14:00   Oral Session 3-GZ – Mini Tutorials
Chair: Anton Nijholt
40 mins
Preference learning for affective modeling
Georgios Yannakakis
Abstract: There is an increasing trend towards personalization of services and interaction. The use of computational models for learning to predict user emotional preferences is of significant importance towards system personalization. Preference learning is a machine learning research area that aids in the process of exploiting a set of specific features of an individual in an attempt to predict her preferences. This paper outlines the use of preference learning for modeling emotional preferences and shows the methodology's promise for constructing accurate computational models of affect.
40 mins
Foundations for Modelling Emotions in Game Characters: Modelling Emotion Effects on Cognition
Eva Hudlicka, Joost Broekens
Abstract: Affective gaming has received much attention lately, as the gaming community recognizes the importance of emotion in the development of engaging games. Affect plays a key role in the user experience, both in entertainment and in ‘serious’ games. Current focus in affective gaming is primarily on the sensing and recognition of the players’ emotions, and on tailoring the game responses to these emotions. A significant effort is also being devoted to generating ‘affective behaviors’ in the game characters, and in player avatars, to enhance their realism and believability. Less emphasis is placed on modeling emotions, both their generation and their effects, in the game characters, and in user models representing the players. This paper accompanies a tutorial presented at ACII2009, whose objective was to provide theoretical foundations for modeling emotions in game characters, as well as practical hands-on guidelines to help game developers construct functional models of emotion. While the tutorial covered models of both emotion generation and emotion effects, this paper focuses on modeling emotion effects on cognition.
40 mins
Emotional Brain-Computer Interfaces
Gary Garcia Molina, Tsvetomira Tsoneva, Anton Nijholt
Abstract: Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide significant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the influence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates. These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of non-invasive EEG based BCIs.