Some decades ago, it was noticed that the distribution function of waiting times in binocular rivalry looked similar to the gamma distribution function. The best fit of experimental data with a gamma distribution function was obtained with an argument very close to 4, for all stimuli and all subjects. The Poisson distribution function is conceptually related to the gamma distribution, ie the distribution function of waiting times for n random events is the gamma distribution with the integer n as argument. The fact that the best fitted parameter is an integer suggests that the process underlying binocular rivalry is a Poisson process. More precisely, it suggests that a percept under binocular rivalry alternates after four successive random events. Without passing judgment about the nature of these events, the above suggestion is currently investigated. One can think of many mechanisms that cause similar distribution functions of waiting times, but distinguishing them is only possible by using dynamic stimuli. The easiest dynamic stimulus, from both experimental and computational viewpoints, is the so-called step function. With a step function, one rivalrous static stimulus is instantaneously replaced by another. Because this operation changes the mean event rate, the time behaviour directly after the step is different from the time behaviour of both stimuli viewed in static conditions separately. When the mean event rates of the two separate static stimuli are known, statistical predictions about the time behaviour directly after the step can be tested.
A Model for Slew Evaluation for On-Chip RC Interconnects using Gamma Distribution Function
