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自然语言指令在神经元网络中诱导了构成性概括
Reidar Riveland1, Alexandre Pouget2
1Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland. reidar.riveland@unige.ch.
Nature neuroscience
|March 19, 2024
概括
人类可以使用语言指令执行新任务,这是一个关键的认知能力. 我们的研究开发了一种神经模型,通过语言指令在零射击学习中显示了83%的准确性,进步了我们对认知概括的理解.
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:
- 人类根据语言指令执行新任务的能力是基本的认知能力.
- 这种概括能力背后的神经机制仍然不太清楚.
- 自然语言处理的进步为模型认知功能提供了新的途径.
研究的目的:
- 开发和评估一个神经模型,能够执行新的任务,仅基于语言指令.
- 为了研究语言如何在灵活的任务执行中对感觉运动表征进行支架.
- 探索AI模型从运动反中生成任务描述的潜力.
主要方法:
- 利用自然语言处理和预训练语言模型的进展.
- 在一系列常见的心理物理任务上训练神经网络模型,并嵌入语言指令.
- 使用零射击学习范式,对未见的任务进行评估模型性能.
主要成果:
- 最好的模型只使用语言指令 (零射击学习) 在新任务中平均达到83%的正确性.
- 演示了语言支架的感觉运动表示,调整任务活动几何与指令语义.
- 展示了一个模型从运动反中生成新任务的语言描述的能力.
结论:
- 语言在构建通用认知能力的感觉运动表征方面发挥着至关重要的作用.
- 神经模型可以有效地从语言指令中学习执行新任务,模仿人类的概括.
- 这些发现为理解语言引导的灵活认知的神经基础提供了可通过实验测试的预测.
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