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The hybrid discrete–dimensional frame method for emotional film selection

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Abstract

Film clips are widely utilized to evoke emotional responses in the laboratory. We found, however, that different fields tend to select emotional film clips using different approaches. Specifically, psychologists focus more on the discreteness of emotions, whereas computer scientists focus more on the valence and arousal of emotions. Different concerns lead to distinct film selection methods, which may challenge the validity of the emotional databases and hinder communication between disciplines. In recent years, the hybrid discrete–dimensional model has been developed. Based on this hybrid theory, in this study, we attempted to synthesize the diverse approaches and developed a possible unified criterion for emotional film selection across disciplines. Twenty-eight film clips aimed at eliciting four basic emotions (i.e., anger, sadness, fear, and happiness) were evaluated by 70 participants. We examined both discrete and dimensional indicators and applied a new integrative film selection criterion. The results showed that compared with the discrete model or the dimensional model, the hybrid model presented the most reasonable film clip selection outcomes, and 12 film clips were recommended to induce strong and discrete emotions. These findings may enlighten further research on emotion in both theoretical and methodological ways.

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References

  • Abadi, M. K., Subramanian, R., Kia, S. M., Avesani, P., Patras, I., & Sebe, N. (2015). DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Transactions on Affective Computing, 6(3), 209–222. https://doi.org/10.1109/TAFFC.2015.2392932

    Article  Google Scholar 

  • Abdulrahman, A., & Baykara, M. (2021). A comprehensive review for emotion detection based on EEG signals: Challenges, applications, and open issues. Traitement du Signal, 38(4), 1189–1200. https://doi.org/10.18280/ts.380430

    Article  Google Scholar 

  • Backs, R. W., da Silva, S. P., & Han, K. (2005). A comparison of younger and older adults’ self- assessment manikin ratings of affective pictures. Experimental Aging Research, 31, 421–440.

    PubMed  Google Scholar 

  • Barrett, L. F. (2006). Solving the emotion paradox: Categorization and the experience of emotion. Personality and Social Psychology Review, 10(1), 20–46. https://doi.org/10.1207/s15327957pspr1001_2

    Article  PubMed  Google Scholar 

  • Barrett, L. F., Mesquita, B., Ochsner, K. N., & Gross, J. J. (2007). The experience of emotion. Annual Review of Psychology, 58(1), 373–403.

    PubMed  PubMed Central  Google Scholar 

  • Baveye, Y., Dellandrea, E., Chamaret, C., & Chen, L. (2015). LIRISACCEDE: A video database for affective content analysis. IEEE Transactions on Affective Computing, 6(1), 43–55.

    Google Scholar 

  • Baveye, Y., Bettinelli, J. N., Dellandréa, E., Chen, L., & Chamaret, C. (2013). A large video database for computational models of induced emotion. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (pp. 13–18). IEEE.

  • Behnke, M., Buchwald, M., Bykowski, A., Kupiński, S., & Kaczmarek, L. D. (2022). Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals. Scientific Data, 9(1), 1–15.

    Google Scholar 

  • Bradley, M. M., & Lang, P. J. (2000). Measuring emotion: Behavior, feeling, and physiology. In R. D. Lane & L. Nadel (Eds.), Cognitive neuroscience of emotion (pp. 242–276). Oxford University Press.

    Google Scholar 

  • Brody, L. R., & Hall, J. A. (2000). Gender, Emotion, and Expression. In M. Lewis & J. M. Haviland-Jones (Eds.), Handbook of emotions: Part IV: Social/personality issues (2nd ed., pp. 325–414). Guilford Press.

    Google Scholar 

  • Bylsma, L. M. (2021). Emotion context insensitivity in depression: Toward an integrated and contextualized approach. Psychophysiology, 58(2), e13715.

    PubMed  Google Scholar 

  • Cacioppo, J. T., & Gardner, W. L. (1999). Emotion. Annual Review of Psychology, 50, 191–214. https://doi.org/10.1146/annurev.psych.50.1.191

    Article  PubMed  Google Scholar 

  • Cahill, L., Uncapher, M., Kilpatrick, C., Alkire, M. T., & Turner, J. (2004). Sex-related hemispheric lateralization of amygdala function in emotionally influenced memory: An fMRI investigation. Learning & Memory, 11, 261–266.

    Google Scholar 

  • Carvalho, S., Leite, J., Galdo-Álvarez, S., & Gonçalves, Ó. F. (2012). The emotional movie database (EMDB): A self-report and psychophysiological study. Applied Psychophysiology and Biofeedback, 37(4), 279–294.

    PubMed  Google Scholar 

  • Christie, I. C., & Friedman, B. H. (2004). Autonomic specificity of discrete emotion and dimensions of affective space: A multivariate approach. International Journal of Psychophysiology, 51, 143–153. https://doi.org/10.1016/j.ijpsycho.2003.08.002

    Article  PubMed  Google Scholar 

  • Correa, J. A. M., Abadi, M. K., Sebe, N., & Patras, I. (2021). AMIGOS: A dataset for affect, personality and mood research on individuals and groups. IEEE Transactions on Affective Computing, 12, 479–493.

    Google Scholar 

  • Davidson, R. J. (1992). A prolegomenon to the structure of emotion: Gleanings from neuropsychology. Cognition and Emotion, 6, 245–268.

    Google Scholar 

  • Davidson, R. J. (1993). Parsing affective space: Perspectives from neuropsychology and psychophysiology. Special section: Neuropsychological perspectives on components of emotional processing. Neuropsychology, 7, 464–475.

    Google Scholar 

  • Deng, Y., Yang, M., & Zhou, R. (2017). A New standardized emotional film database for Asian culture. Frontiers in Psychology, 8, 1941. https://doi.org/10.3389/fpsyg.2017.01941

    Article  PubMed  PubMed Central  Google Scholar 

  • Dunn, J. R., & Schweitzer, M. E. (2005). Feeling and believing: The influence of emotion on trust. Journal of Personality and Social Psychology, 88(5), 736–748. https://doi.org/10.1037/0022-3514.88.5.736

    Article  PubMed  Google Scholar 

  • Ekman, P., & Friesen, W. V. (1975). Unmasking the face: A guide to recognizing emotions from facial clues. Prentice-Hall.

    Google Scholar 

  • Ekman, P., Freisen, W. V., & Ancoli, S. (1980). Facial signs of emotional experience. Journal of Personality and Social Psychology, 39(6), 1125–1134. https://doi.org/10.1037/h0077722

    Article  Google Scholar 

  • Ekman, P., Levenson, R. W., & Friesen, W. V. (1983). Autonomic nervous system activity distinguishes among emotions. Science, 221(4616), 1208–1210. https://doi.org/10.1126/science.6612338

    Article  PubMed  Google Scholar 

  • Erdem, C. E., Turan, C., & Aydin, Z. (2015). BAUM-2: A multilingual audio-visual affective face database. Multimedia Tools and Applications, 74(18), 7429–7459.

    Google Scholar 

  • Fayn, K., Willemsen, S., Muralikrishnan, R., Castaño Manias, B., Menninghaus, W., & Schlotz, W. (2022). Full throttle: Demonstrating the speed, accuracy, and validity of a new method for continuous two-dimensional self-report and annotation. Behavior Research Methods, 54(1), 350–364. https://doi.org/10.3758/s13428-021-01616-3

    Article  PubMed  Google Scholar 

  • Feldman-Barrett, L., Robin, L., Pietromonaco, P. R., & Eyssel, K. M. (1998). Are women the more emotional sex? Evidence from emotional experiences in social context. Cognition and Emotion, 12, 555–578.

    Google Scholar 

  • Fernández-Aguilar, L., Navarro-Bravo, B., Ricarte, J., Ros, L., & Latorre, J. M. (2019). How effective are films in inducing positive and negative emotional states? A meta-analysis. Plos One, 14(11), e0225040. https://doi.org/10.1371/journal.pone.0225040

    Article  PubMed  PubMed Central  Google Scholar 

  • Fernández-Aguilar, L., Lora, Y., Satorres, E., Ros, L., Melendez, J. C., & Latorre, J. M. (2021). Dimensional and discrete emotional reactivity in Alzheimer’s disease: Film clips as a research tool in dementia. Journal of Alzheimer’s Disease, 82(1), 349–360.

    PubMed  Google Scholar 

  • Fredrickson, B. L. (2013). Positive emotions broaden and build. Advances in Experimental Social Psychology, 47, 1–53.

    Google Scholar 

  • Gabert-Quillen, C. A., Bartolini, E. E., Abravanel, B. T., & Sanislow, C. A. (2015). Ratings for emotion film clips. Behavior Research Methods, 47(3), 773–787.

    PubMed  PubMed Central  Google Scholar 

  • Ge, Y., Zhao, G., Zhang, Y., Houston, R. J., & Song, J. (2019). A standardised database of Chinese emotional film clips. Cognition and Emotion, 33(5), 976–990.

    PubMed  Google Scholar 

  • Gerrards-Hesse, A., Spies, K., & Hesse, F. W. (1994). Experimental inductions of emotional states and their effectiveness: A review. British Journal of Psychology, 85(1), 55–78.

    Google Scholar 

  • Gilman, T. L., Shaheen, R., Nylocks, K. M., Halachoff, D., Chapman, J., Flynn, J. J., Matt, L. M., & Coifman, K. G. (2017). A film set for the elicitation of emotion in research: A comprehensive catalog derived from four decades of investigation. Behavior Research Methods, 49(6), 2061–2082.

    PubMed  Google Scholar 

  • Gottman, J. M., Coan, J., Carrere, S., & Swanson, C. (1998). Predicting marital happiness and stability from newlywed interactions. Journal of Marriage and the Family, 5–22.

  • Gray, J. R. (2001). Emotional modulation of cognitive control: Approach-withdrawal states double-dissociate spatial from verbal two-back task performance. Journal of Experimental Psychology: General, 130, 436–452.

    PubMed  Google Scholar 

  • Gross, J. J., & Levenson, R. W. (1995). Emotion elicitation using films. Cognition & Emotion, 9(1), 87–108.

    Google Scholar 

  • Harmon-Jones, E., Harmon-Jones, C., & Summerell, E. (2017). On the importance of both dimensional and discrete models of emotion. Behavioral Sciences, 7(4), 66. https://doi.org/10.3390/bs7040066

    Article  PubMed  PubMed Central  Google Scholar 

  • Hasnul, M. A., Aziz, N. A. A., Alelyani, S., Mohana, M., & Aziz, A. A. (2021). Electrocardiogram-based emotion recognition systems and their applications in healthcare—a review. Sensors, 21(15), 5015.

    PubMed  PubMed Central  Google Scholar 

  • Herring, D. R., Burleson, M. H., Roberts, N. A., & Devine, M. J. (2011). Coherent with laughter: Subjective experience, behavior, and physiological responses during amusement and joy. International Journal of Psychophysiology, 79(2), 211–218.

    PubMed  Google Scholar 

  • Hewig, J., Hagemann, D., Seifert, J., Gollwitzer, M., Naumann, E., & Bartussek, D. (2005). A revised film set for the induction of basic emotions. Cognition & Emotion, 19(7), 1095–1109. https://doi.org/10.1080/02699930541000084

    Article  Google Scholar 

  • Hsieh, D. L., & Hsiao, T. C. (2016). Respiratory sinus arrhythmia reactivity of internet addiction abusers in negative and positive emotional states using film clips stimulation. Biomedical Engineering Online, 15(1), 1–18.

    Google Scholar 

  • Izard, C. E. (1977). Human emotions. Plenum Press.

    Google Scholar 

  • Jenkins, L. M., & Andrewes, D. G. (2012). A new set of standardised verbal and non-verbal contemporary film stimuli for the elicitation of emotions. Brain Impairment, 13(2), 212–227.

    Google Scholar 

  • Kalawski, J. P. (2010). Is tenderness a basic emotion? Motivation and Emotion, 34(2), 158–167.

    Google Scholar 

  • Kelly, J. R., & Hutson-Corneaux, S. L. (1999). Gender emotion stereotypes are context specific. Sex Roles, 40, 107–120.

    Google Scholar 

  • Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2011). DEAP: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31.

    Google Scholar 

  • Kring, A. M., & Gordon, A. H. (1998). Sex differences in emotion: Expression, experience, and physiology. Journal of Personality and Social Psychology, 74(3), 686–703. https://doi.org/10.1037/0022-3514.74.3.686

    Article  PubMed  Google Scholar 

  • Lang, P. J. (1980). Behavioral treatment and bio-behavioral assessment: Computer applications. In J. B. Sidowski, J. H. Johnson, & T. A. Williams (Eds.), Technology in mental health care delivery systems (pp. 119–137). Ablex Publishing.

    Google Scholar 

  • Lang, P. J., Greenwald, M. K., Bradley, M. M., & Hamm, A. O. (1993). Looking at pictures: Affective, facial, visceral, and behavioral reactions. Psychophysiology, 30(3), 261–273. https://doi.org/10.1111/j.1469-8986.1993.tb03352.x

    Article  PubMed  Google Scholar 

  • Laukka, P., & Elfenbein, H. A. (2021). Cross-cultural emotion recognition and in-group advantage in vocal expression: A meta-analysis. Emotion Review, 13(1), 3–11.

    Google Scholar 

  • Levenson, R. W., Ekman, P., & Friesen, W. V. (1990). Voluntary facial action generates emotion-specific autonomic nervous system activity. Psychophysiology, 27, 363–384. https://doi.org/10.1111/j.1469-8986.1990.tb02330.x

    Article  PubMed  Google Scholar 

  • Liang, Y.-C., Hsieh, S., Weng, C.-Y., & Sun, C.-R. (2013). Taiwan corpora of Chinese emotions and relevant psychophysiological data - standard Chinese emotional film clips database and subjective evaluation normative data. Chinese Journal of Psychology, 55(4), 601–621.

    Google Scholar 

  • Malandrakis, N., Potamianos, A., Evangelopoulos, G., & Zlatintsi, A. (2011). A supervised approach to movie emotion tracking. Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic.

  • McHugo, G. J., Smith, C. A., & Lanzetta, J. T. (1982). The structure of self-reports of emotional responses to film segments. Motivation & Emotion, 6(4), 365–385.

    Google Scholar 

  • Mesquita, B., & Haire, A. (2004). Emotion and culture. Encyclopedia of Applied Psychology, 1, 731–737.

    Google Scholar 

  • Mihalache, S., & Burileanu, D. (2021). Dimensional models for continuous-to-discrete affect mapping in speech emotion recognition. University Politehnica of Bucharest Scientific Bulletin, Series C, 83(4), 137–148.

    Google Scholar 

  • Nasoz, F., Alvarez, K., Lisetti, C. L., & Finkelstein, N. (2004). Emotion recognition from physiological signals using wireless sensors for presence technologies. Cognition, Technology & Work, 6(1), 4–14.

    Google Scholar 

  • Nowicki, S., Jr., & Duke, M. P. (1994). Individual differences in the nonverbal communication of affect-the Diagnostic Analysis of Nonverbal Accuracy Scale. Journal of Nonverbal Behavior, 18(1), 9–35.

    Google Scholar 

  • Philippot, P. (1993). Inducing and assessing differentiated emotion-feeling states in the laboratory. Cognition & Emotion, 7(2), 171–193.

    Google Scholar 

  • Plutchik, R. (1980). Emotion: A psychobioevolutionary synthesis. Harper & Row.

    Google Scholar 

  • Rahman, M. M., Sarkar, A. K., Hossain, M. A., Hossain, M. S., Islam, M. R., Hossain, M. B., … & Moni, M. A. (2021). Recognition of human emotions using EEG signals: A review. Computers in Biology and Medicine, 136, 104696.

  • Rainville, P., Bechara, A., Naqvi, N., & Damasio, A. R. (2006). Basic emotions are associated with distinct patterns of cardiorespiratory activity. International Journal of Psychophysiology, 61(1), 5–18. https://doi.org/10.1016/j.ijpsycho.2005.10.024

    Article  PubMed  Google Scholar 

  • Ravindran, N., Zhang, X., Green, L. M., Gatzke-Kopp, L. M., Cole, P. M., & Ram, N. (2021). Concordance of mother-child respiratory sinus arrythmia is continually moderated by dynamic changes in emotional content of film stimuli. Biological Psychology, 161, 108053.

    PubMed  Google Scholar 

  • Rothman, A. D., & Nowicki, S. J., Jr. (2004). A measure of the ability to identify emotion in children’s tone of voice. Journal of Nonverbal Behavior, 28(2), 67–92.

    Google Scholar 

  • Rottenberg, J., Kasch, K. L., Gross, J. J., & Gotlib, I. H. (2002). Sadness and amusement reactivity differentially predict concurrent and prospective functioning in major depressive disorder. Emotion, 2(2), 135–146.

    PubMed  Google Scholar 

  • Rottenberg, J., Ray, R. D., & Gross, J. J. (2007). Emotion elicitation using films. In J. A. Coan & J. J. B. Allen (Eds.), Handbook of emotion elicitation and assessment (pp. 9–28). Oxford University Press.

    Google Scholar 

  • Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178.

    Google Scholar 

  • Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172. https://doi.org/10.1037/0033-295x.110.1.145

    Article  PubMed  Google Scholar 

  • Saganowski, S., Komoszyńska, J., Behnke, M., Perz, B., Kunc, D., Klich, B., … & Kazienko, P. (2022). Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables. Scientific data, 9(1), 1–11.

  • Samson, A. C., Kreibig, S. D., Soderstrom, B., Wade, A. A., & Gross, J. J. (2016). Eliciting positive, negative and mixed emotional states: A film library for affective scientists. Cognition and Emotion, 30(5), 827–856. https://doi.org/10.1080/02699931.2015.1031089

    Article  PubMed  Google Scholar 

  • Sauter, D. A., Eisner, F., Ekman, P., & Scott, S. K. (2010). Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations. Proceedings of the National Academy of Sciences of the United States of America, 107(6), 2408–2412. https://doi.org/10.1073/pnas.0908239106

    Article  PubMed  PubMed Central  Google Scholar 

  • Schaefer, A., Nils, F., Sanchez, X., & Philippot, P. (2010a). Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers. Cognition & Emotion, 24(7), 1153–1172.

    Google Scholar 

  • Schaefer, A., Nils, F., Sanchez, X., & Philippot, P. (2010b). Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers. Cognition and Emotion, 24(7), 1153–1172. https://doi.org/10.1080/02699930903274322

    Article  Google Scholar 

  • Schmidt, L. A., & Trainor, L. J. (2001). Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cognition and Emotion, 15(4), 487–500. https://doi.org/10.1080/02699930126048

    Article  Google Scholar 

  • Scott, J. C. (1930). Systolic blood-pressure fluctuations with sex, anger, and fear. Comparative Psychology, 10(2), 97–114.

    Google Scholar 

  • Shiota, M. N., Campos, B., Keltner, D., & Hertenstein, M. J. (2004). Positive emotion and the regulation of interpersonal relationships. In P. Philippot & R. S. Feldman (Eds.), The regulation of emotion (pp. 127–155). Erlbaum.

    Google Scholar 

  • Simons, R. F., Detenber, B. H., Cuthbert, B. N., Schwartz, D. D., & Reiss, J. E. (2003). Attention to television : Alpha power and its relationship to image motion and emotional content. Media Psychology, 5(3), 283–301. https://doi.org/10.1207/S1532785XMEP0503

    Article  Google Scholar 

  • Soleymani, M., Lichtenauer, J., Pun, T., & Pantic, M. (2011). A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing, 3(1), 42–55.

    Google Scholar 

  • Trnka, M., Darjaa, S., Ritomský, M., Sabo, R., Rusko, M., Schaper, M., & Stelkens-Kobsch, T. H. (2021). Mapping discrete emotions in the dimensional space: An acoustic approach. Electronics, 10(23), 2950.

    Google Scholar 

  • Uhrig, M. K., Trautmann, N., Baumgartner, U., Treede, R.-D., Henrich, F., Hiller, W., & Marschall, S. (2016). Emotion elicitation: A comparison of pictures and films. Frontiers in Psychology, 7(180), 1–12. https://doi.org/10.3389/fpsyg.2016.00180

    Article  Google Scholar 

  • Vytal, K., & Hamann, S. (2010). Neuroimaging support for discrete neural correlates of basic emotions: A voxel-based meta-analysis. Journal of Cognitive Neuroscience, 22(12), 2864–2885. https://doi.org/10.1162/jocn.2009.21366

    Article  PubMed  Google Scholar 

  • Wang, T., Zhao, Y., Xu, Y., & Zhu, Z. (2021). Comparison of response to Chinese and Western videos of mental-health-related emotions in a representative Chinese sample. PeerJ, 9, e10440–e10440. https://doi.org/10.7717/peerj.10440

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., … & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. Information Fusion, 83, 19–52. https://doi.org/10.48550/arXiv.2203.06935

  • Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality & Social Psychology, 54(6), 1063–1070.

    Google Scholar 

  • Westermann, R., Spies, K., Stahl, G., & Hesse, F. W. (1996). Relative effectiveness and validity of mood induction procedures: A meta-analysis. European Journal of Social Psychology, 26(4), 557–580.

    Google Scholar 

  • Witvliet, C. V. O., & Vrana, S. R. (1995). Psychophysiological responses as indices of affective dimensions. Psychophysiology, 32, 436–443.

    PubMed  Google Scholar 

  • Xu, P., Huang, Y., & Luo, Y. (2010). Establishment and assessment of native Chinese affective video system. Chinese Mental Health Journal, 24(7), 551–554+561.

    Google Scholar 

  • Zupan, B., & Babbage, D. R. (2017). Film clips and narrative text as subjective emotion elicitation techniques. The Journal of Social Psychology, 157(2), 194–210. https://doi.org/10.1080/00224545.2016.1208138

    Article  PubMed  Google Scholar 

  • Zupan, B., & Eskritt, M. (2020). Eliciting emotion ratings for a set of film clips: A preliminary archive for research in emotion. The Journal of Social Psychology, 160(6), 768–789.

    PubMed  Google Scholar 

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Acknowledgements

We thank all the participants in the study. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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XYW and SLC designed the study, XYW analyzed the data and wrote the manuscript, HLZ helped with the writing of the introduction, XYW, HLZ, ZBZ, WCJ, JWF, WCX and YFX prepared the experimental materials and collected the data, ZBZ wrote the code of the experimental program, JWF contacted the DECAF staff and obtained authorization, SLC and HC supervised the study and revised the manuscript.

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Correspondence to Shulin Chen.

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The study was approved by the Institutional Review Board (IRB) of the Department of psychology and behavioral science, Zhejiang University (IRB approval code: [2022]059). Informed consent was obtained from all individual participants included in the study.

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The authors have no relevant financial or non-financial interests to disclose. The film clip set (DECAF; Abadi et al., 2015) examined in this study was authorized to be used by the DECAF authors.

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Wang, X., Zhou, H., Xue, W. et al. The hybrid discrete–dimensional frame method for emotional film selection. Curr Psychol 42, 30077–30092 (2023). https://doi.org/10.1007/s12144-022-04038-2

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