The aim of the study was to describe the spontaneous facial expressions elicited by viewers of a compassionate video in terms of the respondents’ muscular activity of single facial action units (AUs). We recruited a convenience sample of 111 undergraduate psychology students, aged 18-25 years (M = 20.53; SD = 1.62) to watch (at home alone) a short video stimulus eliciting compassion, and we recorded the respondents’ faces using webcams. We used both a manual analysis, based on the Facial Action Coding System, and an automatic analysis of the holistic recognition of facial expressions as obtained through EmotionID software. Manual facial analysis revealed that, during the compassionate moment of the video stimulus, AUs 1 = inner-brow raiser, 4 = brow lowerer, 7 = lids tight, 17 = chin raiser, 24 = lip presser, and 55 = head tilt left occurred more often than other AUs. These same AUs also occurred more often during the compassionate moment than during the baseline recording. Consistent with these findings, automatic facial analysis during the compassionate moment showed that anger occurred more often than other emotions; during the baseline moment, contempt occurred less often than other emotions. Further research is necessary to fully describe the facial expression of compassion.
BaránkováM.HalamováJ. (2018). Validating video stimulus for eliciting compassion: A pilot study. In G. Đigić (Ed.), Psychology in and around us. Proceedings of the 13th Days of Applied Psychology 2017 (pp. 283–292). Niš, Serbia: University of Nis.
2.
BaránkováM.HalamováJ.GablíkováM.KoróniováJ.StrnádelováB. (2019).
Analysis of spontaneous facial expression of compassion elicited by the video stimulus. Československá psychologie,
63(1), 75–89.
3.
BaránkováM.HalamováJ.KoróniováJ. (2019).
Non-expert views of compassion: Consensual qualitative research using focus groups.Human Affairs,
29(1), 58–69.
4.
BaveyeY.DellandreaE.ChamaretC.ChenL. (2015).
Liris-accede: A video database for affective content analysis. IEEE Transactions on Affective Computing,
6(1), 43–55.
5.
BeaumontE.Hollins MartinC. J. (2015).
A narrative review exploring the effectiveness of compassion-focused therapy. Counselling Psychology Review,
30(1), 21–32.
6.
BloomP. (2016). Against empathy: The case for rational compassion.
New York, NY:
Harper Collins.
7.
BürknerP. C. (2017).
brms: An R package for Bayesian multilevel models using Stan.Journal of Statistical Software,
80(1), 1–28. doi:10.18637/jss.v080.i01.
8.
BürknerP. C. (2018).
Advanced Bayesian multilevel modeling with the R Package brms.The R Journal,
10(1), 395–411.
9.
CartwrightD. E. (1988).
Schopenhauer’s compassion and Nietzsche’s pity.Schopenhauer Jahrbuch,
69, 557–567.
10.
CatarinoF.GilbertP.McEwanK.BaiãoR. (2014).
Compassion motivations: Distinguishing submissive compassion from genuine compassion and its association with shame, submissive behavior, depression, anxiety and stress.Journal of Social and Clinical Psychology,
33(5), 399–412.
11.
CohnJ. F.De la TorreF. (2015). Automated face analysis for affective computing. In CalvoR.D´MelloS.GratchJ.KappasA.(Eds.), The Oxford handbook of affective computing (pp. 131–150).
New York, NY:
Oxford University Press.
12.
CohnJ. F.ReedL. I.AmbadarZ.XiaoJ.MoriyamaT. (2004, October). Automatic analysis and recognition of brow actions and head motion in spontaneous facial behavior. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583) (Vol. 1, pp. 610–616). Piscataway, NJ: IEEE.
13.
DesbordesG.NegiL. T.PaceT. W. W.WallaceB. A.RaisonC. R.SchwartzE. L. (2012).
Effects of mindful-attention and compassion meditation training on amygdala response to emotional stimuli in an ordinary, non meditative state.Frontiers in Human Neuroscience,
6, 292. doi:10.3389/fnhum.2012.00292
14.
DosseyL. (2007).
Compassion.Explore: The Journal of Science & Healing,
3(1), 1–5.
15.
EisenbergN.FabesR. A.MillerP. A.FultzJ.ShellR.MathyR. M.RenoR. R. (1989).
Relation of sympathy and personal distress to prosocial behavior: A multimethod study.Journal of Personality and Social Psychology,
57(1), 55–66.
16.
Ekman, P., Friesen, W. V., & Hager, J. C. (2002). Facial Action Coding System. Manual and Investigator’s Guide. Salt Lake City, UT Research Nexus.
17.
EkmanP. (1989). The argument and evidence about universals in facial expressions of emotion. In WagnerH.MansteadA.(Eds.), Handbook of social psychophysiology (pp. 144–164).
New York, NY:
John Wiley & Sons.
18.
EkmanP. (1994). All emotions are basic. In EkmanP.DavidsonR. (Eds.), The nature of emotion (pp. 15–19).
New York, NY:
Oxford University Press.
19.
EkmanP. (1998). Universality of emotional expression? A personal history of the dispute. In C.Darwin (Ed.), The expression of the emotions in man and animals(3rd ed., pp. 363–393).
New York, NY:
Oxford University Press.
20.
EkmanP.CordaroD. (2011).
What is meant by calling emotions basic.Emotion Review,
3(4), 364–370.
21.
EkmanP.FriesenW. V. (2003). Unmasking the face: A guide to recognizing emotions from facial clues.
San Jose, CA:
ISHK.
22.
EkmanP.FriesenV. W.HagerJ. C. (2002). Facial Action Coding System: The manual on CD-ROM (Investigator´s Guide).
Salt Lake City, UT:
Research Nexus.
23.
EkmanP.KeltnerD. (1997). Universal facial expressions of emotion: An old controversy and new findings. In SegerstraleU.MolnárP.(Eds.), Nonverbal communication: Where nature meets culture (pp. 27–45).
Hillsdale, NJ:
Lawrence Erlbaum Associates.
24.
FeinbergF. M.GonzalezR. (2012). Bayesian modeling for psychologists: An applied approach. In CooperH.CamicP. M.LongD. L.PanterA. T.RindskopfD.SherK. J. (Eds.), APA handbook of research methods in psychology, vol. 2. Research designs: Quantitative, qualitative, neuropsychological, and biological (pp. 445–464).
Washington, DC:
American Psychological Association. doi:10.1037/13620-024
25.
GablíkováM.HalamováJ. (2016).
Tváre mužov a žien keď sa nikto nepozerá – analýza rozdielov v prejave hnevu. Psychologica,
44, 75–84.
26.
GelmanA.GoodrichB.GabryJ.VehtariA. (2018, December). R-squared for Bayesian regression models, The American Statistician. doi:10.1080/00031305.2018.1549100
27.
GelmanA.RubinD. B. (1992).
Inference from iterative simulation using multiple sequences,Statistical Science,
7, 457–511.
28.
GewekeJ. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In BernadoJ. M.BergerJ. O.DawidA. P.SmithA. F. M. (Eds.), Bayesian statistics 4. (pp. 169–193)
Oxford, England:
Clarendon Press.
29.
GilbertP. (2017). Compassion: Definitions and controversies. In GilbertP. (Ed.), Compassion: Concepts, research and applications (pp. 3–15).
New York, NY:
Routledge.
30.
GilbertP.McEwanK.GibbonsLChotaiS.DuarteJ.MatosM. (2012).
Fears of compassion and happiness in relation to alexithymia, mindfulness, and self-criticism.Psychology and Psychotherapy: Theory, Research and Practice,
85, 374–390. doi:10.1111/j.2044-8341.2011.02046.x
31.
GilbertP.ProcterS. (2006).
Compassionate mind training for people with high shame and self-criticism: Overview and pilot study of a group therapy approach. Clinical Psychology & Psychotherapy: An International Journal of Theory & Practice,
13(6), 353–379.
32.
Haidt, J. (2003). “The Moral Emotions” (PDF). In Davidson, Richard; Scherer, Klaus; Goldsmith, H. (eds.). Handbook of Affective Sciences (pp. 852–870). Oxford University Press.
33.
HalamováJ.BaránkováM.StrnádelováB.KoróniováJ. (2018).
Consensual qualitative research on free associations for compassion and self-compassion.Human Affairs,
28(3), 253–270.
34.
HeidelbergerP.WelchP. D. (1983).
Simulation run length control in the presence of an initial transient.Operations Research,
31(6), 1109–1144. doi:10.1287/opre.31.6.1109
35.
HessU.BanseR.KappasA. (1995).
The intensity of facial expression is determined by underlying affective state and social situation.Journal of Personality and Social Psychology,
69(2), 280.
36.
JazaieriH.JinpaG. T.McGonigalK.RosenbergE. L.FinkelsteinJ.Simon-ThomasE.…GoldinP. R. (2013).
Enhancing compassion: A randomized controlled trial of a compassion cultivation training program.Journal of Happiness Studies,
14, 1113–1126. doi:10.1007/s10902-012-9373-z
37.
JazaieriH.McGonigalK.JinpaT.DotyJ. R.GrossJ. J.GoldinP. R. (2014).
A randomized controlled trial of compassion cultivation training: Effects of mindfulness, affect, and emotion regulation.Motivation and Emotion,
38, 23–35. doi:10.1007/s11031-013-9368-z
38.
KanadeT.CohnJ. F.TianY. (2009). Comprehensive database for facial expression analysis. In Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG′00), Grenoble, France (pp. 484–490). Piscataway, NJ: IEEE.
39.
KeltnerD. (2005a). Signs of appeasement: Evidence for the distinct displays of embarrassment, amusement, and shame. In RosenbergE. L.EkmanP. (Eds.), What the face reveals (2nd ed., pp. 133–157).
New York, NY:
Oxford University Press.
40.
KeltnerD. (2005b). The forms and functions of embarrassment. In RosenbergE. L.EkmanP. (Eds.), What the face reveals (2nd ed., pp. 158–160).
New York, NY:
Oxford University Press.
KeltnerD.ShiotaM. N. (2003).
New displays and new emotions: A commentary on Rozin and Cohen (2003). Emotion,
3(1), 86–91. doi:10.1037/1528-3542.3.1.86
43.
LewinskiP.den UtylT. M.ButlerC. (2014).
Automated facial coding: Validation of basic emotion and FACS AUs in FaseReader.Journal of Neuroscience, Psychology, and Economics,
7(4), 227–236. doi:10.1037/npe0000028
44.
LiS.DengW.DuJ. (2017). Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2852–2861). Piscataway, NJ: IEEE.
45.
LienJ. J. J.KanadeT.CohnJ. F.LiC. C. (2000).
Detection, tracking, and classification of action units in facial expression.Robotics and Autonomous Systems,
31(3), 131–146.
46.
McEwanK.GilbertP.DandeneauS.LipkaS.MaratosF.PatersonK. B.BaldwinM. (2014).
Facial expressions depicting compassionate and critical emotions: The development and validation of a new emotional face stimulus set.PLoS One,
9(2), 1–8. doi:10.3171/journal.pone.0088783
47.
MongrainM.ChinJ. M.ShapiraL. B. (2011).
Practicing compassion increases happiness and self-esteem.Journal of Happiness Studies,
12, 963–981. doi:10.1007/s10902-010-9239-1
48.
PiironenJ.VehtariA. (2017).
Comparison of Bayesian predictive methods for model selection.Statistics and Computing,
27(3), 711–735. doi:10.1007/s11222-016-9649-y
49.
RafteryA. E.LewisS. M. (1995). The number of iterations, convergence diagnostics and generic Metropolis algorithms. In GilksW. R.SpiegelhalterD. J.RichardsonS. (Eds.), Practical Markov Chain Monte Carlo. (pp. 115–130)
London, England:
Chapman and Hall.
50.
R Core Team (2018). R: A language and environment for statistical computing.
Vienna, Austria:
R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
51.
RosenbergE. L. (2005). The study of spontaneous facial expressions in psychology. In RosenbergE. L.EkmanP. (Eds.), What the face reveals (2nd ed., pp. 3–18).
New York, NY:
Oxford University Press.
52.
RozinP.CohenA. B. (2003).
High frequency of facial expressions corresponding to confusion, concentration, and worry in an analysis of naturally occurring facial expressions of Americans.Emotion,
3(1), 68.
53.
RussellJ. A.Feldman BarrettL. (1999).
Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant.Journal of Personality and Social Psychology,
76(5), 805–819.
54.
SamsonA. C.KreibigS. D.SoderstromB.WadeA. A.GrossJ. J. (2016).
Eliciting positive, negative and mixed emotions states: A film library for affective scientists.Cognition and Emotion,
30, 827–856. doi:10.1080/02699931
55.
SingerT.KlimeckiO. (2014).
Empathy and compassion.Current Biology,
24(8), R875–R878. doi:10.1016/j.cub.2014.06.054
56.
SpiegelhalterD. J.BestN. G.CarlinB. P.van der LindeA. (2002).
Bayesian measures of model complexity and fit.Journal of the Royal Statistical Society B,
64, 583–639. doi:10.1111/1467-9868.00353
57.
StellarJ. E.GordonA. M.PiffP. K.CordaroD.AndersonC. L.BaiY.…KeltnerD. (2017).
Self-transcendent emotions and their social functions: Compassion, gratitude, and awe bind us to others through prosociality.Emotion Review,
9(3), 200–207.
58.
StraussC.TalyorB. L.GuJ.KuykenW.BaerR.JonesF.CavanaghK. (2016).
What is compassion and how can we measure it? A review of definitions and measures.Clinical Psychology Review,
47, 15–27. doi:10.1016/j.cpr.2016.05.004
59.
TianY. L.KanadeT.CohnJ. F. (2002, May). Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition (pp. 229–234). Piscataway, NJ: IEEE.
60.
ValdesoloP.DeStenoD. (2011).
Synchrony and the social tuning of compassion.Emotion,
1(2), 262–266.
61.
Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation usingleave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432.
62.
VehtariA.MononenT.TolvanenV.SivulaT.WintherO. (2016).
Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. Journal of Machine Learning Research,
17(103), 1–38.
63.
WagenmakersE-J.MarsmanM.JamilT.LyA.VerhagenJ.LoveJ.…MoreyR. D. (2018).
Bayesian inference for psychology.Psychonomic Bulletin & Review,
25(1), 35–57. doi:10.3758/s13423-017-1343-3
64.
WatanabeS. (2010).
Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory.Journal of Machine Learning Research,
11, 3571–3594.
65.
WidenS. C.ChristyA. M.HewettK.RussellJ. A. (2011).
Do proposed facial expressions of contempt, shame, embarrassment, and compassion communicate the predicted emotion?Cognition and Emotion,
25(5), 898–906.
66.
YaoY.VehtariA.SimpsonD.GelmanA. (2018).
Using stacking to average Bayesian predictive distributions.Bayesian Analysis,
13(3), 917–1007. doi:10.1214/17-BA1091
