探索大脑和非象征性神经网络中的抽象模式表示
1Department of Neurology, Massachusetts General Hospital, Cambridge, MA 02170.
bioRxiv : the preprint server for biology
|December 11, 2023
概括
使用长短期记忆 (LSTM) 网络的深度学习模型可以识别言语中的抽象重复模式,反映人类的认知能力. 这些发现表明神经网络中的关联机制可以解释认知生成性.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 人工智能的人工智能
背景情况:
- 人类的认知和语言生成能力依赖于识别抽象关系.
- 之前的研究表明,与人类婴儿不同,简单的循环神经网络 (SRN) 难以抽象模式概括.
研究的目的:
- 调查深度长期短期记忆 (LSTM) 模型是否可以识别从听觉刺激中抽象的音节重复模式和单词.
- 探索这些模型中的表示机制,并将其与人类大脑活动进行比较.
主要方法:
- 训练深度LSTM模型在表达听觉刺激的耳图上,以识别单个音节三角形单词和重复模式.
- 使用表示相似性分析 (RSA) 来比较模型隐藏节点表示与人类大脑活动模式 (MRI受约束的MEG/EEG数据).
- 检查了培训类别 (词与模式) 和音节预训练对代表性歧视发展的影响.
主要成果:
- LSTM模型成功地发现了能够对抽象重复模式进行分类的表示,证明了学习抽象属性的能力.
- 代表性相似性分析显示,后部区域的模型表示与人类大脑活动之间存在显著的相关性.
- 模型学习抽象模式的能力受到训练重点和先前音节识别的影响.
结论:
- 深度学习模型中的关联机制可以解释抽象模式识别.
- 这些发现挑战了纯粹的关联模型不足以解释认知生成性的观念.
- 建议捕捉抽象属性的可发现表示是认知生成力的关键.
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