Learning General Preference Models from Physiological Responses in Video Games: How Complex Is It?

  • Maurizio Garbarino
  • Simone Tognetti
  • Matteo Matteucci
  • Andrea Bonarini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6974)

Abstract

An affective preference model can be successfully learnt from pairwise comparison of physiological responses. Several approaches to do this obtain different performances. The higher ranked seem to use non linear models and complex feature selection strategies. We present a comparison of three linear and non linear classification methods, combined with a simple and a complex feature selection strategy (sequential forward selection and a genetic algorithm), on two datasets. We apply a strict crossvalidation framework to test the generalization capability of the models when facing physiological data coming from a new user. We show that, when generalization is the goal, complex non-linear models trained using fancy strategies might easily get trapped by overfitting, while linear ones might be preferable. Although this could be expected, the only way to appreciate it has to pass through proper cross-validation, and this is often forgot when rushing in the “best” performance challenge.

Keywords

enjoyment evaluation physiological signals feature selection SFS neuro evolution emotion in games cross validation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Broek, E.L.V.D., Janssen, J.H., Westerink, J.: Guidelines for affective signal processing (asp): From lab to life, pp. 1–6. IEEE, Los Alamitos (2009)Google Scholar
  2. 2.
    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 (2011)Google Scholar
  3. 3.
    Yannakakis, G., Hallam, J.: Entertainment modeling through physiology in physical play. International Journal of Human-Computer Studies 66(10), 741–755 (2008)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Martínez, H.P., Yannakakis, G.N.: Genetic search feature selection for affective modeling: a case study on reported preferences. In: Proceedings of the 3rd International Workshop on Affective Interaction in Natural Environments, AFFINE 2010, pp. 15–20. ACM, New York (2010)Google Scholar
  6. 6.
    Yannakakis, G., Martínez, H., Jhala, A.: Towards affective camera control in games. User Modeling and User-Adapted Interaction 20(4), 313–340 (2010)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Doyle, J.: Prospects for preferences. Computational Intelligence 20(2), 111–136 (2004)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Yannakakis, G.: Preference learning for affective modeling. In: Proceeding of the International Conference on Affective Computing and Intelligent Interaction, ACII 2009, pp. 1–6. IEEE, Los Alamitos (2009)Google Scholar
  10. 10.
    Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. IEEE Intelligent Systems and Their Applications 13(2), 44–49 (1998)CrossRefGoogle Scholar
  11. 11.
    Baker, J.E.: Reducing bias and inefficiency in the selection algorithm, pp. 14–21. Lawrence Erlbaum Associates, Mahwah (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maurizio Garbarino
    • 1
  • Simone Tognetti
    • 1
  • Matteo Matteucci
    • 1
  • Andrea Bonarini
    • 1
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di Milano, IIT UnitMilanoItaly

Personalised recommendations