Designing for Numerical Transcription Typing: Frequent Numbers Matter CANDIDATE Sarah EM Wiseman A dissertation submitted in partial fulfilment of the requirements for the degree of: Doctor of Philosophy of University College London UCL Interaction Centre, Department of Psychology and Language Sciences, University College London DECLARATION I, Sarah Wiseman, confirm that the work presented in this thesis is my own. Where infor- mation has been derived from other sources, I confirm that this has been indicated in the thesis. Abstract In the text entry domain, the task of number entry is often overlooked despite the prevalence of number entry tasks in the real world. Number entry often occurs in safety critical contexts, such as the medical domain, where errors can lead to patient death. In order to prevent errors from happening, it is important to design devices that help the user in their number entry task, and guard against error. To do this effectively, more needs to be known about the task of number transcription so that appropriate design interventions can be created. Current research commonly uses randomly generated numbers in the evaluation of number entry interfaces. However, it is not clear that random numbers are appropriate in this context. The first half of the thesis builds on research that shows that the familiarity of a number can affect how it is read, and investigates how this finding impacts upon transcription of familiar numbers. This is investigated by replicating seminal transcription typing studies using both words and numbers. The results of these experiments suggest that familiar numbers are represented more strongly than non-familiar numbers in memory, and as a result familiar numbers are significantly faster to transcribe. This novel finding then motivates a series of studies that aim to reduce errors in a medical number entry task. First, a log analysis of hospital devices shows that there are clear patterns in the numbers used, providing evidence that medical workers are likely to be more familiar with some numbers rather than others. The knowledge of these frequently used numbers is then utilised in three novel approaches to number entry interface design. First, knowledge of the landscape of frequent numbers in this context is used to create a set of heuristics for the design of number entry interfaces. Second, an experiment shows that adapting the interface specifically for frequent number entry can speed up interaction. Finally an experiment explores how an understanding of the numbers used to program devices can be used to check for and prevent number transcription errors. This thesis highlights the importance of understanding the frequency and familiarity of num- bers used in specific contexts. It explores how this knowledge can improve both evaluation and design of number entry interfaces. Acknowledgements During my PhD I have been surrounded by people who have made the experience not only bearable but enjoyable. I would like to thank a small set of those people here. Unfortunately, there is no space to thank all of my friends who have been there for me when I needed them, but their support has been invaluable. I must thank my PhD supervisors Anna Cox and Duncan Brumby. As a supervisory team they are perfectly balanced and have encouraged me to think big, but still get things done (eventually). Their guidance, at all hours of the day, has helped me produce a piece of work I can feel proud of. Being a part of UCLIC has been an incredibly rewarding experience. I feel very lucky to be able to work in a place where I can call so many people friends. Discussions with many of my colleagues, both in and out of the office, have helped shape this thesis. I would especially like to thank Sandy for chatting throughout the years, and for sharing with me his newly acquired knowledge of the PhD submission process. I was also able to be part of the CHI+MED team. I am thankful to everyone on the team for their useful discussions on my work, and for continuing to be an inspiration by working to make medical devices safer. I would like to thank Anna BD for not only being a proof-reader, but a cheer leader too; both were equally necessary jobs. And thanks too, to Mum for reading the entire thesis and significantly reducing the number of typos and grammar errors. I would finally like to thank my parents and sister for being incredibly supportive of me throughout my academic career. They have always encouraged me in my various pursuits and have inspired me to work hard to achieve my goals. It is thanks to them that I have had the great privilege to be able to do something I love for the last 4 years. 1 Contents 1 Introduction 11 1.1 Motivation . 11 1.2 Thesis Structure . 14 1.3 Contributions . 16 1.3.1 Contribution to Theory . 16 1.3.2 Contribution to Methodology . 17 1.3.3 Contribution to Design . 17 1.4 Summary . 18 2 Motivation 19 2.1 Introduction . 19 2.2 Terminology . 21 2.3 Reading numbers and words . 22 2.3.1 Studies with aphasic patients . 23 2.3.2 Models of number reading . 24 2.3.3 Models of word reading . 26 2.3.4 Summary . 27 2.4 Text Transcription . 28 2.4.1 Number Transcription . 28 2.4.2 Alphabetic Text Transcription . 29 2.4.3 The process of transcription typing . 30 2.4.4 Summary . 36 2.5 Modelling Transcription . 37 2.5.1 TYPIST Model of transcription typing . 37 2.5.2 Queuing Network Model of transcription typing . 39 2.5.3 A model of numerical transcription typing . 40 2.5.4 Summary . 41 2.6 Current Text and Number Entry Research . 42 3 2.6.1 Text Entry . 42 2.6.2 Number Entry . 43 2.6.3 Summary . 46 2.7 Motivation for future work . 47 3 What Is A Familiar Number? 49 3.1 Introduction . 49 3.2 Defining familiar numbers . 50 3.3 Study 1: Gathering a set of familiar numbers . 53 3.3.1 Method . 54 3.3.2 Results . 56 3.3.3 Discussion . 57 3.3.4 Limitations . 59 3.3.5 Conclusion . 60 3.4 Study 2: What makes a number familiar? . 61 3.4.1 Method . 61 3.4.2 Results . 63 3.4.3 Discussion . 64 3.4.4 Limitations . 64 3.4.5 Conclusion . 65 3.5 Summary . 65 4 Applying Transcription Metrics to Numerical Typing 67 4.1 Introduction . 67 4.2 Typing metrics . 68 4.2.1 Eye-Hand Span . 71 4.2.2 Replacement Span . 72 4.2.3 Copy Span . 73 4.2.4 Interkey Interval . 74 4.2.5 Error Rate . 75 4.3 Investigating the phenomena . 75 4.4 Study 3: Eye-Hand Span . 77 4.4.1 Overview . 77 4.4.2 Method . 77 4.4.3 Results . 81 4.4.4 Discussion . 83 4.4.5 Conclusion . 85 4.5 Study 4: Eye-Hand Span Replication . 86 4 4.5.1 Method . 86 4.5.2 Results . 87 4.5.3 Discussion . 89 4.5.4 Limitations . 91 4.5.5 Conclusion . 92 4.6 Study 5: Replacement Span . 93 4.6.1 Overview . 93 4.6.2 Method . 93 4.6.3 Results . 96 4.6.4 Discussion . 99 4.6.5 Limitations . 101 4.6.6 Conclusion . 102 4.7 Study 6: Copy Span . 103 4.7.1 Overview . 103 4.7.2 Method . 103 4.7.3 Results . 106 4.7.4 Discussion . 109 4.7.5 Limitations . 110 4.7.6 Conclusion . 110 4.8 Discussion . 111 4.8.1 Measures of span.