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arXiv · 2506.04167

Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration

Matthew Russell·Aman Shah·Giles Blaney·Judith Amores·Mary Czerwinski·Robert J. K. Jacob
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Abstract

AI-based interactive assistants are advancing human-augmenting technology, yet their effects on users' mental and physiological states remain under-explored. We address this gap by analyzing how Copilot for Microsoft Word, a LLM-based assistant, impacts users. Using tasks ranging from objective (SAT reading comprehension) to subjective (personal reflection), and with measurements including fNIRS, Empatica E4, NASA-TLX, and questionnaires, we measure Copilot's effects on users. We also evaluate users' performance with and without Copilot across tasks. In objective tasks, participants reported a reduction of workload and an increase in enjoyment, which was paired with objective performance increases. Participants reported reduced workload and increased enjoyment with no change in performance in a creative poetry writing task. However, no benefits due to Copilot use were reported in a highly subjective self-reflection task. Although no physiological changes were recorded due to Copilot use, task-dependent differences in prefrontal cortex activation offer complementary insights into the cognitive processes associated with successful and unsuccessful human-AI collaboration. These findings suggest that AI assistants' effectiveness varies with task type-particularly showing decreased usefulness in tasks that engage episodic memory-and presents a brain-network based hypothesis of human-AI collaboration.

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Matthew Russell, Aman Shah, Giles Blaney, Judith Amores, Mary Czerwinski, Robert J. K. Jacob. 2025-06-04. Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration. https://arxiv.org/abs/2506.04167

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