Suggesting multiple target candidates based on touch input is a possible option for high-accuracy target selection on small touchscreen devices. But it can become overwhelming if suggestions are triggered too often. To address this, we propose SATS, a Suggestion-based Accurate Target Selection method, where target selection is formulated as a sequential decision problem. The objective is to maximize the utility: the negative time cost for the entire target selection procedure. The SATS decision process is dictated by a policy generated using reinforcement learning. It automatically decides when to provide suggestions and when to directly select the target. Our user studies show that SATS reduced error rate and selection time over Shift [51], a magnification-based method, and MUCS, a suggestion-based alternative that optimizes the utility for the current selection. SATS also significantly reduced error rate over BayesianCommand [58], which directly selects targets based on posteriors, with only a minor increase in selection time.

With the increasing interest in movement sonification and expressive gesture-based interaction, it is important to understand which factors contribute to movement learning and how. We explore the effects of movement sonification and users’ musical background on motor variability in complex gesture learning. We contribute an empirical study in which musicians and non-musicians learn two gesture sequences over three days, with and without movement sonification. Results show the interlaced interaction effects of these factors and how they unfold in the three-day learning process. For gesture 1, which is fast and dynamic with a direct “action-sound” sonification, movement sonification induces higher variability for both musicians and non-musicians on day 1. While musicians reduce this variability to a similar level as no auditory feedback condition on day 2 and day 3, non-musicians remain to have significantly higher variability. Across three days, musicians also have significantly lower variability than non-musicians. For gesture 2, which is slow and smooth with an “action-music” metaphor, there are virtually no effects. Based on these findings, we recommend future studies to take into account participants’ musical background, consider longitudinal study to examine these effects on complex gestures, and use awareness when interpreting the results given a specific design of gesture and sound.

Aerial hoops are circular, hanging devices for both acrobatic exercise and artistic performance that let us explore the role of interactive sonification in physical activity. We present SonicHoop, an augmented aerial hoop that generates auditory feedback via capacitive touch sensing, thus becoming a digital musical instrument that performers can play with their bodies. We compare three sonification strategies through a structured observation study with two professional aerial hoop performers. Results show that SonicHoop fundamentally changes their perception and choreographic processes: instead of translating music into movement, they search for bodily expressions that compose music. Different sound designs affect their movement differently, and auditory feedback, regardless of type of sound, improves movement quality. We discuss opportunities for using SonicHoop as an aerial hoop training tool, as a digital musical instrument, and as a creative object; as well as using interactive sonification in other acrobatic practices to explore full-body vertical interaction.

Selecting targets accurately and quickly with eye-gaze input remains an open research question. In this paper, we introduce BayesGaze, a Bayesian approach of determining the selected target given an eye-gaze trajectory. This approach views each sampling point in an eye-gaze trajectory as a signal for selecting a target. It then uses the Bayes' theorem to calculate the posterior probability of selecting a target given a sampling point, and accumulates the posterior probabilities weighted by sampling interval to determine the selected target. The selection results are fed back to update the prior distribution of targets, which is modeled by a categorical distribution. Our investigation shows that BayesGaze improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping (CM) method. Our research shows that both accumulating posterior and incorporating the prior are effective in improving the performance of eye-gaze based target selection.

Hick's law is a key quantitative law in Psychology that relates reaction time to the logarithm of the number of stimulus-response alternatives in a task. Its application to HCI is controversial: Some believe that the law does not apply to HCI tasks, others regard it as the cornerstone of interface design. The law, however, is often misunderstood. We review the choice-reaction time literature and argue that: (1) Hick's law speaks against, not for, the popular principle that 'less is better'; (2) logarithmic growth of observed temporal data is not necessarily interpretable in terms of Hick's law; (3) the stimulus-response paradigm is rarely relevant to HCI tasks, where choice-reaction time can often be assumed to be constant; and (4) for user interface design, a detailed examination of the effects on choice-reaction time of psychological processes such as visual search and decision making is more fruitful than a mere reference to Hick's law.

We introduce BIGFile, a new fast file retrieval technique based on the Bayesian Information Gain framework. BIGFile provides interface shortcuts to assist the user in navigating to a desired target (file or folder). BIGFile's split interface combines a traditional list view with an adaptive area that displays shortcuts to the set of file paths estimated by our computationally efficient algorithm. Users can navigate the list as usual, or select any part of the paths in the adaptive area. A pilot study of 15 users informed the design of BIGFile, revealing the size and structure of their file systems and their file retrieval practices. Our simulations show that BIGFile outperforms Fitchett et al.'s AccessRank, a best-of-breed prediction algorithm. We conducted an experiment to compare BIGFile with ARFile (AccessRank instantiated in a split interface) and with a Finder-like list view as baseline. BIGFile was by far the most efficient technique (up to 44% faster than ARFile and 64% faster than Finder), and participants unanimously preferred the split interfaces to the Finder.

While it is well known that menu usage follows a Zipfian distribution, there has been little interest in the impact of menu item frequency distribution on user's behavior. In this note, we explore the effects of frequency distribution on average menu performance as well as individual item performance. We compare three frequency distributions of menu item usage: Uniform; Zipfian with s=1 and Zipfian with s=2. The results show that (1) user's behavior is sensitive to different frequency distributions at both menu and item level; (2) individual item selection time depends on, not only its frequency, but also the frequency of other items in the menu. Finally, we discuss how these findings might have impacts on menu design, empirical studies and menu modeling.

This paper introduces BIGnav, a new multiscale navigation technique based on Bayesian Experimental Design where the criterion is to maximize the information-theoretic concept of mutual information, also known as information gain. Rather than simply executing user navigation commands, BIGnav interprets user input to update its knowledge about the user's intended target. Then it navigates to a new view that maximizes the information gain provided by the user's expected subsequent input. We conducted a controlled experiment demonstrating that BIGnav is significantly faster than conventional pan and zoom and requires fewer commands for distant targets, especially in non-uniform information spaces. We also applied BIGnav to a realistic application and showed that users can navigate to highly probable points of interest on a map with only a few steps. We then discuss the tradeoffs of BIGnav--including efficiency vs. increased cognitive load--and its application to other interaction tasks.