Multistable perception – spontaneous switches of perception when viewing a stimulus compatible with several distinct interpretations – is often characterized by the distribution of durations of individual dominance phases. For continuous viewing conditions, these distributions look remarkably similar for various multistable displays and are typically described using Gamma distribution. Moreover, durations of individual dominance phases show a subtle but consistent dependence on prior perceptual experience with longer dominance phases tending to increase the duration of the following ones, whereas the shorter dominance leads to similarly shorter durations. One way to generate similar switching behavior in a model is by using a combination of cross-inhibition, self-adaptation, and neural noise with multiple useful models being built on this principle. Here, we take a closer look at the history-dependent changes in the distribution of durations of dominance phases. Specifically, we used Gamma distribution and allowed both its parameters – shape and scale – to be linearly dependent on the prior perceptual experience at two timescales. We fit a hierarchical Bayesian model to five datasets that included binocular rivalry, Necker cube, and kinetic-depth effects displays, as well as data on binocular rivalry in children and on binocular rivalry with modulated contrast. For all datasets, we found a consistent change of the distribution shape with higher levels of perceptual history, which can be viewed as a proxy for perceptual adaptation, leading to a more normal-like shape of the Gamma distribution. When comparing real observers to matched simulated dominance phases generated by a spiking neural model of bistability, we found that although it matched the positive history-dependent shift in the shape parameter, it also predicted a negative change of scale parameter that did not match empirical data. We argue that our novel analysis method, the implementation is available freely at the online repository, provides additional constraints for computational models of multistability.
bioRxiv Subject Collection: Neuroscience