Department of Psychology, University of Mannheim, L 13, 17, Mannheim, 68161, Germany.
Psychometrika. 2018 Dec;83(4):893-918. doi: 10.1007/s11336-018-9622-0. Epub 2018 May 24.
Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.
多项加工树模型假设离散的认知状态决定了观察到的反应频率。广义加工树（GPT）模型将这一概念框架扩展到了连续变量，如反应时间、过程追踪测量或神经生理变量。GPT 模型假设有限混合分布，权重由加工树结构决定，连续成分由参数化分布建模，如状态之间具有单独或共享参数的高斯分布。我们讨论了可识别性、参数估计、模型检验、建模语法以及 GPT 估计的精度提高。最后，将语义分类的特征比较模型的 GPT 版本应用于计算机鼠标轨迹。
