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.

Bayesian hierarchical models are probabilistic models that have hierarchical structures and use Bayesian methods for inferences. In this paper, we extend Fitts’ law to be a Bayesian hierarchical pointing model and compare it with the typical pooled pointing models (i.e., treating all observations as the same pool), and the individual pointing models (i.e., building an individual model for each user separately). The Bayesian hierarchical pointing models outperform pooled and individual pointing models in predicting the distribution and the mean of pointing movement time, especially when the training data are sparse. Our investigation also shows that both noninformative and weakly informative priors are adequate for modeling pointing actions, although the weakly informative prior performs slightly better than the noninformative prior when the training data size is small. Overall, we conclude that the expected advantages of Bayesian hierarchical models hold for the pointing tasks. Bayesian hierarchical modeling should be adopted a more principled and effective approach of building pointing models than the current common practices in HCI which use pooled or individual models.

Steering law reveals a linear relationship between the movement time (MT) and the index of difficulty (ID) in trajectory-based steering tasks. However, it does not relate the variance or distribution of MT to ID. In this paper, we propose and evaluate models that predict the variance and distribution of MT based on ID for steering tasks. We first propose a quadratic variance model which reveals that the variance of MT is quadratically related to ID with the linear coefficient being 0. Empirical evaluation on a new and a previously collected dataset show that the quadratic variance model accounts for between 78% and 97% of variance of observed MT variances; it outperforms other model candidates such as linear and constant models; adding the linear coefficient leads to no improvement on the model fitness. The variance model enables predicting the distribution of MT given ID: we can use the variance model to predict the variance (or scale) parameter and Steering law to predict the mean (or location) parameter of a distribution. We have evaluated six types of distributions for predicting the distribution of MT. Our investigation also shows that positively skewed distribution such as Gamma, Lognormal, Exponentially Modified Gaussian (ExGaussian), and Extreme value distributions outperformed the symmetric distribution such as Gaussian and truncated Gaussian distribution in predicting the MT distribution, and Gamma distribution performed slightly better than other positively skewed distributions. Overall, our research advances the MT prediction of steering tasks from a point estimate to variance and distribution estimates, which provides a more complete understanding of steering behavior and quantifies the uncertainty of MT prediction.

Modeling touch pointing is essential to touchscreen interface development and research, as pointing is one of the most basic and common touch actions users perform on touchscreen devices. Finger-Fitts Law [4] revised the conventional Fitts' law into a 1D (one-dimensional) pointing model for finger touch by explicitly accounting for the fat finger ambiguity (absolute error) problem which was unaccounted for in the original Fitts' law. We generalize Finger-Fitts law to 2D touch pointing by solving two critical problems. First, we extend two of the most successful 2D Fitts law forms to accommodate finger ambiguity. Second, we discovered that using nominal target width and height is a conceptually simple yet effective approach for defining amplitude and directional constraints for 2D touch pointing across different movement directions. The evaluation shows our derived 2D Finger-Fitts law models can be both principled and powerful. Specifically, they outperformed the existing 2D Fitts' laws, as measured by the regression coefficient and model selection information criteria (e.g., Akaike Information Criterion) considering the number of parameters. Finally, 2D Finger-Fitts laws also advance our understanding of touch pointing and thereby serve as the basis for touch interface designs.