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Research Articles

Real is the new sexy: the influence of perceived realness on self-reported arousal to sexual visual stimuli

ORCID Icon, ORCID Icon, ORCID Icon, , ORCID Icon & ORCID Icon
Pages 348-360 | Received 14 Jul 2023, Accepted 29 Nov 2023, Published online: 16 Jan 2024

References

  • Azevedo, R., Tucciarelli, R., De Beuklaer, S., Ambroziak, K., Jones, I., & Tsakiris, M. (2020). A body of evidence: ‘Feeling in seeing’ predicts realness judgments for photojournalistic images. PsyArXiv.
  • Balas, B., & Pacella, J. (2017). Trustworthiness perception is disrupted in artificial faces. Computers in Human Behavior, 77, 240–248. https://doi.org/10.1016/j.chb.2017.08.045
  • Becker, C., & Laycock, R. (2023). Embracing deepfakes and AI-generated images in neuroscience research. European Journal of Neuroscience. https://doi.org/10.1111/ejn.16052
  • Chamberlain, R., Mullin, C., Scheerlinck, B., & Wagemans, J. (2018). Putting the art in artificial: Aesthetic responses to computer-generated art. Psychology of Aesthetics, Creativity, and the Arts, 12(2), 177–192. https://doi.org/10.1037/aca0000136
  • Chesney, B., & Citron, D. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107, 1753.
  • Chiarella, S. G., Torromino, G., Gagliardi, D. M., Rossi, D., Babiloni, F., & Cartocci, G. (2022). Investigating the negative bias towards artificial intelligence: Effects of prior assignment of AI-authorship on the aesthetic appreciation of abstract paintings. Computers in Human Behavior, 137, 107406. https://doi.org/10.1016/j.chb.2022.107406
  • Chivers, M. L., Rieger, G., Latty, E., & Bailey, J. M. (2004). A sex difference in the specificity of sexual arousal. Psychological Science, 15(11), 736–744. https://doi.org/10.1111/j.0956-7976.2004.00750.x
  • Chivers, M. L., Seto, M. C., & Blanchard, R. (2007). Gender and sexual orientation differences in sexual response to sexual activities versus gender of actors in sexual films. Journal of Personality and Social Psychology, 93(6), 1108–1121.
  • Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98
  • Dennett, D. C. (2023). The problem with counterfeit people. https://www.theatlantic.com/technology/archive/2023/05/problem-counterfeit-people/674075/
  • Di Dio, C., Ardizzi, M., Schieppati, S. V., Massaro, D., Gilli, G., Gallese, V., & Marchetti, A. (2023). Art made by artificial intelligence: The effect of authorship on aesthetic judgments. Psychology of Aesthetics, Creativity, and the Arts. https://doi.org/10.1037/aca0000602
  • Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046
  • Fallis, D. (2021). The epistemic threat of deepfakes. Philosophy & Technology, 34(4), 623–643. https://doi.org/10.1007/s13347-020-00419-2
  • Flynn, A., Powell, A., Scott, A. J., & Cama, E. (2022). Deepfakes and digitally altered imagery abuse: A cross-country exploration of an emerging form of image-based sexual abuse. The British Journal of Criminology, 62(6), 1341–1358. https://doi.org/10.1093/bjc/azab111
  • Gallucci, M. (2022). GAMLj: General Analyses for the Linear Model in Jamovi (2.6.6). https://gamlj.github.io/
  • Geen, R. G. (1975). The meaning of observed violence: Real vs. fictional violence and consequent effects on aggression and emotional arousal. Journal of Research in Personality, 9(4), 270–281. https://doi.org/10.1016/0092-6566(75)90002-1
  • Harris, K. R. (2021). Video on demand: What deepfakes do and how they harm. Synthese, 199(5–6), 13373–13391. https://doi.org/10.1007/s11229-021-03379-y
  • Henry, N., McGlynn, C., Flynn, A., Johnson, K., Powell, A., & Scott, A. J. (2020). Image-based sexual abuse: A study on the causes and consequences of non-consensual nude or sexual imagery. Routledge.
  • IBM, C. (2020). IBM SPSS statistics for windows (version 27).
  • Itkes, O., & Kron, A. (2019). Affective and semantic representations of valence: A conceptual framework. Emotion Review, 11(4), 283–293. https://doi.org/10.1177/1754073919868759
  • Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103(1), 54–69. https://doi.org/10.1037/a0028347
  • Judd, C. M., Westfall, J., & Kenny, D. A. (2017). Experiments with more than one random factor: Designs, analytic models, and statistical power. Annual Review of Psychology, 68, 601–625.
  • Kirk, U., Skov, M., Hulme, O., Christensen, M. S., & Zeki, S. (2009). Modulation of aesthetic value by semantic context: An fMRI study. Neuroimage, 44(3), 1125–1132. https://doi.org/10.1016/j.neuroimage.2008.10.009
  • Lees, D., Bashford-Rogers, T., & Keppel-Palmer, M. (2021). The digital resurrection of Margaret Thatcher: Creative, technological and legal dilemmas in the use of deepfakes in screen drama. Convergence: The International Journal of Research Into New Media Technologies, 27(4), 954–973. https://doi.org/10.1177/13548565211030452
  • Liefooghe, B., Barbosa de Oliveira, M., Leisten, L. M., Hoogers, E., Aarts, H., & Hortensius, R. (2023). Are Natural Faces Merely Labelled as Artificial Trusted Less?. Collabra: Psychology, 9(1), 73066.
  • Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in R. Behavior Research Methods, 49(4), 1494–1502. https://doi.org/10.3758/s13428-016-0809-y
  • Makowski, D., Sperduti, M., Pelletier, J., Blondé, P., La Corte, V., Arcangeli, M., Zalla, T., Lemaire, S., Dokic, J., Nicolas, S., & Piolino, P. (2019). Phenomenal, bodily and brain correlates of fictional reappraisal as an implicit emotion regulation strategy. Cognitive, Affective, & Behavioral Neuroscience, 19(4), 877–897. https://doi.org/10.3758/s13415-018-00681-0
  • Mocaiber, I., Pereira, M. G., Erthal, F. S., Machado-Pinheiro, W., David, I. A., Cagy, M., Volchan, E., & de Oliveira, L. (2010). Fact or fiction? An event-related potential study of implicit emotion regulation. Neuroscience Letters, 476(2), 84–88. https://doi.org/10.1016/j.neulet.2010.04.008
  • Mocaiber, I., Sanchez, T. A., Pereira, M. G., Erthal, F. S., Joffily, M., Araújo, D. B. D., Volchan, E., & De Oliveira, L. (2011). Antecedent descriptions change brain reactivity to emotional stimuli: A functional magnetic resonance imaging study of an extrinsic and incidental reappraisal strategy. Neuroscience, 193, 241–248. https://doi.org/10.1016/j.neuroscience.2011.07.003
  • Mori, M. (1970). The uncanny valley: The original essay by Masahiro Mori. IEEE Spectrum, 6, 1–6.
  • Murnen, S. K., & Stockton, M. (1997). Gender and self-reported sexual arousal in response to sexual stimuli: A meta-analytic review. Sex Roles, 37(3/4), 135–153. https://doi.org/10.1023/A:1025639609402
  • Ng, V. K. Y., & Cribbie, R. A. (2017). Using the gamma generalized linear model for modeling continuous, skewed and heteroscedastic outcomes in psychology. Current Psychology, 36(2), 225–235. https://doi.org/10.1007/s12144-015-9404-0
  • Nightingale, S. J., & Farid, H. (2022). AI-synthesized faces are indistinguishable from real faces and more trustworthy. Proceedings of the National Academy of Sciences, 119(8), e2120481119. https://doi.org/10.1073/pnas.2120481119
  • Olivera-La Rosa, A. (2018). Wrong outside, wrong inside: A social functionalist approach to the uncanny feeling. New Ideas in Psychology, 50, 38–47. https://doi.org/10.1016/j.newideapsych.2018.03.004
  • Parker, C., Scott, S., & Geddes, A. (2020). Snowball sampling. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), SAGE research methods foundations. SAGE. https://doi.org/10.4135/9781526421036831710
  • Ragot, M., Martin, N., & Cojean, S. (2020). Ai-generated vs. human artworks. a perception bias towards artificial intelligence? In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–10.
  • Rupp, H. A., & Wallen, K. (2008). Sex differences in response to visual sexual stimuli: A review. Archives of Sexual Behavior, 37(2), 206–218. https://doi.org/10.1007/s10508-007-9217-9
  • Rupp, H. A., & Wallen, K. (2009). Sex-specific content preferences for visual sexual stimuli. Archives of Sexual Behavior, 38(3), 417–426. https://doi.org/10.1007/s10508-008-9402-5
  • Ruscio, J. (2008). A probability-based measure of effect size: Robustness to base rates and other factors. Psychological Methods, 13(1), 19–30. https://doi.org/10.1037/1082-989X.13.1.19
  • Sands, S., Ferraro, C., Demsar, V., & Chandler, G. (2022). False idols: Unpacking the opportunities and challenges of falsity in the context of virtual influencers. Business Horizons, 23(8), 56–70.
  • Schepman, A., & Rodway, P. (2022). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400
  • Shahzad, H. F., Rustam, F., Flores, E. S., Luís Vidal Mazón, J., de la Torre Diez, I., & Ashraf, I. (2022). A review of image processing techniques for deepfakes. Sensors, 22(12), 4556. https://doi.org/10.3390/s22124556
  • Shank, D. B., Stefanik, C., Stuhlsatz, C., Kacirek, K., & Belfi, A. M. (2023). AI composer bias: Listeners like music less when they think it was composed by an AI. Journal of Experimental Psychology: Applied, 29(3), 676–692. https://doi.org/10.1037/xap0000447
  • Sperduti, M., Arcangeli, M., Makowski, D., Wantzen, P., Zalla, T., Lemaire, S., Dokic, J., Pelletier, J., & Piolino, P. (2016). The paradox of fiction: Emotional response toward fiction and the modulatory role of self-relevance. Acta Psychologica, 165, 53–59. https://doi.org/10.1016/j.actpsy.2016.02.003
  • Sperduti, M., Makowski, D., Arcangeli, M., Wantzen, P., Zalla, T., Lemaire, S., Dokic, J., Pelletier, J., & Piolino, P. (2017). The distinctive role of executive functions in implicit emotion regulation. Acta Psychologica, 173, 13–20. https://doi.org/10.1016/j.actpsy.2016.12.001
  • Stroup, W. W. (2013). Generalized linear mixed models: Modern concepts, methods and applications. CRC Press, Taylor & Francis Group.
  • The jamovi project. (2022). Jamovi (2.3.9). https://www.jamovi.org
  • Thomas, M. H., & Tell, P. M. (1974). Effects of viewing real versus fantasy violence upon interpersonal aggression. Journal of Research in Personality, 8(2), 153–160. https://doi.org/10.1016/0092-6566(74)90016-6
  • Tucciarelli, R., Vehar, N., Chandaria, S., & Tsakiris, M. (2022). On the realness of people who do not exist: The social processing of artificial faces. iScience, 25(12), 105441. https://doi.org/10.1016/j.isci.2022.105441
  • van der Nagel, E. (2020). Verifying images: Deepfakes, control, and consent. Porn Studies, 7(4), 424–429. https://doi.org/10.1080/23268743.2020.1741434
  • Viola, M., & Voto, C. (2023). Designed to abuse? Deepfakes and the non-consensual diffusion of intimate images. Synthese, 201(1), 1–20. https://doi.org/10.1007/s11229-022-04012-2
  • Wu, Y., Mou, Y., Li, Z., & Xu, K. (2020). Investigating American and Chinese Subjects’ explicit and implicit perceptions of AI-Generated artistic work. Computers in Human Behavior, 104, 106186. https://doi.org/10.1016/j.chb.2019.106186