The Prompting Brain: Neurocognitive Markers of Expertise in Guiding Large Language Models (2508.14869v1)

Abstract: Prompt engineering has rapidly emerged as a critical skill for effective interaction with 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 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.

• The paper demonstrates that experts show distinct neurocognitive markers, with significantly higher low-to-high frequency power ratios in critical brain networks compared to intermediates.
• The paper employs complementary fMRI pipelines, including group ICA and seed-to-voxel connectivity, to quantify differences in functional connectivity and spectral dynamics.
• The paper discusses practical implications for human-AI interface design and adaptive training programs by linking neural biomarkers to strategic prompt formulation.

Neurocognitive Signatures of Prompt Engineering Expertise in LLM Interaction

Introduction

This paper presents a pilot fMRI investigation into the neurocognitive correlates of prompt engineering expertise, specifically focusing on individuals who guide LLMs through natural language prompts. The paper addresses a critical gap in the literature: while prompt engineering is recognized as a key skill for effective LLM utilization, its underlying neural mechanisms have not been systematically characterized. By stratifying participants into expert and intermediate groups using a validated Prompt Engineering Literacy Scale (PELS), the authors examine differences in resting-state brain network dynamics and functional connectivity, providing empirical evidence for distinct neural signatures associated with prompting proficiency.

Methodological Framework

The paper employs a cross-sectional design with 22 right-handed participants, screened for neurological and psychiatric health. Expertise classification is based on the PELS, a psychometrically validated instrument assessing prompt construction, advanced techniques, verification/optimization, and ethical/cultural sensitivity. MRI data acquisition utilizes a Siemens MAGNETOM Spectra 3T scanner, with both high-resolution anatomical and resting-state functional scans.

Data analysis integrates two complementary pipelines:

• Group ICA (GIFT toolbox): Decomposition of fMRI data into spatially independent components, focusing on spectral power ratios (low-frequency vs. high-frequency) within established cognitive networks.
• Seed-to-Voxel Connectivity (CONN toolbox): ROI-based functional connectivity analysis, quantifying Pearson correlations and applying Fisher's r-to-z transformation for group-level statistical comparison.

Reliability of the PELS is supported by a Cronbach's alpha of 0.90, and the cutoff for expertise (score >37/50) is empirically derived and expert-validated, though acknowledged as preliminary.

Key Findings

Intrinsic Network Dynamics

Experts exhibit significantly higher low-to-high frequency power ratios (LHR) in core cognitive networks:

• Ventral Visual Network (VVN): LHR of 63.0 (experts) vs. 36.7 (intermediates)
• Posterior Default Mode Network (pDMN): LHR of 44.4 vs. 33.2
• Left Lateral Parietal Network (LLPN): LHR of 53.3 vs. 36.7

These results indicate stronger low-frequency synchronization and more stable intrinsic network dynamics in experts, consistent with efficient neural processing and greater network integration. The LLPN, implicated in semantic processing and episodic memory retrieval, is particularly relevant for prompt formulation tasks.

Functional Connectivity

Seed-to-voxel analyses reveal:

• Left Middle Temporal Gyrus (MTG): Increased connectivity in experts (p < 0.03), supporting enhanced semantic processing and multimodal integration.
• Left Frontal Pole (FP): Increased connectivity in experts (p < 0.05), associated with strategic planning, goal-directed behavior, and metacognition.

Additionally, experts demonstrate consistently lower fractional amplitude of low-frequency fluctuations (fALFF) across multiple components (e.g., 0.534 vs. 0.852 in Component 18), suggesting reduced spontaneous neural activity and potentially more efficient cognitive control at rest.

Theoretical and Practical Implications

The observed neural distinctions have several implications:

• Cognitive Models of Prompting: The findings support the hypothesis that prompt engineering expertise is reflected in specialized neural configurations, particularly in networks supporting semantic retrieval, executive planning, and visual reasoning. This aligns with theories of expertise development in other complex cognitive domains.
• Human-AI Interface Design: Interfaces that scaffold semantic, visual, and executive processes (e.g., prompt templates, flowchart-style visualizations) may facilitate novice-to-expert transitions by offloading cognitive demands and aligning with expert neural strategies.
• Training and Evaluation: Neurocognitive markers could inform adaptive training programs and serve as user-centered evaluation metrics, favoring tools that optimize cognitive load and task performance.
• Alignment with AI Architectures: The enhanced connectivity in regions implicated in semantic and strategic processing suggests that future LLMs and prompting platforms could be designed to anticipate and support expert cognitive workflows, potentially leveraging unified embedding spaces that map onto cortical hierarchies.

Limitations and Future Directions

The pilot nature of the paper (N=22) limits generalizability and precludes causal inference. The expertise cutoff is heuristic and requires further psychometric calibration. Resting-state fMRI, while informative, does not directly capture task-specific neural dynamics; future work should employ task-based paradigms manipulating prompt complexity and constraints. Longitudinal studies are needed to track neural adaptations as individuals acquire prompting expertise, and to assess the impact of evolving LLM architectures on cognitive demands.

Conclusion

This paper provides preliminary evidence for distinct neurocognitive markers of prompt engineering expertise, including altered low-frequency power dynamics and increased functional connectivity in language and executive control regions. These findings have actionable implications for NLP, HCI, and the design of cognitively-aligned AI systems. Future research should expand on these results with larger samples, longitudinal designs, and task-based neuroimaging, advancing the integration of cognitive neuroscience and AI to optimize human-LLM interaction.