Red or rough, what makes materials warmer?

Materials and Design 42 (2012) 441–449 Contents lists available at SciVerse ScienceDirect Materials and Design journal homepage: www.elsevier.com/locate/matdes Red or rough, what makes materials warmer? Lisa Wastiels a,c,⇑, Hendrik N.J. Schifferstein b, Ann Heylighen c, Ine Wouters a a Vrije Universiteit Brussel, Department of Architectural Engineering, Pleinlaan 2, 1050 Brussel, Belgium Delft University of Technology, Department of Industrial Design, Landbergstraat 15, 2628 CE Delft, The Netherlands c Katholieke Universiteit Leuven, Department of Architecture, Urbanism & Planning, Kasteelpark Arenberg 1/2431, 3001 Leuven, Belgium b a r t i c l e i n f o Article history: Received 18 March 2012 Accepted 14 June 2012 Available online 23 June 2012 Keywords: E. Properties of materials H. Selection of materials Architecture Material experience Warmth perception a b s t r a c t The warmth of a material is generally related to the material’s thermal behavior. However, the multisensory experience of warmth is also affected by other material aspects, such as the color or surface roughness. In the current study, we use an experimental approach to investigate the single and combined effects of color and surface roughness on the assessment of material warmth. Participants are asked to evaluate the material warmth of different material samples with controlled colors and roughnesses. The results illustrate that the material color and the local surface roughness influence our perception of warmth irrespective of each other. A relative comparison of the effect sizes shows that a change in color has a larger influence on the perceived warmth than a comparable change in roughness. These results are relevant to architects and other designers wanting to manipulate the intended warmth for a space or building through its materials. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction The future user experience is one of the aspects considered by architects while designing a space or building [1]. The user experience is related to the building design and more specifically also the selection of materials. The feeling of warmth is one example of an aspect important to the experience of constructed environments [2]. Warmth is commonly used to describe the physical environment [3] and the materiality of building elements [4]. The warmth of materials is usually associated with its thermal properties and mostly refers to the tactile warmth [5–7]. Several sources, however, indicate that people’s overall experience of warmth is influenced by other aspects as well [8,9]. Previous research has illustrated the importance of color in warmth perception [10] and suggests the influence of roughness on warmth perception [4]. There is a lack of common vocabulary and definitions to describe and evaluate the experience evoked by materials [11,12]. While selecting materials, architects often rely on their personal experience and previous encounters with materials to judge the perceived attributes [1]. This intuitive approach may work for familiar materials, but new and unknown materials lack such references. Moreover, subtle manipulation of specific material aspects might have effects on the material experience that architects do not know of. Being able to value the contribution of certain aspects (such as color or roughness) to the experience of warmth, could assist architects and other designers when selecting materials for a design. The ⇑ Corresponding author at: Vrije Universiteit Brussel, Department of Architectural Engineering, Pleinlaan 2, 1050 Brussel, Belgium. Tel.: +32 2 629 28 40. E-mail address: wastiels@post.harvard.edu (L. Wastiels). 0261-3069/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.matdes.2012.06.028 warmth of colors has been studied extensively (e.g. [13,14]), but the combination with other material aspects is usually not considered. The surface roughness of a material can be manipulated by use of different fabrication techniques or surface treatments and affects the visual, tactile, as well as auditory experience of the material. The effect of differences in surface roughness on the perceived warmth has not been researched so far. This paper explores to what extent roughness influences the experience of material warmth and how this effect relates to that of color. 1.1. Material warmth In a literal sense, material warmth refers to how cold or warm a material feels to the touch [5]. This phenomenon is influenced by the material’s temperature as well as its thermal behavior. Whereas the first issue depends on the temperature of the material’s environment, the second aspect relates to the material itself. A material feels colder to the touch if it conducts heat away from the skin quickly and feels warm if it does not [5]: metal feels colder than wood, even when both materials are at room temperature. The thermal effusivity of a material returns in a number of studies as a good technical measure to describe the tactile warmth of a material [15–17]. The multisensory experience of material warmth is influenced by a combination of factors. Different senses contribute to the experience and the simultaneous use of these senses influences the overall perception. Research shows that vision often dominates people’s multisensory experience [18]. In the context of material assessment in architecture, the perceived warmth appears to be dominated by visual considerations as well [19]. Color is the most apparent visual aspect to distinguish between materials and also the most 442 L. Wastiels et al. / Materials and Design 42 (2012) 441–449 researched visual aspect. Most psychological research on colors indicates that colors ranging between yellow, yellow–red, red and red–violet are experienced as warm and that colors ranging between blue–violet, blue, blue–green and yellow–green are experienced as cold. A study by Fenko et al. reveals that color warmth is productdependent as the perceived color warmth follows general color theory for breakfast trays but not for scarves [9]. However, in the context of architecture and interior design, we only have knowledge of studies confirming general color theory. Research has shown that the perceived coldness of a space painted in blue–green is 3–4 °C lower than that of a space painted in orange–red [20]. A study making use of colored squares reveals that darker colors and colors with higher saturation are perceived as being warmer and that color hue has a separate effect on the perception of warmth [10,21]. Most color research does not consider the interaction with other parameters, such as gloss or roughness. In the study referred to above, Fenko et al. investigated the relative importance of color and material in the warmth experience of scarves and breakfast trays and demonstrated that both color and material contributed equally to the judgment of warmth in these products [9]. This suggests that color has a large impact on the experience of warmth but that the material aspects, such as thermal properties, roughness, pattern, or gloss, should not be neglected when considering material warmth. In a study on packaging materials, Chen et al. revealed that the tactile warmth of materials is related to the thermal properties as well as the compliance [22]. This study, however, only considered the experience by touch and did not consider the overall perception. As stated above, in an architectural context, the experience of warmth will not only be related to tactile triggers but to the multisensory perception. Thiis-Evensen stated that a space with smooth walls seems colder than a comparable space with finely textured walls [4], but – to our knowledge – the relation between roughness and warmth has not been researched in depth so far. 1.2. The present study In the present study we manipulate the surface roughness and material color of several material samples to investigate their importance in the experience of warmth. The study consists of two phases, including a pre-study and the main study. In the pre-study, two sample sets are used: one with varying roughness and one with varying color. Participants are asked to assess the ‘warmth’ of the different material samples on a 7-point scale. The results from the pre-study allow us to discuss the single effects of color and roughness on warmth and are used to create a basis of comparison for the main study. In the main study we create specific samples using warm and cold stimuli of the different parameters investigated, and combine those in 2  2 designs (see [23]). This leads to a set of four stimuli according to the following principle: (CC) cold roughness + cold color, (CW) cold roughness + warm color, (WC) warm roughness + cold color, (WW) warm roughness + warm color. Participants are asked to assess the ‘warmth’ of the different material samples on a 7-point scale. We expect the combination of a warm color and a warm roughness to have high ratings for warm, and the combinations of a cold color and cold roughness to have the lowest ratings for warm. We guess that the samples with combined stimuli (cold–warm) will lead to in-between results and tell us something about the relative impact of roughness compared to color. 2. Pre-study: single effects 2.1. Participants Participants were recruited among students of the first year in Architectural Engineering at the Vrije Universiteit Brussel. Twenty full time students participated in the study, including 12 male. Their ages ranged from 17 to 22 with an average of 18. Previous research showed no effect of the years of study on the assessment of material warmth [19,24], which suggests that the results will not be influenced by study year. 2.2. Material sample sets We used the stone-like material Inorganic Phosphate Cement (IPC) as a base material to create the different samples. In its processing IPC is very similar to polyester; but after hardening it behaves and looks like a ceramic [25]. Because of its straightforward production technique, differences in color and surface finishing can easily be achieved while keeping the other parameters fixed. The use of a real building material for studying these individual effects maintains the link with the reality of building where the natural combination of different aspects influences the perception [26]. Two different sample sets were created which each varied in one of the investigated aspects: color or roughness. All samples had a size of 20  20 cm. 2.2.1. Set I: color samples The first set was formed by ten samples spray painted in different colors that could originate from real building materials (Fig. 1). White refers to plaster walls or silicate stone; light gray refers to concrete; brown, red, and yellow are all commonly used brick colors; beige could refer to either bricks or colored plasterwork; black and dark blue correspond to the color of natural stones, such as marble or blue stone; finally, light green and light blue were added to complete the color set in terms of hues and darkness. A qualitative naming of the colors, as suggested by the paint manufacturer, can be found in Table 1. To keep the other variables constant for the experiment, all samples had an even surface and semi-mat appearance. CIELAB color measurements were performed using the Minolta CR-310 colorimeter with D/8° geometry and D65/10° illuminant (Table 1 – Technical measurements). In the CIELAB color system a color is represented by three coordinates (L⁄, a⁄, b⁄) [27]. L⁄ is the lightness factor and varies from black (0) to white (1 0 0) [28]. The white sample has the highest lightness of the set (with L⁄ = 95), followed by the light gray, ivory beige, white green, ice blue and mango yellow samples (with L⁄ = 75–80). The black sample is the darkest sample (L⁄ = 29), and the remaining samples are also rather dark. The a⁄-value and b⁄-value are the chromaticity coordinates:+a⁄ is the red direction, a⁄ the green direction, +b⁄ is the yellow direction, and b⁄ the blue direction. The center is achromatic and as a⁄ and b⁄ increase, the saturation of the color (=color intensity I⁄) increases [28]. 2.2.2. Set II: roughness samples The second set consisted of ten samples with varying surface roughness that would be realistic to use in an architectural setting (Fig. 2). Different types of roughness were explored by varying the height of the surface irregularities, as well as the surface profile. The roughness of the sample set ranged from very smooth and flat without surface irregularities (R01) to very rough and bumpy (R10). All samples were sprayed in a semi-mat light gray paint (corresponding to the color of sample C02) to keep the color and gloss equal for the different stimuli. A light color was selected because roughness is best perceived on lightly colored surfaces which reveal the shadows dropped by the surface irregularities. In material science, one distinguishes between roughness and waviness of a surface [29]. Roughness is a measure of the fine, closely-spaced random irregularities of a surface, caused by the production process. Waviness is a measure of the more widelyspaced repetitive irregularities which can result from vibration, 443 L. Wastiels et al. / Materials and Design 42 (2012) 441–449 Fig. 1. Set I with 10 color samples. Going from left to right, top row: C01–C05; bottom row: C06–C10. Table 1 Data on the color samples (Set I). Qualitative descriptions of the colors, technical measurements of the color samples (L⁄, a⁄, b⁄ and I⁄) and mean warmth ratings from the participants’ evaluation of the color samples, including standard errors of the mean (SEM). Color samples Qualitative description Technical measurements L⁄ C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 a Pure white Light graya Deep black Chocolate brown Ruby red Mango yellowa Ivory beige White green Ice bluea Sapphire bluea 94.80 80.15 28.79 33.17 37.58 75.65 79.40 79.36 75.94 32.22 a⁄ 0.48 0.85 0.00 4.06 32.80 15.55 0.77 14.58 7.73 1.28 Participant evaluations b⁄ 8.39 0.83 0.68 2.65 13.20 74.02 26.15 9.91 17.03 13.03 I⁄ Cold–warm SEM 8.41 1.19 0.68 4.85 35.36 75.64 26.16 17.63 18.70 13.09 2.60 2.45 3.80 4.95 6.30 5.30 4.45 3.55 2.45 3.60 0.28 0.28 0.42 0.25 0.16 0.82 0.30 0.32 0.25 0.34 Colors used for the main study. Fig. 2. Set II with 10 different roughness samples. Going from left to right, top row: R01–R05, bottom row: R06–R10. chatter or deflections [29,30]. Roughness is super-imposed on waviness. Roughness and waviness together constitute surface texture (see Fig. 3). Following these definitions, the roughness sample set is composed of samples with varying surface texture and can be divided into two subsets: samples R01–R05 have an increasing roughness but no waviness; samples R06–R10 can be described 444 L. Wastiels et al. / Materials and Design 42 (2012) 441–449 by an increasing waviness and varying roughness. Table 2 includes a qualitative description of the samples according to these definitions from material science. The terms surface texture, surface roughness, and surface topography are generally used interchangeably and refer to the threedimensional representation of geometric surface irregularities [30]. Architects often speak of surface roughness when referring to the overall concept of surface texture. The term ‘‘roughness’’ then refers to a large range of surface irregularities, which might be confusing compared to the distinguished features roughness and waviness as used in material science. For the sake of clarity, the material science roughness can be referred to as local roughness and waviness is sometimes called global roughness. Because of the large differences between the samples (varying in local as well as global roughness) it was not possible to quantify the roughness of the samples by a single parameter. Furthermore, measuring techniques specified in international standards typically focus on the microscopic aspects of local roughness and are, therefore, also insufficient for the description of the current stimuli. To maintain the link with architecture in this study, we asked five engineerarchitects to describe the roughness of the different samples. The results revealed that no common vocabulary is available to describe roughness, and that a combination of several adjectives such as ‘smooth’, ‘soft’, ‘bumpy’, ‘sharp’, ‘flat’, ‘mat’, and ‘textured’ is used to describe a material’s surface roughness. Table 2 provides an ‘‘architecture description’’ of the samples’ roughness by means of the adjectives that were used by at least two participants to describe the surface. 2.3. Procedure The experiment took place in a room at the Vrije Universiteit Brussel under diffuse lighting conditions with a combination of natural and artificial lighting. Participants received an instruction page that explained the test procedure. They were told that the study investigated the appropriateness of materials for use as indoor wall material. After having read the instructions, the participants were presented with the samples one by one, in a vertical position. The order of the sample sets was assigned randomly to the participants: half of them started with the evaluation of colored samples, the other half started with the roughness set. Within each set, the sequence of stimuli varied for each participant. Participants were not explicitly informed that the samples would vary in roughness or color. For each sample, participants were asked to assess the material warmth by the question: ‘‘I think an indoor wall covered with this material is. . . [cold ————— warm]’’. Their responses were recorded on a 7-point scale going from cold to warm. In order to conceal the purpose of the test, the cold–warm adjective pair was presented together with four other word pairs, such as unpleasant–pleasant or soft–hard. Responses to these other word pairs were not considered for further evaluation. In order to encourage a general assessment using multiple senses, the instructions asked participants to explore the materials visually, as well as by touching them. Fig. 3. Representation of a material’s surface characteristics and terminology. After [30]. Spontaneous comments of the participants during and after the test were recorded. The participants were not provided with an explicit definition of ‘‘warmth’’ and the mode of interaction and assessment was not described in detail. This approach was chosen to leave the interpretation of ‘‘warmth’’ open to the participants. Providing a definition of ‘‘warmth’’ or describing the interaction in detail would have interfered with the intuitive evaluation of what people find ‘‘warm’’ in a material, which is most relevant for understanding their subjective experiences. 2.4. Data analysis Effects on the warmth ratings were investigated through repeated measures analyses of variance (ANOVA), with the Color (10 levels) or Roughness (10 levels) as within-subjects variables. The degrees of freedom were corrected with the GreenhouseGeisser e for e < 0.7, and averaged over Greenhouse-Geisser and Huyn-Feldt for e > 0.7 [31]. Paired comparisons with Bonferroni adjustments investigated the differences between individual samples. Gender was first included as a between-subjects variable in all analyses, but no main effects or interaction effects of Gender were found. Because Gender is not the interest of this study, it is not considered a variable in the further analyses and discussion of the results. 2.5. Results and discussion 2.5.1. Evaluation of set I: color effect The repeated measures ANOVA shows a significant main effect of Color [F(5,88) = 20.4; p < 0.001; g2 = 0.517]. This implies that the colored samples are perceived significantly different from each other in terms of warmth. For an indoor wall application, Ruby red and Mango yellow are perceived as the warmest colors from the set, Light gray and Ice blue are the coldest colors (see Table 1 for means). Paired comparisons with Bonferroni correction reveal that the mean ratings of warm of the Pure white, Light gray, and Ice blue samples are significantly different (p < 0.02) from the means of the Chocolate brown, Ruby red, Mango yellow, and Ivory beige samples. The mean perceived warmth of the Ruby red sample differs significantly (p < 0.001) from all other color samples, except the Mango yellow one. This implies that the combination of hue, lightness and saturation for that specific reddish color adds together to a warm perception compared to the other combinations. The remaining paired comparisons are not significant at the 0.05level. To compare the general color theory with the perceived warmth of the color samples, each sample of the first set is graphically represented in Fig. 4. The warmth perception of colors for a wall application can be discussed based on the three typical color parameters: hue, saturation and lightness. Hue and saturation can be inferred from the (a⁄, b⁄)–plane of the CIELAB color space. Samples that are being perceived as warm (mean score between 4 and 7) are represented by black bubbles, samples with a cold perception (mean score between 1 and 4) are marked by white bubbles. The larger the diameter of a bubble, the more extreme the perception of warmth or coldness; the smaller a bubble, the closer the perception is to medium cold–warm (score 4). Fig. 4 shows that the colors being perceived as warm are all situated in the quadrant with yellowish (+b⁄) and reddish (+a⁄) hues. Bluish ( b⁄) and greenish ( a⁄) colors are being perceived as colder than reddish, yellowish and brownish colors. For the perceived warmth of materials, positive correlations are found with both a⁄ and b⁄ (Pearson’s r = 0.79 and r = 0.54, respectively). These findings are in accordance with the general color theory [20]. Achromatic colors, such as white, gray and black, lead to rather cold perceptions. However, the Chocolate 445 L. Wastiels et al. / Materials and Design 42 (2012) 441–449 Table 2 Information on the roughness samples (Set II), divided into subset without waviness (R01–05) and subset with waviness (R06–10): qualitative descriptions according to material science and architecture; mean ratings on warmth, including standard errors of the mean (SEM). Roughness samples Qualitative description R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 ‘‘Architecture description’’ Cold–warm SEM Flat – no local roughnessa,b Flat – small local roughness Flat – medium local roughness Flat – high local roughnessa,b Flat – highest local roughness Roughness + waviness Smooth waviness Roughness + waviness Sharp waviness Large waviness Smooth and shiny Smooth Soft and mat Lightly rough and grainy Rough, grainy, porous and grating Coarse and structured Soft, wavy, bumpy and smooth Rough and spiky Very rough, sharp and coarse Very rough, bumpy, coarse and irregular 3.00 3.35 3.20 3.95 4.15 2.95 3.55 3.10 3.30 3.05 0.27 0.41 0.46 0.35 0.30 0.29 0.29 0.38 0.31 0.32 Roughness used for the main study. 100 +b* LIGHT > 80 mango yellow YELLOW > a,b Participant evaluations ‘‘Material science description’’ L* pure white 90 60 80 70 light gray ivory beige mango yellow white green ice blue 40 60 ivory beige ruby red 20 50 pure white white green ruby red 40 light gray chocolate brown 0 chocolate brown +a* deep black sapphire blue 30 ice blue deep black sapphire blue 20 -40 RED > < GREEN -40 < DARK < BLUE -20 -20 0 20 (a) 40 10 0 (b) Fig. 4. Representation of the perceived warmth of the sample colors in terms of: (left) hue and saturation, (right) lightness L⁄, with black dots corresponding to a rather warm evaluation and white dots to a rather cold evaluation. brown sample also has a small saturation and is perceived as rather warm. This suggests that color hue is more important in warmth perception than color saturation. However, as the highly saturated samples (ruby red and mango yellow) in the test also have warm hues, these findings should be considered with care and be investigated further. The lightness of a color is represented by its L⁄ value, which can be graphed on a vertical axis ranging from 0-black to 100-white. Fig. 4 plots the lightness of the different color samples according to the same notations as used for the (a⁄, b⁄)-plane. The horizontal position of the bubbles has no meaning and is only used to display all data. Darker shades, like Chocolate brown and Ruby red, are perceived as warm. Most of the lighter shades (Pure white, Light gray, Ice blue and White green) lead to a cold perception. In addition, several dark and light colors are perceived neutral in terms of warmth: Deep black, Sapphire blue and Ivory beige. A moderate negative 446 L. Wastiels et al. / Materials and Design 42 (2012) 441–449 correlation is found between perceived warmth and lightness L⁄ (Pearson’s r = 0.48). These results suggest a trend of darker colors being warmer and lighter colors being colder (Fig. 4). 2.5.2. Evaluation of set II: roughness effect No significant effect of Roughness is found in the repeated measures ANOVA [F(5,92) = 2.0; p = 0.09; g2 = 0.095]. However, as the effect approaches significance (p = 0.09), we will discuss and investigate the effect of roughness on warmth assessment in more detail. The main study will further explore and clarify the possible effects of surface roughness on warmth perception. Paired comparisons with Bonferroni correction did not yield any significant differences between the individual samples. The Bonferroni correction is a very conservative method to detect significance, especially when comparing a large number of means. Separate t-tests reveal significant differences between the more extreme results. Samples situated at both ends of the spectrum are being perceived as the most cold (smooth sample R01 versus rough and wavy surface R10). The warmest roughness can be found for the sample that is flat but has the highest local roughness (R05). Keeping in mind the distinction between local roughness and waviness, the effect on the assessment of warmth should be discussed at two levels, considering the two subsets discussed in Section 2.2.2. A visual representation of the means (Fig. 5) suggests that, for flat materials (R01–R05), an increasing local roughness leads to an increasing perception of warmth. The results from the second subset (R06–R10) do not reflect a specific trend. For the first question, we aim to find out whether the effect of roughness is color dependent, and vice versa whether the effect of color changes in relation to the roughness. The main experiment combined color and roughness stimuli in order to investigate this interaction. For the second question, we aim to gain insights in the relative strength of the effects. Comparing results from the pre-study illustrated that varying between extreme colors has a larger impact on warmth perception than varying between extremes in roughness. A change in color from Ice blue (C09) to Ruby red (C05) led to an increase in mean warmth from 2.45 to 6.30, a difference of 3.85 on a scale from one to seven. When changing the surface from smooth (R01) to rough (R05) the mean warmth rose from 3.00 to 4.15, an increase of only 1.15 on a scale from one to seven. These results suggest that color has a larger effect on warmth perception than roughness. However, we wonder what will happen if we keep the difference in warmth ratings obtained in the pre-study constant, and then check whether the same difference in warmth for color or for roughness has the same impact on the overall warmth rating. 3.1. Participants Thirty-eight students from the bachelor and master program in Architecture at the Vrije Universiteit Brussel participated in the study. Their ages ranged from 18 to 24 with an average age of 21, and 71% of the participants were female. 3.2. Materials 3. Main study: combined effects The results of the pre-study suggest that the perception of material warmth can be modified by changing the material’s color as well as by changing the local surface roughness. Warmer colors lead to warmer perceptions of the material. Materials with a higher local roughness lead to warmer perceptions than materials with a smooth surface. Keeping in mind the reality of building, the effects of color and roughness should be considered together. As stated in the introduction we are interested in the effect of roughness compared to that of color. Thus for the main study we formulate two research questions: (Q1) Do color and roughness interact in terms of warmth perception? (Q2) Which change has, relatively speaking, the largest effect on the perception of material warmth: a change in color or a change in roughness? warm 7 3.2.1. Sample set A To investigate the interactions between color and roughness (Q1), we combined warm and cold stimuli based on the results from the pre-study. A warm and a cold color were selected based on their difference with the neutral warmth of 4 on the scale: Mango yellow was chosen as warm color, Light gray as cold color. As the perceived warmth difference between the roughness samples in the pre-study was much smaller, we selected the samples with the most extreme warmth perception for the main study, namely sample R01 and R05. The selected colors and roughnesses are marked with ‘a’ in Tables 1 and 2. The difference in mean ratings in the pre-study for the color samples in sample set A was 2.85 and for the roughness samples it was 1.15. Differences between the means of the different stimuli were significant at the 0.01-level (tested in separate t-tests). 6 5 4 3 2 R09 R10 R08 R07 R06 R05 R04 R03 R02 1 R01 cold Eight samples were created with combined color and roughness stimuli, resulting in two sets of 2  2 designs. Set A constituted of smooth and rough samples, colored in Light gray and Mango yellow; Set B constituted of smooth and rough samples, colored in Ice blue and Sapphire blue. The material, size and production technique of the samples were identical to that of the pre-study. Fig. 5. Visual representation of the mean ratings on warmth sample set II. 3.2.2. Sample set B To investigate the relative effects of color and roughness (Q2), we also combined two colors and two roughnesses from the pretest with similar warmth ratings. The difference in perceived warmth between the roughest and smoothest sample (R05 and R01, respectively) was taken as a reference to select the colors. Results from the pre-study showed that the colored samples Sapphire blue and Ice blue had similar mean warmth ratings. The selected colors and roughnesses are marked with ‘b’ in Tables 1 and 2. The difference in mean ratings in the pre-study for the color samples in sample set B was 1.15 and for the roughness samples it was 1.15. Differences between the means of each stimulus were significant at the 0.01-level (tested in separate t-tests). 447 L. Wastiels et al. / Materials and Design 42 (2012) 441–449 In total, this leads to a full factorial (42) design with four different colors for two degrees of roughness. A summary of the creation of the samples in reference to the pre-study can be found in Table 3. 3.3. Procedure The experiment took place under the same conditions as the pre-study (see Section 2.3). Each stimulus was presented to the participants in a random order. On average the test took about six minutes to be completed. 3.4. Data analysis Sample set A was designed to investigate the interaction effect between color and roughness. However, the samples from set B can also be considered in this investigation as they deliver additional data in terms of color and roughness. Therefore, a (42) repeated measures ANOVA with Color and Roughness as within-subjects factors provided insights in the first research question. Sample set B was designed to investigate the relative importance of color and roughness in the perception of warmth. A (22) repeated measures ANOVA with Color and Roughness as within-subjects factors provided insights in the second research question. The degrees of freedom were corrected with the Greenhouse-Geisser e for e < 0.7, and with an averaged e over Greenhouse-Geisser and Huyn-Feldt for e > 0.7 [31]. No main effect or interaction effects of Gender were found for any of the analyses, so Gender was not considered as a factor in the analyses. 3.5. Results and discussion 3.5.1. Interaction between color and roughness The ANOVA with Color (light gray, mango yellow, ice blue, sapphire blue) and Roughness (smooth, rough) as independent variables, shows significant main effects of both Color [F(3,101) = 54.3; p < 0.001; g2 = 0.595] and Roughness [F(1,37) = 9.5; p < 0.005; g2 = 0.204] on the assessment of material warmth. The absence of a significant interaction effect Color⁄Roughness [F(3,103) = 0.4; p > 0.5] illustrates that the effect of roughness on material warmth is independent of the material’s color and vice versa. The main effects of Color and Roughness confirm that the material color and the surface roughness both affect the warmth perception. As expected, warmer colors (mango yellow and sapphire blue) lead to a warmer overall perception than colder colors (light gray and ice blue), and rough surfaces lead to warmer perceptions than smooth surfaces (Fig. 6). A combination of the warmest stimuli (mango yellow, rough surface) leads to the warmest perception. A combination of the coldest stimuli (light gray, smooth surface) leads to the coldest perception. The combination of warm and cold stimuli results in warmth perceptions in between the previous two. In Fig. 6 the effects of roughness can be found by comparing mean results within one color zone (compare vertically between different curves); the effects of color are represented by the differences within one level of roughness (compare height difference between points on one curve). Paired comparisons with Bonferroni adjustment show significant differences (0.01-level) between all color pairs, except between the Light blue-Light gray and Light blue-Dark blue (both p > 0.05). 3.5.2. Relative effect of color and roughness A (22) repeated measures ANOVA with Color (ice blue, sapphire blue) and Roughness (smooth, rough) as independent variables is run to analyze the relative importance of roughness compared to color on the assessment of warmth. In accordance with the previous ANOVA, no interaction effect between Color and Roughness is found (p > 0.1). A significant main effect of Color is shown [F(1,37) = 7.3; p < 0.05; g2 = 0.164]. The effect of Roughness just failed to reach significance at the 0.05 level [F(1,37) = 3.8; p = 0.06; g2 = 0.093]. The lack of an effect of Roughness implies that the relative effects of color and roughness cannot be compared based on these samples. However, as a main effect is found for color but not for roughness, these results suggest that the relative effect of color will be larger than that of roughness. To consolidate these findings an additional ANOVA is run. As argued when discussing the selection of colors and roughnesses for the samples (see Section 3.2.2), the relative effects of color and roughness can be analyzed by the consideration of two colors and two roughnesses with similar warmth ratings. The results from the pre-study show that Light gray and Ice blue have the same mean ratings. Hence, we can perform the same test again by comparing the Light gray samples from set A and the Sapphire blue samples from set B. The repeated measures ANOVA with Color (light gray, sapphire blue) and Roughness (smooth, rough) as independent variables, shows a significant main effect of both Color [F(1,37) = 16.3; p < 0.001; g2 = 0.306] and Roughness [F(1,37) = 8.6; p < 0.01; g2 = 0.189]. No interaction effect between Color and Roughness is found. The marginal means resulting from the ANOVA allow us to quantify the effects of the investigated variables. The difference in warmth perception between the light gray (mean 2.72) and the sapphire blue samples (mean 3.67) is 0.95. The difference in warmth perception between the smooth samples (mean 2.92) and the rough samples (mean 3.47) is 0.55. Relatively speaking, the effect of color on the perception of material warmth thus is 1.7 times larger than the effect of increasing the local roughness of a surface. 4. General discussion 4.1. Effect of color The influence of colors on warmth perception can be derived from general color theory. Warm colors, ranging between yellow and red, lead to warmer perceptions than cool colors, which range Table 3 Overview of the eight samples created for the main study, based on results from the pre-study. Color C02 (light gray) Color C06 (mango yellow) Color C09 (ice blue) Color C10 (sapphire blue) Roughness R01(smooth) Roughness R05(rough) Set A Set B 448 warm L. Wastiels et al. / Materials and Design 42 (2012) 441–449 7 6 rough 5 smooth 4 3 cold 2 1 light gray ice blue sapphire blue mango yellow Fig. 6. Comparing the effects of a rough or a smooth surface finish on the assessment of material warmth for different colors. from blue to yellow–green. The hue seems to be the most important color aspect to determine material warmth. This corresponds with findings from Wright and Rainwater, who showed that the warmth perception of colored squares is mainly defined by the hue [21]. They also stated that darker and more chromatic colors tend to be viewed as warmer. This corresponds to our finding that achromatic colors, like white, gray, and black, tend to be perceived as colder. These results should be considered with care, however, because of the limited number of achromatic samples in our study. Previous research showed that vision dominates the multisensory assessment of material warmth in architecture [19]. Analogously, whereas participants in the present study were asked to assess the samples by use of all their senses, it is possible that the results reflect mainly the participants’ visual assessment. In our study, the dominance of vision in our multisensory experience [33,34] may have had two effects. On the one hand, the dominance of vision might explain the large effect of color on the perception of warmth. On the other hand, the effect of roughness in our study may contain both a visual and a tactile component, because participants can both see and feel the roughness differences between the samples. In our case, we cannot be sure whether the increase in material warmth perception is due to its visual roughness or tactual roughness. If the latter is the case, the effect of roughness on warmth perception might gain importance when choosing a material for an application in which users are likely to touch the material frequently, like doors and stairs. Nonetheless, as material perception is context-dependent [35,36], the results presented here should be considered within the context of indoor wall applications. Previous research has shown a strong correlation between the perceived warmth of a material and its thermal properties [5– 17,19]. Whereas the present study has illustrated the influence of color and roughness on warmth perception, it would be interesting to study how these effects relate to the effect of a material’s thermal properties. Will a red metal, for example, be perceived as being warmer than a bluely stained piece of wood? The current research could be taken a step further by studying the warmth perception for other materials, as well as for new compared to known materials. 4.2. Effect of roughness 4.4. Combined effect of color and roughness Setting up the roughness sample set revealed a semantic problem that complicates the understanding of the relation between warmth perception and the physical attribute roughness. Bergmann Tiest and Kappers already showed that people’s personal definition of roughness (perceived roughness) is different from physical roughness [32]. This study shows that in an architectural context the word ‘roughness’ reflects a concept that covers two different surface phenomena describing the irregularities of the surface at the micro and meso scale, being defined by material scientists respectively as local roughness and waviness. Making this distinction leads to a better understanding of the influence on material warmth, namely, waviness has no effect on material warmth but local roughness does: locally rough surfaces are perceived as being warmer than smooth surfaces. This finding confirms the idea that a space with smooth walls seems colder than a comparable space with finely textured walls [4]. In further research, the distinctions between different parameters of surface texture, including roughness, waviness and patterning, are likely to contribute to a more subtle understanding of the effect of surface treatments on the perception of experiential qualities in building materials. 4.3. Comparison of effects The results from the main study show that color has a larger impact than roughness on the warmth perception of materials for indoor walls. This might explain why various studies have focused on the warmth perception of colors, whereas we have no knowledge of studies considering the warmth perception of roughness. However, in some cases in practice it is not desirable or possible to radically change the material color. For example, if an architect wants to modify the warmth perception of a concrete wall without the addition of color pigments or painting the surface, he/she can work the surface texture by using a different finishing technique. Results from the main study showed no interaction between the effects of color and those of roughness. This implies that the individual effects of color and roughness are mutually independent and can be summed when colors and roughness are combined. Given the fact that the effect of color on warmth perception is larger than the effect of roughness, materials with a rough surface and cold color will generally be perceived as colder than materials with a smooth surface and warm color. 5. Conclusion An experimental approach was used to study the effects of color and surface roughness on the perceived warmth of an indoor wall. Results showed that the local surface roughness has an effect on warmth perception, irrespective of the material’s color, and vice versa. The sample sets used for this study revealed that the obtained changes in warmth perception can be larger when varying between extremes of color (e.g. blue versus red) than when varying between extremes in roughness (e.g. smooth versus rough). In addition, a comparison of the sizes of the effects of color and roughness on the perception of warmth showed that a change in color has a larger effect than a comparable change in local roughness. Color adjustments are clearly more effective than surface roughness adjustments in changing the perceived warmth of walls. As it is not always possible or desirable to radically change a material’s color or surface roughness, these findings provide architects and other designers with insights on how to alter the experience of warmth by the selection and/or manipulation of the materials. The application of different finishing techniques – and their impact on changes in color and roughness – increases or decreases the perceived warmth. Future research could focus on different aspects of surface texturing, including roughness at local and global scale as well as surface patterning. L. Wastiels et al. / Materials and Design 42 (2012) 441–449 Acknowledgements The research reported in this paper was conducted in the context of Lisa Wastiels’ PhD research at the department of Architectural Engineering of the VUB, funded by the Research Foundation–Flanders (FWO). The writing of this paper was in part made possible through funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement No. 201673. The authors thank Daniel Debondt (MEMC-VUB) for the preparation/ installation, and Heidi Ottevaere (TONA-VUB) for the roughness measurements of the material samples. They also thank the VUB Architectural Engineering students for participating in the study. References [1] Wastiels L, Wouters I. Architects’ considerations while selecting materials. Mater Des 2012;34:584–93. [2] Fernandez J. Material architecture: emergent materials for innovative buildings and ecological construction. Amsterdam: Architectural Press; 2006. [3] Vielhauer Kasmar J. The development of a usable lexicon of environmental descriptors. In: Nasar JL, editor. Environmental aesthetics theory, research, and applications. New York: Cambridge University Press; 1988. p. 144–55. [4] Thiis-Evensen T. Archetypes in architecture, Norwegian University Press. Oslo; Oxford: Oxford University Press; 1987. [5] Ashby MF, Johnson K. Materials design: the art and science of material selection in product design. Oxford: Butterworth-Heinemann; 2002. [6] Sonneveld MH, Schifferstein HNJ. The tactual experience of objects. In: Schifferstein HNJ, Hekkert P, editors. Product experience. Amsterdam, Oxford: Elsevier Science; 2008. [7] Bergmann Tiest WM, Kappers AML. Thermosensory reversal effect quantified. Acta Psychol 2008;127:46–50. [8] Karana E, Hekkert P, Kandachar PV. Meanings of materials through sensorial properties and manufacturing processes. Mater Des 2009;30:2778–84. [9] Fenko A, Schifferstein HNJ, Hekkert P. Looking hot or feeling hot: what determines the product experience of warmth? Mater Des 2010;31: 1325–31. [10] Wright B. The influence of hue, brightness, and colour on the apparent warmth. Am J Psychol 1962;75:232–41. [11] van Kesteren IEH. Product designers’ information needs in materials selection. Mater Des 2008;29:133–45. [12] Wastiels L, Wouters I., Lindekens J. Material Knowledge for Design: The architect’s material vocabulary. In: Poggenpohl S, editors. Proceedings from the conference of the international societies of design research 2007, Hong Kong, SAR: Hong Kong Polytechnic University; 2007. [13] Dalke H, Little J, Niemann E, Camgoz N, Steadman G, Hill S. Colour and lighting in hospital design. Opt Laser Technol 2006;38:343–65. 449 [14] Yildirim K, Akalin-Baskaya A, Hidayetoglu ML. Effects of indoor color on mood and cognitive performance. Build Environ 2007;42:3233–40. [15] Marin E. Teaching thermal physics by touching. Lat Am J Phys Edu 2008;2:15–7. [16] Myers G. Analytical methods in conduction heat transfer. New York: McGrawHill Book Company; 1971. [17] Obata Y, Takeuchi K, Furuta Y, Kanayama K. Research on better use of wood for sustainable development: quantitative evaluation of good tactile warmth of wood. Energy 2005;30:1317–28. [18] Schifferstein HNJ, Desmet PMA. The effects of sensory impairments on product experience and personal well-being. Ergonomics 2007;50:2026–48. [19] Wastiels L, Schifferstein HNJ, Heylighen A, Wouters I. Relating material experience to technical parameters: A case study on visual and tactile warmth perception of indoor wall materials. Build Environ 2012;49:359–67. [20] Itten J. Kleurenleer. Cantecleer: de Bilt; 1970. [21] Wright B, Rainwater L. The meaning of colour. J Gen Psychol 1962;67:89–99. [22] Chen X, Barnes CJ, Childs THC, Henson B, Shao F. Materials’ tactile testing and characterisation for consumer products’ affective packaging design. Mater Des 2009;30:4299–310. [23] Fenko A, Schifferstein HNJ, Huang T-C, Hekkert P. What makes products fresh: the smell or the colour? Food Qual Prefer 2009;20:372–9. [24] Wastiels L, Schifferstein HNJ, Wouters I, Heylighen A. Touching materials visually. About the dominance of vision in architectural material assessment. Int J Des. [accepted]. [25] De Bolster E, Cuypers H, Van Itterbeeck P, Wastiels J, De Wilde WP. Use of hypar-shell structures with textile reinforced cement matrix composites in lightweight constructions. Compos Sci Technol 2009;69:1341–7. [26] Chen X, Shao F, Barnes C, Childs T, Henson B. Exploring relationships between touch perception and surface physical properties. Int J Des 2009;3:67–76. [27] Commission Internationale de l’Eclairage (CIE), Colorimetry, Commission Internationale de l’Eclairage (CIE), Wien; 1986. [28] Bodart M, de Penaranda R, Deneyer A, Flamant A. Photometry and colorimetry characterisation of materials in daylighting evaluation tools. Build Environ 2008;43:2046–58. [29] Whitehouse DJ. Surfaces and their measurement. 1st ed. London, UK: Butterworth-Heinemann; 2002. [30] Corporation Wyko. WYKO surface profilers technical reference manual. United States: Wyko Corporation; 1996. [31] Stevens JP. Applied multivariate statistics for the social sciences. 4th ed. Mahwah, NJ: Erlbaum; 2002. [32] Bergmann Tiest WM, Kappers AML. Haptic and visual perception of roughness. Acta Psychol 2007;124:177–89. [33] Schifferstein HNJ. The perceived importance of sensory modalities in product usage: a study of self-reports. Acta Psychol 2006;121:41–64. [34] Pallasmaa J. The Eyes of the Skin. Architecture and the senses. London: Wiley; 2005. [35] Karana E, Hekkert P, Kandachar PV. A tool for meaning driven materials selection. Mater Des 2010;31:2932–41. [36] Wastiels L, Wouters I. Framework of material considerations in architectural design: the interaction between context, manufacturing, experience, and material aspects. In: Desmet PMA, Tzvetanova SA, Hekkert P, Justice L, editors. Dare to desire. Proceedings from the 6th conference on design and emotion 2008, Hong Kong, SAR: Hong Kong Polytechnic University; 2008.