I'm Shota Yamanaka from Yahoo Japan Research.
In this study, we propose a model to predict the success rates in manual tracking tasks.
Many interactive systems require continuous tracking operations,
such as menu navigation, video recording, and video games.
In these operations, the success or failure is judged based on the entire operation; not
a single endpoint location.
To our knowledge, there are no established models to predict error rates, or success
rates in these tasks.
First, we derive a model for steering tasks.
Users move the cursor along a narrow path.
The key idea to predict the success rate is, to interpret a steering motion as repetitions
of ballistic submovements.
This is called the servo-mechanism model.
So, in a steering motion, users perceive the cursor position, and determine the speed and
direction of the next submovement.
And, each submovement is performed based on a ballistic manner.
First, endpoint distributions of ballistic motions follow a Gaussian distribution.
And based on the steering law, a submovement distance is proportional to the path width,
And the distribution (sigma) can be predicted by the path width.
We can predict the success rate on whether a submovement endpoint falls inside the path
boundaries.
So, we obtain Equation 4 as the success rate of a single submovement.
Then, we can compute the number of submovements in a trial.
And finally, the success rate of whole trial is Equation 5.
This Study 1 follows the method of classical steering law experiment with a mouse.
We tested four path lengths (A), and five path widths (W).
To obtain the coefficients f and g, we adopt the parameter optimization.
The results show that, when we use all data points, the R-squared is 0.943, and the mean
absolute error is about 3%.
Also, we check the prediction accuracy by the cross-validation, which shows the good prediction
accuracy of the model.
Next, we derive a model for pursuit of a moving target.
The target automatically moves rightwards, and users have to keep the cursor inside the target.
Again, we adopt the servo-mechanism model, which means that, users successively perform
ballistic motions to keep the cursor inside the target.
We interpret each submovement as a moving-target pointing task.
The μ and σ are modeled in Huang and colleagues' work on moving-target pointing.
We again use the power function, whose power index is the number of submovements in a trial.
Finally, we obtain the success rate model for the whole trial as Equation seven.
In Study 2, we tested three target speeds (V) and three target widths (W).
The result shows that, when we use the all data points, the R-squared is 0.972, and the
mean absolute error is about 6%.
Also, the result of cross-validation indicates the good prediction accuracy of our model.
In summary of the two studies, the success rates were accurately predicted.
One important point is that, the two tasks were very different.
Path steering is self-paced tracking, while pursuit is externally-paced tracking.
And, the cursor motion was constrained on the y-axis in the path steering, while the cursor
was constrained on the x-axis in the pursuit task.
This identifies a certain generalizability of our Servo-Gaussian Model.
As takeaways, We can reduce the efforts for conducting costly
user studies to measure success rates.
We can design better UIs based on success rates, and this work informs future research
topics on success rate prediction modeling in more complicated tasks.
This is the summary of this work.
Thank you for watching.