This chapter introduced four concepts relevant to this thesis: usability, VEs, individual differences and adaptivity. It also emphasised the relationships between these concepts in the light of goal of this thesis: designing adaptive VEs through accommodating individual differences in experiencing presence and navigational patterns. The usability of VEs is challenged by both lack of design methodology and technological problems. While a great deal of efforts has been invested in improving VEs for increased usability by manipulating technological aspects, there is a lack of research on human factors whose impact on perceived usability is even higher (see Section 3.3.2).
In light of this, the chapter argues for the suitability of VEs as alternative method- ology for investigating aspects related to spatial cognition, and for accommodating individual differences for increased performance of spatial task. The relevant aspects regarding adaptive VEs are briefly outlined, with a particular emphasis on the central role of user modelling. Given the relevance of navigation for this thesis, this theme has been briefly introduced, while the following chapter will be entirely dedicated to it.
Chapter 3
Navigation in Virtual
Environments
Chapter 1 Introduction Background Chapters Chapter 2 Usability of VE Chapter 3 Navigation in VE Chapter 4 Sense of Presence Methodological Chapters Chapter 6 Artificial Intelligence forAdvanced Data Interpretation and Exploitation Chapter 5 Methodology Chapter 12 Conclusions Results Chapters Chapter 8
Individual Differences in Navigational Patterns Machine Learning Approach
Chapter 9
Individual Differences in Navigational Patterns Geometry of Curve Approach
Chapter 10
Individual Differences in Experiencing Presence Chapter 7
Individual Differences related to Usability
Chapter 11 Towards Accommodating Individual Differences: Design Guidelines
3.1
Introduction
The need for understanding human spatial behaviour in both real and virtual worlds has been largely acknowledged. This is due to the prevalence and significance of this specific behaviour and to the high psychological distress associated with its failure (i.e., getting lost). The study of spatial behaviour provides both theoretical and practical benefits.
At a theoretical level, the investigation of spatial mental models enriches the under- standing of howhumans perceive the space, make sense of space and exploit it. The first part of this chapter (Section 3.2) focuses on this aspect. It particularly discusses how humans acquire spatial knowledge and how this knowledge can be elicited by investiga- tors for understanding both the acquisition process and its product, such as cognitive maps.
Spatial mental representations reflect the inherent complexity of human spatial be- haviour. Such complexity contributes to the challenges and error-proneness which define spatial behaviour. These difficulties are even larger in the case of navigation in Virtual Environment (VEs) (Waller, 2000) (see Section 2.5). Therefore, the understanding of human spatial behaviour, in both physical and virtual worlds, may have a tremendous practical impact. One way to exploit this understanding is through identifying guide- lines to support efficient spatial behaviour. When employed for designing VEs, these guidelines could considerably improve the usability of VEs (see Section 2.6.1).
Most of the studies concerned with the development of adaptive VEs have focused on technological factors and navigational tools applied irrespective of any user model. Thus, this chapter presents also a reviewof the designing guidelines elaborated for making VEs better places to navigate in. Such guidelines present some limitations which this work tries to address.
The difficulties of investigating spatial mental models and the limitations of tech- niques developed for this purpose explain the lack of studies in this area. This thesis aims to address this gap, by focusing on investigating user spatial mental model. One of its major contributions is the proposal of machine learning techniques to overcome the limitations of traditional methods for eliciting such models.
This chapter addresses some fundamental aspects of spatial cognition whose basic concepts have already been introduced in Section 2.3. It takes the reader on a journey starting with a review of spatial mental models. The chapter introduces the concept of representations of spatial knowledge together with the associated methods which have been used in externalising and analysing them. A prototypical instantiation of these representations is captured by the construct ofcognitive maps, whose features are briefly described. In particular, the chapter presents a critical reviewof both direct and indirect methods employed in eliciting or accessing mental models, and spatial mental models in particular, together with their strengths and limitations. Accessing the spatial representations paves the way towards the understanding of the mental model of navigation. This chapter examines different models of navigation developed in the fields
like: experimental psychology, artificial intelligence, robots and neuroscience (Montello, 2003). The reviewed literature lacks systematic work in addressing the rules or strategies describing efficient spatial behaviour. I propose an alternative methodology for eliciting spatial knowledge through stressing the rigour and plausibility of connectionist models aimed to extract those efficient navigational rules and strategies.
After reviewing the theoretical and empirical work focusing on spatial mental mod- els, spatial learning within VEs is considered. Given the prevalence of spatial tasks and their impact on the perceived usability of VEs, the topic of designing better VEs for assisting navigation lies at the core of this thesis. This chapter places this topic in the frame of identifying and accommodating individual differences associated with navigation in VEs.