Challenging cognitivism: computational versus embodied and embedded creativity in AI generative music
Resumo
This paper aims to discuss different perspectives on the nature of cognition in relation to artificial intelligence as a tool for artistic creativity. We bring forth the debate between two important lines of thought in cognitive science: first, we present a brief overview of the computational and representational theory of mind, which posits that the mind functions similarly to a computer, gathering information through the sensory organs and processing this information by means of an internal, symbolic language. We argue that this cognitive point of view is closely linked to the development of the first artificial neural networks and represents a dominant perspective in the field of artificial intelligence to this day. Then, we discuss the idea of embodied and extended cognition, which differs radically from the first by stating that the process of acquiring knowledge is carried out not solely by the brain, but by the entire organism, engaging in constant exchange with its environment through complex mutual modeling. From this debate, we explore the field of generative artificial intelligence by presenting artistic experiences and perspectives in AI-generated music that challenge the cognitivist notion that the brain and computer function in fundamentally similar ways.Referências
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L. Quínamo, “Entre a imagem, a prática e a música: analisando os processos criativos de A Dream Within A Dream, de Tatiana Catanzaro, e de Furnas 1, de Felipe de Almeida Ribeiro,” Dissertação, Campinas, 2023. [Online]. Available: [link]
W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115–133, Dec. 1943, DOI: 10.1007/BF02478259.
C. H. A. Watanabe, “A teoria computacional da mente e o dilema de Searle,” Mestre em Filosofia, Universidade Estadual de Campinas, Campinas, 2021. DOI: 10.47749/T/UNICAMP.2021.1235358.
H. R. Maturana and F. J. Varela, A Árvore do Conhecimento. São Paulo: Palas Athena, 2004.
C. Vear, S. Benford, J. M. Avila, and S. Moroz, “Human-AI Musicking: A Framework for Designing AI for Music Co-creativity,” 2023.
J. A. Fodor, The language of thought. in The Language & thought series. New York: Crowell, 1975.
S. Marsland, Machine Learning: An Algorithmic Perspective, 2nd ed. Chapman and Hall/CRC, 2014. DOI: 10.1201/b17476.
L. F. Oliveira, “O DEBATE SOBRE O REPRESENTACIONALISMO NAS CIÊNCIAS COGNI TIVAS,” Kínesis, vol. 8, no. 17, pp. 85–114, Nov. 2016, DOI: 10.36311/1984-8900.2016.v8.n17.06.p85.
J. Searle, Minds, brains and science. Cambridge: Harvard University Press, 1984.
H. R. Maturana and F. J. Varela, The tree of knowledge: The biological roots of human understanding. Boston: Shambhala, 1992.
D. Van Der Schyff, A. Schiavio, A. Walton, V. Velardo, and A. Chemero, “Musical creativity and the embodied mind: Exploring the possibilities of 4E cognition and dynamical systems theory,” Music & Science, vol. 1, p. 2059204318792319, Jan. 2018, DOI: 10.1177/2059204318792319.
F. Truck and H. Moravec, “Mind Children: The Future of Robot and Human Intelligence,” Leonardo, vol. 24, no. 2, p. 242, 1991, DOI: 10.2307/1575314.
F. J. Varela, E. Thompson, and E. Rosch, A mente incorporada: Ciências cognitivas e a experiência humana. Porto Alegre: Artmed, 2003.
F. J. Varela, Ethical know-how. Stanford: Stanford University Press, 1999.
E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Canadá: ACM, Mar. 2021, pp. 610–623. DOI: 10.1145/3442188.3445922.
M. Mitchell, “Why AI is Harder Than We Think,” Apr. 28, 2021, arXiv: arXiv:2104.12871. DOI: 10.48550/arXiv.2104.12871.
H. Herndon, “Proto,” Stanford, Califórnia, 2019.
K. Crawford and T. Paglen, “Excavating AI: the politics of images in machine learning training sets,” AI & Soc, vol. 36, no. 4, pp. 1105–1116, Dec. 2021, DOI: 10.1007/s00146-021-01162-8.
T. Silva, “Colonialidade difusa no aprendizado de máquina: camadas de opacidade algorítmica na ImageNet,” in Colonialismo de dados: como opera a trincheira algorítmica na guerra neoliberal, São Paulo: Autonomia Literária, 2021, pp. 84–104.
J. ZYLINSKA, AI Art: Machine Visions and Warped Dreams. in MEDIA : ART : WRITE : NOW. Londres: Open Humanities Press, 2020.
H. H. Jiang et al., “AI Art and its Impact on Artists,” presented at the AIES ’23: AAAI/ACM Conference on AI, Ethics, and Society, Montréal: ACM, Aug. 2023, pp. 363–374. DOI: 10.1145/3600211.3604681.
T. Broad, F. F. Leymarie, and M. Grierson, “Network Bending: Expressive Manipulation of Deep Generative Models,” Mar. 12, 2021, arXiv: arXiv:2005.12420. Accessed: Oct. 11, 2024. [Online]. Available: [link]
A. Baio, “Invasive Diffusion: How one unwilling illustrator found herself turned into an AI model,” Waxy.org. Accessed: Oct. 18, 2024. [Online]. Available: [link]
M. Tromble, “Ask not what AI can do for art... but what art can do for AI,” Artn., no. 26, Jul. 2020, DOI: 10.7238/a.v0i26.3368.
G. E. Lewis, “Too Many Notes: Computers, Complexity and Culture in ‘Voyager,’” Leonardo Music Journal, vol. 10, pp. 33–39, 2000, [Online]. Available: [link]
C. Vear and J. Benerradi, “Jess+: designing embodied AI for interactive music-making,” Dec. 09, 2024, arXiv: arXiv:2412.06469. DOI: 10.48550/arXiv.2412.06469.
P. Saint-Germier, C. Canonne, and M. Fiorini, “The corpus’ body. Embodied Interaction from Machine-Learning in Human-Machine Improvisation.,” 2024.
L. Quínamo, “Entre a imagem, a prática e a música: analisando os processos criativos de A Dream Within A Dream, de Tatiana Catanzaro, e de Furnas 1, de Felipe de Almeida Ribeiro,” Dissertação, Campinas, 2023. [Online]. Available: [link]
Publicado
15/09/2025
Como Citar
GARCIA, Matheus P..
Challenging cognitivism: computational versus embodied and embedded creativity in AI generative music. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 19. , 2025, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 14-21.
DOI: https://doi.org/10.5753/sbcm.2025.13065.
