Perceptual multistability, alternative perceptions of an unchanging stimulus, gives important clues to neural dynamics. The present study examined 56 perceptual dominance time series for a Necker cube stimulus, for ambiguous motion, and for binocular rivalry. We made histograms of the perceptual dominance times, based on from 307 to 2478 responses per time series (median=612), and compared these histograms to gamma, lognormal and Weibull fitted distributions using the Kolmogorov-Smirnov goodness-of-fit test. In 40 of the 56 tested cases a lognormal distribution provided an acceptable fit to the histogram (in 24 cases it was the only fit). In 16 cases a gamma distribution, and in 11 cases a Weibull distribution, were acceptable but never as the only fit in either case. Any of the three distributions were acceptable in three cases and none provided acceptable fits in 12 cases. Considering only the 16 cases in which a lognormal distribution was rejected ( p<0.05) revealed that minor adjustments to the fourth-moment term of the lognormal characteristic function restored good fits. These findings suggest that random fractal theory might provide insight into the underlying mechanisms of multistable perceptions.

Perceptual multistability during ambiguous visual perception is an important clue to neural dynamics. We examined perceptual switching during ambiguous depth perception using a Necker cube stimulus, and also during binocular rivalry. Analysis of perceptual switching time series using variance-sample size analysis, spectral analysis and time series shuffling shows that switching times behave as a 1/f noise and possess very long range correlations. The long memory feature contrasts sharply with the traditional satiation models of multistability, where the memory is not incorporated, as well as with recently published models of multistability and neural processing, where memory is excluded. On the other hand, the long memory feature favors the concept of "dynamic core" or coalition of neurons, where neurons form transient coalitions. Perceptual switching then corresponds to replacement of one coalition of neurons by another. The inertia and memory measures the stability of a coalition: a strong and stable coalition has to be won over by another similarly strong and stable coalition, resulting in long switching times. The complicated transient dynamics of competing coalitions of neurons may be addressable using a combination of functional imaging, measurement of frequency-tagged magnetoencephalography and frequency-tagged encephalography, simultaneous recordings of groups of neurons in many areas of the brain, and concepts from statistical mechanics and nonlinear dynamics theory.

Multistability in vision is an intriguing phenomenon that is currently not well understood. In this paper, we present a new, stochastic model for multistable visual perception. It is based on results of time series analysis of experimental data, yielding evidence for it being a linear, stochastic process. This is the outcome of testing for unstable periodic orbits and comparing the correlation dimension of the data to that of white noise. In the model, all degrees of freedom but one can be determined by general knowledge, thus resulting in a high degree of parsimony. The remaining parameter is used to model the individual characteristics that vary between subjects. Fitting simulations to the experimental data proves the parameter to be in a physiologically highly plausible range.

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