通过强化学习探索语言规则学习的特点:语义和语法视角
Yingyu Li1, Xiyuan Wang1, Weiwei Zhang1
1Institute of Psychological and Brain Science, Liaoning Normal University, Dalian 116029, China; Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province 116029, China.
Biological psychology
|July 9, 2025
概括
这项研究揭示了语言规则的学习比象征规则的学习更复杂. 强化学习模型表明,语言中的语义处理需要更多的资源,而语法处理则受益于固有的结构.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
背景情况:
- 规则学习是人类认知的基础.
- 强化学习 (RL) 模型可以模拟非语言系统中的规则学习.
- 了解语言和非语言规则学习之间的差异至关重要.
研究的目的:
- 在语言和非语言符号中模拟语义和语法规则学习,使用RL.
- 为了调查认知和神经机制是否在语言和非语言符号处理之间存在差异.
- 分析RL参数对行为和神经振荡的影响.
主要方法:
- 利用强化学习来模拟试验逐试验的学习动态.
- 对语言符号 (人工语言) 和非语言符号 (形状) 的比较学习过程.
- 分析了与预测错误信号相关的行为性能 (响应时间) 和神经振荡 (贝塔频段功率).
主要成果:
- 语言中的语义处理更复杂,需要更多的认知资源和更慢的,深思熟虑的过程,以更慢的响应时间和降低的β频段功率与不断增加的预测错误来表示.
- 与非语言任务不同的是,语言中的语法处理显示了结构性线索的增强学习,随着预测错误信号的增加,β频段功率的增加证明了这一点.
- 在语义和语法处理之间,以及在语言和非语言符号之间,RL参数效应有所不同.
结论:
- 与人工符号规则学习相比,语言规则处理,特别是语义,涉及不同的和更资源密集的认知和神经机制.
- 语言中的语法处理利用固有的结构线索,使其与RL框架内的非语言符号任务区别开来.
- 这些发现为人类语言学习与人工规则学习的计算和神经基础提供了新的见解.
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