The Emotional Movie Database (EMDB): An Expanded Toolkit for Emotion Research

The Emotional Movie Database (EMDB): An Expanded Toolkit for Emotion Research | 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 The Emotional Movie Database (EMDB): An Expanded Toolkit for Emotion Research Sandra Carvalho, Catarina GomesCoelho, Augusto J. Mendes, Óscar Gonçalves, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6401734/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Emotion-eliciting film clips play a critical role in psychological, neuroscientific, and affective computing research by providing standardized stimuli for studying emotional responses. The Emotional Movie Database (EMDB) was initially developed to offer silent film clips for emotion research, but its limited stimulus diversity necessitated an update. This study expands the EMDB by introducing four new emotional categories—social exclusion, unpleasant landscapes, extreme sports, and social inclusion—along with an enhanced set of neutral clips. Two assessment experiments were conducted to validate the new film clips. Experiment 1 (lab-based; n = 117) examined social exclusion, social inclusion, unpleasant landscapes, and extreme sports, while Experiment 2 (online; n = 128) focused on social exclusion, social inclusion, and newly recorded neutral clips. Participants rated the clips on valence, arousal, and dominance using the Self-Assessment Manikin (SAM) and reported the emotions experienced. Findings indicated that social exclusion clips elicited negative valence ( M = 2.16, SD = 1.07) and moderate-to-high arousal ( M = 5.97, SD = 2.06), while social inclusion clips had positive valence ( M = 7.17, SD = 0.92) and lower arousal ( M = 4.68, SD = 1.72). Unpleasant landscapes were rated negatively in valence ( M = 2.77, SD = 0.99) with low arousal ( M = 4.53, SD = 2.01), and extreme sports clips were positively valenced ( M = 6.25, SD = 1.12) with intermediate arousal ( M = 5.34, SD = 1.95). Newly recorded neutral clips consistently produced neutral valence ( M = 5.11, SD = 0.42) and low arousal ( M = 2.31, SD = 1.36), confirming their effectiveness as control stimuli. Emotional Movie database EMDB social inclusion social exclusion neutral films clips extreme sports pollution neutral film clips Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Understanding how humans process emotions is a crucial area of research in psychology, neuroscience, and affective computing. The ability to elicit and measure emotional responses in a standardized manner has significant implications for studying mental health, decision-making, and human-computer interaction. To facilitate such research, databases of emotion-inducing stimuli have become essential tools, allowing for greater experimental control and improving the reliability of findings across studies. Over the years, standardized emotional content databases have expanded significantly, contributing to more accurate comparisons in emotion research. There are already a considerable number of emotional datasets freely available to research scientists composed of different stimuli, such as pictures (Dan-Glauser & Scherer, 2011; Lang et al., 1999; Marchewka et al., 2014), sounds (Redondo et al., 2008), words (Lang et al., 1999; Soares et al., 2012; Warriner et al., 2013), music (Aljanaki et al., 2017; Eerola & Vuoskoski, 2011), film clips (Gross & Levenson, 1995; Schaefer et al., 2010), among others. However, these databases need to be constantly updated in order to provide researchers with more stimulus from the already existing categories or by adding newer categories, thus expanding their potential usefulness. One of those databases is the emotional movie database (EMDB) (Carvalho et al., 2012). The EMDB was originally constructed to provide researchers with soundless film clips that could be used to elicit emotions. Film clips are often short snippets of a full-length film and seem to be more effective in activating sustained emotional processing on both subjective and physiological levels; in addition, they have relatively higher ecological validity than pictures (Gross & Levenson, 1995). For instance, a previous comprehensive meta-analysis of experimental studies on emotions reported that film clips are very effective and powerful ways to evoke positive but specify negative emotional states (Forgas, 1994; Gerrards‐Hesse et al., 1994; Jallais & Gilet, 2010; Schaefer et al., 2010). In particular, film clips are easily standardized, exhibit no deceptive manipulation, can increase the vividness of the stimuli, and represent dynamic stimuli with relatively high ecological plausibility (Jallais & Gilet, 2010; Samson et al., 2016; Schaefer et al., 2010; Strohminger et al., 2016; Uhrig et al., 2016). Undoubtedly, movies can be utilized to show naturalistic, energetic social intelligence, which can give a sensible estimation of real-life circumstances. The EMDB was created in 2011 and has been extensively used worldwide (Carvalho et al., 2012). The main objective of this work was to create an emotional dataset that could be used across cultures. However, the videos were validated for the Portuguese population without any auditory content, so they could be adapted by adding any language or by superimposing an auditory task to the film clips (Carvalho et al., 2011). The assessment of the emotional clips was based on a dimensional approach to emotional stimuli (valence, arousal, dominance). Thus, the self-report rating of a total of 113 healthy volunteers was assessed and resulted in an assessment of a total of 52 film clips to elicit emotional states from different quadrants of affective space and across 5 emotional categories: high arousing film clips (horror and erotic), low arousing film clips (social positive, social negative, and scenery) and neutral (object manipulation). Prior research utilizing the EMDB has proven the database's efficacy in investigating attention-emotion interactions within cognitively challenging environments. Carvalho et al. (2011) investigated the psychophysiological correlates of sexually and non-sexually motivated attention to EMDB video clips during a demanding task. Their findings indicated that emotionally significant stimuli, especially those with motivational relevance, might impair task performance and alter autonomic responses, underscoring the sensitivity of EMDB clips in provoking affective states even in dual-task contexts. The current work enhances the EMDB by incorporating new emotional categories aimed at addressing deficiencies in affective diversity and arousal intensity, providing a more robust and complete instrument for future emotion research. Several databases of emotion-eliciting film clips have been developed over time, each designed with different purposes and methodologies. A notable early contribution is the comprehensive database created by Gross and Levenson (1995), which systematically categorized film clips based on the discrete emotions they reliably evoked, such as sadness, anger, fear, and happiness. Their database provided standardized stimuli for emotion research and laid the foundation for a robust theoretical and methodological framework for studying emotional responses through dynamic, real-world stimuli. This framework emphasized the importance of developing stimuli that could consistently elicit targeted emotional states in a controlled yet ecologically valid manner. More recently, Rottenberg and collaborators (2007) expanded this database by adding seven additional emotion-inducing film clips, further enhancing its utility. Today, Gross and Levenson’s database remains one of the most widely used tools in emotional research, facilitating the elicitation of discrete emotional responses in both laboratory and online settings across diverse populations. Studies investigating emotional processing, emotional regulation, and related phenomena in psychological and neuroscientific research frequently apply it, demonstrating its continued relevance. Schaefer et al. (2010) previously created a comprehensive database of emotion-eliciting films that have been widely used in psychological research. Their work provided a significant tool for assessing the emotional impact of various film stimuli. Building on this foundational research, Jenkins and Andrewes (2012) developed an emotional movie database consisting of 60 film clips validated by 109 volunteers aged between 18 and 88 years. These clips were designed to elicit five target emotions: amusement, disgust, fear, happiness, and sadness, as well as a neutral emotional state. However, the database proved less effective in consistently eliciting anger. Notably, results from their study highlighted differences in emotional responses between age groups, showing that older participants (aged 46 to 88 years) reported higher levels of arousal in response to both positive and negative stimuli compared to younger participants (aged 18 to 45 years). This finding suggests that emotional arousal may increase with age, adding an important dimension to emotion research across the lifespan. Together, these databases contribute significantly to the field by offering reliable tools for studying emotion elicitation and age-related emotional differences in a standardized and ecologically valid manner. Later, Gabert-Quillen et al. (2015) created and validated film clips in a sample of 304 undergraduate students to evoke 9 discrete emotions: amusement, anger, calmness, disgust, excitement, fear, happiness, sadness, and surprise. Additionally, other researchers have developed emotional film databases that consider cultural specificities often overlooked in existing datasets. For example, Deng et al. (2017)created a standardized emotional film database specifically for Asian cultures, comprising 64 film clips that 110 volunteers evaluated. These clips successfully elicited eight emotions: fear, disgust, anger, sadness, neutrality, surprise, amusement, and pleasure. Along with subjective assessments of valence, arousal, and dominance, physiological responses such as heart rate and respiration rate were also measured for each film clip, offering a comprehensive tool for emotion research in Asian populations. Despite the availability of several film clip databases, including the Emotional Movie Database (EMDB), important gaps limit the diversity of emotional states these tools can elicit. While the original EMDB sets were effective in provoking both positive and negative valence states, as well as high and low arousal responses, the database lacked clips that could evoke high arousal without relying on horror or erotic content. Additionally, there were few film clips designed to elicit intermediate arousal states, which are important for studying the full spectrum of emotional responses. Another limitation of the original EMDB was the limited number of neutral stimuli, restricting its utility in research requiring balanced comparisons between emotional and non-emotional states. Therefore, the primary objective of the present study was to evaluate the emotional impact of five newly developed film clip categories within the EMDB: social exclusion, social inclusion, unpleasant landscapes, extreme sports, and neutral stimuli . To this end, two validation experiments were conducted. Experiment 1, a lab-based assessment ( n = 117), tested the effects of social exclusion, social inclusion, unpleasant landscapes, and extreme sports clips. Experiment 2, an online assessment ( n = 128), focused on social exclusion, social inclusion, and newly recorded neutral clips. The development and validation of these stimuli were grounded in the Motivational Attention Model (Bradley & Lang, 1994), which emphasizes the role of valence and arousal in guiding emotional attention and physiological responses. Across both experiments, participants rated the clips using the Self-Assessment Manikin (SAM), producing normative data for each category. By expanding the EMDB to cover a broader and more balanced spectrum of the affective space—including previously underrepresented states such as emotionally neutral and positively valenced, high-arousal stimuli—this study provides researchers with a theoretically grounded, empirically validated, and freely accessible tool. The updated EMDB supports various applications in psychology, neuroscience, and affective computing, offering enhanced ecological validity and versatility for studying emotion in controlled laboratory and real-world settings. Methods General Overview (insert figure 1 here) The study was conducted in three phases. The first phase involved selecting and editing 60 film clips from a pool of 130 commercial films. In the second phase, referred to as Experiment 1, the film clips were validated in a lab-based setting. The third phase, Experiment 2, focused on the web-based assessment of 20 of these film clips (10 social exclusion and 10 social inclusion) and the addition of 10 neutral clips recorded by the research team. The assessment protocol used in this study was similar to that of Carvalho et al. (2012). The emotional categories chosen for the database were based on the two-dimensional affective model by Bradley and Lang, encompassing valence (pleasant to unpleasant) and arousal (low to high). We selected 10 film clips per category, with the exception of the neutral category, which had 13 clips. The research team pre-evaluated the clips to ensure homogeneous valence and arousal ratings within each category. The categories resulting from this selection and pre-assessment process add diversity to the existing EMDB and are as follows: 1. Social Exclusion The social exclusion category refers to film clips depicting instances of social discrimination, marginalization, or social disadvantage in social interaction. The clips in this category showcase situations involving conflict between individuals, including scenes of racism, bullying, and social tension. These emotionally charged scenarios are designed to evoke feelings of exclusion and isolation. For detailed descriptions of each film clip, please refer to Table 2. 2. Social Inclusion Social inclusion film clips portray scenarios where individuals are integrated into groups and participate in social activities without experiencing any form of discrimination. These clips highlight moments where individuals, especially those at a disadvantage, experience improved abilities, opportunities, and dignity. The emotional content in this category is designed to reflect positive social interactions and inclusivity. For a detailed description of each film clip, please refer to Table 2. 3. Unpleasant Landscapes Unpleasant landscapes film clips depict scenes where natural environments have been negatively altered, such as by pollution. Similar to the "scenery" category of the original EMDB, which was rated as pleasant and low-arousing, these clips do not feature any animals or humans. Instead, they focus solely on the degradation of natural landscapes to evoke discomfort or unease. For detailed descriptions of each film clip in this category, please refer to Table 2. 4. Extreme Sports Extreme sports film clips showcase action sports that are perceived to involve a high level of risk and are designed to evoke high arousal. These clips feature activities that include elements such as speed, altitude, or advanced physical exertion. Unlike the social inclusion and social exclusion clips performed by actors, the extreme sports clips feature real athletes performing these activities. For detailed descriptions of each film clip in this category, please refer to Table 2. 5. Neutral Neutral film clips were recorded by the research team and, similar to the neutral category in the EMDB (Carvalho et al., 2012), they do not elicit strong appetitive or defensive motivations. These clips typically depict scenarios like object manipulation or simple games without featuring human or animal faces. The purpose of these clips is to provide emotionally neutral content, free from emotionally charged stimuli. For detailed descriptions of each film clip in this category, please refer to Table 2. Ethics approval and Consent to participate The study received prior approval by the local ethical review board, the Subcomissão de Ética para as Ciências Sociais e Humanas da Universidade do Minho (reference nº SECSH 028/2017). It was in accordance with the Declaration of Helsinki. All participants gave their written informed consent prior to their inclusion in the study. Procedure Film clips selection Phase 1 After identifying the thematic categories (social inclusion, social exclusion, unpleasant scenery, and extreme sports), two researchers selected and edited 60 film clips from 130 commercial films, along with 10 original clips recorded by the project team. The selected clips were then reviewed by 20 independent researchers who validated them using the Self-Assessment Manikin (SAM) scale. Specifically, the researchers assessed the clips based on ratings of valence (positive ratings close to 9, negative ratings close to 1, and neutral ratings close to 5) and arousal (low arousal close to 1, high arousal close to 9). From this evaluation, 40 film clips (10 per category) were selected, while 20 were discarded due to high variability in SAM scores. The selection criteria at this stage were based on 1) the stability of the context throughout the clip and 2) consistent hedonic valence, ensuring that each clip maintained either positive, negative, or neutral emotions throughout its entire 40-second duration without simultaneously eliciting mixed emotions. The selected clips were edited to ensure consistency in terms of both acting and emotional content. Unlike the neutral and scenery categories, all others featured human presence and social interactions. Film clips assessment Experiment 1: laboratory-based assessment Materials For the laboratory-based experiment, multiple sessions of a relatively small group of participants watched each film clip on a large projection screen. The data collection was performed in the same room for each group of participants. It was under similar light conditions, and participants were seated at a 90º angle arc facing the screen. Participants watched a total of 40 film clips of 40-s duration each, with a resolution of 720 x 576. The film clips were presented in 4 random blocks of 10 film clips each, in a pseudorandom trial order. The videos were displayed using a projector and a projector screen. The maximum image size displayed was 1.5 m wide and 1.2 m high. The self-report procedure occurred in eight sessions, with at least 10-15 participants in each session. In each session, volunteers watched film clips from 4 different categories: social exclusion, social inclusion, unpleasant landscapes, and extreme sports. Before starting each experimental session, participants were told that they would watch several emotion-inducing film clips and that some of them could be potentially uncomfortable. However, they were also told that actors performed all situations depicted in the film clips and that they could withdraw from the experiment at any time. The team of researchers were psychologists and offered support in case any participant requested so. However, none of the enrolled participants drop out of the experiment and self-reported elevated levels of discomfort at the end of the experiment. Therefore, participants were given instructions about the self-assessment manikin (SAM) to rate valence, arousal, and dominance on a visual 9-point Likert scale, as well as to respond to simple questions related to the film clip such as: ‘‘Have you watched this movie before?’’; and ‘‘Have you closed your eyes or looked away during the clip presentation?’’. They were also asked to select at least one (and up to three) emotions evoked by the film clip from a list of emotions (Happy, Sad, Anger, Fear, Aversion, Surprise, Neutral, Supportive, Compassion, Envy, Love, Longing, Guilt, Pity, Shame, Jealousy, Embarrassment). Responses were collected immediately after watching each film clip. Similar to Carvalho et al.: 1), participants were instructed to report (1) How the clip actually made them feel, rather than what they believed they should feel; 2) how he/she felt when they saw the film clip, not their overall mood; 3) Whether they recognized the original movie. The latter was collected to remove from the database all clips previously watched by 30% or more of the participants. Experiment 2: Web-based EMDB Assessment For the web-based EMDB assessment, participants received a link to a Google Forms platform, which included the following components: a consent form, a sociodemographic questionnaire, and the Positive and Negative Affect Schedule (PANAS). After reading the online consent form, participants were prompted with the statement: "I understand the terms of the study. I have no questions. I would like to continue the online experience." They could then select either "Start Experience" to participate or "End Participation" to cancel their involvement. In the sociodemographic questionnaire, participants provided details such as age, gender, nationality, education level, medical history, and other relevant information. Following this, they completed the PANAS, which consists of 20 items (positive and negative emotions). Participants were asked to rate each emotion on a scale from 1 (Very slightly or not at all) to 5 (Extremely). Similar to Experiment 1, participants received instructions and viewed a training film clip (unrelated to the EMDB) before starting the assessment session. The 36 EMDB film clips were divided into two sets (Presentation A and Presentation B) and presented via either the Qualtrics or Google Forms platforms. Participants viewed 18 film clips and an initial training clip during their session. Each film clip lasted 40 seconds, and participants were required to evaluate each clip immediately after watching it. The self-report process took place over four sessions (two on Google Forms and two on Qualtrics), each involving 20 and 62 participants. The entire procedure took approximately 60 minutes to complete. Data analysis The grand mean for each thematic category was calculated to summarize the results. A general linear model analysis was applied to both the lab-based and web-based assessment groups. Separate within-subject repeated measures ANOVAs were conducted to evaluate the dimensions of valence, arousal, and dominance. The analysis for the lab-based assessment group included four levels (Social Exclusion, Unpleasant Landscapes, Extreme Sports, and Social Inclusion). In contrast, three levels were analyzed for the web-based assessment group (Neutral, Social Exclusion, and Social Inclusion). Post-hoc multiple pairwise comparisons were performed using Bonferroni’s correction to account for potential Type I errors. The significance level for all statistical tests was set at p < .05. All analyses were conducted using IBM SPSS Statistics, version 23 (IBM®). Transparency and Openness This study followed APA’s Transparency and Openness Promotion (TOP) Guidelines. All measures, materials, and procedures have been described in detail in the manuscript. The complete set of film clips from the expanded Emotional Movie Database (EMDB) is freely available for scientific research purposes upon request and signature of a user agreement. Interested researchers may contact the authors via [email protected] and/or [email protected] to obtain access. This study was not preregistered. Data sharing is subject to ethical constraints due to the inclusion of identifiable stimuli (film content), but summary statistics are available upon reasonable request. No automated data analysis scripts were used beyond the standard statistical procedures described in the Results section. We adhered to the Journal Article Reporting Standards (JARS) and provide full methodological transparency to support reproducibility. Results Participants A total of 245 healthy volunteers participated in this study, with 117 individuals taking part in the lab-based assessment of the film clips and 128 participants in the web-based assessment conducted using the Qualtrics and Google Forms platforms. The mean age of the participants in the lab-based group was 21.10 years ( SD = 4.40), while the web-based group had a mean age of 22.86 years ( SD = 7.50). Both samples were predominantly female, with 79.5% female participants in the lab-based group and 74.2% in the web-based group. The majority of participants in the web-based assessment were Portuguese nationals (84.4%), single (92.2%), and students (79.7%), with most having completed high school (72.7%). Additionally, most of the web-based participants completed the task at home (90.6%) and used a computer for the task (90.6%). For more detailed information on the sample characteristics, please refer to Table 1. Sample size calculations for both studies were performed utilizing GPower 3.1 (Faul et al., 2009). In Experiment 1 (lab-based), we determined the necessary sample size for a repeated-measures ANOVA (within-subjects design) based on a medium effect size (f = 0.25), α = 0.05, power (1–β) = 0.95, and 4 conditions. The analysis revealed a minimum of 44 participants. We deliberately surpassed this figure (final n = 117) to enhance statistical robustness and accommodate potential data exclusions. In Experiment 2 (web-based), a comparable power analysis for comparisons among three emotional categories and eighteen film clips indicated a requisite minimum of 48 participants. A larger sample (n = 128) was gathered to enhance external validity and facilitate exploratory subgroup analyses. Consistent with best practices for transparency (Simmons et al., 2012), we disclose our sample size determination, all data exclusions, all manipulations, and all metrics included in the study. (Insert Table 1 here) Normative Ratings Table 2 describes each film clip and the normative valence, arousal, and dominance ratings. These ratings are presented separately for males and females, along with the percentage of participants who reported recognizing each clip. Importantly, all film clips were included in the analysis, as none were recognized by more than 30% of the participants. The scores for valence, arousal, and dominance were assessed using the Self-Assessment Manikin (SAM; Lang, 1980). The paper-and-pencil version of the SAM used graphical figures for each film clip, with participants rating each dimension on a 9-point Likert scale (see Lang et al., 2008 2008 for a detailed review). Higher scores on the dominance subscale indicate the degree to which participants felt subjectively overwhelmed by the content. Figure 2 illustrates the distribution of ratings for valence, arousal, and dominance, suggesting that the selected film categories occupy distinct sub-quadrants of the affective space (pleasure, valence, and arousal) for both male and female participants. Before the data analysis, the percentage of participants familiar with each film was examined. Despite one clip being recognized by more than 30% of the participants (46.9%), it was maintained in the analyses (for complete results, see Supplementary Material, Table S1). Table 2 shows a general description of each film, followed by normative ratings of valence, arousal, and dominance. First, the overall means of ratings for each film are presented, and then the scores are divided by sex within the lab-based assessment and web-based assessment groups. (Insert Table 2 here) (Insert Figure 2 here) Figure 3 depicts the distribution of all EMDB film categories within the affective space, where valence (x-axis) and arousal (y-axis) are the primary emotional dimensions. Every point shows the grand mean, or average ratings of ten film clips per category, therefore providing a whole picture of the emotional orientation of every category. Thus, the expanded EMDB now includes a wide spectrum of emotional categories covering many degrees of valence and arousal, hence improving the database's relevance for emotional research in many contexts. High-arousal positive film clips are still underrepresented, though; both sexual and extreme sports movies were evaluated as just mildly arousing (between 5 and 6 on the arousal scale). This restriction implies that although the new categories effectively cover a wide spectrum of emotional states, future developments of the EMDB should try to integrate higher-arousal stimuli to further improve its utility in studies aiming at intense emotional experiences. (Insert Figure 3 here) Lab-based assessment group Social exclusion This category triggered low levels of valence, and anger (36.4%), sadness (26.3%) and aversion (11.4%) were the emotions that better represented what participants felt with these clips(Figures 3 and 4; for complete data, see Supplementary Material, Table S1). The arousal results demonstrated moderate agitation, and the dominance results indicated that participants felt slightly dominated by these categories. Social inclusion The results indicated higher valence scores when compared to the previous categories, and the emotions most frequently used to represent the films were happiness (40.4%), love (28.5%) and compassion (12.6%) (Figures 3 and 4; Table S1). The arousal results presented moderate agitation, and dominance results demonstrated that participants did not feel dominated or domineering after watching these films. Unpleasant landscape These films caused low valence levels, and the most frequently chosen emotions were sadness (36.2%), compassion (12.9%) and, solidarity (10.5%), pity (10.5%) (Figures 3 and 4; Table S1). The arousal results showed moderate agitation, and the dominance results displayed that participants felt slightly dominated by these films. Extreme sports The results showed higher valence scores when compared to the previous categories, and the emotions most frequently used to represent the films were fear (29.3%), happiness (26.2%), and surprise (16.3%) (Figures 3 and 4; Table S1). The arousal results presented moderate agitation, and the dominance scores indicated that participants did not feel dominated or domineering after watching these films. Web-based assessment group Social exclusion The results of these films showed low levels of valence, and more than 33% of participants chose anger as the emotion that better represented what they felt with these clips (Fig. 4; Table S1). The arousal results indicated moderate agitation and dominance, indicating that these films slightly dominated participants. Social inclusion The films displayed higher valence scores compared to the other categories; the emotions most frequently used to represent the films were happiness (37.5%), love (25%), and compassion (16.65%) (Figures 3 and 4; Table S1). The arousal dimension showed moderate agitation, and the dominance results indicated that participants did not feel dominated or domineering by these films. Neutral The valence results demonstrated a neutral state after the films, and 74.6% of participants chose this emotional state to represent better what they felt with each clip. Additionally, around 10% of participants reported positive emotions, specifically happiness (6.6%) and surprise (3.9%) (Figures 3 and 4; for complete data, see Supplementary Material, Table S1). The arousal results indicated low activation after the film clips, and the dominance results showed that participants mostly felt dominant after watching the films. (Insert Figure 4 here) (insert table 3 here) Differences between Lab-based and Web-based groups Social Exclusion The lab-based group scored higher in terms of valence (total: M = 2.16, SD = 1.07) and dominance (total: M = 4.61, SD = 2.09) on most social exclusion films compared to the web-based group (total valence: M = 2.11, SD = 1.13; total dominance: M = 4.55, SD = 2.08). On the other hand, the web-based group showed higher levels of arousal (total: M = 6.11, SD = 1.89) than the lab-based group (total: M = 5.97, SD = 2.07) (Table 3). Despite these differences, the results were not statistically significant for any clip (total valence: t (24) = .37, p > .05; total arousal: t (24) = -.55, p > .05; total dominance: t (24) = .18, p > .05). Social Inclusion The lab-based group demonstrated higher mean scores on valence (total: M = 7.17, SD = 0.92) and arousal (total: M = 4.68, SD = 1.72) in the social inclusion category compared to the web-based group (total valence: M = 7.14, SD = .92; total arousal: M = 5.53, S D = 1.81). Furthermore, the web-based group had high dominance scores (total: M = 5.89, SD = 1.82) relative to the lab-based group (total: M = 5.32, SD = 2.07) (Table 3). Specifically, differences in valence were statistically significant for the films 10,000 (lab-based group: M = 7.74, SD = 1.20; web-based group: M = 7.23, SD = 1.98; t (179.04) = 2.33, p < .05) and 10,007 (lab-based group: M = 6.30, SD = 1.54; web-based group: M = 7.02, SD = 1.53; t (22) = -3.48, p < .01), whereas differences in arousal were statistically significant for the films 10,001 (lab-based group: M = 5.42, SD = 2.33; web-based group: M = 4.73, SD = 2.53; t (23) = 2.14, p < .05) and 10,006 (lab-based group: M = 4.91, SD = 2.31; web-based group: M = 4.22, SD = 2.52; t (22) = 2.13, p < .05). Additionally, results were statistically significant for the total dominance ( t (24) = -2.32 ., p < .05) and for films 10,004 (lab-based group: M = 5.20, SD = 2.48; web-based group: M = 6.00, SD = 2.40; t (22) = -2.44, p < .05), 10,005 (lab-based group: M = 5.19, SD = 2.51; web-based group: M = 6.56, SD = 2.17; t (22) = -4.32, p < .001), 10,007 (lab-based group: M = 5.27, SD = 2.86; web-based group: M = 6.25, SD = 2.42; t (22) = -2.73, p < .01), 10,008 (lab-based group: M = 5.23, SD = 2.51; web-based group: M = 6.13, SD = 2.48; t (22) = -2.66, p < .01), and 10,009 (lab-based group: M = 5.24, SD = 2.78; web-based group: M = 6.06, SD = 2.23; t (220.01) = -2.48, p < .05). Figure 2 shows the mean ratings of valence and arousal for each film. The results suggest that each category occupies a distinct quadrant of the affective space. This pattern was consistent when the results were divided by sex (Fig. 3). Category Comparison The overall means for each film category in all dimensions were calculated to analyze the differences in valence, arousal, and dominance scores between categories (Table 3; Table 4). Lab-based assessment group Valence Effects A One-Way repeated measures ANOVA showed a significant effect of each film category on valence scores ( F (3.348) = 645.157, p < .001, η2 = .848). On average, the values of valence were lower in the social exclusion ( M = 2.16, SD = 1.07) and unpleasant landscape ( M = 2.77, SD = .99) categories, and higher in the extreme sports ( M = 6.25, SD = 1.12) and social inclusion ( M = 7.17, SD = .92) films. Post hoc comparisons were performed using the Bonferroni correction. The differences between the social exclusion and the unpleasant landscape (-.61 95% CI [-.77, -.44]), extreme sports (-4.09 95% CI [-4.53, -3.65]) and social inclusion films (-5.01 95% CI [-5.44, -4.58]) were statistically significant ( p < .001). Similarly, the differences between the unpleasant landscape and extreme sports (-3.48 95% CI [-3.92, -3.04]) and social inclusion films (-4.41 95% CI [-4.83, -3.98]) were statistically significant ( p < .001). The difference between the extreme sports and social inclusion films (-.93 95% CI [-1.16, -.69]) was also statistically significant ( p < .001) (Table 3). Arousal Effects The results of the One-Way repeated measures ANOVA analysis showed a significant effect of each film category on arousal scores ( F (3.348) = 38.30, p < .001, η2 = .248). In general, the social exclusion ( M = 5.97, SD = 2.06) and extreme sports ( M = 5.34, SD = 1.95) categories showed higher scores of arousal compared with the unpleasant landscape ( M = 4.53, SD = 2.01) and social inclusion ( M = 4.68, SD =1.72) categories. Post hoc comparisons carried out using the Bonferroni correction demonstrated that the differences between the social exclusion and unpleasant landscape (1.44 95% CI [1.11, 1.78]), extreme sports (.63 95% CI [.14, 1.13]) and social inclusion films (1.30 95% CI [.90, 1.70]) were statistically significant ( p < .01). Likewise, the differences between the unpleasant landscape and extreme sports films (-.81 95% CI [-1.30, -.32]), as well as between the extreme sports and social inclusion films (.66 95% CI [.30, 1.03]) were statistically significant ( p .05). Dominance Effects A One-Way repeated measures ANOVA indicated a significant effect of film categories on dominance scores ( F (3.348) = 11.74, p < .001, η2 = .092). The results showed that the social exclusion ( M = 4.61 SD = 2.09) and unpleasant landscape ( M = 4.48, SD = 2.21) categories presented lower scores than the extreme sports ( M = 5.14, SD = 2.08) and social inclusion ( M = 5.32, SD = 2.07) films. Post hoc comparisons performed with Bonferroni correction pointed out statistically significant differences ( p < .05) between the social exclusion and extreme sports (-.54 95% CI [-1.06, -.01]) and social inclusion films (-.71 95% CI [-1.26, -.17]). In the same way, the differences between the unpleasant landscapes and extreme sports (-.66 95% CI [-1.10, -.22]) and social inclusion films (-.83 95% CI [-1.23, -.44]) were statistically significant ( p < .01) (Table 3). On the contrary, the differences in dominance scores between the social exclusion and unpleasant landscape categories and between extreme sports and social inclusion films were not significant ( p > .01). Web-based assessment group Valence Effects A One-Way repeated measures ANOVA analysis demonstrated a significant effect of film categories on valence scores ( F (1.12) = 724.52, p < .001, η2 = .851). Results showed that the social inclusion ( M = 7.14, SD = 1.13) category presented higher scores of valence than neutral ( M = 5.11, SD = .42) and social exclusion ( M = 2.11, SD = 1.13) films. Post hoc comparisons performed with Bonferroni correction showed that the differences between the neutral and social exclusion (3.00 95% CI [2.74, 3.26]) and social inclusion films (-2.03 95% CI [-2.28, -1.79]) were statistically significant ( p < .001). Likewise, the difference between the social exclusion and social inclusion (-5.03 95% CI [-5.47, -4.60]) categories was statistically significant ( p < .001) (Table 4). Arousal Effects A One-Way repeated measures ANOVA showed a significant effect of film categories on arousal scores ( F (1.65) = 297.13, p < .001, η2 = .701). On average, the values of arousal were lower in the neutral ( M = 2.31, SD =1.36) and social inclusion ( M = 4.53, SD = 1.81) categories when compared with social exclusion ( M = 6.11, SD = 1.89) films. Post hoc comparisons carried out using the Bonferroni correction indicated that the differences between the neutral and social exclusion (-3.80 95% CI [-4.25, -3.35]) and social inclusion (-2.21 95% CI [-2.60, -1.83]) categories were statistically significant ( p < .001). Additionally, the difference between the social exclusion and social inclusion (-1.59 95% CI [-1.87, -1.29]) categories was also statistically significant ( p < .001) (Table 4). Dominance Effects The One-Way repeated measures ANOVA analysis indicated a significant effect of film categories on dominance scores ( F (1.48) = 85.27, p < .001, η2 = .402). In general, the neutral category ( M = 7.21, SD = 1.94) showed higher scores of dominance compared with the social exclusion ( M = 4.56, SD = 2.08) and social inclusion ( M = 5.89, SD = 1.82) films. Post hoc comparisons performed with Bonferroni correction pointed out that the differences between the neutral and social exclusion (2.65 95% CI [2.03, 3.27]) and social inclusion (1.32 95% CI [.88, 1.76]) categories were statistically significant ( p < .001). Similarly, the difference between the social exclusion and social inclusion films (-1.34 95% CI [-1.72, -.95]) was statistically significant ( p < .001) (Table 4). (Insert Table 4 here) Discussion This study aimed to expand the Emotional Movie Database (EMDB) by introducing five new emotional categories: social exclusion, social inclusion, unpleasant landscapes, extreme sports, and neutral film clips. This expansion addressed key limitations of the original EMDB, such as the lack of film clips capable of eliciting high arousal outside of horror and erotic contexts. It also filled the gap for clips that evoke intermediate arousal and increased the representation of neutral stimuli, which were previously underrepresented. A secondary objective of this study was to evaluate the emotional impact of the newly introduced film clips using both lab-based and web-based methodologies, assessing participants’ emotional responses across three key dimensions: valence, arousal, and dominance in two major emotional categories—social inclusion and social exclusion. Additionally, we incorporated 10 additional neutral film clips in the web-based evaluation to further expand the object manipulation category from Carvalho et al. (2012). As a result, a total of 245 participants took part in the study, with 117 assigned to the lab-based condition and 128 to the web-based condition. Lab-Based Assessment The laboratory assessment demonstrated that the Social Exclusion clips successfully provoked anger and moderate arousal, with participants indicating a modest feeling of being dominated by the content. According to the literature, social conflict, exclusion, and marginalization are associated with intense negative emotions such as wrath (Leary et al., 2006). The moderate arousal levels noted here align with the findings of Gross and Levenson (1995) which suggested that social conflict elicits moderate-to-high arousal without overpowering the audience. The results substantiate the new social exclusion category as an effective mechanism for eliciting emotional states associated with conflict and rejection. Conversely, social inclusion clips generated happiness, which was characterized by high valence and moderate arousal and was indicative of favorable social connections. Participants experienced comfort and empowerment, with no notable dominance effects, indicating that inclusion fosters well-being without disturbing spectators. This conclusion aligns with Schaefer et al., (2010), who demonstrated that pleasant social interactions consistently elicit happiness. Schaefer et al. (2010) emphasized that social inclusion fosters happy emotions without overwhelming individuals. The newly established social inclusion category is a significant instrument for examining positive social feelings in a regulated environment. Likewise, the extreme sports clips generated a distinctive emotional amalgamation of fear and high valence, encapsulating exhilaration in non-threatening yet high-risk scenarios. In contrast to the adverse feelings of fear commonly linked to threats, participants in this study indicated a greater valence, implying they perceived the dangerous behaviors as exhilarating rather than alarming. This aligns with Samson et al. (2016), who observed that extreme sports frequently generate high arousal without negative valence, as spectators regard these activities as exhilarating rather than perilous. The findings demonstrate that these videos effectively elicited the thrill-seeking emotional response characteristic of extreme sporting situations, wherein fear is interwoven with excitement and pleasure. Finally, the unpleasant landscape clips predominantly elicited feelings of melancholy among participants, characterized by low valence and moderate arousal. These findings are consistent with research by Deng et al. (2017), which suggests that environmental degradation often triggers negative emotions, including sadness and despair. The low arousal levels observed in this study suggest also muted emotional reactions typically associated with deteriorated or polluted natural environments. This observation aligns with the work of Ulrich (1983), who found that exposure to degraded landscapes can lead to reduced emotional engagement and lower arousal levels. These clips effectively illustrate the emotional repercussions of environmental damage, contributing to the growing body of research on the emotional impacts of environmental stimuli. As the literature suggests, the emotional responses elicited by such settings underscore the significant relationship between our surroundings and emotional well-being, emphasizing the need for continued investigation into how environmental factors influence psychological states (Kaplan & Kaplan, 1989; Ulrich, 1983). Web-based assessment The social exclusion clips in the web-based group also evoked anger, with moderate arousal levels. The social exclusion videos in the online group elicited anger, accompanied by moderate degrees of arousal. Participants in this group had elevated dominance scores relative to the lab-based group, presumably owing to the self-directed online testing format. In contrast to a controlled laboratory environment, where participants have restricted control over stimulus presentation, web-based participants engage more adaptable and self-directedly with the activity, perhaps alleviating feelings of powerlessness or subordination (Gabert-Quillen et al., 2015). Previous studies indicate that enhanced autonomy in experimental settings correlates with elevated dominance ratings (Betella & Verschure, 2016), potentially elucidating this trend. The diminished emotional intensity commonly noted in online studies (Samson et al., 2016) may influence these results, necessitating additional research. Studies such as Samson et al. (2016), have shown that online environments reduce the intensity of emotional responses compared to in-lab testing, where participants are more immersed in the stimuli. In contrast, Carvalho et al. (2012) suggested that the controlled lab environment fosters stronger emotional engagement, which may explain the higher dominance scores in the lab group. For the social inclusion clips, participants in both lab and web-based groups reported happiness with high valence and moderate arousal. However, web-based participants reported feeling more dominant, likely due to the self-paced nature of the online environment, where participants feel more in control of their emotional experience. Gabert-Quillen et al. (2015) found that web-based participants tend to report higher levels of emotional regulation, as the absence of a researcher mitigates social pressure. In contrast to previous emotional databases, such as Gross and Levenson (1995) and Rottenberg et al. (2007), which focused on eliciting intense emotions, this study introduced a wider spectrum of emotional states, from high to intermediate arousal across positive and negative contexts. This expanded approach is comparable to Jenkins and Andrewes (2012), who used video snippets to evoke specific emotions in a diverse sample. The new categories offer a more comprehensive range of emotional stimuli, making the EMDB more versatile for studying emotional responses across different contexts. The neutral clips successfully delivered emotionally neutral content, as participants indicated neutral emotional states and low arousal. This confirms their function as baseline stimuli, crucial for contrasting emotional and non-emotional responses. These findings correspond with Gross and Levenson (1995), who underscored the necessity of neutral stimuli in emotion research, and Schaefer et al. (2010), who illustrated that well-crafted neutral films create emotional balance, facilitating precise comparisons. Lab-Based vs. Web-Based Assessments: Social Exclusion and Social Inclusion The relatively consistent findings between lab-based and web-based assessments further validate the newly introduced emotional categories. While minor differences in dominance scores were observed—particularly for social inclusion clips, where web-based participants reported higher dominance—these variations may stem from the self-directed nature of online testing. As suggested by Betella and Verschure (2016), dominance ratings can fluctuate depending on the testing context. In this study, the absence of a researcher in the web-based condition may have contributed to an increased sense of control, thereby influencing participants’ dominance ratings. The findings highlight the importance of continuously updating emotion-eliciting databases to reflect diverse emotional experiences and cultural nuances. Recent work by İyilikci et al. (2024), who developed the EGEFILM database for a Turkish sample, demonstrates how cultural contexts influence emotional processing. By incorporating a large and heterogeneous participant pool and a broad spectrum of emotional states, EGEFILM provides valuable insights into culturally driven variations in emotional responses. Expanding databases like EMDB to include culturally diverse stimuli enhances their generalizability, ensuring that emotion research remains contextually relevant and applicable across different populations. Regarding the elicited emotions, in the lab-based trial, unpleasant environments were linked with fear, melancholy, and aversion; social exclusion clips mostly evoked anger and unhappiness. Extreme sports clips inspired great degrees of surprise, which fit their exciting and forceful character. In contrast, social inclusion clips were connected to positive feelings, including happiness, solidarity, and love, supporting their function in generating prosocial affective states. Similar trends showed up in the web-based assessment. Social inclusion clips were linked with happiness and solidarity; social exclusion clips provoked wrath, grief, and compassion. As anticipated, neutral clips provoked minimal emotional responses across all assessed dimensions, affirming their appropriateness as baseline stimuli. These data further corroborate the extended EMDB categories, illustrating their consistency and reliability in laboratory and online study environments. The results underscore the distinct emotional effects of each category, validating their incorporation into the updated database. The use of lab-based and web-based methodologies was critical for ensuring the generalizability of the findings. Lab-based assessments offer a controlled atmosphere that helps lower outside distractions and standardize trial settings. Capturing fine-grained emotional reactions with great internal validity depends especially on this degree of control. Such regulated environments, however, might not adequately portray the complexity of emotional experiences as they arise in real-world circumstances. Although the unpredictability of uncontrolled testing contexts causes web-based assessments to lack inherent ecological validity, they are a useful substitute for future research especially for studies needing extensive data collecting and more general participant recruitment. This method raises sample diversity and accessibility, therefore enabling researchers to access bigger and more geographically scattered populations. Furthermore, web-based approaches might enable longitudinal studies with repeated measurements over time and aid in lowering logistical restrictions. Given the growing reliance on online experimental paradigms in psychological research, the findings of this study contribute to the refinement of web-based methodologies, supporting their potential use in future large-scale and cross-cultural investigations of emotional processing. Limitations Some limitations should be considered. The participant samples were primarily composed of young people, limiting direct inferences about developmental or lifespan differences. The consistent 40-second duration of each clip may limit the depth or complexity of the emotional response in comparison to longer stories or multi-scene stimuli. Subsequent research could address these problems by creating variants of the existing film of differing lengths, sampling from other age demographics, or including audio content customized to specific ethnicities or languages. Moreover, a main limitation of this study is that not all emotional categories were evaluated in both lab-based and online environments. This helps to avoid direct comparisons between all kinds of stimuli and might cause variation in how various emotional events are handled in different environments. Future studies should try to assess every category in both experimental settings methodically, guaranteeing a thorough comparison and enhancing the validity and applicability of web-based approaches in emotional research. Future Directions The expanded EMDB now encompasses various emotional categories, spanning various valence and arousal levels. This broader emotional representation enhances the database’s applicability for emotion research across different contexts, making it a more versatile tool for studying emotional processing. Nonetheless, stimuli with high arousal and positive valence are still inadequately represented, as illustrated in Figure 3. Rectifying this deficiency would optimize the equilibrium of emotional classifications and augment the database's relevance for subsequent research. Thus, this study confirms the utility and reliability of the updated EMDB while identifying opportunities for additional enhancement. Future study should concentrate on incorporating additional intricate emotional categories to encapsulate subtle emotional experiences. Research should also investigate individual variances in emotional reactions to enhance the database's generalizability across varied groups. Broadening the spectrum of high-arousal, positive-valence stimuli would offer a more extensive array of exhilarating and enjoyable experiences for studies on emotion regulation and affective states. Psychophysiological researchers can use the database to assess autonomic responses, including heart rate, skin conductance, and facial electromyography, to measure emotional reactivity. Furthermore, the new categories offer more thorough assessments of emotion control mechanisms, encompassing cognitive reappraisal and attentional deployment. The EMDB enables a thorough examination of emotional regulation in diverse contexts by covering a wide emotional range, from low-arousal neutral states to socially significant negative and positive emotions. Future technological improvements may augment the efficacy of the EMDB. Combining Virtual Reality (VR) with 360-degree media may improve ecological validity, facilitating more immersive emotional experiences for study objectives. Machine learning and computational modeling can enable the automatic classification of emotional responses via facial expressions, vocalizations, and physiological data. These advancements would augment research in emotional computing and human-computer interaction. Conclusion The present study enhances the Emotional Movie Database (EMDB) by integrating novel emotional categories that specifically target the deficiencies found in prior iterations. These enhancements improve the database's usefulness for many research applications by offering a wider selection of emotional stimuli. The meticulous assessment approach, which includes both face-to-face and online methodologies, verifies the dependability of the new film clips in evoking precise emotional reactions. Hence, the enhanced EMDB provides a comprehensive instrument for investigating emotions, facilitating both fundamental research and practical implementations in several psychological fields. This ongoing enhancement guarantees that the database remains pertinent and efficient for modern emotion research. Declarations Funding: This research was conducted at the Psychology Research Centre (CIPsi – PSI/01662), School of Psychology, University of Minho, and supported by the Portuguese Foundation for Science and Technology (FCT) through national funds (UID/01662/2020). C.G.C. was supported by a doctoral scholarship from FCT (grant number 2022.14063.BD). Conflicts of interest/Competing interests : N/A Authors' contributions : The study’s conception and design were led by SC and JL. SC and CGC, and AGM were responsible for data collection and analysis. SC drafted the manuscript, while OG provided critical revisions and contributed to the study design. All authors reviewed, revised, and approved the final manuscript References Aljanaki, A., Yang, Y. H., & Soleymani, M. (2017). Developing a benchmark for emotional analysis of music. PLoS ONE , 12 (3), 1–22. https://doi.org/10.1371/journal.pone.0173392 Betella, A., & Verschure, P. F. M. J. (2016). The affective slider: A digital self-assessment scale for the measurement of human emotions. PLoS ONE , 11 (2), 1–11. https://doi.org/10.1371/journal.pone.0148037 Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. 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Wohlwill (Eds.), Behavior and the natural environment (Vol. 6, pp. 85–125). Plenum Press. Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods , 45 (4), 1191–1207. https://doi.org/10.3758/s13428-012-0314-x Tables Tables 1 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6401734","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465460457,"identity":"35e9d912-95ff-46fc-b3ae-70f2ad465ebf","order_by":0,"name":"Sandra Carvalho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJACCRDBD8QHgJgHwiVGi2QDipYEIrQYHEDh4tEi33724O2KmnvyxrfbHx5gbNsmYy7dfIC58AduLQZn8pItzxwrNtx254wBUMttHss5xxKYZ+CxxYAhx0yygS0hwexGDgNYi8GNHANmHnwO638D1PIvIcF4RvoD4rQw3ADa0tiWkGAgkWBAnBaDG2+MLRv7EgxngPyScA6kJS3h8Iw0fA7LMbzZ8C1Bnn92++MPH8pu2xvcSD74uMAGj8PgABQdMOccJkYDA0o6YSZOyygYBaNgFIwQAAC1KFNaXJJRhwAAAABJRU5ErkJggg==","orcid":"","institution":"Psychological Neuroscience Laboratory - CIPsi, Department of Basic Psychology, School of Psychology, University of Minho, Braga, Portugal","correspondingAuthor":true,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Carvalho","suffix":""},{"id":465460458,"identity":"b09fe42d-3709-442d-9e10-4b89a4b024d9","order_by":1,"name":"Catarina GomesCoelho","email":"","orcid":"","institution":"Psychological Neuroscience Laboratory - CIPsi, Department of Basic Psychology, School of Psychology, University of Minho, Braga, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Catarina","middleName":"","lastName":"GomesCoelho","suffix":""},{"id":465460460,"identity":"4b2ec4fd-db17-4e43-a180-a8f5dcb9f18a","order_by":2,"name":"Augusto J. Mendes","email":"","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Augusto","middleName":"J.","lastName":"Mendes","suffix":""},{"id":465460461,"identity":"38115211-dff5-499d-8749-6978403e0b71","order_by":3,"name":"Óscar Gonçalves","email":"","orcid":"","institution":"CINTESIS@RISE, CINTESIS.UPT, Portucalense University","correspondingAuthor":false,"prefix":"","firstName":"Óscar","middleName":"","lastName":"Gonçalves","suffix":""},{"id":465460463,"identity":"3a6a83ab-20eb-4861-904e-8f41cf68b911","order_by":4,"name":"Jorge Leite","email":"","orcid":"","institution":"CINTESIS@RISE, CINTESIS.UPT, Portucalense University","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Leite","suffix":""}],"badges":[],"createdAt":"2025-04-08 09:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6401734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6401734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84186614,"identity":"1fe1203e-eaaa-4646-beb6-397747d7c9f9","added_by":"auto","created_at":"2025-06-09 05:39:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107561,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of experiment 1: the lab-based assessment of the new EMDB categories (social exclusion, social inclusion, unpleasant landscapes, and extreme sports), and experiment 2: the web-based assessment of the new EMDB categories ((social exclusion, social inclusion, and neutral). PANAS: Positive and Negative Affects Schedule\u003c/p\u003e","description":"","filename":"FIgure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6401734/v1/e56b615a3d58f326909c5509.jpg"},{"id":84186616,"identity":"1f21f984-0750-476a-a8cf-a00213a1fe84","added_by":"auto","created_at":"2025-06-09 05:39:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of Film Categories in the Affective Space Across Overall, Male, and Female Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis figure presents the distribution of valence (x-axis) and arousal (y-axis) ratings for the film clips in the lab-basedand web-based assessments. The three scatter plots represent ratings for all participants (left), men (middle), and women (right).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6401734/v1/01d1098b88cadf6b7cb0a117.jpg"},{"id":84186617,"identity":"25d58333-982d-4fa1-9506-f5844c4eed32","added_by":"auto","created_at":"2025-06-09 05:39:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42764,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of All EMDB Film Categories in Affective Space\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis figure illustrates the distribution of all Emotional Movie Database (EMDB) film categories in the affective space, plotted along two dimensions: valence (x-axis) and arousal (y-axis).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6401734/v1/256778eb78f36fcb116b4cf5.jpg"},{"id":84186620,"identity":"88455f0b-0499-4fde-a5a0-8b114d3c251a","added_by":"auto","created_at":"2025-06-09 05:39:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":166042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eElicited Emotions in Lab-Based (Left) and Web-Based (Right) Assessments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis figure presents radar plots illustrating the intensity of elicited emotions in the lab-based (left) and web-based (right) assessments. Each axis represents a different reported emotion: anger, happiness, sadness, compassion, fear, aversion, love, and surprise. The plotted lines indicate the percentage of participants who reported experiencing each emotion in response to the film clips. Values range from 0% (center of the plot) to 50% (outermost edge), with increasing intensity moving outward.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6401734/v1/969c3e204f7f40a1187dece6.png"},{"id":84187892,"identity":"7ba61c98-87e2-4c6b-b547-89a28e37be69","added_by":"auto","created_at":"2025-06-09 06:03:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1351388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6401734/v1/915d49b8-3a67-4131-b087-0748c4331696.pdf"},{"id":84186615,"identity":"9d962402-872d-492b-849e-6793331ef88e","added_by":"auto","created_at":"2025-06-09 05:39:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":69920,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6401734/v1/59dadf9a0b35309dca8fca01.docx"},{"id":84186619,"identity":"f590cd40-642a-4e9a-8b0b-41fe0b784f35","added_by":"auto","created_at":"2025-06-09 05:39:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44665,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6401734/v1/190915185ea79b6d8e39683f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Emotional Movie Database (EMDB): An Expanded Toolkit for Emotion Research","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding how humans process emotions is a crucial area of research in psychology, neuroscience, and affective computing. The ability to elicit and measure emotional responses in a standardized manner has significant implications for studying mental health, decision-making, and human-computer interaction. To facilitate such research, databases of emotion-inducing stimuli have become essential tools, allowing for greater experimental control and improving the reliability of findings across studies. Over the years, standardized emotional content databases have expanded significantly, contributing to more accurate comparisons in emotion research.\u003c/p\u003e\n\u003cp\u003eThere are already a considerable number of emotional datasets freely available to research scientists composed of different stimuli, such as pictures (Dan-Glauser \u0026amp; Scherer, 2011; Lang et al., 1999; Marchewka et al., 2014), sounds (Redondo et al., 2008), words (Lang et al., 1999; Soares et al., 2012; Warriner et al., 2013), music (Aljanaki et al., 2017; Eerola \u0026amp; Vuoskoski, 2011), film clips (Gross \u0026amp; Levenson, 1995; Schaefer et al., 2010), among others. However, these databases need to be constantly updated in order to provide researchers with more stimulus from the already existing categories or by adding newer categories, thus expanding their potential usefulness.\u003c/p\u003e\n\u003cp\u003eOne of those databases is the emotional movie database (EMDB) (Carvalho et al., 2012). The EMDB was originally constructed to provide researchers with soundless film clips that could be used to elicit emotions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFilm clips are often short snippets of a full-length film and seem to be more effective in activating sustained emotional processing on both subjective and physiological levels; in addition, they have relatively higher ecological validity than pictures (Gross \u0026amp; Levenson, 1995). For instance, a\u0026nbsp;previous\u0026nbsp;comprehensive\u0026nbsp;meta-analysis\u0026nbsp;of\u0026nbsp;experimental\u0026nbsp;studies\u0026nbsp;on\u0026nbsp;emotions\u0026nbsp;reported\u0026nbsp;that\u0026nbsp;film\u0026nbsp;clips\u0026nbsp;are\u0026nbsp;very effective\u0026nbsp;and\u0026nbsp;powerful\u0026nbsp;ways\u0026nbsp;to\u0026nbsp;evoke\u0026nbsp;positive but specify negative\u0026nbsp;emotional states (Forgas, 1994; Gerrards‐Hesse et al., 1994; Jallais \u0026amp; Gilet, 2010; Schaefer et al., 2010). In\u0026nbsp;particular,\u0026nbsp;film\u0026nbsp;clips\u0026nbsp;are\u0026nbsp;easily\u0026nbsp;standardized,\u0026nbsp;exhibit\u0026nbsp;no\u0026nbsp;deceptive\u0026nbsp;manipulation,\u0026nbsp;can increase the vividness of the stimuli,\u0026nbsp;and\u0026nbsp;represent\u0026nbsp;dynamic\u0026nbsp;stimuli\u0026nbsp;with\u0026nbsp;relatively\u0026nbsp;high\u0026nbsp;ecological\u0026nbsp;plausibility (Jallais \u0026amp; Gilet, 2010; Samson et al., 2016; Schaefer et al., 2010; Strohminger et al., 2016; Uhrig et al., 2016).\u0026nbsp;Undoubtedly,\u0026nbsp;movies\u0026nbsp;can be\u0026nbsp;utilized\u0026nbsp;to\u0026nbsp;show\u0026nbsp;naturalistic,\u0026nbsp;energetic\u0026nbsp;social\u0026nbsp;intelligence, which can\u0026nbsp;give\u0026nbsp;a\u0026nbsp;sensible\u0026nbsp;estimation\u0026nbsp;of real-life\u0026nbsp;circumstances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe EMDB was created in 2011 and has been extensively used worldwide (Carvalho et al., 2012). The main objective of this work was to create an emotional dataset that could be used across cultures. However, the videos were validated for the Portuguese population without any auditory content, so they could be adapted by adding any language or by superimposing an auditory task to the film clips (Carvalho et al., 2011). The assessment of the emotional clips was\u0026nbsp;based on a dimensional approach to emotional stimuli (valence, arousal, dominance). Thus, the self-report rating of a total of 113 healthy volunteers was assessed and resulted in an assessment of a total of 52 film clips to elicit emotional states from different quadrants of affective space and across 5 emotional categories: high arousing film clips (horror and erotic), low arousing film clips (social positive, social negative, and scenery) and neutral (object manipulation).\u003c/p\u003e\n\u003cp\u003ePrior research utilizing the EMDB has proven the database\u0026apos;s efficacy in investigating attention-emotion interactions within cognitively challenging environments. Carvalho et al. (2011) investigated the psychophysiological correlates of sexually and non-sexually motivated attention to EMDB video clips during a demanding task. Their findings indicated that emotionally significant stimuli, especially those with motivational relevance, might impair task performance and alter autonomic responses, underscoring the sensitivity of EMDB clips in provoking affective states even in dual-task contexts. The current work enhances the EMDB by incorporating new emotional categories aimed at addressing deficiencies in affective diversity and arousal intensity, providing a more robust and complete instrument for future emotion research.\u003c/p\u003e\n\u003cp\u003eSeveral databases of emotion-eliciting film clips have been developed over time, each designed with different purposes and methodologies. A notable early contribution is the comprehensive database created by Gross and Levenson (1995), which systematically categorized film clips based on the discrete emotions they reliably evoked, such as sadness, anger, fear, and happiness. Their database provided standardized stimuli for emotion research and laid the foundation for a robust theoretical and methodological framework for studying emotional responses through dynamic, real-world stimuli. This framework emphasized the importance of developing stimuli that could consistently elicit targeted emotional states in a controlled yet ecologically valid manner. More recently, Rottenberg and collaborators (2007) expanded this database by adding seven additional emotion-inducing film clips, further enhancing its utility. Today, Gross and Levenson\u0026rsquo;s database remains one of the most widely used tools in emotional research, facilitating the elicitation of discrete emotional responses in both laboratory and online settings across diverse populations. Studies investigating emotional processing, emotional regulation, and related phenomena in psychological and neuroscientific research frequently apply it, demonstrating its continued relevance.\u003c/p\u003e\n\u003cp\u003eSchaefer et al. (2010) previously created a comprehensive database of emotion-eliciting films that have been widely used in psychological research. Their work provided a significant tool for assessing the emotional impact of various film stimuli. Building on this foundational research, Jenkins and Andrewes (2012) developed an emotional movie database consisting of 60 film clips validated by 109 volunteers aged between 18 and 88 years. These clips were designed to elicit five target emotions: amusement, disgust, fear, happiness, and sadness, as well as a neutral emotional state. However, the database proved less effective in consistently eliciting anger. Notably, results from their study highlighted differences in emotional responses between age groups, showing that older participants (aged 46 to 88 years) reported higher levels of arousal in response to both positive and negative stimuli compared to younger participants (aged 18 to 45 years). This finding suggests that emotional arousal may increase with age, adding an important dimension to emotion research across the lifespan. Together, these databases contribute significantly to the field by offering reliable tools for studying emotion elicitation and age-related emotional differences in a standardized and ecologically valid manner. Later, Gabert-Quillen et al. (2015) created and validated film clips in a sample of 304 undergraduate students to evoke 9 discrete emotions:\u0026nbsp;amusement, anger, calmness, disgust, excitement, fear, happiness, sadness, and surprise.\u003c/p\u003e\n\u003cp\u003eAdditionally, other researchers have developed emotional film databases that consider cultural specificities often overlooked in existing datasets. For example, Deng et al. (2017)created a standardized emotional film database specifically for Asian cultures, comprising 64 film clips that 110 volunteers evaluated. These clips successfully elicited eight emotions: fear, disgust, anger, sadness, neutrality, surprise, amusement, and pleasure. Along with subjective assessments of valence, arousal, and dominance, physiological responses such as heart rate and respiration rate were also measured for each film clip, offering a comprehensive tool for emotion research in Asian populations.\u003c/p\u003e\n\u003cp\u003eDespite the availability of several film clip databases, including the Emotional Movie Database (EMDB), important gaps limit the diversity of emotional states these tools can elicit. While the original EMDB sets were effective in provoking both positive and negative valence states, as well as high and low arousal responses, the database lacked clips that could evoke high arousal without relying on horror or erotic content. Additionally, there were few film clips designed to elicit intermediate arousal states, which are important for studying the full spectrum of emotional responses. Another limitation of the original EMDB was the limited number of neutral stimuli, restricting its utility in research requiring balanced comparisons between emotional and non-emotional states.\u003c/p\u003e\n\u003cp\u003eTherefore, the primary objective of the present study was to evaluate the emotional impact of five newly developed film clip categories within the EMDB: \u003cem\u003esocial exclusion, social inclusion, unpleasant landscapes, extreme sports,\u003c/em\u003e and \u003cem\u003eneutral stimuli\u003c/em\u003e. To this end, two validation experiments were conducted. Experiment 1, a lab-based assessment (\u003cem\u003en\u003c/em\u003e = 117), tested the effects of social exclusion, social inclusion, unpleasant landscapes, and extreme sports clips. Experiment 2, an online assessment (\u003cem\u003en\u003c/em\u003e = 128), focused on social exclusion, social inclusion, and newly recorded neutral clips. The development and validation of these stimuli were grounded in the Motivational Attention Model (Bradley \u0026amp; Lang, 1994), which emphasizes the role of \u003cspan class=\"s1\"\u003evalence and arousal\u003c/span\u003e in guiding emotional attention and physiological responses. Across both experiments, participants rated the clips using the Self-Assessment Manikin (SAM), producing normative data for each category.\u003c/p\u003e\n\u003cp\u003eBy expanding the EMDB to cover a broader and more balanced spectrum of the affective space\u0026mdash;including previously underrepresented states such as emotionally neutral and positively valenced, high-arousal stimuli\u0026mdash;this study provides researchers with a theoretically grounded, empirically validated, and freely accessible tool. The updated EMDB supports various applications in psychology, neuroscience, and affective computing, offering enhanced ecological validity and versatility for studying emotion in controlled laboratory and real-world settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eGeneral Overview\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(insert figure 1 here)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was conducted in three phases. The first phase involved selecting and editing 60 film clips from a pool of 130 commercial films. In the second phase, referred to as Experiment 1, the film clips were validated in a lab-based setting. The third phase, Experiment 2, focused on the web-based assessment of 20 of these film clips (10 social exclusion and 10 social inclusion) and the addition of 10 neutral clips recorded by the research team. The assessment protocol used in this study was similar to that of Carvalho et al. (2012). The emotional categories chosen for the database were based on the two-dimensional affective model by Bradley and Lang, encompassing valence (pleasant to unpleasant) and arousal (low to high). We selected 10 film clips per category, with the exception of the neutral category, which had 13 clips. The research team pre-evaluated the clips to ensure homogeneous valence and arousal ratings within each category. The categories resulting from this selection and pre-assessment process add diversity to the existing EMDB and are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eSocial Exclusion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe social exclusion category refers to film clips depicting instances of social discrimination, marginalization, or social disadvantage in social interaction. The clips in this category showcase situations involving conflict between individuals, including scenes of racism, bullying, and social tension. These emotionally charged scenarios are designed to evoke feelings of exclusion and isolation. For detailed descriptions of each film clip, please refer to Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eSocial Inclusion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial inclusion film clips portray scenarios where individuals are integrated into groups and participate in social activities without experiencing any form of discrimination. These clips highlight moments where individuals, especially those at a disadvantage, experience improved abilities, opportunities, and dignity. The emotional content in this category is designed to reflect positive social interactions and inclusivity. For a detailed description of each film clip, please refer to Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eUnpleasant Landscapes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnpleasant landscapes film clips depict scenes where natural environments have been negatively altered, such as by pollution. Similar to the \"scenery\" category of the original EMDB, which was rated as pleasant and low-arousing, these clips do not feature any animals or humans. Instead, they focus solely on the degradation of natural landscapes to evoke discomfort or unease. For detailed descriptions of each film clip in this category, please refer to Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eExtreme Sports\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtreme sports film clips showcase action sports that are perceived to involve a high level of risk and are designed to evoke high arousal. These clips feature activities that include elements such as speed, altitude, or advanced physical exertion. Unlike the social inclusion and social exclusion clips performed by actors, the extreme sports clips feature real athletes performing these activities. For detailed descriptions of each film clip in this category, please refer to Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eNeutral\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeutral film clips were recorded by the research team and, similar to the neutral category in the EMDB (Carvalho et al., 2012), they do not elicit strong appetitive or defensive motivations. These clips typically depict scenarios like object manipulation or simple games without featuring human or animal faces. The purpose of these clips is to provide emotionally neutral content, free from emotionally charged stimuli. For detailed descriptions of each film clip in this category, please refer to Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received prior approval by the local ethical review board, \u003cem\u003ethe Subcomissão de Ética para as Ciências Sociais e Humanas da Universidade do Minho\u003c/em\u003e (reference nº SECSH 028/2017). It was in accordance with the Declaration of Helsinki. All participants gave their written informed consent prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFilm clips selection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhase 1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter identifying the thematic categories (social inclusion, social exclusion, unpleasant scenery, and extreme sports), two researchers selected and edited 60 film clips from 130 commercial films, along with 10 original clips recorded by the project team. The selected clips were then reviewed by 20 independent researchers who validated them using the Self-Assessment Manikin (SAM) scale. Specifically, the researchers assessed the clips based on ratings of valence (positive ratings close to 9, negative ratings close to 1, and neutral ratings close to 5) and arousal (low arousal close to 1, high arousal close to 9). From this evaluation, 40 film clips (10 per category) were selected, while 20 were discarded due to high variability in SAM scores.\u003c/p\u003e\n\u003cp\u003eThe selection criteria at this stage were based on 1) the stability of the context throughout the clip and 2) consistent hedonic valence, ensuring that each clip maintained either positive, negative, or neutral emotions throughout its entire 40-second duration without simultaneously eliciting mixed emotions. The selected clips were edited to ensure consistency in terms of both acting and emotional content. Unlike the neutral and scenery categories, all others featured human presence and social interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFilm clips assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment 1: laboratory-based assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMaterials\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the laboratory-based experiment, multiple sessions of a relatively small group of participants watched each film clip on a large projection screen. The data collection was performed in the same room for each group of participants. It was under similar light conditions, and participants were seated at a 90º angle arc facing the screen. Participants watched a total of 40 film clips of 40-s duration each, with a resolution of 720 x 576. The film clips were presented in 4 random blocks of 10 film clips each, in a pseudorandom trial order. The videos were displayed using a projector and a projector screen. The maximum image size displayed was 1.5 m wide and 1.2 m high. The self-report procedure occurred in eight sessions, with at least 10-15 participants in each session. In each session, volunteers watched film clips from 4 different categories: social exclusion, social inclusion, unpleasant landscapes, and extreme sports.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBefore starting each experimental session, participants were told that they would watch several emotion-inducing film clips and that some of them could be potentially uncomfortable. However, they were also told that actors performed all situations depicted in the film clips and that they could withdraw from the experiment at any time. The team of researchers were psychologists and offered support in case any participant requested so. However, none of the enrolled participants drop out of the experiment and self-reported elevated levels of discomfort at the end of the experiment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore,\u0026nbsp;participants\u0026nbsp;were\u0026nbsp;given\u0026nbsp;instructions\u0026nbsp;about the\u0026nbsp;self-assessment\u0026nbsp;manikin\u0026nbsp;(SAM)\u0026nbsp;to\u0026nbsp;rate\u0026nbsp;valence,\u0026nbsp;arousal,\u0026nbsp;and\u0026nbsp;dominance\u0026nbsp;on\u0026nbsp;a\u0026nbsp;visual\u0026nbsp;9-point\u0026nbsp;Likert\u0026nbsp;scale, as well as to respond to simple questions related to the film clip such as: ‘‘Have you watched this movie before?’’; and ‘‘Have you closed your eyes or looked away during the clip presentation?’’. They were also asked to select at least one (and up to three) emotions evoked by the film clip from a list of emotions (Happy, Sad, Anger, Fear, Aversion, Surprise, Neutral, Supportive, Compassion, Envy, Love, Longing, Guilt, Pity, Shame, Jealousy, Embarrassment). Responses were collected immediately after watching each film clip.\u003c/p\u003e\n\u003cp\u003eSimilar\u0026nbsp;to\u0026nbsp;Carvalho\u0026nbsp;et\u0026nbsp;al.:\u0026nbsp;1),\u0026nbsp;participants\u0026nbsp;were\u0026nbsp;instructed\u0026nbsp;to\u0026nbsp;report\u0026nbsp;(1) How the clip actually made them feel, rather than what they believed they should feel; 2) how he/she felt when they saw the film clip, not their overall mood; 3) Whether they recognized the original movie. The latter was collected to remove from the database all clips previously watched by 30% or more of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment 2: Web-based EMDB Assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the web-based EMDB assessment, participants received a link to a Google Forms platform, which included the following components: a consent form, a sociodemographic questionnaire, and the Positive and Negative Affect Schedule (PANAS). After reading the online consent form, participants were prompted with the statement: \"I understand the terms of the study. I have no questions. I would like to continue the online experience.\" They could then select either \"Start Experience\" to participate or \"End Participation\" to cancel their involvement.\u003c/p\u003e\n\u003cp\u003eIn the sociodemographic questionnaire, participants provided details such as age, gender, nationality, education level, medical history, and other relevant information. Following this, they completed the PANAS, which consists of 20 items (positive and negative emotions). Participants were asked to rate each emotion on a scale from 1 (Very slightly or not at all) to 5 (Extremely).\u003c/p\u003e\n\u003cp\u003eSimilar to Experiment 1, participants received instructions and viewed a training film clip (unrelated to the EMDB) before starting the assessment session. The 36 EMDB film clips were divided into two sets (Presentation A and Presentation B) and presented via either the Qualtrics or Google Forms platforms. Participants viewed 18 film clips and an initial training clip during their session. Each film clip lasted 40 seconds, and participants were required to evaluate each clip immediately after watching it.\u003c/p\u003e\n\u003cp\u003eThe self-report process took place over four sessions (two on Google Forms and two on Qualtrics), each involving 20 and 62 participants. The entire procedure took approximately 60 minutes to complete.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe grand mean for each thematic category was calculated to summarize the results. A general linear model analysis was applied to both the lab-based and web-based assessment groups. Separate within-subject repeated measures ANOVAs were conducted to evaluate the dimensions of valence, arousal, and dominance. The analysis for the lab-based assessment group included four levels (Social Exclusion, Unpleasant Landscapes, Extreme Sports, and Social Inclusion). In contrast, three levels were analyzed for the web-based assessment group (Neutral, Social Exclusion, and Social Inclusion).\u003c/p\u003e\n\u003cp\u003ePost-hoc multiple pairwise comparisons were performed using Bonferroni’s correction to account for potential Type I errors. The significance level for all statistical tests was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; .05. All analyses were conducted using IBM SPSS Statistics, version 23 (IBM®).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransparency and Openness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed APA’s Transparency and Openness Promotion (TOP) Guidelines. All measures, materials, and procedures have been described in detail in the manuscript. The complete set of film clips from the expanded Emotional Movie Database (EMDB) is freely available for scientific research purposes upon request and signature of a user agreement. Interested researchers may contact the authors via [email protected] and/or [email protected] to obtain access.\u003c/p\u003e\n\u003cp\u003eThis study was not preregistered. Data sharing is subject to ethical constraints due to the inclusion of identifiable stimuli (film content), but summary statistics are available upon reasonable request. No automated data analysis scripts were used beyond the standard statistical procedures described in the Results section. We adhered to the Journal Article Reporting Standards (JARS) and provide full methodological transparency to support reproducibility.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 245 healthy volunteers participated in this study, with 117 individuals taking part in the lab-based assessment of the film clips and 128 participants in the web-based assessment conducted using the Qualtrics and Google Forms platforms. The mean age of the participants in the lab-based group was 21.10 years (\u003cem\u003eSD\u003c/em\u003e = 4.40), while the web-based group had a mean age of 22.86 years (\u003cem\u003eSD\u003c/em\u003e = 7.50). Both samples were predominantly female, with 79.5% female participants in the lab-based group and 74.2% in the web-based group. The majority of participants in the web-based assessment were Portuguese nationals (84.4%), single (92.2%), and students (79.7%), with most having completed high school (72.7%). Additionally, most of the web-based participants completed the task at home (90.6%) and used a computer for the task (90.6%). For more detailed information on the sample characteristics, please refer to Table 1.\u003c/p\u003e\n\u003cp\u003eSample size calculations for both studies were performed utilizing GPower 3.1 (Faul et al., 2009). In Experiment 1 (lab-based), we determined the necessary sample size for a repeated-measures ANOVA (within-subjects design) based on a medium effect size (f = 0.25), α = 0.05, power (1–β) = 0.95, and 4 conditions. The analysis revealed a minimum of 44 participants. We deliberately surpassed this figure (final n = 117) to enhance statistical robustness and accommodate potential data exclusions. In Experiment 2 (web-based), a comparable power analysis for comparisons among three emotional categories and eighteen film clips indicated a requisite minimum of 48 participants. A larger sample (n = 128) was gathered to enhance external validity and facilitate exploratory subgroup analyses.\u003cbr\u003e\u0026nbsp;Consistent with best practices for transparency (Simmons et al., 2012), we disclose our sample size determination, all data exclusions, all manipulations, and all metrics included in the study.\u003c/p\u003e\n\u003cp\u003e(Insert Table 1 here)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNormative Ratings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 describes each film clip and the normative valence, arousal, and dominance ratings. These ratings are presented separately for males and females, along with the percentage of participants who reported recognizing each clip. Importantly, all film clips were included in the analysis, as none were recognized by more than 30% of the participants.\u003c/p\u003e\n\u003cp\u003eThe scores for valence, arousal, and dominance were assessed using the Self-Assessment Manikin (SAM; Lang, 1980). The paper-and-pencil version of the SAM used graphical figures for each film clip, with participants rating each dimension on a 9-point Likert scale (see Lang et al., 2008 2008 for a detailed review). Higher scores on the dominance subscale indicate the degree to which participants felt subjectively overwhelmed by the content. Figure 2 illustrates the distribution of ratings for valence, arousal, and dominance, suggesting that the selected film categories occupy distinct sub-quadrants of the affective space (pleasure, valence, and arousal) for both male and female participants.\u003c/p\u003e\n\u003cp\u003eBefore the data analysis, the percentage of participants familiar with each film was examined. Despite one clip being recognized by more than 30% of the participants (46.9%), it was maintained in the analyses (for complete results, see Supplementary Material, Table S1).\u003c/p\u003e\n\u003cp\u003eTable 2 shows a general description of each film, followed by normative ratings of valence, arousal, and dominance. First, the overall means of ratings for each film are presented, and then the scores are divided by sex within the lab-based assessment and web-based assessment groups.\u003c/p\u003e\n\u003cp\u003e(Insert Table 2 here)\u003c/p\u003e\n\u003cp\u003e(Insert Figure 2 here)\u003c/p\u003e\n\u003cp\u003eFigure 3 depicts the distribution of all EMDB film categories within the affective space, where valence (x-axis) and arousal (y-axis) are the primary emotional dimensions. Every point shows the grand mean, or average ratings of ten film clips per category, therefore providing a whole picture of the emotional orientation of every category. Thus, the expanded EMDB now includes a wide spectrum of emotional categories covering many degrees of valence and arousal, hence improving the database's relevance for emotional research in many contexts. High-arousal positive film clips are still underrepresented, though; both sexual and extreme sports movies were evaluated as just mildly arousing (between 5 and 6 on the arousal scale). This restriction implies that although the new categories effectively cover a wide spectrum of emotional states, future developments of the EMDB should try to integrate higher-arousal stimuli to further improve its utility in studies aiming at intense emotional experiences.\u003c/p\u003e\n\u003cp\u003e(Insert Figure 3 here)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLab-based assessment group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial exclusion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis category triggered low levels of valence, and anger (36.4%), sadness (26.3%) and aversion (11.4%) were the emotions that better represented what participants felt with these clips(Figures 3 and 4;\u0026nbsp;for complete data, see Supplementary Material, Table S1). The arousal results demonstrated moderate agitation, and the dominance results indicated that participants felt slightly dominated by these categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial inclusion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results indicated higher valence scores when compared to the previous categories, and the emotions most frequently used to represent the films were happiness (40.4%), love (28.5%) and compassion (12.6%) (Figures 3 and 4;\u0026nbsp;Table S1). The arousal results presented moderate agitation, and dominance results demonstrated that participants did not feel dominated or domineering after watching these films.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUnpleasant landscape\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese films caused low valence levels, and the most frequently chosen emotions were sadness (36.2%), compassion (12.9%) and, solidarity (10.5%), pity (10.5%) (Figures 3 and 4;\u0026nbsp;Table S1). The arousal results showed moderate agitation, and the dominance results displayed that participants felt slightly dominated by these films.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExtreme sports\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results showed higher valence scores when compared to the previous categories, and the emotions most frequently used to represent the films were fear (29.3%), happiness (26.2%), and surprise (16.3%) (Figures 3 and 4;\u0026nbsp;Table S1). The arousal results presented moderate agitation, and the dominance scores indicated that participants did not feel dominated or domineering after watching these films.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeb-based assessment group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial exclusion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of these films showed low levels of valence, and more than 33% of participants chose anger as the emotion that better represented what they felt with these clips (Fig. 4;\u0026nbsp;Table S1). The arousal results indicated moderate agitation and dominance, indicating that these films slightly dominated participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial inclusion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe films displayed higher valence scores compared to the other categories; the emotions most frequently used to represent the films were happiness (37.5%), love (25%), and compassion (16.65%) (Figures 3 and 4;\u0026nbsp;Table S1). The arousal dimension showed moderate agitation, and the dominance results indicated that participants did not feel dominated or domineering by these films.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNeutral\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe valence results demonstrated a neutral state after the films, and 74.6% of participants chose this emotional state to represent better what they felt with each clip. Additionally, around 10% of participants reported positive emotions, specifically happiness (6.6%) and surprise (3.9%) (Figures 3 and 4;\u0026nbsp;for complete data, see Supplementary Material, Table S1). The arousal results indicated low activation after the film clips, and the dominance results showed that participants mostly felt dominant after watching the films.\u003c/p\u003e\n\u003cp\u003e(Insert Figure 4 here)\u003c/p\u003e\n\u003cp\u003e(insert table 3 here)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences between Lab-based and Web-based groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial Exclusion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lab-based group scored higher in terms of valence (total: \u003cem\u003eM\u003c/em\u003e = 2.16, \u003cem\u003eSD\u003c/em\u003e = 1.07) and dominance (total: \u003cem\u003eM\u003c/em\u003e = 4.61, \u003cem\u003eSD\u003c/em\u003e = 2.09) on most social exclusion films compared to the web-based group (total valence: \u003cem\u003eM\u003c/em\u003e = 2.11, \u003cem\u003eSD\u003c/em\u003e = 1.13; total dominance: \u003cem\u003eM\u003c/em\u003e = 4.55, \u003cem\u003eSD\u003c/em\u003e = 2.08). On the other hand, the web-based group showed higher levels of arousal (total: \u003cem\u003eM\u003c/em\u003e = 6.11, \u003cem\u003eSD\u003c/em\u003e = 1.89) than the lab-based group (total: \u003cem\u003eM\u003c/em\u003e = 5.97, \u003cem\u003eSD\u003c/em\u003e = 2.07) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these differences, the results were not statistically significant for any clip (total valence: \u003cem\u003et\u003c/em\u003e(24) = .37, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026gt; .05; total arousal: \u003cem\u003et\u003c/em\u003e(24) = -.55, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026gt; .05; total dominance: \u003cem\u003et\u003c/em\u003e(24) = .18, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026gt; .05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial Inclusion\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lab-based group demonstrated higher mean scores on valence (total: \u003cem\u003eM\u003c/em\u003e = 7.17, \u003cem\u003eSD\u003c/em\u003e = 0.92) and arousal (total: \u003cem\u003eM\u003c/em\u003e = 4.68, \u003cem\u003eSD\u003c/em\u003e = 1.72) in the social inclusion category compared to the web-based group (total valence: \u003cem\u003eM\u003c/em\u003e = 7.14, \u003cem\u003eSD\u003c/em\u003e = .92; total arousal: \u003cem\u003eM\u003c/em\u003e = 5.53, S\u003cem\u003eD\u003c/em\u003e = 1.81). Furthermore, the web-based group had high dominance scores (total: \u003cem\u003eM\u003c/em\u003e = 5.89, \u003cem\u003eSD\u003c/em\u003e = 1.82) relative to the lab-based group (total: \u003cem\u003eM\u003c/em\u003e = 5.32, \u003cem\u003eSD\u003c/em\u003e = 2.07) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifically, differences in valence were statistically significant for the films 10,000 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 7.74, \u003cem\u003eSD\u003c/em\u003e = 1.20; web-based group: \u003cem\u003eM\u003c/em\u003e = 7.23, \u003cem\u003eSD\u003c/em\u003e = 1.98; \u003cem\u003et\u003c/em\u003e(179.04) = 2.33, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .05) and 10,007 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 6.30, \u003cem\u003eSD\u003c/em\u003e = 1.54; web-based group: \u003cem\u003eM\u003c/em\u003e = 7.02, \u003cem\u003eSD\u003c/em\u003e = 1.53; \u003cem\u003et\u003c/em\u003e(22) = -3.48, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .01), whereas differences in arousal were statistically significant for the films 10,001 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 5.42, \u003cem\u003eSD\u003c/em\u003e = 2.33; web-based group: \u003cem\u003eM\u003c/em\u003e = 4.73, \u003cem\u003eSD\u003c/em\u003e = 2.53; \u003cem\u003et\u003c/em\u003e(23) = 2.14, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .05) and 10,006 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 4.91, \u003cem\u003eSD\u003c/em\u003e = 2.31; web-based group: \u003cem\u003eM\u003c/em\u003e = 4.22, \u003cem\u003eSD\u003c/em\u003e = 2.52; \u003cem\u003et\u003c/em\u003e(22) = 2.13, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .05). Additionally, results were statistically significant for the total dominance (\u003cem\u003et\u003c/em\u003e(24) = -2.32 ., \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .05) and for films 10,004 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 5.20, \u003cem\u003eSD\u003c/em\u003e = 2.48; web-based group: \u003cem\u003eM\u003c/em\u003e = 6.00, \u003cem\u003eSD\u003c/em\u003e = 2.40; \u003cem\u003et\u003c/em\u003e(22) = -2.44, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .05), 10,005 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 5.19, \u003cem\u003eSD\u003c/em\u003e = 2.51; web-based group: \u003cem\u003eM\u003c/em\u003e = 6.56, \u003cem\u003eSD\u003c/em\u003e = 2.17; \u003cem\u003et\u003c/em\u003e(22) = -4.32, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001), 10,007 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 5.27, \u003cem\u003eSD\u003c/em\u003e = 2.86; web-based group: \u003cem\u003eM\u003c/em\u003e = 6.25, \u003cem\u003eSD\u003c/em\u003e = 2.42; \u003cem\u003et\u003c/em\u003e(22) = -2.73, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .01), 10,008 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 5.23, \u003cem\u003eSD\u003c/em\u003e = 2.51; web-based group: \u003cem\u003eM\u003c/em\u003e = 6.13, \u003cem\u003eSD\u003c/em\u003e = 2.48; \u003cem\u003et\u003c/em\u003e(22) = -2.66, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .01), and 10,009 (lab-based group: \u003cem\u003eM\u003c/em\u003e = 5.24, \u003cem\u003eSD\u003c/em\u003e = 2.78; web-based group: \u003cem\u003eM\u003c/em\u003e = 6.06, \u003cem\u003eSD\u003c/em\u003e = 2.23; \u003cem\u003et\u003c/em\u003e(220.01) = -2.48, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .05).\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the mean ratings of valence and arousal for each film. The results suggest that each category occupies a distinct quadrant of the affective space. This pattern was consistent when the results were divided by sex (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCategory Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall means for each film category in all dimensions were calculated to analyze the differences in valence, arousal, and dominance scores between categories (Table 3; Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLab-based assessment group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValence Effects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA One-Way repeated measures ANOVA showed a significant effect of each film category on valence scores (\u003cem\u003eF\u003c/em\u003e (3.348) = 645.157, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2\u003c/em\u003e = .848). On average, the values of valence were lower in the social exclusion (\u003cem\u003eM\u003c/em\u003e = 2.16, \u003cem\u003eSD\u003c/em\u003e = 1.07) and unpleasant landscape (\u003cem\u003eM\u003c/em\u003e = 2.77, \u003cem\u003eSD\u003c/em\u003e = .99) categories, and higher in the extreme sports (\u003cem\u003eM\u003c/em\u003e = 6.25, \u003cem\u003eSD\u003c/em\u003e = 1.12) and social inclusion (\u003cem\u003eM\u003c/em\u003e = 7.17, \u003cem\u003eSD\u003c/em\u003e = .92) films. Post hoc comparisons were performed using the Bonferroni correction. The differences between the social exclusion and the unpleasant landscape (-.61 95% \u003cem\u003eCI\u003c/em\u003e [-.77, -.44]), extreme sports (-4.09 95% \u003cem\u003eCI\u003c/em\u003e [-4.53, -3.65]) and social inclusion films (-5.01 95% \u003cem\u003eCI\u003c/em\u003e [-5.44, -4.58]) were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Similarly, the differences between the unpleasant landscape and extreme sports (-3.48 95% \u003cem\u003eCI\u003c/em\u003e [-3.92, -3.04]) and social inclusion films (-4.41 95% \u003cem\u003eCI\u003c/em\u003e [-4.83, -3.98]) were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The difference between the extreme sports and social inclusion films (-.93 95% \u003cem\u003eCI\u003c/em\u003e [-1.16, -.69]) was also statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eArousal Effects\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the One-Way repeated measures ANOVA analysis showed a significant effect of each film category on arousal scores (\u003cem\u003eF\u003c/em\u003e (3.348) = 38.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2\u003c/em\u003e = .248). In general, the social exclusion (\u003cem\u003eM\u003c/em\u003e = 5.97, \u003cem\u003eSD\u003c/em\u003e = 2.06) and extreme sports (\u003cem\u003eM\u003c/em\u003e = 5.34, \u003cem\u003eSD\u003c/em\u003e = 1.95) categories showed higher scores of arousal compared with the unpleasant landscape (\u003cem\u003eM\u003c/em\u003e = 4.53, \u003cem\u003eSD\u003c/em\u003e = 2.01) and social inclusion (\u003cem\u003eM\u003c/em\u003e = 4.68, \u003cem\u003eSD\u003c/em\u003e =1.72) categories. Post hoc comparisons carried out using the Bonferroni correction demonstrated that the differences between the social exclusion and unpleasant landscape (1.44 95% \u003cem\u003eCI\u003c/em\u003e [1.11, 1.78]), extreme sports (.63 95% \u003cem\u003eCI\u003c/em\u003e [.14, 1.13]) and social inclusion films (1.30 95% \u003cem\u003eCI\u003c/em\u003e [.90, 1.70]) were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .01). Likewise, the differences between the unpleasant landscape and extreme sports films (-.81 95% \u003cem\u003eCI\u003c/em\u003e [-1.30, -.32]), as well as between the extreme sports and social inclusion films (.66 95% \u003cem\u003eCI\u003c/em\u003e [.30, 1.03]) were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .01) (Table 3). On the other hand, the difference in arousal scores between the unpleasant landscape and social inclusion categories was not statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDominance Effects\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA One-Way repeated measures ANOVA indicated a significant effect of film categories on dominance scores (\u003cem\u003eF\u003c/em\u003e (3.348) = 11.74, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2\u003c/em\u003e = .092). The results showed that the social exclusion (\u003cem\u003eM\u003c/em\u003e = 4.61 \u003cem\u003eSD\u003c/em\u003e = 2.09) and unpleasant landscape (\u003cem\u003eM\u003c/em\u003e = 4.48, \u003cem\u003eSD\u003c/em\u003e = 2.21) categories presented lower scores than the extreme sports (\u003cem\u003eM\u003c/em\u003e = 5.14, \u003cem\u003eSD\u003c/em\u003e = 2.08) and social inclusion (\u003cem\u003eM\u003c/em\u003e = 5.32, \u003cem\u003eSD\u003c/em\u003e = 2.07) films. Post hoc comparisons performed with Bonferroni correction pointed out statistically significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05) between the social exclusion and extreme sports (-.54 95% \u003cem\u003eCI\u003c/em\u003e [-1.06, -.01]) and social inclusion films (-.71 95% \u003cem\u003eCI\u003c/em\u003e [-1.26, -.17]). In the same way, the differences between the unpleasant landscapes and extreme sports (-.66 95% \u003cem\u003eCI\u003c/em\u003e [-1.10, -.22]) and social inclusion films (-.83 95% \u003cem\u003eCI\u003c/em\u003e [-1.23, -.44]) were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .01) (Table 3). On the contrary, the differences in dominance scores between the social exclusion and unpleasant landscape categories and between extreme sports and social inclusion films were not significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; .01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeb-based assessment group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValence Effects\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA One-Way repeated measures ANOVA analysis demonstrated a significant effect of film categories on valence scores (\u003cem\u003eF\u003c/em\u003e (1.12) = 724.52, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2\u003c/em\u003e = .851). Results showed that the social inclusion (\u003cem\u003eM\u003c/em\u003e = 7.14, \u003cem\u003eSD\u003c/em\u003e = 1.13) category presented higher scores of valence than neutral (\u003cem\u003eM\u003c/em\u003e = 5.11, \u003cem\u003eSD\u003c/em\u003e = .42) and social exclusion (\u003cem\u003eM\u003c/em\u003e = 2.11, \u003cem\u003eSD\u003c/em\u003e = 1.13) films. Post hoc comparisons performed with Bonferroni correction showed that the differences between the neutral and social exclusion (3.00 95% \u003cem\u003eCI\u003c/em\u003e [2.74, 3.26]) and social inclusion films (-2.03 95% \u003cem\u003eCI\u003c/em\u003e [-2.28, -1.79]) were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Likewise, the difference between the social exclusion and social inclusion (-5.03 95% \u003cem\u003eCI\u003c/em\u003e [-5.47, -4.60]) categories was statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eArousal Effects\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA One-Way repeated measures ANOVA showed a significant effect of film categories on arousal scores (\u003cem\u003eF\u003c/em\u003e (1.65) = 297.13, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2\u003c/em\u003e = .701). On average, the values of arousal were lower in the neutral (\u003cem\u003eM\u003c/em\u003e = 2.31, \u003cem\u003eSD\u003c/em\u003e =1.36) and social inclusion (\u003cem\u003eM\u003c/em\u003e = 4.53, \u003cem\u003eSD\u003c/em\u003e = 1.81) categories when compared with social exclusion (\u003cem\u003eM\u003c/em\u003e = 6.11, \u003cem\u003eSD\u003c/em\u003e = 1.89) films. Post hoc comparisons carried out using the Bonferroni correction indicated that the differences between the neutral and social exclusion (-3.80 95% \u003cem\u003eCI\u003c/em\u003e [-4.25, -3.35]) and social inclusion (-2.21 95% \u003cem\u003eCI\u003c/em\u003e [-2.60, -1.83]) categories were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Additionally, the difference between the social exclusion and social inclusion (-1.59 95% \u003cem\u003eCI\u003c/em\u003e [-1.87, -1.29]) categories was also statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDominance Effects\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe One-Way repeated measures ANOVA analysis indicated a significant effect of film categories on dominance scores (\u003cem\u003eF\u003c/em\u003e (1.48) = 85.27, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2\u003c/em\u003e = .402). In general, the neutral category (\u003cem\u003eM\u003c/em\u003e = 7.21, \u003cem\u003eSD\u003c/em\u003e = 1.94) showed higher scores of dominance compared with the social exclusion (\u003cem\u003eM\u003c/em\u003e = 4.56, \u003cem\u003eSD\u003c/em\u003e = 2.08) and social inclusion (\u003cem\u003eM\u003c/em\u003e = 5.89, \u003cem\u003eSD\u003c/em\u003e = 1.82) films. Post hoc comparisons performed with Bonferroni correction pointed out that the differences between the neutral and social exclusion (2.65 95% \u003cem\u003eCI\u003c/em\u003e [2.03, 3.27]) and social inclusion (1.32 95% \u003cem\u003eCI\u003c/em\u003e [.88, 1.76]) categories were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Similarly, the difference between the social exclusion and social inclusion films (-1.34 95% \u003cem\u003eCI\u003c/em\u003e [-1.72, -.95]) was statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (Table 4).\u003c/p\u003e\n\u003cp\u003e(Insert Table 4 here)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to expand the Emotional Movie Database (EMDB) by introducing five new emotional categories: social exclusion, social inclusion, unpleasant landscapes, extreme sports, and neutral film clips. This expansion addressed key limitations of the original EMDB, such as the lack of film clips capable of eliciting high arousal outside of horror and erotic contexts. It also filled the gap for clips that evoke intermediate arousal and increased the representation of neutral stimuli, which were previously underrepresented.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA secondary objective of this study was to evaluate the emotional impact of the newly introduced film clips using both lab-based and web-based methodologies, assessing participants\u0026rsquo; emotional responses across three key dimensions: valence, arousal, and dominance in two major emotional categories\u0026mdash;social inclusion and social exclusion. Additionally, we incorporated 10 additional neutral film clips in the web-based evaluation to further expand the object manipulation category from Carvalho et al. (2012). As a result, a total of 245 participants took part in the study, with 117 assigned to the lab-based condition and 128 to the web-based condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLab-Based Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe laboratory assessment demonstrated that the Social Exclusion clips successfully provoked anger and moderate arousal, with participants indicating a modest feeling of being dominated by the content. According to the literature, social conflict, exclusion, and marginalization are associated with intense negative emotions such as wrath (Leary et al., 2006). The moderate arousal levels noted here align with the findings of Gross and Levenson (1995) which suggested that social conflict elicits moderate-to-high arousal without overpowering the audience. The results substantiate the new social exclusion category as an effective mechanism for eliciting emotional states associated with conflict and rejection.\u003c/p\u003e\n\u003cp\u003eConversely, social inclusion clips generated happiness, which was characterized by high valence and moderate arousal and was indicative of favorable social connections. Participants experienced comfort and empowerment, with no notable dominance effects, indicating that inclusion fosters well-being without disturbing spectators. This conclusion aligns with Schaefer et al., (2010), who demonstrated that pleasant social interactions consistently elicit happiness. Schaefer et al. (2010) emphasized that social inclusion fosters happy emotions without overwhelming individuals. The newly established social inclusion category is a significant instrument for examining positive social feelings in a regulated environment.\u003c/p\u003e\n\u003cp\u003eLikewise, the extreme sports clips generated a distinctive emotional amalgamation of fear and high valence, encapsulating exhilaration in non-threatening yet high-risk scenarios. In contrast to the adverse feelings of fear commonly linked to threats, participants in this study indicated a greater valence, implying they perceived the dangerous behaviors as exhilarating rather than alarming. This aligns with Samson et al. (2016), who observed that extreme sports frequently generate high arousal without negative valence, as spectators regard these activities as exhilarating rather than perilous. The findings demonstrate that these videos effectively elicited the thrill-seeking emotional response characteristic of extreme sporting situations, wherein fear is interwoven with excitement and pleasure.\u003c/p\u003e\n\u003cp\u003eFinally, the unpleasant landscape clips predominantly elicited feelings of melancholy among participants, characterized by low valence and moderate arousal. These findings are consistent with research by Deng et al. (2017), \u0026nbsp;which suggests that environmental degradation often triggers negative emotions, including sadness and despair. The low arousal levels observed in this study suggest also muted emotional reactions typically associated with deteriorated or polluted natural environments. This observation aligns with the work of Ulrich (1983), who found that exposure to degraded landscapes can lead to reduced emotional engagement and lower arousal levels.\u003c/p\u003e\n\u003cp\u003eThese clips effectively illustrate the emotional repercussions of environmental damage, contributing to the growing body of research on the emotional impacts of environmental stimuli. As the literature suggests, the emotional responses elicited by such settings underscore the significant relationship between our surroundings and emotional well-being, emphasizing the need for continued investigation into how environmental factors influence psychological states (Kaplan \u0026amp; Kaplan, 1989; Ulrich, 1983).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeb-based assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe social exclusion clips in the web-based group also evoked anger, with moderate arousal levels. \u0026nbsp;The social exclusion videos in the online group elicited anger, accompanied by moderate degrees of arousal. Participants in this group had elevated dominance scores relative to the lab-based group, presumably owing to the self-directed online testing format. In contrast to a controlled laboratory environment, where participants have restricted control over stimulus presentation, web-based participants engage more adaptable and self-directedly with the activity, perhaps alleviating feelings of powerlessness or subordination (Gabert-Quillen et al., 2015). Previous studies indicate that enhanced autonomy in experimental settings correlates with elevated dominance ratings (Betella \u0026amp; Verschure, 2016), potentially elucidating this trend. The diminished emotional intensity commonly noted in online studies (Samson et al., 2016) may influence these results, necessitating additional research. Studies such as Samson et al. (2016), have shown that online environments reduce the intensity of emotional responses compared to in-lab testing, where participants are more immersed in the stimuli. In contrast, Carvalho et al. (2012) suggested that the controlled lab environment fosters stronger emotional engagement, which may explain the higher dominance scores in the lab group.\u003c/p\u003e\n\u003cp\u003eFor the social inclusion clips, participants in both lab and web-based groups reported happiness with high valence and moderate arousal. However, web-based participants reported feeling more dominant, likely due to the self-paced nature of the online environment, where participants feel more in control of their emotional experience. Gabert-Quillen et al. (2015) found that web-based participants tend to report higher levels of emotional regulation, as the absence of a researcher mitigates social pressure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to previous emotional databases, such as Gross and Levenson (1995) and Rottenberg et al. (2007), which focused on eliciting intense emotions, this study introduced a wider spectrum of emotional states, from high to intermediate arousal across positive and negative contexts. This expanded approach is comparable to Jenkins and Andrewes (2012), who used video snippets to evoke specific emotions in a diverse sample. The new categories offer a more comprehensive range of emotional stimuli, making the EMDB more versatile for studying emotional responses across different contexts.\u003c/p\u003e\n\u003cp\u003eThe neutral clips successfully delivered emotionally neutral content, as participants indicated neutral emotional states and low arousal. This confirms their function as baseline stimuli, crucial for contrasting emotional and non-emotional responses. These findings correspond with Gross and Levenson (1995), who underscored the necessity of neutral stimuli in emotion research, and Schaefer et al. (2010), who illustrated that well-crafted neutral films create emotional balance, facilitating precise comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLab-Based vs. Web-Based Assessments: Social Exclusion and Social Inclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relatively consistent findings between lab-based and web-based assessments further validate the newly introduced emotional categories. While minor differences in dominance scores were observed\u0026mdash;particularly for social inclusion clips, where web-based participants reported higher dominance\u0026mdash;these variations may stem from the self-directed nature of online testing. As suggested by Betella and Verschure (2016), dominance ratings can fluctuate depending on the testing context. In this study, the absence of a researcher in the web-based condition may have contributed to an increased sense of control, thereby influencing participants\u0026rsquo; dominance ratings.\u003c/p\u003e\n\u003cp\u003eThe findings highlight the importance of continuously updating emotion-eliciting databases to reflect diverse emotional experiences and cultural nuances. Recent work by İyilikci et al. (2024), who developed the EGEFILM database for a Turkish sample, demonstrates how cultural contexts influence emotional processing. By incorporating a large and heterogeneous participant pool and a broad spectrum of emotional states, EGEFILM provides valuable insights into culturally driven variations in emotional responses. Expanding databases like EMDB to include culturally diverse stimuli enhances their generalizability, ensuring that emotion research remains contextually relevant and applicable across different populations.\u003c/p\u003e\n\u003cp\u003eRegarding the elicited emotions, in the lab-based trial, unpleasant environments were linked with fear, melancholy, and aversion; social exclusion clips mostly evoked anger and unhappiness. Extreme sports clips inspired great degrees of surprise, which fit their exciting and forceful character. In contrast, social inclusion clips were connected to positive feelings, including happiness, solidarity, and love, supporting their function in generating prosocial affective states. Similar trends showed up in the web-based assessment. Social inclusion clips were linked with happiness and solidarity; social exclusion clips provoked wrath, grief, and compassion. As anticipated, neutral clips provoked minimal emotional responses across all assessed dimensions, affirming their appropriateness as baseline stimuli. These data further corroborate the extended EMDB categories, illustrating their consistency and reliability in laboratory and online study environments. The results underscore the distinct emotional effects of each category, validating their incorporation into the updated database. The use of lab-based and web-based methodologies was critical for ensuring the generalizability of the findings. Lab-based assessments offer a controlled atmosphere that helps lower outside distractions and standardize trial settings. Capturing fine-grained emotional reactions with great internal validity depends especially on this degree of control. Such regulated environments, however, might not adequately portray the complexity of emotional experiences as they arise in real-world circumstances. Although the unpredictability of uncontrolled testing contexts causes web-based assessments to lack inherent ecological validity, they are a useful substitute for future research especially for studies needing extensive data collecting and more general participant recruitment. This method raises sample diversity and accessibility, therefore enabling researchers to access bigger and more geographically scattered populations. Furthermore, web-based approaches might enable longitudinal studies with repeated measurements over time and aid in lowering logistical restrictions. Given the growing reliance on online experimental paradigms in psychological research, the findings of this study contribute to the refinement of web-based methodologies, supporting their potential use in future large-scale and cross-cultural investigations of emotional processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome limitations should be considered. The participant samples were primarily composed of young people, limiting direct inferences about developmental or lifespan differences. The consistent 40-second duration of each clip may limit the depth or complexity of the emotional response in comparison to longer stories or multi-scene stimuli. Subsequent research could address these problems by creating variants of the existing film of differing lengths, sampling from other age demographics, or including audio content customized to specific ethnicities or languages. Moreover, a main limitation of this study is that not all emotional categories were evaluated in both lab-based and online environments. This helps to avoid direct comparisons between all kinds of stimuli and might cause variation in how various emotional events are handled in different environments. Future studies should try to assess every category in both experimental settings methodically, guaranteeing a thorough comparison and enhancing the validity and applicability of web-based approaches in emotional research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expanded EMDB now encompasses various emotional categories, spanning various valence and arousal levels. This broader emotional representation enhances the database\u0026rsquo;s applicability for emotion research across different contexts, making it a more versatile tool for studying emotional processing. Nonetheless, stimuli with high arousal and positive valence are still inadequately represented, as illustrated in Figure 3. Rectifying this deficiency would optimize the equilibrium of emotional classifications and augment the database\u0026apos;s relevance for subsequent research. Thus, this study confirms the utility and reliability of the updated EMDB while identifying opportunities for additional enhancement. Future study should concentrate on incorporating additional intricate emotional categories to encapsulate subtle emotional experiences. Research should also investigate individual variances in emotional reactions to enhance the database\u0026apos;s generalizability across varied groups. Broadening the spectrum of high-arousal, positive-valence stimuli would offer a more extensive array of exhilarating and enjoyable experiences for studies on emotion regulation and affective states. Psychophysiological researchers can use the database to assess autonomic responses, including heart rate, skin conductance, and facial electromyography, to measure emotional reactivity.\u003c/p\u003e\n\u003cp\u003eFurthermore, the new categories offer more thorough assessments of emotion control mechanisms, encompassing cognitive reappraisal and attentional deployment. The EMDB enables a thorough examination of emotional regulation in diverse contexts by covering a wide emotional range, from low-arousal neutral states to socially significant negative and positive emotions. Future technological improvements may augment the efficacy of the EMDB. Combining Virtual Reality (VR) with 360-degree media may improve ecological validity, facilitating more immersive emotional experiences for study objectives. Machine learning and computational modeling can enable the automatic classification of emotional responses via facial expressions, vocalizations, and physiological data. These advancements would augment research in emotional computing and human-computer interaction.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study enhances the Emotional Movie Database (EMDB) by integrating novel emotional categories that specifically target the deficiencies found in prior iterations. These enhancements improve the database\u0026apos;s usefulness for many research applications by offering a wider selection of emotional stimuli. The meticulous assessment approach, which includes both face-to-face and online methodologies, verifies the dependability of the new film clips in evoking precise emotional reactions. Hence, the enhanced EMDB provides a comprehensive instrument for investigating emotions, facilitating both fundamental research and practical implementations in several psychological fields. This ongoing enhancement guarantees that the database remains pertinent and efficient for modern emotion research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was conducted at the Psychology Research Centre (CIPsi – PSI/01662), School of Psychology, University of Minho, and supported by the Portuguese Foundation for Science and Technology (FCT) through national funds (UID/01662/2020). C.G.C. was supported by a doctoral scholarship from FCT (grant number 2022.14063.BD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e: N/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e: The study’s conception and design were led by SC and JL. SC and CGC, and AGM were responsible for data collection and analysis. SC drafted the manuscript, while OG provided critical revisions and contributed to the study design. All authors reviewed, revised, and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAljanaki, A., Yang, Y. H., \u0026amp; Soleymani, M. (2017). Developing a benchmark for emotional analysis of music. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 1\u0026ndash;22. https://doi.org/10.1371/journal.pone.0173392\u003c/li\u003e\n\u003cli\u003eBetella, A., \u0026amp; Verschure, P. F. M. J. (2016). The affective slider: A digital self-assessment scale for the measurement of human emotions. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 1\u0026ndash;11. https://doi.org/10.1371/journal.pone.0148037\u003c/li\u003e\n\u003cli\u003eBradley, M. M., \u0026amp; Lang, P. J. (1994). 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Norms of valence, arousal, and dominance for 13,915 English lemmas. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(4), 1191\u0026ndash;1207. https://doi.org/10.3758/s13428-012-0314-x\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"motivation-and-emotion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Motivation and Emotion](https://link.springer.com/journal/11031)","snPcode":"11031","submissionUrl":"https://submission.springernature.com/new-submission/11031/3","title":"Motivation and Emotion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Emotional Movie database, EMDB, social inclusion, social exclusion, neutral films clips, extreme sports, pollution, neutral film clips","lastPublishedDoi":"10.21203/rs.3.rs-6401734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6401734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmotion-eliciting film clips play a critical role in psychological, neuroscientific, and affective computing research by providing standardized stimuli for studying emotional responses. The Emotional Movie Database (EMDB) was initially developed to offer silent film clips for emotion research, but its limited stimulus diversity necessitated an update. This study expands the EMDB by introducing four new emotional categories—social exclusion, unpleasant landscapes, extreme sports, and social inclusion—along with an enhanced set of neutral clips.\u003c/p\u003e\n\u003cp\u003eTwo assessment experiments were conducted to validate the new film clips. Experiment 1 (lab-based; \u003cem\u003en\u003c/em\u003e = 117) examined social exclusion, social inclusion, unpleasant landscapes, and extreme sports, while Experiment 2 (online; \u003cem\u003en\u003c/em\u003e= 128) focused on social exclusion, social inclusion, and newly recorded neutral clips. Participants rated the clips on valence, arousal, and dominance using the Self-Assessment Manikin (SAM) and reported the emotions experienced.\u003c/p\u003e\n\u003cp\u003eFindings indicated that social exclusion clips elicited negative valence (\u003cem\u003eM\u003c/em\u003e = 2.16, \u003cem\u003eSD\u003c/em\u003e = 1.07) and moderate-to-high arousal (\u003cem\u003eM\u003c/em\u003e = 5.97, \u003cem\u003eSD\u003c/em\u003e= 2.06), while social inclusion clips had positive valence (\u003cem\u003eM\u003c/em\u003e = 7.17, \u003cem\u003eSD\u003c/em\u003e= 0.92) and lower arousal (\u003cem\u003eM\u003c/em\u003e = 4.68, \u003cem\u003eSD\u003c/em\u003e = 1.72). Unpleasant landscapes were rated negatively in valence (\u003cem\u003eM\u003c/em\u003e = 2.77, \u003cem\u003eSD\u003c/em\u003e = 0.99) with low arousal (\u003cem\u003eM\u003c/em\u003e = 4.53, \u003cem\u003eSD\u003c/em\u003e = 2.01), and extreme sports clips were positively valenced (\u003cem\u003eM\u003c/em\u003e = 6.25, \u003cem\u003eSD\u003c/em\u003e = 1.12) with intermediate arousal (\u003cem\u003eM\u003c/em\u003e = 5.34, \u003cem\u003eSD\u003c/em\u003e = 1.95). Newly recorded neutral clips consistently produced neutral valence (\u003cem\u003eM\u003c/em\u003e = 5.11, \u003cem\u003eSD\u003c/em\u003e = 0.42) and low arousal (\u003cem\u003eM\u003c/em\u003e = 2.31, \u003cem\u003eSD\u003c/em\u003e = 1.36), confirming their effectiveness as control stimuli.\u003c/p\u003e","manuscriptTitle":"The Emotional Movie Database (EMDB): An Expanded Toolkit for Emotion Research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 05:39:05","doi":"10.21203/rs.3.rs-6401734/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T16:53:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T15:17:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124914582880140206424180561464050151236","date":"2025-07-02T12:19:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T20:53:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188539118201191651440096755737411494811","date":"2025-06-02T20:04:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-02T15:27:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-12T14:02:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-12T14:02:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Motivation and Emotion","date":"2025-04-08T09:22:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"motivation-and-emotion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Motivation and Emotion](https://link.springer.com/journal/11031)","snPcode":"11031","submissionUrl":"https://submission.springernature.com/new-submission/11031/3","title":"Motivation and Emotion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"edaf842f-3522-4464-bef4-0e7fc60faa5c","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T09:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 05:39:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6401734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6401734","identity":"rs-6401734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}