A stochastic model of multistable visual perception

Abstract

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.

Perceptual Alternation Induced by Visual Transients

“…In bistable perception, adaptation to the dominant percept is widely considered as one of the key factors causing perceptual alternations (Köhler & Wallach, 1944;Cornwell, 1976;Babich & Standing, 1981;Merk & Schnakenberg, 2002;Blake, Sobel & Gilroy, 2003). How is the effect of a flash related to the adaptation process in bistable percepts?…”

Perceptual dominance time distributions in multistable visual perception

“…Multistable visual perception, such as of the Necker cube, is a phenomenon that intrigued scientists for centuries. For example, psychologists have performed numerous experiments with the Necker cube to explore the effects of contrast, luminance, completeness, size, base angle, use of a fixation point, interpolation of squares or cubes, and varying the sideto-base ratio of the stimuli, as well as subject-related variables such as experience, gender, volitional control, attention, or heat and noise in the environment (see review by Merk and Schnakenberg 2002). The significance of research on multistable perception is that it can offer a starting point to investigate consciousness (Crick and Koch 1990) as well as visual information processing, perceptual organization, and the transition from sensation to perception (Blake and Logothetis 2002).…”

A nonlinear dynamics model for simulating long range correlations of cognitive bistability

“…It supports the relevance of the chaotic and fractal character of brain dynamics, with additional stochastic noise components. It contradicts, however, the assumption of a purely stochastic (e.g., Poisson) process (e.g., De Marco et al 1977;Merk and Schnakenberg 2002) as origin of the dwell time statistics.…”

“…Concerning the interpretation of experimental results of bistable perception, there is an ongoing controversial discussion on the possible deterministic, even chaotic or fractal dynamics of cognitive bistability (Poston and Stewart 1978;Ditzinger and Haken 1989;Wilson 1999;Richards et al 1994;Fürstenau 2004;Noest et al 2007) versus dominance of stochastic properties (De Marco et al 1977;Lehky 1995;Merk and Schnakenberg 2002), as derived from statistical analysis of reversal time series. From the above given overview, it appears reasonable to assume that both deterministic and stochastic contributions to the cognitive system dynamics have to be considered.…”