Fitts’ law is very robust and used extensively in human-computer interaction (HCI). It predicts the movement time (MT) of pointing tasks under various conditions. However, the impact of visual inversion on hand movement performance has not been quantitatively investigated. This is really of essence as it provides a means to “unlearn” what we know and thus investigate how the eye and hand coordinate works. This thesis comprises three experiments with three different factors to investigate their impact as well as the underlying mechanism of aimed movement. The first and the third experiments were related to visual inversion. In order to determine the dominant factor of distal-pointing, the second experiment was designed.

The first experiment focused on the impact of eye-hand incompatibili...[ Read more ]

Fitts’ law is very robust and used extensively in human-computer interaction (HCI). It predicts the movement time (MT) of pointing tasks under various conditions. However, the impact of visual inversion on hand movement performance has not been quantitatively investigated. This is really of essence as it provides a means to “unlearn” what we know and thus investigate how the eye and hand coordinate works. This thesis comprises three experiments with three different factors to investigate their impact as well as the underlying mechanism of aimed movement. The first and the third experiments were related to visual inversion. In order to determine the dominant factor of distal-pointing, the second experiment was designed.

The first experiment focused on the impact of eye-hand incompatibility and investigated the substructure of the complete movemenet to investigate the presence of a ballistic region when task difficulty is low. For this purpose, MT was separated into initiation time (IT), distance covering time (DCT), and acquisition time (AT). The AT gives a better separation of the ballistic and visually controlled regions on the index of difficulty (ID) than movement time indicating the existence of a ballistic region.

The second experiment focused on pointing at different depths. The visual angles of movement amplitude and target width are better predictors of movement time than the linear dimensions and depth does not have a significant impact. With increasing depth, hand and arm tremor effects increase.

The third experiment noted that the individual kinematic perception is correlated to one’s MT and people do not need to see the hand during the whole movement when aiming. What we need is a window nearer to the target. The duration and endpoint variability of the first ballistic submovement of the first and the third experiment were compared and that helped explain the performance difference among the different conditions. This validates that different visual conditions affect not only the total MT but also the submovement properties. In addition, the comparison turns out that people can perform delicate control strategies viewing direct, and this ability is heavily reduced with screen feedback.

Further analysis using multiple datasets verified a few hypotheses and gives better models without overfitting of data. Using data from the three experiments and several published studies proved that the log₂MT has a better normal distribution than MT. Moreover, the use of log₂MT, angular A and angular W, and two-part variations of original Fitts’ models were proved to have more accurate prediction on MT. Besides, the model MT = a + b ∗ ID + c ∗ Gain/W, is consistently valid over aimed movement with multiple depth and control-display gain (Gain).