Correlation Between Human Aesthetic Judgement and Spatial Complexity Measure

Javid, Mohammad Ali Javaheri; Blackwell, Tim; Zimmer, Robert; al-Rifaie, Mohammad Majid

doi:10.1007/978-3-319-31008-4_6

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…Figure 16 shows the results of applying an information-gain-based approach proposed in [ 29 ] for the images in Figure 11 a. The results show a link between information gain and empirical aesthetic judgement in the case of the asymmetrical patterns, but not for the symmetrical patterns.…”

Section: Resultsmentioning

confidence: 99%

“…Birkhoff [ 25 ] proposed an aesthetic measure, where the measure of aesthetic quality is in a direct relation to the degree of order O, and in a reverse relation to the complexity C, M = O/C. Eysenck [ 26 , 27 , 28 ] conducted a series of experiments on Birkhoff’s model; he argued that the aesthetic measure has to be in direct relation to the complexity rather than an inverse relation M = O × C. Javid et al [ 29 ] conducted a survey on the use of entropy to quantify order and complexity; they also proposed a computational measure of complexity. Their measure is based on the information gain from specifying the spatial distribution of pixels and their uniformity and non-uniformity.…”

Section: Related Workmentioning

confidence: 99%

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An Information Theory Approach to Aesthetic Assessment of Visual Patterns

Khalili

Bouchachia

2021

Entropy

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The question of beauty has inspired philosophers and scientists for centuries. Today, the study of aesthetics is an active research topic in fields as diverse as computer science, neuroscience, and psychology. Measuring the aesthetic appeal of images is beneficial for many applications. In this paper, we will study the aesthetic assessment of simple visual patterns. The proposed approach suggests that aesthetically appealing patterns are more likely to deliver a higher amount of information over multiple levels in comparison with less aesthetically appealing patterns when the same amount of energy is used. The proposed approach is evaluated using two datasets; the results show that the proposed approach is more accurate in classifying aesthetically appealing patterns compared to some related approaches that use different complexity measures.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Information Theory Approach to Aesthetic Assessment of Visual Patterns

Khalili

Bouchachia

2021

Entropy

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“…We defined six measures that could be potential influencers of subjective ratings: density, entropy, local spatial complexity (LSC), KC (approximate), intricacy, and symmetry; these are described below. We chose these six measures since they are commonly studied in the literature as potential determinants of complexity and beauty judgments (Arnheim, 1956(Arnheim, , 1966Attneave, 1957;Bense, 1960Bense, , 1969Chikhman et al, 2012;Damiano et al, 2021;Fan et al, 2022;Friedenberg & Liby, 2016;Gartus & Leder, 2017;Javaheri Javid et al, 2016;Moles, 1958;Rigau et al, 2008;Roberts, 2007;Schmidhuber, 2009;Singh & Shukla, 2017;Silva et al, 2021;Snodgrass, 1971). We do not consider other popular measures such as number of vertices or edges since as remarked in the previous section, they are hard to define for our CA patterns.…”

Section: Computational Measures For Pattern Quantificationmentioning

confidence: 99%

Relating objective complexity, subjective complexity, and beauty in binary pixel patterns.

Nath,

Brändle,

Schulz

et al. 2024

Psychology of Aesthetics, Creativity, and the Arts

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The complexity of images critically influences our assessment of their beauty. However, studies relating assessments of complexity and beauty to potential objective measures are hampered by the use of hand-crafted stimuli which are hard to reproduce and manipulate. To tackle this, we developed a systematic method for generating 2D black-and-white pixel patterns using cellular automata and collected ratings of complexity and beauty from 80 participants. We developed various computational measures of pattern quantification such as density, entropies, local spatial complexity, Kolmogorov complexity, and asymmetries. We also introduced an “intricacy” measure quantifying the number of components in a pattern using a graph-based approach. We related these objective measures with participant judgments of complexity and beauty to find that a weighted combination of local spatial complexity and intricacy was an effective predictor (R2test = .47) of complexity. This implies that people’s complexity ratings depended on the local arrangement of pixels along with the global number of components in the pattern. Furthermore, we found a positive linear influence of complexity ratings on beauty, with a negative linear influence of disorder (asymmetry and entropy), and a negative interaction between the two quantities (R2test = .65). This implies that there is beauty in complexity as long as there is sufficient order. Lastly, a moderated mediation analysis showed that subjective complexity mediates the influence of objective complexity (OC) on beauty, implying that subjective complexity provides useful information over and above OC.

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“…We defined six measures that could potentially explain subjective complexity ratings: density, entropy, local spatial complexity, (approximate) Kolmogorov complexity, intricacy, and symmetry; these are described below. We chose these six measures since they are commonly studied in the literature as potential determinants of subjective complexity (Friedenberg and Liby, 2016;Fan et al, 2022;Nadal, 2007;Attneave, 1957;Gartus and Leder, 2017;Damiano et al, 2021;Snodgrass, 1971;Arnheim, 1956Arnheim, , 1966Bense, 1960Bense, , 1969Moles, 1958;Schmidhuber, 2009;Javid, 2016;Rigau, 2008;Chikhman et al, 2012;Singh and Shukla, 2017;Silva, 2021). We do not consider other popular measures such as number of vertices or edges since as remarked in the previous section, they are hard to define for our CA patterns.…”

Section: Computational Measures For Pattern Quantificationmentioning

confidence: 99%

“…To relate participant ratings of complexity on these patterns to objective measures, we programmed six computational complexity measures including density, entropy, local spatial complexity, Kolmogorov complexity, and local and global asymmetry. These measures have been considered frequently in past studies (Friedenberg and Liby, 2016;Fan et al, 2022;Nadal, 2007;Attneave, 1957;Gartus and Leder, 2017;Damiano et al, 2021;Snodgrass, 1971;Arnheim, 1956Arnheim, , 1966Bense, 1960Bense, , 1969Moles, 1958;Schmidhuber, 2009;Javid, 2016;Rigau, 2008;Chikhman et al, 2012;Singh and Shukla, 2017;Silva, 2021). We also introduced a novel intricacy measure which quantified the number of visual elements in a pattern using a graph-based approach.…”

Section: Metric For Complexitymentioning

confidence: 99%

Relating Objective Complexity, Subjective Complexity and Beauty in Binary Pixel Patterns

Nath¹,

Brändle²,

Schulz³

et al. 2023

Preprint

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The complexity of images critically influences our impression of them and our assessment of their beauty. However, there is no consensus on the best way to formalize an objective measure of complexity for images. Moreover, studies relating subjective assessments of complexity and beauty to potential objective measures are hampered by the use of hand-crafted stimuli from which it is hard to generalize. To tackle these issues, we generated 2D black-and-white patterns algorithmically using cellular automata, and collected the ratings of 80 participants of their subjective complexity and beauty. We then assessed the relationship between beauty and complexity ratings, and objective measures of complexity such as density, asymmetry, entropy, local spatial complexity, and (approximate) Kolmogorov complexity. We also introduced an “intricacy” measure that quantifies the number of components in a pattern using a graph-based approach. We found that a weighted combination of local spatial complexity and intricacy was an effective predictor (R2test = 0.46) of subjective complexity. This implies that people’s complexity ratings depend on the number of distinct elements in the pattern along with the elements’ local spatial distribution and therefore that global and local image features are integrated to determine complexity judgements. Furthermore, we found a positive linear relationship between beauty and complexity ratings, with a negative linear influence of disorder, namely asymmetry and entropy, and a negative interaction between the two (with a total predictive accuracy of R2test = 0.64). This implies that there is beauty in complexity as long as there is sufficient order in the form of low asymmetry and randomness. In addition, a moderated mediation analysis showed that subjective complexity mediates the influence of objective complexity on beauty at all levels of disorder. Lastly, we found some evidence for individual differences with different people displaying different degrees of preference towards intricacy (in their complexity assessments) and dislike of disorder (in their beauty assessments).

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Correlation Between Human Aesthetic Judgement and Spatial Complexity Measure

Cited by 8 publications

References 35 publications

An Information Theory Approach to Aesthetic Assessment of Visual Patterns

An Information Theory Approach to Aesthetic Assessment of Visual Patterns

Relating objective complexity, subjective complexity, and beauty in binary pixel patterns.

Relating Objective Complexity, Subjective Complexity and Beauty in Binary Pixel Patterns

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