In Fitts’ law studies to investigate pointing, throughput is used to characterize the performance of input devices and users, which is claimed to be independent of task difficulty or the user’s subjective speed-accuracy bias. While throughput has been recognized as a useful metric for target-pointing tasks, the corresponding formulation for path-steering tasks and its evaluation have not been thoroughly examined in the past. In this paper, we conducted three experiments using linear, circular, and sine-wave path shapes to propose and investigate a novel formulation for the effective parameters and the throughput of steering tasks. Our results show that the effective width substantially improves the fit to data with mixed speed-accuracy biases for all task shapes. Effective width also smoothed out the throughput across all biases, while the usefulness of the effective amplitude depended on the task shape. Our study thus advances the understanding of user performance in trajectory-based tasks.

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.

In typical experiments to evaluate novel pointing-facilitation techniques, participants are asked to perform a task as rapidly and accurately as possible. However, the balance can differ among participants, and the techniques’ effectiveness would change if the majority of participants give weight to either speed or accuracy. We investigated the effects of three subjective biases (emphasizing speed, neutral, and emphasizing accuracy) on the evaluation results of pointing-facilitation techniques, namely Bubble Cursor and Bayesian Touch Criterion (BTC). The results indicate that Bubble Cursor outperformed the baseline in terms of movement time and error rate under all bias conditions, while BTC underperformed a simpler target-prediction technique, which was an inconsistent outcome to the original study. Examining multiple biases enables researchers to discuss the (dis)advantages of novel or existing techniques more precisely, which can be beneficial to reach a more reliable conclusion.