The Predictive Roles of Musical Sophistication and AI Literacy in AI Artistic Subjectivity and Aesthetic Bias

doi:10.12139/j.1672-0628.2026.04.013

Abstract

Abstract:

Artificial intelligence (AI) is reshaping music creation, yet public attitudes toward AI-generated music may not be unitary, but may instead diverge across cognitive and evaluative dimensions. The cognitive dimension primarily concerns judgments of AI’s artistic agency, whereas the evaluative dimension manifests as aesthetic bias. Across two studies, we examined whether musical sophistication and AI literacy differentially predicted these two dimensions of attitude. Study 1, a questionnaire study (n=1203), found that both musical sophistication and AI literacy positively predicted perceived AI artistic agency, with AI literacy partially mediating the association between musical sophistication and AI artistic agency. Study 2 employed a creator-label manipulation paradigm (n=84) and found that aesthetic ratings were significantly lower when music was labeled as independently created by AI than when it was labeled as independently created by a human. Further exploratory analyses indicated that musical sophistication positively predicted this aesthetic bias, whereas AI literacy negatively predicted it, with these associations emerging primarily in the innovation style dimension. AI artistic agency was not significantly associated with aesthetic bias. These findings suggest that musical sophistication and AI literacy exhibit distinct patterns of association with cognitive and evaluative attitudes toward AI-generated music.

Key words: AI music, musical sophistication, AI literacy, AI artistic subjectivity, aesthetic bias

摘要：

AI正在重塑音乐创作，但公众对AI音乐的态度可能并非单一维度，而是存在认知层面与评价层面的分化，其中认知层面主要表现为个体对AI艺术主体性的判断，而评价层面则表现为审美偏差。本研究通过两项研究考察音乐素养和AI素养对这两个态度层面的差异化预测作用。研究1(n=1203)采用问卷法发现，音乐素养和AI素养均正向预测AI艺术主体性，且AI素养在音乐素养与AI艺术主体性之间发挥部分中介作用。研究2(n=84)采用创作来源标签操纵范式发现，AI独立创作标签下的审美评分显著低于人类独立创作标签。探索性分析进一步发现，音乐素养正向、AI素养负向预测这一审美偏差，且该现象主要体现在创新风格维度上；AI艺术主体性与审美偏差无显著关联。这表明音乐素养和AI素养对AI音乐的认知态度及评价态度呈现出不同的关联模式。

关键词: AI音乐, 音乐素养, AI素养, AI艺术主体性, 审美偏差

CLC Number:

B849

Na WANG, Baidong HUANG, Qihan ZHANG. The Predictive Roles of Musical Sophistication and AI Literacy in AI Artistic Subjectivity and Aesthetic Bias[J]. Studies of Psychology and Behavior, 2026, 24(4): 533-541.

王娜, 黄柏栋, 张琪涵. 音乐素养与AI素养对AI艺术主体性及审美偏差的预测作用[J]. 心理与行为研究, 2026, 24(4): 533-541.

Figures/Tables 4

References 0

	Ajzen, I. Nature and operation of attitudes. Annual Review of Psychology, 2001, 52, 27- 58. DOI
	Amabile, T. M. Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology, 1982, 43 (5): 997- 1013.
	Barnett, S. M., & Ceci, S. J. When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 2002, 128 (4): 612- 637. DOI
	Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., ... Seli, P. Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research: Principles and Implications, 2023, 8 (1): 42. DOI
	Bigand, E., & Poulin-Charronnat, B. Are we “experienced listeners”? A review of the musical capacities that do not depend on formal musical training. Cognition, 2006, 100 (1): 100- 130. DOI
	Casini, L., Vila, L. C., Dalmazzo, D., Kaila, A. K., & Sturm, B. L. T. (2025). Data-driven analysis of text-conditioned AI-generated music: A case study with Suno and Udio. Retrieved December 23, 2025, from https://arxiv.org/abs/2509.11824
	Chiarella, S. G., Torromino, G., Gagliardi, D. M., Rossi, D., Babiloni, F., & Cartocci, G. 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, 2022, 137, 107406. DOI
	Coeckelbergh, M. Can machines create art?. Philosophy & Technology, 2017, 30 (3): 285- 303. DOI
	Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 1989, 13 (3): 319- 340. DOI
	Grötschla, F., Solak, A., Lanzendörfer, L. A., & Wattenhofer, R. (2025). Benchmarking music generation models and metrics via human preference studies. In ICASSP 2025 - 2025 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 1–5). Hyderabad, India: IEEE.
	Haverals, W., & Martin, M. (2025). Everyone prefers human writers, including AI. Retrieved December 23, 2025, from https://arxiv.org/abs/2510.08831
	Hitsuwari, J., Ueda, Y., Yun, W., & Nomura, M. Does human-AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry. Computers in Human Behavior, 2023, 139, 107502. DOI
	Hong, J. W., Peng, Q. Y., & Williams, D. Are you ready for artificial Mozart and Skrillex? An experiment testing expectancy violation theory and AI music. New Media & Society, 2021, 23 (7): 1920- 1935. DOI
	Horton, C. B., Jr., White, M. W., & Iyengar, S. S. Bias against AI art can enhance perceptions of human creativity. Scientific Reports, 2023, 13 (1): 19001. DOI
	Hullman, J., Holtzman, A., & Gelman, A. (2023). Artificial intelligence and aesthetic judgment. Retrieved December 23, 2025, from https://arxiv.org/abs/2309.12338
	Juslin, P. N., & Västfjäll, D. Emotional responses to music: The need to consider underlying mechanisms. Behavioral and Brain Sciences, 2008, 31 (5): 559- 575. DOI
	Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 2022, 3, 100101. DOI
	Long, D. R., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–16). Honolulu, HI: Association for Computing Machinery.
	Ma, S., & Chen, Z. Z. The development and validation of the artificial intelligence literacy scale for Chinese college students (AILS-CCS). IEEE Access, 2024, 12, 146419- 146429. DOI
	Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 2022, 175, 121390. DOI
	Mikalonytė, E. S., & Kneer, M. Can artificial intelligence make art?: Folk intuitions as to whether AI-driven robots can be viewed as artists and produce art. ACM Transactions on Human-Robot Interaction, 2022, 11 (4): 43. DOI
	Millet, K., Buehler, F., Du, G. Z., & Kokkoris, M. D. Defending humankind: Anthropocentric bias in the appreciation of AI art. Computers in Human Behavior, 2023, 143, 107707. DOI
	Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. The musicality of non-musicians: An index for assessing musical sophistication in the general population. PLoS One, 2014, 9 (2): e89642. DOI
	Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 2022, 53 (4): 914- 931. DOI
	Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2021, 2, 100041. DOI
	North, A., & Hargreaves, D. (2008). The social and applied psychology of music. New York: Oxford University Press.
	Perkins, D. N., & Salomon, G. (1992). Transfer of learning. In T. Husén & T. N. Postlethwaite (Eds.), International encyclopedia of education (2nd ed, pp. 425–441), Oxford: Pergamon Press.
	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 (pp. 1–10). Honolulu, HI: Association for Computing Machinery.
	Raj, M., Berg, J., & Seamans, R. (2023). Art-ificial intelligence: The effect of AI disclosure on evaluations of creative content. Retrieved December 23, 2025, from https://arxiv.org/abs/2303.06217
	Reber, R., Schwarz, N., & Winkielman, P. Processing fluency and aesthetic pleasure: Is beauty in the perceiver's processing experience?. Personality and Social Psychology Review, 2004, 8 (4): 364- 382. DOI
	Rentfrow, P. J., Goldberg, L. R., & Levitin, D. J. The structure of musical preferences: A five-factor model. Journal of Personality and Social Psychology, 2011, 100 (6): 1139- 1157. DOI
	Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A., & Zatorre, R. J. Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nature Neuroscience, 2011, 14 (2): 257- 262. DOI
	Shi, J. Y., Jain, R., Duan, R. L., & Ramani, K. (2023). Understanding generative AI in art: An interview study with artists on G-AI from an HCI perspective. Retrieved December 23, 2025, from https://arxiv.org/abs/2310.13149
	Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., … Kizilcec, R. F. What explains teachers’ trust in AI in education across six countries?. International Journal of Artificial Intelligence in Education, 2025, 35 (3): 1288- 1316. DOI
	Vuust, P., Heggli, O. A., Friston, K. J., & Kringelbach, M. L. Music in the brain. Nature Reviews Neuroscience, 2022, 23 (5): 287- 305. DOI
	Zajonc, R. B. Attitudinal effects of mere exposure. Journal of Personality and Social Psychology, 1968, 9 (2): 1- 27.
	Zentner, M., Grandjean, D., & Scherer, K. R. Emotions evoked by the sound of music: Characterization, classification, and measurement. Emotion, 2008, 8 (4): 494- 521. DOI

变量	M	SD	1	2
1.音乐素养	85.14	16.92
2.AI素养	57.09	7.88	0.13***
3.AI艺术主体性	27.23	7.79	0.10***	0.39***

变量	M	SD	1	2
1.音乐素养	85.14	16.92
2.AI素养	57.09	7.88	0.13***
3.AI艺术主体性	27.23	7.79	0.10***	0.39***

变量	M	SD	1	2	3
1.总体审美偏差	0.33	0.77
2.音乐素养	91.42	14.52	0.14
3.AI素养	55.33	8.65	−0.17	0.49***
4.AI艺术主体性	23.99	7.78	−0.02	0.51***	0.39**

变量	M	SD	1	2	3
1.总体审美偏差	0.33	0.77
2.音乐素养	91.42	14.52	0.14
3.AI素养	55.33	8.65	−0.17	0.49***
4.AI艺术主体性	23.99	7.78	−0.02	0.51***	0.39**

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