Computers in Human Behavior 27 (2011) 2380–2385
Contents lists available at SciVerse ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
Social evaluations of embodied agents and avatars
Rosanna E. Guadagno a,⇑, Kimberly R. Swinth b, Jim Blascovich b
a
b
Department of Psychology, University of Alabama, United States
Department of Psychology, University of California, Santa Barbara, United States
a r t i c l e
i n f o
Article history:
Available online 19 August 2011
Keywords:
Social interaction
Embodied agents
Avatars
Facial expressions
Collaborative virtual environments
Nonverbal behavior
a b s t r a c t
The purpose of this study was to examine social evaluations (i.e., perceptions of empathy and positivity)
following peoples’ interactions with digital human representations. Female research participants
engaged in a 3-min interaction while immersed in a 3-D immersive virtual environment with a ‘‘peer
counselor.’’ Participants were led to believe that the peer counselor was either an embodied agent (i.e.,
computer algorithm) or an avatar (i.e., another person). During the interaction, the peer counselor either
smiled or not. As predicted, a digitally-rendered smile was found to affect participants’ social evaluations.
However, these effects were moderated by participants’ beliefs about their interaction partner. Specifically, smiles enhanced social evaluations of embodied agents but degraded them for avatars. Although
these results are consistent with other findings concerning the communicative realism of embodied
agents and avatars they uniquely demonstrate that people’s beliefs alone, rather than actual differences
in virtual representations, can impact social evaluations.
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Facial expressions play an important role in communication and
social interaction. Facial displays express participants’ emotional
states, involvement, responsiveness, understanding, and validate
their thoughts and feelings (Bavelas, Black, Lemery, & Mullett,
1986; Fridlund, Ekman, & Oster, 1987). As a pervasive facial expression, smiling has received considerable attention in the literature.
The ability to smile and to recognize a smile is well-developed early
in life (Srofe & Waters, 1976). Moreover, a smile is a readily understood gesture of friendliness (Thompson & Meltzer, 1964) and is an
important antecedent to interpersonal attraction (Byrne, 1971).
Smiling affects the ways individuals are perceived and evaluated
by others (Lau, 1982; Reis et al., 1990). There are many types of
smiles and subtly different ones are associated with different feelings and functions. Ekman and Friesen (1982), for example, distinguished between Duchenne (i.e. genuine) and non-Duchenne (i.e.
false) smiles and noted that Duchenne and non-Duchenne smiles
involve different facial muscles. Although research examining the
effect of different kinds of smiles on social evaluations is limited,
Frank, Ekman, and Friesen (1993) found that individuals can reliably distinguish between Duchenne and non-Duchenne smiles
and that Duchenne smiles are associated with more positive
impressions and evaluations of others.
⇑ Corresponding author. Address: Department of Psychology, University of
Alabama, P.O. Box 870348, Tuscaloosa, AL 35487-0348, United States. Tel.: +1 205
348 7803.
E-mail address: [email protected] (R.E. Guadagno).
0747-5632/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.chb.2011.07.017
In the current study, female research participants engaged in a
short session with a peer counselor within an immersive virtual
environment. Participants’ beliefs about their interaction partner
were manipulated. Half were told that they were interacting in
real-time with a digital representation of one of our undergraduate
research assistants. In this condition, both the participant and research assistant were represented by a 3-dimensional virtual human or avatar, which is a digital representation of another actual
person in real time. The other half were led to believe that their peer
counselor was a computer-generated ‘‘embodied agent’’ (a digital
representation of a computer algorithm designed to look like a person; Blascovich et al., 2002). During the interaction, the digital peer
counselor either smiled when it was socially appropriate to do so, or
never smiled. The digitally rendered smiles were animated so as to
approximate a Duchenne smile. Following the interaction, research
participants evaluated their peer counselor on a number of dimensions including perceived empathy and positivity.
1.1. Study goals
Although there is much research examining how communicative realism – the extent to which a virtual human acts like a real
person – affects the way people evaluate and respond to virtual humans (Bailenson, Blascovich, Beall, & Loomis, 2003; Bailenson et al.,
2005; Baylor & Soyoung, 2009; Blascovich, 2002; Blascovich &
Bailenson, 2011; Fridlund, 1991; Garau, 2003; Guadagno, Blascovich, Bailenson, & McCall, 2007; Von der Pütten, Krämer, Gratch,
& Kang, 2010), most previous research examining communicative
realism has focused on the role of eye gaze or head movements.
Much less is known about how the inclusion of facial non-verbal
R.E. Guadagno et al. / Computers in Human Behavior 27 (2011) 2380–2385
behaviors affect evaluations of embodied agents compared to avatars. Thus, this study adds to the existing literature by examining a
socially-important non-verbal behavior, namely smiling, and compares people’s reactions to identical digitally-rendered smiles that
are exhibited by embodied agents and avatars.
1.2. Predictions
Based on previous research examining the effect of smiling on social evaluations (Lau, 1982; Reis et al., 1990; Srofe & Waters, 1976;
Thompson & Meltzer, 1964), we expected participants to evaluate
virtual humans more positively when the latter smiled than when
they did not. We also expected an interaction between the presence
of smiles on the part of virtual humans and participants’ beliefs
regarding the virtual humans with whom they interacted.
Previous research has shown that communicative realism
manipulations such as facial expressions and other non-verbal
behaviors such as mimicry (Bailenson, Yee, Patel, & Beall, 2008;
Stel et al., 2010) sometimes affect people’s perceptions and evaluations of embodied agents and avatars differently (Bailenson et al.,
2003; Guadagno et al., 2007). As a result of these prior research
findings, we were interested in examining the effect of smiling
and agency beliefs (i.e., beliefs about whether one’s virtual partner
was a human or computer) on social evaluations such as perceived
empathy and positivity. Given its social nature (Fridlund, 1991),
smiling may have a greater impact on people when they believe
they are interacting with another person, an avatar, relative to a
computer, an agent. Thus, we might expect smiling to result in
more positive evaluations for avatars. However, research has also
shown that the fidelity of representations of actual others enters
the mix (Slater & Steed, 2001). Because people may be more sensitive to the slightest imprecision in animations of avatars compared
to agents, we might expect even slightly imperfect renderings of
avatars’ facial expressions to elicit less positive or even negative
feelings. Given these competing hypotheses that we examined
the interaction between smiling and agency beliefs without making an a priori directional hypothesis in terms of smiling effects
on interactants by avatars vs. agents.
2. Method
2.1. Participants
Participants were 38 undergraduate women whose average age
was M = 20.2 (SD = 1.55). They received psychology course credit or
were paid for their participation. Owing to women’s greater sensitivity to non-verbal behavior (Hall, 1978), we only used female
participants. This provided us with a methodologically cleanest
test of our hypotheses.
2.2. Design
A 2 (Type of Interaction Partner: agent vs. avatar) X 2 (Smile
Condition: present vs. absent) between-subjects factorial design
was employed. Type of interaction partner referred to participants’
beliefs about whether their interaction partner was a computer
algorithm (embodied agent) or an actual person in real time (avatar). The smile condition, reflected whether the virtual peer counselor smiled (smiles present) or not (smiles absent).
2.3. Procedure
Participants were greeted by a female experimenter, blind to
condition, and escorted to a private room. After providing consent,
participants completed the first part of the study on a desktop
2381
computer. Participants read information on the computer research
involving digital virtual humans and the difference between an
avatar and an embodied agent was explained. They were also informed that immersive virtual environment technology (IVET)
had advanced such that it was difficult for people to tell whether
they were interacting with a computer or another person.
Participants were told that they would be discussing a personal
topic with a digital representation of a peer counselor in an immersive virtual world and that following their interaction they would
provide feedback about their partner and the interaction. They
were presented with three discussion topics (aspects of yourself
that make feel you uncomfortable or embarrassed; an event that
damaged your sense of self-worth; or problems with a past or current relationship) and were asked to rank them in the order of discussion preference. Everyone discussed her second-choice topic.
Next, participants were given 3 min to prepare by identifying their
discussion points.
Participants assigned to the avatar condition were told that
their peer counselor, ‘‘Beth,’’ was an undergraduate research assistant who would join them in a virtual world and that both of them
would be represented by a 3-dimensional virtual human during
their real-time interaction. Participants in the embodied agent condition, were told that they would be interacting with a peer counselor named ‘‘Beth’’ and they were asked to imagine what it would
be like visit a counseling-oriented website to discuss their
thoughts and feelings with a computer-generated counselor who
looked and acted like Beth. Regardless of condition, all participants
actually interacted with a human research assistant, blind to condition, who was trained to respond in a consistent manner during
the interactions.
After the partner type manipulation, participants were brought
into another room for the peer counseling session. To provide the
participant with privacy, the experimenter left the room after helping her don a head-mounted display. During the session, the research assistant ‘‘smiled’’ via the digital representation when it
was socially appropriate to do so by pushing gamepad button. In
the smiles enabled condition, the button triggered a smile response
on the virtual human. In the smile disabled condition, nothing happened when she pushed the button. After the interaction, participants filled out post-interaction measures using RiddleMeThis
(Loewald, 2009), were debriefed, and released.
2.4. The virtual environment
All participants were immersed in the same 3-dimensional virtual world for their interaction and they all interacted with the
same peer counselor. The virtual environment and virtual human
bodies were created using 3D Studio Max and Blaxxun Avatar Studio, respectively (see Fig. 1). The heads and faces of the virtual humans were created using 3D Me Now Software (see Fig. 2). The
selected face was rated as above average on a variety of interpersonal traits (i.e., warm, kind, supportive, open-minded, attractive,
intelligent, etc.) during pre-testing.
2.5. Equipment
Participants and the research assistant donned Virtual Research
V8 stereoscopic head mounted displays (HMDs). The HMDs featured dual 680 horizontal by 480 vertical pixel resolution LCD panels that refreshed at 60 Hz. The display optics presented a visual
field subtending approximately 50 degrees horizontally by 38 degrees vertically. Perspectively correct stereoscopic images were
rendered by a 450 MHz Pentium III dual processor computer with
an Evans & Sutherland Tornado 3000 dual pipe graphics card and
were updated at an average frame rate of 36 Hz. The simulated
viewpoint was continually updated as a function of the
2382
R.E. Guadagno et al. / Computers in Human Behavior 27 (2011) 2380–2385
Fig. 3. Mean counselor empathy ratings as a function of interaction partner type
(avatar vs. embodied agent) and smile condition.
Fig. 1. The virtual environment.
as realistic and natural as possible since the goal was to create a
Duchenne smile. Thus, the corners of the mouth were pulled upwards, mimicking the movement of the zygomatic major muscle,
and the eyes narrowed slightly to approximate the movement of
the orbicularis oculi muscle (see Fig. 3).
2.8. Dependent measures
2.8.1. Social evaluations
Participants evaluated the peer counselor on 19 interpersonal
traits using a 7-point scale where 3 = strongly disagree and
+3 = strongly agree. An exploratory factor analysis revealed that
the 19 traits loaded onto two factors, thus two composite scales
were created which we labeled: counselor empathy and general
positivity. Counselor empathy was comprised of 12 items: supportive, responsive, warm, sympathetic, caring, open, accepting,
genuine, distant, uninvolved, judgmental, and cold, a = .90. General
Positivity was comprised of 7 items: friendly, attractive, intelligent,
professional, polite, likeable, and open-minded, a = .82).
Fig. 2. The peer counselor without (left panel) and with (right panel) a smile.
participants’ head movements. The orientation of the participant’s
head was tracked by a three axis orientation sensing system (Intersense IS300, update rate of 150 Hz). The system latency was 65 ms.
The software used to render and track was Vizard 2.0.
2.6. The interaction
Once immersed in the virtual environment, the participant was
introduced to her peer counselor who was seated in the virtual
world facing her. The peer counselor greeted her by name and informed her that she could begin talking about her topic when she
was ready. The participant then talked about her thoughts and
feelings about her topic for 3 min. During the interaction, the lips
of the virtual humans moved in sync with the participant’s and
peer counselor’s speech and the eyes blinked in a realistic manner.
Their head movements were veridically rendered based on their
actual head movements. Other facial expressions (i.e., mouth and
eye area movements) were explicitly manipulated. The peer counselor maintained eye contact throughout the interaction, nodded
encouragingly, and provided verbal feedback (e.g., ‘‘I see’’; ‘‘go
on’’). If necessary, minimal additional verbal prompts facilitated
further self-disclosure (e.g., ‘‘how did that make you feel?’’).
2.8.2. Other dependent variables
Participants also rated the peer counselor along a number of
other dimensions on a 7-point scale where 3 = strongly disagree
and +3 = strongly agree. Four items (a = .86) assessed the peer
counselor supportiveness. Two items (a = .83) assessed peer counselor likeability. Three items (a = .88) assessed comfort with the
interaction. Interaction satisfaction and interaction enjoyableness
were both assessed by 1 item. Six items (a = .86) assessed social
presence. Nine items (a = .90) peer counselor trustworthiness. Four
items (a = .86) assessed participants’ satisfaction interaction mode.
3. Results
All of the dependent variables showed strong, positive correlations with one another (See Table 1). We first examined our dependent measures to determine whether topic discussed or research
assistant acting as the peer counselor differed by session and
experimental condition. There were no significant differences by
condition or session for the topic discussed or the research assistant portraying the peer counselor. Thus, we were assured that
there were no experimenter effects or differential discussion topics
across the experimental design.
3.1. Social evaluations
2.7. The smile manipulation
Smiles were animated so that they had a gradual onset and offset and lasted for 1 s. On average, the research assistants smiled
three times per interaction. The smiles were programmed to be
In order to examine the impact agency and smiling behavior on
social evaluations, we conducted a multivariate analysis of variance
(MANOVA) using interaction partner type and smile condition as the
independent variables and counselor empathy and general
2383
R.E. Guadagno et al. / Computers in Human Behavior 27 (2011) 2380–2385
Table 1
Means, standard deviations, and inter-correlations for dependent variables.
1
2
3
***
4
5
***
6
***
7
***
8
***
9
***
10
1. Counselor empathy
2. General positivity
3. Supportiveness
4. Liking
5. Comfort
6. Copresence
7. Interaction satisfaction
8. Interaction enjoyment
9. Trust & openness
10. Satisfaction w/medium
–
.75
–
.73
.52***
–
.68
.73***
.72***
–
.76
.56***
.63***
.64***
–
.79
.54***
.73***
.72***
.76***
–
.62
.47**
.56***
.53***
.70***
.69***
–
.71
.62***
.55***
.56***
.79***
.68***
.67***
–
.53
.31+
.59***
.34*
.71***
.56***
.56***
.55***
–
.69***
.49 **
.47**
.58***
.80***
.79***
.71***
.68***
.51*
–
M
SD
.15
1.18
1.00
.79
.82
1.38
.24
1.36
.26
1.74
.38
1.41
.21
1.77
.32
1.40
.14
1.36
.29
1.60
All variables were measured on 7-point scales ranging from
indicate disagreement.
+
p < .10.
*
p < .05.
**
p < .01.
***
p < .001.
***
***
3 (strongly disagree) to +3 (strongly agree). Thus, positive scores indicate agreement and negative scores
positivity as the dependent variables. Results of this analysis revealed no significant main effects for either interaction partner type
or smile condition. The interaction of partner type by smile condition
was significant, Wilks’ K = .78, F (2, 33) = 4.55, p < .05, g2p = .22. In
addition, there was a significant univariate interaction for counselor empathy, F (1, 34) = 6.28, p < .05, g2p = .16, but not positivity.
Because counselor empathy and general positivity were
strongly correlated with one another, follow-up tests to compare
the effect of interaction partner type and smile condition on counselor empathy were conducted controlling for general positivity. As
with the MANOVA results, a univariate analysis of covariance (ANCOVA), with general positivity as the covariate, revealed a significant interaction partner type by smile condition interaction for
counselor empathy, F (1, 33) = 8.36, p < .01, g2p = .21. As seen in
Fig. 3, simple effects comparing the effect of smiling on counselor
empathy separately for avatars and embodied agents revealed that
when participants thought that they were interacting with another
actual person, the peer counselor was viewed as more empathic
when she did not smile (M = .72, SD = .86) compared to when she
did smile (M = .13, SD = .85), F (1, 33) = 5.88, p < .05, g2p = .15. For
participants who thought they were interacting with a computer,
the peer counselor was seen as somewhat more empathic when
she smiled (M = .30, SD = .83) than when she did not (M = .25,
SD = .85), F (1, 33) = 2.73, p = .11, g2p = .08. Thus, the same digitally
rendered smile enhanced social evaluations of empathy for embodied agents, but inhibited them for avatars.
Similarly, simple effects comparing avatars and embodied
agents separately within each smile condition revealed that when
smiling was disabled, avatars were viewed as significantly more
empathic relative to embodied agents, F (1, 33) = 7.36, p < .01,
g2p = .18. When smiling was enabled, however, embodied agents
were viewed as somewhat more empathic than avatars, F
(1, 33) = 1.76, p = .19, g2p = .05.
eight dependent variables by entering general positivity in Step 1
followed by empathy in Step 2. As seen in Table 2, general positivity towards the counselor was significantly related to the eight
dependent variables. Except for liking, adding counselor empathy
at Step 2 resulted in a significant increase in the R2 and reduced
the effect of general positivity non-significant. Thus, perceptions
of counselor empathy explained a significant proportion of the variance for seven of the eight dependent variables, even after controlling for general positive feelings towards the counselor. Moreover,
the only unique effect for these seven variables was for counselor
empathy. Thus, greater perceptions of empathy led participants
to view their counselor as more supportive, they were more comfortable and satisfied with their interaction, they enjoyed their
interaction with her more, they experienced greater copresence
with her, they enjoyed interacting using immersive virtual environment technology more, and they felt more trusting and open
in their interaction. Interestingly, although participants’ beliefs
about their partner and whether she smiled or not did not have
any direct effect on any of the interaction outcome variables, the
amount of perceived empathy did vary as a function of both
whether participants thought they were interacting with another
person or not and the nonverbal behavior of their partner. Empathy
then predicted participants’ evaluation of their partner and their
feelings about their interaction (see Fig. 4).
On the other hand, general positivity explained the majority of
the variance in liking and adding counselor empathy in Step 2 resulted in only a marginal DR2 while general positivity remained
uniquely related to liking. The interpersonal traits comprising the
general positivity scale (i.e., friendly, attractive, intelligent, professional, polite, likeable, and open-minded) might all be viewed as
tapping general liking for the counselor, thus their strong association with participants’ liking ratings is not surprising. Moreover,
the pattern of results for general positivity with the remaining
dependent variables mirrored those for liking.
3.2. Other dependent measures
Results of a MANOVA using interaction partner type and smile
condition as the independent variables and the remaining eight
interaction outcome measures as the dependent variables revealed
no significant main effects or interactions.
3.3. Counselor empathy
Next, a series of hierarchical linear regressions examined the effect of counselor empathy and general positivity on the remaining
4. Discussion
Overall, the results of this study indicate that choosing to render
a basic nonverbal behavior such as a smile can have a profound impact on how people experience their virtual interactions with others. Smiling increased perceptions of counselor empathy and
empathy was positively associated with people’s evaluation of their
peer counselor and their interaction. Specifically, higher empathy
was associated with more positive counselor impressions, a greater
2384
R.E. Guadagno et al. / Computers in Human Behavior 27 (2011) 2380–2385
Table 2
Beta predicting interaction outcome variables from counselor empathy and general positivity ratings.
Dependent variable
Supportiveness
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
Liking
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
Comfort
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
Copresence
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
R2
b
DR 2
Dependent variable
Interaction satisfaction
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
Interaction enjoyment
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
Trust & Openness
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
Satisfaction w/Medium
Step 1:
General positivity
Step 2:
General positivity
Counselor empathy
.27***
***
.52
.54***
.27***
.06
.78***
.53***
.73***
.04+
.57***
.49**
.31+
.32***
.56***
.57***
.26***
.01
.76***
.29***
.54***
.62***
b
.33***
.11
.86***
R2
DR2
.23**
.47**
.38**
.16**
.03
.60**
.39***
.62***
.53***
.14**
.21
.56**
.10+
.31+
.30**
.20**
.20
.68**
.24**
.49**
.48***
.24***
.07
.75***
⁄
p < .05.
p < .10.
**
p < .01.
***
p < .001.
+
Supportiveness
Comfort
Enjoyment
Smiling
Counselor Empathy
Satisfaction
Satisfaction w/ Medium
Interaction Partner Type
Copresence
Trust & Openness
Fig. 4. Model showing the relationship between smiling, empathy, and other variables.
sense of connection with and trust for the counselor, and more
comfort, enjoyment, and satisfaction with the interaction.
Importantly, however, the effect of rendering a smile on counselor empathy was impacted by whether people thought they were
interacting with another person (i.e., an avatar) or a computer (i.e.,
an embodied agent). Rendering a digital smile enhanced perceived
empathy for embodied agents, but doing so decreased perceived
empathy for avatars. In fact, an avatar who did not smile was perceived as significantly more empathic than one who did.
Previous research has shown that the effect of the communicative realism of virtual humans on a variety of social outcomes is complex and not necessarily intuitive (Bailenson et al., 2005; Fox &
Bailenson, 2009; Garau, 2003; Garau et al., 2003; Guadagno et al.,
2007; Yee & Bailenson, 2007; Yee, Bailenson, & Ducheneaut, 2009).
While it might seem that increasing the extent to which an embodied agent or avatar possesses the verbal and non-verbal capacities of
humans should increase its social influence, this does not seem to
always be the case. For example, Bailenson et al. (2005) demonstrated that although communicative realism affects perceptions
of copresence and various social responses to embodied agents,
the effect of communicative realism varies as a function of the extent
to which the embodied agent looks like a real human being.
Others have been found similar effects such that the effect of an
avatar’s realism depends on the extent to which the avatar looks
like a real person (Garau, 2003; Garau et al., 2003). For example,
Garau found that interactions with realistic-looking avatars were
viewed as more natural (i.e., more like a face-to-face interaction),
they elicited greater copresence, and interaction partners were
evaluated more positively when avatars exhibited inferred eye
gaze rather than random eye gaze. In contrast, interactions with
less realistic-looking avatars were viewed more positively when
the avatar exhibited random eye gaze (Ekman & Friesen, 1982).
Taken together, these studies on the effect of embodied agent or
avatar appearance and behavior suggest that there needs to be a
R.E. Guadagno et al. / Computers in Human Behavior 27 (2011) 2380–2385
match between the level of both appearance and behavior. When a
mismatch occurs, interactions are inhibited and social influence is
reduced. According to Slater and Steed (2001), this is because people tend to hold higher expectations for more human-like representations than they do for less realistic-looking ones. Thus, the
more realistic-looking an embodied agent or avatar appears, the
more realistic its communicative behaviors such as facial expressions need to be in order for the digital representation to elicit social responses. In contrast, less communicative realism is needed in
interactions with less realistic-looking embodied agents or avatars.
In this study, we attempted to increase the communicative realism of our peer counselor by adding a digitally-rendered smile. Because the same digital peer counselor representation and same
smiling animation were used for all interactions, one might expect
that a smile would affect the positivity of social evaluations equally
regardless of whether participants thought they were interacting
with another person or a computer. However, this was not the
case. Instead, a digitally-rendered smile enhanced social evaluations of embodied agents while it decreased them for avatars.
Unfortunately, one limitation here is that the design did not allow us to determine the reason for people’s negative reaction to a
smiling avatar. Recall that the animations attempted to mimic the
movement of the zygomaticus major and orbicularis oculi muscles
as occurs with Duchenne smiling. Our animations, however, were
no doubt imperfect. Thus, something about the animation itself
may have affected people differently depending on whether they
thought their partner was a human or a computer. Alternatively,
participants in the avatar condition may have been more sensitive
to the timing of the onset of the smile or its duration. Future research is needed to address these important issues.
References
Bailenson, J. N., Blascovich, J., Beall, A. C., & Loomis, J. M. (2003). Interpersonal
distance in immersive virtual environments. Personality and Social Psychology
Bulletin, 29, 1–15.
Bailenson, J. N., Swinth, K. R., Hoyt, C. L., Persky, S., Dimov, A. E., & Blascovich, J.
(2005). The independent and interactive effects of embodied agent appearance
and behavior on self-report, cognitive, and behavioral markers of copresence in
immersive virtual environments. Presence. Teleoperators and Virtual
Environments, 14, 379–393.
Bailenson, J. N., Yee, N., Patel, K., & Beall, A. C. (2008). Detecting digital chameleons.
Computers in Human Behavior, 24(2008), 66–87.
Bavelas, J. B., Black, A., Lemery, C. R., & Mullett, J. (1986). ‘‘I show you how I feel’’:
Motor mimicry as a communicative act. Journal of Personality and Social
Psychology, 50, 322–329.
Baylor, A. L., & Soyoung, K. (2009). Designing nonverbal communication for
pedagogical agents: When less is more. Computers in Human Behavior, 25,
450–457.
2385
Blascovich, J. (2002). A theoretical model of social influence for increasing the utility
of collaborative virtual environments. In Proceedings of the 4th International
Conference on Collaborative Virtual Environments, Bonn, Germany.
Blascovich, J., & Bailenson, J. B. (2011). Infinite reality avatars, eternal life, new Worlds,
and the Dawn of the virtual revolution. New York: HarperCollins.
Blascovich, J., Loomis, J., Beall, A., Swinth, K., Hoyt, C., & Bailenson, J. N. (2002).
Immersive virtual environment technology as a methodological tool for social
psychology. Psychological Inquiry, 13, 103–124.
Byrne, D. (1971). The Attraction Paradigm. New York: Academic Press.
Ekman, P., & Friesen, W. V. (1982). Felt, false, and miserable smiles. Journal of
Nonverbal Behavior, 6, 238–252.
Fox, J. A., & Bailenson, J. N. (2009). Virtual self-modeling: The effects of vicarious
reinforcement and identification on exercise behaviors. Media Psychology, 12(1),
1–25.
Frank, M. G., Ekman, P., & Friesen, W. V. (1993). Behavioral markers and
recognizability of the smile of enjoyment. Journal of Personality and Social
Psychology, 64, 83–93.
Fridlund, A. J. (1991). Sociality of solitary smiling: Potentiation by an implicit
audience. Journal of Personality and Social Psychology, 60, 229–240.
Fridlund, A. J., Ekman, P., & Oster, H. (1987). Facial expressions of emotion: Review
of the literature, 1970–1983. In A. W. Siegman & S. Feldsteing (Eds.), Nonverbal
behaviour and communication (pp. 143–224). Hillsdale, NJ: Lawrence Erlbaum
Associates, Inc.
Garau, M. (2003). The impact of avatar fidelity on social interaction in virtual
environments. Unpublished Doctoral Dissertation, University College, London.
Garau, M., Slater, M., Vinayagamoorthy, V., Brogni, A., Steed, A., & Sasse, M. A.
(2003). The impact of avatar realism and eye gaze control on perceived quality
of communication in a shared immersive virtual environment. In Proceedings of
the SIG-CHI Conference on Human Factors in Computing Systems, April 5–10,
2003, Fort Lauderdale, FL, USA, pp. 529–536.
Guadagno, R. E., Blascovich, J., Bailenson, J. N., & McCall, C. (2007). Virtual humans
and persuasion: The effects of agency and behavioral realism. Media Psychology,
10, 1–22.
Hall, J. A. (1978). Gender effects in decoding non-verbal cues. Psychological Bulletin,
85, 845–857.
Lau, S. (1982). The effect of smiling on person perception. The Journal of Social
Psychology, 117, 63–67.
Loewald, T. A. (2009). RiddleMeThis. Tuscaloosa, AL: Loewald New Media. [Computer
software available online at Riddlemethis.net.] .
Reis, H. T., Wilson, I. M., Monestere, C., Bernstein, S., Clark, K., & Seidl, E. (1990).
What is smiling is beautiful and good. European Journal of Social Psychology, 20,
259–267.
Slater, M., & Steed, A. (2001). Meeting people virtually: Experiments in virtual
environments. In R. Schroeder (Ed.), The social life of avatars: Presence and
interaction in shared virtual environments. Berlin: Springer Verlag.
Srofe, L. A., & Waters, E. (1976). The ontogenesis of smiling and laughter: A
perspective on the organization of development in infancy. Psychological
Review, 83, 173–189.
Stel, M., Blascovich, J., McCall, C., Mastop, J., Van Baaren, R. B., & Vonk, R. (2010).
Mimicking disliked others: Effects of a priori liking on the mimicry-liking link.
European Journal of Social Psychology, 40, 876–880.
Thompson, D. A., & Meltzer, L. (1964). Communication of emotional intent by facial
expression. Journal of Abnormal and Social Psychology, 68, 129–135.
Von der Pütten, A. M., Krämer, N. C., Gratch, J., & Kang, S. (2010). ‘‘It doesn’t matter
what you are!’’ Explaining social effects of agents and avatars. Computers in
Human Behavior, 26, 1641–1650.
Yee, N., & Bailenson, J. (2007). The Proteus effect: The effect of transformed selfrepresentation on behavior. Human Communication Research, 33, 271–290.
Yee, N., Bailenson, J. N., & Ducheneaut, N. (2009). The Proteus Effect: Implications of
transformed digital self-representation on online and offline behavior.
Communication Research, 36(2), 285–312.