Information Technology Reference
In-Depth Information
Tabl e 2.
Pearson's correlation coe
cient between average facial expression intensities
and average scores from each GEQ dimension, for Draw My Thing (all
p<
0
.
001)
GEQ Dimension
Anger
Joy
Surprise
Competence
-0.37
-0.18
0.53
Immersion
-0.10
0.07
0.08
Flow
0.18
0.25
-0.07
Tension
0.14
-0.14
-0.25
Challenge
0.35
0.38
-0.30
Negative affect
-0.35
-0.42
0.22
Positive affect
-0.12
0.12
0.15
4 Preliminary Conclusions and Future Work
One key finding is that significant correlations were exhibited between the fa-
cial expressions and the GEQ dimensions. In this study, only challenge showed
consistency of a large effect size and direction across both games played. This
correlation implies that challenge intensities can potentially be automatically
inferred from facial expressions across different game genres. There were also
large correlations exhibited in the rest of the GEQ dimensions but they did not
align in both games, which means that different games might induce different
bindings of facial expressions with gameplay experiences.
The next major step will be to analyse these results in combination with our
prior results that involve further qualitative and quantitative measures [4,5].
For future experiments, we also hope to evaluate the correlations of the facial
expressions with other physiological signals like EDA and EMG, in order to
perform a finer-grained analysis.
Acknowledgements.
This research was supported by the Games Studio and
the Centre for Human Centred Technology Design at the University of Technol-
ogy, Sydney.
References
1. Bernhaupt, R.: Evaluating User Experience in Games: Concepts and Methods.
Springer (2010)
2. Nacke, L.E.: Affective Ludology: Scientific Measurement of User Experience in In-
teractive Entertainment. PhD thesis, Blekinge Institute of Technology (2009)
3. Tan, C., Johnston, A.: Towards a Nondisruptive, Practical, and Objective Auto-
mated Playtesting Process. In: Workshops at the Seventh Artificial Intelligence and
Interactive Digital Entertainment Conference, pp. 25-28 (2011)
4. Tan, C.T., Pisan, Y.: Towards Automated Player Experience Detection With Com-
puter Vision Techniques. In: CHI Workshop on Game User Experience (2012)
5. Tan, C.T., Rosser, D., Bakkes, S., Pisan, Y.: A feasibility study in using facial
expressions analysis to evaluate player experiences. In: Proceedings of the 8th Aus-
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pp. 1-10. ACM Press, New York (2012)
