Research Article
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Camouflage - Exploring the AI-Generated Beauty Ideal

Year 2022, , 100 - 121, 21.10.2022
https://doi.org/10.32739/etkilesim.2022.5.10.171

Abstract

The retouching and altering of portrait photographs used to require extreme professionalism from the artists, thus it was a privilege of photographers. However, due to the modern image editing techniques, the process has become completely automated, to the point where it is no longer uncommon to find artificial intelligence-based software to perform portrait editing.
The present research uses a self-report questionnaire to investigate the functioning of an image editing software that uses artificial intelligence to anatomically transform portrait photos beautiful at one push of a button. Participants are asked to give their opinions on pairs of photographs, one of which is an original, unedited picture, meanwhile the other is its idealised version created by the artificial intelligence-based software.
The results of the research showed that the viewer's perception of the automatic image retouching was not influenced by the gender or age of the model, nor by the age of the recipients. However, the participants’ first judgement of the models’ beauty influenced their attitude towards both the model and the photograph. In most cases, the photo version that participants considered more beautiful was the one they would have preferred to see both in their own social media news feed and on the cover of a magazine, furthermore, more people would have preferred to meet the model based on the photo they thought to be beautiful. The research also reveals that viewers tend to associate negative emotions with image manipulation.
Although the enhancement of photographs is a phenomenon that media users regularly encounter, not much research have been conducted on the ideal of beauty in relation to AI-technology. The current study aims to contribute to the filling of the aforementioned research gap.

Supporting Institution

Ministry for Innovation and Technology, Hungary

Project Number

ÚNKP-22-3 New National Excellence Program

Thanks

„Supported by the ÚNKP-22-3 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.”

References

  • Anstee, J. (10 Novembre 2021). PortraitPro 22 software launched. https://www.electronicspecifier.com/products/artificial-intelligence/portraitpro-22-software-launched. 1 August 2022.
  • Baker, B. W. and Woods, M. J. (2001). The role of the divine proportion in the esthetic improvement of patients undergoing combined orthodontic/orthognathic surgical treatment. The International journal of adult orthodontics and orthognathic surgery, 16, 108-120.
  • Bank, A. (2001). Anthropology and portrait photography: Gustav Fritsch's' Natives of South Africa', 1863-1872. Kronos: Journal of Cape History, 27(1), 43-76.
  • Barry, A. M. S. (1997). Visual intelligence: Perception, image, and manipulation in visual communication. Albany: State University of New York Press.
  • Bock, M. A. (2017). Visual communication effects: Photography. The International Encyclopedia of Media Effects, 1-10.
  • Britt, R. K. (2015). Effects of self-presentation and social media use in attainment of beauty ideals. Studies in Media and Communication, 3(1), 79-88.
  • Brugioni, D. A. (1999). Photo Fakery: A History of Deception and Manipulation. Dulles, US: Brassey's.
  • Clark, P. (20 October 2020). Photoshop: Now the world’s most advanced AI application for creatives. https://blog.adobe.com/en/publish/2020/10/20/photoshop-the-worlds-most-advanced-ai-application-for-creatives. 1 August 2022.
  • Das, B. and Chakrabarti, D. (2022). Photography Is a Tool of Social Awareness. In: Chakrabarti, D., Karmakar, S., Salve, U.R. (eds.), Ergonomics for Design and Innovation (619-626). Berlin: Springer.
  • Di Dio, C., Macaluso, E. and Rizzolatti, G. (2007). The golden beauty: brain response to classical and renaissance sculptures. PloS one, 2(11), e1201.
  • Dunlap, R. A. (1997). The golden ratio and Fibonacci numbers. Singapore: World Scientific.
  • Ertem, F. (2006). The pose in early portrait photography: questioning attempts to appropriate the past. Image and narrative, 8(1).
  • Farid, H. (2009). Seeing is not believing. IEEE Spectrum, 46(8), 44-51.
  • Farid, H., and Bravo, M. J. (2010). Image forensic analyses that elude the human visual system. Media Forensics and Security II, 7541, 1-10.
  • Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society, B: Biological Sciences, 360, 815-836.
  • Gill, R. (2007). Postfeminist media culture: Elements of a sensibility. European Journal of Cultural Studies, 10(2), 147-166.
  • Godinho, J., Gonçalves, R. P. and Jardim, L. (2020). Contribution of facial components to the attractiveness of the smiling face in male and female patients: A cross-sectional correlation study. American Journal of Orthodontics and Dentofacial Orthopedics, 157(1), 98-104.
  • Henriques, M. and Patnaik, D. (2020). Social Media and Its Effects on Beauty. In: M. P. Levine and J. S. Santos (eds.), Beauty - Cosmetic Science, Cultural Issues and Creative Developments (1-9). London: IntechOpen.
  • Hofer, M. and Swan, K. O. (2005). Digital image manipulation: A compelling means to engage students in discussion of point of view and perspective. Contemporary Issues in Technology and Teacher Education, 5(3), 290-299.
  • Hume, D. (1757). “Of the Standard of Taste,” Essays Moral and Political. London: George Routledge and Sons, 1894.
  • Hurter, B. (2008). The Best of Portrait Photography: Techniques and Images from the Pros. New York: Amherst Media. Josephson, S., Kelly, J. and Smith, K. (eds.). (2020). Handbook of visual communication: Theory, methods, and media. New York: Routledge.
  • Kant, I. (1790). Critique of Judgement. New York: Macmillan, 1951.
  • Kolta, M. and Tőry, K. (2007). The history of photography. Budapest: Digitalfoto Ltd.
  • Kovács-Bálint, Zs. (2013). Kiben bízhatunk? Komplex szociális arckifejezések felismerésének vizsgálata kognitív neuropszichológiai módszerekkel. Pécs: University of Pécs.
  • Liu, S., Fan, Y. Y., Samal, A. and Guo, Z. (2016). Advances in computational facial attractiveness methods. Multimedia Tools and Applications, 75(23), 16633-16663.
  • Newman, E. J., Garry, M., Bernstein, D. M., Kantner, J. and Lindsay, D. S. (2012). Nonprobative photographs (or words) inflate truthiness. Psychonomic Bulletin & Review, 19, 969-974.
  • Nightingale, S. J., Wade, K. A., & Watson, D. G. (2017). Can people identify original and manipulated photos of real-world scenes?. Cognitive research: principles and implications, 2(1), 1-21.
  • Olshausen, B. A. and Field, D. J. (2000). Vision and the coding of natural images. American Scientist, 88, 238-245.
  • Pallett, P. M., Link, S. and Lee, K. (2010). New “golden” ratios for facial beauty. Vision research, 50(2), 149-154.
  • Prokopakis, E. P., Vlastos, I. M., Picavet, V. A., Nolst Trenite, G., Thomas, R., Cingi, C. and Hellings, P. W. (2013). The golden ratio in facial symmetry. Rhinology, 51(1), 18-21.
  • Pusztai, V. (2021). Vizuális önkifejezési lehetőségek az újmédiában: Uniformizálódik-e a (képi) kommunikáció?. Közösségi Kapcsolódások-tanulmányok kultúráról és oktatásról, 1(1-2), 136-145.
  • Qiu, L., Lu, J., Yang, S., Qu, W. and Zhu, T. (2015). What does your selfie say about you?. Computers in Human Behavior, 52, 443-449.
  • Rab, Á. (2015). A digitális kultúra hatása az emberi viselkedésre a gamifikáció példáján keresztül. Budapest: University of Szeged.
  • Rand, G. and Meyer, T. (2014). The Portrait: understanding portrait photography. San Rafael, CA: Rocky Nook.
  • Rhodes G. (2006). The evolutionary psychology of facial beauty. Annu. Rev. Psychol., 57, 199-226.
  • Sartwell, C. (2022). Beauty. In: Edward N. Zalta (eds.) The Stanford Encyclopedia of Philosophy (1-13). Stanford, CA: The Metaphysics Research Lab.
  • Schmid, K., Marx, D. and Samal, A. (2008). Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios. Pattern Recognition, 41(8), 2710-2717.
  • Senft, T. M. and Baym, N. K. (2015). What does the selfie say? Investigating a global phenomenon. International journal of communication, 9, 1588-1606.
  • Sless, D. (2019). Learning and visual communication. New York: Routledge.
  • Sontag, S. (1973) On Photography. New York: Delta.
  • Sontag, S. (1977). In Plato's Cave. New York: Farrar, Straus and Giroux.
  • Szarka, K. and Fejér, Z. (1999). Photo history. Budapest: Technological Publisher.
  • Thakur, R. and Rohilla, R. (2020). Recent advances in digital image manipulation detection techniques: A brief review. Forensic science international, 312, 110311.
  • Van Dijck, J. (2008). Digital photography: communication, identity, memory. Visual communication, 7(1), 57-76.
  • Van House, N. A. (2011). Personal photography, digital technologies and the uses of the visual. Visual studies, 26(2), 125-134.
  • Vandenbosch, L. and Eggermont, S. (2012). Understanding sexual objectification: A comprehensive approach toward media exposure and girls' internalization of beauty ideals, self-objectification, and body surveillance. Journal of Communication, 62(5), 869-887.
  • Veres, G. (2010). Kutatásalapú tanulás - a feladatok tükrében. Iskolakultúra, 12, 61-77.
  • Veszelszki Á., Horváth, E. and Kovács, G. (2022). New media literacy in the light of image manipulation and deepfake technology. In: Azad Mammadov and Barbara Lewandowska-Tomaszczyk (eds.) Analyzing media discourse: traditional and new (148-178). Newcastle: Cambridge Scholars Publishing.
  • Villi, M. (2007). Mobile visual communication: Photo messages and camera phone photography. Nordicom review, 28(1), 49-62.
  • Winston, J. (2013). Photography in the Age of Facebook. Intersect: The Stanford Journal of Science, Technology, and Society, 6(2), 1-11.
  • Wong, C. H., Wu, W. T. and Mendelson, B. (2021). Invited Discussion on: what is beauty?. Aesthetic Plast Surg, 45(5), 2177-2179.
  • Worth, S. (2016). Studying visual communication. Philadelphia: University of Pennsylvania Press.
  • Wue, R. (2005). Essentially Chinese: the Chinese portrait subject in nineteenth-century photography. In: Wu Hung and Katherine R. Tsiang (eds.), Body and face in Chinese visual culture (257-280). Cambridge, MA: Harvard University Asia Center.
  • Zaidel, D. & Deblieck, C. (2007). Attractiveness of natural faces compared to computer constructed perfectly symmetrical faces. The International journal of neuroscience, 117, 423-431.
  • Zhan, J., Liu, M., Garrod, O. G., Daube, C., Ince, R. A., Jack, R. E. and Schyns, P. G. (2021). Modeling individual preferences reveals that face beauty is not universally perceived across cultures. Current Biology, 31(10), 2243-2252.
  • Zimmer, M. (2013). Arcészlelés. Budapest: Akadémiai Publisher.

Camouflage - Exploring the AI-Generated Beauty Ideal

Year 2022, , 100 - 121, 21.10.2022
https://doi.org/10.32739/etkilesim.2022.5.10.171

Abstract

Eskiden portre fotoğraflarının rötuşlanması ve değiştirilmesi sanatçıların son derece profesyonel olmasını gerektirdiğinden fotoğrafçıların bir ayrıcalığıydı. Ancak modern görüntü düzenleme teknikleri sayesinde bu süreç tamamen otomatik hale geldi; öyle ki artık portre düzenlemesi yapmak için yapay zeka tabanlı yazılımlar bulmak hiç de nadir değil.
Bu araştırma, tek bir düğmeye basarak portre fotoğraflarını anatomik olarak güzelleştirmek için yapay zeka kullanan bir görüntü düzenleme yazılımının işleyişini araştırmak için bir öz bildirim anketi kullanmaktadır. Katılımcılardan, biri orijinal, düzenlenmemiş bir resim, diğeri ise yapay zeka tabanlı yazılım tarafından oluşturulan idealize edilmiş versiyonu olan fotoğraf çiftleri hakkında görüşlerini bildirmeleri istenmiştir.
Araştırmanın sonuçları, izleyicinin otomatik görüntü rötuşuna ilişkin algısının ne modelin cinsiyetinden ya da yaşından ne de alıcıların yaşından etkilendiğini göstermiştir. Ancak, katılımcıların modellerin güzelliğine ilişkin ilk yargıları hem modele hem de fotoğrafa yönelik tutumlarını etkilemiştir. Çoğu durumda, katılımcıların daha güzel olduğunu düşündükleri fotoğraf versiyonu, hem kendi sosyal medya haber akışlarında hem de bir derginin kapağında görmeyi tercih edecekleri fotoğraf versiyonuydu; ayrıca, daha fazla insan güzel olduğunu düşündükleri fotoğrafa dayanarak modelle tanışmayı tercih ederdi. Araştırma ayrıca izleyicilerin olumsuz duyguları görüntü manipülasyonu ile ilişkilendirme eğiliminde olduğunu ortaya koyuyor.
Fotoğrafların iyileştirilmesi medya kullanıcılarının düzenli olarak karşılaştığı bir olgu olmasına rağmen, yapay zeka teknolojisiyle ilişkili olarak güzellik ideali üzerine çok fazla araştırma yapılmamıştır. Bu çalışma, yukarıda bahsedilen araştırma boşluğunun doldurulmasına katkıda bulunmayı amaçlamaktadır.

Project Number

ÚNKP-22-3 New National Excellence Program

References

  • Anstee, J. (10 Novembre 2021). PortraitPro 22 software launched. https://www.electronicspecifier.com/products/artificial-intelligence/portraitpro-22-software-launched. 1 August 2022.
  • Baker, B. W. and Woods, M. J. (2001). The role of the divine proportion in the esthetic improvement of patients undergoing combined orthodontic/orthognathic surgical treatment. The International journal of adult orthodontics and orthognathic surgery, 16, 108-120.
  • Bank, A. (2001). Anthropology and portrait photography: Gustav Fritsch's' Natives of South Africa', 1863-1872. Kronos: Journal of Cape History, 27(1), 43-76.
  • Barry, A. M. S. (1997). Visual intelligence: Perception, image, and manipulation in visual communication. Albany: State University of New York Press.
  • Bock, M. A. (2017). Visual communication effects: Photography. The International Encyclopedia of Media Effects, 1-10.
  • Britt, R. K. (2015). Effects of self-presentation and social media use in attainment of beauty ideals. Studies in Media and Communication, 3(1), 79-88.
  • Brugioni, D. A. (1999). Photo Fakery: A History of Deception and Manipulation. Dulles, US: Brassey's.
  • Clark, P. (20 October 2020). Photoshop: Now the world’s most advanced AI application for creatives. https://blog.adobe.com/en/publish/2020/10/20/photoshop-the-worlds-most-advanced-ai-application-for-creatives. 1 August 2022.
  • Das, B. and Chakrabarti, D. (2022). Photography Is a Tool of Social Awareness. In: Chakrabarti, D., Karmakar, S., Salve, U.R. (eds.), Ergonomics for Design and Innovation (619-626). Berlin: Springer.
  • Di Dio, C., Macaluso, E. and Rizzolatti, G. (2007). The golden beauty: brain response to classical and renaissance sculptures. PloS one, 2(11), e1201.
  • Dunlap, R. A. (1997). The golden ratio and Fibonacci numbers. Singapore: World Scientific.
  • Ertem, F. (2006). The pose in early portrait photography: questioning attempts to appropriate the past. Image and narrative, 8(1).
  • Farid, H. (2009). Seeing is not believing. IEEE Spectrum, 46(8), 44-51.
  • Farid, H., and Bravo, M. J. (2010). Image forensic analyses that elude the human visual system. Media Forensics and Security II, 7541, 1-10.
  • Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society, B: Biological Sciences, 360, 815-836.
  • Gill, R. (2007). Postfeminist media culture: Elements of a sensibility. European Journal of Cultural Studies, 10(2), 147-166.
  • Godinho, J., Gonçalves, R. P. and Jardim, L. (2020). Contribution of facial components to the attractiveness of the smiling face in male and female patients: A cross-sectional correlation study. American Journal of Orthodontics and Dentofacial Orthopedics, 157(1), 98-104.
  • Henriques, M. and Patnaik, D. (2020). Social Media and Its Effects on Beauty. In: M. P. Levine and J. S. Santos (eds.), Beauty - Cosmetic Science, Cultural Issues and Creative Developments (1-9). London: IntechOpen.
  • Hofer, M. and Swan, K. O. (2005). Digital image manipulation: A compelling means to engage students in discussion of point of view and perspective. Contemporary Issues in Technology and Teacher Education, 5(3), 290-299.
  • Hume, D. (1757). “Of the Standard of Taste,” Essays Moral and Political. London: George Routledge and Sons, 1894.
  • Hurter, B. (2008). The Best of Portrait Photography: Techniques and Images from the Pros. New York: Amherst Media. Josephson, S., Kelly, J. and Smith, K. (eds.). (2020). Handbook of visual communication: Theory, methods, and media. New York: Routledge.
  • Kant, I. (1790). Critique of Judgement. New York: Macmillan, 1951.
  • Kolta, M. and Tőry, K. (2007). The history of photography. Budapest: Digitalfoto Ltd.
  • Kovács-Bálint, Zs. (2013). Kiben bízhatunk? Komplex szociális arckifejezések felismerésének vizsgálata kognitív neuropszichológiai módszerekkel. Pécs: University of Pécs.
  • Liu, S., Fan, Y. Y., Samal, A. and Guo, Z. (2016). Advances in computational facial attractiveness methods. Multimedia Tools and Applications, 75(23), 16633-16663.
  • Newman, E. J., Garry, M., Bernstein, D. M., Kantner, J. and Lindsay, D. S. (2012). Nonprobative photographs (or words) inflate truthiness. Psychonomic Bulletin & Review, 19, 969-974.
  • Nightingale, S. J., Wade, K. A., & Watson, D. G. (2017). Can people identify original and manipulated photos of real-world scenes?. Cognitive research: principles and implications, 2(1), 1-21.
  • Olshausen, B. A. and Field, D. J. (2000). Vision and the coding of natural images. American Scientist, 88, 238-245.
  • Pallett, P. M., Link, S. and Lee, K. (2010). New “golden” ratios for facial beauty. Vision research, 50(2), 149-154.
  • Prokopakis, E. P., Vlastos, I. M., Picavet, V. A., Nolst Trenite, G., Thomas, R., Cingi, C. and Hellings, P. W. (2013). The golden ratio in facial symmetry. Rhinology, 51(1), 18-21.
  • Pusztai, V. (2021). Vizuális önkifejezési lehetőségek az újmédiában: Uniformizálódik-e a (képi) kommunikáció?. Közösségi Kapcsolódások-tanulmányok kultúráról és oktatásról, 1(1-2), 136-145.
  • Qiu, L., Lu, J., Yang, S., Qu, W. and Zhu, T. (2015). What does your selfie say about you?. Computers in Human Behavior, 52, 443-449.
  • Rab, Á. (2015). A digitális kultúra hatása az emberi viselkedésre a gamifikáció példáján keresztül. Budapest: University of Szeged.
  • Rand, G. and Meyer, T. (2014). The Portrait: understanding portrait photography. San Rafael, CA: Rocky Nook.
  • Rhodes G. (2006). The evolutionary psychology of facial beauty. Annu. Rev. Psychol., 57, 199-226.
  • Sartwell, C. (2022). Beauty. In: Edward N. Zalta (eds.) The Stanford Encyclopedia of Philosophy (1-13). Stanford, CA: The Metaphysics Research Lab.
  • Schmid, K., Marx, D. and Samal, A. (2008). Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios. Pattern Recognition, 41(8), 2710-2717.
  • Senft, T. M. and Baym, N. K. (2015). What does the selfie say? Investigating a global phenomenon. International journal of communication, 9, 1588-1606.
  • Sless, D. (2019). Learning and visual communication. New York: Routledge.
  • Sontag, S. (1973) On Photography. New York: Delta.
  • Sontag, S. (1977). In Plato's Cave. New York: Farrar, Straus and Giroux.
  • Szarka, K. and Fejér, Z. (1999). Photo history. Budapest: Technological Publisher.
  • Thakur, R. and Rohilla, R. (2020). Recent advances in digital image manipulation detection techniques: A brief review. Forensic science international, 312, 110311.
  • Van Dijck, J. (2008). Digital photography: communication, identity, memory. Visual communication, 7(1), 57-76.
  • Van House, N. A. (2011). Personal photography, digital technologies and the uses of the visual. Visual studies, 26(2), 125-134.
  • Vandenbosch, L. and Eggermont, S. (2012). Understanding sexual objectification: A comprehensive approach toward media exposure and girls' internalization of beauty ideals, self-objectification, and body surveillance. Journal of Communication, 62(5), 869-887.
  • Veres, G. (2010). Kutatásalapú tanulás - a feladatok tükrében. Iskolakultúra, 12, 61-77.
  • Veszelszki Á., Horváth, E. and Kovács, G. (2022). New media literacy in the light of image manipulation and deepfake technology. In: Azad Mammadov and Barbara Lewandowska-Tomaszczyk (eds.) Analyzing media discourse: traditional and new (148-178). Newcastle: Cambridge Scholars Publishing.
  • Villi, M. (2007). Mobile visual communication: Photo messages and camera phone photography. Nordicom review, 28(1), 49-62.
  • Winston, J. (2013). Photography in the Age of Facebook. Intersect: The Stanford Journal of Science, Technology, and Society, 6(2), 1-11.
  • Wong, C. H., Wu, W. T. and Mendelson, B. (2021). Invited Discussion on: what is beauty?. Aesthetic Plast Surg, 45(5), 2177-2179.
  • Worth, S. (2016). Studying visual communication. Philadelphia: University of Pennsylvania Press.
  • Wue, R. (2005). Essentially Chinese: the Chinese portrait subject in nineteenth-century photography. In: Wu Hung and Katherine R. Tsiang (eds.), Body and face in Chinese visual culture (257-280). Cambridge, MA: Harvard University Asia Center.
  • Zaidel, D. & Deblieck, C. (2007). Attractiveness of natural faces compared to computer constructed perfectly symmetrical faces. The International journal of neuroscience, 117, 423-431.
  • Zhan, J., Liu, M., Garrod, O. G., Daube, C., Ince, R. A., Jack, R. E. and Schyns, P. G. (2021). Modeling individual preferences reveals that face beauty is not universally perceived across cultures. Current Biology, 31(10), 2243-2252.
  • Zimmer, M. (2013). Arcészlelés. Budapest: Akadémiai Publisher.
There are 56 citations in total.

Details

Primary Language English
Subjects Communication and Media Studies
Journal Section Research Articles
Authors

Evelin Horváth 0000-0002-3769-4099

Project Number ÚNKP-22-3 New National Excellence Program
Publication Date October 21, 2022
Published in Issue Year 2022

Cite

APA Horváth, E. (2022). Camouflage - Exploring the AI-Generated Beauty Ideal. Etkileşim(10), 100-121. https://doi.org/10.32739/etkilesim.2022.5.10.171
AMA Horváth E. Camouflage - Exploring the AI-Generated Beauty Ideal. Etkileşim. October 2022;(10):100-121. doi:10.32739/etkilesim.2022.5.10.171
Chicago Horváth, Evelin. “Camouflage - Exploring the AI-Generated Beauty Ideal”. Etkileşim, no. 10 (October 2022): 100-121. https://doi.org/10.32739/etkilesim.2022.5.10.171.
EndNote Horváth E (October 1, 2022) Camouflage - Exploring the AI-Generated Beauty Ideal. Etkileşim 10 100–121.
IEEE E. Horváth, “Camouflage - Exploring the AI-Generated Beauty Ideal”, Etkileşim, no. 10, pp. 100–121, October 2022, doi: 10.32739/etkilesim.2022.5.10.171.
ISNAD Horváth, Evelin. “Camouflage - Exploring the AI-Generated Beauty Ideal”. Etkileşim 10 (October 2022), 100-121. https://doi.org/10.32739/etkilesim.2022.5.10.171.
JAMA Horváth E. Camouflage - Exploring the AI-Generated Beauty Ideal. Etkileşim. 2022;:100–121.
MLA Horváth, Evelin. “Camouflage - Exploring the AI-Generated Beauty Ideal”. Etkileşim, no. 10, 2022, pp. 100-21, doi:10.32739/etkilesim.2022.5.10.171.
Vancouver Horváth E. Camouflage - Exploring the AI-Generated Beauty Ideal. Etkileşim. 2022(10):100-21.