大型语言模型相互作用对解决问题和决策的认知影响,使用EEG分析
Ting Jiang1, Jihua Wu1, Stephen C H Leung2
1Pediatric Neurological Department, Anhui Children's Hospital, Hefei, Anhui, China.
Frontiers in computational neuroscience
|July 31, 2025
概括
本研究引入了一种新的框架,使用脑电图 (EEG) 来测量大型语言模型 (LLM) 如何影响用户认知. 该系统准确地评估了人与人工智能交互期间的认知变化,为更具适应性的人工智能铺平了道路.
科学领域:
- 神经科学和人工智能 人工智能
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 越来越多地集成到人类-人工智能协作中,需要了解它们对用户的认知影响.
- 传统的LLM评估侧重于任务执行,忽视了用户交互的神经动态.
- 在评估LLM使用期间的注意力,认知负载和决策等认知影响方面存在差距.
研究的目的:
- 引入一种新的框架来评估LLM交互的认知影响,使用脑电图 (EEG).
- 通过整合神经数据,提供对LLM诱导的认知变化的细粒度,可解释的评估.
- 为了实现更适应性和认知意识的LLM系统的设计.
主要方法:
- 开发了一个整合交互意识语言转换器 (IALT) 的框架,用于增强的代币级建模.
- 整合了一个交互优化推理策略 (IORS),使用强化学习来进行认知一致的推理.
- 将这些组件与实时EEG信号相结合,以进行全面的认知评估.
主要成果:
- 该框架在四个基准EEG数据集 (DEAP,AMIGOS,SEED,DREAMER) 的情绪分类准确性和认知信号对齐方面表现出卓越的表现.
- 在各种EEG配置中实现了高性能,包括低密度和便携式系统,表明了强度.
- 提供了对LLM诱导的认知改变的可解释的见解.
结论:
- 开发的框架为评估LLM互动的认知影响提供了一个强大的方法.
- 这些发现支持设计更具适应性和认知意识的AI系统.
- 在人工智能和神经科学的交叉点开辟了新的研究方向.
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