揭示专家提示工程的脑神经特征，助力人机协作优化。
The Prompting Brain: Neurocognitive Markers of Expertise in Guiding Large Language Models
- 对比专家与中级用户脑功能连接差异，定位关键认知区域。
- 发现左中颞回与左额极连接增强，认知网络频域动力学改变。
- 为构建更契合人类认知的智能交互系统提供神经依据。
提示工程已成为高效使用大语言模型（LLMs）的关键技能，但其认知与神经基础仍不明确。本研究通过一项横断面初步fMRI实验，比较了专家与中级提示工程师在脑功能连接与网络活动上的差异。结果揭示了与高提示工程素养相关的独特神经标志：左中颞回与左额极的功能连接增强，以及关键认知网络的功率-频率动态变化。这些发现首次提供了提示工程能力的神经生物学证据。研究讨论了这些神经认知标记在自然语言处理（NLP）中的意义，有助于设计更符合人类认知习惯的人机界面，完善人机交互认知模型，并可能指导开发更契合人类工作流的AI系统。该跨学科方法旨在弥合人类认知与机器智能之间的鸿沟，深化对人类如何学习与适应复杂AI系统的理解。
原文摘要 · Abstract (English)
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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