Linking Recognition Accuracy and User Experience in an Affective Feedback Loop

In an affective feedback loop, the computer maps various measurements to affective variables such as enjoyment, then adapts its behavior based on the recognized affects. The affect recognition is never perfect, and its accuracy (percentage of times the correct affective state is recognized) depends on many factors. However, it is unclear how this accuracy relates to the overall user experience. As recognition accuracy is difficult to control in a real affective feedback loop, we describe a method of simulating recognition accuracy in a game where difficulty is increased or decreased after each round. The game was played by 261 participants at different simulated recognition accuracies. Participants reported their satisfaction with the recognition algorithm as well as their overall game experience. We observed that in such a game, the affective feedback loop must adapt game difficulty with an accuracy of at least 80 percent to be accepted by users. Furthermore, users who do not enjoy the game are likely to stop playing it rather than continue playing and report low enjoyment. However, the acceptable recognition accuracy may not generalize to other contexts, and studies of affect recognition accuracies in other applications are needed.