通过非参数的等级贝叶斯模型来识别感知学习中的组件过程
Yukai Zhao1,2, Jiajuan Liu3,4, Barbara Anne Dosher3,5
1Center for Neural Science, New York University, New York, NY, USA.
Journal of vision
|May 23, 2024
概括
我们开发了一个层次化的贝叶斯模型 (HBM) 来分析感知学习曲线. HBM准确地捕捉了一般学习,遗忘和快速重新学习,超过了传统的贝叶斯推理程序.
科学领域:
- 认知心理学 认知心理学
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 感知学习涉及复杂的过程,如巩固和重新学习,通常被传统的学习曲线分析所掩盖.
- 高时间分辨率对于在感知学习中剖析这些组成过程至关重要.
研究的目的:
- 开发和比较用于分析感知学习的新型非参数推理程序.
- 评估这些方法在学习曲线内解决组件流程的能力.
主要方法:
- 开发了一个贝叶斯推理程序 (BIP) 和一个层次化的贝叶斯模型 (HBM) 来估计对比度值.
- 在六个不同块大小 (L=10到320) 的会话中,将这两种方法应用于Gabor定向识别数据.
- 利用非参数推断来分析具有高时间分辨率的学习曲线.
主要成果:
- 在所有块大小中,HBM提供了比BIP更好的匹配,更小的标准偏差和更精确的估计.
- HBM产生了公正的估计,而BIP则显示了较小块大小的偏差.
- 具有HBM的小块大小 (L=10,20,40) 成功识别了一般学习,会话间忘记和会话内重新学习.
结论:
- 非参数的HBM提供了一个强大的框架,用于详细评估感知学习.
- 这种方法可以精确识别感知学习中的组件过程.
- HBM推进了学习曲线的分析,为认知过程提供了更深入的见解.
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