In this video, I am presenting our work with the title “The Persistence of First Impressions:
The Effect of Repeated Interactions on the Perception of a Social Robot” which was
carried out by the Uppsala Social Robotics Lab.
This paper is about first impressions, but to better understand what we mean by that,
let’s start by taking a look at Hannah who is about to meet Adam.
Adam is a person previously unknown to her, which is why we call this situation a zero
acquaintance context.
Within the first milliseconds of seeing Adam and before even talking to him, Hannah will
decide whether Adam constitutes a threat or is to be trusted.
The first few minutes of conversation between the two will then set the ground for their
future relationship.
These rapid judgements based on the first five minutes of interaction are called first
impressions.
The conclusions we draw from these first impressions are based on our previous experiences and
since we are meeting new people on a daily basis, related work from social psychology
has shown that these impressions are a good predictor of the future relationship.
However, being exposed to another human more often or having frequent interactions with
them can alter our experience.
Usually, repeated interactions make us grow closer to the other, but if we develop interpersonal
conflicts or become bored, the relationship could also suffer.
Now what happens if Adam is not a human but a robot?
The experience people have with meeting robots is rather sparse, so our first impressions
may not be a good predictor of the relationship we will develop with a robot over time.
In our previous work, we found that even an interaction of a few minutes improved people’s
first impression of a robot on dimensions like anthropomorphism or likability.
What we don’t know, however, is how these first impressions develop over multiple interactions
with several days of zero exposure in between.
Or in other words, how persistent are our first impressions of robots?
Following up on our previous research, the study we present in this paper is aimed at
better understanding how playing a collaborative game with a robot in comparison to just having
a social chat influences people’s initial perception of the robot.
The main focus of our investigation was how repeated interactions with a robot do or do
not alter the perception of a robot.
Unlike related studies, we used true repeated interactions with multiple days of zero exposure
in between, instead of multiple measurements within just one interaction session.
We also kept the type, structure and length of the interaction constant across the three
sessions to reduce the confounding factors observed in other long-term investigations.
Since people had multiple days of zero exposure in between their interaction sessions, we
were also interested in the effect that these times of no exposure had on people’s perception
of the robot.
In other words, would they recall their interaction with the robot differently after they had
some time to process it?
First impressions of a robot can, of course, be influenced by many factors.
From previous work we know that the appearance and specifically the level of humanlikeness
of a robot play an important role in people’s perception of it.
With our study, we would like to understand whether these differences between robot appearances
persist over repeated interactions.
When it comes to anthropomorphism, interesting types of robots are those that carry mismatching
cues in their humanlikeness since these robots usually elicit uncanny feelings.
We designed one of the robot appearances to be a morph between a humanlike and a mechanical
robot, thus embodying these mismatching cues and likely eliciting initial negative feelings
so we were able to study their persistence over time as well.
The cooperative game we developed to study repeated interactions with a robot is designed
to enhance people’s geographic literacy when playing with a robot team partner.
The players have the role of the tutor in this scenario.
On the iPad, they see the world map with one country highlighted as the target.
Their goal is to get the robot to select the same country on the shared screen which is
placed in between the two.
The robot thus acts as a learner in the pedagogical game.
Any verbal description, including the use of the country’s name, is allowed to identify
the target.
The goal for the human-robot team is to score as many points as possible during the ten
minutes of game interaction.
[Participant] So it's to the east of of India and North of Australia.
[Robot] Got it.
[Participant] You got it?
[Robot] Is it this one?
[Participant] Yup, that's the one.
[Participant] Good job.
[Robot] Nice.
[Participant] So shall we take the next one?
[Robot] Yes, let's move on.
[Participant] Alright, you remember where India was?
[Robot] Yes.
[Participant] So this country is bordering to India to the west of it.
[Robot] Is it this one?
[Participant] Yes, that's the one.
[Robot] Yay!
[Participant] Good.
We used the blended robot head Furhat to play the game.
The appearance of the robot can easily be changed by applying a different facial texture,
making the robot look humanlike, mechanical or like a morph between the humanlike and
mechanical version.
The robot was teleoperated according to a Wizard of Oz protocol by a researcher trained
during 50 online game interactions.
Before and after the game, participants engaged in a brief social chat with the teleoperated
robot.
[Robot] Hi, I'm Neil.
[Robot] What's your name?
[Participant] Sophia [Robot] Awesome, nice to meet you.
[Robot] Well, I guess we get to play a game with each other.
[Robot] That sound pretty nice, don't you think?
We conducted an experiment in this scenario in which 49 participants had two to three
interaction sessions with the robot with three to ten days of zero exposure in between.
Participants were divided into three groups, each playing with either the humanlike, mechanical
or morph version of the robot.
[Participant] No, I'm from France.
[Robot] Ah, that's just around the corner.
[Robot] What would you recommend to visit in your country?
[Robot] Last time you told me that you go back home not often.
[Robot] Do you miss being in France?
[Participant] A little.
[Participant] It's nice here, so it's okay.
[Robot] What do you miss about it?
[Participant] The food
[Robot] Have you been to one of those countries before?
[Participant] I have to some of them.
[Robot] Which one?
[Participant] I've been to Ireland and I've been to South East Asia.
[Robot] I had a lot of fun playing with you.
[Robot] I'm very sorry that this is our last meeting.
[Robot] Would you like to meet again in the future?
[Participant] Yeah, why not?
[Robot] Nice.
At different times throughout the interaction sessions, participants were asked to report
their perception of the robot on multiple dimensions, including their perception of
the robot’s anthropomorphism, likability and discomfort with it.
We found repeated interactions to have an overall positive effect on people’s perception
of the robot.
In line with our previous work, we found that merely interacting with the robot for an extended
period increased the robot’s perceived anthropomorphism.
The perceived anthropomorphism was then stable for the second and third interaction.
Interestingly, people’s recall of the robot’s humanlikeness was inaccurate to the disadvantage
of the robot: They recalled the robot to be significantly less humanlike than they rated
it at the end of the previous session.
We hypothesize that the widespread conception of a robot as a mechanical device might have
distorted the mental image of the robot.
While the perceived anthropomorphism stabilized after the first game interaction, the likability
and perceived threat were less persistent.
We saw the likability increasing even after the second session, and the perceived threat
was significantly decreasing up to the end of the third and last session of our experiment.
Our findings thus indicate that the mental image of a robot is built over time as the
perceptual dimensions that contribute to it stabilize at different moments.
Our results suggest that the effects of repeated interactions with a robot generalize across
all levels of anthropomorphism that we investigated.
This implies that initial biases against a robot do not wear out over time.
Participants judged the humanlike robot as more competent, warmer and talked with it
as with another person only based on its humanlike facial appearance, and these differences in
judgement persisted even after three exactly identical interaction sessions.
Even though our robot had very advanced remote-controlled interaction capabilities, it could not recover
from its initial unappealing appearance when applying the texture of the morph between
the humanlike and the machinelike version.
Our findings are important for the HRI community in several ways: First of all, by assessing
the perception of a robot at different moments, we find that perceptual dimensions evolve
differently over time.
By highlighting that initial biases against robots persist over time, we strengthen the
importance of a robot's visual design, but also shed new light on the persistence of
the uncanny valley effect.
We also show how pre-existing expectations can influence people's situated perception
of a social robot and negatively impact how they recall a robot after several days.