Humor Frequency as a Social-Affective Cue: How Humorous Pedagogical Agents Shape Learners’ Motivation, Emotions, and Perceived Agent Value

Humor Frequency as a Social-Affective Cue: How Humorous Pedagogical Agents Shape Learners’ Motivation, Emotions, and Perceived Agent Value | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Humor Frequency as a Social-Affective Cue: How Humorous Pedagogical Agents Shape Learners’ Motivation, Emotions, and Perceived Agent Value Xinjie Xie, Xiangen Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8627679/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Humor can function as a powerful social-affective cue that shapes learners’ emotional and motivational experiences in digital learning environments. This study investigated how humorous pedagogical agents influence intrinsic motivation, positive emotions, and perceived agent value across two between-subjects 2×2 experiments. Experiment 1 used a Humor × Watching dialogue design, and Experiment 2 used a Humor frequency × Mental load design. University students were randomly assigned to conditions in each experiment. Data were analyzed using SPSS factorial ANOVA, followed by simple effect analyses for significant interactions. In Experiment 1, agent humor enhanced intrinsic motivation, positive emotions, and perceived agent value, while watching dialogue suppressed positive emotions without hindering knowledge transfer. In Experiment 2, higher humor frequency increased positive emotions and Human-likeness in perceived agent value, and interacted with mental load to buffer its negative impact on intrinsic motivation. These findings clarify the social-affective mechanisms through which humor and humor frequency support learners’ motivational and emotional engagement in cognitively demanding online contexts. Educational Psychology Pedagogical agents Humor Humor frequency Intrinsic motivation Positive emotions Mental load Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Is humor an effective teaching strategy? Recent research suggests that incorporating humor into online learning environments can enhance student engagement and promote learning across multiple dimensions (Erdoğdu & Çakıroğlu, 2021 ). Pedagogical agents play a comparable role in online education; these virtual teacher figures support learning by providing instructional guidance and social cues. One of their key advantages is the ability to simulate multiparty dialogues. Prior studies have shown that observing dialogues between pedagogical agents can foster active learning and improve performance, although this approach does not improve learning interests. (Novick et al., 2019 ). In contrast, Buttussi and Chittaro ( 2020 ) found that humorous pedagogical agents evoke more positive emotions and receive higher evaluations, although research in this area remains limited. Thus, the present study examines the interaction between humorous pedagogical agents and dialogue-based presentations, aiming to combine the strengths of both approaches. In face-to-face settings, instructors who use humor frequently tend to receive higher student satisfaction ratings and evaluations (Shoda & Yamanaka, 2021 ), particularly in cognitively demanding tasks where humor can help offset excessive mental load especially by exceeding learners’ cognitive capacity during complex tasks (Hu et al., 2017 ). According to Social Agency Theory (SAT) (Mayer et al., 2003 ), learners engage more deeply when instructional systems provide social cues—such as voice, gestures, and humor—that emulate human interactions. However, the mechanisms through which humorous social cues in pedagogical agents influence motivational and emotional processes in cognitively demanding environments remains underexplored. In this study, humorous pedagogical agents are defined as animated instructional characters that employ verbal or visual humor to support learner engagement and motivation. Watching dialogue refers to a condition in which learners observe two agents engaging in a conversational exchange, in contrast to a monologic presentation. Across two experiments, we examined four key dependent variables: intrinsic motivation, positive emotions, learning performance, and perceived agent value. Perceived agent value reflects learner's subjective evaluations of pedagogical agents, as measured using the revised Agent Persona Instrument Revised scale (Schroeder et al., 2018 ), encompassing four dimensions of agent existence: facilitation of learning, credibility, human-likeness, and engagement. 2. Literature review 2.1 The positive impact of humor on learning Humor is a communicative approach that elicits laughter and enjoyment by highlighting incongruities (Buttussi & Chittaro, 2020 ). According to Instructional Humor Processing Theory (IHPT), humor must be recognized, understood, and perceived as appropriate to avoid impeding learning; optimal humor should be relevant to the learning content and contribute directly to learning outcomes (Wanzer et al., 2010 ). Appropriateness is closely linked to humor style. Martin et al. ( 2003 ) classified humor into four types: affiliative, self-enhancing, aggressive, and self-defeating. More recently, Robinson et al. ( 2024 ) introduced the category of festive humor. Among these, affiliative humor— nonhostile and socially bonding—is widely regarded as the most suitable for educational contexts. Self-defeating humor, which involves making oneself the target, may reduce self-esteem but has been found to increase learner effort in conversational agents (Ceha et al., 2021 ). In contrast, aggressive humor is generally inappropriate, and the effects of other humor types remain less understood. In essence, appropriate humor should first align with the learning content, avoid aggressiveness, and be contextually relevant without introducing ambiguity or confusion. Despite these distinctions, many empirical studies treat humor as a generalized pedagogical strategy rather than distinguishing between its forms. For example, Tang et al. ( 2019 ) found that humorous stories improved reading interest and comprehension. Baleghizadeh and Karamzade ( 2020 ) reported that beginners benefit more from humor on terms of retention, whereas advanced learners demonstrated greater gains in delayed tests. Shoda and Yamanaka ( 2021 ) observed that instructors who frequently used humor received higher student participation and evaluations, although they did not specify the humor types and likely avoided offensive styles. Given that our study examined online learning, it is necessary to reflect on the role of humor within this context. Online environments tend to offer greater learner autonomy while imposing fewer external constraints, which makes sustaining engagement a recurring challenge (Erdoğdu & Çakıroğlu, 2021 ; Moore et al., 2011 ). Previous research illustrates this point. Miller et al. ( 2016 ) reported that humorous videos increased student interest and comprehension, particularly when interaction was involved teacher and student engagement. Similarly, Erdoğdu and Çakıroğlu ( 2021 ) incorporated humor into four instructional element —course materials, discussions, assignments, and quizzes—and analyzed learner activity through LMS log data. Their findings revealed that humor enhanced behavioral, emotional, and cognitive engagement, thereby improving the usability of online learning systems. Our study differs from previous research in that it focuses not on human teachers but on pedagogical agents—virtual characters designed to simulate teacher presence in online environments. Pedagogical agents can provide instructional support in the absence of human instructors, and has been shown to positively influence learners’ motivation, emotional responses, and interest (Schroeder & Adesope, 2014 ). According to SAT, pedagogical agents that offer social cues—such as voice and appearance—can lead learners to perceive the interaction as a human-like learning experience, thereby enhancing learning performance (Martha & Santoso, 2019 ; Mayer et al., 2003 ). Since humor is a highly complex human linguistic behavior, its integration may amplify the human-like qualities of pedagogical agents and strengthen the delivery of social cues. Buttussi and Chittaro ( 2020 ) compared agents expressing anger, neutrality, and humor, and found that humorous agents not only improved learners’ emotional states but also increased perceived agent value—suggesting that humor aligns more closely with the goals of SAT. However, their study did not investigate the effects on learner motivation, a critical variable in pedagogical agent research (Schroeder & Adesope, 2014 ), particularly due to its direct relationship with learner engagement in online learning (Erdoğdu & Çakıroğlu, 2021 ). Furthermore, functional Magnetic Resonance Imaging (fMRI) studies have demonstrated that the appreciation of humor is closely associated with activity in specific brain regions, including the temporal poles, midbrain (especially the ventral tegmental area and substantia nigra), mesolimbic system, nucleus accumbent, and the ventromedial prefrontal cortex. These regions are critically involved in reward processing, social motivation, and emotional experience (Chan et al., 2018 ). Activation in these areas may help explain how humorous pedagogical agents enhance learners’ emotional responses and increase their perceived social presence, thereby potentially improving motivation and engagement in online learning. In line with previous studies, our research focuses on how humor embedded in pedagogical agents affects teaching outcomes. We examined four core constructs relevant to online learning: intrinsic motivation, positive emotions, learning performance, and perceived agent value. 2.2 Potential drawbacks of humor on learning Despite its potential, the use of humor in education remains controversial. Fisher ( 1997 ) examined the effects of adding humor to a planetarium presentation and found that learners in the non-humorous condition outperformed those in the humorous condition. Hu et al. ( 2017 ) suggested that this may be due to the humor being unrelated to the learning content, thereby increasing the extraneous cognitive load (ECL). Similarly, Bolkan et al. ( 2018 ) conducted two experiments and reported comparable findings, wherein humorous instructional conditions led to lower academic performance. They proposed that this effect may have been caused by humor distracting learners from the core material. However, Wegener ( 2022 ) refuted this view, arguing that humor can enhance attention. Cazes et al. ( 2024 ) supported this claim with physiological evidence: using eye-tracking data, they demonstrated that humorous learning content attracted longer visual attention. This finding aligns with previous eye-tracking research demonstrating that humorous images generally receive longer fixation times (Brigaud et al., 2021 ; Ferstl et al., 2016 ; Gironzetti et al., 2016 ). Taken together, these results suggest that distraction may not be the primary concern. Other researchers have proposed different explanations. Ceha et al. ( 2021 ) speculated that an overly high frequency of humor may negatively affect learning by increasing cognitive load. Similarly, Hu et al. ( 2017 ) argued that humorous elements, when layered on top of already complex content, such as STEM materials, could overload learners' mental resources. Regarding humor frequency, Shoda and Yamanaka ( 2021 ) found that courses featuring a higher humor frequency were more popular among students, resulting in higher participation and better teacher evaluations. However, this study was based on video recordings of face-to-face teaching. Inspired by these findings, the present study adopted an experimental approach to explore the impact of humor frequency on online learning environments. Regarding concerns about cognitive load, Peng ( 2025 ) found that humor reduced learners’ overall cognitive load and affective filtering, thereby enhancing learning efficiency and engagement in online settings. Nonetheless, the concerns raised by Hu et al. ( 2017 ) remain relevant, particularly because their study focused on inherently complex and cognitively demanding materials typical of STEM education. They argued that when instructional content induces a high intrinsic cognitive load (ICL), the addition of excessive humor may further increase ECL, ultimately hindering learning. According to the Cognitive Load Theory (CLT), cognitive load can be categorized into ICL, ECL, and germane cognitive load (GCL). ICL stems from the inherent complexity of the content and is generally unaffected by instructional design. ECL arises from poor instructional design or the inclusion of irrelevant elements—such as content-unrelated humor—which unnecessarily consume cognitive resources. GCL refers to the effort invested in constructing and refining mental schemas, and can be enhanced through effective instructional strategies (Sweller et al., 2019 ). Within this framework, the complexity of learning materials must be considered alongside humor, as humor may impact all three types of cognitive load, depending on its form and relevance (Hu et al., 2017 ). However, few empirical studies have investigated the interaction between different frequencies of humor and the mental load imposed by the learning materials. To address this gap, our second experiment introduced mental load as an additional independent variable to examine how humor frequency interacts with content complexity. Following the design of Beege et al. ( 2020 ), our study implemented a supplementary memory task to manipulate the perceived difficulty of the learning material while keeping the instructional content consistent across conditions (See the Materials section of Experiment 2 for details). 2.3 Dialogue design in humorous pedagogical agents Unlike traditional learning environments, pedagogical agents in online learning systems can be presented in pairs or groups rather than individually. This type of presentation supports learning. Existing literature suggests that watching dialogues is generally more effective for learning than viewing monologues. Geertshuis et al. ( 2021 ) found that learners who watched dialogues between a human teacher and a pedagogical agent extracted more social cues than those who viewed monologues, resulting in improved learning performance. Similarly, Chi et al. ( 2016 ) demonstrated that dialogue-based videos outperformed monologues in terms of supporting comprehension. In a recent study by Kuang et al., ( 2024 ), students who watched pedagogical agents engaged in interactive questioning, regardless of question type, achieved better learning retention scores than those who watched monologues. Although watching dialogues has been demonstrated to improve attention and academic performance (Geertshuis et al., 2021 ; Novick et al., 2019 ; Nugraha et al., 2020 ), it does not appear to significantly enhance learners’ interest (Nugraha et al., 2020 ). From a physiological perspective, watching dialogues may activate the orbitofrontal cortex, a region associated with attentional processes. However, simultaneous activation of the amygdala may signal the emergence of negative emotions such as anxiety or discomfort (Lewis, 2002 ). Moreover, the increased presence of multiple pedagogical agents may trigger the uncanny valley effect (Mori et al., 2012 ), a phenomenon arising from the discomfort humans feel toward entities that appear almost, but not entirely, human. While this issue remains underexplored, such effects could potentially hinder the development of positive emotional responses. Humor, owing to its established role in fostering positive emotions, may help mitigate the discomfort associated with the uncanny valley effect. Therefore, this study integrates humor with dialogue-based presentations to compare their effects on learners’ intrinsic motivation, emotional responses, and perceived agent value. 2.4 Research questions and hypotheses In Experiment 1, drawing from a literature review, we aimed to explore the effects of humorous pedagogical agents and dialogue-based presentations on online learning. First, existing humor research has rarely addressed the role of pedagogical agents in online learning environments. To address this gap, we designed humorous pedagogical agents that prioritize their influence on learners’ intrinsic motivation, emotions, and perceived agent values. Accordingly, we propose the following hypothesis: H1: Humorous pedagogical agents enhance learners' intrinsic motivation, emotions, and perceived agent value. H2: Humorous pedagogical agents enhance learners' knowledge transfer Second, although dialogue-based learning has been demonstrated to directly improve knowledge transfer (Geertshuis et al., 2021 ), it does not necessarily increase engagement or interest (Nugraha et al., 2020 ). Moreover, due to the heightened attentional demands of dialogue-based formats, we speculate that such presentations may activate the amygdalae (Lewis, 2002 ) and potentially trigger the uncanny valley effect (Mori et al., 2012 ), both of which could negatively influence learners’ emotional responses. Based on this reasoning, we propose: H3: Watching dialogue may negatively affect learners' emotional responses. H4: Watching dialogue can enhance learners' knowledge transfer. Finally, considering humor’s capacity to foster enjoyment and positive emotions, we hypothesize that it may offset the potential shortcomings of dialogue-based presentation. Thus, we propose: H5: There is a significant interaction effect between humor and dialogue on learners’ intrinsic motivation, emotions, and perceived agent value. In Experiment 2, building on the findings of Experiment 1, we explored the effects of humor frequency (high vs. low) and mental load (high vs. low) on online learning outcomes. Previous research has demonstrated that professors who frequently use humor are more favorably evaluated by students and promote greater engagement (Shoda & Yamanaka, 2021 ). However, the effects of humor frequency in pedagogical agents have not yet been extensively studied. Extending H1, we propose: H6: High humor frequency in pedagogical agents is more beneficial in promoting learners' intrinsic motivation, emotional responses, and perceived agent value. H7: High humor frequency in pedagogical agents is more effective in improving learning performance. Additionally, while keeping the learning materials content consistent across all experimental groups, we introduced a supplementary task for the high mental load group to occupy their working memory capacity. Thus, we propose: H8: Mental load negatively affects learners' intrinsic motivation, emotional responses, and perceived agent value. H9: Mental load does not negatively impact learning performance. Finally, since high mental load can increase learners’ anxiety, we hypothesize that higher humor frequency may buffer against such negative effects by enhancing motivation and fostering positive evaluations of pedagogical agents. Thus, we propose: H10: Under high mental load conditions, pedagogical agents with high humor frequency can enhance learners' intrinsic motivation, emotions, and perceived agent value. 3. Experiment 1 First, we examined the effect of using humor and watching dialogue on learners. 3.1 Methods The study employed a 2×2 between-subjects design with humor (with vs. without) and dialogue format (monologue vs. dialogue) as independent variables. The dependent variables included intrinsic motivation, emotional response, perceived agent value, and knowledge transfer. 3.1.1 Participants We conducted an a priori power analysis using G*Power. Based on general conventions in psychological research, we estimated effect sizes of 0.25 (large) and 0.4 (medium), yielding a required sample size range of 52 to 128 participants. This ensured adequate statistical power (0.80) to detect potential effects, with each group requiring 13–32 participants. A total of 118 participants were recruited through an online platform from a national “Double First-Class” comprehensive university in China. The sample included both undergraduate and master’s students and was representative of the higher education learner population. Participants came from various academic disciplines including foreign languages, history, science, engineering, and the arts. Philosophy majors were excluded due to their prior exposure to formal logic, which could interfere with the instructional design and confounded experimental effects. Among the 118 participants, 96 were women (81.36%). The average age was 20.33 years (SD = 1.70). Participants were randomly assigned to one of four experimental conditions: (a) humor monologue, (b) no humor monologue, (c) humor dialogue, and (d) no humor dialogue. Final group sizes were 29, 29, 30, and 30, respectively. All participants signed informed consent forms prior to the experiment and were informed of its purpose, as well as the potential risks and benefits. All data were collected anonymously. 3.1.2 Materials The instruction materials were adapted from Section 3.2 of A Concise Introduction to Logic (10th ed.) by Hurley ( 2008 ), focusing on eight types of fallacies. The learning process introduced the names and definitions of the fallacies (see Fig. 1 ). After the lecture, learners were asked to identify the types of fallacies in the sentences in the learning system quiz and provide correct and incorrect feedback. There is a total of 5 multiple-choice questions. Only students who found all the errors could end the learning (see Fig. 2 ). [Figures 1 and 2 here] In the monologue condition, a single pedagogical agent appeared on the left side of the screen, with one voice delivering the monologue. In contrast, the dialogue condition featured two agents on the left and right sides, each with a distinct voice alternating to simulate dialogue (see Fig. 3 ). [Figures 3 and 4 here] In the humorous condition, humorous expressions were embedded into the agents’ scripts and delivered vocally during instruction. Additionally, humorous memes were displayed on a blackboard within the interface (see Fig. 4 ). The humor materials were curated from platforms such as TikTok and selected for their cultural relevance to Chinese learners, including vocabulary, puns, and memes. Whenever possible, humorous content was aligned with the instructional material. For example, to illustrate the fallacy of appeal to force, the humorous version stated: If I, Giant, go out to buy a bottle of water and come back to find fewer than 100 likes, everyone here will suffer the consequences. This comment—TikTok’s most popular of the year—uses the anime character Giant (Doraemon's child king, known for resorting to violence), to satirize the excessive pursuit of praise. In contrast, the non-humorous version simply stated: If you disagree with me, I’ll hit you. In the context of watching dialogue, humor could be attributed to either of the two agents, allowing for the use of aggressive humor in a culturally appropriate manner. This approach resembled traditional Chinese “cross talk,” a familiar comedic form in Chinese culture. For instance, topic-shifting occurs when one speaker subtly changes the subject to distract their opponent. This technique was used in the humorous condition. Yu Gang: “This example starts with nuclear plant safety but quietly switches to electricity safety. The conclusion is just like Guo Qian’s head—ridiculous to the bone.” Guo Qian: “Hey! You're the one who's bald!” This is actually a pun on the Chinese idiom “ridiculous to the bone,” which can imply both extremeness and baldness in Chinese. In Fig. 3 , Yu Gang is indicated on the left and Guo Qian is on the right. Their names are reversals of the famous Chinese crosstalk pair Guo Degang and Yu Qian, creating deliberate incongruity. In addition, Yugang and Chinese fish tank are homophonic, forming a pun. In the non-humorous group, responses were neutral or explanatory: Teacher Guo: “This example starts with nuclear power plant safety, but quietly switches to electricity safety.” Teacher Yu: “It's just ridiculous.” In both conditions, the pedagogical agents remained expressionless and static, without any facial expressions or physical gestures. To ensure cultural appropriateness and alignment with humor theory, all instructional and humorous materials were reviewed by three graduate students and one psychology professor. To assess learners’ subjective experiences, we administered the humor arousal dimension of the Aroused Fear and Humor Questionnaire. 3.1.3 Measures The instruments used in this study are summarized in Table 1 . Table 1 Instrument Instrument Items / Dimensions Scale Reliability Developer/Reviewer Demographic questionnaire Gender, Age, College, Major, and Educational Background N/A N/A N/A Humor Arousal Dimension of the Aroused Fear and Humor Questionnaire 6 items (e.g., “I found myself laughing while using this system.”) 6-point Likert (1 = “strongly disagree,” 6 = “strongly agree”) Cronbach’s α = 0.70 Buttussi and Chittaro ( 2020 ) Agent Persona Instrument Revised scale 25 items (Facilitation learning: 10 items (e.g., “The agent has led me to think more deeply about the learning content”), Credible: 5 items (e.g., “The agent is knowledgeable”), Human-like: 5 items (e.g., “The agent is personable”), Engaging: 5 items (e.g., “The agent is charming”) 6-point Likert (1 = “strongly disagree,” 6 = “strongly agree”) Facilitation learning: Cronbach’s α = 0.90, Credible: Cronbach’s α = 0.90, Human-likeness: Cronbach’s α = 0.90, Engaging: Cronbach’s α = 0.90, and total Cronbach’s α = 0.95 Schroeder et al. ( 2018 ) Internal Motivation Questionnaire 8 items (e.g., “This learning experience has sparked my curiosity.”) 6-point Likert (1 = “strongly disagree,” 6 = “strongly agree”) Cronbach’s α = 0.93 Isen and Reeve ( 2005 ) Positive and Negative Affect Schedule 20 items (Positive emotions: 10 items (e.g., “Active”), Negative emotions: 10 items (e.g., Irritable) 7-point Likert (1 = “extremely slight,” 7 = “extremely strong”) Positive emotions: Cronbach’s α = 0.90, Negative emotions: Cronbach’s α = 0.84, and total Cronbach’s α = 0.86 Watson et al. ( 1988 ) Knowledge transfer test 22 items (5-option single-choice, including one distractor such as “None of the above.”) A correct choice earns 1 point; the total points are 22. McDonald’s ω = 0.60 Hurley ( 2008 ) 3.1.4 Procedure The procedure followed in Experiment 1 is illustrated in Fig. 5 . The interface of the online learning course used in the experiment is shown in Fig. 6 . [Figures 5 and 6 here] a. Pre-test phase: Following a briefing on the procedure that covered group numbers, account details, and passwords, participants logged in and accessed the pre-test via a course page link. The pre-test consisted of a demographic questionnaire. Both pre-test and post-test data were collected using the questionnaire. b. Learning phase: Once the pre-test was completed, participants were redirected to the course page and instructed to click the link corresponding to their assigned group, Accounts were configured so that they only the appropriate link could be accessed, this ensuring experimental control. The entire learning session lasted approximately 30 minutes. c. Post-test phase: After the learning module, participants were redirected to the post-test page which included measures of humor arousal, intrinsic motivation, emotional states and agent value. 3.2 Results Descriptive statistics for all variables are presented in Table 2 . The correlations between the key variables are presented in Table 3 . Results of the ANOVAs examining the main effects of humor and dialogue viewing, including the humor arousal, are summarized in Table 4 . All analyses were conducted using SPSS. No significant interaction effects were found; therefore, simple effects were not examined. Table 2 Mean (M) and Standard Deviation (SD) of Each Variable Monologue Dialogue Not humor (n = 29) Humor (n = 29) Not humor (n = 30) Humor (n = 30) M SD M SD M SD M SD Humor arousal 3.80 0.78 4.98 0.69 3.72 0.97 4.71 1.05 Facilitation learning Credibility Human-likeness Engagement 4.39 4.28 2.88 3.50 0.69 0.66 0.74 0.97 4.87 4.78 4.12 4.66 0.67 0.77 0.93 0.81 4.36 4.37 2.62 3.43 0.69 0.87 1.06 1.13 4.57 4.49 4.24 4.41 0.75 0.98 1.10 1.00 Agent value 3.76 0.62 4.61 0.73 3.69 0.79 4.43 0.84 Motivation 4.55 0.72 4.95 0.69 4.54 0.76 4.79 0.75 Positive emotion 3.90 0.81 4.29 0.81 3.65 0.76 3.87 0.67 Negative emotion 2.57 0.55 2.41 0.42 2.45 0.48 2.41 0.47 Knowledge transfer 13.07 2.98 12.48 3.29 13.83 2.07 13.67 1.99 Table 3 Pearson Correlations Among Main Study Variables Variable 1 2 3 4 5 6 7 8 9 10 Humor arousal 1 .726 ** .591 ** .699 ** .818 ** .823 ** .611 ** .440 ** .015 − .048 Facilitation learning .726 ** 1 .687 ** .504 ** .637 ** .776 ** .701 ** .539 ** .004 − .028 Credibility .591 ** .687 ** 1 .593 ** .676 ** .832 ** .487 ** .313 ** − .047 − .126 Human-likeness .699 ** .504 ** .593 ** 1 .832 ** .888 ** .404 ** .374 ** − .046 − .119 Engagement .818 ** .637 ** .676 ** .832 ** 1 .933 ** .505 ** .431 ** .025 − .120 Agent value .823 ** .776 ** .832 ** .888 ** .933 ** 1 .583 ** .470 ** − .019 − .119 Motivation .611 ** .701 ** .487 ** .404 ** .505 ** .583 ** 1 .599 ** .005 − .019 Positive emotion .440 ** .539 ** .313 ** .374 ** .431 ** .470 ** .599 ** 1 .180 − .167 Negative emotion .015 .004 − .047 − .046 .025 − .019 .005 .180 1 − .150 Knowledge transfer − .048 − .028 − .126 − .119 − .120 − .119 − .019 − .167 − .150 1 Note: **. Correlation is significant at the .01 level (2-tailed). Table 4 Summary of ANOVA Results Independent Variable Dependent Variable Type III Sum of Squares df MS F p η² Humor Humor arousal 34.635 1.000 34.635 44.078 0.000 0.279 Facilitation learning 3.504 1.000 3.504 7.139 0.009 0.059 Credibility 2.864 1.000 2.864 4.157 0.044 0.035 Human-likeness 60.365 1.000 60.365 63.971 0.000 0.359 Engagement 33.714 1.000 33.714 34.484 0.000 0.232 Agent value 18.362 1.000 18.362 32.604 0.000 0.222 Motivation 3.080 1.000 3.080 5.776 0.018 0.048 Positive emotion 2.803 1.000 2.803 4.833 0.030 0.041 Negative emotion 0.272 1.000 0.272 1.169 0.282 0.010 Knowledge transfer 4.179 1.000 4.179 0.603 0.439 0.005 Watch dialogue Humor arousal 0.923 1.000 0.923 1.175 0.281 0.010 Facilitation learning 0.829 1.000 0.829 1.690 0.196 0.015 Credibility 0.301 1.000 0.301 0.437 0.510 0.004 Human-likeness 0.159 1.000 0.159 0.169 0.682 0.001 Engagement 0.748 1.000 0.748 0.766 0.383 0.007 Agent value 0.464 1.000 0.464 0.823 0.366 0.007 Motivation 0.226 1.000 0.226 0.424 0.516 0.004 Positive emotion 3.306 1.000 3.306 5.702 0.019 0.048 Negative emotion 0.105 1.000 0.105 0.452 0.503 0.004 Knowledge transfer 27.986 1.000 27.986 4.039 0.047 0.034 Humor* Watch dialogue Humor arousal 0.264 1.000 0.264 0.336 0.563 0.003 Facilitation learning 0.562 1.000 0.562 1.145 0.287 0.010 Credibility 1.009 1.000 1.009 1.464 0.229 0.013 Human-likeness 1.057 1.000 1.057 1.120 0.292 0.010 Engagement 0.201 1.000 0.201 0.205 0.651 0.002 Agent value 0.086 1.000 0.086 0.153 0.696 0.001 Motivation 0.177 1.000 0.177 0.332 0.565 0.003 Positive emotion 0.230 1.000 0.230 0.396 0.530 0.003 Negative emotion 0.116 1.000 0.116 0.498 0.482 0.004 Knowledge transfer 1.298 1.000 1.298 0.187 0.666 0.002 Humor had significant main effects on humor arousal, intrinsic motivation, positive emotions, and all dimensions of perceived agent value, including facilitation learning, credibility, human-likeness, and engagement. Dialogue viewing had significant main effects on positive emotions and knowledge transfer. 3.3 Discussion Based on the results of Experiment 1, we draw the following conclusions: 3.3.1 The impact of humorous pedagogical agents on online learning Our findings support H1. Learners exposed to humorous pedagogical agents reported significantly higher levels of intrinsic motivation, positive emotional responses, and perceived agent value compared to those in the non-humor group. The increase in intrinsic motivation is consistent with previous studies demonstrating that instructors’ use of humor positively affects students’ motivation to learn (Damanik et al., 2025 ; Kavandi & Kavandi, 2016 ; Tsukawaki & Imura, 2020 ). Similar effects have been observed in digital environments—for instance, in a study by Lee and Hao ( 2015 ) on instructional games. However, direct evidence regarding the use of humor by pedagogical agents and its impact on intrinsic motivation remains limited. Neuroimaging studies offer a possible explanation, suggesting that humorous stimuli may activate the ventral striatum (associated with reward and dopamine release) and the dorsal striatum (linked to goal-directed behavior) (Prenger et al., 2023 ). Additionally, regions such as the middle temporal gyrus and superior frontal gyrus has been associated with improved comprehension and engagement during humorous learning tasks (Zulazli et al., 2024 ). Although our study did not employ neuroimaging, these findings may explain why humorous pedagogical agents enhance intrinsic motivation. Thus, our study contributes to this underexplored area by demonstrating that humor embedded in agents’ instructions can enhance motivation in online learning contexts. We also found a significant increase in learners’ positive emotional responses under the humor condition, consistent with earlier research. For example, Bieg et al. ( 2018 ) found that teacher humor fosters positive emotional responses and helps buffer negative affect. Similar outcomes were observed in agent-based settings by Buttussi and Chittaro ( 2020 ), who demonstrated that humorous agents elicited more positive emotions. Farkas et al. ( 2021 ) further demonstrated through fMRI that different types of humor activate distinct neural pathways. Auditory humor activated the bilateral inferior frontal gyrus, medial superior frontal gyrus, and superior temporal gyrus. Visual humor, divided into pictorial and text-based subtypes, activated the temporal pole and fusiform gyrus, with additional responses depending on the subtype: pictorial humor specifically activated the bilateral amygdala and medial prefrontal cortex, while text-based humor was associated with the inferior frontal gyrus and superior temporal gyrus. These areas are strongly associated with emotional processing, offering further explanation for our findings. Our study extends this literature by demonstrating that pedagogical agents can elicit positive emotional responses in learners. Although a decrease in negative emotions was observed, it was not statistically significant. Beyond emotional and motivational outcomes, our findings offer new insights into learners’ perceptions of agent value. Previous research has indicated that humor improves students’ perceptions of human instructors (Nienaber et al., 2019 ). Buttussi and Chittaro ( 2020 ) explored similar effects in AI-based pedagogical agents. Our results indicate that learners rated humorous agents significantly higher in terms of facilitation learning, credibility, human-likeness and engagement. These results suggest that humor enhances not only the social presence of pedagogical agents but also learners’ perceptions of their instructional value. This aligns with the SAT, which posits that social cues—such as humor—can humanize digital agents and thereby enhance knowledge transfer (Mayer et al., 2003 ). Finally, although we observed a downward trend in knowledge transfer, the difference was not statistically significant. This is consistent with (Buttussi & Chittaro, 2020 ), who noted that humor’s influence on knowledge transfer may be indirect (Lujan & DiCarlo, 2016 ), with greater benefits emerging during delayed testing (Baleghizadeh & Karamzade, 2020 ). 3.3.2 The impact of watching dialogue on online learning Our findings support H3 and H4, indicating that watching dialogues between pedagogical agents significantly reduced learners’ positive emotional responses but did significantly increase negative affect. Interestingly, despite this decline in positive affect, the dialogue condition was associated with improved conceptual understanding, suggesting that watching dialogues may promote deeper cognitive processing and better integration of learning materials. This finding partially aligns with prior studies. Moreno et al. ( 2000 ) suggested that watching dialogue may heighten learners’ alertness, potentially diminishing emotional enjoyment. From a physiological perspective, heightened attention may activate the orbitofrontal cortex—responsible for attentional control—which is closely connected to the amygdala, a region governing emotional responses such as anxiety, fear, and discomfort (Lewis, 2002 ). Furthermore, our results are consistent with the uncanny valley hypothesis (Mori et al., 2012 ). When two agents interact without human presence, their anthropomorphic appearance—despite limitations in facial expression or natural prosody—may enhance perceived human-likeness to an unsettling degree. This perceptual mismatch between appearance and behavior can provoke emotional discomfort. While most extant studies on watching agent dialogues has focused on engagement and comprehension, the potential affective costs of overly anthropomorphic agents remain underexplored. Our findings highlight this gap. Despite the observed decrease in positive affect, watching dialogues significantly enhanced knowledge transfer, in line with previous findings (Geertshuis et al., 2021 ; Li et al., 2019 ; Nugraha et al., 2020 ). The attentional demands of dialogue may foster deeper cognitive processing, thereby facilitating the integration and application of learned concepts. According to SAT, dialogue provides rich social and contextual cues that support schema construction and long-term memory (Mayer et al., 2003 ). Our results support this interpretation, suggesting that the benefits of watching dialogues for knowledge transfer remain robust even in the presence of reduced positive emotions. 3.3.3 Interaction effects of humor and watching dialogue on online learning Contrary to expectations, H5 was not supported. The results revealed no significant interaction effect between humor and watching dialogue on any of measured outcomes, including motivation, affect, or perceived agent value. This suggests that the effects of humor and of watching dialogue operate independently in this context. Nevertheless, our findings offer important insights for the design of multi-agent instructional systems. As discussed, watching dialogues between agents was found to reduce positive emotional responses—potentially due to increased attentional demand or the uncanny valley effect, wherein enhanced human-likeness clashes with the artificial nature of the agents’ voices or gestures (Mori et al., 2012 ). In such scenarios, humor may serve as a useful design element to mitigate emotional discomfort. By promoting positive emotions and increasing agent likeability, humor may alleviate the eeriness associated with watching artificial dialogue and improve the emotional quality of the learning experience. Although this compensatory effect did not emerge as a statistically significant interaction in our study, the trend suggests that humor could buffer the affective cost of watching dialogue and should be considered in future agent design. Future research should further explore this hypothesis by isolating learners’ emotional responses to different types of agent dialogues—with and without humor—and incorporating physiological and behavioral engagement measures to capture these dynamics more precisely. 4. Experiment 2 In Experiment 2, we adopted a uniform dialogue model of pedagogical agents to examine the effects of humor frequency and mental load on learning. 4.1 Methods A 2×2 between-subjects design was employed, with humor frequency (low vs. high) and mental load (low vs. high) as independent variables. The dependent variables included intrinsic motivation, emotion, perceived agent value, and knowledge testing. 4.1.1 Participants A total of 119 participants were recruited from a national “Double First-Class” comprehensive university in China via an online recruitment platform. The sample included both undergraduate and master’s students and was representative of the higher education learner population. Participants came from diverse academic disciplines, including foreign languages, history, science, engineering, and the arts. Students majoring in philosophy were excluded, as their prior exposure to formal logic could interfere with the instructional design and confound the experimental effects. Of the 119 participants, 96 (80.67%) were women. The average age was 20.29 years (SD = 1.67). Participants were randomly assigned to one of four experimental conditions: (a) low humor frequency-low mental load, (b) high humor frequency-low mental load, (c) low humor frequency-high mental load, and (d) high humor frequency-high mental load. Final group sizes were 29, 29, 31, and 30, respectively. All participants signed informed consent forms before the experiment and were informed of its purpose, as well as potential risks and benefits. All data were collected anonymously. 4.1.2 Materials The learning materials were adapted from Section 3.3 of A Concise Introduction to Logic (10th ed.) by Hurley ( 2008 ), and focused on six types of fallacies. The learning process was identical to that in Experiment 1, but the content was expanded into 17 text segments. Drawing on the findings of Shoda and Yamanaka ( 2021 ), who reported an average of 12.92 humor instances in popular lecture videos compared with 3.92 in others, we set the high-humor frequency condition to include 13 instances, aligning with the more engaging lecture format. The low-humor-frequency condition was limited to four instances, reflecting the more traditional format. The mental load condition was modeled following the experimental paradigm set by Beege et al. ( 2020 ). Prior to the commencement of formal learning, a remembering task was established. Participants were exposed to a task image for 30 seconds before starting the actual learning process (Fig. 5 ). Considering the working memory capacity of 7 ± 2 items, the high mental load condition required participants to remember all 10 items in the picture, whereas the low mental load condition required only two. To verify compliance with the memory task, we added an instruction to the final test: “Please write down the items you remembered.” To determine whether the participants met the experimental conditions, scores were assigned based on the number of items recalled. Specifically, participants in the low mental load group were considered valid if they recalled five or fewer items, while those in the high mental load group were considered valid if they recalled 7 ± 2 items, or all of the items. Although invalid data were to be excluded, all data from both groups met the inclusion criteria. All other procedures were identical to those in Experiment 1. 4.1.3 Measures The instruments used in Experiment 2 are summarized in Table 5 . Table 5 Instruments Instrument Items / Dimensions Scale Reliability Developer / Reviewer Demographic Questionnaire Gender, Age, College, Major, and Educational Background N/A N/A N/A Humor Arousal Dimension of the Aroused Fear and Humor Questionnaire 5 items (One reverse-scored item was removed due to low item-total correlation) Other same as experiment 1 6-point Likert (1 = “strongly disagree,” 6 = “strongly agree”) Cronbach’s α = 0.92 Buttussi and Chittaro ( 2020 ) The Agent Persona Instrument Revised scale 25 items (Facilitation learning: 10 items (e.g., “The agent has led me to think more deeply about the learning content”), Credible: 5 items (e.g., “The agent is knowledgeable”), Human-like: 5 items (e.g., “The agent is personable”), Engaging: 5 items (e.g., “The agent is charming”) 6-point Likert (1 = “strongly disagree,” 6 = “strongly agree”) Facilitation learning: Cronbach’s α = 0.88, Credible: Cronbach’s α = 0.87, Human-likeness: Cronbach’s α = 0.85, Engaging: Cronbach’s α = 0.87, and total Cronbach’s α = 0.95 Schroeder et al. ( 2018 ) The Internal Motivation Questionnaire 8 items (e.g., “This learning experience has sparked my curiosity.”) 6-point Likert (1 = “strongly disagree,” 6 = “strongly agree”) Cronbach’s α = 0.93 Isen and Reeve ( 2005 ) The Positive and Negative Affect Schedule 20 items (Positive emotions: 10 items (e.g., “Active”), Negative emotions: 10 items (e.g., Irritable) 7-point Likert (1 = “extremely slight,” 7 = “extremely strong”) Positive emotions: Cronbach’s α = 0.90, Negative emotions: Cronbach’s alpha = 0.93, and total Cronbach’s α = 0.89 Watson et al. ( 1988 ) The Cognitive Load Questionnaires 8 items (ICL (and GCL): 5 items (e.g., “The content in the learning task is very complex”), ECL: 3 items (e.g., “This learning task is designed to be very detrimental to learning”)) 7-point Likert (1 = strongly disagree, 7 = strongly agree) ICL (and GCL): Cronbach’s α = 0.60 ECL: Cronbach’s α = 0.67 Klepsch et al. ( 2017 ) Knowledge test 17 items (12 items for single-choice same as experiment 1. 5 items for self-developed multiple-choice, with 5 options) Single-choice same as experiment 1, Multiple-choice: partial correctness earned 1 point, and complete correctness earned 2 points; incorrect responses received 0 points. McDonald’s ω = 0.60 Hurley ( 2008 ) and self-developed 4.1.4 Procedure See Fig. 7 for the procedure used in Experiment 2. The procedure mirrored that of Experiment 1, with one modification: before beginning the learning session, participants completed a 30-second memory task. The learning process was initiated automatically. In the post-test, a separate question was included to record whether participants had successfully remembered the content as instructed. 4.2 Results Descriptive statistics are presented in Table 6 , and Pearson correlations are indicated in Table 7 . Results from the two-way ANOVA for humor frequency and mental load are summarized in Table 8 . Analyses were conducted using SPSS, with simple effects for significant interactions examined using R. Table 6 Mean (M) and Standard Deviation (SD) of Each Variable Low load High load Low frequency (n = 29) High frequency (n = 29) Low frequency (n = 31) High frequency (n = 30) M SD M SD M SD M SD Humor arousal 4.91 0.88 4.86 0.89 4.47 1.11 5.23 0.65 Facilitation learning 4.76 0.71 4.68 0.79 4.54 0.67 4.86 0.54 Credibility 4.71 0.77 4.53 0.87 4.47 0.89 4.75 0.78 Human-likeness 3.96 0.84 4.14 1.08 3.57 1.18 4.15 0.78 Engaging 4.42 0.81 4.50 1.00 4.09 0.96 4.54 0.73 Agent value 4.46 0.63 4.46 0.85 4.17 0.82 4.58 0.59 Motivation 5.01 0.59 4.97 0.62 4.53 0.86 5.05 0.51 Positive emotion 4.14 0.99 4.43 1.20 3.71 1.23 4.24 0.79 Negative emotion 1.68 0.85 1.78 0.90 1.79 1.06 2.11 1.16 ICL (GCL) 5.10 0.68 5.31 0.57 5.02 0.71 5.30 0.67 ECL 2.99 1.12 3.33 0.91 3.27 0.94 2.81 0.94 Knowledge testing 14.31 3.32 13.41 2.95 12.87 3.46 13.93 2.97 Mental load task 2.38 1.12 2.34 0.72 7.87 1.41 6.93 1.39 Note: In the mental workload task, one point was awarded for each correctly remembered item (maximum = 10). Table 7 Pearson Correlations Among Main Study Variables Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 Humor arousal 1 .533 ** .507 ** .611 ** .689 ** .679 ** .702 ** .495 ** − .005 − .036 − .036 − .034 − .068 Facilitation learning .533 ** 1 .728 ** .596 ** .668 ** .837 ** .646 ** .470 ** − .038 .109 − .010 − .043 − .030 Credibility .507 ** .728 ** 1 .612 ** .691 ** .863 ** .561 ** .399 ** .101 .006 .003 − .105 .012 Human-likeness .611 ** .596 ** .612 ** 1 .742 ** .871 ** .497 ** .366 ** .010 .048 .077 .078 − .044 Engagement .689 ** .668 ** .691 ** .742 ** 1 .899 ** .602 ** .462 ** .016 − .053 .095 − .064 − .058 Agent value .679 ** .837 ** .863 ** .871 ** .899 ** 1 .655 ** .483 ** .028 .027 .053 − .032 − .036 Motivation .702 ** .646 ** .561 ** .497 ** .602 ** .655 ** 1 .580 ** .026 − .075 − .081 − .045 − .174 Positive emotion .495 ** .470 ** .399 ** .366 ** .462 ** .483 ** .580 ** 1 .139 .091 .133 − .059 − .128 Negative emotion − .005 − .038 .101 .010 .016 .028 .026 .139 1 .022 .054 − .077 .067 ICL (GCL) − .036 .109 .006 .048 − .053 .027 − .075 .091 .022 1 .137 .057 − .061 ECL − .036 − .010 .003 .077 .095 .053 − .081 .133 .054 .137 1 − .010 − .026 Knowledge Testing − .034 − .043 − .105 .078 − .064 − .032 − .045 − .059 − .077 .057 − .010 1 − .032 Mental load task − .068 − .030 .012 − .044 − .058 − .036 − .174 − .128 .067 − .061 − .026 − .032 1 Note: **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Table 8 Summary of Two-Way ANOVA Results Independent Variable Dependent Variable Type III Sum of Squares df MS F p η² Humor frequency Humor arousal 3.790 1.000 3.790 4.671 0.033 0.039 Facilitation learning 0.448 1.000 0.448 0.970 0.327 0.008 Credible 0.089 1.000 0.089 0.130 0.720 0.001 Human-likeness 4.200 1.000 4.200 4.306 0.040 0.036 Engagement 2.053 1.000 2.053 2.642 0.107 0.022 Agent value 1.238 1.000 1.238 2.307 0.132 0.020 Motivation 1.674 1.000 1.674 3.831 0.053 0.032 Positive emotion 4.955 1.000 4.955 4.357 0.039 0.037 Negative emotion 1.331 1.000 1.331 1.327 0.252 0.011 ICL (GCL) 1.750 1.000 1.750 4.028 0.047 0.034 ECL 0.095 1.000 0.095 0.099 0.754 0.001 Knowledge testing 0.204 1.000 0.204 0.020 0.887 0.000 Mental load task 7.023 1.000 7.023 4.903 0.029 0.041 Mental load Humor arousal 0.034 1.000 0.034 0.042 0.837 0.000 Facilitation learning 0.014 1.000 0.014 0.029 0.864 0.000 Credibility 0.004 1.000 0.004 0.006 0.938 0.000 Human-likeness 1.049 1.000 1.049 1.075 0.302 0.009 Engagement 0.612 1.000 0.612 0.787 0.377 0.007 Agent value 0.247 1.000 0.247 0.460 0.499 0.004 Motivation 1.141 1.000 1.141 2.611 0.109 0.022 Positive emotion 2.850 1.000 2.850 2.506 0.116 0.021 Negative emotion 1.470 1.000 1.470 1.466 0.229 0.013 ICL (GCL) 0.071 1.000 0.071 0.163 0.687 0.001 ECL 0.435 1.000 0.435 0.453 0.502 0.004 Knowledge testing 6.288 1.000 6.288 0.620 0.433 0.005 Mental load task 755.143 1.000 755.143 527.175 0.000 0.821 Humor frequency* Mental load Humor arousal 4.884 1.000 4.884 6.019 0.016 0.050 Facilitation learning 1.172 1.000 1.172 2.540 0.114 0.022 Credibility 1.629 1.000 1.629 2.367 0.127 0.020 Human-likeness 1.149 1.000 1.149 1.178 0.280 0.010 Engagement 1.039 1.000 1.039 1.337 0.250 0.011 Agent value 1.238 1.000 1.238 2.307 0.132 0.020 Motivation 2.338 1.000 2.338 5.349 0.023 0.044 Positive emotion 0.443 1.000 0.443 0.389 0.534 0.003 Negative emotion 0.348 1.000 0.348 0.347 0.557 0.003 ICL (GCL) 0.038 1.000 0.038 0.087 0.768 0.001 ECL 4.787 1.000 4.787 4.988 0.027 0.042 Knowledge testing 28.518 1.000 28.518 2.811 0.096 0.024 Mental load task 6.062 1.000 6.062 4.232 0.042 0.035 4.2.1 Main effects and interaction summary Humor frequency had significant effects on aroused humor, positive emotions, ICL (also representing GCL), human-likeness, and performance in the mental load task. Intrinsic motivation had a marginal effect ( p = .053) mental load significantly main effects on mental load task. Significant interactions were observed between humor frequency and mental load for aroused humor, intrinsic motivation, ECL, and mental load. 4.2.2 Simple effects summary In terms of aroused humor, under high mental load, learners in the high humor frequency group (M = 5.23, SE = 0.16) reported significantly higher arousal than those in the low humor frequency group (M = 4.47, SE = 0.16), t (115) = − 3.31, and p = .001. Under low mental load, the difference between high humor frequency (M = 4.86, SE = 0.17) and low humor frequency (M = 4.91, SE = 0.17) was not significant, t (115) = 0.20, and p = .839. In the low humor frequency group, arousal tended to decrease from low to high mental load (M = 4.91 vs. 4.47); however, the difference was marginally significant, with t (115) = 1.89, and p = .062. Within the high humor frequency group, arousal did not differ significantly by mental load condition (M = 4.86 vs. 5.23), t (115) = − 1.58, and p = .116 (see Fig. 8 ). For intrinsic motivation, under high mental load, learners in the high humor frequency condition (M = 5.05, SE = 0.12) demonstrated significantly greater motivation than those in the low humor frequency group (M = 4.53, SE = 0.12), t (115) = − 3.06, and p = .003. Under a low mental load, no significant difference was observed between high (M = 4.97, SE = 0.12) and low humor frequencies (M = 5.01, SE = 0.12), t (115) = 0.25, and p = .804. In the low humor frequency group, motivation was significantly higher under low mental load (M = 5.01) than under high mental load (M = 4.53), t (115) = 2.79, and p = .006. Within the high humor frequency group, no significant difference emerged between low (M = 4.97) and high mental loads (M = 5.05), t (115) = − 0.49, and p = .625 (see Fig. 9 ). In terms of ECL, under high mental load, learners in the high humor frequency group (M = 2.81, SE = 0.18) demonstrated marginally lower ECL than those in the low humor frequency group (M = 3.27, SE = 0.18), t (115) = 1.82, and p = .071. Under low mental load, no significant difference was found between high (M = 3.33, SE = 0.18) and low humor frequencies (M = 2.99, SE = 0.18), t (115) = − 1.34, and p = .183. Within the high humor frequency group, ECL was significantly lower under high mental load (M = 2.81) than under low mental load (M = 3.33), t (115) = 2.05, and p = .043. Within the low humor frequency group, ECL did not differ significantly across mental load conditions (M = 2.99 vs. 3.27), t (115) = − 1.11, and p = .270 (see Fig. 10 ). In the mental load task, under high mental load, learners in the low humor frequency group (M = 7.87, SE = 0.22) outperformed those in the high humor frequency group (M = 6.93, SE = 0.22), t (115) = 3.06, and p = .003. Under a low mental load, no difference was observed between low (M = 2.38, SE = 0.22) and high humor frequencies (M = 2.34, SE = 0.22), t (115) = 0.11, and p = .913. Within both humor frequency conditions, learners scored significantly higher under high mental load (low humor frequency: M = 7.87 vs. 2.38, t = − 17.76, p < .001; high humor frequency: M = 6.93 vs. 2.34, t = − 14.72, p < .001), confirming the effectiveness of the task manipulation (see Fig. 11 ). 4.3 Discussion 4.3.1 Influence of humor frequency on online learning The experimental results partially supported H6. Learners exposed to more humor demonstrated significantly more positive emotions and a marginal increase in intrinsic motivation. Notably, the high humor frequency group demonstrated a noticeable increase in the human-like dimension and an upward trend in engagement, although the latter was not statistically significant. Consistent with prior research, teachers who frequently use humor are generally favored by students (Ruch & Heintz, 2019 ; Shoda & Yamanaka, 2021 ). Our study found that pedagogical agents using high humor frequency, while not significantly enhancing perceived agent value, improved human-like assessment. Although previous studies have not extensively examined the overall impact of humor frequency on learning, our findings indicate that high humor frequency fosters positive emotions. Furthermore, H7 was not supported because humor frequency (high vs. low) did not significantly influence learners’ academic performance. Nonetheless, our findings are informative. Previous studies have raised concerns that excessive humor may impair learning by increasing cognitive load (Ceha et al., 2021 ). However, our results did not reveal any adverse effects. Although learners in the high humor frequency group exhibited significantly higher ICL, we combined ICL and GCL into a single score due to scale limitations. According to CLT, a moderate increase in the GCL can enhance learning by supporting schema construction. Moreover, given the limited reliability of the cognitive load used in this study, we interpreted these findings exploratorily rather than conclusively. Overall, we found no evidence that high humor frequency impairs learning performance. In contrast, learners in the high-humor frequency condition reported significantly greater positive emotions and higher perceived agent value, which may serve as motivational and social-affective support. These findings highlight the potential benefits of well-designed humor interventions for sustaining learner engagement in online environments, particularly when pedagogical agents mediate instruction. 4.3.2 Influence of mental load level on online learning Our study did not support H8 and H9; no significant differences were found in learners’ motivation, positive emotions, perceived agent value, or learning performance between the high and low mental load conditions. First, the mental load manipulation was effective, as evidenced by significantly higher memory task scores in the high-load condition. This finding confirms the validity of our experimental manipulation. Current research on cognitive load and learning primarily emphasizes the role of working memory and the influence of extraneous tasks on cognitive performance (Mutlu-Bayraktar et al., 2019 ). In line with this, we manipulated mental load by requiring learners to retain either a small or large set of items in working memory, simulating the varying demands of simple versus complex learning tasks. Second, although some scholars have suggested that a high frequency of humor might impose an additional cognitive burden (Schnickel & Martchev, 2018) and potentially impede learning (Ceha et al., 2021 ), no such effects were observed in the present study. This may be attributed to our intentional integration of the most humorous content with the instructional material, following the principles of the Incongruity-Humor Processing Theory (IHPT). While some humor not directly tied to the content was included, participants did not perceive it as distracting or inappropriate. The results suggest that using humor 13 times per lesson is acceptable, as it does not generate excessive negative effects. Nonetheless, the primary focus of this study was to examine interaction effects. 4.3.3 Interaction between humor frequency and mental load level on online learning The results of our study supported H10, which revealed that the intrinsic motivation of the high humor frequency group was significantly higher than in the low humor frequency group under conditions of high mental load. Previous research has paid limited attention to the direct relationship between motivation and cognitive load (Mutlu-Bayraktar et al., 2019 ). Whereas, most prior studies have investigated how enhancing intrinsic motivation promotes learning in low cognitive load conditions (Liao et al., 2019 ; Schneider et al., 2018 . In contrast, the present study explored how these dynamics interact across varying mental load levels. The findings suggest that as task complexity increases, higher humor frequency is more likely to enhance learners' intrinsic motivation. Furthermore, several additional interactions emerged. First, learners' perception of humor varied with different levels of mental load. Under high mental load, low humor frequency was perceived as less humorous, while high humor frequency was perceived as more humorous. We suggest that this is due to the inherent interestingness of the research material, which can reduce anxiety and thus lower humor sensitivity among learners experiencing low psychological load. Based on CLT, participants under high mental load likely experienced cognitive tension due to working memory overload, and humor may have helped alleviate that tension. Second, at low levels of mental load, participants in the high humor frequency group reported an increase in perceived ECL, whereas at high levels of mental load, they reported reduced ECL. This variation in perception may be attributed to increased intrinsic motivation, which is theorized to temporarily enhance working memory capacity and facilitate cognitive processing (Mutlu-Bayraktar et al., 2019 ; Schnotz et al., 2009 ). However, we offer this explanation with caution, as our study did not directly investigate this mechanism. As such, no conclusions can be drawn regarding the impact of humor on ECL or working memory. Further research is needed to explore the potential underlying mechanisms and their influence on learning performance. Finally, the number of items remembered by the high humor frequency group was significantly lower than those remembered by the low humor frequency group. In this study, the humor used during the mental load task was content-irrelevant and not part of the learning material. This finding aligns with the IHPT, which suggests that content-irrelevant humor may hinder learning (Bieg et al., 2018 ). However, since the mental load task was not a learning task, we observed only the effect of humor conditions in this context. This result should therefore be interpreted as preliminary rather than conclusive. Further research is required to better understand how content-irrelevant humor influences cognitive load and memory retention, particularly during actual learning tasks. 5. Discussion This study demonstrates that the use of humor by pedagogical agents positively influences learners' intrinsic motivation, positive emotional response, and perceived agent value, compared to conditions without humor. Agents employing a high frequency of humor were more effective in eliciting positive emotional responses than those using humor infrequently. Additionally, learners perceived agents with a high humor as more humanlike. These findings align with prior research. From a neurophysiological perspective, the observed enhancement of motivation, emotion, and perceived agent value may be associated with activation in brain regions such as the dopaminergic midbrain, bilateral amygdala, inferior frontal gyrus, and medial prefrontal cortex (Chan et al., 2018 ). Higher humor frequency has been linked to increased activation in these regions, thereby boosting intrinsic motivation and emotional engagement. This study contributes to the literature by validating the positive influence of pedagogical agents’ humor on learners' motivation and emotional responses, while also emphasizing its role in enhancing perceptions of the agent. Moreover, although participants reported lower positive emotions when exposed to agents' dialogues, this did not diminish the effectiveness of dialogue in promoting knowledge transfer. One possible explanation is that the decrease in positive emotions may be related to heightened attention triggered by watching dialogue (Geertshuis et al., 2021 ; Nugraha et al., 2020 ). Attention regulated by the orbitofrontal cortex may inhibit amygdalar activity, thereby dampening the expression of positive emotions (Lewis, 2002 ). Additionally, this finding aligns with the Uncanny Valley Theory (Mori et al., 2012 ), which posits that increasing human-like qualities in pedagogical agents —such as through dialogue—can lead to a sense of dissonance or discomfort among learners. However, despite the negative impact on emotional experience, the overall effect of watching dialogue on learning outcomes may still be beneficial. Finally, regarding the interaction between mental load and humor frequency, under high mental load, low humor frequency was associated with reduced intrinsic motivation, whereas high humor frequency was more effective in enhancing it. This finding highlights an underexplored dimension in existing literature: the potential relationship between mental load and learning motivation. 6. Limitations and future directions First, the reliability of the cognitive load scale and the knowledge test used in this study was not satisfactory. The low internal consistency of the cognitive load scale may stem from the limited number of items and lack of prior validation, allowing only preliminary exploration. Future research should use more established instruments to examine how humor and other instructional factors influence cognitive load dimensions. The relatively low reliability of the knowledge test may reflect the fragmented nature of the concepts assessed. However, overly high reliability can indicate overly narrow assessments, which may not suit knowledge evaluation tasks (Taber, 2018 ). Future research should expand item types—e.g., short-answer or applied questions—or increase item count to improve internal consistency. Second, no direct effect of humor frequency on learning performance was observed. While consistent with previous online learning studies, this finding contrasts with the IHPT, which posits that content-related humor directly supports learning (Wanzer et al., 2010 ). A possible explanation is that the selected instructional materials lacked complexity or discriminative power. Although A Concise Introduction to Logic (10th ed.). suits higher education critical thinking, the experiment utilized only one brief section on informal fallacies. These abstract, foundational concepts may not have produced measurable effects. Future studies should use more complex, domain-specific content, such as STEM-related materials, as considered by Hu et al. ( 2017 ). Third, although several physiological studies were cited in the discussion, all data in this study relied on self-reported scales. While practical, such instruments lack objectivity and sensitivity. Future research should incorporate objective physiological measures (e.g., EEG recordings or eye-tracking) to better assess cognitive and affective responses to humor. Fourth, this study examined humor frequency but not its timing within the instruction. Shoda and Yamanaka ( 2021 ) found that humor is typically used at the beginning and end of lessons, which may influence attention and cognitive processing differently. Future research should explore how humor placement within instructional sequences affects learning outcomes. Finally, participants were undergraduate students from a single university, though from diverse departments. While typical for humor studies and small-scale experiments, future research should expand sample diversity for greater generalizability. This could include cross-cultural studies and learners at different educational levels. As online learning expands beyond higher education (Moore et al., 2011 ), studying K–12 learners may reveal age-related differences in humor perception and instructional impact. 7. Conclusion This study examined the impact of humorous pedagogical agents in online learning. Humor—whether in dialogues or monologues—enhanced learners’ intrinsic motivation and positive emotions, and agents using humor were rated more favorably. A higher frequency of humor was associated with stronger positive emotional responses, and such agents were perceived as more human-like. These findings suggest that incorporating humor can improve the design and effectiveness of pedagogical agents. Although dialogues may slightly reduce positive emotions, they still support knowledge transfer—a paradox supported by earlier research (Geertshuis et al., 2021 ). Humor can offset this emotional drawback, maintaining engagement and attention while promoting learning. Strategically integrating humor may thus create a more balanced and effective learning experience. Finally, under high mental load, humor was found to boost intrinsic motivation. This highlights the value of humor in designing pedagogical agents for complex and cognitively demanding learning tasks. Declarations This experiment was approved by the Life Sciences Ethics Review Committee of Central China Normal University. References Baleghizadeh S, Karamzade T (2020) A study of comparative effects of humorous versus non-humorous text types on vocabulary learning of Iranian EFL learners at two proficiency levels. Crit Literary Stud 2(2):155–177. https://www.doi.org/10.34785/J014.2020.439 Beege M, Schneider S, Nebel S, Rey GD (2020) Does the effect of enthusiasm in a pedagogical agent’s voice depend on mental load in the learner’s working memory? 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J Acad Librariansh 48:102482. https://doi.org/10.1016/j.acalib.2021.102482 Zulazli AH, Mokhtar M, Albakri MMA, Mohd Tahir IS, Khalid MHM, P. Z., Zaini K (2024) An investigation on the types of humour in English Language Teaching among Malaysian lecturers in higher education. Eur J Humour Res 12(2):163–175. https://doi.org/10.7592/EJHR.2024.12.2.889 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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07:08:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3961425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe interactive effect of ECL\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-8627679/v1/f3ae26382e5bd6b567cacb0c.png"},{"id":100890839,"identity":"470fd2a2-4ca9-4268-b075-d795e8dbc65a","added_by":"auto","created_at":"2026-01-22 13:23:01","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":6279237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe interactive effect of mental load task\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-8627679/v1/470af72db98fd79952582363.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHumor Frequency as a Social-Affective Cue: How Humorous Pedagogical Agents Shape Learners’ Motivation, Emotions, and Perceived Agent Value\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIs humor an effective teaching strategy? Recent research suggests that incorporating humor into online learning environments can enhance student engagement and promote learning across multiple dimensions (Erdoğdu \u0026amp; \u0026Ccedil;akıroğlu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Pedagogical agents play a comparable role in online education; these virtual teacher figures support learning by providing instructional guidance and social cues. One of their key advantages is the ability to simulate multiparty dialogues. Prior studies have shown that observing dialogues between pedagogical agents can foster active learning and improve performance, although this approach does not improve learning interests. (Novick et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, Buttussi and Chittaro (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that humorous pedagogical agents evoke more positive emotions and receive higher evaluations, although research in this area remains limited. Thus, the present study examines the interaction between humorous pedagogical agents and dialogue-based presentations, aiming to combine the strengths of both approaches.\u003c/p\u003e \u003cp\u003eIn face-to-face settings, instructors who use humor frequently tend to receive higher student satisfaction ratings and evaluations (Shoda \u0026amp; Yamanaka, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), particularly in cognitively demanding tasks where humor can help offset excessive mental load especially by exceeding learners\u0026rsquo; cognitive capacity during complex tasks (Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Social Agency Theory (SAT) (Mayer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), learners engage more deeply when instructional systems provide social cues\u0026mdash;such as voice, gestures, and humor\u0026mdash;that emulate human interactions. However, the mechanisms through which humorous social cues in pedagogical agents influence motivational and emotional processes in cognitively demanding environments remains underexplored.\u003c/p\u003e \u003cp\u003eIn this study, humorous pedagogical agents are defined as animated instructional characters that employ verbal or visual humor to support learner engagement and motivation. Watching dialogue refers to a condition in which learners observe two agents engaging in a conversational exchange, in contrast to a monologic presentation.\u003c/p\u003e \u003cp\u003eAcross two experiments, we examined four key dependent variables: intrinsic motivation, positive emotions, learning performance, and perceived agent value. Perceived agent value reflects learner's subjective evaluations of pedagogical agents, as measured using the revised Agent Persona Instrument Revised scale (Schroeder et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), encompassing four dimensions of agent existence: facilitation of learning, credibility, human-likeness, and engagement.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The positive impact of humor on learning\u003c/h2\u003e \u003cp\u003eHumor is a communicative approach that elicits laughter and enjoyment by highlighting incongruities (Buttussi \u0026amp; Chittaro, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to Instructional Humor Processing Theory (IHPT), humor must be recognized, understood, and perceived as appropriate to avoid impeding learning; optimal humor should be relevant to the learning content and contribute directly to learning outcomes (Wanzer et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Appropriateness is closely linked to humor style. Martin et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) classified humor into four types: affiliative, self-enhancing, aggressive, and self-defeating. More recently, Robinson et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) introduced the category of festive humor. Among these, affiliative humor\u0026mdash; nonhostile and socially bonding\u0026mdash;is widely regarded as the most suitable for educational contexts. Self-defeating humor, which involves making oneself the target, may reduce self-esteem but has been found to increase learner effort in conversational agents (Ceha et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, aggressive humor is generally inappropriate, and the effects of other humor types remain less understood. In essence, appropriate humor should first align with the learning content, avoid aggressiveness, and be contextually relevant without introducing ambiguity or confusion.\u003c/p\u003e \u003cp\u003eDespite these distinctions, many empirical studies treat humor as a generalized pedagogical strategy rather than distinguishing between its forms. For example, Tang et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that humorous stories improved reading interest and comprehension. Baleghizadeh and Karamzade (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that beginners benefit more from humor on terms of retention, whereas advanced learners demonstrated greater gains in delayed tests. Shoda and Yamanaka (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed that instructors who frequently used humor received higher student participation and evaluations, although they did not specify the humor types and likely avoided offensive styles.\u003c/p\u003e \u003cp\u003eGiven that our study examined online learning, it is necessary to reflect on the role of humor within this context. Online environments tend to offer greater learner autonomy while imposing fewer external constraints, which makes sustaining engagement a recurring challenge (Erdoğdu \u0026amp; \u0026Ccedil;akıroğlu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Moore et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Previous research illustrates this point. Miller et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that humorous videos increased student interest and comprehension, particularly when interaction was involved teacher and student engagement. Similarly, Erdoğdu and \u0026Ccedil;akıroğlu (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) incorporated humor into four instructional element \u0026mdash;course materials, discussions, assignments, and quizzes\u0026mdash;and analyzed learner activity through LMS log data. Their findings revealed that humor enhanced behavioral, emotional, and cognitive engagement, thereby improving the usability of online learning systems.\u003c/p\u003e \u003cp\u003eOur study differs from previous research in that it focuses not on human teachers but on pedagogical agents\u0026mdash;virtual characters designed to simulate teacher presence in online environments. Pedagogical agents can provide instructional support in the absence of human instructors, and has been shown to positively influence learners\u0026rsquo; motivation, emotional responses, and interest (Schroeder \u0026amp; Adesope, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). According to SAT, pedagogical agents that offer social cues\u0026mdash;such as voice and appearance\u0026mdash;can lead learners to perceive the interaction as a human-like learning experience, thereby enhancing learning performance (Martha \u0026amp; Santoso, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mayer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Since humor is a highly complex human linguistic behavior, its integration may amplify the human-like qualities of pedagogical agents and strengthen the delivery of social cues. Buttussi and Chittaro (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) compared agents expressing anger, neutrality, and humor, and found that humorous agents not only improved learners\u0026rsquo; emotional states but also increased perceived agent value\u0026mdash;suggesting that humor aligns more closely with the goals of SAT. However, their study did not investigate the effects on learner motivation, a critical variable in pedagogical agent research (Schroeder \u0026amp; Adesope, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), particularly due to its direct relationship with learner engagement in online learning (Erdoğdu \u0026amp; \u0026Ccedil;akıroğlu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, functional Magnetic Resonance Imaging (fMRI) studies have demonstrated that the appreciation of humor is closely associated with activity in specific brain regions, including the temporal poles, midbrain (especially the ventral tegmental area and substantia nigra), mesolimbic system, nucleus accumbent, and the ventromedial prefrontal cortex. These regions are critically involved in reward processing, social motivation, and emotional experience (Chan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Activation in these areas may help explain how humorous pedagogical agents enhance learners\u0026rsquo; emotional responses and increase their perceived social presence, thereby potentially improving motivation and engagement in online learning.\u003c/p\u003e \u003cp\u003eIn line with previous studies, our research focuses on how humor embedded in pedagogical agents affects teaching outcomes. We examined four core constructs relevant to online learning: intrinsic motivation, positive emotions, learning performance, and perceived agent value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Potential drawbacks of humor on learning\u003c/h2\u003e \u003cp\u003eDespite its potential, the use of humor in education remains controversial. Fisher (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) examined the effects of adding humor to a planetarium presentation and found that learners in the non-humorous condition outperformed those in the humorous condition. Hu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) suggested that this may be due to the humor being unrelated to the learning content, thereby increasing the extraneous cognitive load (ECL). Similarly, Bolkan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted two experiments and reported comparable findings, wherein humorous instructional conditions led to lower academic performance. They proposed that this effect may have been caused by humor distracting learners from the core material. However, Wegener (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) refuted this view, arguing that humor can enhance attention. Cazes et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) supported this claim with physiological evidence: using eye-tracking data, they demonstrated that humorous learning content attracted longer visual attention. This finding aligns with previous eye-tracking research demonstrating that humorous images generally receive longer fixation times (Brigaud et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ferstl et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gironzetti et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Taken together, these results suggest that distraction may not be the primary concern.\u003c/p\u003e \u003cp\u003eOther researchers have proposed different explanations. Ceha et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) speculated that an overly high frequency of humor may negatively affect learning by increasing cognitive load. Similarly, Hu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) argued that humorous elements, when layered on top of already complex content, such as STEM materials, could overload learners' mental resources.\u003c/p\u003e \u003cp\u003eRegarding humor frequency, Shoda and Yamanaka (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that courses featuring a higher humor frequency were more popular among students, resulting in higher participation and better teacher evaluations. However, this study was based on video recordings of face-to-face teaching. Inspired by these findings, the present study adopted an experimental approach to explore the impact of humor frequency on online learning environments.\u003c/p\u003e \u003cp\u003eRegarding concerns about cognitive load, Peng (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that humor reduced learners\u0026rsquo; overall cognitive load and affective filtering, thereby enhancing learning efficiency and engagement in online settings. Nonetheless, the concerns raised by Hu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) remain relevant, particularly because their study focused on inherently complex and cognitively demanding materials typical of STEM education. They argued that when instructional content induces a high intrinsic cognitive load (ICL), the addition of excessive humor may further increase ECL, ultimately hindering learning.\u003c/p\u003e \u003cp\u003eAccording to the Cognitive Load Theory (CLT), cognitive load can be categorized into ICL, ECL, and germane cognitive load (GCL). ICL stems from the inherent complexity of the content and is generally unaffected by instructional design. ECL arises from poor instructional design or the inclusion of irrelevant elements\u0026mdash;such as content-unrelated humor\u0026mdash;which unnecessarily consume cognitive resources. GCL refers to the effort invested in constructing and refining mental schemas, and can be enhanced through effective instructional strategies (Sweller et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Within this framework, the complexity of learning materials must be considered alongside humor, as humor may impact all three types of cognitive load, depending on its form and relevance (Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, few empirical studies have investigated the interaction between different frequencies of humor and the mental load imposed by the learning materials. To address this gap, our second experiment introduced mental load as an additional independent variable to examine how humor frequency interacts with content complexity. Following the design of Beege et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our study implemented a supplementary memory task to manipulate the perceived difficulty of the learning material while keeping the instructional content consistent across conditions (See the Materials section of Experiment 2 for details).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Dialogue design in humorous pedagogical agents\u003c/h2\u003e \u003cp\u003eUnlike traditional learning environments, pedagogical agents in online learning systems can be presented in pairs or groups rather than individually. This type of presentation supports learning. Existing literature suggests that watching dialogues is generally more effective for learning than viewing monologues. Geertshuis et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that learners who watched dialogues between a human teacher and a pedagogical agent extracted more social cues than those who viewed monologues, resulting in improved learning performance. Similarly, Chi et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that dialogue-based videos outperformed monologues in terms of supporting comprehension. In a recent study by Kuang et al., (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), students who watched pedagogical agents engaged in interactive questioning, regardless of question type, achieved better learning retention scores than those who watched monologues.\u003c/p\u003e \u003cp\u003eAlthough watching dialogues has been demonstrated to improve attention and academic performance (Geertshuis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Novick et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nugraha et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it does not appear to significantly enhance learners\u0026rsquo; interest (Nugraha et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From a physiological perspective, watching dialogues may activate the orbitofrontal cortex, a region associated with attentional processes. However, simultaneous activation of the amygdala may signal the emergence of negative emotions such as anxiety or discomfort (Lewis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Moreover, the increased presence of multiple pedagogical agents may trigger the uncanny valley effect (Mori et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), a phenomenon arising from the discomfort humans feel toward entities that appear almost, but not entirely, human. While this issue remains underexplored, such effects could potentially hinder the development of positive emotional responses. Humor, owing to its established role in fostering positive emotions, may help mitigate the discomfort associated with the uncanny valley effect.\u003c/p\u003e \u003cp\u003eTherefore, this study integrates humor with dialogue-based presentations to compare their effects on learners\u0026rsquo; intrinsic motivation, emotional responses, and perceived agent value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research questions and hypotheses\u003c/h2\u003e \u003cp\u003eIn Experiment 1, drawing from a literature review, we aimed to explore the effects of humorous pedagogical agents and dialogue-based presentations on online learning.\u003c/p\u003e \u003cp\u003eFirst, existing humor research has rarely addressed the role of pedagogical agents in online learning environments. To address this gap, we designed humorous pedagogical agents that prioritize their influence on learners\u0026rsquo; intrinsic motivation, emotions, and perceived agent values. Accordingly, we propose the following hypothesis:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH1: Humorous pedagogical agents enhance learners' intrinsic motivation, emotions, and perceived agent value.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH2: Humorous pedagogical agents enhance learners' knowledge transfer\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSecond, although dialogue-based learning has been demonstrated to directly improve knowledge transfer (Geertshuis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), it does not necessarily increase engagement or interest (Nugraha et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, due to the heightened attentional demands of dialogue-based formats, we speculate that such presentations may activate the amygdalae (Lewis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and potentially trigger the uncanny valley effect (Mori et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), both of which could negatively influence learners\u0026rsquo; emotional responses. Based on this reasoning, we propose:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH3: Watching dialogue may negatively affect learners' emotional responses.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH4: Watching dialogue can enhance learners' knowledge transfer.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, considering humor\u0026rsquo;s capacity to foster enjoyment and positive emotions, we hypothesize that it may offset the potential shortcomings of dialogue-based presentation. Thus, we propose:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH5: There is a significant interaction effect between humor and dialogue on learners\u0026rsquo; intrinsic motivation, emotions, and perceived agent value.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Experiment 2, building on the findings of Experiment 1, we explored the effects of humor frequency (high vs. low) and mental load (high vs. low) on online learning outcomes.\u003c/p\u003e \u003cp\u003ePrevious research has demonstrated that professors who frequently use humor are more favorably evaluated by students and promote greater engagement (Shoda \u0026amp; Yamanaka, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the effects of humor frequency in pedagogical agents have not yet been extensively studied. Extending H1, we propose:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH6: High humor frequency in pedagogical agents is more beneficial in promoting learners' intrinsic motivation, emotional responses, and perceived agent value.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH7: High humor frequency in pedagogical agents is more effective in improving learning performance.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAdditionally, while keeping the learning materials content consistent across all experimental groups, we introduced a supplementary task for the high mental load group to occupy their working memory capacity. Thus, we propose:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH8: Mental load negatively affects learners' intrinsic motivation, emotional responses, and perceived agent value.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH9: Mental load does not negatively impact learning performance.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, since high mental load can increase learners\u0026rsquo; anxiety, we hypothesize that higher humor frequency may buffer against such negative effects by enhancing motivation and fostering positive evaluations of pedagogical agents. Thus, we propose:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH10: Under high mental load conditions, pedagogical agents with high humor frequency can enhance learners' intrinsic motivation, emotions, and perceived agent value.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Experiment 1","content":"\u003cp\u003eFirst, we examined the effect of using humor and watching dialogue on learners.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Methods\u003c/h2\u003e \u003cp\u003eThe study employed a 2\u0026times;2 between-subjects design with humor (with vs. without) and dialogue format (monologue vs. dialogue) as independent variables. The dependent variables included intrinsic motivation, emotional response, perceived agent value, and knowledge transfer.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Participants\u003c/h2\u003e \u003cp\u003eWe conducted an a priori power analysis using G*Power. Based on general conventions in psychological research, we estimated effect sizes of 0.25 (large) and 0.4 (medium), yielding a required sample size range of 52 to 128 participants. This ensured adequate statistical power (0.80) to detect potential effects, with each group requiring 13\u0026ndash;32 participants.\u003c/p\u003e \u003cp\u003eA total of 118 participants were recruited through an online platform from a national \u0026ldquo;Double First-Class\u0026rdquo; comprehensive university in China. The sample included both undergraduate and master\u0026rsquo;s students and was representative of the higher education learner population. Participants came from various academic disciplines including foreign languages, history, science, engineering, and the arts. Philosophy majors were excluded due to their prior exposure to formal logic, which could interfere with the instructional design and confounded experimental effects.\u003c/p\u003e \u003cp\u003eAmong the 118 participants, 96 were women (81.36%). The average age was 20.33 years (SD\u0026thinsp;=\u0026thinsp;1.70). Participants were randomly assigned to one of four experimental conditions: (a) humor monologue, (b) no humor monologue, (c) humor dialogue, and (d) no humor dialogue. Final group sizes were 29, 29, 30, and 30, respectively.\u003c/p\u003e \u003cp\u003e All participants signed informed consent forms prior to the experiment and were informed of its purpose, as well as the potential risks and benefits. All data were collected anonymously.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Materials\u003c/h2\u003e \u003cp\u003eThe instruction materials were adapted from Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e of \u003cem\u003eA Concise Introduction to Logic\u003c/em\u003e (10th ed.) by Hurley (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), focusing on eight types of fallacies. The learning process introduced the names and definitions of the fallacies (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After the lecture, learners were asked to identify the types of fallacies in the sentences in the learning system quiz and provide correct and incorrect feedback. There is a total of 5 multiple-choice questions. Only students who found all the errors could end the learning (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eIn the monologue condition, a single pedagogical agent appeared on the left side of the screen, with one voice delivering the monologue. In contrast, the dialogue condition featured two agents on the left and right sides, each with a distinct voice alternating to simulate dialogue (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figures \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the humorous condition, humorous expressions were embedded into the agents\u0026rsquo; scripts and delivered vocally during instruction. Additionally, humorous memes were displayed on a blackboard within the interface (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The humor materials were curated from platforms such as TikTok and selected for their cultural relevance to Chinese learners, including vocabulary, puns, and memes. Whenever possible, humorous content was aligned with the instructional material. For example, to illustrate the fallacy of appeal to force, the humorous version stated:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIf I, Giant, go out to buy a bottle of water and come back to find fewer than 100 likes, everyone here will suffer the consequences.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis comment\u0026mdash;TikTok\u0026rsquo;s most popular of the year\u0026mdash;uses the anime character Giant (Doraemon's child king, known for resorting to violence), to satirize the excessive pursuit of praise.\u003c/p\u003e \u003cp\u003eIn contrast, the non-humorous version simply stated:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIf you disagree with me, I\u0026rsquo;ll hit you.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the context of watching dialogue, humor could be attributed to either of the two agents, allowing for the use of aggressive humor in a culturally appropriate manner. This approach resembled traditional Chinese \u0026ldquo;cross talk,\u0026rdquo; a familiar comedic form in Chinese culture. For instance, topic-shifting occurs when one speaker subtly changes the subject to distract their opponent. This technique was used in the humorous condition.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eYu Gang: \u0026ldquo;This example starts with nuclear plant safety but quietly switches to electricity safety. The conclusion is just like Guo Qian\u0026rsquo;s head\u0026mdash;ridiculous to the bone.\u0026rdquo;\u003c/p\u003e\u003cp\u003eGuo Qian: \u0026ldquo;Hey! You're the one who's bald!\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis is actually a pun on the Chinese idiom \u0026ldquo;ridiculous to the bone,\u0026rdquo; which can imply both extremeness and baldness in Chinese. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Yu Gang is indicated on the left and Guo Qian is on the right. Their names are reversals of the famous Chinese crosstalk pair Guo Degang and Yu Qian, creating deliberate incongruity. In addition, Yugang and Chinese fish tank are homophonic, forming a pun.\u003c/p\u003e \u003cp\u003eIn the non-humorous group, responses were neutral or explanatory:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTeacher Guo: \u0026ldquo;This example starts with nuclear power plant safety, but quietly switches to electricity safety.\u0026rdquo;\u003c/p\u003e\u003cp\u003eTeacher Yu: \u0026ldquo;It's just ridiculous.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn both conditions, the pedagogical agents remained expressionless and static, without any facial expressions or physical gestures.\u003c/p\u003e \u003cp\u003eTo ensure cultural appropriateness and alignment with humor theory, all instructional and humorous materials were reviewed by three graduate students and one psychology professor. To assess learners\u0026rsquo; subjective experiences, we administered the humor arousal dimension of the Aroused Fear and Humor Questionnaire.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Measures\u003c/h2\u003e \u003cp\u003eThe instruments used in this study are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInstrument\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrument\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems / Dimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeveloper/Reviewer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender, Age, College, Major, and Educational Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumor Arousal Dimension of the Aroused Fear and Humor Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 items (e.g., \u0026ldquo;I found myself laughing while using this system.\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-point Likert (1 = \u0026ldquo;strongly disagree,\u0026rdquo; 6 = \u0026ldquo;strongly agree\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eButtussi and Chittaro (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgent Persona Instrument Revised scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 items (Facilitation learning: 10 items (e.g., \u0026ldquo;The agent has led me to think more deeply about the learning content\u0026rdquo;), Credible: 5 items (e.g., \u0026ldquo;The agent is knowledgeable\u0026rdquo;), Human-like:\u003c/p\u003e \u003cp\u003e5 items (e.g., \u0026ldquo;The agent is personable\u0026rdquo;), Engaging: 5 items (e.g., \u0026ldquo;The agent is charming\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-point Likert (1 = \u0026ldquo;strongly disagree,\u0026rdquo; 6 = \u0026ldquo;strongly agree\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacilitation learning:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.90, \u003c/p\u003e \u003cp\u003eCredible: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.90, \u003c/p\u003e \u003cp\u003eHuman-likeness:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.90, \u003c/p\u003e \u003cp\u003eEngaging: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.90, \u003c/p\u003e \u003cp\u003eand total Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchroeder et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Motivation Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 items (e.g., \u0026ldquo;This learning experience has sparked my curiosity.\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-point Likert (1 = \u0026ldquo;strongly disagree,\u0026rdquo; 6 = \u0026ldquo;strongly agree\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsen and Reeve (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive and Negative Affect Schedule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 items (Positive emotions: 10 items (e.g., \u0026ldquo;Active\u0026rdquo;), Negative emotions:\u003c/p\u003e \u003cp\u003e10 items (e.g., Irritable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7-point Likert (1 = \u0026ldquo;extremely slight,\u0026rdquo; 7 = \u0026ldquo;extremely strong\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive emotions:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.90,\u003c/p\u003e \u003cp\u003eNegative emotions:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.84,\u003c/p\u003e \u003cp\u003eand total Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWatson et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge transfer test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 items (5-option single-choice, including one distractor such as\u003c/p\u003e \u003cp\u003e\u0026ldquo;None of the above.\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA correct choice earns 1 point; the total points are 22.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMcDonald\u0026rsquo;s ω\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHurley (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Procedure\u003c/h2\u003e \u003cp\u003eThe procedure followed in Experiment 1 is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The interface of the online learning course used in the experiment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ea. Pre-test phase: Following a briefing on the procedure that covered group numbers, account details, and passwords, participants logged in and accessed the pre-test via a course page link. The pre-test consisted of a demographic questionnaire. Both pre-test and post-test data were collected using the questionnaire.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eb. Learning phase: Once the pre-test was completed, participants were redirected to the course page and instructed to click the link corresponding to their assigned group, Accounts were configured so that they only the appropriate link could be accessed, this ensuring experimental control. The entire learning session lasted approximately 30 minutes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ec. Post-test phase: After the learning module, participants were redirected to the post-test page which included measures of humor arousal, intrinsic motivation, emotional states and agent value.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Results\u003c/h2\u003e \u003cp\u003eDescriptive statistics for all variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The correlations between the key variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Results of the ANOVAs examining the main effects of humor and dialogue viewing, including the humor arousal, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. All analyses were conducted using SPSS. No significant interaction effects were found; therefore, simple effects were not examined.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMean (M) and Standard Deviation (SD) of Each Variable\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMonologue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDialogue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNot humor (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHumor (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNot humor (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHumor (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003cp\u003e4.28\u003c/p\u003e \u003cp\u003e2.88\u003c/p\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003cp\u003e0.66\u003c/p\u003e \u003cp\u003e0.74\u003c/p\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003cp\u003e4.78\u003c/p\u003e \u003cp\u003e4.12\u003c/p\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e0.93\u003c/p\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003cp\u003e4.37\u003c/p\u003e \u003cp\u003e2.62\u003c/p\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003cp\u003e0.87\u003c/p\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003cp\u003e4.49\u003c/p\u003e \u003cp\u003e4.24\u003c/p\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePearson Correlations Among Main Study Variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.726\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.591\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.699\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.818\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.823\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.611\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.440\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.726\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.687\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.504\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.637\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.776\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.701\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.539\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.591\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.687\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.593\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.676\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.832\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.487\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.313\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.699\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.504\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.593\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.832\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.888\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.404\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.374\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.818\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.637\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.676\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.832\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.933\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.505\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.431\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.823\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.776\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.832\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.888\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.933\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.583\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.470\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.611\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.701\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.487\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.404\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.505\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.583\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.599\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.440\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.539\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.313\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.374\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.431\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.470\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.599\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: **. Correlation is significant at the .01 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSummary of ANOVA Results\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III Sum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eη\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eHumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eWatch dialogue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eHumor*\u003c/p\u003e \u003cp\u003eWatch dialogue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHumor had significant main effects on humor arousal, intrinsic motivation, positive emotions, and all dimensions of perceived agent value, including facilitation learning, credibility, human-likeness, and engagement.\u003c/p\u003e \u003cp\u003eDialogue viewing had significant main effects on positive emotions and knowledge transfer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Discussion\u003c/h2\u003e \u003cp\u003eBased on the results of Experiment 1, we draw the following conclusions:\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 The impact of humorous pedagogical agents on online learning\u003c/h2\u003e \u003cp\u003eOur findings support H1. Learners exposed to humorous pedagogical agents reported significantly higher levels of intrinsic motivation, positive emotional responses, and perceived agent value compared to those in the non-humor group.\u003c/p\u003e \u003cp\u003eThe increase in intrinsic motivation is consistent with previous studies demonstrating that instructors\u0026rsquo; use of humor positively affects students\u0026rsquo; motivation to learn (Damanik et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kavandi \u0026amp; Kavandi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tsukawaki \u0026amp; Imura, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar effects have been observed in digital environments\u0026mdash;for instance, in a study by Lee and Hao (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) on instructional games. However, direct evidence regarding the use of humor by pedagogical agents and its impact on intrinsic motivation remains limited. Neuroimaging studies offer a possible explanation, suggesting that humorous stimuli may activate the ventral striatum (associated with reward and dopamine release) and the dorsal striatum (linked to goal-directed behavior) (Prenger et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, regions such as the middle temporal gyrus and superior frontal gyrus has been associated with improved comprehension and engagement during humorous learning tasks (Zulazli et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although our study did not employ neuroimaging, these findings may explain why humorous pedagogical agents enhance intrinsic motivation. Thus, our study contributes to this underexplored area by demonstrating that humor embedded in agents\u0026rsquo; instructions can enhance motivation in online learning contexts.\u003c/p\u003e \u003cp\u003eWe also found a significant increase in learners\u0026rsquo; positive emotional responses under the humor condition, consistent with earlier research. For example, Bieg et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that teacher humor fosters positive emotional responses and helps buffer negative affect. Similar outcomes were observed in agent-based settings by Buttussi and Chittaro (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who demonstrated that humorous agents elicited more positive emotions. Farkas et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further demonstrated through fMRI that different types of humor activate distinct neural pathways. Auditory humor activated the bilateral inferior frontal gyrus, medial superior frontal gyrus, and superior temporal gyrus. Visual humor, divided into pictorial and text-based subtypes, activated the temporal pole and fusiform gyrus, with additional responses depending on the subtype: pictorial humor specifically activated the bilateral amygdala and medial prefrontal cortex, while text-based humor was associated with the inferior frontal gyrus and superior temporal gyrus. These areas are strongly associated with emotional processing, offering further explanation for our findings. Our study extends this literature by demonstrating that pedagogical agents can elicit positive emotional responses in learners. Although a decrease in negative emotions was observed, it was not statistically significant.\u003c/p\u003e \u003cp\u003eBeyond emotional and motivational outcomes, our findings offer new insights into learners\u0026rsquo; perceptions of agent value. Previous research has indicated that humor improves students\u0026rsquo; perceptions of human instructors (Nienaber et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Buttussi and Chittaro (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) explored similar effects in AI-based pedagogical agents. Our results indicate that learners rated humorous agents significantly higher in terms of facilitation learning, credibility, human-likeness and engagement. These results suggest that humor enhances not only the social presence of pedagogical agents but also learners\u0026rsquo; perceptions of their instructional value. This aligns with the SAT, which posits that social cues\u0026mdash;such as humor\u0026mdash;can humanize digital agents and thereby enhance knowledge transfer (Mayer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, although we observed a downward trend in knowledge transfer, the difference was not statistically significant. This is consistent with (Buttussi \u0026amp; Chittaro, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who noted that humor\u0026rsquo;s influence on knowledge transfer may be indirect (Lujan \u0026amp; DiCarlo, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with greater benefits emerging during delayed testing (Baleghizadeh \u0026amp; Karamzade, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 The impact of watching dialogue on online learning\u003c/h2\u003e \u003cp\u003eOur findings support H3 and H4, indicating that watching dialogues between pedagogical agents significantly reduced learners\u0026rsquo; positive emotional responses but did significantly increase negative affect. Interestingly, despite this decline in positive affect, the dialogue condition was associated with improved conceptual understanding, suggesting that watching dialogues may promote deeper cognitive processing and better integration of learning materials.\u003c/p\u003e \u003cp\u003eThis finding partially aligns with prior studies. Moreno et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) suggested that watching dialogue may heighten learners\u0026rsquo; alertness, potentially diminishing emotional enjoyment. From a physiological perspective, heightened attention may activate the orbitofrontal cortex\u0026mdash;responsible for attentional control\u0026mdash;which is closely connected to the amygdala, a region governing emotional responses such as anxiety, fear, and discomfort (Lewis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Furthermore, our results are consistent with the uncanny valley hypothesis (Mori et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). When two agents interact without human presence, their anthropomorphic appearance\u0026mdash;despite limitations in facial expression or natural prosody\u0026mdash;may enhance perceived human-likeness to an unsettling degree. This perceptual mismatch between appearance and behavior can provoke emotional discomfort. While most extant studies on watching agent dialogues has focused on engagement and comprehension, the potential affective costs of overly anthropomorphic agents remain underexplored. Our findings highlight this gap.\u003c/p\u003e \u003cp\u003eDespite the observed decrease in positive affect, watching dialogues significantly enhanced knowledge transfer, in line with previous findings (Geertshuis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nugraha et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The attentional demands of dialogue may foster deeper cognitive processing, thereby facilitating the integration and application of learned concepts. According to SAT, dialogue provides rich social and contextual cues that support schema construction and long-term memory (Mayer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Our results support this interpretation, suggesting that the benefits of watching dialogues for knowledge transfer remain robust even in the presence of reduced positive emotions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Interaction effects of humor and watching dialogue on online learning\u003c/h2\u003e \u003cp\u003eContrary to expectations, H5 was not supported. The results revealed no significant interaction effect between humor and watching dialogue on any of measured outcomes, including motivation, affect, or perceived agent value. This suggests that the effects of humor and of watching dialogue operate independently in this context.\u003c/p\u003e \u003cp\u003eNevertheless, our findings offer important insights for the design of multi-agent instructional systems. As discussed, watching dialogues between agents was found to reduce positive emotional responses\u0026mdash;potentially due to increased attentional demand or the uncanny valley effect, wherein enhanced human-likeness clashes with the artificial nature of the agents\u0026rsquo; voices or gestures (Mori et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn such scenarios, humor may serve as a useful design element to mitigate emotional discomfort. By promoting positive emotions and increasing agent likeability, humor may alleviate the eeriness associated with watching artificial dialogue and improve the emotional quality of the learning experience. Although this compensatory effect did not emerge as a statistically significant interaction in our study, the trend suggests that humor could buffer the affective cost of watching dialogue and should be considered in future agent design.\u003c/p\u003e \u003cp\u003eFuture research should further explore this hypothesis by isolating learners\u0026rsquo; emotional responses to different types of agent dialogues\u0026mdash;with and without humor\u0026mdash;and incorporating physiological and behavioral engagement measures to capture these dynamics more precisely.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Experiment 2","content":"\u003cp\u003eIn Experiment 2, we adopted a uniform dialogue model of pedagogical agents to examine the effects of humor frequency and mental load on learning.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Methods\u003c/h2\u003e \u003cp\u003eA 2\u0026times;2 between-subjects design was employed, with humor frequency (low vs. high) and mental load (low vs. high) as independent variables. The dependent variables included intrinsic motivation, emotion, perceived agent value, and knowledge testing.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Participants\u003c/h2\u003e \u003cp\u003eA total of 119 participants were recruited from a national \u0026ldquo;Double First-Class\u0026rdquo; comprehensive university in China via an online recruitment platform. The sample included both undergraduate and master\u0026rsquo;s students and was representative of the higher education learner population. Participants came from diverse academic disciplines, including foreign languages, history, science, engineering, and the arts. Students majoring in philosophy were excluded, as their prior exposure to formal logic could interfere with the instructional design and confound the experimental effects.\u003c/p\u003e \u003cp\u003eOf the 119 participants, 96 (80.67%) were women. The average age was 20.29 years (SD\u0026thinsp;=\u0026thinsp;1.67). Participants were randomly assigned to one of four experimental conditions: (a) low humor frequency-low mental load, (b) high humor frequency-low mental load, (c) low humor frequency-high mental load, and (d) high humor frequency-high mental load. Final group sizes were 29, 29, 31, and 30, respectively.\u003c/p\u003e \u003cp\u003e All participants signed informed consent forms before the experiment and were informed of its purpose, as well as potential risks and benefits. All data were collected anonymously.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Materials\u003c/h2\u003e \u003cp\u003eThe learning materials were adapted from Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e of \u003cem\u003eA Concise Introduction to Logic\u003c/em\u003e (10th ed.) by Hurley (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and focused on six types of fallacies. The learning process was identical to that in Experiment 1, but the content was expanded into 17 text segments.\u003c/p\u003e \u003cp\u003eDrawing on the findings of Shoda and Yamanaka (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who reported an average of 12.92 humor instances in popular lecture videos compared with 3.92 in others, we set the high-humor frequency condition to include 13 instances, aligning with the more engaging lecture format. The low-humor-frequency condition was limited to four instances, reflecting the more traditional format.\u003c/p\u003e \u003cp\u003eThe mental load condition was modeled following the experimental paradigm set by Beege et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Prior to the commencement of formal learning, a remembering task was established. Participants were exposed to a task image for 30 seconds before starting the actual learning process (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Considering the working memory capacity of 7\u0026thinsp;\u0026plusmn;\u0026thinsp;2 items, the high mental load condition required participants to remember all 10 items in the picture, whereas the low mental load condition required only two.\u003c/p\u003e \u003cp\u003eTo verify compliance with the memory task, we added an instruction to the final test: \u0026ldquo;Please write down the items you remembered.\u0026rdquo; To determine whether the participants met the experimental conditions, scores were assigned based on the number of items recalled. Specifically, participants in the low mental load group were considered valid if they recalled five or fewer items, while those in the high mental load group were considered valid if they recalled 7\u0026thinsp;\u0026plusmn;\u0026thinsp;2 items, or all of the items. Although invalid data were to be excluded, all data from both groups met the inclusion criteria.\u003c/p\u003e \u003cp\u003eAll other procedures were identical to those in Experiment 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Measures\u003c/h2\u003e \u003cp\u003eThe instruments used in Experiment 2 are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInstruments\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrument\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems / Dimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeveloper / Reviewer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender, Age, College, Major, and Educational Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumor Arousal Dimension of the Aroused Fear and Humor Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 items (One reverse-scored item\u003c/p\u003e \u003cp\u003ewas removed due to low item-total correlation)\u003c/p\u003e \u003cp\u003eOther same as experiment 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-point Likert (1 = \u0026ldquo;strongly disagree,\u0026rdquo; 6 = \u0026ldquo;strongly agree\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eButtussi and Chittaro (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Agent Persona Instrument Revised scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 items (Facilitation learning: 10 items (e.g., \u0026ldquo;The agent has led me to think more deeply about the learning content\u0026rdquo;), Credible: 5 items (e.g., \u0026ldquo;The agent is knowledgeable\u0026rdquo;), Human-like:\u003c/p\u003e \u003cp\u003e5 items (e.g., \u0026ldquo;The agent is personable\u0026rdquo;), Engaging: 5 items (e.g., \u0026ldquo;The agent is charming\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-point Likert (1 = \u0026ldquo;strongly disagree,\u0026rdquo; 6 = \u0026ldquo;strongly agree\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacilitation learning:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.88, \u003c/p\u003e \u003cp\u003eCredible: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.87, \u003c/p\u003e \u003cp\u003eHuman-likeness:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.85, \u003c/p\u003e \u003cp\u003eEngaging: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.87, \u003c/p\u003e \u003cp\u003eand total Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchroeder et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Internal Motivation Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 items (e.g., \u0026ldquo;This learning experience has sparked my curiosity.\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-point Likert (1 = \u0026ldquo;strongly disagree,\u0026rdquo; 6 = \u0026ldquo;strongly agree\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsen and Reeve (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Positive and Negative Affect Schedule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 items (Positive emotions: 10 items (e.g., \u0026ldquo;Active\u0026rdquo;), Negative emotions:\u003c/p\u003e \u003cp\u003e10 items (e.g., Irritable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7-point Likert (1 = \u0026ldquo;extremely slight,\u0026rdquo; 7 = \u0026ldquo;extremely strong\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive emotions:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.90,\u003c/p\u003e \u003cp\u003eNegative emotions:\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.93,\u003c/p\u003e \u003cp\u003eand total Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWatson et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Cognitive Load Questionnaires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 items (ICL (and GCL): 5 items (e.g., \u0026ldquo;The content in the learning task is very complex\u0026rdquo;),\u003c/p\u003e \u003cp\u003eECL: 3 items (e.g., \u0026ldquo;This learning task is designed to be very detrimental to learning\u0026rdquo;))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7-point Likert (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICL (and GCL):\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e \u003cp\u003eECL: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKlepsch et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 items\u003c/p\u003e \u003cp\u003e(12 items for single-choice same as experiment 1.\u003c/p\u003e \u003cp\u003e5 items for self-developed multiple-choice, with 5 options)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle-choice same as experiment 1,\u003c/p\u003e \u003cp\u003eMultiple-choice: partial correctness earned 1 point, and complete correctness earned 2 points; incorrect responses received 0 points.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMcDonald\u0026rsquo;s ω\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHurley (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and self-developed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Procedure\u003c/h2\u003e \u003cp\u003eSee Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for the procedure used in Experiment 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe procedure mirrored that of Experiment 1, with one modification: before beginning the learning session, participants completed a 30-second memory task. The learning process was initiated automatically. In the post-test, a separate question was included to record whether participants had successfully remembered the content as instructed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results\u003c/h2\u003e \u003cp\u003eDescriptive statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and Pearson correlations are indicated in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Results from the two-way ANOVA for humor frequency and mental load are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Analyses were conducted using SPSS, with simple effects for significant interactions examined using R.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMean (M) and Standard Deviation (SD) of Each Variable\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eLow load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eHigh load\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLow frequency (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHigh frequency (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLow frequency (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eHigh frequency (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEngaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eICL (GCL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eECL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKnowledge testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMental load task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: In the mental workload task, one point was awarded for each correctly remembered item (maximum\u0026thinsp;=\u0026thinsp;10).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePearson Correlations Among Main Study Variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.533\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.507\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.611\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.689\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.679\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.702\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.495\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.533\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.728\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.596\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.668\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.837\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.646\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.470\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.507\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.728\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.612\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.691\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.863\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.561\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.399\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.611\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.596\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.612\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.742\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.871\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.497\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.366\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.689\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.668\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.691\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.742\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.899\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.602\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.462\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.679\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.837\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.863\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.871\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.899\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.655\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.483\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.702\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.646\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.561\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.497\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.602\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.655\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.580\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.495\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.470\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.399\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.366\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.462\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.483\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.580\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICL (GCL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge Testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental load task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eNote: **. Correlation is significant at the 0.01 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSummary of Two-Way ANOVA Results\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III Sum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eη\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003eHumor frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCredible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICL (GCL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMental load task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003eMental load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICL (GCL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMental load task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e755.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e755.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e527.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eHumor frequency*\u003c/p\u003e \u003cp\u003eMental load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumor arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCredibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman-likeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgent value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICL (GCL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMental load task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Main effects and interaction summary\u003c/h2\u003e \u003cp\u003eHumor frequency had significant effects on aroused humor, positive emotions, ICL (also representing GCL), human-likeness, and performance in the mental load task. Intrinsic motivation had a marginal effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.053) mental load significantly main effects on mental load task.\u003c/p\u003e \u003cp\u003eSignificant interactions were observed between humor frequency and mental load for aroused humor, intrinsic motivation, ECL, and mental load.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Simple effects summary\u003c/h2\u003e \u003cp\u003eIn terms of aroused humor, under high mental load, learners in the high humor frequency group (M\u0026thinsp;=\u0026thinsp;5.23, SE\u0026thinsp;=\u0026thinsp;0.16) reported significantly higher arousal than those in the low humor frequency group (M\u0026thinsp;=\u0026thinsp;4.47, SE\u0026thinsp;=\u0026thinsp;0.16), \u003cem\u003et\u003c/em\u003e(115) = \u0026minus;\u0026thinsp;3.31, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001. Under low mental load, the difference between high humor frequency (M\u0026thinsp;=\u0026thinsp;4.86, SE\u0026thinsp;=\u0026thinsp;0.17) and low humor frequency (M\u0026thinsp;=\u0026thinsp;4.91, SE\u0026thinsp;=\u0026thinsp;0.17) was not significant, \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;0.20, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.839. In the low humor frequency group, arousal tended to decrease from low to high mental load (M\u0026thinsp;=\u0026thinsp;4.91 vs. 4.47); however, the difference was marginally significant, with \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;1.89, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.062. Within the high humor frequency group, arousal did not differ significantly by mental load condition (M\u0026thinsp;=\u0026thinsp;4.86 vs. 5.23), \u003cem\u003et\u003c/em\u003e(115) = \u0026minus;\u0026thinsp;1.58, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.116 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor intrinsic motivation, under high mental load, learners in the high humor frequency condition (M\u0026thinsp;=\u0026thinsp;5.05, SE\u0026thinsp;=\u0026thinsp;0.12) demonstrated significantly greater motivation than those in the low humor frequency group (M\u0026thinsp;=\u0026thinsp;4.53, SE\u0026thinsp;=\u0026thinsp;0.12), \u003cem\u003et\u003c/em\u003e(115) = \u0026minus;\u0026thinsp;3.06, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003. Under a low mental load, no significant difference was observed between high (M\u0026thinsp;=\u0026thinsp;4.97, SE\u0026thinsp;=\u0026thinsp;0.12) and low humor frequencies (M\u0026thinsp;=\u0026thinsp;5.01, SE\u0026thinsp;=\u0026thinsp;0.12), \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;0.25, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.804. In the low humor frequency group, motivation was significantly higher under low mental load (M\u0026thinsp;=\u0026thinsp;5.01) than under high mental load (M\u0026thinsp;=\u0026thinsp;4.53), \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;2.79, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006. Within the high humor frequency group, no significant difference emerged between low (M\u0026thinsp;=\u0026thinsp;4.97) and high mental loads (M\u0026thinsp;=\u0026thinsp;5.05), \u003cem\u003et\u003c/em\u003e(115) = \u0026minus;\u0026thinsp;0.49, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.625 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of ECL, under high mental load, learners in the high humor frequency group (M\u0026thinsp;=\u0026thinsp;2.81, SE\u0026thinsp;=\u0026thinsp;0.18) demonstrated marginally lower ECL than those in the low humor frequency group (M\u0026thinsp;=\u0026thinsp;3.27, SE\u0026thinsp;=\u0026thinsp;0.18), \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;1.82, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.071. Under low mental load, no significant difference was found between high (M\u0026thinsp;=\u0026thinsp;3.33, SE\u0026thinsp;=\u0026thinsp;0.18) and low humor frequencies (M\u0026thinsp;=\u0026thinsp;2.99, SE\u0026thinsp;=\u0026thinsp;0.18), \u003cem\u003et\u003c/em\u003e(115) = \u0026minus;\u0026thinsp;1.34, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.183. Within the high humor frequency group, ECL was significantly lower under high mental load (M\u0026thinsp;=\u0026thinsp;2.81) than under low mental load (M\u0026thinsp;=\u0026thinsp;3.33), \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;2.05, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.043. Within the low humor frequency group, ECL did not differ significantly across mental load conditions (M\u0026thinsp;=\u0026thinsp;2.99 vs. 3.27), \u003cem\u003et\u003c/em\u003e(115) = \u0026minus;\u0026thinsp;1.11, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.270 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the mental load task, under high mental load, learners in the low humor frequency group (M\u0026thinsp;=\u0026thinsp;7.87, SE\u0026thinsp;=\u0026thinsp;0.22) outperformed those in the high humor frequency group (M\u0026thinsp;=\u0026thinsp;6.93, SE\u0026thinsp;=\u0026thinsp;0.22), \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;3.06, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003. Under a low mental load, no difference was observed between low (M\u0026thinsp;=\u0026thinsp;2.38, SE\u0026thinsp;=\u0026thinsp;0.22) and high humor frequencies (M\u0026thinsp;=\u0026thinsp;2.34, SE\u0026thinsp;=\u0026thinsp;0.22), \u003cem\u003et\u003c/em\u003e(115)\u0026thinsp;=\u0026thinsp;0.11, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.913. Within both humor frequency conditions, learners scored significantly higher under high mental load (low humor frequency: M\u0026thinsp;=\u0026thinsp;7.87 vs. 2.38, \u003cem\u003et\u003c/em\u003e = \u0026minus;\u0026thinsp;17.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; high humor frequency: M\u0026thinsp;=\u0026thinsp;6.93 vs. 2.34, \u003cem\u003et\u003c/em\u003e = \u0026minus;\u0026thinsp;14.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming the effectiveness of the task manipulation (see Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Discussion\u003c/h2\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Influence of humor frequency on online learning\u003c/h2\u003e \u003cp\u003eThe experimental results partially supported H6. Learners exposed to more humor demonstrated significantly more positive emotions and a marginal increase in intrinsic motivation. Notably, the high humor frequency group demonstrated a noticeable increase in the human-like dimension and an upward trend in engagement, although the latter was not statistically significant.\u003c/p\u003e \u003cp\u003eConsistent with prior research, teachers who frequently use humor are generally favored by students (Ruch \u0026amp; Heintz, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shoda \u0026amp; Yamanaka, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our study found that pedagogical agents using high humor frequency, while not significantly enhancing perceived agent value, improved human-like assessment. Although previous studies have not extensively examined the overall impact of humor frequency on learning, our findings indicate that high humor frequency fosters positive emotions.\u003c/p\u003e \u003cp\u003eFurthermore, H7 was not supported because humor frequency (high vs. low) did not significantly influence learners\u0026rsquo; academic performance. Nonetheless, our findings are informative. Previous studies have raised concerns that excessive humor may impair learning by increasing cognitive load (Ceha et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, our results did not reveal any adverse effects. Although learners in the high humor frequency group exhibited significantly higher ICL, we combined ICL and GCL into a single score due to scale limitations. According to CLT, a moderate increase in the GCL can enhance learning by supporting schema construction. Moreover, given the limited reliability of the cognitive load used in this study, we interpreted these findings exploratorily rather than conclusively.\u003c/p\u003e \u003cp\u003eOverall, we found no evidence that high humor frequency impairs learning performance. In contrast, learners in the high-humor frequency condition reported significantly greater positive emotions and higher perceived agent value, which may serve as motivational and social-affective support. These findings highlight the potential benefits of well-designed humor interventions for sustaining learner engagement in online environments, particularly when pedagogical agents mediate instruction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Influence of mental load level on online learning\u003c/h2\u003e \u003cp\u003eOur study did not support H8 and H9; no significant differences were found in learners\u0026rsquo; motivation, positive emotions, perceived agent value, or learning performance between the high and low mental load conditions.\u003c/p\u003e \u003cp\u003eFirst, the mental load manipulation was effective, as evidenced by significantly higher memory task scores in the high-load condition. This finding confirms the validity of our experimental manipulation. Current research on cognitive load and learning primarily emphasizes the role of working memory and the influence of extraneous tasks on cognitive performance (Mutlu-Bayraktar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In line with this, we manipulated mental load by requiring learners to retain either a small or large set of items in working memory, simulating the varying demands of simple versus complex learning tasks.\u003c/p\u003e \u003cp\u003eSecond, although some scholars have suggested that a high frequency of humor might impose an additional cognitive burden (Schnickel \u0026amp; Martchev, 2018) and potentially impede learning (Ceha et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), no such effects were observed in the present study. This may be attributed to our intentional integration of the most humorous content with the instructional material, following the principles of the Incongruity-Humor Processing Theory (IHPT). While some humor not directly tied to the content was included, participants did not perceive it as distracting or inappropriate.\u003c/p\u003e \u003cp\u003eThe results suggest that using humor 13 times per lesson is acceptable, as it does not generate excessive negative effects. Nonetheless, the primary focus of this study was to examine interaction effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Interaction between humor frequency and mental load level on online learning\u003c/h2\u003e \u003cp\u003eThe results of our study supported H10, which revealed that the intrinsic motivation of the high humor frequency group was significantly higher than in the low humor frequency group under conditions of high mental load. Previous research has paid limited attention to the direct relationship between motivation and cognitive load (Mutlu-Bayraktar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whereas, most prior studies have investigated how enhancing intrinsic motivation promotes learning in low cognitive load conditions (Liao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schneider et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e. In contrast, the present study explored how these dynamics interact across varying mental load levels. The findings suggest that as task complexity increases, higher humor frequency is more likely to enhance learners' intrinsic motivation.\u003c/p\u003e \u003cp\u003eFurthermore, several additional interactions emerged. First, learners' perception of humor varied with different levels of mental load. Under high mental load, low humor frequency was perceived as less humorous, while high humor frequency was perceived as more humorous. We suggest that this is due to the inherent interestingness of the research material, which can reduce anxiety and thus lower humor sensitivity among learners experiencing low psychological load. Based on CLT, participants under high mental load likely experienced cognitive tension due to working memory overload, and humor may have helped alleviate that tension.\u003c/p\u003e \u003cp\u003eSecond, at low levels of mental load, participants in the high humor frequency group reported an increase in perceived ECL, whereas at high levels of mental load, they reported reduced ECL. This variation in perception may be attributed to increased intrinsic motivation, which is theorized to temporarily enhance working memory capacity and facilitate cognitive processing (Mutlu-Bayraktar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schnotz et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, we offer this explanation with caution, as our study did not directly investigate this mechanism. As such, no conclusions can be drawn regarding the impact of humor on ECL or working memory. Further research is needed to explore the potential underlying mechanisms and their influence on learning performance.\u003c/p\u003e \u003cp\u003eFinally, the number of items remembered by the high humor frequency group was significantly lower than those remembered by the low humor frequency group. In this study, the humor used during the mental load task was content-irrelevant and not part of the learning material. This finding aligns with the IHPT, which suggests that content-irrelevant humor may hinder learning (Bieg et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, since the mental load task was not a learning task, we observed only the effect of humor conditions in this context. This result should therefore be interpreted as preliminary rather than conclusive. Further research is required to better understand how content-irrelevant humor influences cognitive load and memory retention, particularly during actual learning tasks.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study demonstrates that the use of humor by pedagogical agents positively influences learners' intrinsic motivation, positive emotional response, and perceived agent value, compared to conditions without humor. Agents employing a high frequency of humor were more effective in eliciting positive emotional responses than those using humor infrequently. Additionally, learners perceived agents with a high humor as more humanlike. These findings align with prior research. From a neurophysiological perspective, the observed enhancement of motivation, emotion, and perceived agent value may be associated with activation in brain regions such as the dopaminergic midbrain, bilateral amygdala, inferior frontal gyrus, and medial prefrontal cortex (Chan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Higher humor frequency has been linked to increased activation in these regions, thereby boosting intrinsic motivation and emotional engagement. This study contributes to the literature by validating the positive influence of pedagogical agents\u0026rsquo; humor on learners' motivation and emotional responses, while also emphasizing its role in enhancing perceptions of the agent.\u003c/p\u003e \u003cp\u003e Moreover, although participants reported lower positive emotions when exposed to agents' dialogues, this did not diminish the effectiveness of dialogue in promoting knowledge transfer. One possible explanation is that the decrease in positive emotions may be related to heightened attention triggered by watching dialogue (Geertshuis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nugraha et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Attention regulated by the orbitofrontal cortex may inhibit amygdalar activity, thereby dampening the expression of positive emotions (Lewis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Additionally, this finding aligns with the Uncanny Valley Theory (Mori et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which posits that increasing human-like qualities in pedagogical agents \u0026mdash;such as through dialogue\u0026mdash;can lead to a sense of dissonance or discomfort among learners. However, despite the negative impact on emotional experience, the overall effect of watching dialogue on learning outcomes may still be beneficial.\u003c/p\u003e \u003cp\u003eFinally, regarding the interaction between mental load and humor frequency, under high mental load, low humor frequency was associated with reduced intrinsic motivation, whereas high humor frequency was more effective in enhancing it. This finding highlights an underexplored dimension in existing literature: the potential relationship between mental load and learning motivation.\u003c/p\u003e"},{"header":"6. Limitations and future directions","content":"\u003cp\u003eFirst, the reliability of the cognitive load scale and the knowledge test used in this study was not satisfactory. The low internal consistency of the cognitive load scale may stem from the limited number of items and lack of prior validation, allowing only preliminary exploration. Future research should use more established instruments to examine how humor and other instructional factors influence cognitive load dimensions. The relatively low reliability of the knowledge test may reflect the fragmented nature of the concepts assessed. However, overly high reliability can indicate overly narrow assessments, which may not suit knowledge evaluation tasks (Taber, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Future research should expand item types\u0026mdash;e.g., short-answer or applied questions\u0026mdash;or increase item count to improve internal consistency.\u003c/p\u003e \u003cp\u003eSecond, no direct effect of humor frequency on learning performance was observed. While consistent with previous online learning studies, this finding contrasts with the IHPT, which posits that content-related humor directly supports learning (Wanzer et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A possible explanation is that the selected instructional materials lacked complexity or discriminative power. Although \u003cem\u003eA Concise Introduction to Logic\u003c/em\u003e (10th ed.). suits higher education critical thinking, the experiment utilized only one brief section on informal fallacies. These abstract, foundational concepts may not have produced measurable effects. Future studies should use more complex, domain-specific content, such as STEM-related materials, as considered by Hu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, although several physiological studies were cited in the discussion, all data in this study relied on self-reported scales. While practical, such instruments lack objectivity and sensitivity. Future research should incorporate objective physiological measures (e.g., EEG recordings or eye-tracking) to better assess cognitive and affective responses to humor.\u003c/p\u003e \u003cp\u003eFourth, this study examined humor frequency but not its timing within the instruction. Shoda and Yamanaka (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that humor is typically used at the beginning and end of lessons, which may influence attention and cognitive processing differently. Future research should explore how humor placement within instructional sequences affects learning outcomes.\u003c/p\u003e \u003cp\u003eFinally, participants were undergraduate students from a single university, though from diverse departments. While typical for humor studies and small-scale experiments, future research should expand sample diversity for greater generalizability. This could include cross-cultural studies and learners at different educational levels. As online learning expands beyond higher education (Moore et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), studying K\u0026ndash;12 learners may reveal age-related differences in humor perception and instructional impact.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study examined the impact of humorous pedagogical agents in online learning. Humor\u0026mdash;whether in dialogues or monologues\u0026mdash;enhanced learners\u0026rsquo; intrinsic motivation and positive emotions, and agents using humor were rated more favorably. A higher frequency of humor was associated with stronger positive emotional responses, and such agents were perceived as more human-like. These findings suggest that incorporating humor can improve the design and effectiveness of pedagogical agents.\u003c/p\u003e \u003cp\u003eAlthough dialogues may slightly reduce positive emotions, they still support knowledge transfer\u0026mdash;a paradox supported by earlier research (Geertshuis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Humor can offset this emotional drawback, maintaining engagement and attention while promoting learning. Strategically integrating humor may thus create a more balanced and effective learning experience.\u003c/p\u003e \u003cp\u003eFinally, under high mental load, humor was found to boost intrinsic motivation. This highlights the value of humor in designing pedagogical agents for complex and cognitively demanding learning tasks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis experiment was approved by the Life Sciences Ethics Review Committee of Central China Normal University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaleghizadeh S, Karamzade T (2020) A study of comparative effects of humorous versus non-humorous text types on vocabulary learning of Iranian EFL learners at two proficiency levels. 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Z., Zaini K (2024) An investigation on the types of humour in English Language Teaching among Malaysian lecturers in higher education. Eur J Humour Res 12(2):163\u0026ndash;175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7592/EJHR.2024.12.2.889\u003c/span\u003e\u003cspan address=\"10.7592/EJHR.2024.12.2.889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"7a6c71af-0625-4171-be1d-e1dfadfd7b62","identifier":"10.13039/501100001809","name":"National Natural Science Foundation of China","awardNumber":"Key Project No. 61937001","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"central china normal university pschology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pedagogical agents, Humor, Humor frequency, Intrinsic motivation, Positive emotions, Mental load","lastPublishedDoi":"10.21203/rs.3.rs-8627679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8627679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHumor can function as a powerful social-affective cue that shapes learners\u0026rsquo; emotional and motivational experiences in digital learning environments. This study investigated how humorous pedagogical agents influence intrinsic motivation, positive emotions, and perceived agent value across two between-subjects 2\u0026times;2 experiments. Experiment 1 used a Humor \u0026times; Watching dialogue design, and Experiment 2 used a Humor frequency \u0026times; Mental load design. University students were randomly assigned to conditions in each experiment. Data were analyzed using SPSS factorial ANOVA, followed by simple effect analyses for significant interactions. In Experiment 1, agent humor enhanced intrinsic motivation, positive emotions, and perceived agent value, while watching dialogue suppressed positive emotions without hindering knowledge transfer. In Experiment 2, higher humor frequency increased positive emotions and Human-likeness in perceived agent value, and interacted with mental load to buffer its negative impact on intrinsic motivation. These findings clarify the social-affective mechanisms through which humor and humor frequency support learners\u0026rsquo; motivational and emotional engagement in cognitively demanding online contexts.\u003c/p\u003e","manuscriptTitle":"Humor Frequency as a Social-Affective Cue: How Humorous Pedagogical Agents Shape Learners’ Motivation, Emotions, and Perceived Agent Value","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 13:22:56","doi":"10.21203/rs.3.rs-8627679/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13b6cd55-898e-464e-99b1-258793413b99","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61303186,"name":"Educational Psychology"}],"tags":[],"updatedAt":"2026-01-22T13:22:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 13:22:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8627679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8627679","identity":"rs-8627679","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}