Time-to-Event Models for Analyzing Reaction Time Sequences

We investigate reaction time (RT) sequences obtained from lexical decision experiments by applying Time-to-Event modelling (Survival Analysis). This is a branch of statistics for analyzing the expected duration until one or more events happen, associated with a set of potential ‘causes’ (in our case the decision for a ‘word’ judgment as a function of conventional predictors such as lexical frequency, stimulus duration, reduction, etc.). In this analysis, RTs are considered a by-product of an (unobservable) cumulative incidence function that results in a decision when it exceeds a certain threshold. We show that Survival Analysis can be effectively used to narrow the gap between data-oriented models and process-oriented models for RT data from lexical decision experiments. Results of this analysis technique are presented for two different RT data sets. The analysis reveals time-varying patterns of predictors that reflect the differences in cognitive processes during the presentation of auditory stimuli.