用层次贝叶斯模型估计感知学习中的试验逐试验学习曲线
Yukai Zhao1, Jiajuan Liu2, Barbara Anne Dosher2
1Center for Neural Science, New York University, New York, USA.
Research square
|December 4, 2023
概括
我们开发了新的方法来估计逐试验的感知学习曲线. 具有协差 (HBMc) 的等级贝叶斯模型最好地捕捉了学习动态,并预测了未来的性能.
科学领域:
- 认知心理学 认知心理学
- 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:
- 感知学习曲线是理解人类表现的关键.
- 现有的方法汇总数据,掩盖试验逐试验的动态.
- 学习包括一般学习,忘记和快速重新学习.
结论:
- HBMc为分析感知学习组件提供了一个强大的框架.
- 这种方法可以在未测量的时间点预测学习曲线.
- 有助于详细了解学习,遗忘和适应过程.
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