Cognitive networks highlight differences and similarities in the STEM mindsets of human and LLM-simulated trainees, experts and academics

• URL: http://arxiv.org/abs/2502.19529v1
• Date: Wed, 26 Feb 2025 20:02:51 GMT
• Title: Cognitive networks highlight differences and similarities in the STEM mindsets of human and LLM-simulated trainees, experts and academics
• Authors: Edith Haim, Lars van den Bergh, Cynthia S. Q. Siew, Yoed N. Kenett, Daniele Marinazzo, Massimo Stella,
• Abstract summary: This study uses behavioural forma mentis networks to investigate the STEM-focused mindset.<n>Human forma mentis networks exhibited significantly higher clustering coefficients compared to GPT-3.5.<n>Human experts, in particular, demonstrated robust clustering coefficients, reflecting better integration of STEM concepts into their cognitive networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding attitudes towards STEM means quantifying the cognitive and emotional ways in which individuals, and potentially large language models too, conceptualise such subjects. This study uses behavioural forma mentis networks (BFMNs) to investigate the STEM-focused mindset, i.e. ways of associating and perceiving ideas, of 177 human participants and 177 artificial humans simulated by GPT-3.5. Participants were split in 3 groups - trainees, experts and academics - to compare the influence of expertise level on their mindset. The results revealed that human forma mentis networks exhibited significantly higher clustering coefficients compared to GPT-3.5, indicating that human mindsets displayed a tendency to form and close triads of conceptual associations while recollecting STEM ideas. Human experts, in particular, demonstrated robust clustering coefficients, reflecting better integration of STEM concepts into their cognitive networks. In contrast, GPT-3.5 produced sparser mindsets. Furthermore, both human and GPT mindsets framed mathematics in neutral or positive terms, differently from STEM high schoolers, researchers and other large language models sampled in other works. This research contributes to understanding how mindset structure can provide cognitive insights about memory structure and machine limitations.

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