Combining speed and accuracy to control for speed-accuracy trade-offs(?)

In psychological experiments, participants are typically instructed to respond as fast as possible without sacrificing accuracy. How they interpret this instruction and, consequently, which speed–accuracy trade-off they choose might vary between experiments, between participants, and between conditions. Consequently, experimental effects can appear unpredictably in either RTs or error rates (i.e., accuracy). Even more problematic, spurious effects might emerge that are actually due only to differential speed–accuracy trade-offs. An often-suggested solution is the inverse efficiency score (IES; Townsend & Ashby, 1983), which combines speed and accuracy into a single score. Alternatives are the rate-correct score (RCS; Woltz & Was, 2006) and the linear-integrated speed–accuracy score (LISAS; Vandierendonck, 2017, 2018). We report analyses on simulated data generated with the standard diffusion model (Ratcliff, 1978) showing that IES, RCS, and LISAS put unequal weights on speed and accuracy, depending on the accuracy level, and that these measures are actually very sensitive to speed–accuracy trade-offs. These findings stand in contrast to a fourth alternative, the balanced integration score (BIS; Liesefeld, Fu, & Zimmer, 2015), which was devised to integrate speed and accuracy with equal weights. Although all of the measures maintain “real” effects, only BIS is relatively insensitive to speed–accuracy trade-offs.