At the IBT, we study behavior and technology from a human-centered perspective. We use latest methods to conduct fundamental research and to study technological applications and their implications for individuals, organizations, and society at large.
Our studies are part of a burgeoning field of research, seeking to develop systems and devices that can recognize, interpret, process, forecast, and simulate human emotion. Specific examples include the development of a hybrid machine learning approach to improve emotion recognition, based on facial or vocal characteristics. Further, we investigate how affect-related consumption outcomes such as satisfaction can be predicted from written user input. In another research project, we investigate how to engineer empathetic and vulnerable conversational agents able to transmit emotions via simulating human vocal features. Such agents could potentially adapt to the emotional state of the user and possibly motivate prosocial behaviors, for instance in the context of donation advertisements. All the aforementioned projects can be leveraged in both research and industry contexts.
- Busquet, F., & Hildebrand, C. (2020). Black-Box Emotion Detection: On the Variability and Predictive Accuracy of Automated Emotion Detection Algorithms. ACR North American Advances. [Link]