Edited by: Hasan Ayaz, Drexel University, United States
Reviewed by: Meltem Izzetoglu, Villanova University, United States; Murat Ozgoren, Dokuz Eylül University, Turkey
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Emotional labor is characterized by two main regulation strategies: surface acting and deep acting. However, which strategy consumes more energy? To explore this, we used functional near-infrared spectroscopy (fNIRS) to measure changes in hemoglobin density while participants performed a task requiring them to make the opposite emotional facial expression of that presented in a picture. We found that (1) neither surface nor deep acting led to a significant change in hemoglobin concentration in the prefrontal cortex; (2) making negative and positive facial expressions activated the same left front and middle areas of the prefrontal cortex; and (3) making positive facial expressions activated the rear portion of the prefrontal cortex, but making negative facial expressions did not. Based on these findings and past work, we can infer that deep and surface acting may not significantly differ in terms of the activity in the prefrontal cortex energy consumed. Furthermore, engaging in positive and negative emotional labor appear to utilize some of the same neurological mechanisms, although they differ in others.
Emotional labor is the act of regulating one’s emotion to conform to organizational standards. Presently, it is central to numerous service occupations that employees are the first point of contact that customers have with the organization. Scholars have been continually seeking to understand the emotional labor process. Some have proposed that emotional labor consists of three components (
Since
For this reason, some researchers have tried experimental methods to explore differences between surface acting and deep acting. For example,
According to the definition of emotional labor, which refers to one kind of emotional regulation, so for surface acting and deep acting, they can be seen as the two emotional regulation strategies (
In this study, the purpose was to verify whether people would need more psychological resources when engaging in deep acting than when engaging in surface acting. To induce use of the different strategies, we used emotional facial expressions. Because emotional labor involves the display of positive and negative emotions, we presented participants with positive or negative facial expressions and asked them to make the opposite facial expression. This also enabled us to explore whether there was a difference in the effort for these strategies between positive and negative emotional expressions.
All research procedures were approved by the Ethical Committee of Guizhou Normal University, Guiyang, Guizhou province, China. All participants provided written informed consent after fully understanding the study.
Twenty male and 20 female senior undergraduates were recruited from a university in Hunan province, China. All participants came from a teacher training specialty (e.g., education or psychology) and were healthy and right-handed. None of them had a history of psychiatric or neurological disorders. Participants were randomized to the surface acting or deep acting condition. In the deep acting condition, one male and one female participant’s fNIRS data were not correctly obtained, so the data were excluded. The average age of all participants was 21.5 ± 1.4 years.
We used a 24-fiber (38-channel) fNIRS system (SHIMADZU LABNIRS System, Kyoto, Japan) in this study. Semiconductor lasers with wavelengths of 780 nm, 805 nm, and 830 nm are employed as light sources; every emitter in the LABNIRS system emits the three wavelength light, and as well as every detector measures the absorbance for each of these wavelengths. fNIRS measures the amount of relative change from the initial value of oxygenated hemoglobin (OxyHb), deoxygenated hemoglobin (deOxyHb), and total hemoglobin (totalHb), using the near-infrared rays. The increase in OxyHb and the concomitant decrease in deOxyHb reflects an increase in local arteriolar vasodilatation, which increases local cerebral blood flow and cerebral blood volume, a mechanism known as neurovascular coupling. This produces a change in the amount of light absorbed by this tissue, which can be measured by near infrared spectroscopy systems (
Numerous recent studies have identified the prefrontal cortex (PFC) as a key region for the induction and regulation of emotional responses (
Functional near-infrared spectroscopy (fNIRS) settings. Ten emitters and 10 detectors were deployed with reference to the International 10–20 system, with a total of 31 channels. For channel 10 and channel 13, the distance of emitter and receptor was over 3 cm, so the measurement results were canceled. The “cross” mark was used to represent the canceled results.
We determined the anatomical locations of the optodes in relation to the standard head landmarks, including the nasion, Cz, left tragus (T3), and right tragus (T4), using a FASTTRAK 3D tracking system (Polhemus). The Montreal Neurological Institute (MNI) coordinates (
MNI locations for the channels.
Registered positions of fNIRS measurement channels on the standard brain MRI atlas.
Brodmann area | Channel |
---|---|
BA 6 | 29, 30, 31 |
BA 8 | 24, 25, 26, 28 |
BA 9 | 15, 16, 17, 19, 20, 21, 22, 23, 27 |
BA 10 | 2, 3, 5, 6, 7, 8, 9, 11, 12 |
BA 11 | 1, 4 |
BA 45 | 18 |
BA 46 | 10, 13, 14 |
We selected 20 positive, 20 neutral, and 20 negative emotional face pictures from the Chinese Facial Affective Picture System (CFAPS;
To identify whether participants had engaged with the tasks, we asked participants to complete a 4-item questionnaire after the experiment, as follows: (1) What did you think about the difficulty of the experiment? (2) How would rate the degree of effort put into the experiment? (3) How would you rate your degree of concentration on the experiment? (4) How would you rate your performance in executing the experimental instructions? Each item was rated on a six-point scale.
Participants were instructed to sit on a chair in front of a 17-in (32 × 24 cm) monitor. The distance between each participant and the monitor was set to around 70 cm. The fNIRS equipment was attached to participants’ heads. The experiment was divided into the exercise and formal experimental phases. In the exercise phase, participants practiced how to respond according to the instructions until they completely understood it. There were 24 facial expression pictures (eight positive, eight neutral, and eight negative), so in this phase, the participant would experience 24 trials under the set condition. Thereafter, the formal experiment began. Before the formal experiment, participants were instructed to avoid any head and body movements as much as possible while the fNIRS was operating. Subsequently, emotional face pictures were presented on the screen (315 × 356 pixels), and participants were asked to make the opposite facial expression of the pictures they viewed (see
One trial experimental procedure (written informed consent was obtained for the publication of this image).
In this condition, we used the followed instructions to ensure that participants performed surface acting:
As with the surface acting condition, we used instructions to induce participants to perform deep acting:
As the distance between the emitting and detecting optodes was over 3 cm, channel 10 was interpolated by channels 1, 5, 6, 14, 15, and 19; and channel 13 was interpolated by channels 4, 8, 9, 17, 18, and 22. For this analysis, we chose to focus on changes in the concentration of OxyHb, as it is regarded as the most sensitive measure of changes in regional cerebral blood flow (
We compared the beta value of each channel with a 0 value. If the
To determine which channels were activated while participants engaged in surface or deep acting, we subjected the beta values estimated from the NIRS-SPM tool to a one sample
Comparison of activated channels among different conditions.
Surface acting ( |
Neutral ( |
Deep acting ( |
||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Positive–negative | Negative–positive | Positive–negative | Negative–positive | |||||||||||
Channel | ||||||||||||||
1 | 1.676 | 0.110 | 0.341 | -0.398 | 0.695 | 0.798 | -1.313 | 0.197 | 2.626 | 0.018* | 0.110 | 1.521 | 0.147 | 0.301 |
2 | -0.325 | 0.749 | 0.987 | 0.090 | 0.930 | 0.935 | -0.719 | 0.477 | 3.348 | 0.004** | 0.041* | -0.398 | 0.696 | 0.768 |
5 | 1.431 | 0.169 | 0.475 | 0.786 | 0.441 | 0.624 | -0.841 | 0.406 | 1.639 | 0.120 | 0.265 | 2.336 | 0.032* | 0.138 |
9 | -0.22 | 0.982 | 0.987 | 1.275 | 0.218 | 0.562 | -0.225 | 0.823 | 3.330 | 0.004** | 0.041* | 0.951 | 0.355 | 0.440 |
10 | 1.910 | 0.071 | 0.276 | 0.882 | 0.389 | 0.624 | -0.578 | 0.567 | 3.139 | 0.006** | 0.046* | 2.416 | 0.027* | 0.138 |
14 | 2.327 | 0.031 | 0.265 | 3.245 | 0.004** | 0.044* | 1.048 | 0.301 | 4.440 | 0.000*** | 0.011* | 3.571 | 0.002** | 0.073 |
15 | 1.083 | 0.292 | 0.620 | 0.992 | 0.334 | 0.624 | 0.897 | 0.376 | 2.255 | 0.038* | 0.167 | 2.916 | 0.010* | 0.133 |
18 | 1.766 | 0.093 | 0.322 | -0.427 | 0.674 | 0.798 | 1.043 | 0.304 | 2.436 | 0.026* | 0.135 | 0.367 | 0.718 | 0.768 |
23 | -0.856 | 0.403 | 0.734 | 2.218 | 0.047* | 0.272 | 1.158 | 0.254 | 1.386 | 0.184 | 0.380 | 2.777 | 0.013* | 0.133 |
24 | -2.283 | 0.034* | 0.265 | 1.334 | 0.198 | 0.562 | -0.181 | 0.857 | -1.769 | 0.095 | 0.263 | 1.237 | 0.233 | 0.344 |
25 | -2.011 | 0.059 | 0.266 | 2.247 | 0.037* | 0.272 | -0.571 | 0.571 | -0.319 | 0.754 | 0.899 | 1.355 | 0.193 | 0.340 |
26 | -2.615 | 0.017* | 0.264 | 0.973 | 0.343 | 0.624 | -0.162 | 0.872 | 0.060 | 0.953 | 0.992 | 0.977 | 0.342 | 0.440 |
28 | -2.811 | 0.011* | 0.264 | 1.918 | 0.070 | 0.272 | 0.951 | 0.348 | -1.318 | 0.205 | 0.397 | 2.282 | 0.036* | 0.138 |
29 | -1.999 | 0.060 | 0.266 | 3.519 | 0.002** | 0.035* | -0.659 | 0.514 | -0.627 | 0.539 | 0.795 | 2.509 | 0.023* | 0.138 |
30 | -2.135 | 0.046* | 0.266 | 4.306 | 0.000*** | 0.012* | -0.683 | 0.499 | 0.010 | 0.992 | 0.992 | 2.061 | 0.055 | 0.155 |
31 | -1.370 | 0.187 | 0.482 | 1.963 | 0.064 | 0.272 | 0.579 | 0.566 | -0.541 | 0.596 | 0.839 | 2.626 | 0.018* | 0.137 |
We then subjected the OxyHb data to a 2 × 3 repeated measures analysis of variance (rmANOVA). The independent variables were group and facial expressions. Group was a between-subject variable, and included two levels: surface acting and deep acting. Facial expression was a within-subject variable, and had three levels: positive, negative, and neutral valence pictures. The results indicated a non-significant interaction of the group and facial expressions for all channels (all
Main effects of facial expression (
Channel | η |
PHMC | Channel | η |
PHMC | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 6.473 | 0.004** | 0.018* | 0.27 | a > c | 17 | 0.071 | 0.931 | 0.931 | 0.004 | None |
2 | 0.861 | 0.431 | 0.534 | 0.047 | None | 18 | 2.693 | 0.082 | 0.146 | 0.133 | None |
3 | 1.087 | 0.348 | 0.490 | 0.058 | None | 19 | 3.020 | 0.062 | 0.124 | 0.147 | None |
4 | 3.314 | 0.048* | 0.106 | 0.159 | None | 20 | 3.420 | 0.044* | 0.105 | 0.163 | None |
5 | 7.621 | 0.002** | 0.012* | 0.303 | a > c, b > c | 21 | 2.115 | 0.136 | 0.211 | 0.108 | None |
6 | 2.976 | 0.064 | 0.124 | 0.145 | None | 22 | 0.244 | 0.785 | 0.811 | 0.014 | None |
7 | 0.997 | 0.379 | 0.490 | 0.054 | None | 23 | 2.649 | 0.085 | 0.146 | 0.131 | None |
8 | 1.023 | 0.370 | 0.490 | 0.055 | None | 24 | 5.908 | 0.006** | 0.021* | 0.252 | a < b, a < c |
9 | 0.747 | 0.481 | 0.552 | 0.041 | None | 25 | 6.172 | 0.005** | 0.019* | 0.261 | a < b, a < c |
10 | 7.822 | 0.002** | 0.012* | 0.309 | a > c, b > c | 26 | 4.433 | 0.019* | 0.054 | 0.202 | None |
11 | 3.922 | 0.029* | 0.075 | 0.183 | None | 27 | 2.514 | 0.095 | 0.156 | 0.126 | None |
12 | 0.686 | 0.510 | 0.565 | 0.038 | None | 28 | 10.775 | 0.000*** | 0.002** | 0.381 | a < b, a < c, b > c |
13 | 0.357 | 0.702 | 0.750 | 0.020 | None | 29 | 13.710 | 0.000*** | 0.000*** | 0.439 | a < b, a < c, b > c |
14 | 6.903 | 0.003** | 0.015* | 0.283 | a > c, b > c | 30 | 14.414 | 0.000*** | 0.000*** | 0.452 | a < b, b > c |
15 | 1.712 | 0.195 | 0.288 | 0.089 | None | 31 | 5.599 | 0.008** | 0.025* | 0.242 | a < b, b > c |
16 | 0.775 | 0.468 | 0.552 | 0.042 | None | ||||||
At channels 5, 10, and 14, the
To further visualize the OxyHb concentration change differences between positive, negative, and neutral expressions, we used the results of the main effects analysis to calculate
Topographical map of significant
As shown in the topographical maps, compared to presenting a neutral face picture (thereby requiring participants to make no emotional expression), presenting positive and negative face pictures significantly increased OxyHb concentration in the left front and left middle areas of the PFC. Additionally, when presenting a positive face picture (thereby requiring participants to make a negative expression), the OxyHb concentration significantly decreased in the left pre-motor and supplementary motor cortex near the rear area of the PFC. Finally, when presenting a negative face picture (thereby requiring participants to make a positive expression), the OxyHb density significantly increased in the pre-motor and supplementary motor cortex near the rear area of PFC.
Using the Student’s
Status of participants after completing the experiment.
Comparison between groups |
|||
---|---|---|---|
Q1 | 1.750 ± 0.910 | 1.944 ± 0.938 | -0.648 |
Q2 | 1.800 ± 1.005 | 1.889 ± 1.023 | -0.270 |
Q3 | 4.100 ± 0.788 | 4.167 ± 1.043 | -0.224 |
Q4 | 5.150 ± 0.745 | 5.056 ± 0.725 | 0.395 |
For the means of all four questionnaire items, we found no significant difference between the surface acting and deep acting groups.
The main hypothesis of our study, which was based on action theory, is that surface acting and deep acting consume different amounts of psychological resources—particularly, deep acting leads to greater energy consumption. However, the study results appear somewhat contradictory. When we consider only the number of activated areas in the brain, deep acting activated more channels than surface acting did, thus supporting our study hypothesis. However, when looking at the ANOVA results, we observed neither significant differences in activation between surface and deep acting nor a significant interaction of the group and facial expression. In other words, these results do not support our hypothesis. Thus, when using a more strict criterion (the ANOVA results), it is possible that there is no difference in energy consumption between surface and deep acting in the PFC based on this study. However, this does not mean that we can absolutely make a conclusion: there is no difference of energy consumption between surface and deep acting. This is because in addition to the PFC, other brain regions may also be associated with emotional labor processes, such as subcortical tissue amygdala, hippocampus, and so on. In addition, in this study, we only used the instruction to ask participants to perform surface acting and deep acting, and used a video monitor to record their facial expressions. However, for the deep acting, we did not monitor whether the participants were experiencing the emotion corresponding to their facial expression (in fact it is very difficult to do). This made it difficult to confirm whether the participants really performed deep acting.
In addition to action theory, we might consider the results in light of the conservation of resources theory. According to this theory, surface acting, because it involves the suppression of emotions, consumes more resources than deep acting does (
We found that facial expression had a significant main effect for some channels (see
Additionally, compared to making no facial expressions, when participants engaged in a positive facial expression, the posterior frontal lobe (mainly the frontal eye fields and supplementary motor cortex) showed activity; however, this was not found when they made a negative facial expression. This finding might indicate that positive facial expressions are related to stronger motion control.
First, in this study, we only presented participants with emotional face pictures, and asked them to display the opposite facial expression to the pictures they saw, using instructions to manipulate whether they would engage in surface or deep acting. Accordingly, our findings may not reflect real-life emotional labor. Further studies should consider motivational factors in order to improve the external validity of the study. Then, in this study, we primarily focused on PFC activity. Although the PFC is the most important zone for emotion regulation (
Based on the results of this study, we infer that deep acting and surface acting may not show a significant difference in energy consumption. Furthermore, engaging in positive and negative emotional labor may rely on some of the same psychological mechanisms, though there could be differences as well.
All research procedures were approved by the Research Ethical Committee of Guizhou Normal University Educational School according to the Declaration of Helsinki. All participants were given written informed consent after they fully understand the study.
WW designed the experiments. YL, HZ, DL, and DP collected the data. YL and GM analyzed the data. YL, WW, and SZ wrote the main manuscript. YL and WW prepared the figures. All authors reviewed the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.