Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908393635053568 |
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| author | Russell, Matthew Shah, Aman Blaney, Giles Amores, Judith Czerwinski, Mary Jacob, Robert J. K. |
| author_facet | Russell, Matthew Shah, Aman Blaney, Giles Amores, Judith Czerwinski, Mary Jacob, Robert J. K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration Russell, Matthew Shah, Aman Blaney, Giles Amores, Judith Czerwinski, Mary Jacob, Robert J. K. Human-Computer Interaction 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. |
| title | Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.04167 |