Int J Soc Robot
DOI 10.1007/s12369-011-0122-y

How Does the Difference Between Users’ Expectations
and Perceptions About a Robotic Agent Affect Their Behavior?
An Adaptation Gap Concept for Determining Whether Interactions Between Users
and Agents Are Going Well or Not
Takanori Komatsu · Rie Kurosawa · Seiji Yamada

Accepted: 7 November 2011
© Springer Science & Business Media BV 2011

Abstract We assumed that the difference between theusers’ expectations regarding the functions of an agent andthe function that they actually perceived would significantly
affect their behavior toward the agent. We then defined thisdifferences as the adaptation gap and experimentally investigated how the adaptation gap signs affected the acceptance
rate indicating how many of the agent’s suggestions the participants accepted as their behaviors toward the agent. The
results showed that the participants with positive adaptation
gap signs had a significantly higher acceptance rate thanthose with negative ones. This led us to conclude that theadaptation gap signs significantly affected the participants’behavior toward agents in the way that we expected, andthat comprehending these signs will become indispensablefor designing interaction between users and agents.
Keywords Adaptation gap · User expectations andperceptions · User behavior toward agents

T. Komatsu ()IYREC (International Young Researcher Empowerment Center),
Shinshu University, 3-15-1 Tokida, Ueda 3868567, Japane-mail: tkomat@acm.org
R. Kurosawa
Faculty of Textile Science and Technology, Shinshu University,
3-15-1 Tokida, Ueda 3868567, JapanS. YamadaNational Institute of Informatics/SOKENDAI/Tokyo Instituteof Technology, 2-1-2 Hitotsubashi, Tokyo 1018430, Japane-mail: seiji@nii.ac.jp

1 Introduction
Various interactive agents such as robotic agents [1, 2]
and embodied conversational agents (ECA) [3, 4] have recently been developed to assist us with our daily tasks.
Researchers in the human-computer interaction (HCI) andhuman-robot interaction (HRI) communities are working
particularly hard to create such interactive agents. In thesefields, the issue of “how the users’ mental models of an agent
formed before the interaction affect their subsequent interaction with it” is especially focused on [5–7]. Users forma mental model about an agent based on its appearance, its
behaviors, and their own preferences, and this model theycreate has a significant effect on the way they interact withthe agent. For example, when a user encounters a dog-likerobot, he or she expects dog-like behavior from it [8] andnaturally speaks to it using commands and other utterances
intended for a real dog, such as “sit,” “lie down,” and “fetch.”
However, the user would not act this way toward a cat-likerobot.
Several previous studies have focused on the effect ofusers’ mental models about an agent on their ensuing interactions [9]. For example, Matsumoto et al. [10] proposeda “minimal design policy” for designing interactive agentsand concluded that the agent’s appearance should be minimized in its use of anthropomorphic features to ensure thatusers do not over or underestimate an agent’s competency
level. This minimal design policy was applied to the development of Muu [11], an interactive robot, and Talking
Eye [12], a life-like agent. Kiesler [13] argued that the design of an agent should include a process that anticipates auser’s mental model on the basis of the common ground theory [14]; that is, individuals engaged in conversation must
share knowledge (common ground) in order to be understood and to have a meaningful exchange. In particular, sheInt J Soc Robotstated that an agent should be designed in such a way thata user could easily estimate his or her common ground with
it.
The above design policies or “road maps” were quite effective for designers, especially at the beginning of an interaction, because they could determine whether or not a user
would actually start interacting with a given agent. However,these approaches cannot anticipate cases in which an agent
expresses certain behaviors that completely deviate from theuser’s mental model. For example, imagine that a user meetsa humanoid robot that looks very much like a real humanbeing. The user would then intuitively form a mental modelof the robot, expecting fluent human-like speech, dialogue,
and dexterous limb motions [15]. However, if this particularrobot could only express machine-like speech and halting
limb motions that completely deviate from the user’s mental model, he or she will immediately become disappointed
with the robot because of its unexpected behavior. The user
would then probably stop or lose interest in interacting withit.

2 Adaptation Gap Hypothesis
To comprehend this undesirable situation in the hope of
avoiding it, we assumed that the difference between theusers’ expectations regarding the function of the agent andthe actual function they perceive would significantly affect their subsequent behavior. This difference, which wecall the adaptation gap (AG) [16–18], can be defined as
AG = Faf ter − Fbef ore . Here, Faf ter is the function that auser perceives in the agent and Fbef ore is the function thatthe user expected. We assume that this AG has the following
two properties (Fig. 1).

– AG > 0 (Faf ter > Fbef ore ): When the users’ perceived
function of an agent exceeds their expected function of
the agent, there is a positive adaptation gap. In this case,most people are not disappointed by the agent and do trust
the robot’s output.

3 Differences Between our Former and the Present
Study
In our previous study, we experimentally investigated how
the positive or negative signs of adaptation gap (AG signs)
affect the users’ final impressions of an agent during useragent interaction [18]. The results of this previous study
showed that positive or negative AG signs and subjective
impressions of agents before the experiment (Ibef ore in
Fig. 2) significantly affected the user’s final impressions of
the agents (Iaf ter in Fig. 2). Specifically, the participants
who showed positive AG signs and negative impressionsof the agents before the experiment (Ibef ore ) ended up having significantly positive impressions of the agents (Iaf ter ),whereas the participants who showed negative AG signs
and positive impressions of the agents before the experiment ended up having significantly negative impressions after. Therefore, not only the AG signs but also the users’ impressions before the experiment had a significant effect on
the users’ final impressions. While this information is useful, we failed to observe the pure effects of the adaptation
gap on the users’ impressions about agents and their subsequent behavior toward them.
In terms of the above results, the relationship between
the independent variables and the dependent variables was– AG < 0 (Faf ter < Fbef ore ): When the users’ expectedfunction of an agent exceeds their perceived function of
the agent, there is a negative adaptation gap. In this case,most people become disappointed by the agent and do nottrust the robot’s output.

Fig. 1 Intuitive concept of adaptation gap

Fig. 2 Relationship between independent and dependent variables inour previous experiment [18]Int J Soc Robot

Fig. 3 Treasure hunting video gamecomplicated, and we had to use a covariate to properly interpret the results. That is, the independent variables were
the AG signs, the dependent variables were the users’ final
impressions after the interaction (Iaf ter ), and the covariate
was the users’ impressions of the agents before the experiment (Ibef ore ). At first, we had assumed that the users’
expected/perceived functions and their impressions about
agents before/after the interactions would be independent ofeach other, but this assumption turned out to be incorrect.
In the former study, we focused on how the AG signs affectthe users’ impressions about the agents. However, as mentioned in Sect. 1, we also need to focus on how the AG signs
affect the users’ behavior toward agents.
In the current study, we carefully and clearly assigned the
independent variable (AG signs) and the dependent variable(users’ behaviors toward agents) in order to exclude the effects of any covariates. This reassignment of independentand dependent variables helps us clarify how the AG signs
affect the users’ behavior toward the agents.

4 Experiment
We conducted an experiment to investigate how positive or
negative AG signs affected the users’ behavior towards anagent. This experiment consisted of two phases: in the first,
we measured the AG signs as an independent variable (exploration phase), and in the second, we measured the users’
behavior as a dependent variable (exploitation phase).

4.1 Experimental environment
We chose a “treasure hunting” video game (Fig. 3) as theexperimental environment for observing the interaction between a user and an agent in both phases. In this game,a character on a computer monitor operated by a user walks
on a straight road with three tiny hills appearing along theway. A gold coin is inside one of the three hills, while theother two hills have nothing. In the exploration phase, we
ended the game after the character met 40 sets of hills. The
approximate duration of each game was about three minutes.
In the exploitation phase, we ended the game after 20 sets ofhills. The goal of this game is to find as many gold coins as
possible. A robotic agent (MS; MindStorms, LEGO Corporation, Fig. 4) placed next to the user tells the user where it
expects the coin to be each time. MS did this by beeping thenumbers: e.g., one beep meant the first hill (left), two beeps
meant the second hill (middle), and three beeps meant thethird hill (right). The users were free to accept or reject theagents’ suggestions. They were informed that one gold coin
was equivalent to 10 yen (about 10 US cents) and that, after
the experiment, they could purchase some stationery (e.g.,file holders or USB flash memories) of equivalent value with
their acquired coins. The beep sounds of the robotic agent
were remotely operated by an experimenter in the next room
performing in the Wizard of Oz (WOZ) manner via an FMtransmitter and radio tuner loaded on the MS. The treasurehunting video game was projected on a 46-inch LCD screen
in front of the participants (Figs. 5 and 6).

Int J Soc Robot

Fig. 4 MindStorms robot

Fig. 5 Experimental set-up

accepted the robot’s suggestions, even though they thought
and believed the robot’s ability was not perfect. Actually,
a posterior analysis of this preliminary experiment revealed
that the reason for such behavior by the participants was
based on the fact that there were no clues for them to find thecoin in this game. We then concluded that this phenomenon
could be explained by Koehler’s argument [19], which statesthat “the confidence level of another’s hypothesis tends to behigher than one of their own hypotheses, especially in a situation where users could not find any counter-evidence.” In
addition, Kiyokawa, Ueda, and Okada [20] argued that theconfidence level in a user’s own hypothesis increases when
they are given counter-evidence against the other-generated
hypothesis. We therefore introduced the “four-beep” soundas counter-evidence indicating that the robots’ generated hypotheses were not always correct. The presence of counterevidence works to create an environment in which users
must think carefully about the robots’ suggestions.
In the exploration phase, the participants were allowed toknow whether the robots’ and their own selections were correct or not in each trial because the game showed the actual
position of the gold coin after the participants made a selection. In contrast, in the exploitation phase they were notallowed to know whether the given or their own selections
were correct or not: the selected hill just showed a question mark on a closed treasure box (Fig. 3). We did thisbecause we needed the participants to estimate the robots’functions only in the exploration phase in order to determine
the AG signs; in the exploitation phase, we only needed toobserve whether the participants accepted or rejected therobots’ suggestions.
4.2 Participants
Thirty Japanese university students (22 men and 8 women;
19–25 years old) participated in the experiment. The mean
age of these participants was 21.66 years old (SD = 1.56).
4.3 Experimental Group
These participants were randomly divided into the following
two groups in terms of the experimenter’s instructions about
the agent’s ability.

Fig. 6 Experimental scene

In this experiment, the robotic agent also emitted a “fourbeep” sound that did not relate to any of the hills appearing on the display. In the preliminary experiment, in which
the MS only expressed the three different beep sounds (one,two, and three beeps) that correlated to the hills appearing on
the display, we observed that most participants unthinkingly– Lower Expectation Group (15 participants): Before theexploration phase, the experimenter gave these participant
an instruction “the rate at which this robot succeeds in detecting the position of a coin is 10%,” thus this instruction
was for setting the participants’ expectations (Fbef ore ) at
10%.
– Higher Expectation Group (15 participants): Before theexploration phase, the experimenter told these participants that “the rate at which this robot succeeds in detecting the position of a coin is 90%,” thus setting the participants’ expectations (Fbef ore ) at 90%.

Int J Soc Robot

Fig. 7 Relationship between independent and dependent variables inthis study

The actual rate at which the robotic agent succeeded in
detecting the position of a coin in the exploration phase wasset at 33% regardless of the experimental groups. We selected a rate of “33%” which is the same rate as pure chance.
We assumed this rate would become the Faf ter values for allparticipants in both groups, thus automatically determining
the values of AG (AG values). That is, the ideal AG values in the Lower Expectation Group should be around +23(i.e., Faf ter − Fbef ore = 33% − 10%), while the values inthe Higher Expectation Group should be around −57 (i.e.,
Faf ter − Fbef ore = 33% − 90%).
4.4 Analysis
We investigated the effect of the AG signs on the participants’ behavior towards the robotic agent. The independent
variable was the AG signs and the dependent variable was
the participants’ behavior (Fig. 7). In order to acquire theAG signs, we measured the Fbef ore values of participants
who were asked just prior to the exploration phase “At what
rate will this robot succeed in detecting the position of acoin?” and measured the Faf ter values of participants who
were asked just after the exploration phase “At what rate did
this robot succeed in detecting the position of a coin?” Thus,
the values and AG signs can be calculated by the acquired
Fbef ore and Faf ter values (i.e., AG = Faf ter − Fbef ore ).
Here, we assumed that the participants in the Lower Expectation Group would show positive AG signs while those in
the Higher Expectation Group would show negative ones.
In order to acquire the participants’ behaviors as dependent variables, we calculated the acceptance rate indicating
how many of the agents’ suggestions the participants accepted in the exploitation phase. As mentioned previously in
Sect. 1, we predicted that “in the case of AG < 0, users will

eventually stop interacting with an agent, and in the case of
AG > 0, they will continue.” However, in this experiment
it is unrealistic to assume a situation in which participantssuddenly stop interacting with an agent. We therefore assumed that the attitude of such participants would directlyreflect their behavior toward the agent in terms of whether
or not they would accept the agent’s suggestions. In otherwords, if the participants wanted to stop interacting withan agent they would reject the robot’s suggestions, and ifthey wanted to continue the interaction they would accept
the robot’s suggestions. Among 20 trials in the exploitation
phase, the “four-beeps” sound was utilized twice so that the
maximum number of the acceptance rate would be 18.
The purpose of this experiment was to compare the acceptance rates of the two experimental groups. If we could
observe participants in the Lower Expectation Group having a significantly higher acceptance rate than those in theHigher Expectation Group, we would be able to conclude
that the AG signs significantly affected the participants’ behavior toward the agents in the way we had expected. In sucha case, we could argue that the AG properties mentioned in
Sect. 1 were clearly verified.
4.5 Manipulation Check
As manipulation checks, we firstly checked to determine
whether the values of Fbef ore were appropriately set or notin both groups just before starting the experiment (exploration phase). The average value of Fbef ore in the Lower Expectation Group was 16.2 (SD = 8.38) and that in the Higher
Expectation Group was 75.1 (SD = 22.0). This result showsthat the Fbef ore values in both groups were not rigidly set
as 10% and 90%. However, the results of a t-test (betweensubjects design, independent variable: Lower/Higher Expectation Group, dependent variable: Fbef ore values) showeda significant difference between these two experimentalgroups (t (28) = 9.361, p < .01, effect size r = .87). Therefore, it can be said that the Fbef ore values were appropriately
set by means of the experimenter’s verbal instruction.
We then checked if the acquired independent variables(i.e., AG signs) were appropriately set in the exploration
phase; specifically, whether the participants in the Lower
Expectation Group showed positive AG signs and whether
the ones in the Higher Expectation Group showed negative ones. For the 15 participants in the Lower Expectation
Group, the average AG value was +6.0 (SD = 8.79) a ndfor the 15 participants in the Higher Expectation Group, theaverage AG value was −43.9 (SD = 25.2). Although thesevalues were not the same as the ideal AG values (i.e., +23
in the Lower Expectation Group and −57 in the Higher
Expectation Group), the participants in the Lower Expectation Group actually showed positive AG signs while theInt J Soc Robot

Fig. 8 Acceptance rate in each experimental groupones in the Higher Expectation Group showed the negative AG signs. Moreover, the result of a t-test (betweensubjects design, independent variable: Lower/Higher Expectation Group, dependent variable: AG values) showed significant differences between the two AG values (t (28) =7.009, p < .01, effect size r = .80). This confirmed that theindependent variables were appropriately set in the experiment.
4.6 Results
We then calculated the acceptance rate as a dependent
variable. For the 15 participants in the Lower Expectation
Group, the average acceptance rate of the robot’s 18 suggestions was 9.40 (SD = 4.33), and for the 15 participants in
the Higher Expectation Group it was 5.13 (SD = 4.47) (seeFig. 8). The acceptance rates for both experimental groups
were then analyzed using a t-test (between-subjects design,
independent variable: Lower/Higher Expectation Group, dependent variable: acceptance rate). The results of the t-test
showed a significant difference between the two experimental groups (t (28) = 2.564, p < .05, effect size r = .44); thatis, the participants in the Lower Expectation Group showeda significantly higher acceptance rate compared to those in
the Higher Expectation Group. We therefore concluded that
the AG signs significantly affected the participants’ behavior
towards the agents. We also confirmed that the AG properties mentioned in Sect. 1 could be clearly verified.

5 Discussion
5.1 Design Policy Utilizing Adaptation Gap Concept
From the results of this experiment, we eventually succeeded in showing evidence that AG signs affect the participants’ behavior toward an agent, which suggests that
understanding the AG signs will become indispensable fordesigning interactions between users and agents. Actually,
these experimental results are in clear agreement with the
AG properties mentioned in Sect. 1, which suggests thatthey can play a role in the development of novel interaction
design policies. For example, our finding that “agents thatevoke higher expectations than their actual functions should
not be used for interaction with users” should prove to bea key contribution. These results seem to suggest a specificdesign policy, such as “The Fbef ore values should be set as
low as possible to easily make the AG signs positive.” However, such lower Fbef ore values would likely result in someusers being deeply disappointed with the agent before theinteraction, leading to a failure to start the interaction at all.
Therefore, clarifying the appropriate Fbef ore range is also asignificant issue in terms of utilizing the AG concept for an
actual interaction design policy, along with the issue of how
to create Fbef ore values for users. However, in this study,
we did not manipulate the appearance or design of robotic
agents per se. So considering the relationship between theFbef ore and the elements of the agents’ appearance or design is definitely our next target.
Here, the design policy derived from AG and Matsumoto’s minimal design policy [10] are quite similar because both mentioned that the Fbef ore value was importantfor designing the agents. However, the clear difference between these two concepts was that AG was proposed basedon the concrete hypothesis focusing on the relationship between Fbef ore and Faf ter and this was experimentally verified, while the minimum design policy was not.
5.2 How to Determine the Fbef ore Values
In this experiment, the experimenter gave verbal instructions
to the participants, such as “The rate at which this robot succeeds in detecting the position of a coin is 10%/90%” inorder to create specific Fbef ore values for each of the experimental groups. Some may argue that the Fbef ore values should be naturally and spontaneously determined by
the participants themselves without any outside interference.
However, the reason the experimenter gave verbal instructions to participants about the Fbef ore values was based on
the results of our former study [18]. In that study, we prepared two different types of robotic agent (one was the same
MS we used in the current study and the other was an AIBO
robot (Sony Corporation, ERS-7, Fig. 9)) and then had theparticipants acquire the Fbef ore values by asking “At what
rate will this robot succeed in detecting the position of acoin?” in the same treasure hunting video game environment. We expected that the participants would assign lower
Fbef ore values to the MS and higher ones to the AIBO.
However, the results showed that the average Fbef ore value
for MS was 57.0% (SD = 19.97) and that for AIBO wasalso 57.0% (SD = 16.91), meaning there was no significant

Int J Soc Robot

5.3 Effects of Values of Adaptation Gap (AG Values)

Fig. 9 AIBO robot (Sony Corporation, ERS-7)

difference of Fbef ore values between the two robots. This
meant that it was quite difficult to create specific Fbef ore
values among participants with various background details,i.e., gender, educational level, technophobia [21], and so on;that is, Fbef ore can be determined by the participants’ various kinds of preferences. Actually this notion accords with
our former study “we assumed that this phenomenon was
due to the fact that the agents’ appearances do not have astrong enough effect to make the participants evoke uniform
Fbef ore and Ibef ore ”; that is, Fbef ore could not be determined by the agents’ appearance in previous study. We assumed that the differences of the participants’ backgrounds
were what caused the wide range of standard deviation of
the Fbef ore values (i.e., SD = 19.97 for MS and SD = 16.91
for AIBO). Therefore, we gave verbal instructions about theFbef ore values in order to force our desired Fbef ore value
setting in this experiment.
While the Faf ter values would be automatically determined by the actually implemented function of the agents,
so determining how to set the appropriate Fbef ore values
without using verbal instruction becomes a critical issue.
We are currently planning to conduct a questionnaire based
survey, in collaboration with product designers and social
psychologists, to clarify the relationship between specificappearances or behaviors and the functions they evoke interms of the type of given task and users’ preferences. We
believe such collaborations would provide an elegant solution to controlling the Fbef ore values and contribute to amuch more sophisticated AG concept. As mentioned previously, Matsumoto et al. [10] and Kiesler [13] have proposed
design “road maps” to design agents that must interact withusers, so it would be worthwhile to consider how these studies could be used to create specific Fbef ore values amongusers.

Some might point out that the comparison of the negative
AG as −57% (= 33% − 90% = Faf ter − Fbef ore ) of thepositive AG as +23% (= 33% − 10%) is unfair because theabsolute values are different. In this study, we did not focus
on the AG values but rather on the AG signs. Therefore, thepurpose of this study was to investigate whether the hypothesis of AG can be verified as a primary step by introducing
extreme conditions.
Actually, we currently do not have any specific methodologies that can precisely comprehend or measure the users’
expected or perceived functions in terms of percentage. Although the functions of the agents cannot always be quantified in a percentage manner, we utilized the percentage fordescribing their expected and perceived functions in order
to comprehend the relative differences among them, and webelieve that our approach utilizing a percentage was worthwhile to show the concept of adaptation gap objectively.
Specifically, we investigated the relationship between theAG values and the participants’ acceptance rate by calculating the correlation coefficient between them. As a result,
we were able to observe the medium, positive correlation(r = .529, p < .01), so it can be said that the AG values affected the users’ behaviors to some degree.
However, from our viewpoint as experimenters, the participants judged the abilities of the agent (both Fbef ore andFaf ter ) compared to the chance level for detecting the position of a coin as “33%”; when they felt that the ability of theagent was good, they rated it as better than 33%, whereas
when they felt that the ability was not very good, they rated
it as worse than 33%. Such a decision would also be affected by differences in the participants’ backgrounds, asmentioned in Sect. 4.1. Therefore, finding or developing amethodology that can precisely comprehend or measure thefunctions a user expects and perceives in terms of percentage
is also a critical issue when it comes to comprehending theeffects of AG values. If we succeeded in finding or developing such a methodology, we could tackle the reminding
issue of the adaptation gap, for example, by analyzing thecase of AG = 0 in which the user is informed of the actual
function of the agent prior to their interaction.1

6 Conclusions
We assumed that the difference between the users’ expectations regarding the functions of an agent and the function that they actually perceived would significantly affect
1 Actually, we did not focus on this condition AG = 0 because it wasquite difficult to realize it in this experimental setting even though theparticipants were told the actual function of the agent prior to the interaction.

Int J Soc Robot

their behavior toward the agent. That is, when the users’
expectations exceed their perceptions, they would be disappointed by the agent and do not believe the agents’ outputs. In contrast, when their perceptions exceed their expectations, they would get interested in the agent and do believe the agents’ outputs. We then defined this differences
as the adaptation gap and experimentally investigated how
the adaptation gap signs affected the users’ behavior toward
the agents in order to determine if the above assumptions
were correct. The independent variable was the adaptation
gap signs and the dependent variable was the acceptance rate
indicating how many of the agents’ suggestions the participants accepted. Specifically, we assumed that the higher acceptance rate meant that the participants wanted to continueinteracting with the agent, while the lower acceptance ratemeant that they would stop interaction. The results showedthat the participants with positive adaptation gap signs hada significantly higher acceptance rate than those with negative ones. This led us to conclude that the adaptation gap
signs significantly affected the participants’ behavior toward
agents in the way that we expected, and that comprehending
these signs will become indispensable for designing interaction between users and agents. The results of this experiment also highlighted the need to clarify the unsolved issue of “how to create specific Fbef ore values among users.”
Needless to say, this issue is our immediate challenge.
Acknowledgements Our deepest appreciation goes to anonymous
reviewers whose comments and suggestions were of inestimable value
for this study. We also would like to thank NII Collaborative Researchthat made it possible to complete this study.

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Takanori Komatsu is an Assistant Professor in International Young
Researcher Empowerment Center (IYREC) at Shinshu University,
Japan. He earned his Ph.D. (2003) from the University of Tokyo, Japan,
and the B.E. (1997) degree from Shibaura Insitute of Technology,
Japan. His research interests includes rethinking of human-computerinteraction by means of human’s cognitive features.
Rie Kurosawa earned her B.E. (2010) degree from Shinshu University, Japan. Now she is a systems engineer at Fujitsu Nagano Systems
Engineering Limited.
Seiji Yamada is a Professor at the National Institute of Informatics,
SOKENDAI, and Tokyo Institute of Technology. He received B.S.
(1984), M.S. (1986) and the Ph.D. (1989) degrees in artificial intelligence from Osaka University. His research interests are in the designof intelligent interaction including Human-Agent Interaction, intelligent interactive systems.