统计学学习的音节序列作为轨迹通过一个感知相似空间
1Department of Psychology, University of Southern California, 3620 S. McClintock Ave, Los Angeles, CA 90089, United States.
Cognition
|January 14, 2024
概括
这项研究引入了一个新的统计学习 (SL) 模型,将序列学习视为相似空间中的轨迹,而不仅仅是概率计算. 这种新方法准确地模拟了人类的学习,并预测了语音细分中的新效应.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 心理语言学 心理语言学
背景情况:
- 统计学习 (SL) 是一种基本的认知能力,用于处理跨物种的顺序信息.
- 通过SL进行语音细分的传统模型侧重于离散单元之间的过渡概率.
- 现有的模型通常将语音单元视为抽象的,没有特征的实体.
研究的目的:
- 通过相似空间提出和评估基于轨迹的统计学学习的替代模型.
- 调查反复的神经网络是否可以模拟人类在经典SL任务中的表现.
- 探索相似性表示如何影响人工语言的学习结果.
主要方法:
- 开发了循环神经网络模型,在连续相似空间中表示音节.
- 使用这些网络模型模拟了经典的语音细分研究.
- 设计了人工语言,并与人类参与者进行了实验,以测试模型预测.
主要成果:
- 循环网络模型成功模拟了经典统计学习研究的结果.
- 在特定条件下,模型预测和实验证实了反向SL效应 (部分词比单词更熟悉).
- 分析显示,音节相似性特征显著影响来自具有相同过渡概率的人造语言的学习.
结论:
- 序列学习,特别是在语音细分中,可以有效地被模拟为在相似空间中导航轨迹.
- 在连续相似空间中表示语音单元提供了比离散概率计算更丰富的统计学学习描述.
- 这一框架为人类统计学学习和语言学习的基础机制提供了新的见解.
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