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Jaan Aru – Journal of Creative Behavior, 2025
Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative process. In particular, the advanced capabilities of AI underscore the importance of studying the internal…
Descriptors: Artificial Intelligence, Creativity, Creative Thinking, Neurology
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Ronald Mtenga; Mathias Bode; Radwa Khalil – Journal of Creative Behavior, 2025
Creative thinking stems from the cognitive process that fosters the creation of new ideas and problem-solving solutions. Artificial intelligence systems and neural network models can reduce the intricacy of understanding creative cognition. For instance, the generation of ideas could be symbolized as patterns of binary code in which clusters of…
Descriptors: Inhibition, Creative Thinking, Cognitive Processes, Concept Formation
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Stephanie Alcock; Aline Ferreira-Correia; Kate Cockcroft – Journal of Creative Behavior, 2023
Creativity involves generating novel and valuable ideas. While the importance of creative thinking is widely acknowledged, its cognitive basis is poorly understood, particularly in older adults. This study aimed to develop and test an explanatory model of creative thinking to elucidate its underlying cognitive functions in an elderly sample. The…
Descriptors: Creativity, Older Adults, Cognitive Processes, Creative Thinking
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Gal Sasson Lazovsky; Tuval Raz; Yoed N. Kenett – Journal of Creative Behavior, 2025
As artificial intelligence and natural language processing methods rapidly develop, communication plays a pivotal role in every-day interactions. In this theoretical paper, we explore the overlap and commonalities between question-asking and prompt engineering. While seemingly distinct, these processes share a common foundation in essential skills…
Descriptors: Creativity, Questioning Techniques, Inquiry, Artificial Intelligence
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Sundararajan, Louise – Journal of Creative Behavior, 2014
Harold Cohen is a renowned painter who has developed a computer program, AARON, to create art. While AARON has been hailed as one of the most creative AI programs, Cohen consistently rejects the claims of machine creativity. Questioning the possibility for AI to model human creativity, Cohen suggests in so many words that the human mind takes a…
Descriptors: Creativity, Artificial Intelligence, Art Products, Artists
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Palmiero, Massimiliano; Di Giacomo, Dina; Passafiume, Domenico – Journal of Creative Behavior, 2016
Cognitive reserve relies on the ability to effectively cope with aging and brain damage by using alternate processes to approach tasks when standard approaches are no longer available. In this study, the issue if creativity can predict cognitive reserve has been explored. Forty participants (mean age: 61 years) filled out: the Cognitive Reserve…
Descriptors: Creativity, Correlation, Predictor Variables, Cognitive Processes
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Doherty, William J.; Corsini, David A. – Journal of Creative Behavior, 1976
This research investigates the relationship between creativity as a cognitive process and the level of moral reasoning as well as the relationship between moral reasoning level and various combinations of creativity and intelligence. (Author/RK)
Descriptors: Cognitive Processes, College Students, Creativity, Creativity Research
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Guastello, Stephen J.; Guastello, Denise D.; Hanson, Casey A. – Journal of Creative Behavior, 2004
The study addressed two findings in the creativity literature that show, on the one hand, that bipolar disorder and other clinical dysfunctions are overrepresented among eminently creative people, and that positive affect is positively associated with creativity. The central hypothesis of the study was that emotional intelligence could be an…
Descriptors: Emotional Intelligence, Creativity, Cognitive Style, Creative Thinking
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Plucker, Jonathan A. – Journal of Creative Behavior, 1999
A reanalysis of data from three previously published studies on whether creativity is content general or content specific suggest that, contrary to the results of recent research, a content-general factor may explain 40-50% of the variance in creativity checklist scores. Results are analyzed in light of other studies. (Author/CR)
Descriptors: Ability Identification, Check Lists, Cognitive Ability, Cognitive Processes