Beaty, R. E., & Silvia, P. J. (2012). Why do ideas get more creative across time? An executive interpretation of the serial order effect in divergent thinking tasks. Psychology of Aesthetics, Creativity, and the Arts, 6(4), 309–319.
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"Two other mechanisms—strategy use and goal maintenance— deserve mention. Strategy use received an extensive treatment in Gilhooly et al.’s (2007) protocol analysis of people’s verbal reports during unusual uses tasks. As we mentioned earlier, that research found a wide range of strategies that varied in effectiveness. People who came up with better ideas were able to stop the most intuitive strategy (search memory for known uses), identify a better one, and keep it in mind while enacting it. Goal maintenance—the ability to keep a task’s goal in mind while performing the task— has not yet been studied in creativity tasks, but it is probably important. People with better cognitive control, such as people with high working memory spans, are less likely to have a task’s goal (such as “to be creative” and “generate only creative uses”) slip from mind during the task (Kane & Engle, 2003; Marcovitch, Boseovski, Knapp, & Kane, 2010). "
"If smart people are simply inhibiting the same prepotent associates that everyone else is typing, then we would expect them to take longer to type their first response, assuming that the rate of semantic spread is similar across intelligence levels and that the process of inhibiting and rejecting ideas requires time."
"Traditional methods for scoring divergent thinking, particularly uniqueness scoring and its variants, lead to unusually high correlations between creativity and fluency (e.g., Hocevar, 1979; Silvia, 2008b). Subjective ratings, in contrast, appear to effectively divorce creative quality from mere quantity (Silvia, Martin, & Nusbaum, 2009; Silvia et al., 2008)."
"If anything, the long-standing spatial metaphor of moving away from close, obvious ideas toward remote, original ones may be misleading (cf. Moran, 2009). Good ideas needn’t be “farther away” in semantic space than bad ideas—indeed, good ideas are often generated by people on the spot, not waiting to be found in the frontiers of memory (Gilhooly et al., 2007). Instead, people who are more effective at managing their minds can recruit the strategies and processes needed to generate ideas despite interference generated from obvious ideas and entrenched ways of thinking."
"The serial order effect diminished as intelligence increased, and it was effectively absent at the highest levels of intelligence in our sample (see Figure 2)."
Fluid intelligence
"A straightforward way to examine the role of executive processes is to examine the role of fluid intelligence, a domain-general executive ability (Carroll, 1993). People higher in fluid intelligence can use complex strategies more effectively (Nusbaum & Silvia, 2011a, Study 2), switch idea categories more often (Nusbaum & Silvia, 2011a, Study 1), manage interference (Unsworth, 2010), and discern patterns, rules, and structure in complex problems (McGrew, 2005). Fluid intelligence is closely tied to working memory span (Su¨ß, Oberauer, Wittman, Wilhelm, & Schulze, 2002), another ability fundamental to the deliberate control of cognition and attention. Fluid intelligence can thus represent variability in how well people can exert control over the mental resources needed to generate good ideas"
"Third, interference management during a divergent thinking would lead to later responses being better than earlier ones. Creating good responses involves managing interference from obvious uses, previously generated responses, and responses closely connected to the object’s salient features (e.g., a brick’s color, shape, and density). Managing interference is a central executive process (Unsworth, 2010), and several cognitive abilities, particularly fluid intelligence and working memory span (Burgess, Gray, Conway, & Braver, 2011), predict how well people can do it."
"Second, executive switching—when people stop generating ideas from one conceptual category and switch to another— unfolds over time. Research on verbal fluency tasks, which are close cousins of divergent thinking tasks (Carroll, 1993), shows that people higher in intelligence and working memory span switch categories more often (Troyer, Moscovitch, & Winocur, 1997; Unsworth, Spillers, & Brewer, 2011)."
Becker, H. S. (1978). Arts and crafts. American Journal of Sociology, 83(4), 862–889.
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"The histories of various art forms include typical sequences in which what has been commonly understood by practitioners and public to be a craft becomes redefined as an art or, conversely, an art becomes redfined as a craft."
Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., & Barr, N., et al. (2023). Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research: Principles and Implications, 8.
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"Firstly, people preferred (purportedly) human-created art over AI-created art. This preference was particularly evident for criteria that communicated deeper meanings of the art (e.g., Profundity, Worth). On more-sensory levels, the difference between human- and AI-labelled art was much more modest, though significant differences were nonetheless observed. As such, AI-labelled art can still be greatly appreciated (almost as much as human-labelled art) when people utilize varying levels of engagement processes.
Interestingly, the sensory versus communicative-level judgement processes were further distinguished by different interaction processes: the former were moderated by rates of story-telling and perceived effort (though in opposite directions), while the latter were moderated by personal positive attitudes toward AI. These interactions shed light on when and why individuals may appreciate art made by different creators, as posed in our initial research question, and also reflect how different stages of multi-processing models of aesthetics may interact with one another.
In conclusion, people tend to perceive art as reflecting a human-specific experience, though creator labels seem to mediate the ability to derive deeper evaluations from art. Thus, creative products like art may be achieved — according to human raters—by non-human AI models, but only to a limited extent that still protects a valued anthropocentrism."
Beloyianni, V., Zbainos, D., & Karagianni, M.-P. (2024). From mindreading to originality: Exploring the relationship between Theory of Mind and creativity across the lifespan. The British journal of developmental psychology, 42(2), 215–233.
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Boden, M. A. (2004). The creative mind: Myths & mechanisms. 2nd ed. New York: Routledge.
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On values and creativity
"I said earlier that ‘new’ has two meanings, and that ‘surprising’ has three. I didn’t say how many meanings ‘valuable’ has – and nobody could. Our aesthetic values are difficult to recognize, more difficult to put into words, and even more difficult to state really clearly. (For a computer model, of course, they have to be stated really, really clearly.)
Moreover, they change: who will proudly admit, today, to having worn a beehive hairdo or flared trousers in the 1960s? They vary across cultures. And even within a given ‘culture’, they are often disputed: different subcultures or peer groups value different types of dress, jewellery or music. And where transformational creativity is concerned, the shock of the new may be so great that even fellow artists find it difficult to see value in the novel idea.
Even in science, values are often elusive and sometimes changeable. Just what ‘simplicity’ or ‘elegance’ means, as applied to scientific theories, is something that philosophers of science have long tried – and failed – to pin down precisely. And whether a scientific finding or hypothesis is ‘interesting’ depends on the other theories current at the time, and on social questions too (might it have some medical value, for instance?).
Because creativity by definition involves not only novelty but value, and because values are highly variable, it follows that many arguments about creativity are rooted in disagreements about value. This applies to human activities no less than to computer performance. So even if we could identify and program our aesthetic values so as to enable the computer to inform and monitor its own activities accordingly there would still be disagreement about whether the computer even appeared to be creative."
Parallel to intelligece. Intelligence is also tied to several value parameters, and as Boden argues, creativity is one of them. Those who accumulate great wealth and thereby power are often viewed as intelligent, but the two need not have any correlation, for example. What constitutes value is often what is viewed as succesful, but surely that is a reduction of such a rich concept.
Boden, M. A. (2009). Computer models of creativity. AI Magazine, 30(3), 23–34.
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Boden, M. A. (2011). Creativity & art: Three roads to surprise. Oxford: Oxford University Press.
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"Some aesthetic values (as suggested in Chapter 4) may be enduring, because they are part of our biological heritage. But many are culturally based. [...] Values in general, whether enduring or not, can’t be justified—even though they may sometimes be explained—in scientific terms. In that sense, and in that sense alone, both creativity and art lie for ever beyond the reach of science."
What is valuable is more difficult to say. "Our aesthetic values are difficult to recognize, more difficult to put into words, and even more difficult to state really clearly" and values change across cultures, from subculture to subculture, and so on, even within science where descriptions can be vague or the value placed on a hypothesis (worthwhile or not and within what context) can vary. "Because creativity by definition involves not only novelty but value, and because values are highly variable, it follows that many arguments about creativity are rooted in disagreements about value."
"An idea may be surprising because it’s unfamiliar, or even unlikely— like a 100-to-1 outsider winning the Derby. This sort of surprise goes against statistics. The second sort of surprise is more interesting. An unexpected idea may ‘‘fit’’ into a style of thinking that you already had—but you’re surprised because you hadn’t realized that this particular idea was part of it. Maybe you’re even intrigued to find that an idea of this general type fits into the familiar style. And the third sort of surprise is more interesting still: this is the astonishment you feel on encountering an apparently impossible idea. It just couldn’t have entered anyone’s head, you feel—and yet it did. It may even engender other ideas which, yesterday, you’d have thought equally impossible. What on earth can be going on?"
"P-creativity involves coming up with a surprising, valuable idea that’s new to the person who comes up with it. It doesn’t matter how many people have had that idea before. But if a new idea is H-creative, that means that (so far as we know) no one else has had it before: it has arisen for the first time in human history."
"Creativity is the ability to come up with ideas or artefacts that are new, surprising, and valuable."
There are three paths to creativity:
combinational: Novel combinations of familiar things/ideas leading to an "intelligible conceptual pathway between them for the combination to ‘‘make sense’’" (p.32) rather than just a random combination.
exploratory: Conceptual spaces are pre-existing "structured styles of thought" (p.32). Ways of writing, sculptural forms, navigating geography and physical space, etc. Not all ways of navigating those spaces have been explored, although they do exist; new possibilities might be found and this might lead further to questioning the bounds of that space and what further potential possibilities might exist . . .
transformational: If exploratory creativity leads to questioning the limits of the conceptual space, transformational creativity takes it the obvious step further. Some conceptual spaces (e.g., a cartographic map) can't be changed; mental maps can be changed. "[T]hinking something which, with respect to the conceptual spaces in their minds, they couldn’t have thought before. The supposedly impossible idea can come about only if the creator changes the pre-existing style in some way" (p.34).
Bondebjerg, I. (2017). The creative mind: Cognition, society and culture. Palgrave Communications, 3(19).
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Davis, D. (1995). The work of art in the age of digital reproduction (an evolving thesis: 1991-1995). Leonardo, 28(5), 381–386.
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Gotlieb, R. J., Hyde, E., Immordino-Yang, M. H., & Kaufman, S. B. (2019). Imagination is the seed of creativity. The Cambridge handbook of creativity.
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Grimshaw, M., & Garner, T. A. (2012). The use of sound to represent data and concepts as a means to engender creative thought: Some thoughts on implementation and a research agenda. In Proceedings of the 7th Audio Mostly Conference: A Conference on Interaction with Sound. (pp. 9–15). Association for Computing Machinery.
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Ilya, B., Vadim, M., & Silnov, D. 2020 Automatic generation of questions based on semantic text analysis. Paper presented at 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT).
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Janiszewska-Szczepanik, A. (2019). The compulsion to create: Definition and genesis of the phenomenon. Creativity, 6(2), 250–263.
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Jefferson, G. (1949). The mind of mechanical man. BMJ, 1(4616), 1105–1110.
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In comparing the brain with (what was then) an early computer, Jefferson identifies "the machines' lack of opinions, of creative thinking in verbal concepts. I shall be surprised, indeed, if that gap is bridged, for even supposing that electrical charges could be made to represent words, what then? Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain—that is, not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants."
Liu, H. (2019). Automatic idea generation and analysis using NLP and ML techniques. Unpublished PhD thesis, University of Nottingham, United Kingdom.
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Marks, R. J. (2022). Did the GPT3 chatbot pass the Lovelace test? Mind Matters, Retrieved November 18, 2025, from https://mindmatters.ai/ ... pass-the-lovelace-test/
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Paraphrasing the Lovelace test: "computer creativity will be demonstrated when a machine’s performance is beyond the intent or explanation of its programmer."
McCormack, J., & d'Inverno, M. 2014, April 1–January 4, On the future of computers and creativity. Unpublished paper presented at AISB Symposium on Computational Creativity.
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Mishra, S. K., Goel, P., Sharma, A., Jagannatha, A., Jacobs, D., & Daumé, H. (2020). Towards automatic generation of questions from long answers. arXiv.
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Moon, K., Kushlev, K., Bank, A., Luttges, B., Viskontas, I., & Kaufman, J., et al. (2026). The link between diverse words and original ideas is weakening in the AI-era college admissions.
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Moruzzi, C. (2020). Measuring creativity: An account of natural and artificial creativity. European Journal for Philosophy of Science, 11(1).
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