Anticipation of Time Spans: New Data from the Foreperiod Paradigm and the Adaptation of a Computational Model

To act successfully, it is necessary to adjust the timing of one's behavior to events in the environment. One way to examine human timing is the foreperiod paradigm. It requires experimental participants to react to events that occur at more or less unpredictable time points after a warning stimulus (foreperiod). In the current article, we first review the empirical and theoretical literature on the foreperiod paradigm briefly. Second, we examine how behavior depends on either a uniform or peaked (at 500ms) probability distribution of many (15) possible foreperiods. We report adaptation to different probability distribution with a pronounced adaptation for the peaked (more predictable) distribution. Third, we show that Los and colleagues' [1] computational model accounts for our results. A discussion of specific findings and general implications concludes the paper.