What do colors communicate

Conversely, blue can also carry some negative color meanings such as depression and can bring about a sense of coldness. Some retailers add their guarantee, trust certification or free shipping icons in a blue color to strengthen the trust aspect the color is known for. Tech brands like Facebook, Twitter and Skype often use blue in their marketing.

But retailers like Walmart and Oral B also use the color. The blue in the Walmart logo can help position the brand as trustworthy , reliable, and relaxing. After all, Walmart is a place where you can buy groceries and do shopping all in one convenient location.

Oral B is a dental health brand that sells toothbrushes. Healthcare niches, like Oral B, typically use blue in their branding to help people associate the brand with a quality, reliable and safe product. In color psychology, purple is a royal color. The color meaning for purple is connected to power, nobility, luxury, wisdom, and spirituality. But avoid using the color too much as it can cause feelings of frustration. Some perceive its overuse as arrogant. Purple is a color brands like Hallmark and Yahoo use.

On Hallmark, the logo and the top navigation are purple but the rest of the website uses a variety of other colors. On Yahoo, the logo, top navigation words, and Yahoo icons like Mail use the color purple. In color psychology, white showcases innocence, goodness, cleanliness, and humility. Keep in mind, that this is the meaning in North American culture. In some parts of the world, white has the opposite meaning.

The color meaning for white also has a negative side where it symbolizes sterility and cold. On an ecommerce website, white tends to be the most used color. Your pages will likely have a white background with a black font. This is because, black font on a white background is the best color combination for readability.

On ASOS, the words in the header, logo, and background are white. When the background is grey or black, the font is white and when the background is white the font is black. The use of a white logo helps create contrast. Many brands who have white as a central color tend to pair it with black or grey.

Black is a popular color in retail. In contrast, the color meaning can also evoke emotions such as sadness and anger. Many fashion retailers have used black in their logos. Some brands choose to use black and white photos for lifestyle banner images or icons to create a certain tone or consistency on their website. Black is a color retailers such as Chanel and Nike use.

Chanel uses black for their logo and has several black and white images on their website to maintain a consistent look. Once you start browsing their website, a thick black top navigation background appears.

They use a black font on their graphics for images and for their text. Noticeably, their call to actions are also black. Many retailers in the fashion niche, especially, use black call to actions that contrast well against a white background. Nike also uses a black, white and grey color scheme for their website. Their logo and font is black throughout their website.

Thus, making the website easy to read. In color psychology, grey represents neutrality and balance. Its color meaning likely comes from being the shade between white and black. However, grey does carry some negative connotations, particularly when it comes to depression and loss. Its absence of color makes it dull. Grey can be used for font color, headers, graphics, and even products to appeal to a mass audience.

Apple is an example of a brand who uses the color grey in their branding. On their website, they use the color grey for their header to contrast against a white logo. Brown is an earthy color. So naturally, color psychology highlights that the color meaning for brown relates to comfort, security and a down to earth nature. They know red splotches on a weather map signal impending storms, red traffic lights signal stop, and red milk cartons signal that the container holds whole milk.

Given this ability, people use colors to communicate important and time-sensitive information. For example, a recent surgical protocol for separating conjoined twins used green and purple tape to signal which monitors and equipment were dedicated to each twin Associated Press, , presumably so they did not get mixed up during surgery. Color is one of many visual features that can be used to communicate abstract information, with others including size, texture, orientation, and shape Bertin, ; Ware, However, color is especially useful for signaling because it can be observed quickly from a distance and it provides meaningful information that is independent from spatial structure.

In human-made artifacts, differences in font color can signal different meanings in signs and maps without affecting legibility of the text. Most relevant to the present study, differences in surface colors can signal different kinds of recycling bins without interfering with the ability to insert objects into the bins.

Yet, interpreting colors is complicated because there is no one-to-one correspondence between colors and concepts Fig. There are one-to-many mappings Fig. How, then, do observers interpret reliable and meaningful signals from colors? Mappings between colors squares and concepts circles that are a one-to-one, b one-to-many, and c many-to-one.

We addressed this question by investigating how observers interpret colors in color-coding systems designed for visual communication. When people attempt to communicate through visual media e. Ideally, observers will be able to decode the same message that was encoded by the designer. For example, observers are faster at interpreting bar graphs depicting fruit sales when the bar colors match the colors of the fruit they represent e.

One might argue that if color-coding systems are clearly labeled, then interpreting those systems is trivial—you just look up the answer. However, Lin et al. We approach this question by considering visual communication as a set of assignment problems. In optimization and operations research, assignment problems also known as maximum-weight matching problems are mathematical models that describe how to pair items from two different categories Kuhn, ; Munkres, Here, we consider two types of assignment problems for generating and interpreting color-coding systems, which correspond to the encoding and decoding tasks described above: encoding assignment problems and decoding assignment problems.

Although we focus on color-coding systems here, the principles can generalize to any coding system in which concepts map onto perceptual features. Designers can use encoding assignment problems to generate color-coding systems by determining optimal assignments between colors and concepts.

Figure 2a illustrates an encoding assignment problem as a bipartite graph. Here, and henceforth, we typically refer to "objects" instead of "concepts" because the focus of this paper is on color-coding systems for objects to be discarded in trash and recycling bins. The choice of numerical labels is arbitrary and only serves to simplify the explanation.

Merit scores can be thought of as weights on each of the edges of the graph in Fig. Bipartite graphs illustrating color-object assignments. Here, there are more colors than objects. Black edges represent assigned color-object pairs and gray edges represent unassigned color-object pairs.

Dashed black edges represent inferred assignments that match the encoded assignments. Solving an encoding assignment problem means to select a subset of the edges such that each object is assigned to exactly one color and the sum of merit scores along selected edges is maximized. In Fig. The optimal assignment will depend on the particular choice of merit scores.

Footnote 2. Lin et al. To encode the color-concept pairings for their test stimuli e. This approach rewards strong color-object associations for the intended pairing while penalizing the associations of unintended pairings. We propose that when people interpret color-coding systems, they solve a decoding assignment problem. To do so, they make inferences about how the designer had mapped colors onto concepts while generating the color-coding system. In the decoding assignment problem in Fig.

The Color Inference Framework Schloss, in press proposes that people make inferences about colors color inferences based on an internal representation of color-concept associations that is stored in their minds. There are different kinds of color inference processes that operate on the same internal representation, which are modulated by perceptual context e.

Here, we aim to understand the assignment inference process for interpreting mappings between colors and concepts, which we believe enables people to solve decoding assignment problems.

We studied assignment color inference in the domain of recycling, where the color-coding system mapped different colored bins to different kinds of objects to be discarded. As described below, our approach was to manipulate input into the color inference system i. We selected the colors for each experiment based on color-object association ratings obtained from 49 participants in a pilot experiment see Additional file 1 for methodological details.

Approximations of the colors are shown in Fig. The mean association ratings are displayed in Fig. We describe the details of how we selected the colors for Experiment 1 and Experiment 2 within the sections on each experiment below.

Colors in the figure are for illustration only and are not colorimetrically accurate. Mean color-object association ratings for paper, plastic, glass, metal, compost, and trash.

Colors are sorted along the x-axis from most weakly associated to most strongly associated with each object. Bar colors represent the colors that were judged also see x-axis label and corresponding coordinates in Additional file 1 : Table S1. See Additional file 1 for details on how the data were collected. Participants saw images of unlabeled colored bins along with the name of object to discard e. We proposed and evaluated two hypotheses about how people perform assignment color inference.

When people are given a single object and are asked to map it onto one color from a given set of colors, the local assignment hypothesis predicts that they simply match the object with its most strongly associated color. This means that two different objects could be mapped to the same color if that color is the strongest associate for both objects. In contrast, the global assignment hypothesis predicts that people not only consider the association strength between the object and candidate colors, but also account for the association strengths between all other objects and colors within the scope of the color-coding system.

This can result in pairing colors with objects that are weakly associated if it results in better overall pairings for all objects considered. In this study, we investigated assignment color inference in two experiments.

Experiment 1 tested whether people perform local or global assignment in a simple scenario with two objects paper and trash and sets of two colors. We chose paper and trash because the colors most associated with these objects are distinct see Fig. This avoids conflicts that arise from one-to-many and many-to-one mappings and therefore makes the task relatively easy, at least when one of the colors is strongly associated with paper and the other is strongly associated with trash.

Experiment 2 tested whether people can still perform assignment inference with a larger set of six objects and six colors that contain conflicts due to one-to-many and many-to-one mappings.

These conflicts make the task of selecting which six colors to use a nontrivial one. We determined which colors to use by designing different merit functions and solving the corresponding encoding assignment problems.

In Experiment 1, we compared the local assignment and global assignment hypothesis predictions for a simple case of discarding one of two kinds of objects into one of two unlabeled colored bins. Participants saw images of colored bins along with a description of a target object paper or trash and indicated which bin was appropriate for discarding the target object. We only tested paper and trash in this experiment because among all pairs of objects, this pair has the most different pattern of color-object association ratings, determined by the pilot experiment described above also see Additional file 1 : Table S2.

We tested all pairs of four colors: the two colors that were most strongly associated with paper white; WH and trash dark-yellow; DY and two colors that were most weakly associated with paper saturated-red; SR and trash saturated-purple; SP , see Fig. Figure 5 illustrates the local and global assignment hypotheses for two color sets. Each panel contains two parts. The top part contains a bipartite graph showing the predicted assignments thick black lines between two possible objects paper P and trash T ; circles and two possible colored bins squares.

The bottom part shows the corresponding example trial, with a red arrow showing the predicted response. Illustration of the assignment inference process for two objects and two colors, under the local assignment a and b or global assignment c and d hypotheses. Thick black lines represent assigned color-object pairings and thin gray lines represent unassigned color-object pairings.

Under local assignment, the target object trash is matched with its strongest associate association ratings indicated on the graph edges. Under global assignment, the target is matched by accounting for the association strengths of both objects and both colors.

The blue region in Fig. For the local assignment hypothesis, only the target object for the particular trial in this case, trash is in the scope and the other, non-target object in this case, paper is outside the scope. As a result, the target object always gets discarded into the bin whose color is most strongly associated with it, regardless of the association strength between the colors and the non-target object. As shown in Fig. For the global assignment hypothesis, both the target and non-target are within the scope of the assignment problem.

As a result, the target object gets discarded into the bin that allows for maximization of the total association strength over all possible color-object pairings. However, in Fig. Trash gets discarded in the saturated-purple bin even though trash is more strongly associated with white than with saturated-purple, because the global assignment specifies that white should be reserved for paper.

In this experiment, we quantified the predictions of the local and global assignment hypotheses and evaluated which predictions better capture participant responses. All had normal color vision as screened using the H. The bins were 3. The colors were those that were most strongly associated with paper white; WH and trash dark-yellow; DY , and the colors that were most weakly associated with paper saturated-red; SR and trash saturated-purple; SP.

There was text at the top of the screen that indicated which target object was to be discarded on each trial paper or trash. The displays were presented on a The experiment was programmed using Presentation www. Participants were asked to imagine they had paper and trash to throw away and they wanted to figure out where the objects should be discarded. On each trial, there was text at the top of the screen indicating which object to discard on that trial paper or trash , with a pair of colored bins below Fig.

Participants indicated whether the object should be discarded in the left or right bin by pressing the left or right arrow key.

Before the test trials, there were five practice trials. If participants asked questions about which bin they should choose, they were told to follow their intuition. The trials were presented in a random order separated by a ms inter-trial interval. The participants were given a break after each set of 20 trials.

We generated predictions for the local and global assignment hypotheses using the color-object associations data from the pilot experiment described in Additional file 1 and shown in Fig. To solve an assignment problem under the local assignment hypothesis, we simply match each object with its highest rated color. Under the global assignment hypothesis, we consider both objects and both colors together and pick the pairings that yields the largest total association rating.

One approach for generating these predictions might be to solve assignment problems under each hypothesis by calculating merit scores using the mean color-object association ratings presented in Fig.

This approach would be problematic because solving an assignment problem is a deterministic and absolute procedure. This means that if the outcome e. However, we want predictions that reflect the sensitivity of outcomes to small changes in the merit scores. To produce predictions with this sensitivity property, we used a sampling approach. We describe the sampling procedure for generating predictions in detail in Additional file 1. Roughly, we added small random perturbations to the association ratings in Fig.

This procedure has the desired effect because when association ratings are very different, adding small perturbations has little influence on the outcome of the assignment problem. However, when two association ratings are similar, perturbing them will sometimes cause a reversal in which association rating is largest and yield a different solution to the assignment problem. Repeating many times produces a distribution of outcomes that reflects the magnitude of the differences between merit scores.

This approach should approximate the uncertainty in human judgments when different objects have similar color-object associations. Figure 6 shows the predictions of the local assignment hypothesis Fig. It also shows the mean proportion of trials out of 12 that participants chose each color within each color set for each object Fig.

Predictions from a the local assignment hypothesis and b the global assignment hypothesis in terms of the proportion of times each color would be chosen for each target object x-axis within each color set separate plots.

The model predictions were generated from the color-object association data shown in Fig. Footnote 5 We coded predictors for the local and global assignment hypotheses as the probability of choosing the left color, using the values from Fig. For 18 out of 24 participants, the beta weights for the global assignment model were higher than the beta weights for the local assignment model.

They change from trial to trial, depending on the other colors in the scene. For example, participants almost always responded that red was correct for paper when paired with dark-yellow, almost never responded that red was correct for paper when paired with white, and responded near chance 0.

Further, the results demonstrate that people can accurately interpret encoded assignments when none of the colors are strongly associated with the target object.

They can do so as long as one of the colors is associated with the non-target object at least when there are only two objects. For example, saturated-purple and dark-yellow are similarly weakly associated with paper see Fig.

They can do so because dark-yellow is strongly associated with trash the non-target object for that trial , and solving the assignment problem tells them if dark-yellow is for trash, then saturated-purple must be the color for paper. Although people can solve decoding assignment problems when both colors are weakly associated with the target, the response time RT data suggest doing so is more difficult compared to cases when one of the colors is strongly associated with the target Fig.

We analyzed the RT data by first calculating the median RT across all 12 trials within each condition. In summary, Experiment 1 provided evidence that participants approached interpreting the color-coding system in our task as a global assignment problem; they determined which assignments between colors and objects optimized the color-object associations of the entire set.

This process sometimes resulted in observers interpreting that objects were intended to be assigned to colors that were their weakest associates, even when there was a stronger associate on the screen. However, there was a processing cost when the target did not have a strongly associated color in the color set. In Experiment 1, we found evidence that people interpret color-coding systems by solving a decoding assignment problem with a global scope. Decoding was somewhat straightforward when there were only two objects and two colors.

However, decoding becomes more complicated when there are several objects and colors and there are conflicts arising from one-to-many and many-to-one mappings. This is the case for the objects we studied in Experiment 2: paper, plastic, glass, metal, compost, and trash.

Generally, a color-coding system should be easier to decode if: 1 each object has a color that is strongly associated with it; and 2 each object has only one color that is strongly associated with it. When these objectives are competing, we must choose how to prioritize one over the other. We tested two different color sets that were selected using two different merit functions: an isolated merit function and a balanced merit function. These merit functions trade off the relative weight placed on 1 and 2.

We also tested a third set of colors that were selected using a baseline merit function , which attempts to select colors that are each equally associated with all the objects, thus maximizing confusion for the participants. We tested the color sets above for two tasks.

It can be a great alternative to white, and give things a modern, yet timeless, look. Earthy and organic is what best describes this color. Dirt, trees, potatoes— all things found in the earth. When used properly, it can be a soothing element and give an organic feel to your design. The lightest color both visually and in psychological weight.

It gives a light feel to your design, and is best used to give your design breathing room. Associated with purity, cleanliness and clarity I think white is a great color to incorporate into any design.

The color of elegance, power, and authority; boldly stated and seemingly unshakeable. I am of course talking about all these colors in their most basic form. Surely a neon-green will be more attention-getting than a dull red any day of the week. So these are merely a starting point and general guide to what colors communicate. Please feel free to share! Unfortunately I have way to much to say about picking the right colors, so instead of making this post the size of a small novel, I decided to break it up over different posts.

Has any of this caused you to re-think the colors you are currently using in your blog design? You can leave a comment by clicking here. A social media blogger and speaker who loves helping people tell bigger, better stories online. Founder of SoVisual. The best social media management tool for sophisticated marketers. Share Tweet Pin 3.