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Mobile keyboards often present error corrections and word completions (suggestions) as candidates for anticipated user input. However, these suggestions are not cognitively free: they require users to attend, evaluate, and act upon them. To understand this trade-off between suggestion savings and interaction costs, we conducted a text transcription experiment that controlled interface assertiveness: the tendency for an interface to present itself. Suggestions were either always present (extraverted), never present (introverted), or gated by a probability threshold (ambiverted). Results showed that although increasing the assertiveness of suggestions reduced the number of keyboard actions to enter text and was subjectively preferred, the costs of attending to and using the suggestions impaired average time performance.

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Figure 1. Transfer functions transform touch scrolling gestures (top) into scrolling output. These gestures can be simulated mechanically by SCARA robots (bottom) to reverse engineer the transfer functions. ABSTRACTTouch scrolling systems use a transfer function to transform gestures on a touch sensitive surface into scrolling output. The design of these transfer functions is complex as they must facilitate precise direct manipulation of the underlying content as well as rapid scrolling through large datasets. However, researchers' ability to refine them is impaired by: (1) limited understanding of how users express scrolling intentions through touch gestures; (2) lack of knowledge on proprietary transfer functions, causing researchers to evaluate techniques that may misrepresent the state of the art; and (3) a lack of tools for examining existing transfer functions. To address these limitations, we examine how users express scrolling intentions in a human factors experiment; we describe methods to reverse engineer existing 'black box' transfer functions, including use of an accurate robotic arm; and we use the methods to expose the functions of Apple iOS and Google Android, releasing data tables and software to assist replication. We discuss how this new understanding can improve experimental rigour and assist iterative improvement of touch scrolling.

Psychological research has shown that 'peak-end' effects influence people's retrospective evaluation of hedonic and affective experience. Rather than objectively reviewing the total amount of pleasure or pain during an experience, people's evaluation is shaped by the most intense moment (the peak) and the final moment (end). We describe an experiment demonstrating that peak-end effects can influence a user's preference for interaction sequences that are objectively identical in their overall requirements. Participants were asked to choose which of two interactive sequences of five pages they preferred. Both sequences required setting a total of 25 sliders to target values, and differed only in the distribution of the sliders across the five pages -with one sequence intended to induce positive peak-end effects, the other negative. The study found that manipulating only the peak or the end of the series did not significantly change preference, but that a combined manipulation of both peak and end did lead to significant differences in preference, even though all series had the same overall effort.