在多个时间尺度上快速学习时间依赖
Cybelle M Smith1, Sharon L Thompson-Schill1, Anna C Schapiro1
1University of Pennsylvania.
Journal of cognitive neuroscience
|August 6, 2024
概括
人类和人工智能模型可以同时学习多个时间尺度上的时间模式. 这项研究揭示了统计学习如何能够同时获取短期和长期的订单信息.
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.
- 机器学习 机器学习
背景情况:
- 环境在多个同时的时间尺度上呈现时间信息.
- 了解人类和人工系统如何学习和表现这些动态信息流是至关重要的.
研究的目的:
- 研究人类统计学学习的同时短期和长时间的意外情况.
- 使用循环神经网络来建模这些学习过程.
主要方法:
- 与人类参与者 (n=96) 进行的统计学习游戏,涉及到目标位置预测.
- 操纵短时间尺度 (顺序对) 和长时间尺度 (第一半与第二半) 订单属性.
- 使用受训为立即预测的封闭循环网络进行建模.
主要成果:
- 人类参与者在两个时间尺度上都表现出了依赖于背景的敏感性,以订购信息,在较短的时间尺度上学习更强.
- 循环网络模型展示了多层次的学习,并反映了人类对游戏相似性结构的敏感性.
- 该模型成功地根据规则结构对游戏进行了分组,并根据低级别的顺序信息进行了区分.
结论:
- 人类和计算模型可以快速且同时获取不同时间尺度的订单信息.
- 这项研究突出了生物和人工系统在多层次的时间信息处理能力.
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