Modeling User Affect from Causes and Effects

We present a model of user affect to recognize multiple user emotions during interaction with an educational computer game. Our model deals with the high level of uncertainty involved in recognizing a variety of user emotions by probabilistically combining information on both the causes and effects of emotional reactions. In previous work, we presented the performance and limitations of the model when using only causal information. In this paper, we discuss the addition of diagnostic information on user affective valence detected via an EMG sensor, and present an evaluation of the resulting model.

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