A Feedback Information-Theoretic Transmission Scheme (FITTS) for Modeling Trajectory Variability in Aimed Movements
This provides a theoretical explanation for the speed-accuracy tradeoff in aimed movements, recovering Fitts' law, but it is incremental as it builds on existing models and datasets.
The paper tackled the problem of variability in human aimed movements by modeling the second phase as a Shannon-like communication problem with feedback, showing that positional variance decreases exponentially at a rate equal to channel capacity C, which was found constant across tasks in re-analyzed datasets.
Trajectories in human aimed movements are inherently variable. Using the concept of positional variance profiles, such trajectories are shown to be decomposable into two phases: In a first phase, the variance of the limb position over many trajectories increases rapidly; in a second phase, it then decreases steadily. A new theoretical model, where the aiming task is seen as a Shannon-like communication problem, is developed to describe the second phase: Information is transmitted from a source (determined by the position at the end of the first phase), to a destination (the movement's end-point) over a channel perturbed by Gaussian noise, with the presence of a noiseless feedback link. Information-theoretic considerations show that the positional variance decreases exponentially with a rate equal to the channel capacity C. Two existing datasets for simple pointing tasks are re-analyzed and observations on real data confirm our model. The first phase has constant duration and C is found constant across instructions and task parameters, which thus characterizes the participant's performance. Our model provides a clear understanding of the speed-accuracy tradeoff in aimed movements: Since the participant's capacity is fixed, a higher prescribed accuracy necessarily requires a longer second phase resulting in an increased overall movement time. The well-known Fitts' law is also recovered using this approach.