The Prompting Brain: Neurocognitive Markers of Expertise in Guiding Large Language Models

• URL: http://arxiv.org/abs/2508.14869v1
• Date: Wed, 20 Aug 2025 17:31:53 GMT
• Title: The Prompting Brain: Neurocognitive Markers of Expertise in Guiding Large Language Models
• Authors: Hend Al-Khalifa, Raneem Almansour, Layan Abdulrahman Alhuasini, Alanood Alsaleh, Mohamad-Hani Temsah, Mohamad-Hani_Temsah, Ashwag Rafea S Alruwaili,
• Abstract summary: This paper presents findings from a cross-sectional pilot fMRI study investigating differences in brain functional connectivity and network activity between experts and intermediate prompt engineers.<n>Our results reveal distinct neural signatures associated with higher prompt engineering literacy, including increased functional connectivity in brain regions such as the left middle temporal gyrus and the left frontal pole.<n>We discuss the implications of these neurocognitive markers in Natural Language Processing (NLP)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Prompt engineering has rapidly emerged as a critical skill for effective interaction with large language models (LLMs). However, the cognitive and neural underpinnings of this expertise remain largely unexplored. This paper presents findings from a cross-sectional pilot fMRI study investigating differences in brain functional connectivity and network activity between experts and intermediate prompt engineers. Our results reveal distinct neural signatures associated with higher prompt engineering literacy, including increased functional connectivity in brain regions such as the left middle temporal gyrus and the left frontal pole, as well as altered power-frequency dynamics in key cognitive networks. These findings offer initial insights into the neurobiological basis of prompt engineering proficiency. We discuss the implications of these neurocognitive markers in Natural Language Processing (NLP). Understanding the neural basis of human expertise in interacting with LLMs can inform the design of more intuitive human-AI interfaces, contribute to cognitive models of LLM interaction, and potentially guide the development of AI systems that better align with human cognitive workflows. This interdisciplinary approach aims to bridge the gap between human cognition and machine intelligence, fostering a deeper understanding of how humans learn and adapt to complex AI systems.

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