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Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties

Overview of attention for article published in Frontiers in Human Neuroscience, July 2016
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Title
Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties
Published in
Frontiers in Human Neuroscience, July 2016
DOI 10.3389/fnhum.2016.00343
Pubmed ID
Authors

Richard H. A. H. Jacobs, Koen V. Haak, Stefan Thumfart, Remco Renken, Brian Henson, Frans W. Cornelissen

Abstract

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed-and presumably for this reason-the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities-including the aesthetic appreciation-are sufficiently universal to be predicted-with reasonable accuracy-based on the computed feature content of the textures.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 30%
Student > Bachelor 2 20%
Researcher 2 20%
Unknown 3 30%
Readers by discipline Count As %
Psychology 4 40%
Physics and Astronomy 1 10%
Neuroscience 1 10%
Unknown 4 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2016.
All research outputs
#17,811,358
of 22,881,154 outputs
Outputs from Frontiers in Human Neuroscience
#5,718
of 7,170 outputs
Outputs of similar age
#265,761
of 364,404 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#151
of 177 outputs
Altmetric has tracked 22,881,154 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,170 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 364,404 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 177 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.