Computational nonlinear dynamics model of percept switching with ambiguous stimuli

Abstract

Simulation results of bistable perception due to ambiguous visual stimuli are presented which are obtained with a behavioral nonlinear dynamics model using perception–attention–memory coupling. As a kind of minimum architecture representing the Thalamo-Cortical V4–InferoTemporal–PraeFrontal–V4 ("what") loop the basic model couples the dynamics of a macroscopic perception state order parameter with an adaptive attention (feedback gain) control parameter with reentrant delay T and additive band limited attention noise (Fürstenau 2006, 2007). Quasiperiodic perceptual switching is induced by attention fatigue with a perception bias which balances the relative duration of the alternative percepts, corresponding to the well known Synergetics model of Ditzinger and Haken (1989). Mean perceptual duration times of 2 – 5 s of the Gamma-distributed dwell time statistics are predicted in agreement with experimental results reported in the literature, if a feedback delay T of 40 ms is assumed which is typical for cortical reentrant loops and the stimulus-V1 latency (Lamme 2003). Numerically determined perceptual transition times of 3 – 5 T are in reasonable agreement with stimulus–conscious perception delay of 150 – 200 ms. Periodic stimulus simulations as a function of stimulus off-time yields the reversal rate variation in surprisingly good quantitative agreement with classical experimental results of Orbach et.al.(1966) when selecting adaptation and recovery time constants of 1 – 2 s. As an additional feature memory effects are introduced by allowing for the slow adaptation of the perception bias parameter via coupling to the perception state. They are quantified by calculation of the self similarity (Hurst) parameter H of the reversal time series (H greater than 0.5). The simulations exhibit long range correlations, i.e. the fractal character of the perceptual duration times in agreement with recent experimental results of Gao et al. (2006). This finding again fits into the proposed picture of underlying nonlinear brain dynamics as derived from analysis and theoretical modeling of EEG time series (e.g. (Lutzenberger et.al. 1995)). Also a straightforward extension appears possible for modeling top-down control of eye movements.