotion: an- gry (percenta), happy (percenth), and sad (percents). Both of the races are indicated in combination with both sexes: Asian female (a_f), Asian male (a_m), Caucasian female (c_f) and Caucasian male (c_m). The total score is given in percentage correct due to the dif- ferent number of stimuli in each category and was compiled from all the durations. Interestingly, the range for the total Caucasian fe- male happy correct responses was only .10, with a mean percent correct response 98.3%. In fact, all of the mean responses for the happy expression were over 95% correct, regardless of the race or sex of the stimulus face. In contrast, the mean correct percentage for Asian angry faces of both sexes was quite low: 53.9% for fe- males and 65.8% for males.

An independent sample t-test was carried out in order to deter- mine if gender differences were shown in the sample. Table 3 shows sex- and race-specific means and standard deviations. There were no sex differences in either trait or ability EI. Independent sample t-tests were also carried out in order to determine if there were racial differences in trait and ability EI. The results indicate that Asian participants scored significantly higher on the TEMINT. However, given the reverse method of scoring on the TEMINT, this result indicates that the Caucasian participants had significantly higher ability EI. Trait EI did not show any significant racial differences.

4.2. Regression analysis

Multiple regression analysis was performed in three blocks in order to determine the significant predictors of the total percent- age correct for each of the emotion IT tasks. In the first block, sex and age were entered as the predictor variables. In the second block, race was added as an independent variable. The third block saw the addition of trait and ability EI as independent variables.

ig. Caucas. Asian t df Sig.

.33 40.73 (9.55)

48.87 (10.73)

�3.70 84 0.00

.61 122.37 (12.82)

123.7 (12.5)

�0.49 85 0.63

Table 5 Analysis of covariance.

Source Type III sum of squares

df Mean square

F Sig.

face_exp 0.15 1.39 0.10 4.37 0.03 face_exp � Trait EI 0.03 1.39 0.02 0.83 0.40 face_exp � Ability EI 0.00 1.39 0.00 0.13 0.80 face_exp � race 0.04 1.39 0.03 1.10 0.32 Error(face_exp) 2.74 114.15 0.02 Facerace 0.05 1.00 0.05 7.28 0.01 facerace � Trait EI 0.01 1.00 0.01 1.41 0.24 facerace � Ability EI 0.00 1.00 0.00 0.19 0.66 facerace � race 0.03 1.00 0.03 4.58 0.04 Error(facerace) 0.51 82.00 0.01 face_exp � facerace 0.02 1.69 0.01 1.15 0.31 face_exp � facerace � Trait

EI 0.01 1.69 0.01 0.74 0.46

face_exp � facerace � Ability EI

0.03 1.69 0.02 1.71 0.19

face_exp � facerace � race 0.03 1.69 0.02 2.15 0.13 Error(face_exp � facerace) 1.28 138.57 0.01

K.P.A. DeBusk, E.J. Austin / Personality and Individual Differences 51 (2011) 764–768 767

The only IT tasks to show any of the independent variables as significant predictors were the Caucasian female angry and sad faces. The Caucasian female angry total showed significant results for the first block, sex and age (p = .031), as well as the second block in which race was added as a predictor (p < .001). The Cauca- sian female sad total displayed significant results for the second block (p = .007). The full results can be seen in Table 4.

Overall, the results indicate that sex and race are the strongest predictors of correct responses on the emotional IT task. Further investigation of the standardised betas reveals that for the first block of the Caucasian female angry regression, sex showed a sig- nificant result (b = �.253, p = .019), but age did not. In the second block of the regression, sex maintained its significant beta, and race was a significant predictor as well (b = �.429, p < .001). For the sec- ond model, the Caucasian female sad total, an investigation of the betas showed that race was the only significant predictor (b = �.306, p < .001). However, none of the results demonstrate trait EI or ability EI as significant predictors.

Note: Faceexp = facial expression of the stimulus; face race = race of the stimulus; race = race of the participants.

Table 6 Post hoc independent sample t-test examining differences in race and emotion of the stimuli.

t df Sig.

Asian_female_percent_angry 0.31 85.00 0.76 Asian_female_percent_happy 0.86 85.00 0.39 Asian_female_percent_sad 1.52 85.00 0.13 Asian_male_percent_angry 0.53 85.00 0.60 Asian_male_percent_happy 1.62 80.79 0.11 Asian_male_percent_sad 0.27 85.00 0.79 Caucasian_female_percent_angry 4.51 80.58 0.00 Caucasian_female_percent_happy �1.09 85.00 0.28 Caucasian_female_percent_sad 2.62 85.00 0.01 Caucasian_male_percent_angry 1.08 85.00 0.28 Caucasian_male_percent_happy 1.22 79.59 0.23 caucasian_male_percent_sad 0.81 85.00 0.42

4.3. Analysis of covariance (ANCOVA)

A repeated measures ANCOVA was carried out in order to deter- mine if there was a significant difference between the total correct for each of the emotions and the race of the stimuli face, as well as to determine if there was a significant interaction between the race of the participant and the race of the stimuli. The within-subjects factors of the ANCOVA used were the three emotions (happy, sad, and angry), the race of the face stimulus (Asian or Caucasian), and the sex of the face stimulus. The between subjects factor were race and sex, with trait and ability EI as covariates.

The results of the ANCOVA revealed a significant main effect for the race of the face stimulus (F (1, 82) = 7.28, p < .01), indicating that participants responded differently to the races of the face stimulus. The results also show a significant interaction between the race of face stimulus and the race of the participant (F (1, 82) = 4.58, p < .05), which shows that participants are better able to correctly identify faces of their own race. The main effect displayed for emo- tions indicates that the emotional facial expressions differed signif- icantly from each other (F (1.39, 114.15) = 4.37, p < .05), displaying that some emotions were easier to correctly identify. However, there was not a significant interaction between the facial expression and the race of the participant, or between the facial expression and the race of the face stimuli. Interestingly, there was not a significant interaction between facial expression, race of the participants, and race of the facial stimulus. Neither trait nor ability EI showed signif- icant effects as covariates. The full ANCOVA results can be seen in Table 5.

Post hoc independent samples t-tests were carried out in order to further investigate the significant differences. The results of the t-test indicate that Caucasians had significantly higher mean cor- rect scores in identifying the Caucasian female angry and sad faces.

Table 4 Multiple regression analysis model summary for Caucasian female angry and sad.

R2 R2

Adj. R2

Change F Change

df1 df2 Sig. F change

C_f_angry 1 0.08 0.06 0.08 3.63 2 83 0.03 2 0.25 0.22 0.17 18.05 1 82 0.00 3 0.29 0.24 0.04 2.23 2 80 0.12

C_f_sad 1 0.02 �0.01 0.02 0.69 2 83 0.50 2 0.10 0.07 0.08 7.70 1 82 0.01 3 0.12 0.06 0.02 0.71 2 80 0.50

Step 1: Sex and age; Step 2: sex, age, and race; Step 3: sex, age, race, trait EI, ability EI.

However, as can be seen in Table 6, there were no significant differ- ences between the races for any of the other stimuli.

Further post hoc analysis indicated that females were signifi- cantly better at identifying the Caucasian female angry faces (t(85) = 2.35, p < .05). None of the other stimuli showed significant sex differences. This result is in keeping with what was found in the regression analysis.

Overall, the results reveal race, both of the participant and of the stimulus, to be the biggest factor in correctly identifying the emo- tion of the target face. Surprisingly, neither trait nor ability EI were significant predictors of success on the inspection time task.

5. Discussion

Previous research has indicated that people are better able to recognise facial affect in another person of their own race and more generally in in-group compared to out-group targets (Elfenbein & Ambady, 2002). More complex effects relating to differences in perception of and speed of response to individual emotions in in- and out-group members have also been identified (Beaupré and Hess, 2003; Dunham, 2011; Hugenberg, 2005) Since emotion per- ception is an important component of EI, it was hypothesised that high EI would be connected to better performance on an emotion recognition task for both own-and other-race targets and that high EI would reduce or remove the in-group advantage in facial expres- sion recognition.

768 K.P.A. DeBusk, E.J. Austin / Personality and Individual Differences 51 (2011) 764–768

The results of the present study indicate that there was a signif- icant difference in the number of correct responses for the different races of the face stimuli, as well as an interaction between the race of the participant and target. This result was in accordance with previous cross-race effect research, which shows that people are better able to identify faces of their own race (Elfenbein & Ambady, 2002). The results also indicate that there was a significant differ- ence in the proportion of correct responses for each of the emo- tions, as well as an interaction between the emotion and the race of the face stimulus. Post hoc analysis showed that there were sig- nificant racial differences in the percentage of correct responses for Caucasian female angry and sad faces but percentage correct re- sponses for the happy face stimuli were all above 96%, regardless of race. This is somewhat similar to the results of Hugenberg (2005) which showed that participants recognised happy faces fas- ter than angry or sad faces, and is likely to be due to happiness being readily distinguished from sadness and anger.

Surprisingly, neither trait nor ability EI were significant predic- tors of the percentage of correct responses for any stimulus type, contradicting previous findings of an EI effect on emotional face inspection time performance (Austin, 2005), and there was no indi- cation of a reduction of in-group bias (where found) related to EI. The results suggest that sex and ethnicity are the factors which determine how well an individual is able to identify facial affect in the inspection time task. Further examination of the effects of EI in cross-group emotion perception is indicated, given the gen- eral expectation that EI should be related to more effective pro- cessing of emotional information. This could involve examining different kinds of emotion perception task, including the use of vo- cal as well as picture stimuli. The use of stimuli with a greater vari- ety and/or subtlety of emotions than in the present study, i.e. changes which would make the emotion identification task more challenging, could also be examined. Speed as well as accuracy in emotion identification could also be investigated by using a reac- tion time paradigm. This would be of interest since it is possible that the use of an inspection time paradigm with a small number of fixed durations of exposure to the stimuli may have obscured more complex effects in the time taken to process different stimuli.

Another area where the putative EI effects on cross-group emo- tion perception could be investigated would be using more ecolog- ically valid tasks, for example emotional/social perception tasks employing video scenes involving in-group or out-group members. The use of such tasks would allow the examination of any effects of EI in situations similar to those encountered in real life, where information on emotional states from multiple channels (face, voice, gesture, etc.) has to be integrated rapidly.

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