培训范例之间的高度一致性促进了面部家族的广泛泛化
Caitlin R Bowman1, Dagmar Zeithamova1
1Department of Psychology, University of Oregon.
概括
用典型的例子进行培训,而不是变量,增强概念知识和概括. 高连贯性培训通过关注共同特征来帮助类别学习,从而导致更快的学习和更广泛的理解.
科学领域:
- 认知心理学 认知心理学
- 学习科学 学习科学
- 人工智能的人工智能
背景情况:
- 概念知识的概括对于学习至关重要.
- 训练集可变性对概括性的影响仍在争论中.
- 了解如何优化学习体验的概念知识形成是关键.
研究的目的:
- 调查训练集的变性 (典型性和集大小) 如何影响概念知识的形成和概括.
- 为了确定高连贯性 (低可变性) 培训是否有利于类别学习和概括.
- 探索潜在的学习机制,例如原型抽象与基于示例的学习.
主要方法:
- 操纵训练在示例典型性 (高与低连贯性) 和集大小 (小与大) 方面显示了集变性.
- 控制了所有条件下的训练试验总数.
- 使用面部家族类别结构,旨在突出共同和独特的特征.
- 采用探索性模型拟合来测试学习机制假设.
主要成果:
- 训练集大小对学习和概括的影响最小.
- 高连贯性 (低可变性) 训练显著改善了类别学习和概括.
- 高连贯性培训鼓励在分类和识别任务中依赖共享特征.
- 一个示例模型,而不是一个原型模型,更好地解释了跨条件的学习模式.
结论:
- 高变量的培训并不总是有利于概念学习,特别是当培训时间受到控制时.
- 在典型例子 (高连贯性) 上进行培训,通过促进共享特征的提取,促进了更快的学习和更广泛的类别知识.
- 学习者受益于对代表性示例的专注培训,以建立强大的概念理解.
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