Hi, everyone. I'm Xiuli Chen. I'm going to present
an adaptive model of gaze-based selection
Gaze-based selection has been studied in HCI since the 1990s. While many significant advances have been made,
We believe that further progress could be made with a cognitive model of eye movement and vision
In this paper,
we propose a reinforcement learning model that learns the gaze-based selection task
from experience of the environment
In the model,
the sequence of saccades and fixations is governed by an attempt to optimize a utility function.
such as selection time, accuracy
or a trade off between these two.
Importantly, the optimization
is constrained by the model
of both motor system and vision system
in a gaze-based selection task,
the user is often asked to move their gaze into a target region as quickly as possible
From a starting point, due to our low acuity
peripheral vsion, a noisy estimate of the target position is made
as shown in location two
then an eye movement is aimed towards location two
However
due to the noise in our ocular modal system, the eyes ended up at location three
the process repeats until it reaches the target
in this case
It takes two saccades to reach the target
Similarly, in the model
the agent chooses an action (a saccade)
based on its current belief of target position
given the action taken and the task environment
a noisy observation is obtained
and used to update agent belief of the target position.
Meanwhile, the reward function is used to update agent's policy.
A reinforcement learning algorithm is used to learn the
optimal policy so that the sequence of the saccades is governed by an attempt to maximize the reward function.
Now let's look at some results
The selection time in a gaze-based selection task is usually composed of two parts
Eye movement time and dwell time
Eye movement times are the time required to find and reach the target
This is sensitive to the size of the target
Specifically
the eye movement time decreases with bigger target sizes as that
The number of saccades required decreases
The second part is dwell time.
in the experiment
A continuous 800 millseconds within target region is required to commit
the target selection
However
it's known that
even during fixations
human eyes do not remain perfectly still
the eyes jitter in a small area
For example, the data show that
when fixating on a target region of
1.2°
This jitter causes the gaze to leave and reenter the target
area several times
resulting in an actual target dwell time of 1,000 and
75 milliseconds
That is 275 milliseconds longer than the dwell criterion
of 800 milliseconds
How does our model do
The model was only fitted
to data of the smallest target size
But as you can see
there is an exceptional correspondence between the model prediction and the data
The model predictions of eye movement time and final selection time decrease the same rate as human data
Our model also predicts a number of other empirical findings in the literature
such as the effect of index of difficulty on the number of the saccades
To conclude, we have reported a gaze based selection model
that adapts eye movement strategies to
target sizes
target distances
as well as to noise in the motor and vision system
The model predicts a number of key empirical findings in the
literature and provides a useful basis for guiding future research
Furtherwork should explore
using the model in design optimization
Thank you