在人类和子子解决问题的语言模型
Qianli Yang1, Zhihua Zhu1, Ruoguang Si2
1Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
我们开发了一个解决问题的语言 (LoPS) 模型来分析人类和子的复杂解决问题的行为. 该模型揭示了在解决问题方面不断演变的层次结构,为智力及其演变提供了洞察力.
科学领域:
- 认知科学 认知科学
- 比较心理学比较心理学
- 计算神经科学是一种神经科学.
背景情况:
- 人类的智能卓越于顺序解决问题的能力.
- 了解解决问题的进化是理解智力的关键.
- 跨物种的比较对于揭示认知机制至关重要.
研究的目的:
- 介绍解决问题的语言 (LoPS) 模型,用于定量分析解决问题的行为.
- 研究人类和子的解决问题策略的结构和演变.
- 将解决问题的能力进行比较,并在物种之间识别潜在的语法结构.
主要方法:
- 在改编的Pac-Man游戏中,将小说LoPS模型应用于人类和的游戏数据.
- 利用语言模型框架从解决问题的序列中提取潜在结构 (语法).
- 与个人表现和物种水平差异相关的LoPS语法复杂性.
主要成果:
- 该LoPS模型确定了非马科夫的时间依赖性和分层结构在解决问题的行为两种物种.
- LoPS语法复杂性与游戏表现相关,区分人类和的能力.
- 人类和子都在学习过程中演变的LoPS语法,从简单的结构发展到复杂的结构.
结论:
- 解决问题的结构不是固定的,而是不断发展以提高效率和复杂性.
- 这项研究揭示了人类和子如何分解复杂的任务并进行导航的见解.
- 这项工作为探索解决问题和智能进化的神经基础奠定了基础.
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