Uzzi, B. & Spiro, J. Collaboration and creativity: the small world problem. Am. J. Sociol. 111, 447–504 (2005).
Article
Google Scholar
Uzzi, B., Mukherjee, S., Stringer, M. & Jones, B. Atypical combinations and scientific impact. Science 342, 468–472 (2013).
Article
CAS
PubMed
Google Scholar
Pomiechowska, B., Bródy, G., Téglás, E. & Kovács, Á. M. Early-emerging combinatorial thought: human infants flexibly combine kind and quantity concepts. Proc. Natl Acad. Sci. USA 121, e2315149121 (2024).
Article
CAS
PubMed
PubMed Central
Google Scholar
Frank, M. R. et al. Toward understanding the impact of artificial intelligence on labor. Proc. Natl Acad. Sci. USA 116, 6531–6539 (2019).
Article
CAS
PubMed
PubMed Central
Google Scholar
Grossmann, I. et al. AI and the transformation of social science research. Science 380, 1108–1109 (2023).
Article
CAS
PubMed
Google Scholar
Shirado, H. & Christakis, N. A. Locally noisy autonomous agents improve global human coordination in network experiments. Nature 545, 370–374 (2017).
Article
CAS
PubMed
PubMed Central
Google Scholar
Shin, M., Kim, J., van Opheusden, B. & Griffiths, T. L. Superhuman artificial intelligence can improve human decision-making by increasing novelty. Proc. Natl Acad. Sci. USA 120, e2214840120 (2023).
Article
CAS
PubMed
PubMed Central
Google Scholar
Rahman, H. A. The invisible cage: workers’ reactivity to opaque algorithmic evaluations. Adm. Sci. Q. 66, 945–988 (2021).
Article
Google Scholar
Guilford, J. P. The Nature of Human Intelligence (McGraw-Hill, 1967).
Stevenson, C., Smal, I., Baas, M., Grasman, R. & van der Maas, H. Putting GPT-3’s creativity to the (alternative uses) test. In Proc. 13th International Conference on Computational Creativity (ICCC’22) (eds Hedblom, M. M. et al.) 164–168 (Association for Computational Creativity, 2022).
Haase, J. & Hanel, P. H. P. Artificial muses: generative artificial intelligence chatbots have risen to human-level creativity. J. Creat. 33, 100066 (2023).
Article
Google Scholar
Chakrabarty, T., Laban, P., Agarwal, D., Muresan, S. & Wu, C.-S. Art or artifice? Large language models and the false promise of creativity. In Proc. 2024 CHI Conference on Human Factors in Computing Systems (Association for Computing Machinery, 2024); https://doi.org/10.1145/3613904.3642731
Tian, Y. et al. MacGyver: are large language models creative problem solvers? In Proc. 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Duh, K. et al.) (Association for Computational Linguistics, 2024); https://doi.org/10.18653/v1/2024.naacl-long.297
Doshi, A. R. & Hauser, O. P. Generative AI enhances individual creativity but reduces the collective diversity of novel content. Sci. Adv. 10, eadn5290 (2024).
Article
PubMed
PubMed Central
Google Scholar
Guo, D. et al. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature 645, 633–638 (2025).
Article
CAS
PubMed
PubMed Central
Google Scholar
Jia, N., Luo, X., Fang, Z. & Liao, C. When and how artificial intelligence augments employee creativity. Acad. Manag. J. 67, 5–32 (2024).
Article
Google Scholar
van den Broek, E., Sergeeva, A. & Huysman, M. When the machine meets the expert: an ethnography of developing AI for hiring. MIS Q. 45, 1557–1580 (2021).
Article
Google Scholar
Olson, J. A., Nahas, J., Chmoulevitch, D., Cropper, S. J. & Webb, M. E. Naming unrelated words predicts creativity. Proc. Natl Acad. Sci. USA 118, e2022340118 (2021).
Article
CAS
PubMed
PubMed Central
Google Scholar
Bao, L., Cao, J., Gangadharan, L., Huang, D. & Lin, C. Effects of lockdowns in shaping socioeconomic behaviors. Proc. Natl Acad. Sci. USA 121, e2405934121 (2024).
Article
CAS
PubMed
PubMed Central
Google Scholar
Beketayev, K. & Runco, M. A. Scoring divergent thinking tests by computer with a semantics-based algorithm. Eur. J. Psychol. 12, 210–220 (2016).
Article
PubMed
PubMed Central
Google Scholar
Brophy, D. R. Understanding, measuring, and enhancing individual creative problem-solving efforts. Creat. Res. J. 11, 123–150 (1998).
Article
Google Scholar
Amabile, T. M. The social psychology of creativity: a componential conceptualization. J. Pers. Soc. Psychol. 45, 357–376 (1983).
Article
Google Scholar
Long, H. & Pang, W. Rater effects in creativity assessment: a mixed methods investigation. Think. Skills Creat. 15, 13–25 (2015).
Article
Google Scholar
Dumas, D., Organisciak, P. & Doherty, M. Measuring divergent thinking originality with human raters and text-mining models: a psychometric comparison of methods. Psychol. Aesthet. Creat. Arts https://doi.org/10.1037/aca0000319. (2020).
Beaty, R. E., Johnson, D. R., Zeitlen, D. C. & Forthmann, B. Semantic distance and the alternate uses task: recommendations for reliable automated assessment of originality. Creat. Res. J. 34, 245–260 (2022).
Article
Google Scholar
Guilford, J. P. Creativity: yesterday, today and tomorrow. J. Creat. Behav. 1, 3–14 (1967).
Article
Google Scholar
Wallach, M. A. & Kogan, N. A new look at the creativity-intelligence distinction. J. Pers. 33, 348–369 (1965).
Article
CAS
PubMed
Google Scholar
Yang, Y., Youyou, W. & Uzzi, B. Estimating the deep replicability of scientific findings using human and artificial intelligence. Proc. Natl Acad. Sci. USA 117, 10762–10768 (2020).
Article
CAS
PubMed
PubMed Central
Google Scholar
Farquhar, S., Kossen, J., Kuhn, L. & Gal, Y. Detecting hallucinations in large language models using semantic entropy. Nature 630, 625–630 (2024).
Article
CAS
PubMed
PubMed Central
Google Scholar
Pividori, M. Chatbots in science: what can ChatGPT do for you? Nature https://doi.org/10.1038/d41586-024-02630-z (2024).
Samdarshi, P. et al. Connecting the dots: evaluating abstract reasoning capabilities of LLMs using the New York Times Connections word game. In Proc. 2024 Conference on Empirical Methods in Natural Language Processing (eds Al-Onaizan, Y. et al.) (Association for Computational Linguistics, 2024); https://doi.org/10.18653/v1/2024.emnlp-main.1182
Todd, G., Merino, T., Earle, S. & Togelius, J. Missed connections: lateral thinking puzzles for large language models. In Proc. 2024 IEEE Conference on Games (CoG) 1–8 (Institute of Electrical and Electronics Engineers, 2024).
Cvrček, V. et al. Comparing web-crawled and traditional corpora. Lang. Resour. Eval. 54, 713–745 (2020).
Article
Google Scholar
Horowitz, J. L. Bootstrap methods in econometrics. Annu. Rev. Econ. 11, 193–224 (2019).
Article
Google Scholar
Efron, B. Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979).
Article
Google Scholar
Jentzsch, S. & Kersting, K. ChatGPT is fun, but it is not funny! Humor is still challenging large language models. In Proc. 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis (eds Barnes, J. et al.) 325–340 (Association for Computational Linguistics, 2023).
Castillo, L., León-Villagrá, P., Chater, N. & Sanborn, A. Explaining the flaws in human random generation as local sampling with momentum. PLoS Comput. Biol. 20, e1011739 (2024).
Article
CAS
PubMed
PubMed Central
Google Scholar
Angelike, T. & Musch, J. A comparative evaluation of measures to assess randomness in human-generated sequences. Behav. Res. Methods 56, 7831–7848 (2024).
Article
PubMed
PubMed Central
Google Scholar
Geva, E. & Ryan, E. Linguistic and cognitive correlates of academic skills in first and second languages. Lang. Learn. 43, 5–42 (1993).
Article
Google Scholar
Henrickson, L. & Meroño-Peñuela, A. Prompting meaning: a hermeneutic approach to optimising prompt engineering with ChatGPT. AI Soc. https://doi.org/10.1007/s00146-023-01752-8 (2023).
Giray, L. Prompt engineering with ChatGPT: a guide for academic writers. Ann. Biomed. Eng. 51, 2629–2633 (2023).
Article
PubMed
Google Scholar
Lin, Z. How to write effective prompts for large language models. Nat. Hum. Behav. 8, 611–615 (2024).
Article
PubMed
Google Scholar
Aggarwal, A., Lohia, P., Nagar, S., Dey, K. & Saha, D. Black box fairness testing of machine learning models. In Proc. 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Association for Computing Machinery, 2019); https://doi.org/10.1145/3338906.3338937
Chao, P. et al. Jailbreaking black box large language models in twenty queries. In Proc. 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 23–42 (Institute of Electrical and Electronics Engineers, 2025).
Lapid, R., Langberg, R., & Sipper, M. Open sesame! Universal black-box jailbreaking of large language models. Appl. Sci. 14, 7150 (2024).
Article
CAS
Google Scholar
Chesebrough, C., Chrysikou, E. G., Holyoak, K. J., Zhang, F. & Kounios, J. Conceptual change induced by analogical reasoning sparks aha moments. Creat. Res. J. 35, 499–521 (2023).
Article
Google Scholar
Beaty, R. E. & Kenett, Y. N. Associative thinking at the core of creativity. Trends Cogn. Sci. 27, 671–683 (2023).
Article
PubMed
Google Scholar
Te’eni, D. et al. Reciprocal human-machine learning: a theory and an instantiation for the case of message classification. Manage. Sci. https://doi.org/10.1287/mnsc.2022.03518 (2023).
Yax, N., Anlló, H. & Palminteri, S. Studying and improving reasoning in humans and machines. Commun. Psychol. 2, 51 (2024).
Article
PubMed
PubMed Central
Google Scholar
Strachan, J. W. A. et al. Testing theory of mind in large language models and humans. Nat. Hum. Behav. 8, 1285–1295 (2024).
Article
PubMed
PubMed Central
Google Scholar
Bzdok, D. et al. Data science opportunities of large language models for neuroscience and biomedicine. Neuron 112, 698–717 (2024).
Article
CAS
PubMed
Google Scholar
Padmakumar, V. & He, H. Does writing with language models reduce content diversity? In Proc. International Conference on Representation Learning (Kim, B. et al.) 642–669 (ICLR, 2024).
Anderson, B. R., Shah, J. H. & Kreminski, M. Homogenization effects of large language models on human creative ideation. In Proc. 16th Conference on Creativity and Cognition (Association for Computing Machinery, 2024); https://doi.org/10.1145/3635636.3656204
Mohammadi, B. Creativity has left the chat: the price of debiasing language models. Preprint at https://arxiv.org/abs/2406.05587 (2024).
Groh, M. et al. Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nat. Med. 30, 573–583 (2024).
Article
CAS
PubMed
PubMed Central
Google Scholar
Marks, M. A., DeChurch, L. A., Mathieu, J. E., Panzer, F. J. & Alonso, A. Teamwork in multiteam systems. J. Appl. Psychol. 90, 964–971 (2005).
Article
PubMed
Google Scholar
Vaccaro, M., Almaatouq, A. & Malone, T. When combinations of humans and AI are useful: a systematic review and meta-analysis. Nat. Hum. Behav. 8, 2293–2303 (2024).
Article
PubMed
PubMed Central
Google Scholar
Bellemare-Pepin, A. et al. Divergent creativity in humans and large language models. Preprint at https://arxiv.org/abs/2405.13012 (2024).
Chen, H. & Ding, N. Probing the ‘creativity’ of large language models: can models produce divergent semantic association? In Findings of the Association for Computational Linguistics: EMNLP 2023 (eds Bouamor, H. et al.) (Association for Computational Linguistics, 2023); https://doi.org/10.18653/v1/2023.findings-emnlp.858
Childs, P. et al. The creativity diamond—a framework to aid creativity. J. Intell. 10, 73 (2022).
Article
PubMed
PubMed Central
Google Scholar
Chen, L. et al. TRIZ-GPT: an LLM-augmented method for problem-solving. In Proc. 36th International Conference on Design Theory and Methodology (DTM) V006T06A010 (American Society of Mechanical Engineers, 2024).
Chen, L. et al. DesignFusion: integrating generative models for conceptual design enrichment. J. Mech. Des. 146, 111703 (2024).
Article
Google Scholar
Hennessey, B. A., Amabile, T. M. & Mueller, J. S. in Encyclopedia of Creativity (Elsevier, 2011); https://doi.org/10.1016/B978-0-12-375038-9.00046-7
Cropley, A. In praise of convergent thinking. Creat. Res. J. 18, 391–404 (2006).
Article
Google Scholar
Wang, D. Presentation in self-posted facial images can expose sexual orientation: Implications for research and privacy. J. Pers. Soc. Psychol. 122, 806–824 (2022).
Article
PubMed
Google Scholar
Taylor, J. E. T. & Taylor, G. W. Artificial cognition: how experimental psychology can help generate explainable artificial intelligence. Psychon. Bull. Rev. 28, 454–475 (2021).
Article
PubMed
Google Scholar
Voudouris, K. et al. Direct human–AI comparison in the animal–AI environment. Front. Psychol. 13, 711821 (2022).
Article
PubMed
PubMed Central
Google Scholar
Hitsuwari, J., Ueda, Y., Yun, W. & Nomura, M. Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry. Comput. Hum. Behav. 139, 107502 (2022).
Article
Google Scholar
Griffiths, T. L. Understanding human intelligence through human limitations. Trends Cogn. Sci. 24, 873–883 (2020).
Article
PubMed
Google Scholar
