在儿童早期概念中跨模式对齐的签名
Kaarina Aho1, Brett D Roads1, Bradley C Love1,2
1Department of Experimental Psychology, University College London, London WC1H 0AP, United Kingdom.
概括
孩子 孩子 孩子 孩子
科学领域:
- 认知科学 认知科学
- 发展心理学 发展心理学
- 人工智能的人工智能
背景情况:
- 学习通常被视为基于事件的,需要直接输入监督或无监督培训.
- 另一种学习机制,系统对齐,根据共享的上下文相似性,推断不同系统 (例如视觉和语言) 之间的映射.
- 这种对齐是可能的,因为镜像相似关系跨模式,允许跨系统的概念推断.
研究的目的:
- 调查儿童早期的概念结构是否有助于系统对学习进行调整.
- 为了评估系统对齐在推断新概念,特别是视觉词映射,在人工智能中的有效性.
- 探索语义社区密度在儿童概念中的作用,以有效地调整系统.
主要方法:
- 用儿童的概念数据进行模拟研究,以建模系统对齐.
- 人工智能被编程为通过利用概念的结构特征,特别是密集的语义社区来推断视觉词语映射.
- 绩效在模拟环境中进行评估,以反映儿童发育,并在环境中排除常见概念.
主要成果:
- 儿童早期的概念非常适合系统对齐,使代理商能够在没有监督的情况下在推断视觉文字映射时达到超过85%的准确性.
- 儿童概念中密集的语义社区的结构性质对于有效的概念选择和推理至关重要.
- 采用这些发展原则的人工代理在各种模拟环境中表现出高效率.
结论:
- 儿童的概念发展通过系统对齐为学习提供了最佳的基础,补充了传统的基于事件的学习.
- 早期概念的结构特征,如语义密度,是有效跨模式学习的关键.
- 将这些发展原理纳入人工系统可以显著提高机器学习能力,特别是在无监督概念获取方面.
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