Determinants involved in the perception of the necker cube: An application of catastrophe theory

Abstract

The study is concerned with evaluating interactions at the organic level within the visual perception subsystem of living systems. The reported work focuses on the identification of some of the determinants of multistable perception by experimentally testing a nonlinear dynamical systems (catastrophe) model of the Necker Cube. This technique serves as an advantage over linear threshold models which cannot effectively study multivalued functional relationships. It was proposed that manipulation of two independent control parameters (bias or changing shape by continuously varying perspective lines and selective stimulus shading) was compatible with the subjective dichotomy of bistable perception of the Necker cube. One hundred and twenty naive subjects, categorized by age, sex, and optical aids, were presented with a computer-generated sequence of 63 stimuli (7 shading levels X 9 perspective levels) to which they had to respond as to whether they saw a "hollow" or "solid" image. The work revealed that bias and shading exerted their effects in opposition and that each influenced the other. Both were decisive factors involved in the perception of the cube. These findings are supported by topological and psychological evidence.

Chaos in percepts?

“…Here we present evidence that processes searching for the appropriate percept have some characteristics associated with lowdimensional, chaotic dynamical systems. Our results confirm a proposal by Poston and Stewart (1978) and conclusions reached by Ta'eed et al (1988) that multistable percepts can be modeled as nonlinear dynamical systems.…”

A catastrophe model of construction conflict behavior

“…These steps were widely adopted by previous studies of catastrophe theory using Cuspfit program [46,48,50,85,86].…”

A stochastic model of multistable visual perception

“…Largely independent of these studies, physicists have been dealing with the topic by applying modelling techniques from other ®elds such as catastrophe theory (Poston and Stewart 1978;Ta'eed et al 1988), synergetics (Ditzinger and Haken 1989) and neural networks (Kawamoto and Anderson 1985). These have been topdown approaches, starting from a given model and ®t-ting it to the behaviour ascribed to experimental data.…”