How Can We Live with Overconfident or Unconfident Systems?:
A Comparison of Artificial Subtle Expressions with Human-like Expression
Takanori Komatsu (tkomat@shinshu-u.ac.jp)
Faculty of Textile Science and Technology, Shinshu University,
3-15-1 Tokida, Ueda 386-8567, Japan

Kazuki Kobayashi (kby@cs.shinshu-u.ac.jp)
Faculty of Engineering, Shinshu University,
4-17-1 Wakasato, Nagano 380-8553, Japan

Seiji Yamada (seiji@nii.ac.jp)
National Institute of Informatics/ SOKEDAI/ Tokyo Institute of Technology,
2-1-2 Hitotsubashi, Tokyo 101-8430, Japan

Kotaro Funakoshi (funakoshi@jp.honda-ri.com)
Honda Research Institute Japan Co., Ltd,
8-1 Honcho, Wako 351-0188, Japan

Mikio Nakano (nakano@jp.honda-ri.com)
Honda Research Institute Japan Co., Ltd,
8-1 Honcho, Wako 351-0188, Japan

Abstract
Expressing the confidence level of a system’s suggestions byusing speech sounds is an important cue to users of the systemfor perceiving how likely it is for the suggestions to be correct.
We assume that expressing the levels of confidence using
human-like expressions will cause users to have a poorer
impression of a system than if artificial subtle expressions(ASEs) were used when the quality of the presented
information does not match the expressed level of confidence.
We confirmed that this assumption was correct by conducting
a psychological experiment.
Keywords: Artificial Subtle Expressions (ASEs), Humanlike Expressions, Confidence, Users’ Subjective Impressions

Introduction
Human-machine communication using speech sounds is
becoming more common (Cohen, Giangola, & Balogh,
2004; Nass & Brave, 2005) because users can obtain
information while engaging in their primary tasks without
facing nor manually operating the information providing
systems (e.g., intelligent home appliances or car navigation
systems). However, due to various reasons (for example,
Benzeghibaa et al., 2007), such as noise in the sensors, theincompleteness of data, immaturity of technology, and thecomplexity of tasks, the reliability of such systems is often
limited. Cai & Lin (2010) experimentally showed how
expressing the levels of confidence for such systems toindicate whether the system’s represented information isaccurate or not to users plays an important role in improving
both the user’s performance and their impressions.

When intending to express a system’s level of confidence,
one can easily have the idea of using human-like verbal
expressions such as “probably,” “definitely,” or “83%confident.” However, expressing levels of confidence using
such human-like expressions might frustrate users when thequality of the presented information does not match theexpressed level of confidence. For example, users might feel
frustrated with systems (like car navigation systems) that
express a higher level of confidence like “you should follow
my suggested route” or “I am 80% confident,” but the
represented information was wrong (this is the case of being“overconfident”). Since human-like expressions make users
expect higher human-like abilities from the systems (forexample, Sholtz & Bahrami, 2003; Kanda et al., 2008), suchinconsistent behaviors eventually make them deeply
disappointed (Aronson & Linder, 1965; Komatsu &Yamada, 2010; Komatsu, Kurosawa & Yamada, 2011).
Related to the above issue, we have proposed artificial
subtle expressions (ASEs) as an intuitive methodology for
notifying users of a system’s internal state. Actually, theASEs only have a complementary role in communicationand should not interfere with communication’s main
protocol. This means that the ASEs themselves do not haveany meaning without a communication context. In particular,
we showed that ASEs implemented as beep-like sounds
succeeded in accurately and intuitively conveying asystem’s confidence to the users (Funakoshi et al., 2010;
Komatsu et al., 2010a; Komatsu et al., 2010b;). Therefore,
we assume that our proposed ASEs are suitable forexpressing levels of confidence in comparison to humanlike expressions.

1816

The purpose of this study is then to confirm the above
assumption that expressing levels of confidence using
human-like expressions gives users a poorer impression of asystem than by using those expressed by ASEs when thequality of the presented information does not match theexpressed levels of confidence (in particular, where thesystem’s suggestions are incorrect/correct even though theexpressed confidence is high/low) by conducting a
psychological experiment to comprehend the users’
subjective impressions.
Such inconsistency between the represented informationand the level of confidence is inevitable due to theimmaturity of the current technology used in media
terminals and due to the fact that the levels of confidence
are just a probability indicating how accurate therepresented information is. Therefore, this study should
contribute to proposing a novel interaction technique on
how to handle this inconsistency without frustrating theusers.

Experiment
Environment
We used a “driving and treasure hunting” video game as
our experimental environment for comprehending theparticipants’ impressions of a system. In this game, a gameimage scrolls forward on a straight road as if the participant
is driving a car using a navigation system with three small
mounds of dirt appearing along the way. A coin is inside
one of the three mounds, while the other two mounds
contain nothing. The game ends after the participant
encounters 20 sets of mounds (20 trials). The purpose of thisgame is to get as many coins as possible. The location of thecoin amongst the three mounds was randomly assigned. In
each trial, the navigation system next to the driver’s seat(circle on top of Figure 1) told them which mound it expects
the coin to be in by using speech. The participants could
freely accept or reject the navigation system’s suggestions.
After the participant selected one mound among the threeusing a computer mouse, they could immediately knowwhether the selected mound contained the coin or not on thedisplay (middle of Figure 1).

Using Speech Sounds
In this experiment, the navigation system used Japanese
speech sounds to suggest to the users the expected locationof the coin; that is, “ichi-ban (no. 1),” “ni-ban (no. 2),” or“san-ban (no. 3).” These speech sounds were created byadding robotic-voice effects to the recorded speech soundsof one of the authors. These sounds were the main protocol(suggestion) of the navigation system. We then prepared thefollowing three experimental stimuli (conditions) to express
the levels of confidence of the main protocol.
Figure 1: Driving and treasure hunting video game1817

ASE Condition: One of the two ASEs was played 0.2
seconds after the speech sounds (Figure 2). These two
ASEs were triangular wave sounds 0.5 seconds induration with different pitch contours (Figure 3); thatis, one was a flat ASE (onset F0: 400 Hz and end F0:
400 Hz) and the other was a decreasing ASE (onset F0:
400 Hz and end F0: 250 Hz). The suggestions withdecreasing ASEs were able to inform users of thesystem’s lower level of confidence in its suggestions
while the ones with flat ASEs were to inform them of ahigher level of confidence (Funakoshi et al., 2010;
Komatsu et al., 2010a; Komatsu et al., 2010b).
Paralinguistic Condition: As a kind of typical humanlike expressions, we prepared two stimuli bymodifying the paralinguistic information of thesuggestions (“ichi-ban,” “ni-ban,” and “san-ban”), e.g.,
the rate of the utterances and intonation patterns; that is,
one was an utterance with a faster rate with a fallingintonation (“ichiban!”, Figure 4 (b)), while the otherwas a slower-rate utterance with a rising intonation(“i..chi..ba..n?”, Figure 4 (c)). We designed the latter
stimulus (slower rate with rising intonation) to inform
users of the system’s lower level of confidence in theform of a question, while the former stimulus (fasterrate and falling intonation) informs them of a higher
level of confidence.
Linguistic condition: As another kind of typicalhuman-like expressions, we prepared two stimuli byadding Japanese linguistic suffixes to the suggestions;that is, one with “desu (definitely)” 0.1 seconds after
the suggestion, and the other with “dato omoi masu (Iguess so).” We designed the suggestions with “datoomoi masu” to inform users of the system’s lower levelof confidence, while the ones with “desu” to informthem of a higher level of confidence.

Among the 20 trials, the navigation system expressed theinformation with a higher level of confidence 10 times andwith a lower one 10 times. The order of these two levels ofconfidence was counterbalanced across the participants.

Figure 4: (a) Original wave form and pitch contour of “ichiban” used in ASEs and Linguistic conditions, (b) wave form
and pitch contour of “ichiban!” and (c) wave form and pitch
contour of “i..chi..ba..n?”Participants

Figure 2: Speech sound “ni-ban (no.2)” and decreasing
ASE

Twenty Japanese university students (15 men and 5
women; 21 - 28 years old) participated. They wererandomly divided into the following two experimentalgroups in terms of the accuracy of the navigation system’slevels of confidence.


Figure 3: Flat and decreasing ASEs (duration: 0.5 second)

Consistent Group (10 participants): The participantsin this group interacted with a system that expressed
levels of confidence that were consistent with thecorrectness of the information it presented; that is,when the system expressed the information at a higher
level of confidence, the rate of the suggested moundcontaining the coin was 100%, and when the systemexpressed the information with a lower level ofconfidence, the rate was 0%.
Inconsistent Group (10 participants): The
participants in this group interacted with a system that
expressed levels of confidence that were inconsistent
with the correctness of the information it presented;
that is, when the system expressed the information
with a higher level of confidence, the rate of thesuggested mound containing the coin was 50%, andwhen the system expressed the information with alower level of confidence, the rate was also 50%.

All the participants experienced all three experimental
stimuli, so the experimental design was a 2 × 3 mixed
design; that is, the between-factor was consistent(consistent/inconsistent groups), while the within-factor was1818the type ofconditions).

stimuli(ASEs/Paralinguistic/Linguistic

those for the paralinguistic and linguistic conditions in theinconsistent group, we would be able to verify ourassumption.

Procedure
We used a web-based questionnaire system tocomprehend the participants’ impression of the navigation
systems and their performances and behaviors using thetreasure hunting video game in this experiment. First, thesystem displayed a consent form and the instructions for theexperiment. Before starting the treasure hunting game, theparticipants were asked to listen to test sounds via a speakeror headphones and to adjust the sound volume to acomfortable level. Afterwards, they played the treasurehunting video game three times to experience all three
conditions. The order of these conditions wascounterbalanced among the participants.
After finishing each condition, the participants were asked
to fill in a questionnaire on the navigation system, which
consisted of 18 questions using a 7-point Likert scale(maximum evaluation: 7 points; minimum evaluation: 1
point). The summed points of these questions were used as
the “participants’ subjective impression scores” of thisnavigation system; that is, more points meant a better
impression of the system (the highest score was 126 points
and the lowest was 18). The questionnaire consisted of amodified love-liking scale (Rubin, 1970) and our originalquestions (Table 1: Cronbach’s alpha: 0.86).
Table 1: 18 questions in the questionnaire
1.
2.
3.
4.
5.
6.
7.

My feeling is the as usual even if I use this system.
This system has good adaptability.
This system deserves to work on responsible tasks.
I place complete reliance on this system.
This system makes favorable impressions on many people.
This system is always preferred among the similar systems.
I prefer this system because this system is similar to my
way of thinking.
8. This system is human-like.
9. This system can offer good services.
10. I am satisfied with the services of this system.
11. I want to use this system again.
12. I cannot stand the mistakes made by this system.
13. This system is polite.
14. This system is a sufficiently reliable one.
15. This system is helpful for me.
16. This system is lovable.
17. I enjoy spending time with this system.
18. I feel tired when I use this system.

Manipulation Check
We assumed that the types of experimental stimuli(ASEs/paralinguistic/linguistic conditions) did not affect theparticipants’ performance and behaviors in this game but
only their subjective impressions of the system. We theninvestigated the game scores, which indicated how manycoins the participants acquired during the game (maximum:
20 coins) to clarify the relationship between the stimuli andtheir performance in this game (Table 2) and the acceptance
rate, which indicated how many of the system’s suggestions
the participants accepted (maximum: 10 times for eachconfidence level) to clarify the relationship between theexperimental stimuli and the participants’ behaviors (Table
3).
The game scores were then analyzed with a 2 × 3 mixed
ANOVA (between independent variable: consistent/
inconsistent groups, within independent variable:
ASEs/paralinguistic/linguistic conditions, and dependent
variable: game score). The results showed no significant
difference on the main effects of the within independent
variable [F(2,36)=0.04, n.s.]. The acceptance rates wereanalyzed with a 2 × 3 × 2 mixed ANOVA (between
independent variable: consistent/inconsistent groups, #1
within independent variable: ASEs/paralinguistic/linguistic
conditions, #2 within independent variable: suggestion withhigh/low confidence, and dependent variable: acceptancerate). The results showed no significant difference on themain effects of the #1 within independent variable[F(2,36)=0.04, n.s.].
Table 2: Game scores for each experimental condition

Table 3: Acceptance rate for each experimental condition
according to confidence level

Assumption
We assumed that expressing the levels of confidence
using human-like expressions would give the users a poorer
impression of the system than expressing these levels with
ASEs when the quality of the presented information does
not match the expressed levels of confidence; in particular,
in the case where the systems’ suggestions wereincorrect/correct even though the confidence was high/low.
That is, if we could observe that the participant’s impression
scores for the ASE condition were significantly higher thanAs a result of this manipulation check, we confirmed thatthe type of experimental stimuli did not affect theparticipants’ performance and behaviors. Therefore, we canfocus purely on the effects of the types of experimental
stimuli on the participants’ subjective impressions scores.

1819

Results
The users’ subjective impression scores for each groupand condition are shown in Figure 5. For the 10 participantsin the consistent group, the average impression score for the
ASE conditions was 58.2 (SD = 15.53), that for theparalinguistic ones was 75.1 (SD = 9.27), and that for the
linguistic ones was 68.1 (SD = 11.85). For the 10participants in the inconsistent group, the average
impression score for the ASE conditions was 60.0 (SD =11.40), that for the paralinguistic ones was 49.4 (SD =11.94), and that for the linguistic ones was 48.6 (SD =12.31).
These subjective impression scores were then analyzed
using a 2 × 3 mixed ANOVA (between independent
variable: consistent/inconsistent group, within independent
variable: ASEs/paralinguistic/linguistic, and dependent
variable: users’ subjective impression scores). The results of
the ANOVA showed significant differences in theinteraction effect [F(2,36)=11.50, p<.01(**), effect size:
η2=0.15] and the main effect between independent variables[F(1,18)=9.99, p<.01(**), η2=0.22]. The simple main effectsof the between and within independent variables were thenanalyzed, and the results showed significant differences inthe scores for the paralinguistic and linguistic conditions
between the consistent and inconsistent groups[paralinguistic: F(1,18)=26.03, p<.01(**), linguistic:
F(1,18)=11.71, p<.01(**)], and in the scores for the threeexperimental stimuli within both groups [consistent:
F(2,36)=7.97, p<.01(**), inconsistent: F(2,36)=4.48,
p<.05(*)]. A multiple comparison using an LSD test on thesimple main effect of the within independent variables
showed that the scores for the paralinguistic and linguistic
conditions in the consistent group were significantly higher
than those for the ASEs (MSe=90.4883, 5% level). In
comparison, the scores for the paralinguistic and linguistic
conditions in the inconsistent group were significantly lower
than those for the ASEs (MSe=90.4883, 5% level).

inconsistent group, so we were able to verify ourassumption. Moreover, we found that the scores for the ASE
conditions in both groups were almost the same, while thescores for the paralinguistic and linguistic conditions
significantly differed. Therefore, this also implies that theusers’ subjective impressions for a system expressing ASEs
were quite robust regardless of the consistency between the
represented information and the levels of confidence.

Discussion
The results of this study showed that expressing
confidence using human-like expressions received a higher
evaluation in comparison to using such expressions with
ASEs when the accuracy of the system’s levels ofconfidence was perfect (in consistent group). However, it isalmost impossible to build a user interface that can always
provide correct suggestions and levels of confidence tousers based on the level of current technology. Therefore,
establishing a concrete methodology for handling theinconsistency between the suggestion and the levels ofconfidence is indispensable and worthwhile for the HCI andcognitive science domains.
We now have to investigate whether the acquired results
can be used in much more realistic applications, e.g., spoken
dialogue systems like an actual car navigation system. If wesucceed, we can strongly argue that expressing the levels ofconfidence of systems by using ASEs is a reasonable
methodology for avoiding frustrating users, and willcontribute to the proposals for a novel interaction techniquefor dealing with the inconsistency between the represented
information and the levels of confidence.
In this experiment, we could not clarify which kinds ofusers’ cognitive basis significantly affected the result of thisexperiment. We assume that several researches such as thegain and loss of esteem (Aronson & Linder, 1965),
adaptation gap hypothesis (Komatsu & Yamada, 2010;
Komatsu, Kurosawa & Yamada, 2011), uncanny valley(Mori, 1970) and human’s cognitive nature foranthropomorphism (Reeves & Nass, 1996) are keys to findout the cognitive basis that could clearly explain theparticipants’ behaviors observed in this experiment. To
tackle with this issue, we are now planning to conduct aconsecutive experiment to investigate the further abilities ofASEs; that is, whether the ASEs could inform users of not
only the higher or lower level of confidence but the otherkinds of meanings? or more detailed level of confidence?
We believe that this consecutive study would clarify whichusers’ cognitive basis affect their behaviors how they
intuitively interpret the ASEs.

Figure 5. Subjective impression scores for eachexperimental condition and group

Conclusions

Therefore, we clearly observed that the users’ impression
scores for the ASE conditions were significantly higher thanthose for the paralinguistic or linguistic conditions in the

Expressing the confidence level of a system’s suggestions
by using speech sounds is an important cue to users of thesystem for perceiving how likely it is for the suggestions to
be correct. We assume that expressing the levels of1820

confidence using human-like expressions will cause users tohave a poorer impression of a system than if the ASEs wereused when the quality of the presented information does notmatch the expressed level of confidence. We confirmed that
this assumption was correct by conducting a psychologicalexperiment. In particular, the users’ impression scores for
the ASE conditions were significantly higher than those forthe paralinguistic or linguistic conditions in the inconsistentgroup.

Acknowledgments
This work was supported by KAKENHI (23700248) as
Grant-in-Aid for Young Scientists (B) by The Ministry of
Education, Culture, Sports, Science and Technology(MEXT), Japan, and by joint research with Honda ResearchInstitute Japan and with National Institute of Informatics,
Japan.

References
Aronson, E., & Linder, D. (1965). Gain and loss of esteem
as determinants of interpersonal attractiveness, Journal of
Experimental Social Psychology, 1 (2), 156-171.
Benzeghibaa, M., De Moria, R., Derooa, O., Dupont S.,
Erbesa, T., Jouveta, D., Fissorea, F., Lafacea, P., Mertinsa,
A., Risa, C., Rosea, R., Tyagia, V., & Wellekensa, C.
(2007). Automatic speech recognition and speechvariability: A review, Speech Communication, 49, 763786.
Cai, H., & Lin, Y. (2010). Tuning Trust Using Cognitive
Cues for Better Human-Machine Collaboration,
Proceedings of Human Factors and Ergonomics Society
2010 (pp. (5) 2437-2441).
Cohen, M. H., Giangola, J. P., & Balogh, J. (2004). Voice
User Interface Design, MA: Addison-Wesley.
Funakoshi, K., Kobayashi, K., Nakano, M., Yamada, S., &Komatsu, T. (2010). Non- humanlike Spoken Dialogue: aDesign Perspective, Proceedings of 12th Annual Meetingof the Special Interest Group on Discourse and Dialogue(pp. 176-184).
Kanda, T., Miyashita, T., Osada, T., Haikawa, Y., &Ishiguro H. (2008). Analysis of Humanoid Appearancesin Human-robot Interaction, IEEE Transactions on
Robotics, 24 (3), 725-735.
Komatsu, T., & Yamada, S. (2010). Adaptation Gap
Hypothesis: How differences between users’ expected andperceived agent functions affect their subjective
impression, Journal of Systemics, Cybernetics andInformatics,9(1), 67-74.
Komatsu, T., Yamada, S., Kobayashi, K., Funakoshi, K., &Nakano, M. (2010a). Artificial Subtle Expressions:
Intuitive Notification Methodology of Artifacts,
Proceedings of the 28th international conference on
Human factors in computing systems (pp. 1941-1944).
Komatsu, T., Yamada, S., Kobayashi, K., Funakoshi, K., &Nakano, M. (2010b). Artificial Subtle Expressions:

Intuitive Notification Methodology of Artifacts,
Proceedings of the 32nd Annual Meeting of Cognitive
Science Society (pp. 447-452).
Komatsu, T., Kurosawa, R., & Yamada, S. (2011). How
Does the Difference Between Users’ Expectations andPerceptions About a Robotic Agent Affect Their
Behavior?, International Journal of Social Robotics,
DOI=10.1007/s12369-011-0122-y.
Mori, M. (1970). Bukimi no tani: The uncanny valley (K. F.
MacDorman & T. Minato, Trans.). Energy, 7(4), 33–35.
(Originally in Japanese).
Nass, C., & Brave, S. (2005). Wired for Speech: How Voice
Activates and Advances the Human-Computer
Relationship, MA: The MIT Press.
Reeves, B., & Nass, C. (1996). The Media Equation: How
People Treat Computers, Television, and New Media Like
Real People and Places, MA: The MIT Press.
Rubin, Z. (1970). Measurement of romantic love, Journal of
Personality and Social Psychology, 16(2), 265-273
Sholtz, J., & Bahrami, S. (2003). Human-Robot interaction:
development of an evaluation methodology for the
bystander role of interaction, Proceedings of the 2003
IEEE System, Man and Cybernetics (pp. 3212 - 3217).

1821