自动思维:催眠状态与大语言模型处理之间的认知平行
Giuseppe Riva1,2, Brenda K Wiederhold3,4, Fabrizia Mantovani5
1Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy.
Cyberpsychology, behavior and social networking
|December 7, 2025
概括
催眠中的认知过程和大型语言模型 (LLM) 显示出惊人的功能平行. 两种系统都表现出自动模式完成,监督有限,从而导致无根据的输出,并突出了整合执行监控的AI架构的需要.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 被催眠的个体的认知过程和大型语言模型 (LLM) 的计算操作具有功能上的相似性.
- 这两种系统都通过自动模式完成,在有限的执行监督下,产生了适合环境的行为.
研究的目的:
- 检查催眠和LLMs在关键原则之间的融合:自动性,抑制监控和上下文依赖.
- 探索没有主观代理的功能代理的概念,以及在这两个领域的策划现象.
主要方法:
- 在催眠和LLM操作中的认知机制的比较分析.
- 关于自动性,抑制监控,上下文依赖,功能代理和策划的文献综述.
主要成果:
- 催眠和LLM表现出自动性,抑制监控 (导致共/幻觉) 和高度的上下文依赖.
- 两者都表现出没有主观代理的功能代理,产生连贯但无根据的输出,需要外部解释.
- 策划,或没有意识的自动目标导向模式生成,在催眠和LLMs中都被观察到.
结论:
- 催眠和LLM之间的相似之处提供了对意图分离和隐藏的动机动态的见解.
- 未来可靠的人工智能可能会从混合架构中受益,将生成流性与执行监控相结合,受到人类思想的启发.
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