Existing guidelines for designing targets on smartphones often focus on single-tap operations for accurate selection. However, smartphone interfaces can support both tap and swipe actions. We explored user-performance differences between tap and swipe in two crowdsourced experiments using bar and square targets. Results indicated longer operation times, higher error rates, and significantly shifted touch points for swipe compared to tap. Our findings imply that current target-size guidelines may not apply to swipe-operated targets, and they reveal new research opportunities for swipeable-target designs.

Steering a cursor through a constrained path is required for operating graphical user interfaces, such as when navigating a cascaded menu. Recently, the accuracy with which users successfully accomplish a task is emerging as an important performance indicator. In this study, we evaluated the performance of a Servo-Gaussian model to predict the success rates in linear and circular paths with 212 and 166 crowdsourced participants, respectively. The results showed that, for linear paths, the model achieved r2=0.9676, MAE=2.036%, and RMSE=2.692%, and for circular paths, it achieved r2=0.9787, MAE=3.199%, and RMSE=3.927%. Shuffle-split cross-validation with five train-test data-size ratios also demonstrated the robust prediction accuracy of the model. These findings will provide designers with a useful tool to judge if an interface can be accurately operated without running costly user studies for new task conditions.

The Dual Gaussian Distribution Model can be utilized for predicting the success rates of tapping targets. However, previous studies have shown that the prediction error increases to as much as 10 points, where "points" represent the percentage difference between the observed and predicted values of the tap success rate, particularly for a small target width W such as 2 mm. We hypothesize that this could be due to the experimental designs with sparse W levels performed by few participants, rather than the model itself. Our experiment involving horizontal and vertical bar targets with W = 2-8 mm (step: 0.2 mm) performed by more than 180 participants showed that the maximum prediction errors were relatively small: 2.769 and 3.185 points, respectively. Furthermore, the correlation between W and the prediction error was statistically small (Pearson's |r| < 0.2), and W was not a significant contributor to changing prediction errors (p>0.05). As these results do not support the concerns that the Dual Gaussian Distribution Model has an issue when used with small targets, the development of applications and refined models is encouraged to continue.

Previous studies on the Finger-Fitts law (FFitts law) are lacking in sufficient experiments to verify its inherent potential. Since the FFitts law is originally a modified version of the effective width method to normalize speed-accuracy biases, the model fit would improve if multiple biases were mixed together and the throughputs would be more stable than using the nominal target width. In this study, we conduct an experiment in which participants tap 1D-bar and 2D-circular targets under three subjective biases: balancing the speed and accuracy, emphasizing speed, and emphasizing accuracy when they perform the tasks. The results showed that applying the effective width to Ko et al.'s refined FFitts law, which represents the touch ambiguity with a free parameter, was the most successful in normalizing biases. Reanalyzing another dataset on ray-casting pointing also led to the same conclusion. We thus recommend using Ko et al.'s model with effective width when researchers compare several experimental conditions such as devices and user groups.

Error rates (ERs) in target-pointing tasks are typically modelled in two steps: predicting the click-point variability (σ) based on target sizes and then computing the probability that a click falls outside a target. This is an indirect approach if the researcher’s purpose is to achieve the accurate prediction of ERs because the model coefficients are optimized to predict σ accurately in the first step. We compared the prediction accuracies of this method with a more direct technique in which the coefficients used for σ are determined in such a way as to optimize the closeness between observed and predicted ERs. Our re-analysis of eight datasets from mouse- and touch-based pointing studies showed that the latter approach consistently outperforms the conventional one if the starting values for the parameter search are appropriate (which can be achieved by hyperparameter optimization), thus enabling the interface configuration on the basis of accurately predicted ERs.

A prior work has recommended adding a 4-mm gap between a target and the edge of a screen, as tapping a target located at the screen edge takes longer than tapping non-edge targets. However, it is possible that this recommendation was created based on statistical errors, and unexplored situations existed in the prior work. In this study, we re-examine the recommendation by utilizing crowdsourced experiments to resolve the issues. If we observe the same results as the prior work through experiments including diversities, we can verify that the recommendation is suitable. We found that increasing the gap between the target and the screen edge decreased the movement time, which was consistent with the prior work. In addition, we newly found that increasing the gap decreased the error rate as well. On the basis of these results, we discuss how the gap and the target should be designed.

The effective width method of Fitts’ law can normalize speed-accuracy biases in 1D target pointing tasks. However, in graphical user interfaces, more meaningful target shapes are rectangular. To empirically determine the best way to normalize the subjective biases, we ran remote and crowdsourced user experiments with three speed-accuracy instructions. We propose to normalize the speed-accuracy biases by applying the effective sizes to existing Fitts’ law formulations including width W and height H. We call this target-size adjustment the bivariate effective width method. We found that, overall, Accot and Zhai’s weighted Euclidean model using the effective width and height independently showed the best fit to the data in which the three instruction conditions were mixed (i.e., the time data measured in all instructions were analyzed with a single regression expression). Our approach enables researchers to fairly compare two or more conditions (e.g., devices, input techniques, user groups) with the normalized throughputs.

We propose a method that predicts the success rate in pointing to 2D rectangular targets by using 1D vertical-bar and horizontal-bar task results. The method can predict the success rates for more practical situations under fewer experimental conditions. This shortens the duration of experiments, thus saving costs for researchers and practitioners. We verified the method through two experiments: laboratory-based and crowdsourced ones. In the laboratory-based experiment, we found that using 1D task results to predict the success rate for 2D targets slightly decreases the prediction accuracy. In the crowdsourced experiment, this method scored better than using 2D task results. Thus, we recommend that researchers use the method properly depending on the situation.

Models of lassoing time to select multiple square icons exist, but realistic lasso tasks also typically involve encircling non-rectangular objects. Thus, it is unclear if we can apply existing models to such conditions where, e.g., the width of the path that users want to steer through changes dynamically or step-wise. In this work, we conducted two experiments where the objects were non-rectangular, with path widths that narrowed or widened, smoothly or step-wise. The results showed that the baseline models for pen-steering movements (the steering and crossing law models) fitted the timing data well, but also that segmenting width-changing areas led to significant improvements. Our work enables the modeling of novel UIs requiring continuous strokes, e.g., for grouping icons.

In touch interfaces, a target, such as an icon, has two widths: the visual width and the touchable width. The visual width is the target's appearance, and the touchable width is the area in which users can touch a target and execute an action. In this study, we conduct two experiments to investigate the effects of the visual and touchable widths on touch pointing performance (movement time and success rate). Based on the results, we build candidate models for predicting the movement time and compare them by the values of adjusted R^2 and AIC. In addition, we build a success rate model and test it through cross-validation. Existing models can be applied only to situations where the visual and touchable widths are equal, and we show that our refined model achieves better model fitness, even when such widths are different. We also discuss the design implications of the touch interfaces based on our models.

The steering law is a model for predicting the time and speed for passing through a constrained path. When people can view only a limited range of the path forward, they limit their speed in preparation of possibly needing to turn at a corner. However, few studies have focused on how limited views affect steering performance, and no quantitative models have been established. The results of a mouse steering study showed that speed was linearly limited by the path width and was limited by the square root of the viewable forward distance. While a baseline model showed an adjusted R2 = 0.144 for predicting the speed, our best-fit model showed an adjusted R2 = 0.975 with only one additional coefficient, demonstrating a comparatively high prediction accuracy for given viewable forward distances.

We propose a Servo-Gaussian model to predict success rates in continuous manual tracking tasks. Two tasks were conducted to validate this model: path steering and pursuit of a 1D moving target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints form a bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls inside the tolerance, and (3) the success rate for a whole trial can be predicted if the number of submovements is known. The cross-validation showed R^2>0.92 and MAE<4.9% for steering and R^2>0.95 and MAE<6.5% for pursuit tasks. These results demonstrate that our proposed model delivers high prediction accuracy even for unknown datasets.

The dual Gaussian distribution hypothesis has been used to predict the success rate of target pointing on touchscreens. Bi and Zhai evaluated their success-rate prediction model in off-screen-start pointing tasks. However, we found that their prediction model could also be used for on-screen-start pointing tasks. We discuss the reasons why and empirically validate our hypothesis in a series of four experiments with various target sizes and distances. The prediction accuracy of Bi and Zhai's model was high in all of the experiments, with a 10-point absolute (or 14.9% relative) prediction error at worst. Also, we show that there is no clear benefit to integrating the target distance when predicting the endpoint variability and success rate.

When users operate smartphones and desktop interfaces with their fingers, there are differences between the motor and visual widths. For example, when a user selects an item from a vertical menu, the area that is physically touched by the user is often larger than the visual width (e.g., of the label for the item selected). Therefore, the user aims for the label assuming that the label width (the visual width) means the motor width. Consequently, the user performs operations more carefully than necessary. We conducted an experiment to investigate the effect of the motor and visual widths on finger pointing. After asking participants to explore the motor width, they performed an experimental task. Our experiment shows that the users' movement time depends on the motor width and can be predicted. We also analyze existing interfaces and discuss the implications.

In graphical user interfaces, users can select multiple objects simultaneously via lasso selection. This can be implemented, for example, by having users select objects by looping around their centers or entire areas. Based on differences in lassoing criteria, we presume that the performance of the criteria also differs. In this study, we compare three lassoing criteria and model lassoing motions while considering these criteria. We conducted two experiments; participants steered through straight-line and one-loop paths by using three criteria. The participants handled the lassoing criteria correctly and performed lassoing at appropriate speeds for each path shape. Although the drawn trajectories varied depending on the lassoing criteria, the criteria in the performance and subjective evaluations did not differ significantly. Additionally, from our results, we build a baseline model to predict the movement time by considering the lassoing criteria. We also discuss further experiments to predict movement time under more complex conditions.