人类类类别学习中的频率效应
Dong-Yu Yang1, Darrell A Worthy1
1Department of Psychological and Brain Sciences, Texas A&M University.
概括
人类类别学习受到类别遇到的频率的影响. 一个近期权重的示例模型准确地预测了更频繁的类别是首选的,即使是新的项目.
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:
- 原型和示例模型是人类类别学习的关键理论.
- 分类频率是影响学习和决策的已知因素.
- 了解分类的计算机制至关重要.
研究的目的:
- 调查类别学习的原型与示例模型.
- 检查类别频率对分类决策的影响.
- 确定最近在学习计算模型中的作用.
主要方法:
- 使用基线和近期权重的原型和示例模型.
- 在四个实验中使用来自双变正常分布的刺激.
- 在训练期间操纵类别的频率和测试的新兴刺激的分类.
主要成果:
- 观察到一个显著的频率效应:参与者更喜欢将项目分类到更频繁的类别中.
- 这种偏好扩展到训练区域以外的刺激.
- 最近权重的通用上下文模型示例模型最适合数据和预测的分类模式.
结论:
- 相对类别频率显著影响人类对新品的分类.
- 分类决定是基于近期权重,累积证据,而不是平均证据.
- 原型模型未能解释观察到的频率效应,与示例模型不同.
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