在现实世界高维类别域中测试分类和旧新识别的正式认知模型
Brian J Meagher1, Robert M Nosofsky1
1Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States.
Cognitive psychology
|September 1, 2023
概括
这项研究使用现实世界的岩石图像来模拟分类和旧新识别记忆. 一个增强的示例模型成功地解释了复杂刺激的两个记忆任务.
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:
- 分类和旧新识别记忆是基本的认知过程.
- 以前的正式模型经常使用简化的刺激和人工类别.
- 缺乏使用现实世界的刺激的两个过程的统一解释.
研究的目的:
- 将认知过程的正式建模扩展到高维,现实世界的刺激.
- 量化建模分类和旧新识别记忆.
- 在复杂的数据上测试不同计算模型的有效性.
主要方法:
- 收集了540张岩石,变态岩石和沉积岩石的图像上的分类和旧新识别判断.
- 训练有素的参与者在现实世界中进行类别分类.
- 在培训和新项目上测试了分类和识别记忆.
- 使用示例,聚类和原型模型建模行为数据.
主要成果:
- 实例和集群模型很适合分类数据;原型模型没有.
- 一个标准示例模型部分解释了旧新识别数据.
- 一个扩展的混合相似性样本模型显著改善了识别数据的合适性.
- 这是对个体,高维的现实世界物品进行量化模拟记忆的第一个研究之一.
结论:
- 认知模型可以扩展,以考虑复杂的真实世界刺激的记忆过程.
- 以示例为基础的模型,特别是具有特征匹配考虑的模型,为理解分类和识别记忆提供了强大的框架.
- 这些发现强调了刺激复杂性和现实世界类别结构在评估认知模型中的重要性.
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