Color preference is an important aspect of human behavior, but little is known about why people like some colors more
than others. Recent results from the Berkeley Color Project (BCP) provide detailed measurements of preferences among
32 chromatic colors as well as other relevant aspects of color perception. We describe the fit of several color preference
models, including ones based on cone outputs, color-emotion associations, and Palmer and Schloss's ecological valence
theory. The ecological valence theory postulates that color serves an adaptive "steering' function, analogous to taste
preferences, biasing organisms to approach advantageous objects and avoid disadvantageous ones. It predicts that people
will tend to like colors to the extent that they like the objects that are characteristically that color, averaged over all such
objects. The ecological valence theory predicts 80% of the variance in average color preference ratings from the
Weighted Affective Valence Estimates (WAVEs) of correspondingly colored objects, much more variance than any of
the other models. We also describe how hue preferences for single colors differ as a function of gender, expertise,
culture, social institutions, and perceptual experience.
KEYWORDS: Electronic imaging, Colorimetry, Psychology, Visualization, Graphic design, Factor analysis, Current controlled current source, Human subjects, Computing systems, Binary data
The previous literature on the aesthetics of color combinations has produced confusing and conflicting claims. For
example, some researchers suggest that color harmony increases with increasing hue similarity whereas others say it
increases with hue contrast. We argue that this confusion is best resolved by considering three distinct judgments about
color pairs: (a) preference for the pair as a whole, (b) perceived harmony of the two colors, and (c) preference for the
figural color when viewed against the background color. Empirical support for this distinction shows that pair
preference and harmony ratings both increase as hue similarity increases, but preference correlates more strongly with
component color preferences and lightness contrast than does harmony. Although ratings of both pair preference and
harmony decrease as hue contrast increases, ratings of figural color preference increase as hue contrast with the
background increases. Our results refine and clarify well-known and often contradictory claims of artistic color theory.
The concept of space and geometry varies across the subjects. Following Poincare, we consider the construction of the perceptual space as a continuum equipped with a notion of magnitude. The study of the relationships of objects in the perceptual space gives rise to what we may call perceptual geometry. Computational modeling of objects and investigation of their deeper perceptual geometrical properties (beyond qualitative arguments) require a mathematical representation of the perceptual space. Within the realm of such a mathematical/computational representation, visual perception can be studied as in the well-understood logic-based geometry. This, however, does not mean that one could reduce all problems of visual perception to their geometric counterparts. Rather, visual perception as reported by a human observer, has a subjective factor that could be analytically quantified only through statistical reasoning and in the course of repetitive experiments. Thus, the desire to experimentally verify the statements in perceptual geometry leads to an additional probabilistic structure imposed on the perceptual space, whose amplitudes are measured through intervention by human observers. We propose a model for the perceptual space and the case of perception of textured surfaces as a starting point for object recognition. To rigorously present these ideas and propose computational simulations for testing the theory, we present the model of the perceptual geometry of surfaces through an amplification of theory of Riemannian foliation in differential topology, augmented by statistical learning theory. When we refer to the perceptual geometry of a human observer, the theory takes into account the Bayesian formulation of the prior state of the knowledge of the observer and Hebbian learning. We use a Parallel Distributed Connectionist paradigm for computational modeling and experimental verification of our theory.
Conference Committee Involvement (1)
Imaging and Printing in a Web 2.0 World
19 January 2010 | San Jose, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.