The effects of signal uncertainty on detection performance were measured using a new procedure that allows precise specification of the initial temporal uncertainty. Five different models of the detection process, two assuming a continuous representation of the sensory input and three assuming a discrete representation, were compared with the obtained data. The effects of varying signal uncertainty (the number of potential signal intervals was one, five, or ten) had little effect on detection performance. The one‐parameter form of the choice model can be rejected without hesitation. The continuous Gaussian model and the symmetric two‐state model are significantly different from the data. The high threshold and sophisticated two‐state models provide accurate descriptions of the data that cannot be rejected on statistical grounds.