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BMC Psychology
Relationship betweenclient laughter
andsession outcomes inmetaverse counseling
Jieun Kang1 , Woo Hyun Baek1 , Yeon Bin Jeong1 , Hyerin Yang1 , Seongchan Lee2 and
Sang Min Lee1*
Abstract
Considering the growing interest in VR psychotherapy, this study investigated the relationship between client laugh-
ter and session outcomes in metaverse counseling. To investigate the relationships between types of client laugh-
ter and session outcomes in metaverse counseling, we employed a multilevel analysis by separating the variables
into two levels: session-level (between-sessions) and client-level (between-clients). The dataset included 159 sessions
nested among 26 clients. This study found that clients’ cheerful and nervous laughter positively impacted session
outcomes at the session level (within individual clients). However, when considering client-level laughter events
(between-client), nervous laughter at the session level was not significantly related to session outcomes. Polite, reflec-
tive, and contemptuous laughter showed no significant relationship with the session outcomes. None of the laughter
events were related to session outcomes at the client level (between clients). However, there was a significant within-
level interaction effect between session and cheerful laughter on session outcomes. The implications of the effects
of client laughter are discussed in metaverse counseling by comparing them with those of in-person counseling.
Keywords Metaverse counseling, Avatar, Virtual psychotherapy, Client laughter, Session outcomes
Relationships betweenClient Laughter andSession
Outcome inMetaverse Counseling
In recent years, the integration of virtual reality (VR) and
the metaverse has steered transformative changes across
various aspects of our lives, impacting our modes of
communication, work, and social interaction [20]. ese
advancements have redefined our daily experiences and
introduced notable shifts within the realm of mental
health care through the introduction of metaverse coun-
seling [7]. is groundbreaking therapeutic approach
capitalizes on the immersive and interactive capabilities
of virtual reality to provide support and interventions
for individuals confronting a spectrum of mental health
challenges [8]. In South Korea, metaverse counseling has
gained significant traction in elementary and secondary
schools, universities, and Employee Assistance Programs.
Notably, 141 educational institutions have adopted the
metaverse counseling platform, aptly named “Meta-
Forest” (see https:// youtu. be/ Y8xkK OyipoM). is plat-
form, which has over 10,000 students, has become a vital
hub for delivering counseling services, underscoring the
widespread acceptance and utilization of this innovative
approach [23].
Metaverse counseling broadens the scope of traditional
in-person therapy by immersing clients and therapists in
a virtual setting, enabling them to engage in and tackle
emotional and psychological challenges. Cho et al. [8]
explored the outcomes of counseling in metaverse and
traditional in-person modalities. eir findings indicated
that metaverse counseling resulted in more noticeable
improvements in specific aspects of psychological symp-
toms, such as depression and anxiety, than in-person
*Correspondence:
Sang Min Lee
leesang@korea.ac.kr
1 Department of Education, Korea University, Seoul, South Korea
2 YATAV, Inc, Seoul, South Korea
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Page 2 of 11
Kangetal. BMC Psychology (2024) 12:755
counseling. Furthermore, the study reported a higher
level of therapeutic rapport and client satisfaction with
the metaverse modality than with traditional face-to-
face therapy. ese results suggest a potential inclina-
tion toward establishing a more robust working alliance
between counselors and clients at an accelerated pace
within the metaverse counseling framework.
Although Cho et al. [8] demonstrated the usefulness
and effectiveness of metaverse counseling, there remains
a significant gap in our understanding of the factors that
influence the effectiveness of this therapeutic approach.
An essential aspect that requires further exploration
within the domain of metaverse counseling is the non-
verbal behaviors exhibited by clients during therapy
sessions. Within the metaverse counseling platform
MetaForest, cutting-edge deep learning technology is
employed to identify and interpret facial expressions (see
metaforest.us). is technology enables avatars to mirror
the real facial expressions of the therapist and the client.
Consequently, this capability allows for the interpretation
of non-verbal behaviors, marking a notable advancement
in the provision of metaverse counseling services.
Previous studies have produced mixed findings on
whether avatars’ non-verbal cues in virtual environments
resemble real-life interactions, reflecting the complexity
of translating social signals into digital spaces [32]. Some
researchers have reported that digital avatars often fail
to capture the full range of non-verbal cues (e.g., micro-
expressions, subtle shifts in body language, complex
facial expressions) [11]. Yet, other studies suggest that
avatars’ non-verbal cues can emulate real-life social inter-
actions effectively, depending on the therapeutic pres-
ence or immersion level [43]. is may especially vary
depending on the context and the counseling medium
used; therefore specifically, this study tried to focus on
the impact of non-verbal expressions within metaverse
counseling and how these might exhibit different dynam-
ics compared to real-life counseling interactions.
Among various client non-verbal behaviors, laughter
emerges as a critical element, as it possesses the unique
potential to empower therapists to enhance their profi-
ciency in forging a robust therapeutic alliance, playing
a pivotal role in advancing and optimizing counseling
outcomes [14]. Laughter can be part of the therapeutic
process. Laughter may mirror the mechanisms underly-
ing spoken communication and serve as an indicator of
the effectiveness of therapeutic exchange. Occasionally,
client laughter can affect the counseling process, such as
when a client has a big emotional release or when a client
uses laughter to hide their genuine emotions. Clients can
use laughter in unique and creative ways. erapists must
be careful because of the risks, especially when clients
use dark or insensitive laughter [5].
Numerous scholars, including Gupta [14], Marci
et al. [33], and Nelson [41], have conducted empirical
research to examine the significance of client laughter in
the therapeutic process and its outcomes. Nevertheless,
the specific dynamics of client laughter in a metaverse
counseling environment are yet to be explored. is
study aims to uncover the role of client laughter and its
ramifications in the therapeutic process in the context of
metaverse counseling.
Empirical Studies onClient Laughter andSession
Outcomes
Gervaize et al. [12], Falk and Hill [9], Marci etal. [33],
and Gupta [14] investigated client laughter in the con-
text of psychotherapy. First, Gervaize etal. [12] suggest
risky therapist behaviors can induce strong laughter.
Consequently, Falk and Hill [9] critically reviewed and
expanded Gervaize et al.’s [12] research by investigat-
ing the causes of client laughter in psychotherapy. eir
study found that humorous therapist interventions were
more effective in eliciting laughter than risky therapist
behaviors. Specific types of humor intervention, such as
“other humor” and “release of tension,” played a signifi-
cant role in client laughter. Marci etal. [33] explored the
impact of client laughter in psychotherapy by assessing
skin conductivity, a measure of electrical activity in sweat
glands. ey observed elevated skin conductivity during
laughter episodes and even more so when the therapist
and the client shared laughter, highlighting the signifi-
cance of laughter in shaping interpersonal dynamics dur-
ing therapy.
Gupta [14] provided a framework for categorizing
laughter characteristics based on observations of laugh-
ter episodes from various cases. Gupta focused on the
observable verbal and non-verbal aspects of laughter,
such as the number of “ha” sounds and duration, rather
than the laughter’s function, like its role in establishing a
therapeutic relationship. Laughter has five distinct char-
acteristics: (a) cheerful/happy, (b) polite, (c) reflective, (d)
contemptuous, and (e) nervous. Each of these character-
istics was assessed on a 5-point scale ranging from 1 (no
presence) to 5 (strong presence). is classification sys-
tem allows for a more nuanced analysis of laughter in a
therapeutic context. Gupta employed a classification sys-
tem to investigate the relationship between five distinct
types of laughter and session outcomes. Gupta found
that sessions characterized by an abundance of reflec-
tive laughter received more positive evaluations from
clients. Additionally, therapists whose clients exhibited
higher levels of reflective laughter tended to receive more
favorable session evaluations from these clients. Interest-
ingly, among the therapists’ caseloads, clients displaying
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Kangetal. BMC Psychology (2024) 12:755
the most nervous and contemptuous laughter reported
the highest client-rated session evaluations.
Purpose ofStudy
We carefully analyzed the therapy sessions conducted in
the metaverse to explore different types of client laugh-
ter within metaverse counseling based on Gupta’s [14]
classification system. During the coding process, four
independent judges conducted initial assessments of the
client laughter events, followed by collaborative discus-
sions to reach a consensus. As recommended by Hill [17],
this approach is deemed more valid than independent
ratings because it involves clinical intuition, reasoning,
and multiple perspectives. Subsequently, we compared
our findings with those of Gupta [14] to identify the spe-
cific characteristics and considerations of laughter in the
digital context. Finally, employing a multilevel analysis,
we will investigate the relationships between the types of
client laughter and session outcomes, specifically session
satisfaction, in metaverse counseling. is analysis distin-
guishes between session-level variations in client laughter
across sessions and client-level variations in client laugh-
ter. is comprehensive examination of client laughter in
metaverse counseling aims to enhance the understanding
and effectiveness of this emerging counseling approach
and facilitate its integration into mainstream therapeutic
practices.
Method
Participants
Clients
is study involved 26 clients (24 females and 2 males;
university students), all of whom completed the study
without dropping out. Each participant was Korean,
representing the racial and ethnic identities of the local
population. For privacy and anonymity in metaverse
counseling, each client used a nickname, and the counse-
lors did not have access to the participants’ real informa-
tion. e clients’ personal data, including their student
identification number, age, grade, and contact details,
remained confidential unless overtly shared with the
counselor during the session.
Counselors
Seven (six female, one male) master’s and doctoral coun-
selors at a metaverse counseling center participated in
this study. e number of clients served by counselors
employed at the metaverse counseling center varied from
one to eight. e counselors ranged in age from 23 to
33years (M = 27.57, SD = 3.78). Additionally, 28.6% had a
doctoral degree and an average of 2.09years of clinical
experience (SD = 1.82). Every counselor was required to
attend monthly case conferences, receive individual clini-
cal supervision, and participate in weekly small-group
peer supervision sessions.
Coders
Four undergraduate students (two male and two female)
majoring in psychology with a fundamental understand-
ing of counseling served as coders. e work of these
coders was supervised during each session by two pro-
fessors specializing in counseling and one doctoral stu-
dent in the field of counseling. After learning the coding
standards as a team, they were divided into two teams
(one male and one female per team) and proceeded to
code each counseling session.
Procedures
is study was approved by the Institutional Review
Board (IRB Number: 2022–0010) affiliated with the
authors’ academic institution. Potential research par-
ticipants were recruited at a metaverse counseling center
from August 2022 to June 2023. e data was collected
using a secure digital platform. Before the counseling
sessions began, participants received an overarching
research introduction and were asked to complete a con-
sent form, ensuring they were informed about the study.
Intake andscreening
Clients were recruited through Internet discoveries, and
their consent and eligibility were determined through a
screening questionnaire. During this screening phase,
the severity of symptom factors, such as intensity and
frequency, and the content of primary psychological con-
cerns were considered to make suitable counselor assign-
ments. Several mental health issues have been reported,
including problems related to interpersonal relationships,
depression, anxiety, career-related challenges, and aca-
demic stress. Clients were provided with comprehensive
instructions on how to use the “MetaForest” counseling
program, covering aspects such as accessing the platform,
using it on their devices, and providing feedback about
their counseling experiences through a secure online sur-
vey. While counselors were advised to complete therapy
within five sessions owing to the policy of the metaverse
counseling center, they had the flexibility to extend coun-
seling as needed. On average, metaverse counseling con-
sisted of approximately 6.1 sessions (SD = 2.29), with each
session lasting approximately 50min.
Counseling sessions
For supervision and coding, all counseling sessions were
recorded for approximately 50 min each. When pre-
sented to the coders, each session was assigned a specific
code to ensure anonymity and confidentiality.
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Kangetal. BMC Psychology (2024) 12:755
Training andcoding measures
e coders viewed the metaverse counseling videos and
analyzed two aspects: the duration of laughter lasting
over three seconds, laughter frequency throughout the
entire counseling session, and the level of satisfaction
in each session. Before commencing the actual coding
process based on the metaverse counseling videos, the
coders engaged in discussions and training regarding
the analysis of laughter lasting over three seconds and
categorized these instances into five specific categories.
Each coder underwent training to achieve an intraclass
correlation coefficient (ICC) of 0.7 or higher, indicating a
strong level of agreement among the raters. A high level
of agreement was constantly supervised in each session
to be maintained throughout coding, and they were also
given a manual for reference. Additionally, the coders
assessed the interaction dynamics between the clients
and counselors during the sessions. After the four asses-
sors coded the categories as each team, they were divided
into two pairs, and the variables were collectively re-eval-
uated until a consensus was reached. In this process, we
adhered to Gupta’s [14] guidelines, which emphasize that
consensus judgments involving clinical intuition, reason-
ing, and various perspectives are considered more reli-
able than independent ratings.
Metaverse Counseling Platform
is research utilized a specially designed metaverse
counseling platform named “MetaForest” for conduct-
ing counseling sessions through avatars in a virtual set-
ting. MetaForest offers several unique features within the
counseling environment. ese features include facial
recognition, motion gestures, themed spaces for coun-
seling, voice modulation, password-protected private
counseling areas within the metaverse, and screen shar-
ing. One of the standout features of the platform is its
use of advanced deep learning technology to recognize
and replicate the real facial expressions of counselors and
clients through their avatars. is technology allows for
the interpretation of non-verbal cues while preserving
the confidentiality of the counseling process. Addition-
ally, MetaForest enables non-verbal interactions through
gesture buttons, allowing avatars to perform actions such
as greetings, nodding, and raising hands. e platform
also provides various themed spaces tailored to different
types of counseling sessions, such as individual coun-
seling, group counseling, conferences, and healing gar-
dens, ensuring that the environment matches the specific
purpose of each counseling session. To enhance privacy
and security during counseling, MetaForest incorpo-
rates voice filtering and password protection. MetaForest
is accessible on both computers and mobile devices,
making it a versatile cross-platform application. More
in-depth information on the MetaForest can be found in
Cho etal. [7] and Cho etal. [8].
Measures
Laughter characteristics
Gupta [14] focused on identifying and categorizing dif-
ferent laughter characteristics based on a review of exist-
ing literature and refined them by observing laughter
episodes from various counseling cases. ese charac-
teristics were categorized into five types—cheerfulness,
politeness, reflectiveness, contemptuousness, and nerv-
ousness—each rated on a 5-point scale to assess their
presence (i.e. intensity) in counseling interactions.
1. Cheerfulness: Cheerfulness is characterized by
mutual enjoyment and awareness of the laughter
context between the client and therapist. It is often
marked by non-verbal cues such as smiles, relaxed
body posture, and facial muscle movements asso-
ciated with genuine smiles. is is more observed
when clients genuinely describe pleasant feelings
than when they superficially express pleasure, which
is contrary to their inner self.
2. Politeness: Politeness in laughter is related to brief,
low-energy laughter that often occurs during pleas-
antness or small talk. It is characterized by a lack of
intense non-verbal cues, such as wrinkles in the skin
around the eyes, and is common at the beginning of
sessions.
3. Reflectiveness: Reflective laughter is associated with
the verbal cues of pondering, thinking, or explor-
ing in the counseling context. Tones are often phil-
osophical and offer new insights or perspectives.
Non-verbal cues included steady eye contact, relaxed
body posture, a pensive voice tone, and congruence
between verbal and non-verbal cues.
4. Contemptuousness: Contemptuous laughter is
marked by verbal cues expressing hostility or disap-
proval toward oneself or others. Non-verbal cues
include sighing, scoffing, and physical signs of anger
or withdrawal.
5. Nervousness: Nervous laughter occurs when there is
incongruence between the content of the discussion
and the client’s reactions. It often involves discom-
fort, tension, high-pitch laughter, trembling and fidg-
eting.
ese laughter characteristics provide a nuanced
understanding of laughter in counseling sessions, helping
assess the emotional and relational dynamics between
clients and therapists.
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Kangetal. BMC Psychology (2024) 12:755
Session Outcome
e session outcomes were assessed using the Korean
version of the Client Satisfaction Questionnaire (K-CSQ).
e original CSQ was developed by Larsen et al. [27],
while Hwang [19] translated and back translated the
CSQ items into Korean. Subsequently, this translation
was cross-validated using a confirmatory factor analysis.
is study employed an abridged version of the original
questionnaire consisting of eight items, known as the
K-CSQ-8 [19]. It was rated on a 7-point Likert scale from
1 (Not at all) to 7 (Very much) with a total score range
from 8 to 56. Higher scores indicated greater satisfaction
with metaverse counseling. Example items in the ques-
tionnaire included statements such as “e client would
be content with the level of assistance they received dur-
ing counseling.” and “e client would have received
the type of counseling service they desired.” To allow an
objective third-party coder to assess and evaluate coun-
seling satisfaction, the survey questions were modified
from self-reporting to being observed by an external
observer. Hwang [19] reported a high level of internal
consistency reliability, with a coefficient of 0.97 for meas-
uring client satisfaction, and the reliability of session sat-
isfaction assessed by raters in this study was 0.86.
Data Analysis
e study began by examining the participants’ demo-
graphic characteristics, investigating the correlations
between variables, and analyzing the frequency of dif-
ferent types of laughter during metaverse counseling.
e data collected in this study exhibited a hierarchical
structure involving sessions and clients, which created
a multilevel structure with repeated measurements and
group clusters. To address this issue, a multilevel mod-
eling approach was employed to handle the clustered lon-
gitudinal data. For sampling, models that aim to explicitly
account for the third level of clustering frequently face
challenges when there are fewer than 10 clusters [38]. As
there were only seven counselors in the present study and
were not nested with enough clients (M = 3.71) within
each cluster, it was not deemed sufficient for a three-level
model. As a result, the therapist effect at level 3 was not
measured. All data analyses were performed using IBM
SPSS Statistics for Windows, version 27.0. To mitigate the
bias stemming from the small sample size and absence of
missing data, the restricted maximum likelihood estima-
tion method (REML) was used following the recommen-
dations of Kenward and Roger [22]. When using REML
to estimate model parameters, even a relatively small
number of clusters as few as 10 with small cluster sizes
as low as 5 [30] can yield unbiased estimates of fixed
effects [3, 37]. As a group size of average 25 is generally
considered small but acceptable number and treated
normal in educational research [25], the sample size was
deemed sufficient. is was proved again by utilizing a
priori power analysis with power of 0.8 (d = 0.50). While
REML does not fully correct for the downward bias for
small sample’s standard errors, the Kenward–Roger
approximation was further applied to maintain the nomi-
nal Type I error rate in small samples [10, 22]. Addition-
ally, the normal distribution of residuals was validated
at both levels, and homoscedasticity [18] was verified by
examining residual histograms and a residual scatter plot,
which led to confirmed basic multilevel assumptions. e
dependent variable was then measured using the scores
for session outcomes (session satisfaction) as assessed by
the raters. e independent variables included session
number (time) and different categories of laughter.
In the initial null model, the intra-class correlation
coefficient (ICC; [36]) was computed to gauge the extent
to which variances could be attributed to individual dif-
ferences. is step aims to assess the suitability of mul-
tilevel modeling. ICC values generally range from zero
to one and indicate the degree of dependency within the
nested data structure. Level 1 predictors included session
time and laughter categories centered on the group mean
for each session. Group mean-centered laughter was cal-
culated by subtracting the average laughter level for each
client (Level 2 mean) from the specific value within each
session. At Level 2, grand mean centering was performed
for predictors related to the aggregated group mean
of laughter across sessions. While observing the fixed
effects and cross-level interaction terms, random inter-
cepts at the client and counselor levels were integrated
into the analysis to account for individual variation.
Results
We first described the ratios of various types of client
laughter characteristics that appeared in metaverse coun-
seling in Table1. In the metaverse counseling sessions,
25.8% of the laughter were rated cheerful, and 34.0% were
rated with polite laughter. Conversely, reflective laughter
had the lowest rate (7.8%). Contemptuous laughter was
Table 1 Occurrence of Different Types of Laughter
Note. The numbers in the table represent percentages of laughter frequency
throughout whole counseling sessions
Types of laughter Occurrence rate
Cheerful 25.8
Polite 34
Reflective 7.8
Contemptuous 12.9
Nervous 19.5
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Kangetal. BMC Psychology (2024) 12:755
observed at 12.9%, with nervous laughter showing 19.5%
of instances.
Table2 shows the correlations between the variables
of interest at both levels. e two levels are categorized
as session (Level 1) and client (Level 2). Cheerful, polite,
and nervous laughter showed significant positive rela-
tionships with session outcomes at the session level (level
1). ere was no significant relationship between reflec-
tive or contemptuous laughter and session outcomes at
the session level (Level 1). However, at the client level
(level 2), only polite and reflective laughter showed a sig-
nificant positive relationship with the session outcomes.
Furthermore, a stronger positive correlation between
laughter and session outcomes was observed at the client
level (level 2) than at the session level (level 1).
Subsequently, we observed fixed and random effects in
a multilevel model predicting session outcomes (outcome
and dependent variable), divided into five subcategories,
into which laughter (predictors and independent vari-
ables) was classified. Table3 shows the results of the mul-
tilevel analysis predicting session outcomes. A null model
(Model 1) with no level predictors was first used to deter-
mine the ICC (0.40) of the session outcome. Inter-indi-
vidual differences explained 40% of the total variance
in session outcomes. Model 2 then presents the added
session-level parameters, showing that session outcome
(est. = 0.06, p < 0.05) increased as sessions passed. It also
showed that the levels of cheerful laughter (est. = 0.04,
p < 0.05) and nervous laughter (est. = 0.04, p < 0.05) had a
significantly positive relationship with session outcomes
at the session level (Level 1). is indicates that cheer-
ful and nervous laughter positively affects the session
outcomes. Finally, Model 3 observes the added Level 2
parameters at the client level. Although none of the cli-
ents’ laughter significantly affected session outcomes
at the client level (Level 2) within laughter categories,
cheerful laughter (est. = 0.04, p < 0.05) only showed a sig-
nificantly positive relationship with session outcomes at
the session level. Finally, a within-level interaction effect
between session and cheerful laughter was observed for
session outcomes (est. = 0.01, p < 0.05). e positive rela-
tionship between session outcomes and cheerful laughter
increased as the sessions proceeded (see Fig.1). Table3
presents the random intercepts at the client level in addi-
tion to the fixed effects while Table4 presents the concise
model and equations with each parameter added in each
level.
Discussion
is study aimed to reveal the effects of client laugh-
ter on metaverse counseling. Among the five distinct
characteristics of client laughter, cheerful and polite
laughter occurred most frequently in all sessions. How-
ever, nervous and contemptuous laughter were less fre-
quently seen, while reflective laughter was rarely seen in
metaverse counseling sessions. Furthermore, cheerful
laughter and nervous laughter had a positive effect on
session outcomes at the session level, while other types
of laughter resulted in non-significant results. More spe-
cifically, a significant effect of cheerful laughter on ses-
sion outcomes was observed regardless of the influence
of the client’s overall laughter on session outcomes. is
implies that the influence of cheerful laughter on session
outcomes was considerably significant between sessions.
Conversely, nervous laughter did not show any signifi-
cance in the relationship between overall client laughter
and session outcomes. Our findings also showed that as
the counseling sessions progressed, cheerful laughter
between sessions had a greater impact on session out-
comes. is implies that session satisfaction was rein-
forced, with the influence of cheerful laughter becoming
more crucial during the later stages of metaverse
counseling.
In the clinical context, laughter describes its meaning
in various performances related to the release of psychic
energy, stress, anxiety, depression, and interpersonal
relationships [4, 6, 24, 35, 45]. Especially in the coun-
seling context, the presence or absence of laughter could
Table 2 Descriptive Statistics and Intercorrelation between Measures per Session and Client
Note. The session level is above the diagonal line, and the client level is below it. M = mean; SD = standard deviation; N = number
* p < .05, **p < .01
M SD 123456
Sessions (n = 159) Clients (n = 26)
1. Cheerful 2.37 3.86 .378** .079 -.018 .072 .237**
2. Polite 3.56 3.04 .537** .102 .078 .364** .245**
3. Reflective 0.17 0.45 .078 .338 .036 -.018 .150
4. Contemptuous 0.52 1.29 .008 .456*.095 .477** .055
5. Nervous 2.28 3.73 .118 .496*.080 .628** .250**
6. Session Outcome 5.82 0.66 .273 .527** .418*.236 .344
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Kangetal. BMC Psychology (2024) 12:755
Table 3 Fixed and Random Effects of Multilevel Models for Session Outcomes
Note. The AIC and BIC were estimated using REML estimation, and xed eects were estimated with robust standard errors. W, within-client laughter; B, between-client
laughter
Model 1 Model 2 Model 3
Fixed Eects Est p Est P Est p
Level 1
Intercept 5.74 (0.09) < .001 5.34 (0.12) < .001 5.18 (0.17) < .001
Session 0.06 (0.02) < .05 0.06 (0.02) < .05
Cheerful
W0.04 (0.01) < .05 0.04 (0.17) < .05
Polite
W0.00 (0.02) 0.87 −0.01 (0.19) 0.73
Reflective
W0.13 (0.09) 0.16 0.09 (0.1) 0.37
Contemptu-
ous W
−0.03 (0.04) 0.51 −0.03 (0.04) 0.52
Nervous
W0.04 (0.02) < .05 0.04 (0.02) 0.05
Ses-
sion × CheerfulW
0.01 (0.00) < .05
Level 2
Cheerful
B−0.03 (0.04) 0.38
Polite
B0.06 (0.06) 0.34
Reflective
B0.59 (0.44) 0.20
Contemptu-
ous B
−0.03 (0.20) 0.89
Nervous
B−0.01 (0.05) 0.91
Random Eects 95% CI 95% CI 95% CI
Residual Level 1 0.26 [0.21–0.38] 0.24 [0.18–0.30] 0.24 [0.18–0.30]
Intercept Level 2 0.17 [0.08–0.35] 0.12 [0.56–0.26] 0.12 [0.54–0.28]
ICC Level 2 0.40 0.33 0.33
AIC 274.29 280.20 290.95
BIC 280.33 286.15 296.83
Fig. 1 Relationships between Cheerful Laughter and Session Outcomes over the Sessions
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Kangetal. BMC Psychology (2024) 12:755
support or interrupt therapeutic work and the relation-
ship between the client and the therapist in perceiving
genuineness or defensiveness [14]. erefore, it is impor-
tant to investigate the specific types of laughter elicited
during therapy sessions.
In this study, polite laughter was observed to be the
most frequent type of laughter, which is consistent with
the findings of Provine [42] and Gupta [14]. However,
this study found that polite laughter had no significant
effect on session outcomes. is could be explained by
differences in the structure and features of cheerful and
polite laughter. Previous researchers have suggested that
polite laughter is generally triggered by interpersonal
relationships and that this laughter can function as a flex-
ible social factor used in early communication situations
with others, such as social lubricants [42]. In such situ-
ations, polite laughter is used to maintain conversation,
loosen or assess the situation softly, or hide nervous-
ness by minimizing the emotions [14, 44]. Meanwhile,
Sabonytė [44] proposed evidence of differences between
cheerful and polite laughter, examining structure and
acoustic features of two types of social laughter. Accord-
ing to this study, polite laughter is considered a one-bout
laughter, which comprises one or two syllables shorter in
duration than cheerful laughter. In other words, the type
of laughter distinguished by laughter tempo with shorter
duration and tempo was considered polite. Similarly, in
a slightly different context, polite laughter occurred fre-
quently at the beginning of the session. ese anteced-
ents might explain why polite laughter can be treated as a
factor that occurs consecutively and is captured in social
interactions rather than a factor that affects counseling
satisfaction.
Empirical research has emphasized the frequency and
importance of cheerful laughter in therapeutic processes
and outcomes. Falk and Hill [9] specifically uncovered
the implications of humorous and cheerful laughter in
releasing tension during psychotherapy. Further, it is
recognized as a treatment intervention to reduce pain,
stress, and anxiety, and positive changes at the biologi-
cal level have been observed in psychotherapy. Lapierre
etal. [26] found that the humor test group prevented a
decrease in pain tolerance by eliciting cheerful laughter
after watching a ceremony video for 30min. Similarly, Ko
and Youn [24] and Mbiriri [35] suggested that laughter
therapy itself helps decrease stress and depression and
heal pain or unpleasant feelings. Akimbekov and Raz-
zaque [1] revealed the effects of cheerful laughter during
the ongoing stressful period of the COVID-19 pandemic
and asserted the importance of creating an environment
that accelerates laughter to lessen anxiety and pro-stress
factors. e results of our study can be extended in that
we observed the role of cheerful laughter by referring to
the significant relationship between client laughter and
session outcomes. Addressing variables such as a sense
of humor or cheerfulness in therapy sessions may sug-
gest positive effects resulting from happiness and may be
linked to mitigating the effects of stress [2, 4, 14]. Based
on this assumption, cheerful laughter can be expanded to
be a major variable for predicting session outcomes.
is study also found a positive relationship between
clients’ nervous laughter and session outcomes at the
session level. is explanation could be interpreted
as freeing negative parts of the self to elicit authen-
tic psychological anxiety through laughter across
sessions. Clients using nervous laughter may first expe-
rience dissonance between the situational context and
their responses [14]. Laughter of nervousness can be
interpreted in terms of cognitive incongruity. Mar-
tin [34] emphasizes humor duality, indicating similari-
ties between anxiety and laughter responses. Based on
this emphasis, Granitsas [13] considered laughter a
Table 4 Models and Equations
Note. To keep the equations concise, only the parameters for session, cheerfulness and nervousness laughter are included in this table. C refers to the aggregated
mean value
Models Equations
Unconstrained Model (no independent variables) Level 1: CSQij = β0j + rij
Level 2: β0j = γ00 + u0j
Model 1 (Level 1 independent variables) Level 1: CSQij = β0j + β1j*(SESSij) + β2j*(CHEERij) + β3j*(NERVij) + rij
Level 2: β0j = γ00 + u0j, β1j = γ10, β2j = γ20, β3j = γ30
Model 2 (Levels 1 and 2 independent variables) Level 1: CSQij = β0j + β1j*(SESSij) + β2j*(CHEERij) + β3j*(NERVij) + rij
Level 2: β0j = γ00 + γ01*(CCHEERj) + γ02*(CNERVj) + u0j
β1j = γ10, β2j = γ20, β3j = γ30
Model 3 (Levels 1 and 2 independent variables with cross-level interaction) Level 1: CSQij = β0j + β1j*(SESSij) + β2j*(CHEERij) + β3j*(NERVij) + rij
Level 2: β0j = γ00 + γ01*(CCHEERj) + γ02*(CNERVj) + u0j
β1j = γ10 + γ11*(CCHEERj) + γ12*(CNERVj)
β2j = γ20 + γ21*(CCHEERj) + γ22*(CNERVj)
β3j = γ30 + γ31*(CCHEERj) + γ32*(CNERVj)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 9 of 11
Kangetal. BMC Psychology (2024) 12:755
perspective of nervousness that can generate anxiety
and can be stimulated from incongruous happenings
by suspicion and confusion. Incongruity, as the root of
anxiety, could lead to the treatment of humor as suffer-
ing or defensive, and without laughter, it would retain a
negative sense of reality [13]. Although Gupta [14] agreed
that nervous laughter induces cognitive dissonance, cli-
ents can use nervous laughter as an expression to free
themselves, which can lead to higher session evaluations.
Although nervous laughter is significant for counseling
satisfaction between sessions, the method of using nerv-
ous laughter may differ among clients. Our study found
that nervous laughter did not significantly affect session
outcomes when client-level variables were added—it can
be assumed that the impact of nervous laughter on ses-
sion outcomes is not as strong as that of cheerful laughter
in metaverse counseling.
Unlike Gupta [14] with in-person therapy, the results
of this study indicated that reflective laughter was not
significantly related to session outcomes. Gupta [14]
insisted that reflective laughter is a prominent factor in
non-verbal behaviors when explicating session evalua-
tion. ese different results may have been influenced by
the therapeutic surroundings (i.e., counseling method),
which these two studies demonstrated within different
counseling modalities: metaverse and in-person. As the
information about the emotional state transmitted when
laughing is caught through actual facial expressions, body
movements, and sounds [44], it becomes more difficult to
capture reflective laughter compared to in-person coun-
seling due to the specific characteristics and limitations
of the metaverse counseling modality. Reflective laughter
might be described as the genesis of contradictions, such
as incongruity in nervous laughter, which is the occur-
rence of laughter due to the uncanniness of inherent psy-
chological incongruity along with enlightenment gained
by comparing it with the incapacitation of others or one’s
past [48]. In other words, reflective laughter can ten-
tatively be classified as nervous laughter in a metaverse
counseling setting. Moreover, Gupta [14] interpreted
client laughter rated highly in terms of reflectiveness as
somewhat ruefully amused when they came to new reali-
zations about themselves. erefore, it can be specu-
lated that reflective laughter might be captured as part of
laughter. In this study, reflective laughter was observed at
the lowest frequency of 7.8%,therefore, it may be difficult
for it to appear as a variable that significantly affects ses-
sion outcomes.
is study also found that session outcomes improved
as metaverse counseling sessions progressed. In other
words, the satisfaction level with the counseling ses-
sions increased as the metaverse counseling sessions
progressed. Moreover, in the later phases of treatment,
cheerful laughter during sessions was linked to stronger
session satisfaction. In other words, the more cheerful
the laughter in each session, the stronger the relationship
between laughter and session outcomes in later stages of
counseling. Strong client cheerful laughter signifies the
development of a positive client-counselor relationship,
referred to as warmth and acceptance, intimacy, and a
reduction in emotional distance [31, 39, 40]. e early
sessions of metaverse counseling may reflect specificity
that the absence of actual contact and practical presence
can evoke a sense of alienation, making it more difficult
for the counselors to cultivate a reliable relationship with
the client quickly [29]. In other words, the effect of cheer-
ful laughter in metaverse counseling could be remark-
able in reinforcing session outcomes as sessions proceed
toward later phases. Togetherness is another principal
aspect of laughter in the development of therapeutic rela-
tionships. When two people laugh together rather than
alone, it is possible to contribute to an increase in posi-
tive emotions by inducing a stronger laughter reaction by
building a social scene [26]. Additionally, Marci etal. [33]
found that client laughter was shared with therapists to
form interpersonal dynamics during sessions. e posi-
tive consequences of the shared laughter of therapists
and clients in therapeutic relationships can be reflected
continuously when they laugh together as the counseling
session passes. e way laughter emerges between the
client and therapist can become a baseline for the initial
establishment of a therapeutic relationship. Furthermore,
laughter can be the basis for the client’s transference
responses and the emotional bond between the client and
the therapist that develops over time.
is study had several limitations that have implica-
tions for future research. First, there may be differences
in prehensible laughter between the two counseling
modalities of metaverse and in-person counseling.
Counselors and clients use subtle non-verbal behaviors
to recognize important interaction cues, such as body
gestures and facial expressions, and immediately dis-
cern emotional and relational information [15, 46]. Non-
verbal communication can be more severely affected by
metaverse counseling in online circumstances. Further,
some restrictions might occur in fully adopting Gupta’s
[14] laughter classification system, as laughter is likely to
be expressed differently in each counseling modality and
may cause trouble when implemented under certain cir-
cumstances. It is necessary to examine the meaningful
aspects of laughter in the context of online counseling,
such as metaverse counseling, as the references primarily
focus on in-person psychodynamic studies. Considering
this, further empirical research should be conducted by
clearly comparing metaverse counseling with in-person
counseling to investigate specifically how variables that
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 10 of 11
Kangetal. BMC Psychology (2024) 12:755
affect counseling satisfaction work depend on different
counseling modalities.
Second, there may be differences in the cultural con-
texts of laughter in Korea. Clients in other cultures may
have different characteristics, preferred/denied types,
and communication styles regarding laughter [14]. Pre-
vious research has argued that specific cultural contexts
can shape how emotions and expressions are felt and
expressed once formed [28, 47]. us, it may be difficult
to generalize the relationship between client laughter and
session outcomes to other cultures.
ird, it will be necessary to obtain a larger sample size
while considering other nested factor effects. Although
we achieved an appropriate sample size for using REML
and obtained a suitable ICC value, increasing the sam-
ple size would enhance the generalizability of the study.
is limitation may be attributed to the contextual con-
straints of the unique medium of metaverse counseling
[21]. Yet on the opposite side, this can also be seen as an
opportunity to examine the unique aspects of metaverse
counseling in greater depth. Additionally, future research
could enhance the model by incorporating counselor
characteristics as an additional nested level above the cli-
ent cluster, which would lead to increased sample size.
Exploring individual counselor characteristics, which
may influence client laughter, could also be valuable.
Finally, all clients opted for voluntary participation in
metaverse counseling instead of in-person counseling.
Clients who accept metaverse counseling instead of in-
person counseling may have higher expectations or moti-
vations for that type of counseling service, which may be
related to positive evaluations of counseling sessions [16].
In other words, it was difficult to consider the distinct
characteristics of each client who chose metaverse coun-
seling, which may have biased their reported laughter
during counseling. A potential explanation for the speci-
ficity of clients choosing metaverse counseling might be
openness, extraversion, and neuroticism [8], personality
factors of those who choose metaverse counseling must
be further explored.
Despite these limitations, this study is significant in
that it applied Gupta’s laughter classification system to
a metaverse counseling environment and investigated
the variables that cause therapeutic effects in metaverse
counseling, which have not yet been developed. Our
findings further suggest that clients’ laughter might
reflect a range of laughter characteristics while revealing
the specific characterized types of laughter in metaverse
counseling. In this vein, this study empirically verified the
kind of laughter that should be seen in metaverse coun-
seling to affect counseling satisfaction, further dispens-
ing for the practical viability of metaverse counseling. In
summary, the results imply that counselors can derive
successful session outcomes from clients in metaverse
counseling by paying attention to clients’ laughter and
recognizing certain characteristics of it during coun-
seling sessions.
Acknowledgements
We would like to thank the counselors and clients who participated in this
study.
Authors‘ contributions
Conceptualization, S.M.L.; methodology, J.K.; formal analysis, Y.B.J.; investiga-
tion, S.M.L.; data curation, Y.B.J; raw data management, J.K; writing—original
draft preparation, J.K., W.H.B., Y.B.J., H.Y., and S.M.L.; writing—review and edit-
ing, J.K., S.L., S.M.L., ; supervision, S.M.L.; project administration, S.L., S.M.L.; and
funding acquisition, S.L. All authors have read and agreed to the published
version of the manuscript.
Funding
This work was supported by the Technology development Program [RS-
2024–00468584] through the Ministry of SMEs and Startups (MSS, Korea)
and the Humanities and Social Sciences Institute Support Program (NRF-
2023S1A5C2A02095547) through the Ministry of Education of the Republic of
Korea and the National Research Foundation of Korea.
Data availability
The data presented in this study are available on request from the correspond-
ing author.
Declarations
Ethics Approval and Consent to Participate
The research protocol underwent a thorough review and received approval
from Korea University’s Institutional Review Board (IRB #: KU-2022–0010). All
participants provided consent to participate in this study. All participants
agreed to take part in this research and sign the informed consent form.
Consent for Publication
All participants gave their consent for publication.
Competing Interests
The authors declare no competing interests.
Received: 31 January 2024 Accepted: 5 December 2024
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