We introduce a distinction between disambiguation supporting continuous versus discrete ambiguous text entry. With continuous ambiguous text entry methods, letter selections are treated as ambiguous due to expected imprecision rather than due to discretized letter groupings. We investigate the simple case of a one-dimensional character layout to demonstrate the potential of techniques designed for imprecise entry. Our rotation-based sight-free technique, Rotext, maps device orientation to a layout optimized for disambiguation, motor efficiency, and learnability. We also present an audio feedback system for efficient selection of disambiguated word candidates and explore the role that time spent acknowledging word-level feedback plays in text entry performance. Through a user study, we show that despite missing on average by 2.46--2.92 character positions, with the aid of a maximum a posteriori (MAP) disambiguation algorithm, users can average a sight-free entry speed of 12.6wpm with 98.9% accuracy within 13 sessions (4.3 hours). In a second study, expert users are found to reach 21wpm with 99.6% accuracy after session 20 (6.7 hours) and continue to grow in performance, with individual phrases entered at up to 37wpm. A final study revisits the learnability of the optimized layout. Our modeling of ultimate performance indicates maximum overall sight-free entry speeds of 29.0wpm with audio feedback, or 40.7wpm if an expert user could operate without relying on audio feedback.
Response time and accuracy are two of the most frequently collected dependent measures. Tradeoffs between speed and accuracy are often observed, both between people, and between experimental conditions. In this paper we consider how speed, and accuracy, can be combined into a single, overall measure of performance. We consider two different approaches that adjust accuracy scores based on observed speed of responding and we examine how well those measures work with different data sets. We then present a third approach that combines standardized speed and accuracy scores. We show how this latter approach can represent the data fairly well regardless of which (if any) speed-accuracy tradeoff occurs in the data. We also show how this measure can be further generalized by applying differential weightings to the standardized scores of speed, and accuracy, respectively. We conclude by discussing the value of the measure for use in analyzing human performance data where continuous indicators of accuracy or error can be collected or constructed relatively easily. Our goal in developing the global measure of performance is not to accurately model the speed-accuracy relationship, but rather to create a measure that is more sensitive to experimental differences and causal relationships than either speed or accuracy alone.
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