生成性人工智能使用与面部表情衍生的中央执行网络指数之间的关联:试点研究
Keisuke Kokubun1, Yoshinori Yamakawa2, Anna Yoshida2
1Graduate School of Management, Kyoto University, Kyoto 606-8501, Japan.
Brain sciences
|January 28, 2026
概括
生成性AI的使用可能会在创作任务期间影响认知网络,特别是在概念组合过程中减少中央执行网络 (CEN) 的活动. 更高的创造性自我效能可能会缓冲这种效应.
科学领域:
- 认知神经科学 认知神经科学
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 生成性AI的快速增长引发了对其认知效应的质疑,特别是在创作过程中.
- 以前与大脑网络 (默认模式网络/DMN,中央执行网络/CEN) 相关的面部表情指数被用来探索AI的影响.
研究的目的:
- 研究生成性AI使用和面部表情衍生的神经网络指数之间的特定任务关联.
- 探索创造性自我效能与这些神经网络在人工智能辅助任务中的反应之间的潜在相关性.
主要方法:
- 33名商业专业人士参与了人工智能支持的三个写作任务:概念生成,概念组合和混合任务.
- 收集并分析了面部表情数据,以获得与DMN和CEN活动相关的指数.
主要成果:
- 在概念组合任务中观察到与CEN相关的面部表情指数显著下降.
- 对于概念生成或混合任务,没有发现任何重大变化.
- 创造性自我效率的提高与CEN相关指数的较少明显下降相关.
结论:
- 研究结果表明,生成性AI可能会在特定的创意子流程 (如概念组合) 中影响CEN.
- 由于间接措施和缺乏对照组,结果是初步的;需要进一步研究.
- 未来的研究应该采用受控设计和直接的神经成像来证实这些产生假设的发现.
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