ABSTRACT
Voice Assistants (VAs) are becoming a regular part of our daily life. They are embedded in our smartphones or smart home devices. Just as natural language processing has improved the conversation with VAs, ongoing work in speech emotion recognition also suggests that VAs will soon become emotion- and personality-aware. However, the social implications, ethical borders and the users’ general attitude towards such VAs remain underexplored. In this paper, we investigate users’ attitudes towards and preferences for emotionally aware VAs in three different cultures. We conducted an online questionnaire with N = 364 participants in Germany, China, and Egypt to identify differences and similarities in attitudes. Using a cluster analysis, we identified three different basic user types (Enthusiasts, Pragmatists, and Skeptics), which exist in all cultures. We contribute characteristic properties of these user types and highlight how future VAs should support customizable interactions to enhance user experience across cultures.
Supplemental Material
Available for Download
- Takanori Akiyama, Shinnosuke Takamichi, and Hiroshi Saruwatari. 2018. Prosody-aware subword embedding considering Japanese intonation systems and its application to DNN-based multi-dialect speech synthesis. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, IEEE, New York,NY,USA, 659–664. https://doi.org/10.23919/APSIPA.2018.8659465Google ScholarCross Ref
- Isaac Asimov. 1941. Three laws of robotics.Google Scholar
- Matthew P Aylett, Alessandro Vinciarelli, and Mirjam Wester. 2017. Speech synthesis for the generation of artificial personality. IEEE transactions on affective computing 11, 2 (2017), 361–372. https://doi.org/10.1109/TAFFC.2017.2763134Google ScholarCross Ref
- Alice Baird, Shahin Amiriparian, and Björn Schuller. 2019. Can deep generative audio be emotional? Towards an approach for personalised emotional audio generation. In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, IEEE, New York, NY, USA, 1–5. https://doi.org/10.1109/MMSP.2019.8901785Google ScholarCross Ref
- Michael Braun, Anja Mainz, Ronee Chadowitz, Bastian Pfleging, and Florian Alt. 2019. At Your Service: Designing Voice Assistant Personalities to Improve Automotive User Interfaces. Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3290605.3300270Google ScholarDigital Library
- SG Brederoo, FG Nadema, FG Goedhart, AE Voppel, JN De Boer, J Wouts, S Koops, and IEC Sommer. 2021. Implementation of automatic speech analysis for early detection of psychiatric symptoms: What do patients want?Journal of psychiatric research 142 (2021), 299–301. https://doi.org/10.1016/j.jpsychires.2021.08.019Google Scholar
- Malika Charrad, Nadia Ghazzali, Véronique Boiteau, and Azam Niknafs. 2014. NbClust: an R package for determining the relevant number of clusters in a data set. Journal of statistical software 61, 1 (2014), 1–36. https://doi.org/10.18637/jss.v061.i06Google ScholarCross Ref
- Lim Kok Cheng, Ali Selamat, Mohd Hazli Mohamed Zabil, Md Hafiz Selamat, Rose Alinda Alias, Fatimah Puteh, Farhan Mohamed, and Ondrej Krejcar. 2019. Comparing the Accuracy of Hierarchical Agglomerative and K-Means Clustering on Mobile Augmented Reality Usability Metrics. In 2019 IEEE Conference on Big Data and Analytics (ICBDA). IEEE, IEEE, New York, NY, USA, 34–40. https://doi.org/10.1109/ICBDA47563.2019.8987044Google Scholar
- Leigh Clark, Nadia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Justin Edwards, Brendan Spillane, Emer Gilmartin, Christine Murad, Cosmin Munteanu, Vincent Wade, and Benjamin R. Cowan. 2019. What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents. Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300705Google ScholarDigital Library
- Alan Cooper, Robert Reimann, David Cronin, and Christopher Noessel. 2014. About face: the essentials of interaction design. John Wiley & Sons, Hoboken, New Jersey, U.S.Google ScholarDigital Library
- Donald L Day. 1998. Shared values and shared interfaces: The role of culture in the globalisation of human-computer systems. https://doi.org/10.1016/S0953-5438(97)00025-8Google Scholar
- Marieke De Mooij and Geert Hofstede. 2011. Cross-cultural consumer behavior: A review of research findings. Journal of International Consumer Marketing 23, 3-4 (2011), 181–192. https://doi.org/10.1080/08961530.2011.578057Google Scholar
- Chris Ding and Xiaofeng He. 2004. K-Means Clustering via Principal Component Analysis. In Proceedings of the Twenty-First International Conference on Machine Learning (Banff, Alberta, Canada) (ICML ’04). Association for Computing Machinery, New York, NY, USA, 29. https://doi.org/10.1145/1015330.1015408Google ScholarDigital Library
- David Dunning, Chip Heath, and Jerry M Suls. 2004. Flawed self-assessment: Implications for health, education, and the workplace. Psychological science in the public interest 5, 3 (2004), 69–106. https://doi.org/10.1111/j.1529-1006.2004.00018.xGoogle ScholarCross Ref
- Paul Ekman and Wallace V Friesen. 1971. Constants across cultures in the face and emotion. Journal of personality and social psychology 17, 2(1971), 124. https://doi.org/10.1037/h0030377Google ScholarCross Ref
- Vanessa Evers and Donald Day. 1997. The role of culture in interface acceptance. In Human-Computer Interaction INTERACT’97. Springer, Springer, Boston, MA, 260–267. https://doi.org/10.1007/978-0-387-35175-9_44Google Scholar
- Sybil Eysenck and Ahmed Abdel-Khalek. 1989. A Cross-Cultural Study of Personality: Egyptian and English Children. International journal of psychology : Journal international de psychologie 24 (02 1989), 1–11. https://doi.org/10.1080/00207594.1989.10600028Google Scholar
- Gerhard Fischer. 2001. User modeling in human–computer interaction. User modeling and user-adapted interaction 11, 1 (2001), 65–86. https://doi.org/10.1023/A:1011145532042Google ScholarDigital Library
- Olaf Frandsen-Thorlacius, Kasper Hornbæk, Morten Hertzum, and Torkil Clemmensen. 2009. Non-universal Usability?: A Survey of How Usability is Understood by Chinese and Danish Users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI ’09). ACM, New York, NY, USA, 41–50. https://doi.org/10.1145/1518701.1518708Google ScholarDigital Library
- Enrique Frias-Martinez, Sherry Y Chen, and Xiaohui Liu. 2006. Survey of data mining approaches to user modeling for adaptive hypermedia. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 36, 6 (2006), 734–749. https://doi.org/10.1109/TSMCC.2006.879391Google ScholarDigital Library
- Zhenye Gan, Rui Wang, Yue Yu, and Xin Zhao. 2020. Voice Conversion from Tibetan Amdo Dialect to Tibetan U-tsang Dialect Based on StarGAN-VC2. In 2020 International Conference on Big Data Economy and Information Management (BDEIM). IEEE, IEEE, New York, NY, USA, 184–187. https://doi.org/10.1109/BDEIM52318.2020.00049Google ScholarCross Ref
- Asma Ghandeharioun, Daniel McDuff, Mary Czerwinski, and Kael Rowan. 2019. Towards Understanding Emotional Intelligence for Behavior Change Chatbots. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, New York, NY, USA, 8–14. https://doi.org/10.1109/ACII.2019.8925433Google Scholar
- Javier Hernandez, Daniel McDuff, Xavier Benavides, Judith Amores, Pattie Maes, and Rosalind Picard. 2014. AutoEmotive: Bringing Empathy to the Driving Experience to Manage Stress. In Proceedings of the 2014 Companion Publication on Designing Interactive Systems (Vancouver, BC, Canada) (DIS Companion ’14). Association for Computing Machinery, New York, NY, USA, 53–56. https://doi.org/10.1145/2598784.2602780Google ScholarDigital Library
- Morten Hertzum, Torkil Clemmensen, Kasper Hornbæk, Jyoti Kumar, Qingxin Shi, and Pradeep Yammiyavar. 2007. Usability constructs: a cross-cultural study of how users and developers experience their use of information systems. In International Conference on Usability and Internationalization. Springer, Springer, Berlin, Heidelberg, 317–326. https://doi.org/10.1007/978-3-540-73287-7_39Google ScholarCross Ref
- Geert Hofstede. 2010. The GLOBE debate: Back to relevance. Journal of International Business Studies 41, 8 (2010), 1339–1346. https://doi.org/10.1057/jibs.2010.31Google ScholarCross Ref
- Adrian Holliday. 2010. Complexity in cultural identity. Language and Intercultural Communication 10, 2 (2010), 165–177. https://doi.org/10.1080/14708470903267384Google ScholarCross Ref
- Matthew B Hoy. 2018. Alexa, Siri, Cortana, and more: an introduction to voice assistants. Medical reference services quarterly 37, 1 (2018), 81–88. https://doi.org/10.1080/02763869.2018.1404391Google Scholar
- Ellen Isaacs, Artie Konrad, Alan Walendowski, Thomas Lennig, Victoria Hollis, and Steve Whittaker. 2013. Echoes from the Past: How Technology Mediated Reflection Improves Well-Being. Association for Computing Machinery, New York, NY, USA, 1071–1080. https://doi.org/10.1145/2470654.2466137Google ScholarDigital Library
- Anil K Jain. 2010. Data clustering: 50 years beyond K-means. Pattern recognition letters 31, 8 (2010), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011Google ScholarDigital Library
- Xin Jin and Jiawei Han. 2010. K-Medoids Clustering. Springer US, Boston, MA, 564–565. https://doi.org/10.1007/978-0-387-30164-8_426Google Scholar
- P. Juslin, P. Laukka, and T. Bänziger. 2018. The Mirror to Our Soul? Comparisons of Spontaneous and Posed Vocal Expression of Emotion. Journal of Nonverbal Behavior 42 (2018), 1 – 40. https://doi.org/10.1007/s10919-017-0268-xGoogle ScholarCross Ref
- Minna Kamppuri, Roman Bednarik, and Markku Tukiainen. 2006. The Expanding Focus of HCI: Case Culture. In Proceedings of the 4th Nordic Conference on Human-Computer Interaction: Changing Roles (Oslo, Norway) (NordiCHI ’06). Association for Computing Machinery, New York, NY, USA, 405–408. https://doi.org/10.1145/1182475.1182523Google ScholarDigital Library
- Leonard Kaufman and Peter J. Rousseeuw. 2008. Partitioning Around Medoids (Program PAM). John Wiley & Sons, Inc., Hoboken, New Jersey, U.S., 68–125. https://doi.org/10.1002/9780470316801.ch2Google Scholar
- Ruhul Amin Khalil, Edward Jones, Mohammad Inayatullah Babar, Tariqullah Jan, Mohammad Haseeb Zafar, and Thamer Alhussain. 2019. Speech emotion recognition using deep learning techniques: A review. IEEE Access 7(2019), 117327–117345. https://doi.org/10.1109/ACCESS.2019.2936124Google ScholarCross Ref
- Andreas M Klein, Andreas Hinderks, Maria Rauschenberger, and Jörg Thomaschewski. 2020. Exploring Voice Assistant Risks and Potential with Technology-based Users.. In WEBIST. ACM, New York, NY, USA, 147–154. https://doi.org/10.5220/0010150101470154Google Scholar
- Jong-Eun Roselyn Lee and Clifford I Nass. 2010. Trust in computers: The computers-are-social-actors (CASA) paradigm and trustworthiness perception in human-computer communication. In Trust and technology in a ubiquitous modern environment: Theoretical and methodological perspectives. IGI Global, Hershey, Pennsylvania, USA, 1–15. https://doi.org/10.4018/978-1-61520-901-9.ch001Google Scholar
- Yaniv Leviathan and Yossi Matias. 2018. Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.htmlGoogle Scholar
- Jingyi Li, Yong Ma, and Changkun Ou. 2019. Cultivation and Incentivization of HCI Research and Community in China: Taxonomy and Social Endorsements. In CHI ’19 Workshop: HCI in China. ACM, New York, NY, USA.Google Scholar
- Kok Cheng Lim, Ali Selamat, Mohd Hazli Mohamed Zabil, Yunus Yusoff, Md Hafiz Selamat, Rose Alinda Alias, Fatimah Puteh, Farhan Mohamed, and Ondrej Krejcar. 2019. A Comparative Usability Study Using Hierarchical Agglomerative and K-Means Clustering on Mobile Augmented Reality Interaction Data. In Advancing Technology Industrialization Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, Amsterdam, Netherlands, 258–271. https://doi.org/10.1109/ICBDA47563.2019.8987044Google Scholar
- Sebastian Linxen, Christian Sturm, Florian Brühlmann, Vincent Cassau, Klaus Opwis, and Katharina Reinecke. 2021. How WEIRD is CHI?. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 143, 14 pages. https://doi.org/10.1145/3411764.3445488Google ScholarDigital Library
- Zhen-Tao Liu, Abdul Rehman, Min Wu, Weihua Cao, and Man Hao. 2020. Speech personality recognition based on annotation classification using log-likelihood distance and extraction of essential audio features. IEEE Transactions on Multimedia 23 (2020), 3414–3426. https://doi.org/10.1109/TMM.2020.3025108Google ScholarCross Ref
- Yong Ma, Heiko Drewes, and Andreas Butz. 2021. Fake Moods: Can Users Trick an Emotion-Aware VoiceBot?. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1–4. https://doi.org/10.1145/3411763.3451744Google ScholarDigital Library
- Daniel McDuff, Amy Karlson, Ashish Kapoor, Asta Roseway, and Mary Czerwinski. 2012. AffectAura: An Intelligent System for Emotional Memory. Association for Computing Machinery, New York, NY, USA, 849–858. https://doi.org/10.1145/2207676.2208525Google ScholarDigital Library
- Margaret McRorie, Ian Sneddon, Gary McKeown, Elisabetta Bevacqua, Etienne de Sevin, and Catherine Pelachaud. 2012. Evaluation of Four Designed Virtual Agent Personalities. IEEE Transactions on Affective Computing 3, 3 (2012), 311–322. https://doi.org/10.1109/T-AFFC.2011.38Google ScholarDigital Library
- Batja Mesquita and Nico H Frijda. 1992. Cultural variations in emotions: a review. Psychological bulletin 112, 2 (1992), 179. https://doi.org/10.1037/0033-2909.112.2.179Google Scholar
- Catherine Michalopoulou and Maria Symeonaki. 2017. Improving Likert scale raw scores interpretability with K-means clustering. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 135, 1(2017), 101–109. https://doi.org/10.1177/0759106317710863Google ScholarCross Ref
- Clifford Nass and Youngme Moon. 2000. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues 56 (03 2000), 81–103. https://doi.org/10.1111/0022-4537.00153Google Scholar
- Clifford Nass, Jonathan Steuer, and Ellen R Tauber. 1994. Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, New York, NY, USA, 72–78. https://doi.org/10.1145/191666.191703Google ScholarDigital Library
- Behrooz Omidvar-Tehrani, Sihem Amer-Yahia, and Alexandre Termier. 2015. Interactive User Group Analysis. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (Melbourne, Australia) (CIKM ’15). Association for Computing Machinery, New York, NY, USA, 403–412. https://doi.org/10.1145/2806416.2806519Google ScholarDigital Library
- Daphna Oyserman. 2017. Culture three ways: Culture and subcultures within countries. Annual Review of Psychology 68, 1 (01 2017), 435–463. https://doi.org/10.1146/annurev-psych-122414-033617Google ScholarCross Ref
- Malay K Pakhira, Sanghamitra Bandyopadhyay, and Ujjwal Maulik. 2004. Validity index for crisp and fuzzy clusters. Pattern recognition 37, 3 (2004), 487–501. https://doi.org/10.1016/j.patcog.2003.06.005Google Scholar
- M. Pell, S. Paulmann, Chinar Dara, Areej Alasseri, and S. Kotz. 2009. Factors in the recognition of vocally expressed emotions: A comparison of four languages. J. Phonetics 37(2009), 417–435. https://doi.org/10.1016/j.wocn.2009.07.005Google ScholarCross Ref
- Tim Polzehl, Sebastian Möller, and Florian Metze. 2010. Automatically assessing personality from speech. In 2010 IEEE Fourth International Conference on Semantic Computing. IEEE, IEEE, New York, NY, USA, 134–140. https://doi.org/10.1109/ICSC.2010.41Google ScholarDigital Library
- Martin Porcheron, Joel E. Fischer, Stuart Reeves, and Sarah Sharples. 2018. Voice Interfaces in Everyday Life. Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3174214Google ScholarDigital Library
- Georgios Rizos, Alice Baird, Max Elliott, and Björn Schuller. 2020. Stargan for emotional speech conversion: Validated by data augmentation of end-to-end emotion recognition. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, IEEE, New York, NY, USA, 3502–3506. https://doi.org/10.1109/ICASSP40776.2020.9054579Google ScholarCross Ref
- Björn W Schuller. 2018. Speech emotion recognition: Two decades in a nutshell, benchmarks, and ongoing trends. Commun. ACM 61, 5 (2018), 90–99. https://doi.org/10.1145/3129340Google ScholarDigital Library
- Katie Seaborn and Jacqueline Urakami. 2021. Measuring Voice UX Quantitatively: A Rapid Review. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1–8. https://doi.org/10.1145/3411763.3451712Google ScholarDigital Library
- Norin Shamsuddin and Nor Mahat. 2019. Comparison Between k-Means and k-Medoids for Mixed Variables Clustering. Springer, Singapore, 303–308. https://doi.org/10.1007/978-981-13-7279-7_37Google Scholar
- Andy Smith and Fahri Yetim. 2004. Global human–computer systems: cultural determinants of usability. https://doi.org/10.1016/j.intcom.2003.11.001Google Scholar
- Huatong Sun. 2002. Exploring cultural usability. In Proceedings. IEEE International Professional Communication Conference. IEEE, IEEE, New York, NY, USA, 319–330. https://doi.org/10.1109/IPCC.2002.1049114Google Scholar
- Lee Taber and Steve Whittaker. 2018. Personality Depends on The Medium: Differences in Self-Perception on Snapchat, Facebook and Offline. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3173574.3174181Google ScholarDigital Library
- Feng Tian, Xiangshi Ren, Xiangmin Fan, Wei Li, Haipeng Mi, Tun Lu, Chun Yu, and Dakuo Wang. 2019. HCI in China: Research Agenda, Education Curriculum, Industry Partnership, and Communities Building. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1–8. https://doi.org/10.1145/3290607.3299005Google ScholarDigital Library
- Peter Tonn, Yoav Degani, Shani Hershko, Amit Klein, Lea Seule, and Nina Schulze. 2020. Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study. JMIR research protocols 9, 5 (2020), e13852. https://doi.org/10.2196/13852Google Scholar
- Sarah Theres Völkel, Daniel Buschek, Malin Eiband, Benajmin R. Cowan, and Heinrich Hussmann. 2021. Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, 1–15. https://doi.org/10.1145/3411764.3445536Google ScholarDigital Library
- Sarah Theres Völkel, Penelope Kempf, and Heinrich Hussmann. 2020. Personalised Chats with Voice Assistants: The User Perspective. In Proceedings of the 2nd Conference on Conversational User Interfaces (Bilbao, Spain) (CUI ’20). Association for Computing Machinery, New York, NY, USA, Article 53, 4 pages. https://doi.org/10.1145/3405755.3406156Google ScholarDigital Library
- Sarah Theres Völkel, Ramona Schödel, Daniel Buschek, Clemens Stachl, Verena Winterhalter, Markus Bühner, and Heinrich Hussmann. 2020. Developing a Personality Model for Speech-Based Conversational Agents Using the Psycholexical Approach. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376210Google ScholarDigital Library
- Sarah Theres Völkel, Ramona Schödel, and Heinrich Hussmann. 2018. Designing for Personality in Autonomous Vehicles: Considering Individual’s Trust Attitude and Interaction Behavior. In Workshop ”Interacting with Autonomous Vehicles: Learning from other Domains” at CHI 2018. ACM, New York, NY, USA.Google Scholar
- Joseph Weizenbaum. 1966. ELIZA—a Computer Program for the Study of Natural Language Communication between Man and Machine. Commun. ACM 9, 1 (Jan. 1966), 36–45. https://doi.org/10.1145/365153.365168Google ScholarDigital Library
- Pamela J. Wisniewski, Bart P. Knijnenburg, and Heather Richter Lipford. 2017. Making privacy personal: Profiling social network users to inform privacy education and nudging. International Journal of Human-Computer Studies 98 (2017), 95–108. https://doi.org/10.1016/j.ijhcs.2016.09.006Google ScholarDigital Library
- Chunhui Yuan and Haitao Yang. 2019. Research on K-value selection method of K-means clustering algorithm. J 2, 2(2019), 226–235. https://doi.org/10.3390/j2020016Google Scholar
- Guanlong Zhao, Shaojin Ding, and Ricardo Gutierrez-Osuna. 2019. Foreign Accent Conversion by Synthesizing Speech from Phonetic Posteriorgrams. In INTERSPEECH. Elsevier, Amsterdam, Netherlands, 2843–2847. https://doi.org/10.21437/Interspeech.2019-1778Google ScholarCross Ref
- Michelle X. Zhou, Gloria Mark, Jingyi Li, and Huahai Yang. 2019. Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst. 9, 2–3, Article 10 (March 2019), 36 pages. https://doi.org/10.1145/3232077Google ScholarDigital Library
Index Terms
- Enthusiasts, Pragmatists, and Skeptics: Investigating Users’ Attitudes Towards Emotion- and Personality-Aware Voice Assistants across Cultures
Recommendations
Between, within and across cultures
The phenomenon of contemporary composers reaching across cultures in search of inspiration, musical materials and forms, and new ideas is not a new one, but it is occurring now with greater frequency. Some seek to join inherited traditions from within ...
Towards a Model for Personality-Based Agents for Emotional Responses
Webmedia '16: Proceedings of the 22nd Brazilian Symposium on Multimedia and the WebAffective Computing is a promising research area with many open challenges. This area expects to develop computational systems that can monitor and respond to the affective states of an interacting user (IU). These affective states can be observed in ...
Comments