More than three decades of research on human-computer interaction (HCI) have resulted in significant advances in theories, tools, and methods to facilitate, support, and enhance interactions between humans and computing systems. HCI research has also paved the way for many highly important and commercially successful applications, such as touch input on the billions of mobile phones used worldwide, or latest game consoles and car infotainment systems that can be controlled by body movements or hand gestures. However, despite the fundamental importance of HCI for the information society as well as advances towards the grand HCI challenge of making interactions with computers human-like, current general-purpose user interfaces (UI) still fall a long way short in one core human ability – Theory of Mind (ToM). ToM allows us to attribute mental states to others and anticipate their actions and is thus essential for us to interact naturally, effortlessly, and seamlessly with each other.
Two critical requirements for ToM are the impressive human abilities to understand others' attention and to predict their intentions. Deficits in ToM are closely linked to severe developmental disorders, such as autism. We argue that current general-purpose UIs are similarly mind-blind. That is, they fail to sense users' attention and predict their intentions and therefore lack the ability to anticipate and pro-actively adapt to users' actions. This limits them to operating after the fact, i.e. to merely reacting to user input, drastically restricting the naturalness, efficiency, and user experience of current interactions.
The overall objective of ANTICIPATE is to establish the scientific foundations for a new generation of user interfaces that implement ToM and are thus able to anticipate users' future actions and take action on users' behalf. To this end, the project explores fundamental computational methods to sense users' attention and predict their intentions during interactions with general-purpose graphical UIs (i.e. not specialised for a particular task, such as text entry), as well as innovative interaction paradigms for anticipatory UI adaptations. If successful, these UIs will appear near "magical" to users as they will seem to read their minds and to always be one step ahead. For example, these UIs could infer the information that users intend to find and thus only resides in their minds, and pro-actively present that information. By establishing anticipatory HCI as a strong complement to existing interaction and UI adaptation paradigms, we expect to significantly improve the naturalness, efficiency, and
user experience of current human-computer interactions. As such, the project has the potential to drastically improve the billions of interactions that we all perform with computers every day and to enable new types of UI adaptation impossible today.
