在多个时间尺度上快速学习时间依赖性
Cybelle M Smith1, Sharon L Thompson-Schill1, Anna C Schapiro1
1Department of Psychology, University of Pennsylvania.
bioRxiv : the preprint server for biology
|January 31, 2024
概括
人类和人工智能模型可以同时在多个时间尺度上学习订单信息. 这项研究表明,在统计学学习任务中,短时间和长时间的偶然情况的并行学习.
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.
背景情况:
- 人类大脑在不同的时间尺度上处理复杂的环境信息.
- 了解大脑如何学习和表示同时的时间信息流对于认知建模至关重要.
研究的目的:
- 调查人类统计学学习的同时短期和长时间的应急情况.
- 使用循环神经网络来模拟这些学习过程.
主要方法:
- 一个统计学习游戏,有96名人类参与者学习目标位置序列.
- 操纵短期 (顺序对) 和长期 (游戏的一半) 订单信息.
- 训练一个封闭的循环网络来预测目标位置,并将其学习与人类的表现进行比较.
主要成果:
- 人类参与者在两个时间尺度上都表现出了取决于背景的顺序敏感性,在较短的时间尺度上学习更强.
- 经常出现的网络模型展示了多层次的学习,并反映了人类对游戏结构的敏感性.
- 该模型根据规则结构对游戏进行分组,并通过低级别的顺序信息来区分它们.
结论:
- 人类和人工神经网络都可以在不同的时间尺度上同时快速获取和表示订单信息.
- 这些发现表明,在生物和人工系统中处理层次时间信息的共享机制.
- 这项研究提供了关于统计学学习和时间信息处理的神经基础的见解.
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