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Predicting beauty: Fractal dimension and visual complexity in art
Author(s) -
Forsythe A.,
Nadal M.,
Sheehy N.,
CelaConde C. J.,
Sawey M.
Publication year - 2011
Publication title -
british journal of psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.536
H-Index - 92
eISSN - 2044-8295
pISSN - 0007-1269
DOI - 10.1348/000712610x498958
Subject(s) - beauty , psychology , dimension (graph theory) , fractal dimension , variance (accounting) , preference , fractal , cognitive psychology , visual perception , perception , aesthetics , mathematics , statistics , art , mathematical analysis , accounting , business , neuroscience , pure mathematics
Visual complexity has been known to be a significant predictor of preference for artistic works for some time. The first study reported here examines the extent to which perceived visual complexity in art can be successfully predicted using automated measures of complexity. Contrary to previous findings the most successful predictor of visual complexity was Gif compression. The second study examined the extent to which fractal dimension could account for judgments of perceived beauty. The fractal dimension measure accounts for more of the variance in judgments of perceived beauty in visual art than measures of visual complexity alone, particularly for abstract and natural images. Results also suggest that when colour is removed from an artistic image observers are unable to make meaningful judgments as to its beauty.