Rouder Jeffrey N, Tuerlinckx Francis, Speckman Paul, Lu Jun, Gomez Pablo
Department of Psychological Sciences, University of Missouri, Columbia, MO 65211, USA.
Psychon Bull Rev. 2008 Dec;15(6):1201-8. doi: 10.3758/PBR.15.6.1201.
Understanding how response time (RT) changes with manipulations has been critical in distinguishing among theories in cognition. It is well known that aggregating data distorts functional relationships (e.g., Estes, 1956). Less well appreciated is a second pitfall: Minimizing squared errors (i.e., OLS regression) also distorts estimated functional forms with RT data. We discuss three properties of RT that should be modeled for accurate analysis and, on the basis of these three properties, provide a hierarchical Weibull regression model for regressing RT onto covariates. Hierarchical regression model analysis of lexical decision task data reveals that RT decreases as a power function of word frequency with the scale of RT decreasing 11% for every doubling of word frequency. A detailed discussion of the model and analysis techniques are presented as archived materials and may be downloaded from www.psychonomic.org/archive.
理解反应时间（RT）如何随操作而变化，对于区分认知理论至关重要。众所周知，汇总数据会扭曲函数关系（例如，埃斯蒂斯，1956年）。另一个不太为人所知的陷阱是：最小化平方误差（即普通最小二乘法回归）也会扭曲RT数据的估计函数形式。我们讨论了RT的三个属性，为了进行准确分析应对其进行建模，并基于这三个属性，提供了一个将RT回归到协变量上的分层威布尔回归模型。对词汇判断任务数据的分层回归模型分析表明，RT随着词频的幂函数而降低，词频每翻倍，RT的尺度就会降低11%。模型和分析技术的详细讨论作为存档材料呈现，可从www.psychonomic.org/archive下载。
