Comparing time-accuracy curves: beyond goodness-of-fit measures

The speed-accuracy trade-off (SAT) is a ubiquitous phenomenon in experimental psychology. One popular strategy for controlling SAT is to use the response signal paradigm. This paradigm produces time-accuracy curves (or SAT functions), which can be compared across different experimental conditions. The typical approach to analyzing time-accuracy curves involves the comparison of goodness-of-fit measures (e.g., adjusted-R2), as well as interpretation of point estimates. In this article, we examine the implications of this approach and discuss a number of alternative methods that have been successfully applied in the cognitive modeling literature. These methods include model selection criteria (the Akaike information criterion and the Bayesian information criterion) and interval estimation procedures (bootstrap and Bayesian). We demonstrate the utility of these methods with a hypothetical data set.