Color–emotion associations by speakers of Spanish and Mandarin in verbal and visual tasks: a comparison

ARTICLE Color–emotion associations by speakers of Spanish and Mandarin in verbal and visual tasks: a comparison Mingshan Xu 1 , Jingtao Zhu 2 and Antonio Benítez-Burraco 1 1 Department of Spanish, Linguistics, and Theory of Literature (Linguistics), Faculty of Philology, University of Seville, Seville, Spain and 2 ClicAsia, Centre d’Estudis Orientals, Barcelona/Madrid, Spain Corresponding author: Antonio Benítez-Burraco; Email: [email protected] (Received 11 January 2024; Revised 23 June 2024; Accepted 16 August 2024) Abstract This study aims to determine if there are differences in color–emotion association between monolingual speakers of Spanish and Mandarin, depending on how colors are presented (verbally or visually). We tested two groups of 25 speakers of these two languages in two different tasks using the Geneva Emotion Wheel, which encompasses 20 types of emotions. In Task 1,13colorswerepresentedto participants ascolorterms intheirnativelanguage, whereas in Task 2 the same colors were presented as color patches from the Munsell chart. Participants were then asked to associate color terms or color patches to the set of emotion concepts (and intensities of emotion) in the Geneva Emotion Wheel. Overall, differences between languages were not significant, regarding either the type of emotionor individual dimensions of emotion (valence, arousal or power), although significant differences were observed for specific colors. Also, Spanish speakers tended to attribute higher intensity values and higher numbers of emotion values to colors. At the same time, speakers of both languages reacted similarly to color presentation, with color terms being associated with the same emotions as color patches, but eliciting stronger reactions with respect to intensity and the number of emotion values. Finally, we found less variability in color–emotion associations within the Spanish-speaking group. Overall, our study points to a mixed pattern of universality and culture-specificity regarding how colors are used for conveying emotions by people. Keywords: color–emotion associations; color patches; color terms; cultural specificity; Geneva Emotion Wheel; universality 1. Introduction Across languages and cultures, colors are often used to convey emotional states (Adams & Osgood, 1973; Jonauskaite et al., 2020a; Soriano & Valenzuela, 2009). Culture shapes these color–emotion associations, with differences impacting in turn © The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. Language and Cognition (2024), 16: 4, 2130–2147 doi:10.1017/langcog.2024.52 https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press on the perceptive, cognitive and behavioral dimensions of color experience (Elliot et al., 2007; Jiang et al., 2013; Mehta & Zhu, 2009). Although color–emotion associations have been described for selected languages and cultures, more comparative research is needed to identify universal and culture-specific factors mediating color–emotion associations in the world’s societies (Hanada, 2018). More specifically, ongoing research has tried to identify specific dimensions of color that might relate to specific dimensions of emotion. Color dimensions that are typically considered include hue, brightness and saturation, whereas emotion dimensions that are usually analyzed include valence (i.e., the pleasantness of the emotion, ranging from strongly negative to strongly positive), arousal (i.e., the level of activation resulting from the emotion, ranging from strongly calmy to strongly excited) and power (i.e., the sense of dominance brought about by the emotion, ranging from strongly powerless to strongly powerful) (for details, see Fontaine et al., 2007; Fontaine, 2013; Forceville & Renckens, 2013; Littlemore et al., 2023; Safarnejad et al., 2014; Scherer, 2005; Soriano & Valenzuela, 2009; Takei & Imaizumi, 2022; Yu, 2009, among many others). As a general rule, more saturated and brighter colors tend to result in higher arousal and valence ratings, with hue having a lesser impact (Wilms & Oberfeld, 2018). Also, according to Soriano and Valenzuela (2009), arousal and power ratings tend to be less variable than valence ratings, which appear to be more context-sensitive, seemingly because the former are more dependent on our physiology, that is, on the biological mechanisms underlying emotional experience (particularly, if colors can themselves provoke emotional reactions, as observed by, for example, Elliot et al., 2007). Therefore, it frequently happens that different cultures associate the same color with different emotions. Hence, white is typically associated with positive emotions in English, French, Spanish, Turkish or Japanese (Demir, 2020; Jonauskaite et al., 2020b; Kaya & Epps, 2004; Soriano, 2005), whereas it conveys diverse negative meanings in Mandarin, as in baishi ‘white event’for funeral; baiyan ‘white eyes’for scorn; or baiyanlang ‘white eyed wolf’for ungratefulness (He, 2011; Xing, 2009; Zhou, 2010). Still, cross-cultural differences in color–emotion associations do not appear randomly. A recent study by Jonauskaite et al. (2020a) has found that these associations are more similar in cultures that are geographically closer (although see Jiang et al., 2013 for some striking differences between Mainland China and Hong Kong). A related concern is the effect of language on color–emotion associations. Whereas the perception of color can be regarded as similar in all human beings, languagesdifferinthenumberofbasiccolor terms, ranging from a couple to more than a dozen (Berlin & Kay, 1969;Gao&Sutrop,2014;Hsiehetal.,2020; Lillo et al., 2007; Paramei, 2005; Uusküla & Sutrop, 2007;Wu,2011;Xuetal.,2023,among others). Moreover, the color lexiconimpacts,evenifsubtly,oncolorperception and categorization (González-Perilli et al.,2017; Winawer et al., 2007). Language is alsoknowntoplayacrucialroleinshaping emotional associations (Lindquist et al., 2015). Color terms and emotions often intertwine, metaphorically or metonymically (Kövecses, 2000; Soriano & Valenzuela, 2009). As a reflection, to a great extent, of these cross-cultural differences in color–emotion association, different languages can use different color terms for referring to one specific emotion or associate one particular color term with different emotions. For instance, envy is associated with green in Spanish (e.g., estar verde de envidia ‘to be green with envy’), but with red in Mandarin (e.g., yanhong ‘red eyes’); by contrast, red is associated with anger in Spanish (estar rojo de ira ‘to be Language and Cognition 2131 https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press incandescent with anger’),whereasinMandarin,angerisblack(asinhei zhe lian ‘black (darker) face’)(Li,2020). A final concern is whether the mode of presentation of color impacts color– emotion associations. A study by Wang et al. (2014) with Mandarin speakers found differences in valence ratings for red and blue depending on whether the stimuli encompassed color patches or color terms. These authors further found that whereas some color patches activate both biologically based associations (i.e., metonymical, for example, red to blood, fire or danger) and culturally based associations (i.e., metaphorical, for example, red to envy), others can activate only one type. By contrast, a recent study by Jonauskaite et al. (2020b) with French speakers found no differences between color patches and color terms in a color–emotion association task. In this paper, we aim to delve into the potential differences in color–emotion association between native speakers of Spanish and Mandarin. Contrary to previous research that focused either on a few colors (e.g., red and blue in Wang et al., 2014)or a few emotions (e.g., happiness and sadness in Takei & Imaizumi, 2022), here we examine an ample set of colors, as well as a comprehensive set of emotions. In addition, we try to determine the factor(s) that promote(s) the use of one (or more) specific color(s) for referring to a particular emotion. In order to do so, we consider the three fundamental dimensions of emotion as characterized above: valence, arousal and power. Finally, we try to clarify whether color–emotion associations differ when colors are presented visually or verbally. Overall, we expect to find some (remarkable) differences in the color–emotion association between Spanish and Mandarin, considering the notable typological differences between the two languages, the noteworthy cultural differences between speakers and the noticeable distance between the areas where the two languages are spoken. 2. Materials and methods For examining the research questions posited above, we mostly replicated the study by Jonauskaite et al. (2020b) with our own sample of monolingual speakers of Spanish and Mandarin. In brief, we asked participants to associate color terms in their language (Task 1) or color patches (Task 2) with a set of emotion concepts (and intensities of emotion) as found in the Geneva Emotion Wheel (GEW), which is a tested instrument aimed to measure emotional reactions to objects, events and situations (Scherer, 2005; Scherer et al., 2013). 2.1. Stimuli For Task 1, we used the 11 basic color terms in Spanish and their Mandarin equivalents (Spanish –negro ‘black’,blanco ‘white’,rojo ‘red’,amarillo ‘yellow’,verde ‘green’,azul ‘blue’,marrón ‘brown’,naranja ‘orange’,rosa ‘pink’,morado ‘purple’, gris ‘gray’; Mandarin –hei ‘black’,bai ‘white’,hong ‘red’,huang ‘yellow’,lü ‘green’,lan ‘blue’,zong/he ‘brown’,cheng/jü ‘orange’,fen ‘pink’,zi ‘purple’,hui ‘gray’). We also included Spanish celeste ‘light blue’(Mandarin qianlan) and Spanish lila ‘light purple’ (Mandarin qianzi), since in our previous research we found evidence that celeste could be a 12 th basic color in Spanish, whereas lila is quite consistently used by Spanish speakers to refer to light purple (with morado being used for darker tones) 2132 Mingshan Xu et al. https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press (see Xu et al., 2023 for details). This differential usage of lila and morado has been confirmed by independent research by Jonauskaite and colleagues (e.g., Epicoco et al., 2024; Uusküla et al., 2023). For Task 2, we presented participants with 13 color patches corresponding to the best examples of the color terms used in Task 1. These are the color patches that subjects use more consistently for referring to a specific color, as found in our previous research too (see Xu et al., 2023 for details). Color patches were retrieved from The World Color Survey (Kay et al., 2010;https:// www1.icsi.berkeley.edu/wcs/data.html). The Munsell values for each of the color patches used in this task can be found in Supplementary Table S1. With regard to the set of emotions considered in both tasks and their distinctive dimensions (i.e., valence: positive–negative, arousal: high–low and power: strong–weak), we made use, as noted, of the GEW, version 3.0 (Scherer, 2005; Scherer et al., 2013) (see Supplementary Table S2 for details), via the interface that is usually considered as the easiest to use and understand by participants (see Caicedo & Van Beuzekom, 2006; Warpechowski et al., 2019 for details). As for the Spanish and Mandarin versions of the emotion terms, we did not translate them by ourselves, but used instead the versions provided by Jonauskaite et al. (2020b) in their online survey (http://www2.unil.ch/onlinepsylab/colour/main.php). 2.2. Participants The Spanish sample consisted of 50 monolingual speakers of Castilian (i.e., European) Spanish (22 males) with a mean age of 28.56 years (95% confidence interval [CI] = [26.04, 31.08]). All of them lived in Barcelona (a big city in Northeastern Spain), the place where they were tested, although they had different origins (Madrid, Barcelona or Seville). 25 individuals (10 males) participated in Task 1 (M age = 28.53, 95% CI age = [24.36, 32.70], range: 18-58) and the remaining 25 individuals (12 males) participated in Task 2 (M age = 28.6, 95% CI age = [23.92, 33.28], range: 18-62). An independent samples ttest showed that the age was comparable between both groups (t(48) = 0.03, p= .98, d= .009) as well as the gender (χ 2 (1) = .08, p = .78, V = .04). The Mandarin sample consisted of 50 monolingual speakers of Mandarin (25 males) with a mean age of 29.34 years (95% confidence interval [CI] = [26.69, 31.99]). All of them lived in Chengdu (a big city in the province of Sichuan, Southwestern China), where they were tested, although they also had different origins (Chengdu, Wuhan, Qingdao and Beijing). 25 subjects (10 males) took part in Task 1 (M age = 29.04, 95% CI age = [24.95, 33.13], range: 18-59), and the remaining 25 subjects (15 males) participated in Task 2 (M age = 29.64, 95% CI age = [25.96, 33.32], range: 20-55). An independent samples ttest showed that the age was comparable between groups (t(48) = .23, p= .82, d= .07), as also was the gender distribution (χ 2 (1) = 1.28, p = .26, V = .16). Additionally, an independent samples ttest showed that the age of the participants was comparable between the two groups (Spanish vs. Mandarin) participating in Task 1 (t(48) = 0.18, p= .85, d= .053) and Task 2 (t(48) = 0.36, p= .72, d= .10). The same was true for gender distribution (Task1: (χ 2 (1) = 0, p = 1, V =0); Task 2: (χ 2 (1) = .32, p = .57, V = .08)). Using different subsets of participants for these two tasks helps avoiding biases and confounding effects. Participants were not introduced to the subject of the research until the end of the experiment, in order to avoid potential priming effects. Language and Cognition 2133 https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press 2.3. Procedure Before starting with the two tasks, the experimenter first introduced the procedure and participants signed an informed consent document. Participants then received a booklet (15 pages in total) containing a one-page introduction to the GEW, 13 identical wheels and a sheet for gathering some personal data: age, sex, education level, the language(s) they spoke and a confirmation that they did not suffer from color vision problems. In Task 1, color terms were randomly presented to the participants in writing, using white pieces of sheet and black ink. In Task 2, color patches (15 x 15 cm) were also presented randomly on the screen of a laptop (resolution: 2560x1600 (227 ppi); brightness: 300 nits; contrast ratio: 900:1; standard color gamut (sRGB)) in sufficient natural daylight (thus avoiding direct sunlight or deep shade) and using a gray base as the background (N5, Munsell color notation). Participants observed color patches at a distance of around 60 cm with an average visual angle of 3.5 degrees. During both tasks, Correlated Color Temperature (CCT) oscillated between 4000 and 4500 K and illumination ranged from 1750 to 2000 lux. CCT is a measure of the quality of the white light, with low values corresponding to ‘warm’lights (which are richer in low-frequency wavelengths like reds and yellows) and with high values being typical of ‘cool’lights (that are richer in high-frequency blues and violets). Illumination is a measure of the intensity of the light. We secured the same experimental conditions in both testing places (Spain and China). As noted, depending on the task, participants had to associate color terms or color patches with one (or more than one) of the 20 types of emotion included in the GEW (for details, see Figure 1 and Supplementary Table S2). Participants were allowed to associate colors with emotions not included in the GEW. They could also refuse to associate colors with any emotion. Additionally, they were requested to rate the intensity of the emotion(s). For this, they had to choose one among the different circle sizes also included in the GEW, which range from the smallest one (for the weakest emotion, coded as 1) to the biggest (for the strongest emotion, coded as 5). For recording their choices, they just needed to make a checkmark on the circles in the GEW sheets. Since color terms or color chips were presented in a randomized order that changed from participant to participant, the experimenter labeled the order of each playback as well as the participant’s results. 2.4. Data analysis For both tasks, we first determined the number of participants associating a specific color term (Task 1) or color patch (Task 2) with a specific emotion. We also calculated the number of participants associating a specific color with a specific emotion regardless of the mode of presentation. If a particular color term or color patch was not associated with any emotion, we coded this as 0. If a color was associated with an emotion not included in the GEW, we coded this as a missing value. We then used autocluster analyses to uncover the most prominent color– emotion associations for each task and for each language. Autocluster analyses are aimed to find and categorize inherent patterns within datasets in the absence of manual input. We employed Fisher’s test to look for significant differences between conditions (i.e., between types of emotion, between languages and between modes of presentation). We further used the Pearson Correlations Coefficient (PCC) to 2134 Mingshan Xu et al. https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press determine the similarity in color–emotion associations between languages and between tasks. In this latter case, the higher the PCC value, the more similarity (for p< 0.05). Second, for each task, we calculated the average intensity assigned to each color–emotion association. If no emotion was associated with the color, or if the subject associated the color with an emotion not shown in the GEW, we coded this as a missing value, since intensity ranges from 1 to 5. We then conducted a series of independent samples ttests to find differences in color–emotion associations and intensity ratings between speakers of Spanish and Mandarin, and also between Task 1 and Task 2. Finally, we conducted a mixed-design multivariate analysis of variance (MANOVA) to identify the dimension(s) that impact(s) mostly on color–emotion association. Accordingly, we set the broadness of valence (i.e., the number of positive emotions and negative emotions), arousal (i.e., the number of high emotions and low emotions) and power (i.e., the number of strong emotions and weak emotions) as dependent variables, with color presentation mode, color type and language as independent variables (see Supplementary Table S2 for details). Additionally, we calculated the biases in valence, arousal and power when it comes to rate emotions. Our aim was to determine whether specific colors tend to significantly evoke specific emotional responses in any of these two languages. Valence bias is the differencebetweenthepositivemeanandthe negative mean for valence ratings by participants and was calculated as follows: Figure 1. The GEW used for testing the association of color terms or patches with emotions by participants. Emotions are displayed in the outer part of the wheel (in English (A), Spanish (B) and Mandarin (C)). The intensity of each emotion correlates with the size of the circles in the inner part of the wheel (reproduced from Jonauskaite et al., 2020a). Language and Cognition 2135 https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press Valence bias = (sum of the intensity of all negative emotions / number of negative emotions) –(sum of the intensity of all positive emotions / number of positive emotions). We calculated the arousal biasand the power bias similarly: Arousal bias = (sum of the intensity of all low arousal emotions / number of low emotions) - (sum of the intensity of all high arousal emotions / number of high emotions) and Power bias = (sum of the intensity of all weak power emotions / number of weak emotions) –(sum of the intensity of all strong power emotions / number of strong emotions). The intensity of negative emotions, low arousal or weak power all received a negative sign, whereas the intensity of positive emotions, high arousal or strong power, all received a positive sign. Individual mixed-design ANOVAs were then conducted for each of these three dimensions of emotion to determine the effect of diverse factors (languages, color and mode of presentation), with the aim of understanding how these factors impact, solely or coordinately, on each dimension of emotion. 3. Results 3.1. Type of emotion Autocluster analyses revealed that Spanish speakers tend to agree more than Mandarin speakers when it comes to associating specific colors with specific emotions in general, with this difference being significant ((F = .43, 95% CI = [0.34, 0.56], p< .001). The strongest differences between languages were observed for white (F = .26, 95% CI = [0.10, 0.65], p= .005), red (F = .27, 95% CI = [0.11, 0.69], p= .007), yellow (F = .25, 95% CI = [0.10, 0.64], p= .004), blue (F = .29, 95% CI = [0.12, 0.73], p= .009) and light blue (F = .33, 95% CI = [0.13, 0.85], p= .02). For example, Spanish speakers tend to associate the color white with relief (62% of cases) and contentment (44%), whereas Mandarin speakers are more likely to associate it with sadness (34%) and disappointment (22%). Likewise, Spanish speakers tend to associate blue with sadness (54% of cases) and relief (44%), whereas Mandarin speakers preferentially associate it with pleasure (24%) and compassion (22%) (see Supplementary Table S3 for details). At the same time, both Spanish speakers and Mandarin speakers converge more within groups on color–emotion associations when colors are presented verbally instead of visually (F = .61, 95% CI = [0.48, 0.78], p< .001 for Spanish; F = .55, 95% CI = [0.43, 0.71], p< .001 for Mandarin). This difference is mostly attributable to the remarkable differences observed for some specific colors: red and light purple for Spanish speakers, and red, white, yellow and orange for Mandarin speakers. Accordingly, when red is presented verbally, 88% of Spanish speakers associate this color with anger, but this number goes down to only 28% when red is presented visually. Similarly, Mandarin speakers strongly associate white with sadness, but only if the color is presented verbally (52% vs 16% if it is presented as a color patch) (see Supplementary Table S3 for details). Figure 2 shows the heatmaps for general color–emotion associations (above), color term–emotion associations (middle) and color patch–emotion associations (below) for both languages. We performed Pearson Correlations analyses and found a similarity between both languages in overall color–emotion associations (r(258) = .69, p< .001), color term– emotion associations (r(258) = .55, p< .001) and color patch–emotion associations (r(258) = .62, p< .001). However, significant differences were observed for specific 2136 Mingshan Xu et al. https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press colors (see Table 1). Across these three conditions, differences were significant for light purple only. With regard to the mode of presentation, Pearson Correlations analyses indicate that both Spanish speakers (r(258) = .66, p< .001) and Mandarin speakers (r(258) = .73, p< .001) tend to associate the same emotions with the same colors, with the exception of purple and light purple, which are associated with different emotions in each language, whether presented verbally or visually (see Supplementary Table S4 for details). Figure 2. Heatmaps of color–emotion associations for Spanish and Mandarin. (A) Color–emotion associations. (B) Color term–emotion associations. (C) Color patch–emotion associations. In the heatmaps, more intense reds correspond to stronger associations between colors and emotions. Language and Cognition 2137 https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press 3.2. Intensity of emotion Concerning the intensity of emotion, an independent samples ttest showed that Spanish speakers attribute a higher mean intensity to colorsthan Chinese speakers do (M = 3.72, SD = .86 for Spanish; M = 3.27, SD=1.0 for Mandarin), with this difference being statistically significant between groups (t(1164) = 8.17, p< .001, d=.48). At the same time, both groups tend to attribute a higher mean intensity to color terms than to color patches (M = 3.82, SD = .85 vs M = 3.62, SD = .87 for Spanish; M = 3.43, SD = .94 vs. M = 3.12, SD = 1.04 for Mandarin), with this difference being statistically significant between groups too (t (615) = 2.78, p= .006, d=.22 for Spanish; t(547) = 3.78, p= .0001, d=.32 for Mandarin). Still, this disparity is mostly driven by differences in selected colors that differ between the two languages: black, gray and yellow for Spanish; green, orange and purple for Mandarin (see Supplementary Table S5 for details). 3.3. Dimensions of emotion Finally, a mixed-design MANOVA analysis of our data indicated that Spanish speakers tend to associate more different emotion concepts with each individual color (M= 3.46, SD = 2.24 for Spanish; M = 1.84, SD = 1.93 for Mandarin), F(1, 1229) = 36.41, p < .001 (see Supplementary Table S6 for details). The Pillai’s trace values of the MANOVA (which are used to measure the effect of sets of independent variables on sets of dependent variables in small samples like ours) further suggested that this difference is also significant for the three basic dimensions considered in our study: valence, arousal and power (Pillai’s trace value = .15, F(6, 1224) = 34.61, p< .001, η p2 = .15). Likewise, our analysis suggested that for both languages, there is a significant effect of the mode of presentation (Pillai’s trace value = .21, F(18, 3672) = 15.54, p< .001, η p2 = .07), so that participants tend to give higher numbers to color terms than to color patches. Also, for both languages, the interaction between color and mode of presentation was significant too (Pillai’s trace value = .11, Table 1. Pearson Correlation Coefficients (PCCs) for the similarity in color–emotion associations between speakers of Spanish vs Mandarin Spanish vs Mandarin PCC (overall) PCC (Task 1) PCC (Task 2) Red .52* .38 .51* Yellow .53* .51* .40 Green .81*** .70*** .78*** Blue .74*** .36 .73*** Brown .71*** .54* .64** Orange .63** .62** .45 Pink .90*** .91*** .82*** Purple .55* –.24 .24 Gray .94*** .90*** .89*** Black .74*** .58** .62*** White .35 .14 .83*** Light purple .32 .07 .10 Light blue .77*** .56* .69*** Note: Three conditions are compared: overall color–emotion associations, color term–emotion associations (Task 1) and color patch–emotion associations (Task 2). Significance levels are expressed with asterisks (* for p≤0.05, ** for p≤0.01 and *** for p≤0.001). Non-significant correlations are highlighted in bold. 2138 Mingshan Xu et al. https://doi.org/10.1017/langcog.2024.52 Published online by Cambridge University Press Author contribution. M.X. conducted the research, analyzed the data, reviewed the available literature and wrote the paper. J.Z. analyzed the data, reviewed the available literature and wrote thepaper. A.B-B. conceived the paper, reviewed the available literature and wrote the paper. All authors approved the final version of the manuscript. CRediT author statement. Mingshan Xu: Methodology, Investigation, Data curation, Formal analysis, Validation, Writing; Jingtao Zhu: Formal analysis, Writing; Antonio Benítez-Burraco: Conceptualization, Writing, Supervision. Funding statement. This research was conducted in the absence of external funding. Competing interest. The authors declare none. Ethical standard. 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