Approaching subjective interval timing with a non-Gaussian perspective

Perceiving time intervals is an essential ability of many animals, whose psychophysical properties have yet to be fully understood. A common theoretical approach is to consider that internal representations of time intervals are reflected in probability distribution functions. Depending on the mechanism proposed for interval timing inverse Gaussian and log-normal probability distributions are candidate distributions to represent internal representations of time. In this article, we show that these two distributions approximate each other under the assumptions of mean accuracy and scalar timing when considering experimentally-relevant Weber fractions. Afterward, we show that both distributions may be used in the description of the temporal bisection task, predicting bisection times approximately at the geometric mean of reference time intervals for the experimental range of Weber fractions. Taken together these results suggest that the log-normal and the inverse Gaussian, when adapted to model subjective time intervals, are experimentally indistinguishable, and so are the models that use them as benchmarks.