高可变性训练不会在原型-扭曲范式中增强概括性
Mingjia Hu1, Robert M Nosofsky2
1Psychological and Brain Sciences, Indiana University, 1101 E. Tenth Street, Bloomington, IN, 47405, USA.
Memory & cognition
|January 16, 2024
概括
当训练试验被等同时,高可变性训练并没有改善点模式分类任务中的概括性. 这挑战了经典的发现,表明低变化培训可能对更简单的类别结构优越.
科学领域:
- 认知心理学 认知心理学
- 人类分类学习学习学习
背景情况:
- 经典研究表明,高变量训练在原型扭曲范式中增强了概括性.
- 最近的研究表明,当试验数量等同时,低变量训练优越.
研究的目的:
- 通过使用经典的点模式刺激,重新评估训练可变性对概括的影响.
- 调和关于培训变性和类别学习的相互矛盾的发现.
主要方法:
- 采用原型扭曲范式与点模式刺激.
- 在高可变性和低可变性条件中等同训练试验的数量.
- 使用示例和原型模型进行数据分析.
主要成果:
- 高变量训练并没有提高点模式任务的概括性,即使进行了广泛的训练.
- 低变量的训练产生了类似或更好的概括性能.
- 样本和原型模型有质地捕获了观察到的结果.
结论:
- 高变量训练并不总是提高原型扭曲任务的概括性.
- 培训变化的有效性可能取决于类别的复杂性.
- 需要进一步的研究来探索在更复杂的类别结构中的培训效应.
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