An ACT-R Model of Elliptical Endpoint Error Distributions in a Mobile

ii Given the high propensity of users ’ motoric errors with smaller touchscreen buttons, knowing the endpoint distributions for finger-based pointing and tapping is especially important for higher-fidelity predictive modeling of tasks on such devices. One of the most studied models of aimed human motor movement in HCI is Fitts ’ Law. While Fitts ’ Law has a long and successful history of application in predicting mouse-pointing or stylus-tapping times, its traditionally high predictive ability declines when it is applied to finger-pointing tasks involving small touchscreen targets, especially when the finger (input device) is larger than the target itself (commonly known as the “fat finger ” problem). There is still some uncertainty regarding the systematic prediction of endpoint distributions for two-dimensional finger-pointing tasks. Recent work (May, 2012) found endpoint error distributions larger for on-axis than off-axis movement in a mouse-pointing task, with the shape of the error distribution along the movement axis more ovoid than circular around the target center. Since the implementation of endpoint error in ACT-R did not previously distinguish between on-axis and off-axis error, Gallagher and Byrn