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Modeling Color Preference Using Color Space Metrics ⇑ Karen B Vision Research 151 (2018) 99–116 Contents lists available at ScienceDirect Vision Research journal homepage: www.elsevier.com/locate/visres Modeling color preference using color space metrics ⇑ Karen B. Schloss a,b, , Laurent Lessard b,c, Chris Racey a,b, Anya C. Hurlbert d a Department of Psychology, University of Wisconsin–Madison, 1202 West Johnson Street, Madison, WI 53706, USA b Wisconsin Institute for Discovery, University of Wisconsin–Madison, and Wisconsin Institutes for Discovery, 330 N. Orchard St., Madison, WI 53715, USA c Department of Electrical and Computer Engineering, University of Wisconsin–Madison, 1415 Engineering Dr., Madison, WI 53706, USA d Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE2 4HH, UK article info abstract Article history: Studying color preferences provides a means to discover how perceptual experiences map onto cognitive Received 21 February 2017 and affective judgments. A challenge is finding a parsimonious way to describe and predict patterns of Received in revised form 25 June 2017 color preferences, which are complex with rich individual differences. One approach has been to model Accepted 3 July 2017 color preferences using factors from metric color spaces to establish direct correspondences between Available online 28 July 2017 dimensions of color and preference. Prior work established that substantial, but not all, variance in color Number of Reviews = 2 preferences could be captured by weights on color space dimensions using multiple linear regression. The question we address here is whether model fits may be improved by using different color metric specifications. We therefore conducted a large-scale analysis of color space models, and focused in-depth analysis on models that differed in color space (cone-contrast vs. CIELAB), coordinate system within the color space (Cartesian vs. cylindrical), and factor degrees (1st degree only, or 1st and 2nd degree). We used k-fold cross validation to avoid over-fitting the data and to ensure fair comparisons across models. The best model was the 2nd-harmonic Lch model (‘‘LabC Cyl2”). Specified in CIELAB space, it included 1st and 2nd harmonics of hue (capturing opponency in hue preferences and simultaneous liking/disliking of both hues on an opponent axis, respectively), lightness, and chroma. These modeling approaches can be used to characterize and compare patterns for group averages and individuals in future datasets on color preference, or other measures in which correspondences between color appear- ance and cognitive or affective judgments may exist. Ó 2018 Elsevier Ltd. All rights reserved. 1. Introduction see Hurlbert & Owen, 2015; Schloss & Palmer, 2015), the origins of which are not entirely understood and have yet to be captured Central goals in the study of color cognition are to understand fully through a predictive model. how the perceptual experience of color maps onto cognitive and The aim of this study is to determine the best quantitative emotional judgments about color, and to understand how these model for describing and predicting color preference patterns judgments influence people’s beliefs and behaviors. The study of based on color appearance alone. By ‘‘best”, we mean the model color preference provides a direct route to this goal. The results having the smallest number of input factors that captures the lar- of previous studies suggest that there are systematic mappings gest amount of variance in preference across the individuals being between color appearance and preference (Guilford & Smith, studied. The input factors are related to the specification of the 1959; Hurlbert & Ling, 2007; McManus, Jones, & Cottrell, 1981; color stimulus only, and do not include any characteristics of the Ou, Luo, Woodcock, & Wright, 2004; Palmer & Schloss, 2010). For individual. Thus, the aim is not to probe the causal origins of example, hue preferences in industrialized cultures tend to vary individual differences in color preference (e.g., as in Schloss, along a blueness-yellowness axis, with a peak at blue and a trough Hawthorne-Madell, & Palmer, 2015), but instead to describe and around yellowish-green. Despite this robust pattern in average predict color preference from a specification of color appearance preference data, there are large individual differences (for reviews, alone. In this sense, the rationale follows earlier attempts to demonstrate that the ‘‘affective value” of a color may be predicted ⇑ Corresponding author at: University of Wisconsin–Madison, Department of systematically from accurate ‘‘color-specification” (Guilford & Psychology and Wisconsin Institutes for Discovery, Brogden Hall, 1202 West Smith, 1959). Such a model also provides a parsimonious way to Johnson Street, Madison, WI 53706, USA. characterize preference patterns across populations, through E-mail address: [email protected] (K.B. Schloss). http://dx.doi.org/10.1016/j.visres.2017.07.001 0042-6989/Ó 2018 Elsevier Ltd. All rights reserved. 100 K.B. Schloss et al. / Vision Research 151 (2018) 99–116 weights on the input factors (Hurlbert & Ling, 2007), provided the matching judgments made by human observers and specify color model fits the data well. appearance in terms of three basic descriptors. Here, for standard- Given that multiple color spaces and color ordering systems ized appearance spaces, we focus on the near perceptually-uniform exist for specifying color appearance, a first task in building a color spaces CIELAB and CIELUV1, which are based on data from per- model is choosing the color space in which the dimensions of ceptual color-difference measurements of standardized color sam- appearance are specified. These dimensions ultimately should ples and which explicitly define descriptors corresponding to the emerge as the ones that map best to variations in preference. perceptual attributes of hue, chroma, and lightness (Kuehni & Although the causal origins of preferences are not addressed by Schwarz, 2008; Wyszecki & Stiles, 1982). CIELAB space is widely the model, the results may provide a deeper understanding of used to specify color stimuli in visual psychophysics studies, and how and at which stage of visual processing color preferences has specifically been used for relating preference to color appear- are embedded. The second task is to determine the exact form of ance. Ou et al. (2004), for example, found that 70% of the variance the quantitative relationship between the color dimensions and in average preference across a set of 20 colors could be accounted preference, e.g. whether it is linear or nonlinear. We approached for by a nonlinear function of the L⁄, a⁄, and b⁄ color coordinates. these tasks by conducting a large-scale analysis of multiple candi- ‘‘Physiological” color spaces based directly on cone photorecep- date color spaces and different coordinate representations within tor activations are derived from neurobiological or psychophysical them, to determine which were most effective at capturing varia- measurements of color discrimination, and their dimensions are tions in color preferences. We evaluated the models using two pre- more readily related to early stages of visual processing viously obtained datasets from different countries. A practical (Derrington, Krauskopf, & Lennie, 1984; Eskew, McLellan, & outcome of this work is a set of tools for building models that com- Giulianini, 1999). For example, cone-opponent contrast spaces pactly describe and predict variations in color preference patterns (Derrington et al., 1984; Eskew et al., 1999), which we examine across large populations and individuals. here, quantify the appearance of a test color in terms of its contrast against a uniform adapting background along three cardinal direc- 1.1. Considerations in constructing models of color preferences based tions, defined by transformations of the L, M, and S cone photore- on color appearance ceptor activations, [S À (L + M)], [L À M] and [L + M], sometimes referred to as ‘‘blue-yellow”, ‘‘red-green”, and ‘‘luminance” mecha- For a model based on color appearance to be useful in describ- nisms. These dimensions are generally equated with the second- ing and predicting color preferences, it should satisfy at least two stage of color encoding in the human visual system and thought criteria: (1) the dimensions of the color space used should capture to be represented by neurons at early stages in the visual pathway (and allow parameterization of) all possible variations in color (Lennie & Movshon, 2005). For brevity, we subsequently refer to appearance and (2) variations in at least some of these dimensions [S À (L + M)] as ‘‘S À LM”, [L À M]as‘‘LM”, and [L + M]as‘‘Lum”. should elicit variations in color preference. For example, suppose a In developing a method for describing individual differences in model includes no dimension that captured variations in lightness. color preference, Hurlbert and Ling (2007) demonstrated the effec- It might fit color sets dominated by variations in hue and satura- tiveness of these cone-opponent dimensions in capturing prefer- tion but not those that have variations in lightness. The first crite- ence variations. Specifically, they found that individuals’ rion would be violated because it fails to capture all variations in preferences for eight colors, which varied only in hue (iso- color appearance. Further, the second criterion would be violated luminance, iso-saturation) could be decomposed into two principal if color preferences for some datasets varied only along the
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