Perceptual alternation of ambiguous patterns: a model based on an artificial neural network
Massimo Riani, Francesco Masulli, Enrico Simonotto · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
An artificial neural network modelling some peculiar aspects of human perception in the presence of so-called ambiguous figures is described. When one of such patterns is observed, the same visual input can elicit two different interpretations, A and B, giving rise to a cyclic perceptual alternation of the two competitive percepts. The neural network used to model the phenomenon consists of two layers of identical subunits, which have been derived from the brain-state-in-a-box (BSB) model developed by Anderson and co-workers. Computer simulations have demonstrated that the model based on this two-layer neural network allows one to obtain the stochastic gamma distributions of the experimental perceptual durations of the two alternative interpretations of an ambiguous pattern. Moreover, simulation results are in good agreement with some other characteristics of the perceptual alternation phenomenon.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.