User-adaptive systems are a recent trend in technological development. Designed to learn the characteristics of the user interacting with them, they change their own features to provide users with a targeted, personalized experience.
This project investigates human adaptive behavior in mutual-learning situations. A better understanding of adaptive human-machine interactions, and of human sensorimotor learning processes in particular, will provide guidelines, evaluation criteria, and recommendations that will be beneficial for all projects within SFB/Transregio 161 that focus on the design of user-adaptive systems and algorithms.
To achieve this goal, we will carry out behavioral experiments using human participants and base our empirical choices on the framework of optimal decision theory as derived from the Bayesian approach. This approach can be used as a tool to construct ideal observer models against which human performance can be compared.
Previous studies have shown, that adaptive features can create side effects, which might be undesirable. In applications requiring the users to improve their skills, for example, these side effects could impede the human learn process (Fig.1).