Throw Your Response Times in the Bin: Accounting for Measurement Noise in Response Time Modeling

Response times (RTs) are crucial in experimental psychology, providing insights into affective and cognitive processes. Although the finite resolution of stimulus presentation and response recording introduce noise into measured RTs, methods used to fit RT-based cognitive models typically ignore this source of measurement uncertainty. We mathematically characterize the effects of different types of measurement error on RT distributions. We then investigate how a range of realistic levels of measurement noise affect the estimation of five prominent evidence-accumulation models of RT and choice. Although models differ in sensitivity, we find that in all cases the estimation of at least some parameters is distorted by realistic levels of measurement noise. We propose and evaluate a ``binning'' method that not only ameliorates the resulting biases, but can also substantially reduce the computational cost of model fitting.