Adaptation gap hypothesis:
How differences between users’ expected and perceived agent functions affect theirsubjective impression
Takanori Komatsu
International Young Researcher Empowerment Center, Shinshu University
Ueda, Nagano 3868567, Japanand
Seiji Yamada
National Institute of Informatics/SOKENDAI/Tokyo Institute of Technology
Chiyoda, Tokyo 1018430, Japan

ABSTRACTWe describe an “adaptation gap” that indicates the differences
between the functions of artificial agents that users expect
before starting their interactions and the functions they perceive
after their interactions. We investigated the effect of thisadaptation gap on users’ impressions of artificial agents
because any variations in impression before and after the startof an interaction determines whether the user feels that thisagent is worth interacting with. The results showed that
positive or negative signs of the adaptation gap and subjective
impression scores of agents before the experiment significantly
affected the users’ final impressions of the agents.

Keywords: human-agent interaction (HAI), adaptation gap,
users’ impressions of agents, gain and loss of esteem.

1. INTRODUCTION
Various interactive agents such as robotic agents [1] andembedded conversational agents (ECA) [2,3] have beendeveloped to assist us with our daily tasks. In particular,
researchers in the human-computer interaction and humanagent interaction communities are working hard to create suchinteractive agents. One of the hottest topics in these fields
concerns how users’ mental model of an agent affects theirinteraction with it. Because users supposedly base their mental
model on the agent’s appearance, its behaviors, and theirpreferences for the agent, the users’ mental model significantly
affects their interaction [4]. For example, when a user
encounters a dog-like robot, s/he expects dog-like behavior
from it, and s/he naturally speaks to it using commands andother utterances intended for real dogs, such as “sit,” “liedown,” and “fetch.” However, s/he does not act this way
toward a cat-like robot.
Several studies have focused on the effect of users’ mental
models about an agent on their interaction. Matsumoto et al. [5]proposed a “Minimal Design Policy” for designing interactive
agents and concluded that the agent’s appearance should be
minimized in its use of anthropomorphic features so that users
do not overestimate or underestimate the agents’ competences.

In fact, they applied this minimal design policy to developing
Muu, their interactive robot [6] and Talking Eye, a life-likeagent [7]. Kiesler [8] argued that agent design should include a
process that anticipates a user’s mental model about agents on
the basis of the theory of common ground [9]; that is,
individuals engaged in conversation must share knowledge (socalled, common ground) in order to be understood and have ameaningful conversation. Specifically, she stated that agentsshould be designed in such a way that a user could easily
estimate her/his common ground (shared knowledge) withthem. We believe that this design approach would be quite
effective for users, especially at the beginning of an interaction,
because it may determine whether or not a user would start aninteraction with an agent.

2. ADAPTATION GAP BETWEEN A HUMAN AND AN
AGENT
However, approaches like Matsumoto et al.’s [5] or Kiesler’s[8] have a serious problem when an agent expresses behaviors
that completely deviate from the users’ mental model. Imagine
that a user meets a human-like robot that looks very much likea real human being. This user would intuitively form a mental
model of the robot, expecting human-like fluent speech anddexterous limb motions. However, if this particular robot could
only express machine-like speech and halting limb motions thatcompletely deviate from her/his mental model, s/he would be
immediately disappointed with this robot because of its
unexpected behaviors. The user would then stop interactingwith it. To solve this problem, we need to determine the users’
impressions of agents during their interactions; because such
impressions would make users determine whether this agent isworth interacting with.
In this study, we focused on the difference between users’
expectations regarding the function of the agent and users’
actual perceived function, which is one of the factors that
affects the users’ impressions. We called this difference theadaptation gap (AG). Specifically, AG can be defined as AG =Fafter - Fbefore. Here, Fafter is the function of the agent that a user
actually perceived, and Fbefore is the users’ expected function of
the agent. We expect that this adaptation gap would have thefollowing three properties [10,11].







AG<0 (Fafter< Fbefore): When users’ expected function
exceeds their perceived function, it is said that there is anegative adaptation gap. In this case, most people would
be disappointed by the agent and would stop interactingwith it.
AG>0 (Fafter> Fbefore): When users’ perceived function
exceeds their expected function, it is said that there is apositive adaptation gap. In this case, most people would
not be disappointed by the agent and would continueinteracting with it.
AG=0 (Fafter= Fbefore): When the perceived function
equals the expected function, it is said that there is no
adaptation gap. In this case, the agent would be regarded
as just an instrument for users.

For example, when Fbefore is larger than Fafter (say, when a user
meets a human-like robot on the left side of Figure 1), AG
would have a negative value (AG<0), and the user would most
likely be disappointed. However, when Fafter is larger thanFbefore (say, when a use meets a machine-like robot on the rightside of Figure 1), AG would have a positive value (AG>0), andthe user would be interested in interacting with this agent.

In particular, we expected that the value of AG strongly affects
the user’s impressions Iafter. In this study, we then investigated
the relationship between the values of AG and the user’s
subjective impressions after the interaction Iafter (see the arrowon the right side of Figure. 2); that is, the independent variablein this concept is AG while the dependent variable is Iafter. We
assumed that this investigation would lead to verification of theabove three properties about AG. Namely, if Iafter issignificantly influenced by AG (=Fafter - Fbefore), we canconclude that the AG’s properties have been verified.

3. EXPERIMENT
We conducted an experiment to investigate how the positive or
negative signs of the adaptation gap affected the users’
impressions of the agents.
Overview
We chose a “treasure hunting” video game as the experimental
environment to observe the interaction between a user and an
agent (Figure 3). In this game, a character on a computer
monitor operated by a user walks on a straight road, with threetiny hills appearing along the way. A gold coin is inside one of
the three hills, while the other two hills have nothing. The
game ends after the character meets 100 sets of hills and theapproximate duration of the game is about 10 minutes. The
goal of the game is to get as many gold coins as possible.

Figure 1: Intuitive concept of adaptation gap

BeforeInteraction

FbeforeAfterInteraction
Interaction
phase

IbeforeFafter

AG = Fafter – Fbefore

IafterFigure 3: Treasure hunting video game
User’s Mental
Model of An Agent

Figure 2: Systematic concept of adaptation gap

The systematic concept of AG is depicted in Figure 2. Here,Ibefore is the users’ subjective impressions about an agent before
their interactions, and Iafter is the users’ impressions after theinteraction. Note that these impressions (Ibefore and Iafter) aretotally different from the users’ expected and perceived
function of the agents (Fbefore and Fafter); since Fbefore and Fafterreflect the users’ understanding of the behavioral aspects of theagent, e.g., whether the agent could achieve the given task ornot, while Ibefore and Iafter reflect the users’ subjective
impressions of the agent, e.g., whether this agent is aworthwhile interactive partner.

In this experiment, the user got one point for each coin that s/hefound during the first 80 trials, but from trials 81 on, s/he got
ten points per coin. The participants were informed that 1 point
was equivalent to 10 Japanese 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 points. The position of the coin in the three hills was
randomly assigned. A robotic agent (explained later), which
was placed next to the user, told the participant each time
where it expected the coin would be. The participant could
accept or reject the agents’ suggestions.

Two different kinds of agents and participants
Twenty Japanese university students (10 men and 10 women;
21–23 years old) participated. These participants wererandomly divided into the following two groups.

AIBO Group (10 participants): The agent placed next tothe participants was an AIBO robot (Sony Corporation,
ERS-7, Figure 4 and 6). AIBO told the expected positionof the coin by barking the number, e.g., one bark meant
the first hill (the one on the left), two barks meant the
middle hill, and three barks meant the third hill (the oneon the right).
MS Group (10 participants): The agent was a
MindStorms (MS) robot (LEGO corporation, Figure 5
and 7). MS told the user the expected position by beeping
the number, e.g., one beep meant the first hill, two beeps
meant the second hill (middle), and three beeps meant thethird hill.

Figure 6: Photograph of AIBO group in experiment

The rate at which both agents succeeded in detecting theposition of the coin was set at 60%, that is, the rates were equal.
The behaviors of both agents were remotely operated by anexperimenter in the next room performing in the Wizard of Oz(WOZ) manner. The treasure hunting video game wasprojected on a 46 inch LCD screen in front of the participants(Figure 8). Figures 6 and 7 show a photograph of theexperiment.

Figure 7: Photograph of MS group in experiment

46 Inch LCD
Wireless LAN
AIBO

Figure 4: AIBO robot (Sony Corporation, ERS-7)

MS

FM Radio
Tuner
Keyboard

Game
Operation PC
Agent Control PC
FM Transmitter

Place of Agent

ParticipantExperimenter

Figure 8: Experimental setting

Figure 5: MindStorms robot (LEGO corporation)

Analysis
We investigated the effect of the sign of the adaptation gap on
the users’ impressions of the agents. To acquire the users’
impressions Ibefore and Iafter, we had the participants fill in aquestionnaire sheet based on the Liking scale [12], which
consisted of 7 questions scored on a 9-point Likert scale.
Accordingly, the summed score of these 7 questions(maximum: 63 points) was used as the participants’ impression
score for the agents (Table 1). A higher score indicated a better
impression of the agents.

In particular, before the experiment, the participants were asked
to fill in this questionnaire about their expected functions of theagent Fbefore. After the experiment, they were asked to fill in thesame questionnaire about their perceived functions of the agent
Fafter. Therefore, the signs of the adaptation gap were calculated
by using the values of Fbefore and Fafter; that is, AG= Fafter Fbefore.
Table 1: Questionnaire given to acquire participants’impression scores of artificial agents before and after theexperiment.
Questions
1 My feeling did not change even though this agent
was next to me.
2 This agent has adequate adaptabilities for anyuser.
3 I can leave the important tasks to this agent.
4 This agent is quite good as a commercial product.
5 I have a high level of confidence in this agent.
6 This agent will become popular for most users.
7 This agent is the best product compared to theother kinds of agents.

Results
We assumed that the participants’ impressions about the agentswould be affected not only by the signs of the adaptation gap
but also by the different agents or the achievement level of
their interaction. Therefore, we investigated the effects of (1)the different agents (AIBO or MS), (2) the achievement level(high or low game score) and (3) the signs of the adaptationgap (positive or negative) on the users’ impressions of theagents.

Therefore, the differences in the agents did not affect the users’
impressions of them.

Figure 10: Game scores of AIBO group and MS group

Next, to see the effect of the different agents on theachievement level, the game scores in both groups wereanalyzed using a 2 (AIBO or MS) ANOVA. No significant
difference in game scores (AIBO: 140.6 points, MS: 143.8
points, F(1,18)=0.10, n.s.) was obtained (Figure 10).

Case 1. Different agent: Figure 9 depicts theparticipants’ impressions of the two agents, and theimpressions scores of both groups after the experiment did notchange much from before.
Figure 11: Expected and perceived functions of AIBO group
and MS group

Figure 9: Impressions of agents of AIBO group and MS groupFirst, to see the effect of the differences in the agents on theparticipants’ impressions, the participants’ impressions in the
AIBO and MS groups (10 participants each) were analyzed
using a 2 (agent factor: AIBO or MS) x 2 (order factor: before
or after the experiment) mixed ANOVA. The results showed nosignificant difference in interaction effects (F(1,18)=0.02, n.s.),and no main effects for agent or order factors (AIBO or MS:
F(1,18)=0.32, n.s.; before or after: F(1,18)=0.18, n.s.).

Finally, the expected and perceived functions of the agents(Fbefore and Fafter) were analyzed using a 2 (AIBO or MS) x 2(before or after) mixed ANOVA (Figure 11). Again, no
significant differences were obtained for interaction effects(F(1,18)=0.21, n.s.) or both main effects (AIBO or MS:
F(1,18)=0.23, n.s.; before or after: F(1,18)=0.01, n.s.). The
average value of the adaptation gap in the AIBO group was 1.9 (S.D.=20.5), and the average for the MS group was 4.9(S.D.=23.8). Therefore, the differences between the agents did
not affect the achievement level of the interaction or the
expected and perceived functions.
To sum up, the differences in the agents did not affect theusers’ impressions of the agents, the achievement level ofinteraction, or the expected and perceived functions.

Case 2. Achievement level (Game score): All 20
participants, regardless of whether they were in the AIBO or
MS group, were divided into two groups in terms of acquired
game scores: high scoring and low scoring (10 participants ineach group). The average game score of the high scoring groupwas 158.5 points, while the average score of the low scoringgroup was 125.9 points. That is, there was a significant
difference in average game score between high and low scoringgroups (F(1,18)=26.3, p<.01(**), Figure 12).

Figure 14: Expected and perceived function of high-scoring
and low-scoring group

Figure 12: Game scores of high-scoring group and low-scoringgroup

To sum up, the achievement level of the game (game score) did
not affect the users’ impressions of the agents or their expected
and perceived functions.

Case 3. Signs of adaptation gap: All 20 participants,
regardless of whether they were in the AIBO or MS group,
were divided into two groups based on the signs of theadaptation gap (AG=Fafter-Fbefore): positive (AG>0; Fafter>Fbefore,
9 participants) or negative (AG <0; Fafter<Fbefore, 10participants) groups. Note that the one participant who had an
AG of 0 was eliminated from this analysis. The average
expected functions of the agents, Fbefore, were respectively
42.6% and 68.7% in the positive and negative groups, while theaverage perceived functions, Fafter, were 63.4% and 51.0%.
Thus, the average value of the adaptation gap in the negative
group was -17.7 (S.D.=17.7), and it was 20.8 (S.D.=11.8) in thepositive group (Figure 15).
Figure 13: Impressions of agents of high-scoring group andlow-scoring group

Figure 13 depicts the participants’ impressions of the agents.
The impression scores of both groups after the experiment did
not change from before. The participants’ impressions of theagents were then analyzed using a 2 (score factor: high or low)x 2 (order factor: before or after) mixed ANOVA. The results
showed no significant differences in interaction effects(F(1,18)=0.04, n.s.) between groups or on each of the main
effects (high or low: F(1,18)=1.06, n.s.; before or after:
F(1,18)=0.18, n.s.). Therefore, game score as an achievementlevel of interaction did not affect the users’ impressions of theagents.
We also analyzed the expected and perceived functions of theagents (Fbefore and Fafter) using a 2 (high or low) x 2 (before orafter) mixed ANOVA (Figure 14), and the results showed nosignificant differences in interaction effects (F(1,18)=0.03, n.s.)
or in both main effects (high or low: F(1,18)=2.84, n.s.; before
or after: F(1,18)=0.01, n.s.). The average value of theadaptation gap in the high group was 1.6 (S.D.=23.4), and theaverage value in the low group was -0.5 (S.D.=21.1).

Figure 15: Expected and perceived function of positive group
and negative group.

Figure 16 depicts the participants’ impressions of the agents.
The impression scores in the positive group increased, while
the ones in the negative group decreased. The participants’impressions of the agents in the positive and negative groups
were then analyzed using a 2 (positive or negative) x 2 (before
or after) mixed ANOVA. The results showed a significant
difference in the interaction effects (F(1,17)=14.29, p<.01 (**)),and there were significant differences in the participants’impressions before and after the experiment in both groups(positive: F(1,17)=9.94, p<.01 (**); negative: F(1,17)=4.81,

p<.05 (*)) and a significant tendency in the impressions before
the experiment between the two groups (F(1,17)=3.99, p<.1(+)). Therefore, the signs of the adaptation gap actuallyaffected the users’ impressions of the agents; specifically, theimpressions in the positive group significantly increased (about
4.9 points), while the ones in the negative group significantly
decreased (about 3.4 points).

To sum up this analysis, the signs of the adaptation gap did notaffect the achievement level of the interaction but theparticipants’ impressions of the agents. Moreover, theimpression scores before the experiment also affected thevariations of their impressions.

4. DISCUSSION

Figure 16: Impressions of agents of positive group and negative
group.

However, from Figure 16, it seemed that the participants’impression scores acquired before the experiment also hadsome effect on the final users’ impression scores after theexperiment. To confirm these effects, we performed a 2(positive or negative) ANCOVA (independent variable:
positive or negative groups, covariate: impression scores before
the experiment, dependent variable: impressions scores after
the experiment), and the results showed significant differencesin the main effect of the independent variable (F(1,16)=11.69,
p<.01 (**)) and in the main effect of the covariate(F(1,16)=28.89, p<.01 (**)). Therefore, the final impression
scores of the agents acquired after the experiment were affectedby two factors simultaneously; one was the signs of theadaptation gap (independent variable), and the other was theimpressions scores before the experiment (covariate).

Figure 17: Game scores of positive group and negative group.

While no significant differences were found in the game score
between the two groups (positive: 145.9, negative: 138.8;
F(1,17)=0.46, n.s.), we found that the signs of the adaptation
gap did not affect the achievement level of interaction (Figure.
17).

Summary and future plans
The results of our experiment can be summarized as follows:
The differences in the agents (i.e., whether the agent was
AIBO or MS) did not affect the users’ impressions of theagents.

The achievement level of the interaction (i.e., whether theparticipants could get the higher scores or not) did notaffect the users’ impressions.

Two factors, signs of the adaptation gap and impressions
scores before the experiment, significantly affected theusers’ impressions of the agents. Specifically, theparticipants, who showed positive signs of the adaptation
gap and lower impressions scores of the agents before theexperiment, ended up having higher impressions of theagents, whereas the participants, who showed negative
signs of the adaptation gap and higher impressions scores
before the experiment, ended up having lower
impressions of the agents.
Therefore, not only the signs of the adaptation gap but also
users’ impressions before the experiment had a significant
effect on the users’ final impression of the agents. This meansthat the users’ impressions would not become higher even if theadaptation gap was positive. Therefore, these results suggest arigid condition regarding the adaptation gap; one must
comprehend the users’ impressions of the agents before theinteraction in order to have the properties of the adaptation gap
described in section 2 (especially in the case of “AG<0” and“AG>0”).
One of the interesting phenomena of this experiment was that
there were no significant differences in the expected functionof the agents Fbefore and in the impressions scores of the agents
before the experiment Ibefore between the AIBO and MS groups.
We assumed that this phenomenon was due to the fact that theagents’ appearances do not have a strong enough effect to makethe participants evoke uniform Fbefore and Ibefore. This meansthat the participants’ mental models about the agents were
diverse. Therefore, Kiesler’s argument of [5] that agents should
include a process that anticipates a user’s mental model about
agents should be carefully applied, considering not only theagents’ appearance but also its given tasks, relationship withusers, and so on.
However, it can be said that the relationship between the
independent variable (signs of adaptation gap) and dependent
variable (participants’ final subjective impression) was a bitcomplicated in this experiment. In order to simplify thisrelationship, we are planning to construct a new experimental
setting (Figure 18). Specifically, this involves preparing two
experiments; the first experiment is to measure the sign of theadaptation gap, and the second experiment is to measureparticipants’ subjective impressions. After the first experiment,
the participants are divided into two experimental groups, an“AG >0 group” and an “AG <0 group”, based on the signs of
AG. After the second experiment, the subjective impression

scores of participants are analyzed by comparing these twogroups. We believe that this experimental plan would clarify
and refine the concept of the adaptation gap hypothesis.

Figure 18. Plan of new experiment to clarify the relationshipbetween independent variable and dependent variable

After resolving the above issue, we will conduct a subsequent
study to develop a practical design methodology on how tomake users evoke uniform Fbefore and impressions before
interaction Ibefore. If we succeed in developing such amethodology and set up a lower Fbefore and lower Ibefore we
could create a “positive adaptation gap” situation in which theparticipants end up with higher impressions of the agents bywhich Fafter is higher than Fbefore. Such a consecutive study
would be the basis of a novel design methodology to makeagents that users would want to continue interacting with andwithout losing their good impressions, and such a methodology
would make it possible for various interactive agents withwhich users would find worth interacting.

Relevance to existing findings
The results presented here are quite similar to findings in social
psychology on “gain and loss of esteem” [13]; that is, person A
prefers person B who changed her/his evaluation of person A
from positive to negative, to person C who maintained apositive evaluation (gain effect), and person A has a worse
perception of person B who changed her/his positive evaluation
into a negative one, compared with person C who maintained a
negative evaluation (loss effect). This implies that people’s
impressions about others are determined not only by the total
evaluation given by others but also by the latest evaluation
when it changes significantly. While the gain and loss ofesteem states that people’s impressions are determined by theobjective evaluations received from others, our results showthat users’ impressions of agents are determined by the
subjective evaluations they themselves generated. Therefore,
the results of this study did not simply replicate the existingfindings in HCI field on media equation studies [14], etc.; itgenerated new findings about significant personal properties
that are applicable to various HCI areas.
Moreover, the acquired results have bearing on the “uncanny
valley” [15]; that is, agents are becoming similar in appearance
to human beings. At some point, people will start feelinguncanny or loss of familiarity when dealing with such agents
because small differences in appearances of the agents fromthose of humans would become extremely emphasized. Figure
19 qualitatively illustrates this relationship between the agents’
likeness to human beings and the familiarity that human users
feel.

Figure 19. Concept diagram of uncanny valley 1

We believe that the reason why humans suddenly start feelinguncanny can be explained by our adaptation gap hypothesis,
because the “human-likeness” of the agents would form an
expectation about them in the user’s mind, and “familiarity”
would be determined by the perceived functions of the agents.
Therefore, we could assume that the relationship between
human-likeness and familiarity of the agents can be replaced by
one between the expected function and perceived functions of
the agents. If we could measure the familiarity of the agent as adependent variable in our experiment, the above assumption
might be able to be verified, and the results might contribute todevelopment of a novel methodology for designing interactive
agents.

5. CONCLUSIONS
We described an “adaptation gap” that indicates the differences
between the functions of artificial agents users expect before
starting their interactions and the functions they perceive after
the interactions. We investigated the effects of this adaptation
gap on users’ impressions of the artificial agents because any
variations in impressions before and after the start of aninteraction determine whether the user feels that this agent isworth interacting with. The results showed that the positive or
negative signs of the adaptation gap and the subjective
impression scores of the agents before the experimentsignificantly affected the final users’ impressions of the agents.
Thus, this study could contribute to the development of a novel
methodology to make users continue interacting with agents
without losing their impressions of them, and such amethodology could be easily applied to various interactive
agents that could make users believe is worth continuing aninteraction. Although these results were acquired in an
organized experimental environment and their applicability toactual usage is still unclear, a comparison with other findings,
such as on “gain and loss of esteem” or the “uncanny valley
hypothesis”, would contribute to the development of a design
methodology for interactive agents.

1

This figure is copied fromhttp://en.wikipedia.org/wiki/Uncanny_valley

ACKNOWLEDGEMENTS
This study was partially supported by Special Coordination
Funds for Promoting Science and Technology from the
Ministry of Education, Culture, Sports, Science andTechnology (MEXT), Japan.

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