Modeling Behaviour to Predict User State: Self-Reports as Ground Truth
This addresses the problem of accurately detecting user states for interactive systems, but it is incremental as it builds on existing concepts without presenting new empirical results.
The paper argues for model-based approaches that predict user states like emotions by training on user behavior with self-reports as ground truth, comparing benefits to methods using only self-reports or behavior models without such ground truths.
Methods that detect user states such as emotions are useful for interactive systems. In this position paper, we argue for model-based approaches that are trained on user behaviour and self-reported user state as ground truths. In an application context, they record behaviour, extract relevant features, and use the models to predict user states. We describe how this approach can be implemented and discuss its benefits in comparison to solely self-reports in an application and to models of behaviour without the selfreport ground truths. Finally, we discuss shortcomings of this approach by considering its drawbacks and limitations.