通过由相似性驱动的动态模型提高IAT的可靠性和有效性
Peter D Kvam1, Louis H Irving2, Konstantina Sokratous2
1Department of Psychology, University of Florida, Florida, USA. pkvam@ufl.edu.
Behavior research methods
|July 14, 2023
概括
隐性关联测试 (IAT) 的新几何相似性表示 (GSR) 模型提高了可靠性和有效性. 这种方法分析了响应时间和准确性,提供了比传统方法更强大的认知过程测量方法.
科学领域:
- 认知心理学 认知心理学
- 心理测量 心理测量 心理测量
- 计算神经科学是一种神经科学.
背景情况:
- 隐性关联测试 (IAT) 是一种对认知过程的行为测量,但往往受到测试-检查可靠性低的影响.
- 传统的IAT评分方法并不能捕捉影响任务执行的所有决策过程.
研究的目的:
- 开发和验证用于IAT数据分析的新型建模方法.
- 通过整合响应时间和准确性来提高IAT措施的可靠性和有效性.
- 为应用新的IAT分析模型提供一个可访问的工具.
主要方法:
- 为IAT数据开发了一个几何相似性表示 (GSR) 模型.
- 利用响应时间和准确度推断刺激和类别之间的表示相似性.
- 分解认知过程,包括响应谨慎,刺激编码,概念相似性和响应执行.
- 在点击软件中使用预训练的神经网络进行即时模型拟合.
主要成果:
- GSR模型参数显示了高的测试-重新测试可靠性 (约为0.80-90).
- 与IAT D-score,Quad模型和简单扩散模型相比,GSR模型参数显示出更高的预测有效性.
- 模型参数有效预测了与集团间接触和动机相关的结果.
结论:
- GSR模型显著提高了IAT测量的可靠性和有效性.
- 传统IAT的不可靠性主要是由于数据分析方法,而不是任务本身.
- 开发的软件工具使得先进的IAT分析可用于研究人员没有广泛的技术专业知识.
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