Applying a User-centred Approach to Interactive Visualization Design

Analysing users in their context of work and finding out how and why they use different information resources is essential to provide interactive visualisation systems that match their goals and needs. Designers should actively involve the intended users throughout the whole process. This chapter presents a user-centered approach for the design of interactive visualisation systems. We describe three phases of the iterative visualisation design process: the early envisioning phase, the global specification hase, and the detailed specification phase. The whole design cycle is repeated until some criterion of success is reached. We discuss different techniques for the analysis of users, their tasks and domain. Subsequently, the design of prototypes and evaluation methods in visualisation practice are presented. Finally, we discuss the practical challenges in design and evaluation of collaborative visualisation environments. Our own case studies and those of others are used throughout the whole chapter to illustrate various approaches

Pro-active Meeting Assistants : Attention Please!

This paper gives an overview of pro-active meeting assistants, what they are and when they can be useful. We explain how to develop such assistants with respect to requirement definitions and elaborate on a set of Wizard of Oz experiments, aiming to find out in which form a meeting assistant should operate to be accepted by participants and whether the meeting effectiveness and efficiency can be improved by an assistant at all

The Head and the Heart in Crisis: The Temporal Dynamics of the Interplay Between Team Cognitive Processes and Collective Emotions During Crisis Events

Organizations commonly use teams to rapidly and appropriately respond to crises. These teams must face a multidimensional challenge because crises not only present sets of ill-defined, complex problems, but also exert high emotional demands on the team. As a result, effective team functioning in crisis events involves handling each dimension of the crisis through distinct, yet concurrent, types of responses, namely team cognitive processes and collective emotions. Research on groups also suggests that cognitive processes and collective emotions are dynamically intertwined and can influence one another. Studies of crisis events to date, however, have largely examined cognition and emotion in isolation from one another. As a result, we know little about how team cognitive processes and collective emotions go hand in hand over the course a crisis event to shape team performance. This study seeks to address this research gap. Focusing on 20 teams of MBA students dealing with a simulated organizational crisis, I used a longitudinal research design and behavioural observation methods to examine the dynamics of the interplay between team cognitive processes and collective emotions at two different temporal scales. At the micro-temporal scale, I examined the co-occurrence (also called coupling) of team cognitive processes and collective emotions to determine which observed couplings were statistically meaningful in higher- versus lower-performing teams facing a crisis event. Lag sequential analyses revealed that compared with lower-performing teams, higher-performing teams were less likely to engage in explicit situation processing in an emotionally-midaroused team atmosphere. Higher-performing teams were also less likely than lower-performing teams to exhibit implicit situation processing in an emotionally-neutral team atmosphere. Lower-performing teams, on the other hand, had more tendency to engage in implicit situation processing in an emotionally-homogeneous team atmosphere. Finally, lower-performing teams were more likely than higher-performing teams to exhibit implicit action processing in an emotionally-midaroused team atmosphere. At the macro-temporal scale, I tracked the evolution of couplings over the course of the crisis event by means of an exploratory visualization tool called GridWare. GridWare enabled me to characterize and compare the structure and the content of the coupling trajectory of higher- and lower-performing teams. The coupling trajectory of higher performers was not found to be any more or less variable than that of lower performers. However, according to my analyses, the coupling trajectory of higher-performing teams was significantly more likely to become absorbed in a single, strong, attracting coupling, as opposed to the coupling trajectory of lower-performing teams which tended to get drawn toward multiple, weaker, attracting couplings. The single, strong attracting coupling that pulled the trajectory of higher-performing teams was the coupling of explicit action processing and midaroused-neutral collective emotions. This indicates that higher performers had more tendency to keep returning to discussing and updating their decisions/actions in a midaroused-neutral emotional atmosphere. Theoretical contributions of this study and implications of these findings for practice and for future research are discussed

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Social Interactions in Immersive Virtual Environments: People, Agents, and Avatars

Immersive virtual environments (IVEs) have received increased popularity with applications in many fields. IVEs aim to approximate real environments, and to make users react similarly to how they would in everyday life. An important use case is the users-virtual characters (VCs) interaction. We interact with other people every day, hence we expect others to appropriately act and behave, verbally and non-verbally (i.e., pitch, proximity, gaze, turn-taking). These expectations also apply to interactions with VCs in IVEs, and this thesis tackles some of these aspects. We present three projects that inform the area of social interactions with a VC in IVEs, focusing on non-verbal behaviours. In our first study on interactions between people, we collaborated with the Social Neuroscience group at the Institute of Cognitive Neuroscience from UCL on a dyad multi-modal interaction. This aims to understand the conversation dynamics, focusing on gaze and turn-taking. The results show that people have a higher frequency of gaze change (from averted to direct and vice versa) when they are being looked at compared to when they are not. When they are not being looked at, they are also directing their gaze to their partners more compared to when they are being looked at. Another contribution of this work is the automated method of annotating speech and gaze data. Next, we consider agents’ higher-level non-verbal behaviours, covering social attitudes. We present a pipeline to collect data and train a machine learning (ML) model that detects social attitudes in a user-VC interaction. Here we collaborated with two game studios: Dream Reality Interaction and Maze Theory. We present a case study for the ML pipeline on social engagement recognition for the Peaky Blinders narrative VR game from Maze Theory studio. We use a reinforcement learning algorithm with imitation learning rewards and a temporal memory element. The results show that the model trained with raw data does not generalise and performs worse (60% accuracy) than the one trained with socially meaningful data (83% accuracy). In IVEs, people embody avatars and their appearance can impact social interactions. In collaboration with Microsoft Research, we report a longitudinal study in mixed-reality on avatar appearance in real-work meetings between co-workers comparing personalised full-body realistic and cartoon avatars. The results imply that when participants use realistic avatars first, they may have higher expectations and they perceive their colleagues’ emotional states with less accuracy. Participants may also become more accustomed to cartoon avatars as time passes and the overall use of avatars may lead to less accurately perceiving negative emotions. The work presented here contributes towards the field of detecting and generating nonverbal cues for VCs in IVEs. These are also important building blocks for creating autonomous agents for IVEs. Additionally, this work contributes to the games and work industry fields through an immersive ML pipeline for detecting social attitudes and through insights into using different avatar styles over time in real-world meetings