Abstract
One of the frequently employed tasks within the implicit timing paradigm is the foreperiod task. The foreperiod is the time interval spanning from the presentation of a warning signal to the appearance of a target stimulus, during which reaction time trajectory follows time uncertainty. While the typical approach in analyzing foreperiod effects is based on linear approximations, the uncertainty in the estimation of time, expressed by the Weber fraction, implies a nonlinear trend. In the present study, we analyzed the variable foreperiod reaction times from a relatively large sample (n = 109). We found that the linear regression on reaction times and log-transformed reaction times poorly fitted the foreperiod data. However, a nonlinear regression based on an exponential decay function with three distinctive parameters provided the best fit. We discussed the inferential hazards of a simplistic linear approach and demonstrated how a nonlinear formulation can create new opportunities for studies in implicit timing research, which were previously impossible.