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      Taxonomy of Individual Variations in Aesthetic Responses to Fractal Patterns

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          Abstract

          In two experiments, we investigate group and individual preferences in a range of different types of patterns with varying fractal-like scaling characteristics. In Experiment 1, we used 1/f filtered grayscale images as well as their thresholded (black and white) and edges only counterparts. Separate groups of observers viewed different types of images varying in slope of their amplitude spectra. Although with each image type, the groups exhibited the “universal” pattern of preference for intermediate amplitude spectrum slopes, we identified 4 distinct sub-groups in each case. Sub-group 1 exhibited a typical peak preference for intermediate amplitude spectrum slopes (“intermediate”; approx. 50%); sub-group 2 exhibited a linear increase in preference with increasing amplitude spectrum slope (“smooth”; approx. 20%), while sub-group 3 exhibited a linear decrease in preference as a function of the amplitude spectrum slope (“sharp”; approx. 20%). Sub-group 4 revealed no significant preference (“other”; approx. 10%). In Experiment 2, we extended the range of different image types and investigated preferences within the same observers. We replicate the results of our first experiment and show that individual participants exhibit stable patterns of preference across a wide range of image types. In both experiments, Q-mode factor analysis identified two principal factors that were able to explain more than 80% of interindividual variations in preference across all types of images, suggesting a highly similar dimensional structure of interindividual variations in preference for fractal-like scaling characteristics.

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          Most cited references23

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          Fractal and multifractal analysis: a review.

          Over the last years, fractal and multifractal geometries were applied extensively in many medical signal (1D, 2D or 3D) analysis applications like pattern recognition, texture analysis and segmentation. Application of this geometry relies heavily on the estimation of the fractal features. Various methods were proposed to estimate the fractal dimension or multifractal spectral of a signal. This article presents an overview of these algorithms, the way they work, their benefits and their limits. The aim of this review is to explain and to categorize the various algorithms into groups and their application in the field of medical signal analysis.
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            Universal aesthetic of fractals

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              Predicting beauty: fractal dimension and visual complexity in art.

              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. ©2010 The British Psychological Society.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                08 July 2016
                2016
                : 10
                : 350
                Affiliations
                [1] 1School of Psychology, UNSW Australia Sydney, NSW, Australia
                [2] 2Department of Physics, University of Oregon Eugene, OR, USA
                Author notes

                Edited by: Christoph Redies, University of Jena, Germany

                Reviewed by: George Mather, University of Lincoln, UK; Paul Barry Hibbard, University of Essex, UK

                *Correspondence: Branka Spehar b.spehar@ 123456unsw.edu.au
                Article
                10.3389/fnhum.2016.00350
                4937063
                27458365
                ee057ebf-cb6d-4b55-a7c9-53b377bf08bc
                Copyright © 2016 Spehar, Walker and Taylor.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 February 2016
                : 28 June 2016
                Page count
                Figures: 15, Tables: 0, Equations: 0, References: 47, Pages: 18, Words: 10330
                Funding
                Funded by: Australian Research Council 10.13039/501100000923
                Award ID: DP120103659
                Categories
                Neuroscience
                Original Research

                Neurosciences
                individual differences,fractals,visual preference,aesthetics,individual variability
                Neurosciences
                individual differences, fractals, visual preference, aesthetics, individual variability

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