The Affective Triad: Stimuli, Questionnaires, and Measurements
Affective Computing has always aimed to answer the question: which measurement is most suitable to predict the subject’s affective state? Many experiments have been devised to evaluate the relationships among three types of variables (the affective triad): stimuli, self-reports, and measurements. Being the real affective state hidden, researchers have faced this question by looking for the measure most related either to the stimulus, or to self-reports. The first approach assumes that people receiving the same stimulus are feeling the same emotion; a condition difficult to match in practice. The second approach assumes that emotion is what people are saying to feel, and seems more likely.
We propose a novel method, which extends the mentioned ones by looking for the physiological measurement mostly correlated to the self-report due to emotion, not the stimulus. This guarantees to find a measure best related to subject’s affective state.
Keywordsaffective computing emotion emotion model video game stimuli self report physiology affective triad
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- 2.Bradley, M.M., Lang, P.J.: The International Affective Picture System (IAPS) in the Study of Emotion and Attention. In: Coan, J.A., Allen, J.J.B (2007)Google Scholar
- 6.Stanford encyclopedia of philosophy - emotion, http://plato.stanford.edu/entries/emotion/
- 7.Picard, R., Vyzas, E., Healey, J.: Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1175–1191 (2001)Google Scholar
- 10.Rowe, D., Sibert, J., Irwin, D.: Heart rate variability: indicator of user state as an aid to human-computer interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 480–487. ACM Press/Addison-Wesley Publishing Co. (1998)Google Scholar
- 11.Tognetti, S., Alessandro, C., Bonarini, A., Matteucci, M.: Fundamental issues on the recognition of autonomic patterns produced by visual stimuli. In: Proceeding of the International Conference on Affective Computing and Intelligent Interaction, ACII 2009, IEEE, Amsterdam (2009)Google Scholar
- 12.Mandryk, R., Inkpen, K.: Physiological indicators for the evaluation of co-located collaborative play. In: Proceedings of the 2004 ACM Conference on Computer Supported Cooperative Work, p. 111. ACM, New York (2004)Google Scholar
- 13.Tognetti, S., Garbarino, M., Bonarini, A., Matteucci, M.: Modeling player enjoyment from physiological responses in a car racing game. In: 2010 IEEE Symposium on Computational Intelligence and Games (CIG), pp. 321–328. IEEE, Los Alamitos (2010)Google Scholar
- 14.Tognetti, S., Garbarino, M., Bonarini, A., Matteucci, M.: Enjoyment recognition from physiological data in a car racing game. In: Proceedings of the 3rd International Workshop on Affective Interaction in Natural Environments, AFFINE 2010, pp. 3–8. ACM, New York (2010)Google Scholar
- 15.Likert, R.: A technique for the measurement of attitudes. Archives of Psychology 140, 1–55 (1932)Google Scholar
- 17.Calvo, R., D’Mello, S.: Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 18–37 (2010)Google Scholar
- 19.Ortony, A., Clore, G., Collins, A.: The cognitive structure of emotions. Cambridge Univ. Pr., Cambridge (1990)Google Scholar
- 20.Martınez, H., Hullett, K., Yannakakis, G.: Extending Neuro-evolutionary Preference Learning through Player Modeling. In: 2010 IEEE Symposium on Computational Intelligence and Games, CIG (2010)Google Scholar