Estimating the time course of the influence of different factors in human performance is one of the major topics of research in cognitive psychology/neuroscience. Over the past decades, researchers have proposed several methods to tackle this question using latency data. Here we examine a recently proposed procedure that employs survival analyses on latency data to provide precise estimates of the timing of the first discernible influence of a given factor (e.g., word frequency on lexical access) on performance (e.g., fixation durations or response times). A number of articles have used this method in recent years, and hence an exploration of its strengths and its potential weaknesses is in order. Unfortunately, our analysis revealed that the technique has conceptual flaws, and it might lead researchers into believing that they are obtaining a measurement of processing components when, in fact, they are obtaining an uninterpretable measurement.
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Reingold, Sheridan, and colleagues define divergence on survival functions rather than on cumulative distribution functions. The survival function S is 1 − F, where F is the cumulative distribution function. Hence, divergence may be defined equivalently on survival or cumulative distribution functions (CDFs). We choose CDFs because we expect more readers are familiar with cumulative distribution functions than with survival functions.
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Gómez, P., Breithaupt, J., Perea, M. et al. Are divergence point analyses suitable for response time data?. Behav Res 53, 49–58 (2021). https://doi.org/10.3758/s13428-020-01424-1
- Mental chronometry