Should we design robots to be highly predictable in their behaviour, to accomodate to the need of
autistic children for predictable environments? To answer this question, we conducted an experimental
study that I will be presenting. And my name is Bob Schadenberg from the University of Twente.
Autism spectrum condition, or autism in short, affects each child is very differently. However,
there are two main features that all autistic individuals have in common.
There are difficulties in social interaction and communication
and the presence of restrictive repetitive behaviour and interests.
And one of the main manifestations of autism is a need for predictable environments. Which is also
referred to as insistence of sameness This is why many programs for these children are highly
structured so that the children know what will happen during the day to limit unexpected events.
Robots might be able to address this need for predictability. Because they are programmable,
we can design them to be highly predictable. And this predictability of robots is also a commonly
used argument for why robots are promising tools for autism professionals to use. The hypothesis
here is that robots could make it easier for autistic children to engage in learning
in a robot-assisted intervention and maintain this engagement.
However, robots are not inherently predictable. We need to design them as such. This involves
a lot of decisions and trade-offs. For instance, should we add variations of speech and movement to
the robot? Because for typically developing children, we see that this reduces boredom
and increases engagement. Moreover, a highly predictable robot may also not be very useful,
as it would perpetuate in repetitive behaviour, and may also not be representative of the
interaction between people, and thus limit the generalisation of learning to human-human
interaction. So we might need to introduce a degree of unpredictability to the robot s
behaviour. To make an informed decision between having a more predictable robot or trading some
of the predictability off for other beneficial effects, we conducted an experimental study,
where we looked at two research questions. The first question relates to the effect of variance
in the robot s behaviour on the engagement of the children. Here, variance is a way to make
the robot more unpredictable. For instance, we implemented variance in all its movements
and speech. Now because autistic children are so different from each other, we also looked at role
of the individual differences on the children s engagement in relation to the robot s variance.
In our study, autistic children would participate
in a robot-assisted activity which was about learning the basics of emotion recognition.
In total, 24 children participated, and these were children with high support needs.
The activity consisted of 4 sessions which were conducted on different days. Participants
were divided into two conditions. In a high variance condition, where we tried to maximise
the robot s variance, whilst maintaining a normal interaction. And in the low-variance condition,
we limited the variance as much as we could.
children by annotating each video recording of the each sessions.
We also annotated their visual attention. So this relates to the locations the child would look at.
And to measure the individual differences, we included the for symptom severity,
which was measured through the CARS-2. And we measured the intolerance of uncertainty,
because we believed that uncertainty was related to unpredictability And finally, we also included
a bespoke scale of expressive language. To analyse all this we used hierarchical linear modelling.
We hypothesized that the effect of high variance would show in the later sessions. Because
initially, both robots are unpredictable. But as the child would interact more with the robot,
they become more familiar with the robot and learn to predict its behaviour. But in the high-variance
condition this would be more difficult because of all the added variance. However,
as you can see in the graph, we found no difference in behavioural engagement over time.
However, we did find that those with higher symptom severity,
also showed less behavioural engagement overall.
also scored higher on behavioural engagement. So they were more engaged.
For the intolerance of uncertainty we found no effect
For visual attention, we again expect that the effect of variance has an effect over time.
If we look at how often the children would look at the robot, we see that this decreases over time,
but that there is no between conditions. However, when we look at all locations that had nothing to
do with the task at hand, we do see that those in the high-variance condition increasingly look
at such locations. Now for visual attention, none of the individual differences were significant.
So this also means that the difference between the conditions on non-task related
locations was independent of any of the individual differences that we measured.
In conclusion, it appears that the children continued engaging in the robot-assisted activity,
but started to pay less visual attention over time to the activity-relevant locations when the robot
was less predictable. Instead, the children started to pay more attention to locations
that visually did not change, such as the walls, or the floor. Ultimately, this could negatively
influence learning, in particular for tasks with a strong visual component. We also found that
symptom severity and expressive language ability influence how behaviourally engaged children were
in general. The main take-away from our study is that the predictability of the robot is an
important factor in keeping autistic children in a state where learning can occur. And that we should
be mindful when trading off predictability in the robots behaviour in favour of other benefits.
For more details on our study and on robot predictability,
please see our full article, which is freely accessible to everybody.