TOWARDS AN INTEGRATIVE THEORETICAL FRAMEWORK OF INTERACTIVE MACHINE LEARNING SYSTEMS

Interactive machine learning (IML) is a learning process in which a user interacts with a system to iteratively define and optimise a model. Although recent years have illustrated the proliferation of IML systems in the fields of Human-Computer Interaction (HCI), Information Systems (IS), and Computer Science (CS), current research results are scattered leading to a lack of integration of existing work on IML. Furthermore, due to diverging functionalities and purposes IML systems can refer to, an uncertainty exists regarding the underlying distinct capabilities that constitute this class of systems. By reviewing extensive IML literature, this paper suggests an integrative theoretical framework for IML systems to address these current impediments. Reviewing 2,879 studies in leading journals and conferences during the years 1966-2018, we found an extensive range of applications areas that have implemented IML systems and the necessity to standardise the evaluation of those systems. Our framework offers an essential step to provide a theoretical foundation to integrate concepts and findings across different fields of research. The main contribution of this paper is organising and structuring the body of knowledge in IML for the advancement of the field. Furthermore, we suggest three opportunities for future IML research. From a practical point of view, our integrative theoretical framework can serve as a reference guide to inform the design and implementation of IML systems

Measuring, analysing and artificially generating head nodding signals in dyadic social interaction

Social interaction involves rich and complex behaviours where verbal and non-verbal signals are exchanged in dynamic patterns. The aim of this thesis is to explore new ways of measuring and analysing interpersonal coordination as it naturally occurs in social interactions. Specifically, we want to understand what different types of head nods mean in different social contexts, how they are used during face-to-face dyadic conversation, and if they relate to memory and learning. Many current methods are limited by time-consuming and low-resolution data, which cannot capture the full richness of a dyadic social interaction. This thesis explores ways to demonstrate how high-resolution data in this area can give new insights into the study of social interaction. Furthermore, we also want to demonstrate the benefit of using virtual reality to artificially generate interpersonal coordination to test our hypotheses about the meaning of head nodding as a communicative signal. The first study aims to capture two patterns of head nodding signals – fast nods and slow nods – and determine what they mean and how they are used across different conversational contexts. We find that fast nodding signals receiving new information and has a different meaning than slow nods. The second study aims to investigate a link between memory and head nodding behaviour. This exploratory study provided initial hints that there might be a relationship, though further analyses were less clear. In the third study, we aim to test if interactive head nodding in virtual agents can be used to measure how much we like the virtual agent, and whether we learn better from virtual agents that we like. We find no causal link between memory performance and interactivity. In the fourth study, we perform a cross-experimental analysis of how the level of interactivity in different contexts (i.e., real, virtual, and video), impacts on memory and find clear differences between them