Color for the perceptual organization of the pictorial plane: Victor Vasarely's legacy to Gestalt psychology

The Application of Color Psychology in Community Health Environment Design

The topic of health has gained importance in today's society in the context of a healthy China. The success of community environmental design is intimately correlated with everyone's physical and mental health since it is the setting for people's daily lives. At present, although the facilities and equipment of the community environment in our country are improving day by day, the important role of color psychology in the community environment has been neglected. Color has always been a part of human life and a very important component of the community environment. When it comes to the design of communal environments, the color design of various areas will have variable degrees of influence on the psychological space and perspective of individuals. Therefore, the purpose of this paper is to investigate the application value of color psychology in the design of community health environments using color psychology as a scientific foundation. Additionally, the paper attempts to use color psychology as a scientific basis to study the thinking of health issues in the community environment. In addition, the results of the questionnaire survey are used to perform analysis and discussion in order to investigate the regular characteristics of the various research objects, as well as their preferences and psychological needs for color. This is done with the goal of providing empirical support for the improvement of the community environment and the health of the people living in it. In the end, the results of the questionnaire survey are compiled with four different design concepts. These design principles include functionality, naturalness, beauty, and safety. And the design intention and optimization strategy from the three directions the overall building, planting, and paving in the community healthy environment are then presented. This is done in order to create a colorful and comfortable healthy environment for the community life of people and to support the development of people's physical and mental health.

Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry

Symmetry in nature is a result of biological self-organization, driven by evolutionary processes. Detected by the visual systems of various species, from invertebrates to primates, symmetry determines survival relevant choice behaviors and supports adaptive function by reducing stimulus uncertainty. Symmetry also provides a major structural key to bio-inspired artificial vision and shape or movement simulations. In this psychophysical study, local variations in color covering the whole spectrum of visible wavelengths are compared to local variations in luminance contrast across an axis of geometrically perfect vertical mirror symmetry. The chromatic variations are found to delay response time to shape symmetry to a significantly larger extent than achromatic variations. This effect depends on the degree of variability, i.e., stimulus complexity. In both cases, we observe linear increase in response time as a function of local color variations across the vertical axis of symmetry. These results are directly explained by the difference in computational complexity between the two major (magnocellular vs. parvocellular) visual pathways involved in filtering the contrast (luminance vs. luminance and color) of the shapes. It is concluded that color variability across an axis of symmetry proves detrimental to the rapid detection of symmetry, and, presumably, other structural shape regularities. The results have implications for vision-inspired artificial intelligence and robotics exploiting functional principles of human vision for gesture and movement detection, or geometric shape simulation for recognition systems, where symmetry is often a critical property.

Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.