Abstract
People interpret abstract meanings from colors, which makes color a useful perceptual feature for visual communication. This process is complicated, however, because there is seldom a one-to-one correspondence between colors and meanings. One color can be associated with many different concepts (one-to-many mapping) and many colors can be associated with the same concept (many-to-one mapping). We propose that to interpret color-coding systems, people perform assignment inference to determine how colors map onto concepts. We studied assignment inference in the domain of recycling. Participants saw images of colored but unlabeled bins and were asked to indicate which bins they would use to discard different kinds of recyclables and trash. In Experiment 1, we tested two hypotheses for how people perform assignment inference. The local assignment hypothesis predicts that people simply match objects with their most strongly associated color. The global assignment hypothesis predicts that people also account for the association strengths between all other objects and colors within the scope of the color-coding system. Participants discarded objects in bins that optimized the color-object associations of the entire set, which is consistent with the global assignment hypothesis. This sometimes resulted in discarding objects in bins whose colors were weakly associated with the object, even when there was a stronger associated option available. In Experiment 2, we tested different methods for encoding color-coding systems and found that people were better at assignment inference when color sets simultaneously maximized the association strength between assigned color-object parings while minimizing associations between unassigned pairings. Our study provides an approach for designing intuitive color-coding systems that facilitate communication through visual media such as graphs, maps, signs, and artifacts.
Highlights
People can interpret complex messages encoded in visual features
We found that people have expectations for how different colored bins signal different kinds of to-be-discarded objects, based on their color-object associations and contextual cues from other colors in the set
We note that we propose that people interpret color-coding systems by solving decoding assignment problems, we do not claim that there is some part of the brain that performs the same computations as in Matlab’s linprog function using the merit scores defined with Eq 2
Summary
People can interpret complex messages encoded in visual features. 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. Color is one of many visual features that can be used to communicate abstract information, with others including size, texture, orientation, and shape (Bertin, 1983; Ware, 2012). 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. 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
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
