Advertisement

Emotion Elicitation Using Film Clips: Effect of Age Groups on Movie Choice and Emotion Rating

  • Dilana Hazer
  • Xueyao Ma
  • Stefanie Rukavina
  • Sascha Gruss
  • Steffen Walter
  • Harald C. Traue
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 528)

Abstract

In affective computing an accurate emotion recognition process requires a reliable emotion elicitation method. One of the arising questions while inducing emotions for computer-based emotional applications is age group differences. In the present study, we investigate the effect of emotion elicitation on various age groups. Emotion elicitation was conducted using standardized movie clips representing five basic emotions: amusement, sadness, anger, disgust and fear. Each emotion was elicited by three different clips. The different clips are individually rated and the subjective choice of the most relevant clip is analyzed. The results show the influence of age on film-clip choice, the correlation between age and valence/arousal rating for the chosen clips and the differences in valence and arousal ratings in the different age groups.

Keywords

Emotion elicitation Affective computing Emotion recognition Human-computer interaction Film clips Age difference 

Notes

Acknowledgements

This research was supported by grants from the Transregional Collaborative Research Center SFB/TRR 62 Companion Technology for Cognitive Technical Systems funded by the German Research Foundation (DFG) and a doctoral scholarship funded by the China Scholarship Council (CSC) for Xueyao Ma.

References

  1. 1.
    Wendemuth, A., Biundo, S.: A companion technology for cognitive technical systems. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds.) COST 2102. LNCS, vol. 7403, pp. 89–103. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Lang, P., Greenwald, M., Bradley, M., Hamm, A.: Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30(3), 261–273 (1993)CrossRefGoogle Scholar
  3. 3.
    Bradley, M., Lang, P.: The international affective picture system (IAPS) in the study of emotion and attention. In: Coan, J.A., Allen, J.J.B. (eds.) Handbook of Emotion Elicitation and Assessment, vol. 29. Oxford University Press, Oxford (2007)Google Scholar
  4. 4.
    Frantzidis, C., Bratsas, C., Klados, M., Konstantinidis, E., Lithari, C., Vivas, A., Papadelis, C., Kaldoudi, E., Pappas, C., Bamidis, P.: On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications. IEEE Trans. Inf. Technol. Biomed. 14(2), 309–318 (2010)CrossRefGoogle Scholar
  5. 5.
    Daly, I., Malik, A., Hwang, F., Roesch, E., Weaver, J., Kirke, A., Williams, D., Miranda, E., Nasuto, S.J.: Neural correlates of emotional responses to music: an EEG study. Neurosci. Lett. 573, 52–57 (2014)CrossRefGoogle Scholar
  6. 6.
    Kim, J., André, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067–2083 (2008)CrossRefGoogle Scholar
  7. 7.
    Lundqvist, L.O., Carlsson, F., Hilmersson, P., Juslin, P.: Emotional responses to music: experience, expression, and physiology. Psychol. Music (2008)Google Scholar
  8. 8.
    Hewig, J., Hagemann, D., Seifert, J., Gollwitzer, M., Naumann, E., Bartussek, D.: A revised film set for the induction of basic emotions. Cogn. Emot. 19(7), 1095–1109 (2005)CrossRefGoogle Scholar
  9. 9.
    Gross, J., Levenson, R.: Emotion elicitation using films. Cogn. Emot. 9(1), 87–108 (1995)CrossRefGoogle Scholar
  10. 10.
    Kreibig, S., Wilhelm, F., Roth, W., Gross, J.: Cardiovascular, electrodermal, and respiratory response patterns to fear and sadness inducing films. Psychophysiology 44(5), 787–806 (2007)CrossRefGoogle Scholar
  11. 11.
    Mills, C., D’Mello, S.: On the validity of the autobiographical emotional memory task for emotion induction. PLoS ONE 9(4), e95837 (2014)CrossRefGoogle Scholar
  12. 12.
    Kothe, C., Makeig, S., Onton, J.: Emotion recognition from EEG during self-paced emotional imagery. In: Humaine Association Conference IEEE Affective Computing and Intelligent Interaction (ACII), pp. 855–858 (2013)Google Scholar
  13. 13.
    Kolodyazhniy, V., Kreibig, S., Gross, J., Roth, W., Wilhelm, F.: An affective computing approach to physiological emotion specificity: toward subject-independent and stimulus-independent classification of film-induced emotions. Psychophysiology 48, 908–922 (2011)CrossRefGoogle Scholar
  14. 14.
    Rukavina, S., Gruss, S., Tan, J.-W., Hrabal, D., Walter, S., Traue, H.C., Jerg-Bretzke, L.: The impact of gender and sexual hormones on automated psychobiological emotion classification. In: Kurosu, M. (ed.) HCII/HCI 2013, Part V. LNCS, vol. 8008, pp. 474–482. Springer, Heidelberg (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dilana Hazer
    • 1
  • Xueyao Ma
    • 1
  • Stefanie Rukavina
    • 1
  • Sascha Gruss
    • 1
  • Steffen Walter
    • 1
  • Harald C. Traue
    • 1
  1. 1.Medical PsychologyUniversity of UlmUlmGermany

Personalised recommendations