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A neural network account to Kant's philosophical aesthetics

Published 12 Apr 2024 in q-bio.NC | (2404.12395v1)

Abstract: According to Kant's (1724 -- 1804) philosophical aesthetics, laid down in his Critique of the Power of Judgement (1790), beauty is "subjective purposefulness", reflected by the "harmony of the cognitive faculties", which are "understanding" and "imagination". On the one hand, understanding refers to the mental capability to find regularities in sensory manifolds, while imagination refers to intuition, fantasy, and creativity of the mind, on the other hand. Inspired by the reinforcement learning theory of Schmidhuber, I present a neural network analogy for the harmony of the faculties in terms of generative adversarial networks (GAN) - also often employed for artificial music composition -- by identifying the generator module with the faculty of imagination and the discriminator module with the faculty of understanding. According to the GAN algorithm, both modules are engaged in an adversarial game, thereby optimizing a particular objective function. In my reconstruction, the convergence of the GAN algorithm during the reception of art, either music or fine, entails the harmony of the faculties and thereby a neural network analogue of subjective purposefulness, i.e., beauty.

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References (64)
  1. A formalization of Kant’s transcendental logic. Review of Symbolic Logic, 4(2):254 – 289.
  2. beim Graben, P. (2006). Pragmatic information in dynamic semantics. Mind and Matter, 4(2):169 – 193.
  3. beim Graben, P. (2014). Order effects in dynamic semantics. Topics in Cognitive Science, 6(1):67 – 73.
  4. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, pages 610 – 623, New York (NY). ACM.
  5. Blutner, R. (2024). The emotional meaning of pure music. Mind and Matter, this issue.
  6. Deep Learning Techniques for Music Generation. Computational Synthesis and Creative Systems. Springer, Cham.
  7. A modular architecture for transparent computation in recurrent neural networks. Neural Networks, 85:85 – 105.
  8. Uncertainty and surprise jointly predict musical pleasure and amygdala, hippocampus, and auditory cortex activity. Current Biology, 29(23):4084 – 4092.e4.
  9. Christie’s (2018). Is artificial intelligence set to become art’s next medium? Online: https://www.christies.com/en/stories/a-collaboration-between-two-artists-one-human-one-a-machine-0cd01f4e232f4279a525a446d60d4cd1. Retrieved at Saturday, April, 6th, 2024.
  10. Collins, M. (2012). The attractiveness of the average face. Seminars in Orthodontics, 18(3):217 – 228.
  11. Galton, F. (1878). Composite portraits. Nature, 18(447):97 – 100.
  12. Gärdenfors, P. (1988). Knowledge in Flux. Modeling the Dynamics of Epistemic States. MIT Press, Cambridge (MA).
  13. Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  14. Gibbs, A. C. (2022). Aesthetics of music, on psychedelics. Mind and Matter, 20(2):177 – 194.
  15. Ginsborg, H. (1997). Lawfulness without a law: Kant on the free play of imagination and understanding. Philosophical Topics, 25(1):37 – 81.
  16. Ginsborg, H. (2003). Aesthetic judging and the intentionality of pleasure. Inquiry, 46(2):164 – 181.
  17. Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems (NIPS), volume 27. Curran Associates, Inc.
  18. Guyer, P. (2006). The harmony of the faculties revisited. In Kukla, R., editor, Aesthetics and Cognition in Kant’s Critical Philosophy, chapter 7, pages 162 – 193. Cambridge University Press, Cambridge (MA).
  19. Hanslick, E. (1891). The Beautiful in Music. A Contribution to the Revisal of Musical Aesthetics. Novello, London. Translated by G. Cohen.
  20. Introduction to the Theory of Neural Computation, volume I of Lecture Notes of the Santa Fe Institute Studies in the Science of Complexity. Perseus Books, Cambridge (MA).
  21. Long short-term memory. Neural Computation, 9(8):1735 – 1780.
  22. Huron, D. (2006). Sweet Anticipation: Music and the Psychology of Expectation. MIT Press, Cambridge (MA).
  23. Huron, D. (2019). Musical aesthetics: Uncertainty and surprise enhance our enjoyment of music. Current Biology, 29(23):R1238 – R1240.
  24. Bayesian surprise attracts human attention. In Weiss, Y., Schölkopf, B., and Platt, J., editors, Advances in Neural Information Processing Systems, volume 18. MIT Press.
  25. Kant, I. (1764 — 2011). Observations on the Feeling of the Beautiful and Sublime. Cambridge Texts in the History of Philosophy. Cambridge University Press, Cambridge (MA).
  26. Kant, I. (1787 — 1999). Critique of Pure Reason. The Cambridge Edition of The Works Of Immanuel Kant. Cambridge University Press, Cambridge, 2nd edition.
  27. Kant, I. (1788 — 2015). Critique of Practical Reason. Cambridge Texts in the History of Philosophy. Cambridge University Press, Cambridge.
  28. Kant, I. (1790 — 1914). Kant’s Critique of Judgement. Macmillan, London, 2nd edition. Translated by J. H. Bernard.
  29. Kant, I. (2000). First Introduction to the Critique of the Power of Judgment. Cambridge University Press, Cambridge (MA).
  30. A thirst for knowledge: Grounding curiosity, creativity, and aesthetics in memory and reward neural systems. Creativity Research Journal, 35(3):412 – 426.
  31. Kant and Artificial Intelligence. De Gruyter, Berlin.
  32. Krumhansl, C. L. (1990). Cognitive Foundations of Musical Pitch. Oxford University Press, New York.
  33. Aesthetics and Cognition in Kant’s Critical Philosophy. Cambridge University Press, Cambridge (MA).
  34. Deep learning. Nature, 521(7553):436 – 444.
  35. Computers and Creativity. Springer, Berlin.
  36. What are aesthetic emotions? Psychological Review, 126(2):171 – 195.
  37. Meyer, L. B. (1956). Emotion and Meaning in Music. University of Chicago Press, Chicago (IL), paperback 1961 edition.
  38. Michaelis, C. F. (1795). Ueber den Geist der Tonkunst: Mit Rücksicht auf Kants Kritik der aesthetischen Urtheilskraft. Ein ästhetischer Versuch. Schäfersche Buchhandlung, Leipzig.
  39. Mizraji, E. (2010). En busca de las leyes del pensamiento. Editorial Trilce, Montevideo (Uruguay).
  40. Mizraji, E. (2023). Creating order in the mind: Borges’ paradoxical mirror. Journal of Genius and Eminence, 5(2):97 –107.
  41. Mogren, O. (2016). C-RNN-GAN: Continuous recurrent neural networks with adversarial training. In Proceedings of the Constructive Machine Learning Workshop (CML) of the Neural Information Processing Symposium (NIPS).
  42. Moore, G. H. (1978). The origins of Zermelo’s axiomatization of set theory. Journal of Philosophical Logic, 7(1):307 – 329.
  43. Murray, N. (2019). PFAGAN: An aesthetics-conditional GAN for generating photographic fine art. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 3333 – 3341.
  44. Ott, E. (1993). Chaos in Dynamical Systems. Cambridge University Press, New York. Reprinted 1994.
  45. The Beauty of Fractals. Images of Complex Dynamical Systems. Springer, Berlin.
  46. Primas, H. (1990). Mathematical and philosophical questions in the theory of open and macroscopic quantum systems. In Miller, A. I., editor, Sixty-two Years of Uncertainty: Historical, Philosophical and Physics Inquries into the Foundation of Quantum Mechanics, pages 233 – 257. Plenum Press, New York.
  47. Artificial Intelligence: A Modern Approach. Pearson, 3rd edition.
  48. Schmidhuber, J. (2010). Formal theory of creativity, fun, and intrinsic motivation (1990 – 2010). IEEE Transactions on Autonomous Mental Development, 2(3):230 – 247.
  49. Schmidhuber, J. (2012). A formal theory of creativity to model the creation of art, pages 323 – 337. In McCormack and d’Inverno, (2012).
  50. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61:85 – 117.
  51. Silvers, R. (1996). Photomosaics: Putting Pictures in Their Place. PhD thesis, Massachusetts Institute of Technology (MIT), Cambridge (MA).
  52. Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. In Rumelhart, D. E., McClelland, J. L., and the PDP Research Group, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume I, chapter 6, pages 194 – 281. MIT Press, Cambridge (MA).
  53. Smolensky, P. (2006). Harmony in linguistic cognition. Cognitive Science, 30:779 – 801.
  54. The Harmonic Mind. From Neural Computation to Optimality-Theoretic Grammar, volume 1: Cognitive Architecture. MIT Press, Cambridge (MA).
  55. Stangneth, B. (2019). Hässliches Sehen. Rowohlt, Reimbek.
  56. Planning to be surprised: Optimal Bayesian exploration in dynamic environments. In Proceedings of the Fourth Conference on Artificial General Intelligence (AGI-2011).
  57. Tedesco, S. (2024). Starting from Plessner’s “aesthesiology of the spirit”: Sound and normative value of the senses. Mind and Matter, this issue.
  58. Zipf’s law is not a consequence of the central limit theorem. Physical Reviews E, 57(2):1347 – 1355.
  59. The Language of Creative AI: Practices, Aesthetics and Structures. Springer Series on Cultural Computing. Springer, Cham.
  60. Wagner, R. (1852—1984). Oper und Drama, volume 8207 of Universalbibliothek. Reclam, Stuttgart.
  61. Williams, R. J. (1988). On the use of backpropagation in associative reinforcement learning. In Proceedings of the IEEE International Conference on Neural Networks (ICANN), volume 1, pages 263 – 270.
  62. Midinet: A convolutional generative adversarial network for symbolic-domain music generation. In Hu, X., Cunningham, S. J., Turnbull, D., and Duan, Z., editors, Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), pages 324 – 331. arXiv:1703.10847 [cs.SD].
  63. Conditional LSTM-GAN for melody generation from lyrics. ACM Transactions on Multimedia Computation and Communication Applications, 17(1).
  64. Zoeller, M. (2024). How random is random? Mind and Matter, this issue.
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