Dataset Information

#TheDress: Categorical perception of an ambiguous color image.

ABSTRACT: We present a full analysis of data from our preliminary report (Lafer-Sousa, Hermann, & Conway, 2015) and test whether #TheDress image is multistable. A multistable image must give rise to more than one mutually exclusive percept, typically within single individuals. Clustering algorithms of color-matching data showed that the dress was seen categorically, as white/gold (W/G) or blue/black (B/K), with a blue/brown transition state. Multinomial regression predicted categorical labels. Consistent with our prior hypothesis, W/G observers inferred a cool illuminant, whereas B/K observers inferred a warm illuminant; moreover, subjects could use skin color alone to infer the illuminant. The data provide some, albeit weak, support for our hypothesis that day larks see the dress as W/G and night owls see it as B/K. About half of observers who were previously familiar with the image reported switching categories at least once. Switching probability increased with professional art experience. Priming with an image that disambiguated the dress as B/K biased reports toward B/K (priming with W/G had negligible impact); furthermore, knowledge of the dress's true colors and any prior exposure to the image shifted the population toward B/K. These results show that some people have switched their perception of the dress. Finally, consistent with a role of attention and local image statistics in determining how multistable images are seen, we found that observers tended to discount as achromatic the dress component that they did not attend to: B/K reporters focused on a blue region, whereas W/G reporters focused on a golden region.

SUBMITTER: Lafer-Sousa R

PROVIDER: S-EPMC5672910 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:We report associations between vowel sounds, graphemes, and colors collected online from over 1,000 Dutch speakers. We also provide open materials, including a Python implementation of the structure measure and code for a single-page web application to run simple cross-modal tasks. We also provide a full dataset of color-vowel associations from 1,164 participants, including over 200 synesthetes identified using consistency measures. Our analysis reveals salient patterns in the cross-modal associations and introduces a novel measure of isomorphism in cross-modal mappings. We found that, while the acoustic features of vowels significantly predict certain mappings (replicating prior work), both vowel phoneme category and grapheme category are even better predictors of color choice. Phoneme category is the best predictor of color choice overall, pointing to the importance of phonological representations in addition to acoustic cues. Generally, high/front vowels are lighter, more green, and more yellow than low/back vowels. Synesthetes respond more strongly on some dimensions, choosing lighter and more yellow colors for high and mid front vowels than do nonsynesthetes. We also present a novel measure of cross-modal mappings adapted from ecology, which uses a simulated distribution of mappings to measure the extent to which participants' actual mappings are structured isomorphically across modalities. Synesthetes have mappings that tend to be more structured than nonsynesthetes', and more consistent color choices across trials correlate with higher structure scores. Nevertheless, the large majority (~ 70%) of participants produce structured mappings, indicating that the capacity to make isomorphically structured mappings across distinct modalities is shared to a large extent, even if the exact nature of the mappings varies across individuals. Overall, this novel structure measure suggests a distribution of structured cross-modal association in the population, with synesthetes at one extreme and participants with unstructured associations at the other.

Dataset Information

#TheDress: Categorical perception of an ambiguous color image.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets