Touch interactions with current mobile devices have limited expressiveness. Augmenting devices with additional degrees of freedom can add power to the interaction, and several augmentations have been proposed and tested. However, there is still little known about the effects of learning multiple sets of augmented interactions that are mapped to different applications. To better understand whether multiple command mappings can interfere with one another, or affect transfer and retention, we developed a prototype with three pushbuttons on a smartphone case that can be used to provide augmented input to the system. The buttons can be chorded to provide seven possible shortcuts or transient mode switches. We mapped these buttons to three different sets of actions, and carried out a study to see if multiple mappings affect learning and performance, transfer, and retention. Our results show that all of the mappings were quickly learned and there was no reduction in performance with multiple mappings. Transfer to a more realistic task was successful, although with a slight reduction in accuracy. Retention after one week was initially poor, but expert performance was quickly restored. Our work provides new information about the design and use of augmented input in mobile interactions. Mobile touchscreen devices such as smartphones, tablets, and smartwatches are now ubiquitous. The simplicity of touch-based interaction is one of the main reasons for their popularity, but touch interfaces have low expressivenessthey are limited in terms of the number of actions that the user can produce in a single input. As a result, touch interactions often involve additional actions to choose modes or to navigate menu hierarchies. These limitations on touch input can be addressed by adding new degrees of freedom to touch devices. For example, both Android and IOS devices have augmentations that allow the user to specify the difference between scrolling and selecting: Android uses a timeout on the initial touch (i.e., a drag starts with either a short press or a long press), and some IOS devices use pressure-sensitive screens that use different pressure levels to specify selection and scrolling [13] . Researchers have also proposed adding a wide variety of new degrees of freedom for touch devices -including multi-touch and bimanual input [16, 32, 42] , external buttons and force sensors [1, 44] , back-of-device touch [3] , sensors for pen state [26] or screen tilt [39, 43] , and pressure sensors [8, 9] . Studies have shown these additional degrees of freedom to be effective at increasing the expressive power of interaction with a mobile device. However, previous research has only looked at these new degrees of freedom in single contexts, and as a result we know little about how augmented input will work when it is used in multiple different applications: if an augmented input is mapped to a set of actions that are specific to one application, will there be interference when the same augmentations are mapped to a different set of actions in another application? To find out how multiple mappings for a new degree of freedom affect learning and usage, we carried out a study with a prototype device that provides three buttons on the side of a smartphone case. The buttons can be chorded, giving seven inputs that can be used for discrete commands or transient modes. We developed three different mappings for these chording buttons for three different contexts: shortcuts for a launcher app, colour selections for a drawing app; and modes for a text-editing app. Our study looked at three issues: first, whether learning multiple mappings with the chorded buttons would interfere with learning or accuracy; second, whether people could transfer their learning from training to usage tasks that set the button commands into more complex and realistic activities; and third, whether memory of the multiple mappings would be retained over one week, without any intervening practice. Our evaluation results provide new insights into the use of augmented input for mobile devices:  Learning multiple mappings did not reduce performance -people were able to learn all three mappings well, and actually learned the second and third mappings significantly faster than the first;  Multiple mappings did not reduce accuracy -people were as accurate on a memory test with three mappings as they were when learning the individual mappings;  Performance did transfer from training to more realistic usage tasks, although accuracy decreased slightly;  Retention after one week was initially poor (accuracy was half that of the first session), but performance quickly returned to near-expert levels. Our work provides two main contributions. First, we show that chorded input is a successful way to provide a rich input vocabulary that can be used with multiple applications. Second, we provide empirical evidence that mapping augmented input to multiple contexts does not impair performance. Our results provide new evidence that augmented input can realistically increase the expressive power of interactions with mobile devices. [7, 19, 22, 23] ), and researchers have proposed new paradigms of interaction (e.g., for eyes-free ubiquitous computing [29] or for post-WIMP devices [4, 5, 24] ) that can incorporate different types of augmentation. Cechanowicz and colleagues also created a framework specifically about augmented interactions [9] ; they suggest several ways of adding to an interaction, such as adding states to a discrete degree of freedom, adding an entirely new degree of freedom, or "upgrading" a discrete degree of freedom to use continuous input. Our study provides several main findings:  The training phase showed that people were able to learn all three mappings quickly (performance followed a power law), and were able to achieve 90% accuracy within a few training blocks;  Overloading the buttons with three different mappings did not appear to cause any problems for participantsthe second and third mappings were learned faster than the first, and there was no difference in performance across the position of the learned mappings;  People were able to successfully transfer their expertise from the training system to the usage tasks -although performance dropped by a small amount;  Performance in the memory test, which mixed all three mappings together, was very strong, with many of the items remembered at near 100% accuracy;  Retention over one week without any intervening practice was initially poor (about half the accuracy of the first memory test), but recovered quickly in the usage tasks to near the levels seen in the first sessions. In the following paragraphs we discuss the reasons for our results, and comment on how our findings can be generalized and used in the design of richer touch-based interactions. Expressiveness is limited in mobile touch interfaces. Many researchers have devised new ways of augmenting these interactions, but there is still little understanding of issues of interference, transfer, and retention for augmented touch interactions, particularly those that use multiple mappings for different usage contexts. To provide information about these issues, we developed an augmented phone case with three pushbuttons that can be chorded to provide seven input states. The external buttons can provide quick access to command shortcuts and transient modes, increasing the expressive power of interaction. We carried out a four-part study with the system, and found that people can successfully learn multiple mappings of chorded commands, and can maintain their expertise in more-complex usage tasks. Retention was found to be an important issue -accuracy dropped over one week, but was quickly restored after a short period of use. Our work provides new knowledge about augmented interactions for touch devices, and shows that adding simple input mechanisms like chorded buttons are a promising way to augment mobile interactions.
Describes a study investigating interference, transfer, and retention of multiple mappings with the same set of chorded buttons
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