53 research outputs found
Estimating Color-Concept Associations from Image Statistics
To interpret the meanings of colors in visualizations of categorical
information, people must determine how distinct colors correspond to different
concepts. This process is easier when assignments between colors and concepts
in visualizations match people's expectations, making color palettes
semantically interpretable. Efforts have been underway to optimize color
palette design for semantic interpretablity, but this requires having good
estimates of human color-concept associations. Obtaining these data from humans
is costly, which motivates the need for automated methods. We developed and
evaluated a new method for automatically estimating color-concept associations
in a way that strongly correlates with human ratings. Building on prior studies
using Google Images, our approach operates directly on Google Image search
results without the need for humans in the loop. Specifically, we evaluated
several methods for extracting raw pixel content of the images in order to best
estimate color-concept associations obtained from human ratings. The most
effective method extracted colors using a combination of cylindrical sectors
and color categories in color space. We demonstrate that our approach can
accurately estimate average human color-concept associations for different
fruits using only a small set of images. The approach also generalizes
moderately well to more complicated recycling-related concepts of objects that
can appear in any color.Comment: IEEE VIS InfoVis 2019 ACM 2012 CSS: 1) Human-centered computing,
Human computer interaction (HCI), Empirical studies in HCI 2) Human-centered
computing, Human computer interaction (HCI), HCI design and evaluation
methods, Laboratory experiments 3) Human-centered computing, Visualization,
Empirical studies in visualizatio
Effects of data distribution and granularity on color semantics for colormap data visualizations
To create effective data visualizations, it helps to represent data using
visual features in intuitive ways. When visualization designs match observer
expectations, visualizations are easier to interpret. Prior work suggests that
several factors influence such expectations. For example, the dark-is-more bias
leads observers to infer that darker colors map to larger quantities, and the
opaque-is-more bias leads them to infer that regions appearing more opaque
(given the background color) map to larger quantities. Previous work suggested
that the background color only plays a role if visualizations appear to vary in
opacity. The present study challenges this claim. We hypothesized that the
background color modulate inferred mappings for colormaps that should not
appear to vary in opacity (by previous measures) if the visualization appeared
to have a "hole" that revealed the background behind the map (hole hypothesis).
We found that spatial aspects of the map contributed to inferred mappings,
though the effects were inconsistent with the hole hypothesis. Our work raises
new questions about how spatial distributions of data influence color semantics
in colormap data visualizations
Context Matters: A Theory of Semantic Discriminability for Perceptual Encoding Systems
People's associations between colors and concepts influence their ability to
interpret the meanings of colors in information visualizations. Previous work
has suggested such effects are limited to concepts that have strong, specific
associations with colors. However, although a concept may not be strongly
associated with any colors, its mapping can be disambiguated in the context of
other concepts in an encoding system. We articulate this view in semantic
discriminability theory, a general framework for understanding conditions
determining when people can infer meaning from perceptual features. Semantic
discriminability is the degree to which observers can infer a unique mapping
between visual features and concepts. Semantic discriminability theory posits
that the capacity for semantic discriminability for a set of concepts is
constrained by the difference between the feature-concept association
distributions across the concepts in the set. We define formal properties of
this theory and test its implications in two experiments. The results show that
the capacity to produce semantically discriminable colors for sets of concepts
was indeed constrained by the statistical distance between color-concept
association distributions (Experiment 1). Moreover, people could interpret
meanings of colors in bar graphs insofar as the colors were semantically
discriminable, even for concepts previously considered "non-colorable"
(Experiment 2). The results suggest that colors are more robust for visual
communication than previously thought.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphic
Effects of university affiliation and “school spirit” on color preferences: Berkeley versus Stanford
The ecological valence theory (EVT) posits that preference for a color is determined by people’s average affective response to everything associated with it (Palmer & Schloss, Proceedings of the National Academy of Sciences, 107, 8877–8882, 2010). The EVT thus implies the existence of sociocultural effects: Color preference should increase with positive feelings (or decrease with negative feelings) toward an institution strongly associated with a color. We tested this prediction by measuring undergraduates’ color preferences at two rival universities, Berkeley and Stanford, to determine whether students liked their university’s colors better than their rivals did. Students not only preferred their own colors more than their rivals did, but the degree of their preference increased with self-rated positive affect (“school spirit”) for their university. These results support the EVT’s claim that color preference is caused by learned affective responses to associated objects and institutions, because it is unlikely that students choose their university or develop their degree of school spirit on the basis of preexisting color preferences
Manual / Issue 4 / Blue
Manual, a journal about art and its making. Blue.The fourth issue. Indigo blue, ultramarine blue, cobalt blue, cerulean blue, zaffre blue, indanthrone blue, phthalo blue, cyan blue, Han blue, French blue, Berlin blue, Prussian blue, Venetian blue, Dresden blue, Tiffany blue, Lanvin blue, Majorelle blue, International Klein Blue, Facebook blue. The names given to different shades of blue speak of plants, minerals, and modern chemistry; exoticism, global trade, and national pride; capitalist branding and pure invention. The fourth issue of Manual is a meditation on blue. From precious substance to controllable algorithm to the wide blue yonder, join us as we leap into the blue.
Softcover, 64 pages. Published 2015 by the RISD Museum. Proceeds from RISD Museum publications support the work of the museum. Manual 4 (Blue) contributors include Lawrence Berman, A. Will Brown, Linda Catano, Spencer Fitch, Jessica Helfand, Kate Irvin, Oda van Maanen, Dominic Molon, Maggie Nelson, Ingrid A. Neuman, Margot Nishimura, Karen B. Schloss, Anna Strickland, Louis van Tilborgh, and Elizabeth A. Williams.https://digitalcommons.risd.edu/risdmuseum_journals/1003/thumbnail.jp
Aesthetic response to color combinations: preference, harmony, and similarity
Previous studies of preference for and harmony of color combinations have produced confusing results. For example, some claim that harmony increases with hue similarity, whereas others claim that it decreases. We argue that such confusions are resolved by distinguishing among three types of judgments about color pairs: (1) preference for the pair as a whole, (2) harmony of the pair as a whole, and (3) preference for its figural color when viewed against its colored background. Empirical support for this distinction shows that pair preference and harmony both increase as hue similarity increases, but preference relies more strongly on component color preference and lightness contrast. Although pairs with highly contrastive hues are generally judged to be neither preferable nor harmonious, figural color preference ratings increase as hue contrast with the background increases. The present results thus refine and clarify some of the best-known and most contentious claims of color theorists
Characterization of the Fecal Microbiome from Non-Human Wild Primates Reveals Species Specific Microbial Communities
BACKGROUND: Host-associated microbes comprise an integral part of animal digestive systems and these interactions have a long evolutionary history. It has been hypothesized that the gastrointestinal microbiome of humans and other non-human primates may have played significant roles in host evolution by facilitating a range of dietary adaptations. We have undertaken a comparative sequencing survey of the gastrointestinal microbiomes of several non-human primate species, with the goal of better understanding how these microbiomes relate to the evolution of non-human primate diversity. Here we present a comparative analysis of gastrointestinal microbial communities from three different species of Old World wild monkeys. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed fecal samples from three different wild non-human primate species (black-and-white colobus [Colubus guereza], red colobus [Piliocolobus tephrosceles], and red-tailed guenon [Cercopithecus ascanius]). Three samples from each species were subjected to small subunit rRNA tag pyrosequencing. Firmicutes comprised the vast majority of the phyla in each sample. Other phyla represented were Bacterioidetes, Proteobacteria, Spirochaetes, Actinobacteria, Verrucomicrobia, Lentisphaerae, Tenericutes, Planctomycetes, Fibrobacateres, and TM7. Bray-Curtis similarity analysis of these microbiomes indicated that microbial community composition within the same primate species are more similar to each other than to those of different primate species. Comparison of fecal microbiota from non-human primates with microbiota of human stool samples obtained in previous studies revealed that the gut microbiota of these primates are distinct and reflect host phylogeny. CONCLUSION/SIGNIFICANCE: Our analysis provides evidence that the fecal microbiomes of wild primates co-vary with their hosts, and that this is manifested in higher intraspecies similarity among wild primate species, perhaps reflecting species specificity of the microbiome in addition to dietary influences. These results contribute to the limited body of primate microbiome studies and provide a framework for comparative microbiome analysis between human and non-human primates as well as a comparative evolutionary understanding of the human microbiome
A framework for human microbiome research
A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies
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