Bibliography for poster for SMPC 2026:

A Longitudinal Investigation of Generative AI’s Perceived Musical Expressiveness

Agres, K., Forth, J., & Wiggins, G. A. (2016). Evaluation of musical creativity and musical metacreation systems. Computers in Entertainment, 14(3), 1-33. https://doi.org/10.1145/2967506

Ansani, A., Koehler, F., Giombini, L., Hämäläinen, M., Meng, C., Marini, M., & Saarikallio, S. (2025). AI performer bias: Listeners like music less when they think it was performed by an AI. Empirical Studies of the Arts, 43(2), 1137-1161. https://doi.org/10.1177/02762374241308807

Anthis, J. R., Pauketat, J. V. T., Ladak, A., & Manoli, A. (2025). Perceptions of sentient AI and other digital minds: Evidence from the AI, Morality, and Sentience (AIMS) survey. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3706598.3713329

Ariza, C. (2009). The interrogator as critic: The Turing test and the evaluation of generative music systems. Computer Music Journal, 33(2), 48-70.

Bara, I., Ramsey, R., & Cross, E. S. (2025). AI contextual information shapes moral and aesthetic judgments of AI-generated visual art. Cognition, 257, 106063. https://doi.org/10.1016/j.cognition.2025.106063

Bellaiche, O., Bijaoui, E., & Rivenez, M. (2023). AI-generated art perception: Human responses and aesthetic judgments. Cognitive Research: Principles and Implications, 8(42), 1-15. https://doi.org/10.1186/s41235-023-00499-6

Ben-Tal, O., Harris, M. T., & Sturm, B. L. (2020). How music AI is useful: Engagements with composers, performers and audiences. Leonardo, 54(1), 1-13. https://doi.org/10.1162/leon_a_01959

Benjamin, W. (1935). “The Work of Art in the Age of Mechanical Reproduction.” in Illuminations: Essays and Reflections, 217-251. New York: Schocken Books, trans. 1968.

Bigman, Y. E., & Gray, K. (2018). People are averse to machines making moral decisions. Cognition, 181, 21-34.

Bonnefon, J.-F., Rahwan, I., & Shariff, A. (2024). The moral psychology of artificial intelligence. Annual Review of Psychology, 75. https://doi.org/10.1146/annurev-psych-030123-113559

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press.

Block, N. (1995). “The Mind as the Software of the Brain.” In Thinking: An Invitation to Cognitive Science, 2nd ed., edited by Edward E. Smith and Daniel N. Osherson, vol. 3 (377-425). Cambridge, MA: MIT Press.

Braguinski, N. (2022). Mathematical Music: From Antiquity to Music AI. New York: Routledge.

Chalmers, D. J. (2022). Reality+: Virtual Worlds and the Problems of Philosophy. New York: W. W. Norton.

Chu, H. and S. Liu. (2024) Can AI tell good stories? Narrative transportation and persuasion with ChatGPT, Journal of Communication, 74/5: 347–358, https://doi.org/10.1093/joc/jqae029

Cope, D.  (2005). Computer Models of Musical Creativity. Cambridge, MA: MIT Press.

Cox, A. (2016). Music and Embodied Cognition: Listening, Moving, Feeling, and Thinking. Bloomington: Indiana University Press.

Danielsen, A., Haugen, M. R., & Thoresen, J. P. (2010). Rhythm in the age of digital reproduction: Micro-rhythm and rhythmic feel in electronic dance music. In A. Danielsen (Ed.), Musical Rhythm in the Age of Digital Reproduction (pp. 19–36). Ashgate.

Deezer/Ipsos. (2025, November 12). AI and music: Global study on perceptions and attitudes towards AI-generated music. https://newsroom-deezer.com/2025/11/deezer-ipsos-survey-ai-music/

Deruty, E., Grachten, M., Lattner, S., Nistal, J., & Aouameur, C. (2022). On the development and practice of AI technology for contemporary popular music production. Transactions of the International Society for Music Information Retrieval, 5(1), 35-49. https://doi.org/10.5334/tismir.100

Dunham, R. L., van Kleef, G. A., & Stamkou, E. (2025). The threat of synthetic harmony: The effects of AI vs. human origin beliefs on listeners’ cognitive, emotional, and physiological responses to music. Computers in Human Behavior: Artificial Humans. Advance online publication. https://doi.org/10.1016/j.chbah.2025.100205

Edelblum, A. B. & Poe, J.  (2026). Liking without endorsing: Consumer dilemmas in responses to AI-generated music. Psychology & Marketing. https://doi.org/10.1002/mar.70170

Figueiredo, F., Martinelli, G., Sousa, H., Rodrigues, P., Pedrosa, F., & Ferreira, L. N. (2025). Echoes of humanity: Exploring the perceived humanness of AI music. arXiv preprint arXiv:2509.25601.

Fišer, N., Martín-Pascual, M., & Andreu-Sánchez, C. (2025). Emotional impact of AI-generated vs. human-composed music in audiovisual media: A biometric and self-report study. PLOS ONE, 20(6), e0326498. https://doi.org/10.1371/journal.pone.0326498

Fischinger, T., Kaufmann, M., & Schlotz, W. (2018). If it's Mozart, it must be good? The influence of textual information and age on musical appreciation. Psychology of Music, 48(4), 579-597.

Friedrichsen, J. and Schwarz, J. and Michel, C. When Music is Made by AI: Effects on Preferences and Willingness to Pay (2026). CESifo Working Paper No. 12405, Available at SSRN: https://ssrn.com/abstract=6084172

Gorenz, D., & Schwarz, N. (2024). How funny is ChatGPT? A comparison of human- and A.I.-produced jokes. PLOS ONE, 19(7), e0305364

Greenberg, D. M., Kosinski, M., Stillwell, D. J., Monteiro, B. L., Levitin, D. J., & Rentfrow, P. J. (2021). The song is you: Preferences for musical attribute dimensions reflect personality. Social Psychological and Personality Science, 12(3), 300-308.

Haugeland, J. (1996). “What is Mind Design?” In Mind Design: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland. Cambridge, MA: MIT Press: 1-16.

Herbold, S., Hautli-Janisz, A., Heuer, U. et al. (2023). A large-scale comparison of human-written versus ChatGPT-generated essays. Sci Rep 13, 18617. https://doi.org/10.1038/s41598-023-45644-9

Huron, D. (2006). Sweet Anticipation: Music and the Psychology of Expectation. Cambridge: MIT.

Jefferson, G. (1949). “The mind of mechanical man.” British Medical Journal 1 (4616): 1105–1110. doi: 10.1136/bmj.1.4616.1105. PMID: 18153422; PMCID: PMC2050428.

Kobis, N., & Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, 106553.

Lecamwasam, K., & Chaudhuri, T. R. (2025). Exploring listeners’ perceptions of AI-generated and human-composed music for functional emotional applications. arXiv preprint arXiv:2506.02856.

Lermann Henestrosa, A., & Kimmerle, J. (2024). The Effects of Assumed AI vs. Human Authorship on the Perception of a GPT-Generated Text. Journalism and Media, 5(3), 1085-1097. https://doi.org/10.3390/journalmedia5030069

Liang, F., Gotham, M., Johnson, M., & Shotton, J. (2017). Automatic stylistic composition of Bach chorales with deep LSTM. Proceedings of the 18th International Society for Music Information Retrieval Conference, 449-456.

Longoni, C., & Cian, L. (2020). Artificial intelligence in utilitarian vs. hedonic contexts: The “word-of-machine” effect. Journal of Marketing, 85(1), 1-17.

Messingschlager, T. V., & Appel, M. (2025). Mind ascribed to AI and the appreciation of AI-generated art. New Media & Society, 27, 1673-1692. https://doi.org/10.1177/14614448231200248

Mori, M. (1970). The uncanny valley. Energy, 7, 33-35

Oore, S., Simon, I., Dieleman, S., Eck, D., & Simonyan, K. (2020). This time with feeling: Learning expressive musical performance. Neural Computing and Applications, 32(3), 955-967. https://doi.org/10.1007/s00521-018-3758-9

Palfy, C. S. (2022). Musical Agency and the Social Listener. New York: Routledge.

Postman, N. (1985). Amusing Ourselves to Death: Public Discourse in the Age of Show Business. New York: Penguin.

Pelowski, M., Markey, P. S., Forster, M., Gerger, G., & Leder, H. (2017). Move me, astonish me... delight my eyes and brain: The Vienna integrated model of top-down and bottom-up processes in art perception (VIMAP) and corresponding affective, evaluative, and neurophysiological correlates. Physics of Life Reviews, 21, 80-125.

Rohrmeier, M. (2022). On creativity, music's AI completeness, and four challenges for artificial musical creativity. Transactions of the International Society for Music Information Retrieval, 5(1), 50-66. https://doi.org/10.5334/tismir.104

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

Stammer, D., Strauss, H., & Knees, P. (2025). Perception of AI-generated music: The role of composer identity, personality traits, music preferences, and perceived humanness. arXiv preprint arXiv:2512.02785.

Stein, J.-P., Messingschlager, T., Gnambs, T., Hutmacher, F., & Appel, M. (2024). Attitudes towards AI: Measurement and associations with personality. Scientific Reports, 14, 2909. https://doi.org/10.1038/s41598-024-53335-2

Tigre Moura, F., & Maw, M. (2021). Consumer resistance to artificial intelligence in services: The role of authenticity and uniqueness desires. Journal of Service Management, 32(4), 530-550.

Turing, A. (1950). Computing Machinery and Intelligence, Mind, Volume LIX, Issue 236, October 1950, Pages 433 460, https://doi.org/10.1093/mind/LIX.236.433 Epigraph used with permission.

van Hees, J., Grootswagers, T., Quek, G. L., & Varlet, M. (2025). Human perception of art in the age of artificial intelligence. Frontiers in Psychology, 15, 1497469. https://doi.org/10.3389/fpsyg.2024.1497469

White, C. W. (2025). The AI Music Problem: Why machine learning misaligns with musical creativity. New York: Routledge.

Wiggins, G. A. (2006). A Preliminary Framework for Description, Analysis and Comparison of Creative Systems. Knowledge-Based Systems 19 (7): 449–458.

Wu, S. H., & Holmes, K. J. (2026). Is there a ‘mind’ behind the music? Attributing music to AI can suppress narrative meaning-making. Cognitive Research: Principles and Implications, 11, Article 19. https://doi.org/10.1186/s41235-026-00715-z