阻塞训练有助于学习多个方案
Andre O Beukers1, Silvy H P Collin2, Ross P Kempner1
1Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Communications psychology
|September 6, 2024
概括
模式学习通过分割新的表示来避免灾难性的干扰,特别是在受阻训练时. 早期被阻塞的训练比较晚的被阻塞的训练更能提高图表的掌握能力.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种计算神经科学.
- 机器学习是机器学习.
背景情况:
- 人类利用心理模式来理解事件序列.
- 由于对现有知识的灾难性干扰,图表采集面临着挑战.
- 交叉培训和代表性分裂是提出的解决方案.
研究的目的:
- 比较"分割"和"非分割"模式的模式学习.
- 调查培训课程 (封锁与交叉) 对模式获取的影响.
- 确定代表性分裂是否可以防止阻塞训练下的干扰.
主要方法:
- 进行了半自然主义的实验.
- 开发和模拟贝叶斯和神经网络模型.
- 在封闭式和交叉式培训课程下比较学习成绩.
主要成果:
- 封锁的培训课程比交错的课程更好地表现.
- 贝叶斯模型包含代表性分割解释了这些发现.
- 早期插入阻塞训练比后期插入更有好处.
结论:
- 作为对预测错误的响应,表示分离有助于模式学习.
- 培训课程显著影响方案的组成和学习效率.
- 阻塞训练的时间影响了整体学习成绩.
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