Literature DB >> 736877

Nonlinear modeling of multistable perception.

T Poston, I Stewart.   

Abstract

Multistable figures show that the stimulus-percept relation is not a single valued function. We therefore propose a tentative nonlinear model on the hypothesis that the graph of this relation is the equilibrium set of a dynamic system. For simplicity and to obtain testable predictions, we consider a system whose bifurcations are gradient-like and thus generically described by the elementary catastrophes. We motivate this general model, and then show how, in conjunction with the principle of minimal singularity, it implies cusp catastrophe geometry in a specific perceptual example. Indeed, we argue for canonical cusp geometry in this case. The model incorporates naturally certain observed features of multistable perception, such as hysteresis and bias effects. Despite being a continuum model it is naturally compatible with the subjective dichotomy of bistable perception. The model makes testable predictions which may easily be extended to other specific examples of multistable perception.

Mesh:

Year:  1978        PMID: 736877     DOI: 10.1002/bs.3830230403

Source DB:  PubMed          Journal:  Behav Sci        ISSN: 0005-7940


  5 in total

1.  A quantitative population model of whisker barrels: re-examining the Wilson-Cowan equations.

Authors:  D J Pinto; J C Brumberg; D J Simons; G B Ermentrout
Journal:  J Comput Neurosci       Date:  1996-09       Impact factor: 1.621

2.  Simulating bistable perception with interrupted ambiguous stimulus using self-oscillator dynamics with percept choice bifurcation.

Authors:  Norbert Fürstenau
Journal:  Cogn Process       Date:  2014-09-03

3.  Visual inhomogeneity and eye movements in multistable perception.

Authors:  M A García-Pérez
Journal:  Percept Psychophys       Date:  1989-10

4.  Chaos in percepts?

Authors:  W Richards; H R Wilson; M A Sommer
Journal:  Biol Cybern       Date:  1994       Impact factor: 2.086

Review 5.  Dynamical systems, attractors, and neural circuits.

Authors:  Paul Miller
Journal:  F1000Res       Date:  2016-05-24
  5 in total

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