Extending Fitts' law to more than one dimension has been recognized as having important implications for HCI. In spite of the progress made over the years, however, it is still far from a resolved issue. Our work approaches this problem from the viewpoint of a configuration space, which has served as a useful conceptual framework for understanding human preference in perception. Notably, human are found to be biased towards regular configurations. In this work, we extended the configuration space framework to the domain of motor behavior, analyzed 2D pointing, and developed five models to account for the performance. An extensive experiment was conducted to measure the fit of the derived models and that of three previous models. Consistent with our hypothesis, the model reflecting a bias towards regular configuration was found to have the most satisfactory fit with the data. The paper concludes with discussions on improving understanding of Fitts' law and the implications for HCI. © 2010 ACM.