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An Ecological Valence Theory of Human Color Preference

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An Ecological Valence Theory of Human Color Preference An ecological valence theory of human color preference Stephen E. Palmer1 and Karen B. Schloss Department of Psychology, University of California, Berkeley, CA 94720 Edited* by Paul Kay, University of California, Berkeley, CA, and approved January 13, 2010 (received for review June 5, 2009) Color preference is an important aspect of visual experience, but contrast model explained 70% of the variance in Hurlbert and little is known about why people in general like some colors more Ling’s preference data on a limited gamut of colors. Both males’ than others. Previous research suggested explanations based on and females’ preferences weighted positively on the S-axis, mean- biological adaptations [Hurlbert AC, Ling YL (2007) Curr Biol 17:623– ing that both sexes preferred colors that were more violet to colors 625] and color-emotions [Ou L-C, Luo MR, Woodcock A, Wright A that were more yellow-green. On the LM-axis, however, females (2004) Color Res Appl 29:381–389]. In this article we articulate an weighted somewhat positively, preferring redder colors, and males ecological valence theory in which color preferences arise from weighted somewhat negatively, preferring colors that were more people’s average affective responses to color-associated objects. blue-green. This gender difference formed the basis of Hurlbert An empirical test provides strong support for this theory: People and Ling’s evolutionary/behaviorally adaptive hypothesis, in that like colors strongly associated with objects they like (e.g., blues they attributed the difference to hardwired mechanisms that with clear skies and clean water) and dislike colors strongly associ- evolved in hunter-gatherer societies: Females like redder colors ated with objects they dislike (e.g., browns with feces and rotten because their visual systems are specialized for identifying ripe food). Relative to alternative theories, the ecological valence theory fruit/berries against green foliage. Hurlbert and Ling (10–11) did both fits the data better (even with fewer free parameters) and not speculate, however, on why males prefer colors that are more provides a more plausible, comprehensive causal explanation of blue-green or why both genders prefer colors that are more violet color preferences. to colors that are more yellow-green. Later, Ling and Hurlbert (14) showed that for a more diverse set of colors, the fit of the cone- aesthetic preference | color vision | ecological theory contrast model improved if they added two more dimensions to the COGNITIVE SCIENCES PSYCHOLOGICAL AND S-axis and LM-axis predictors: a lightness predictor (S+L+M) and olor preference is an important aspect of visual experience a saturation predictor (Suv from CIELUV color space). Cthat influences a wide spectrum of human behaviors: buying Ou et al. (15, 16) proposed an account based on “color- cars, choosing clothes, decorating homes, and designing websites, emotions,” which they defined as “feelings evoked by either colors to name but a few. Most scientific studies of color preference or color combinations.” Color-emotions can be linked causally to have focused on psychophysical descriptions (1–8), which may be color preferences if colors are preferred to the extent that viewing sufficient for marketing applications but provide no explanation them produces positive emotions in the observer. They found that of why people like the colors they do or even why they have color 67% of the variance in their color preference data could be pre- preferences at all. More recently, a few speculations have been dicted from three factor-analytic dimensions derived from color- offered about the cause of color preferences. emotion data: active/passive (active preferred), heavy/light (light Humphrey (9) proposed that color preferences stem from the preferred), and warm/cool (cool preferred). They did not explain signals that colors convey to organisms in nature: Sometimes how color-emotions arise from viewing colors, however, or why colors send an “approach” signal (e.g., the colors of a flower some color-emotions predict color preferences better than others. attracting pollinating insects), and sometimes they send an “avoid” In this article we propose a more coherent and comprehensive signal (e.g., the colors of a poisonous toad deterring predators). theory of human color preferences that we call the “ecological Humphrey suggested that, even though the colors of many modern valence theory” (EVT) and report an empirical test of the the- artifacts are almost completely arbitrary (e.g., the color of a shirt or ory. The EVT is related to but is different from both previous car) and thus do not have significant signal value, deeply ingrained theories. Consistent with Humphrey’s (9) and Hurlbert and natural color signals (e.g., the redness of a blushing face) may be Ling’s (10, 11) ideas, the EVT is grounded on the premise that strong enough to influence color preferences. human color preferences are fundamentally adaptive: People are Hurlbert and Ling (10) reported findings that they interpreted more likely to survive and reproduce successfully if they are as support for the kind of evolutionary/behaviorally adaptive attracted to objects whose colors “look good” to them and avoid theory of color preferences that Humphrey suggested would arise objects whose colors “look bad” to them. This ecological heu- based on behavioral adaptations. They suggested that color ristic will, in fact, be adaptive, provided that how good/bad colors preferences are wired into the human visual system as weightings look reflects the degree to which objects that characteristically on cone-opponent neural responses that arose from evolutionary have those colors are advantageous/disadvantageous to the selection. Their hypothesis is essentially that the color vision sys- organism’s survival, reproductive success, and general well-being. tem adapted to improve performance on evolutionarily important Whereas Humphrey’s (9) and Hurlbert and Ling’s (10, 11) fi behavioral tasks (e.g., females nding ripe red fruits and berries hypotheses address an evolutionary time scale (i.e., genetic against green leaves) and that genetic tuning to optimize such behaviorally significant discriminations resulted in preferences for the colors of those objects against the colors of their backgrounds, Author contributions: S.E.P. and K.B.S. designed research; S.E.P. and K.B.S. performed independent of their original context (10, 11). research; S.E.P. contributed new reagents/analytic tools; K.B.S. analyzed data; and S.E.P. Hurlbert and Ling (10) analyzed their preference data in terms and K.B.S. wrote the paper. of the two cardinal dimensions of opponent cone-contrasts: the The authors declare no conflict of interest. LM-axis (L-M) that runs roughly from red to blue-green and the *This Direct Submission article had a prearranged editor. S-axis [S-(L+M)], that runs roughly from violet to yellow-green 1To whom correspondence should be addressed. E-mail: [email protected]. “ ”“ ” “ ” (12, 13), where S, M, and L refer to the outputs of short-, This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. medium-, and long-wavelength cone types, respectively. The cone- 1073/pnas.0906172107/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.0906172107 PNAS Early Edition | 1of6 Downloaded by guest on September 24, 2021 Fig. 1. (A) The present sample of 32 chromatic colors as defined by eight hues, consisting of four approximately unique hues (Red, Green, Yellow, Blue) and their approximate angle bisectors (Orange, cHartreuse, Cyan, Purple), at four “cuts” (saturation-lightness levels) in color-space: saturated (s, Upper Left), light (l, Upper Right), dark (d, Lower Right), and muted (m, Lower Left). (B) The projections of these 32 colors onto an isoluminant plane in CIELAB color-space. (C) Color preferences averaged over all 48 participants. Error bars show SEM. (D) WAVEs for the 32 chromatic colors estimated using data from independent participants performing three different tasks. adaptations across generations resulting in hardwired neural Results and Discussion mechanisms), the EVT extends the range of potentially adaptive Each of 48 participants rated each of the 32 chromatic colors of mechanisms to include individual organisms learning color pref- the Berkeley Color Project (BCP) (Fig. 1 A and B) in terms of erences on an ontogenetic time scale. An analogy to taste pref- how much the participant liked the color using a line-mark rating erences is apt: Taste preferences have both an evolutionary scale that was converted to numbers ranging from −100 to +100 component, because some genetic variations in taste are more with a neutral zero-point. Average preference ratings (Fig. 1C) adaptive than others, and a learned component resulting from show that the saturated (s), light (l), and muted (m) colors experiences that arise from eating various flavored foods that have produced approximately parallel functions with a broad peak at affectively different outcomes (17). The connection of the EVT to blue and a narrow trough at chartreuse. The s colors were pre- the emotion-based theory of Ou et al. (15, 16) is that the envi- ferred to the l and m colors [F(1,47) = 9.20, P < 0.01], which did ronmental feedback required for a learning-based heuristic to not differ from each other (F < 1). Although the pattern of hue † work for color preferences is provided by the emotional outcomes preferences across s, m, and l cuts did not differ [F(14, 658) = ’ of color-relevant experiences during a person s lifetime. The more 1.66, P > 0.05], it did vary for the dark (d) cut relative to the enjoyment and positive affect an individual receives from expe- other three [F(7,329) = 17.87, P < 0.001]. In particular, dark riences with objects of a given color, the more the person will tend orange (brown) and dark yellow (olive) were significantly less to like that color.
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