This paper presents a novel mathematical model for visual search and selection time in linear menus. Assuming two visual search strategies, serial and directed, and a pointing sub-task, it captures the change of performance with five factors: 1) menu length, 2) menu organization, 3) target position, 4) absence/presence of target, and 5) practice. The novel aspect is that the model is expressed as probability density distribution of gaze, which allows for deriving total selection time. We present novel data that replicates and extends the Nielsen menu selection paradigm and uses eye-tracking and mouse tracking to confirm model predictions. The same parametrization yielded a high fit to both menu selection time and gaze distributions. The model has the potential to improve menu designs by helping designers identify more effective solutions without conducting empirical studies.

In this paper we present our findings from a lab and a field study investigating how passers-by notice the interactivity of public displays. We designed an interactive installation that uses visual feedback to the incidental movements of passersby to communicate its interactivity. The lab study reveals:(1) Mirrored user silhouettes and images are more effective than avatar-like representations. (2) It takes time to notice the interactivity (approximately 1.2s). In the field study, three displays were installed during three weeks in shop windows, and data about 502 interaction sessions were collected. Our observations show: (1) Significantly more passers-by interact when immediately showing the mirrored user image (+90%) or silhouette (+47%) compared to a traditional attract sequence with call-to-action. (2) Passers-by often notice interactivity late and have to walk back to interact (the landing effect). (3) If somebody is already interacting, others begin interaction behind the ones already interacting, forming multiple rows (the honeypot effect). Our findings can be used to design public display applications and shop windows that more effectively communicate interactivity to passers-by.

International audienceMenus are used for exploring and selecting commands in interactive applications. They are widespread in current systems and used by a large variety of users. As a consequence, they have motivated many studies in Human-Computer Interaction (HCI). Facing the large variety of menus, it is difficult to have a clear understanding of the design possibilities and to ascertain their similarities and differences. In this article, we address a main challenge of menu design: the need to characterize the design space of menus. To do this, we propose a taxonomy of menu properties that structures existing work on visual menus. In order to highlight the impact of the properties on performance, we begin by refining performance through a list of quality criteria and by reviewing existing analytical and empirical methods for quality evaluation. The taxonomy of menu properties is an unavoidable step toward the elaboration of advanced predictive models of menu performance and the optimization of menus. A key point of this work is to focus both on menus and on the properties of menus, to enable a fine-grained analysis in terms of performance

Abstract. We present Wave menus, a variant of multi-stroke marking menus designed for improving the novice mode of marking while preserving their efficiency in the expert mode of marking. Focusing on the novice mode, a criteria-based analysis of existing marking menus motivates the design of Wave menus. Moreover a user experiment is presented that compares four hierarchical marking menus in novice mode. Results show that Wave and compound-stroke menus are significantly faster and more accurate than multi-stroke menus in novice mode, while it has been shown that in expert mode the multi-stroke menus and therefore the Wave menus outperform the compound-stroke menus. Wave menus also require significantly less screen space than compound-stroke menus. As a conclusion, Wave menus offer the best performance for both novice and expert modes in comparison with existing multi-level marking menus, while requiring less screen space than compound-stroke menus.

⌘⎇2 ⌘⇧H ⌘W ⌘M ⌘← ⌘D ⌘T Figure 1. The user wants to open a new tab but is not sure of the hotkey. He visually locates the button in the toolbar (boxed on left), then presses the Command key ( ) to activate ExposeHK, which overlays toolbar items with available hotkeys (right). He completes the command by pressing T. ABSTRACTKeyboard shortcuts allow fast interaction, but they are known to be infrequently used, with most users relying heavily on traditional pointer-based selection for most commands. We describe the goals, design, and evaluation of ExposeHK, a new interface mechanism that aims to increase hotkey use. ExposeHK's four key design goals are: 1) enable users to browse hotkeys; 2) allow non-expert users to issue hotkey commands as a physical rehearsal of expert performance; 3) exploit spatial memory to assist non-expert users in identifying hotkeys; and 4) maximise expert performance by using consistent shortcuts in a flat command hierarchy. ExposeHK supports these objectives by displaying hotkeys overlaid on their associated commands when a modifier key is pressed. We evaluated ExposeHK in three empirical studies using toolbars, menus, and a tabbed 'ribbon' toolbar. Results show that participants used more hotkeys, and used them more often, with ExposeHK than with other techniques; they were faster with ExposeHK than with either pointing or other hotkey methods; and they strongly preferred ExposeHK. Our research shows that ExposeHK can substantially improve the user's transition from a 'beginner mode' of interaction to a higher level of expertise.

Figure 1. iSkin is a thin, flexible, stretchable and visually customizable touch sensor that can be worn directly on the skin. We present three novel classes of on-body devices based on iSkin: (a) FingerStrap, exemplified here with a strap on the index finger for fast, one-handed control of incoming calls; (b) Extensions for wearable devices, exemplified here with a rollout keyboard attached to a smart watch; and SkinStickers, exemplified here with (c) an input surface for a music player attached to the forearm, (d) a click wheel on the back of the hand and (e) a headset control behind the ear. ABSTRACTWe propose iSkin, a novel class of skin-worn sensors for touch input on the body. iSkin is a very thin sensor overlay, made of biocompatible materials, and is flexible and stretchable. It can be produced in different shapes and sizes to suit various locations of the body such as the finger, forearm, or ear. Integrating capacitive and resistive touch sensing, the sensor is capable of detecting touch input with two levels of pressure, even when stretched by 30% or when bent with a radius of 0.5 cm. Furthermore, iSkin supports single or multiple touch areas of custom shape and arrangement, as well as more complex widgets, such as sliders and click wheels. Recognizing the social importance of skin, we show visual design patterns to customize functional touch sensors and allow for a visually aesthetic appearance. Taken together, these contributions enable new types of on-body devices. This includes finger-worn devices, extensions to conventional wearable devices, and touch input stickers, all fostering direct, quick, and discreet input for mobile computing.

One reason that human interaction with technology is difficult to understand is because the way in which people perform interactive tasks is highly adaptive. One such interactive task is menu search. In the current article we test the hypothesis that menu search is rationally adapted to (1) the ecological structure of interaction, (2) cognitive and perceptual limits, and (3) the goal to maximise the trade-off between speed and accuracy. Unlike in previous models, no assumptions are made about the strategies available to or adopted by users, rather the menu search problem is specified as a reinforcement learning problem and behaviour emerges by finding the optimal policy. The model is tested against existing empirical findings concerning the effect of menu organisation and menu length. The model predicts the effect of these variables on task completion time and eye movements. The discussion considers the pros and cons of the modelling approach relative to other well-known modelling approaches.

No abstract