Converting continuous tracking data to pseudo response time distributions

Analysis (and models) of response times typically rely on data from trial-by-trial designs, whereby experimental tasks present participants with a series of trials constructed as a sequence of stimulus presentation, response, and a short break, and all over again. However, real-world behaviours (e.g., driving) often require continuous monitoring of information and accompanied by ongoing responses. In these cases, there is no start and end to a trial, and the researcher cannot measure RT, pre-empting many successful approaches to analysis of RT data (such as Systems Factorial Technology, on which we focus here). We developed and tested a novel technique for converting continuous tracking data to a trial-like form, producing what we call ‘pseudo response times’. These pseudo response times can be conveniently subjected to many existing RT analysis techniques. Participants completed a continuous tracking task. We calculated the absolute tracking error as the distance between the user-controlled needle and to-be-tracked target. We then converted these data to pseudo RTs by setting a threshold of maximum acceptable tracking error, identifying points in the time series when tracking error crossed this threshold, and calculating the time taken to return to acceptable performance. Analyses of pseudo RTs agreed with equivalent analyses of mean tracking error, albeit with less sensitivity.