工作记忆和自我指导的内在语言增强多任务通用化在主动推理中的多任务通用化
Jeffrey Frederic Queißer1, Jun Tani2
1Okinawa Institute of Science and Technology, Okinawa 904-0412, Japan jeffrey.queisser@oist.jp.
Neural computation
|October 29, 2025
概括
这项研究表明,用两个工作记忆模块和内在语言堆叠的循环神经网络 (RNN) 如何获得复杂的任务. 这种方法通过记忆和语言之间的结构化相互作用来提高任务性能和概括性.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知建模认知建模
- 人工智能的人工智能是人工智能.
背景情况:
- 工作记忆对于认知任务至关重要.
- 循环神经网络 (RNN) 用于建模顺序数据.
- 积极推断 (AIF) 为目标导向行为提供了一个框架.
研究的目的:
- 通过堆叠的RNN和多个工作记忆来研究工作记忆任务的同时获取.
- 探索内在语言在任务执行和概括中的作用.
- 在最佳条件下分析模型的内部动态.
主要方法:
- 模拟了一个堆叠的RNN与多个工作内存模块相互作用.
- 员工监督培训通过尽量减少自由能量.
- 利用主动推理 (AIF) 框架来实现目标导向的任务执行.
- 在任务执行过程中加入了自我指导的内在语言.
主要成果:
- 通过两个工作记忆模块和内在语音实现了最佳的任务性能.
- 在最佳条件下,在堆叠的RNN中形成了一个时间层次.
- 该模型展示了跨新任务配置的概括能力.
结论:
- 工作记忆和自主语言生成之间的相互作用支持概括.
- 该模型提供了对工作记忆和语言背后的神经机制的洞察.
- 这些发现表明了人工通用智能的潜在架构.
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