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Inferring Master Painters' Esthetic Biases from the Statistics of Portraits

Overview of attention for article published in Frontiers in Human Neuroscience, March 2017
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
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Title
Inferring Master Painters' Esthetic Biases from the Statistics of Portraits
Published in
Frontiers in Human Neuroscience, March 2017
DOI 10.3389/fnhum.2017.00094
Pubmed ID
Authors

Hassan Aleem, Ivan Correa-Herran, Norberto M. Grzywacz

Abstract

The Processing Fluency Theory posits that the ease of sensory information processing in the brain facilitates esthetic pleasure. Accordingly, the theory would predict that master painters should display biases toward visual properties such as symmetry, balance, and moderate complexity. Have these biases been occurring and if so, have painters been optimizing these properties (fluency variables)? Here, we address these questions with statistics of portrait paintings from the Early Renaissance period. To do this, we first developed different computational measures for each of the aforementioned fluency variables. Then, we measured their statistics in 153 portraits from 26 master painters, in 27 photographs of people in three controlled poses, and in 38 quickly snapped photographs of individual persons. A statistical comparison between Early Renaissance portraits and quickly snapped photographs revealed that painters showed a bias toward balance, symmetry, and moderate complexity. However, a comparison between portraits and controlled-pose photographs showed that painters did not optimize each of these properties. Instead, different painters presented biases toward different, narrow ranges of fluency variables. Further analysis suggested that the painters' individuality stemmed in part from having to resolve the tension between complexity vs. symmetry and balance. We additionally found that constraints on the use of different painting materials by distinct painters modulated these fluency variables systematically. In conclusion, the Processing Fluency Theory of Esthetic Pleasure would need expansion if we were to apply it to the history of visual art since it cannot explain the lack of optimization of each fluency variables. To expand the theory, we propose the existence of a Neuroesthetic Space, which encompasses the possible values that each of the fluency variables can reach in any given art period. We discuss the neural mechanisms of this Space and propose that it has a distributed representation in the human brain. We further propose that different artists reside in different, small sub-regions of the Space. This Neuroesthetic-Space hypothesis raises the question of how painters and their paintings evolve across art periods.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 21%
Professor 6 14%
Researcher 5 12%
Professor > Associate Professor 5 12%
Lecturer > Senior Lecturer 2 5%
Other 6 14%
Unknown 10 23%
Readers by discipline Count As %
Psychology 10 23%
Arts and Humanities 3 7%
Agricultural and Biological Sciences 3 7%
Social Sciences 3 7%
Neuroscience 3 7%
Other 7 16%
Unknown 14 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 05 September 2017.
All research outputs
#7,778,524
of 24,340,143 outputs
Outputs from Frontiers in Human Neuroscience
#3,191
of 7,459 outputs
Outputs of similar age
#117,893
of 311,861 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#92
of 188 outputs
Altmetric has tracked 24,340,143 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,459 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one has gotten more attention than average, scoring higher than 56% of its peers.
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 311,861 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 188 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.