Perceptual dominance time distributions in multistable visual perception

Authors:
Y. H. Zhou;J. B. Gao;K. D. White;I. Merk;K. Yao
Affiliations:
EB 559, University of Florida, Department of Electrical and Computer Engineering, USA;EB 559, University of Florida, Department of Electrical and Computer Engineering, USA;University of Florida, Department of Psychology, USA;Gerling Global Reinsurance Company, Department of Psychology, Germany;University of California, Department of Electrical Engineering, USA
Venue:
Biological Cybernetics
Year:
2004

Citing 0
Cited 6

A model for perceptual averaging and stochastic bistable behavior and the role of voluntary control

Neural Computation
A Computational Model of Bistable Perception- Attention Dynamics with Long Range Correlations

KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
A Multilayered Scheme of Bidirectional Associative Memory for Multistable Perception

Neural Information Processing
Improving identification accuracy by extending acceptable utterances in spoken dialogue system using barge-in timing

IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Modelling and simulation of spontaneous perception switching with ambiguous visual stimuli in augmented vision systems

PIT'06 Proceedings of the 2006 international tutorial and research conference on Perception and Interactive Technologies
Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

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