| Byrne, R. M. J. (2007). The rational imagination: How people create alternatives to reality. Cambridge: The MIT Press. (Original work published 2005). |
 
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| "Inventions of new instances of a category appear to be structured by existing conceptual knowledge." |
| Wang, J. (2021). Half sound, half philosophy: Aesthetics, politics, and history of China’s sound art. New York: Bloomsbury Academic. |
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| Added by: Mark Grimshaw-Aagaard Last edited by: Mark Grimshaw-Aagaard 3/12/25, 1:08 AM |
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| "Based on the Confucian scholar Jung-Yeup Kim’s interpretation of Zhang Zai, it is one primary goal of Zhang Zai to realize in the cosmos and the myriad things the capacity for resonance, which is often veiled or hidden. This capacity for resonance is creativity. As Zhang Zai writes, that which interpenetrates through resonance (感) is creativity (诚) (感而通诚也). Resonance as a transformative interaction among polarities of qi leads to the great harmony, which further produces and sustains life vitality." |
| Waterworth, E. L., & Waterworth, J. A. (2003). The illusion of being creative. In G. Riva, F. Davide & W. A. IJsselsteijn (Eds.), Being There: Concepts, Effects and Measurements of User Presence in Synthetic Environments Vol. 5, (pp. 223–236). Amsterdam: IOS Press. |
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| Added by: Mark Grimshaw-Aagaard 2/14/19, 9:48 AM |
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| experiencing the same data set through different representations and sensory modalities "increases the range of concrete perceptions through which information is experienced. These richer perceptions may then lead to more original concepts." |
| Wingström, R., Hautala, J., & Lundman, R. (2022). Redefining creativity in the era of AI? Perspectives of computer scientists and new media artists. Creativity Research Journal, 36(2), 177–193. |
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| Added by: alexb44 Last edited by: Mark Grimshaw-Aagaard 2/19/25, 12:28 AM |
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| "We further apply Hayles’s theory (2017, pp. 31–32) to demonstrate that AI is a cognizer (i.e., an actor that can autonomously pursue a goal). It differs from noncognizer (i.e., a non-autonomous artifact such as a pen). Thus, researching AI from these perspectives is critical because it is a novel technology that can make decisions and change the process it participates in (cf. Mazzone & Elgammal, 2019)."
Hayles, N. K. (2017). Unthought: The power of the cognitive nonconscious. University of Chicago Press. doi:10.7208/chi cago/9780226447919.001.0001
Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. doi:10.3390/ arts8010026 |
| "The second perspective of computational creativity focuses on developing AI that is co-creative with humans. Human–AI co-creativity aims to blend the creativity of humans and AI in an interactive process “on a shared task in real time” (Karimi et al., 2020, p. 22). Such AI is capable of interacting with humans, learning, and adapting its functions in real time, and this interaction is also known as “human in the loop” (Chung, 2021). Thus, some consider it “an equal creative partner” to humans (Berman & James, 2018, p. 257) or a tool that can support the creativity of a human (Kantosalo & Toivonen, 2016). Research has shown that AI is capable of generating new ideas and inspiration for humans, providing knowledge that enhances humans’ creative abilities, overcoming fixated thinking and “blank canvas paralysis,” and inspiring individuals by presenting sketches of varying similarity (Kantosalo & Toivonen, 2016; Karimi et al., 2020; Maher, 2012)."
Berman, A., & James, V. (2018). Learning as performance: Autoencoding and generating dance movements in real time. In A. Liapis, J. J. R. Cardalda, & A. Ekárt (Eds.), International Conference on Computational Intelligence in Music, Sound, Art and Design (pp. 256–266). Springer. doi:10.1007/978-3-319-77583-8
Chung, N. C. (2021). Human in the loop for machine creativity. In 9th AAAI Conference on Human Computationand Crowdsourcing (HCOMP 2021), Virtual conference.arXiv:2110.03569
Kantosalo, A., & Toivonen, H. (2016). Modes for creative human-computer collaboration: Alternating and task- divided co-creativity. In A. Cardoso, V. Corruble, & F. Ghedini (Eds.), Proceedings of the Seventh International Conference on Computational Creativity (pp. 77–84). Paris, France
Karimi, P., Rezwana, J., Siddiqui, S., Maher, M. L., & Dehbozorgi, N. (2020). Creative sketching partner: An ana
lysis of human-AI co-creativity. In Proceedings of the 25th International Conference on Intelligent User Interfaces, 221–230. Cagliary, Italy. doi:10.1145/3377325.3377522
Maher, M. L. (2012). Computational and collective creativity: Who’s being creative? In M. L. Maher, K. Hammond, A. Pease, R. Pérez y Pérez, D. Ventura & G. Wiggins (Eds.), Proceedings of the Third International Conference on Computational Creativity (pp. 67–71). Dublin, Ireland: University College Dublin |
| Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(3). |
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| Added by: alexb44 Last edited by: alexb44 2/24/25, 3:06 PM |
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| "While generative AI has demonstrated the capability to automatically create new digital artifacts, there remains a significant knowledge gap regarding its impact on productivity in artistic endeavors which lack welldefined objectives, and the long-run implications on human creativity more broadly. In particular, if humans increasingly rely on generative AI for content creation, creative fields may become saturated with generic content, potentially stifling exploration of new creative frontiers." |
| "A simplified view of human creative novelty with respect to art can be summarized via two main channels through which humans can inject creativity into an artifact: Contents and Visuals. These concepts are rooted in the classical philosophy of symbolism in art which suggests that the contents of an artwork is related to the meaning or subject matter, whereas visuals are simply the physical elements used to convey the content (7). In our setting, Contents concern the focal object(s) and relations depicted in an artifact, whereas Visuals consider the pixel-level stylistic elements of an artifact. Thus, Content and Visual Novelty are measured as the pairwise cosine distance between artifacts in the feature space (see Materials and methods for details on feature extraction and how novelty is measured)."
7: Huang S, Grady P, GPT-3. 2022. Generative AI: a creative new world. Sequoia Capital US/Europe. https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/ |
| "We define creative productivity as the log of the number of artifacts that a user posts in a month. Figure 1a reveals that upon adoption, artists experience a 50% increase in productivity on average, which then doubles in the subsequent month. For the average user, this translates to approximately 7 additional artifacts published in the adoption month and 15 artifacts in the following month. Beyond the adoption month, user productivity gradually stabilizes to a level that still exceeds preadoption volume. By automating the execution stage of the creative process, adopters can experience prolonged productivity gains compared to their nonadopter counterparts." |
| "Figure 1b reveals an initial nonsignificant upward trend in the Value of artworks produced by AI adopters. But after 3 months, AI adopters consistently produce artworks judged significantly more valuable than those of nonadopters. This translates to a 50% increase in artwork favorability by the sixth month, jumping from the preadoption average of 2% to a steady 3% rate of earning a favorite per view" |
| "The result shown in Fig. 1e highlights that average Visual Novelty is decreasing over time among adopters when compared to nonadopters. The same result holds for the maximum Visual Novelty seen in Fig. 1f. This suggests that adopters may be gravitating toward a preferred visual style, with relatively minor deviations from it. This tendency could be influenced by the nature of text-to-image workflows, where prompt engineering tends to follow a formulaic approach to generate consistent, high-quality images with a specific style. As is the case with contents, publicly available fine-tuned checkpoints and adapters for these models may be designed to capture specific visual elements from which users can sample from to maintain a particular and consistent visual style. In effect, AI may be pushing artists toward visual homogeneity." |
