exploring the role of complexity, content and individual ... et al artp 2020.pdf · photographs...

© Koninklijke Brill NV, Leiden, 2020 DOI: 10.1163/22134913-20191139

Art & Perception 8 (2020) 89–119

*To whom correspondence should be addressed. E-mail:[email protected]

Exploring the Role of Complexity, Content and Individual Differences in Aesthetic Reactions to Semi-

Abstract Art Photographs

Nathalie Vissers1,*, Pieter Moors1, Dominique Genin2 and Johan Wagemans1

1Laboratory of Experimental Psychology, Department of Brain and Cognition, KU Leuven (University of Leuven), Belgium

2SLAC, Academy for Visual Arts, Leuven, BelgiumReceived 20 July 2019; accepted 7 January 2020

AbstractArtistic photography is an interesting, but often overlooked, medium within the field of empirical aesthetics. Grounded in an art–science collaboration with art photographer Dominique Genin, this project focused on the relationship between the complexity of a photograph and its aesthetic appeal (beauty, pleasantness, interest). An artistic series of 24 semi-abstract photographs that play with multiple layers, recognisability vs unrecognizability and complexity was specifically created and se-lected for the project. A large-scale online study with a broad range of individuals (n = 453, varying in age, gender and art expertise) was set up. Exploratory data-driven analyses revealed two clusters of individuals, who responded differently to the photographs. Despite the semi-abstract nature of the photographs, differences seemed to be driven more consistently by the ‘content’ of the photograph than by its complexity levels. No consistent differences were found between clusters in age, gender or art expertise. Together, these results highlight the importance of exploratory, data-driven work in empirical aesthetics to complement and nuance findings from hypotheses-driven studies, as they allow to go further than a priori assumptions, to explore underlying clusters of participants with dif-ferent response patterns, and to point towards new venues for future research. Data and code for the analyses reported in this article can be found at https://osf.io/2fws6/.

KeywordsArt photography, empirical aesthetics, semi-abstract, complexity, individual differences, content, beauty, interest, pleasure

Downloaded from Brill.com03/17/2020 03:17:46PM by [email protected] Dr Wagemans

mailto:[email protected]

https://osf.io/2fws6/

90 N. Vissers et al. / Art & Perception 8 (2020) 89–119

1. Introduction

Photography is the easiest thing in the world if one is willing to accept pictures that are flaccid, limp, bland, banal, indiscriminately informative, and pointless. But if one insists in a photograph that is both complex and vigorous it is almost impossible. – John Szarkowski, former director of photography New York’s Museum of Modern Art (n.d.)

[…]and what we’re all trying to do is to create a layered, deep, complex, com-plicated photograph that doesn’t look complex or complicated[…] – Sam Abell, photographer at National Geographic (Braun, 2008)

Two ideas stand out from the introductory quotes above: First, it is not always easy to make a captivating photograph. Second, the complexity of a photo-graph seems to play an important role in its aesthetic appeal. What is not clear, however, is the exact nature of this relationship. Where can we find the edge where vigorousness and complexity co-exist, where a layered and complex photograph does not look (too) complex?

This is a relevant question to anyone making photographs, but also to re-searchers in empirical aesthetics — longing to understand what determines our aesthetic reactions to visual stimuli. A collaboration between a photogra-pher and researcher could therefore be an ideal playground for these questions about complexity, photography and aesthetics. What you are reading is the result of exactly such an artist–scientist collaboration. Concretely, we studied the nature between complexity (see Note 1) and aesthetic appeal in a set of art photographs with the following aims: (1) to shed new light on our aes-thetic reactions to art photographs, (2) to focus on the role of complexity in the photographs — a feature often linked to aesthetics, and (3) to engage in a science– art collaboration that is meaningful for both artist and scientist. Be-low, we shortly introduce these three aspects, after which we will outline the approach for our current study.

1.1. Aesthetic Reactions to Photography

At this point, there is limited psychological knowledge on our aesthetic re-actions to (artistic) photography (Jacobsen and Beudt, 2017; McManus and Stöver, 2014). Within the domain of empirical aesthetics, the only review spe-cifically focusing on photography and aesthetics (as far as we know) concludes with the statement: “Surprisingly little effort has been put into a scientific understanding of photographs and photography, be those effects perceptual or aesthetic” (McManus and Stöver, 2014, p. 271). Still, we are currently living in a world where we encounter a flood of photographs with aesthetic inten-tion on a daily basis, where the debate on photography as a valid form of art


N. Vissers et al. / Art & Perception 8 (2020) 89–119 91

is well behind us, and where we are experiencing a rise of art photography works being sold for exuberant prices (e.g., Andreas Gursky’s ‘Rhein II’ was purchased for 4.3 million dollars during an auction at Christie’s New York in 2011). It is therefore high time to introduce art photography into the field of empirical aesthetics, and empirical aesthetics into the field of art photog-raphy to get more insight into what determines our aesthetic impressions of photographs.

Overall, the field is only beginning to emerge (see McManus and Stöver, 2014 for an overview), but some studies have tried to find underlying dimen-sions of — and individual differences in — people’s judgements of photographs (Axelsson, 2007a, b), focused on the role of image quality characteristics such as sharpness or contrast (Tinio et al., 2011), the centre of mass of a photograph (McManus et al., 2011) or automatic vs controlled processing of art photo-graphs (Foytik et al., 2013).

Some of these studies hint towards an important role of (perceived) stim-ulus complexity in people’s preferences for (artistic) photographs. For ex-ample, Axelsson (2007a) was inspired by a framework of Eckblad (1981) on mental representations — or schemes — and how assimilation resistance (the resistance of new input into existing schemes) is associated with certain emo-tions. In particular, a combination of stimulus complexity and the person’s information processing abilities will result in a certain type of emotional re-sponse. For example, as schemes are developed and a certain type of input (e.g., photographs) is mastered, predictive schemes are formed and previously encountered photographs become familiar. As this happens, people will start to appreciate a higher level of assimilation resistance. Moderate resistance, in this framework, would lead to pleasantness, too high assimilation resistance to unpleasantness, and interest would be somewhere in between (above moderate degree of resistance). Building on this framework to study individual differenc-es in preferences to photographs, Axelsson (2007a) hypothesizes that experts in photography — who should have a superior ability to process photographic information, thanks to better developed predictive schemes for photographs — should prefer relatively more uncertain (e.g., complex, ambiguous, full of contradictions) and unfamiliar photographs than novices. In line with this, Axelsson (2007a) found that experts preferred photographs that were more un-certain, dynamic, unfamiliar and expressive, whereas novices preferred more familiar, certain, possibly static, and hedonic or pleasant photographs. Simi-larly, Foytik and colleagues (2013) found that their participants (undergraduate students and mainly photography novices) gave higher preference ratings to photographs that were perceived to be more familiar and easy to process.

These studies provide some first ideas regarding the relationship between complexity and aesthetic preferences for artistic photographs, and possible individual differences in the relationship. However, they rely on relatively few



participants (45 undergraduates in Foytik et al., 2013 and 10 novices and 5 professionals in Axelsson, 2007a), who had to fill out many ratings for each photograph (27 semantic differentials), and results are obtained from sum-marising over these ratings based on principal components analyses, as well as summarizing over a wide range of photography types (e.g., from cat pho-tographs or Stockholm cityscapes to iconic art photographs). Moreover, the studies above focus mainly on linear relationships, whereas the relationship between complexity and aesthetic preference might be better explained by a curvilinear pattern (as outlined in the next paragraph).

Overall, we can conclude that there are some first ideas regarding the role of complexity and aesthetic appreciation of photographs, but research on this specific, possibly non-linear relationship with a larger and more di-verse group of participants has been lacking. This might be surprising, given that complexity is a central concept within the larger field of empirical aes-thetics (overview in the next paragraph) and — as the introductory quotes illustrate — is also an ecologically valid feature within the photography world itself. Moreover, the field of machine learning — inspired by the concept of complexity within empirical aesthetics — has already successfully introduced complexity features in their algorithms to predict human aesthetic preferences of large user-based photography databases (Romero et al., 2012; Sun et al., 2015). However, these algorithms were built for possible future applications, such as browsers that take into account aesthetic preferences, and are not primarily out of interest in the nature of this relationship or in demonstrating “the existence of universal aesthetic principles or rules” (Romero et al., 2012, p. 126).

Therefore, specifically focusing on the relationship between complexity and aesthetics in artistic photographs could be an important step to bridge the gap between the fields of artistic photography and empirical aesthetics.

1.2. Complexity and Empirical Aesthetics

Trying to grasp concepts like beauty and our aesthetic reactions to art is not a new trend, but has existed already since philosophers in ancient Greece. With Fechner’s ‘Vorschule der Aesthetik’ (Fechner, 1876), a field of empiri-cal aesthetics was born, where researchers were trying to understand these philosophical principles in a controlled and empirical fashion. Complexity has popped up as a possible predictor of aesthetic experience already early on in this field. Both Birkhoff (1932) and Eysenck (1941) tried to formulate the role of complexity and order on people’s aesthetic evaluations, but it was not until Berlyne (1971) introduced his psychobiological framework that “the study of complexity’s influence on the appreciation of beauty was based on firm psy-chological and neurological grounds” (Nadal et al., 2010, p. 174).



Berlyne’s psychobiological framework relates arousal to hedonic value, postulating that an intermediate level of arousal will result in the highest he-donic value. As a result, our aesthetic appreciation of a work of art will also be highest if it induces an intermediate level of arousal in the viewer. Several collative properties of an artwork can induce arousal (e.g., novelty, complex-ity, uncertainty, conflict) of which complexity is an important one. Following from these ideas, Berlyne’s theory suggests that the relationship between com-plexity and hedonic value will describe an inverted U-curve, where interme-diate levels of complexity will be liked best; too little is boring, too much is overwhelming. Many researchers have since followed in Berlyne’s footsteps, trying to predict people’s aesthetic reactions from the complexity levels of many different stimuli, ranging from computer-generated patterns (Güçlütürk et al., 2016), to snowflakes (Adkins and Norman, 2016), music (Gordon and Gridley, 2013), architecture (Imamoglu, 2000), and abstract paintings (Kru-pinski and Locher, 1988). However, the results of these studies have diverged: whereas some successfully reproduced the hypothesized inverted U-curve, others have also found positive and negative linear relationships or even U-curve relationships (see Nadal et al., 2010 for an empirical study trying to understand these divergences and Van Geert and Wagemans, in press a, for a recent review of the literature on order, complexity and aesthetic preference).

Thus, the relationship between complexity and aesthetics seems to be com-plex itself. Some have put forward the differences in type of stimuli and opera-tionalization of the involved factors (stimuli and complexity concept — Nadal et al., 2010; conceptualization of liking — Marin et al., 2016), whereas others have focused on the data analytical strategies as possible causes of the diver-gence (e.g., Güçlütürk et al., 2016).

1.2.1. Conceptualization of Aesthetic Response: Beauty, Pleasure and Interest

Depending on the type of aesthetic response, different relationships between complexity and aesthetics could be found. When comparing underlying di-mensions of complexity, interest and pleasingness ratings, Berlyne and his colleagues found that both complexity and interest have two underlying di-mensions, and both interest dimensions correlated highly to the complexity dimensions (labelled ‘information content’ and ‘unitariness vs. articulation into easily recognisable parts’). Pleasingness, however, had three underlying dimensions, of which only one dimension correlated highly to a complexity dimension (to the ‘information content’ component), the second did relate to the second interest component, and the third component was more difficult to interpret (but was labelled ‘meaningfulness’ or ‘associative value’, for it seems to separate more familiar and less familiar objects) (Berlyne et al., 1968). Oth-ers have also found an intricate relationship between complexity and interest.



Silvia (2005) showed that interest can be predicted by the appraisals of com-plexity and ability to understand the image. Muth et al. (2015) found that the perceived degree of ambiguity related positively to different aesthetic respons-es (liking, interest, and strength of affect), but the largest effect was found for interest.

Studies and theories focusing on the importance of complexity and ambigu-ity in predicting aesthetic response have been contrasted to theories that focus on processing fluency as an overarching predictor (comprising aspects such as good Gestalt, symmetry, etc.) for aesthetic pleasure (Reber et al., 2004). To account for these contrasts in predicting aesthetic outcome (complex images vs. fluent images predicting aesthetics), Graf and Landwehr (2015, 2017) proposed a dual-process perspective on fluency-based aesthetics. They hypothesized that processing fluency and processing style (automatic and de-liberate) interact to determine different aesthetic responses. Automatically processed images that are processed fluently would result in aesthetic plea-sure, whereas images that are less fluently processed would — through delib-erate processing, and reducing the disfluency — result in interest responses. Others have put forward similar ideas, but focusing on the distinction between beauty and pleasure, emphasizing that processing fluency could lead to mild aesthetic pleasure (‘pretty’), but it cannot explain more intense aesthetic feel-ings related to novel stimuli (‘beauty’). They relate this to prevention goals and promotion goals, respectively (Armstrong and Detweiler-Bedell, 2008). Finally, an empirical study focusing on the relationship between complexity and different types of aesthetic response (Marin et al., 2016) also found dif-ferences in the relationships. For example, in a set of paintings, they found that — after controlling for familiarity — the relationship between complexity and beauty was significantly positive, whereas for pleasantness it was weakly negative, and it was not present for liking (Marin et al., 2016). In sum, these results highlight the importance of approaching aesthetic response as a multi-dimensional concept, taking into account that complexity might have different relationships with different aesthetic dimensions.

1.2.2. Data Analytic Strategy: Averaging and Individual DifferencesBeside this conceptual factor, there is also a data-analytical factor that might explain the inconsistent findings, namely the aggregation of individual data that exhibits qualitatively different patterns. Specifically, the average relation-ship between complexity and preference does not necessarily accurately re-flect the relationship for all individuals (Estes, 1956). Comparing participants’ individual judgments of novel graphic patterns, Jacobsen and Höfel (2002) found large individual differences. Different participants considered different sometimes even opposing – elements to contribute to beauty. As a result, when they compared group and individual models, the group model that averaged



over participants’ beauty ratings could not accurately describe their data. In fact, it could only account for around half of their participants.

Similarly, Güçlütürk and colleagues (2016) warn against the use of across-participant analyses in psycho-aesthetics. Choosing Berlyne’s inverted U-curve between complexity and liking as an example — and making use of computer-generated geometrical patterns — they put forward cluster analyses as an alternative way to explore the patterns in the data. Using the traditional across-participant analyses, Berlyne’s proposed inverted U-curve was found. However, a data-driven cluster approach of participants’ ratings showed that this inverted U-curve could be decomposed into two groups of participants that showed opposite patterns. The largest cluster (20 out of 30 participants) showed a linear decrease in beauty for increasing complexity, whereas the rest of the participants showed a linear increase, effectively demonstrating that the inverted U-curve might lead to erroneous conclusions. They proposed cluster analyses as a good alternative to the commonly used analyses, because the data-driven approach, followed by further investigation of the differences be-tween participant clusters, could lead to new insights into the mechanisms that underlie aesthetic preferences (Güçlütürk et al., 2016).

Indeed, using this kind of cluster approach could lead to interesting new results on aesthetic preferences. Studying people’s preferences for different fractal-like stimuli, Spehar and colleagues (Spehar et al., 2016) also found that their group-level inverted U-curve was made up of — no less than four — distinct clusters of participants. While half followed the inverted U-curve pat-tern, others showed a linear increase, decrease or no significant preference. Interestingly, by presenting different image types to the same participants, it turned out that these different patterns were stable individual differences.

In sum, whereas many would agree that complexity plays an important role in predicting people’s aesthetic reactions to different types of artistic and non-artistic visual stimuli, the exact nature of this relationship seems to depend on the conceptualization of the involved factors as well as the analysis techniques researchers have used. Despite its incongruent results, however, Berlyne’s psy-chobiological model can still be used as an interesting ‘scientific playground’ (Marin et al., 2016, p. 4) to study aesthetic experience. In this case, we aim to make use of this playground to further our scientific understanding of the aes-thetic appeal of art photographs, while engaging in a meaningful science–art collaboration.

1.3. Art–Science Collaboration

One of the main challenges in psycho-aesthetics is the trade-off between ex-perimental control and ecological validity (Makin, 2017; Wagemans, 2011). Measuring participants’ aesthetic preferences to simple, controlled stimuli,



carefully manipulated to vary on a certain dimension will give experimen-tal control, but cannot always inform us about ‘real’ aesthetic experiences to works of art. On the other hand, opting for ‘real’ artistic stimuli within their natural context takes away a certain level of control. Of course, there are more than just these two options within empirical aesthetics; there are a myriad of possibilities between these extremes, such as doing an ecologically oriented study within a more controlled context (Carbon, 2019).

Here, we opt for the latter, using original artistic photographs within an on-line computer-based task. This gives us the control to randomize the order and choose the presentation format, while also making use of ecologically valid artistic photographs. Using these artistic photographs is not only of interest to us, as scientist, but results in valuable insights for the artist too. The project was part of a larger science–art collaboration (PiLoT1, 2016–2017, https://pilotleuven.wordpress.com/), where scientists of the University of Leuven teamed up with artists of the Leuven art academy (SLAC Leuven) to work on the broad theme of ‘chaos’. The present article is the result of a collaboration with art photographer Dominique Genin (https://www.dominiquegenin.com/). The results of another collaboration within our research group — with painter Lou Bielen (http://www.loubielen.be/) — will appear in a future article.

At the moment the study was set up (2016–2017), Dominique Genin (1956, Belgium) had just successfully graduated from a five-year part-time art pho-tography education at the Leuven art academy (SLAC), and was following a multi-disciplinary specialization course, in which the option for the science–art collaboration was introduced. His photographic style includes playing with abstraction and the border between recognizable/unrecognizable, where he aims to let the viewer take an active role in discovering the work or, as he explains it on his website: “My pictures tend to be at the boundary between abstraction and reality, where the viewer can create his own reality, when he is not just looking but seeing…” (Genin, 2019).

In line with this photographic style, the idea of ‘chaos’ led Dominique Genin to a photography series with semi-abstract photographs that were made up of different layers (through multiple exposures), in which he played with levels of recognisability, coincidence and complexity. The resulting series of photographs proved to be an excellent starting point to examine the relation-ship between complexity and aesthetics. The photographs form a homogenous series, but are still varied in their levels of complexity, layers and recognisa-bility. Together with the artist, 24 photographs were selected that showed an interesting variety in complexity and recognisability for the current research purposes, as well as staying true to the essence of the artistic project. All se-lected photographs can be found in Fig. 1.

The photographer took part in the different phases of the research (from brainstorm to selecting stimuli and recruiting participants, to discussing


https://pilotleuven.wordpress.com/

https://pilotleuven.wordpress.com/

https://www.dominiquegenin.com/

http://www.loubielen.be/


Figure 1. Artistic photographs by photographer Dominique Genin that were used in the study. Photographs are ordered by PHOG complexity values. PHOG complexity and perceived complexity scores (average and standard deviation) can be found below each photograph.



results). These types of close scientist–artist collaborations therefore result in valuable insights for scientist and artist (see Wagemans, 2011 for an elabora-tion on this new kind of experimental psycho-aesthetics).

1.4. The Present Study

Grounded in a science–art collaboration, the present study focuses on the re-lationship between complexity and aesthetic ratings in a series of original ar-tistic photographs.

Specifically, the following research questions were explored:

(1) Does the relationship between complexity of the artistic photographs and beauty ratings follow Berlyne’s inverted U-curve pattern?

(2) Are there individual differences underlying this across-participant pattern?(3) Can individual differences be explained by participant or photograph

characteristics?(4) Does the pattern differ depending on aesthetic response (beauty, pleasure,

interest) or the measure of complexity (objective vs. perceived)?

Given the few available resources on aesthetic reactions to art photographs and the incongruent results of previous studies on complexity and aesthet-ics, the study took a largely exploratory and data-driven approach, while being informed by previous findings from the empirical aesthetics literature. We took into account considerations put forward by previous researchers on the multifaceted nature of complexity (Nadal et al., 2010) and aesthetic re-sponse (Marin et al., 2016) by using multiple measures for both, as well as the warnings against across-participant analyses by following a data-driven cluster approach, followed by an exploration of the clusters (Güçlütürk et al., 2016). Moreover, we tried to step away from a reductionist approach to empirical aesthetics by focusing on real artistic photographs that resulted from a collaboration with both artistic and scientific intent. In doing this, we hope to move towards the new kind of experimental psycho-aesthetics that produces valuable insights for both artist and scientist (Wagemans, 2011).

2. Method

2.1. Participants

To reach a broad audience, the study was set up as an online experiment. Participants were contacted through the networks of the senior author, the artist and the art–science PiLoT1 organization, followed by a snowball effect. A total of 461 participants completed the experiment, of which eight partici-pants completed the experiment more than once and were therefore excluded.



The final set of 453 participants represent a broad audience varying in gender (63% female), age (ranging from 18 to 85, M = 41.15, SD = 16.73) and artistic expertise (37% followed an art education; 21% reported an active art practice). On average, participants seemed to be rather interested in art (M = 5.16; SD = 1.40), and the most often selected answers for art gallery visits and art books were 2–3 times a year (39% of participants) and a lot, more than 10 (33% of participants), respectively.

2.2. Stimuli

Stimuli were 24 photographs by art photographer Dominique Genin, specif-ically created and selected for the purposes of the current art–science col-laboration. Figure 1 shows all photographs. Together, they form a series of semi-abstract photographs that vary in complexity and recognisability, which is ideal for the purposes of our study. Compare, for example, the photograph on the top left showing a relatively simple pattern, in which a viewer might recognize the folded leaves of a book, with the photograph on the bottom right showing a more complex, layered photograph where the photographed content is almost impossible to recognize.

Note that, even though the photographs form one coherent stylistic series, the artist’s way of working (i.e., adding layers, abstracting photographs through certain manipulations, etc.) resulted in a few subgroups of photographs that were inspired by the same type of content (e.g., the top row of photographs all relate to the abovementioned folded book leaves). We will also explore the role of content in our analyses.

2.3. Measures

2.3.1. Art ExpertiseA self-developed scale was used to measure participants’ art expertise. Similar items have been used in previous studies from our research lab (e.g., Augustin, 2012; Chamberlain et al., 2017) and the items are similar to self-developed scales by other researchers (e.g., the art experience questionnaire in Chatter-jee et al., 2010). Specifically, the following six items were used to tap into participants’ art education, art practice and interest in art. All items were con-sidered separately in this article, as there is no clear-cut way to turn these into a summary scale, without making subjective decisions on how these different aspects should be counted and weighed into what makes an ‘art expert’.

Art education (one item) was measured by asking participants whether they received an art and design education. They could indicate whether they stud-ied art school, art history and/or a course that uses applied art, design, visual thinking or drawing, as well as specify the number of years they studied this. Participants were allowed to indicate multiple options.



Art practice (two items) was measured by asking participants whether they considered themselves to be a practicing artist or designer. If so, participants were asked to specify the number of years of their practice and to indicate the specific field of art or design on a multiple-choice list [including drawing/illustration, fine art (e.g., painting, sculpture), conceptual art, graffiti, photog-raphy, film/animation, fashion/textile design, product design, graphic design, typography, architecture]. Participants were allowed to choose multiple items and/or specify another field that was not part of the list.

Art interest (three items) was measured by Likert-scales where people could indicate their interest in art (seven-point Likert scale ranging from 1 = not at all to 7 = yes, very strongly), the number of times they visited an art gallery (four-point scale with options: never; once; 2–3 times a year; often) and the number of art or design books they owned (four-point scale with op-tions: none; at least 1 but less than 5; at least 5 but less than 10; a lot, more than 10).

2.3.2. Complexity MeasuresPHOG complexity. For each photograph, a complexity measure was calculated based on the Pyramid of Histograms of Orientation Gradients (PHOG) meth-od. The measure is based on a shape descriptor originally developed for object recognition and categorization (Bosch et al., 2007; Dalal and Triggs, 2005), but can also be used to derive several image properties that have been studied for their role in aesthetic appreciation, such as visual complexity. Specifically, Christoph Redies and his colleagues (Braun et al., 2013; Redies et al., 2012) have previously used this method of calculating image properties for different types of visual stimuli and the relation to aesthetic appreciation. In this article, we only focus on the measure of PHOG complexity.

To calculate the PHOG of an image, the image is first divided into three images representing the different channels: luminance, red–green and blue–yellow channels, after which a gradient image is calculated for each of these images. A summary of these three gradient images is made by taking the high-est value of the three channels for each pixel. From this gradient image, PHOG features can be derived by dividing the orientations of the gradients in dif-ferent bins and calculating the strength of the gradient per bin, after which the histogram values are normalized. Using this information, complexity is measured as the average of all gradient strengths in an image (see Braun et al., 2013; Redies et al., 2012 for mathematical details). In other words, this means that images that show small changes between pixel values (in luminance or colour channels) will result in a low complexity score, while large changes result in higher complexity scores. Figure 1 shows the photographs organized according to their PHOG complexity scores — and includes the PHOG num-ber below each photograph.



Perceived complexity. To complement the calculated image property of (PHOG) complexity, participants were also asked to rate each photographs’ complexity on a seven-point Likert scale that ranged between 1 (very simple), over 4 (neutral) to 7 (very complex). Throughout the article, we will refer to this measure as ‘perceived complexity’.

2.3.3. Aesthetic ResponsesThree aesthetic responses were measured for each photograph, each of them rated on a seven-point Likert scale:

Interest was measured on a scale ranging from 1 (very boring) over 4 (neutral) to 7 (very interesting).Beauty was measured on a scale ranging from 1 (very ugly), over 4 (neutral), to 7 (very beautiful).Pleasantness was measured on a scale ranging from 1 (very unpleasant), over 4 (neutral), to 7 (very pleasant).

At the end of the experiment, participants were also asked about their general opinion of the photographs created by Dominique Genin by clicking one or more adjectives out of the following list: attractive, abstract, chaotic, complex, busy, ambiguous, monotonous, emotional, expressive, impressive, compli-cated, interesting, meaningless, incomprehensible, touching, overwhelming, strange, restful, special and confusing.

2.4. Procedure

Ethical approval was obtained from the Social and Societal Ethical Committee of the KU Leuven. Participants received an invitation to participate through e-mail. After clicking the link, they could choose their language (Dutch, French, English, Italian) and then received a short introduction about the tasks and the project, indicating that the study was part of the larger artist–scientist collabo-ration PiLoT1, that it was about the perception and appreciation of images by a photographer, Dominique Genin, and that it would take around 15 minutes.

After giving their consent, participants provided their e-mail address to unlock the experiment (or continue their ongoing participation, if they had chosen to do only part of the experiment before) and first answered the short demographic questions (age, gender, mother tongue) and art expertise items. Then, they saw four example photographs by Dominique Genin. These photo-graphs were not part of the selected 24 photographs and did not have to be rat-ed, but rather served as a reference for participants, so that they could anchor their ratings based on the context of this type of photographs. An accompany-ing statement said that these are not the kind of photographs that most people



take (e.g., birthdays, groups of people, holidays), but more abstract images and that they would be introduced to 24 of these images that they would have to rate on four scales.

Following this, participants were introduced to each of the 24 photographs separately (in a randomized order), accompanied by all four rating scales, presented simultaneously below the photograph. The rating scales were dis-played in the following order (from top to bottom): complexity, interest, beau-ty, pleasantness. Finally, at the end of the experiment, participants were also asked about their general impression of the photographs, by selecting one or more adjectives out of a list (see Sect. 2.3.3.)

3. Results

3.1. Data-Analysis Approach

Although guided by our research questions, our analyses were mainly done in an exploratory and data-driven manner, taking into account some of the find-ings previously outlined within the psycho-aesthetics literature. Data and code for the analyses reported in this article can be found at https://osf.io/2fws6/.

First, we calculated general descriptive statistics for each photograph, as well as the correlations among complexity measures (PHOG and perceived complexity) and aesthetic responses (beauty, pleasantness, interest). Then, we explored our four research questions using the following steps:

(1) Visually explore whether the across-participant relationship between PHOG complexity and beauty follows an inverted U-curve pattern.

(2) Perform a data-driven cluster approach to examine whether the aggregate pattern is the result of underlying individual differences (participant clus-ters) and statistically compare which model (overall model vs participant clusters) fits the data best.

(3) Explore whether the differences in the relationship between PHOG complexity and beauty can be explained by participant or photograph characteristics.

(4) Explore whether a different measure of complexity (PHOG vs perceived complexity) or aesthetic response (beauty, pleasure, interest) yields quali-tatively similar or different findings.

3.2. Data Preparation and Descriptive Statistics

3.2.1. OutliersOne photograph deviated strongly from the others on its PHOG complexity score. This photograph (bottom right in Fig. 1, score of 22.16) scored almost double on the complexity measure compared to the second most complex


https://osf.io/2fws6/


photograph (score of 10.66) (Note 2). We therefore dropped it from further analyses, so we would have a continuous range of complexity scores with enough data points to draw reliable conclusions about the relationships of in-terest. The final dataset therefore included 23 photographs, 453 participants and 10,419 observations (453 participants who each rated 23 photographs). All the following analyses, results and conclusions are based on this dataset.

3.2.2. Missing Data ValuesThere were a few missing values for ratings of perceived complexity (n = 13; 0.12% of all complexity ratings); interestingness (n = 28; 0.27% of all inter-estingness ratings) and beauty (n = 15; 0.14% of all beauty ratings), because the online set-up did not prevent participants (accidentally) skipping items. To exploit the maximum amount of available data, cases were only dropped when they had missing values on the variables of interest specific for each respective analysis.

3.2.3. Average Ratings per PhotographFigure 1 shows the average scores on the two complexity measures (PHOG and perceived complexity) for each photograph. The photographs are ordered based on their PHOG value. In our final dataset (excluding the outlier photo-graph), photographs had an average value of 6.70 (SD = 2.96) on the PHOG complexity measure. Generally, aesthetic reactions to the photographs seem to be positive, with average ratings per photograph tending towards the beauti-ful (M = 4.57; SD = 0.22), pleasant (M = 4.54, SD = 0.27), interesting (M = 4.56, SD = 0.30) and complex (M = 4.49, SD = 0.52) side of the scales. The median was always 5 on a scale from 1 (very ugly, unpleasant, boring and simple, resp.) through 4 (neutral) to 7 (very beautiful, pleasant, interesting and complex, resp.).

Considering their general impression of the photographs, the top three ad-jectives that were selected by most people from the list (see Sect. 2.3.3. for the full list) were abstract (69.32%), interesting (49.45%) and expressive (36.20%).

3.3. Correlations among Scales

Figure 2 shows the correlations among the variables of interest. Pearson cor-relations are calculated using the 23 final selected photographs (removing the outlier photograph for complexity). We first standardized all response ratings within each participant (i.e., calculated z-scores per scale per participant) to prevent that differences in scale use would confound the results. All correla-tions were significant (p < 0.001), except for the correlation between inter-est and PHOG complexity (r = 0.01, p = 0.22). Results show that the three aesthetic responses are all positively correlated, with beauty and pleasantness



showing the highest correlation. The two complexity measures (PHOG com-plexity vs perceived complexity) show a moderate positive correlation and perceived complexity is also moderately correlated to interestingness ratings. The other relations between complexity measures and aesthetic responses show smaller correlations, sometimes positive, sometimes negative. This does not mean that both concepts are not related, however. As correlations only capture linear relations between variables, we would not be able to detect the expected curvilinear pattern (such as Berlyne’s inverted U-curve). Moreover, individual differences in how complexity and aesthetics relate could obscure a clear average pattern. In the next sections, we explore whether a curvilinear pattern might be a better fit, followed by a cluster analyses to explore possible individual differences.

3.4. Across-Participant Relationship between PHOG Complexity and Beauty

We visually examined whether a curvilinear pattern would fit the relationship between PHOG complexity and beauty across all participants. Again, beauty ratings were standardized within each participant to prevent that differences in scale use would confound the results. Figure 3A shows the relationship between these average (normalized) beauty ratings and PHOG complexity. A quadratic function fit was added to the data to examine whether the pattern follows Berlyne’s inverted U-curve. Visual inspection reveals that this is not the case; instead the relationship seems to resemble almost the opposite — a U-curve pattern — where the most simple and the most complex photographs receive higher beauty ratings than those in the middle of the set.

Perceived co

mplexity

Beauty

PleasantnessInterest

phog complexity 0.33 –0.11 –0.15 0.01

0.15 0.08 0.38

0.76 0.64

0.56

Perceived complexity

Beauty

Pleasentness

–1 1–0.8 0.8–0.6 0.6–0.4 0.4–0.2 0.20

Figure 2. Correlation matrix with variables of interest. Correlations are calculated on standardized ratings (standardized within participants) for the final 23 photographs (outlier photograph is removed).



3.5. Data-Driven Cluster Approach to Discover Underlying Individual Differences

Following the recommendation by Güçlütürk and colleagues (2016), we used a cluster approach to explore possible individual differences that might un-derly the general pattern.

The cluster method is a data-driven approach which clusters participants based on their rating patterns over the different PHOG complexity levels. Spe-cifically, we started from a matrix where participants are represented as rows, photographs as columns (organized from low to high PHOG complexity) and standardized beauty ratings (standardized within individuals) as cells. Thus, the cluster analyses start from the full dataset for one aesthetic response (in this case beauty), without having to average over photographs or individuals. On this matrix, cluster analyses are performed to examine whether the patterns of individuals’ beauty ratings over the range of the PHOG complexity ratings can be organized into clusters of individuals with similar patterns (and thus show a similar relationship between PHOG complexity and beauty).

Cluster analyses were implemented in R 3.6.1 (R Core Team, 2019), with the pamk (Partitioning Around Medoids With Estimation Of Number Of Clus-ters) function from the package fpc (v2.2-3; Hennig, 2019). Specifically, this package uses the partitioning around medoids (pam) approach (see Chapter 2 of Kaufman and Rousseeuw, 1990 for an introduction to this approach), a more robust version of K-means clustering, and the algorithm itself deter-mines the optimal number of clusters. The algorithm first looks for a good set

B C

Aver

age

beau

ty ra

ting

(nor

mal

ized

)

A

–0.8

2.5 5.0 7.5PHOG complexity

10.0

–0.6

–0.4

–0.2

0.8

1.0

0.6

0.4

0.2

0.0

–0.8


10.0

–0.6

–0.4

–0.2

0.8

1.0

0.6

0.4

0.2

0.0

–0.8


10.0

–0.6

–0.4

–0.2

0.8

1.0

0.6

0.4

0.2

0.0

Cluster Cluster 1 (n = 295) Cluster 2 (n = 158)

Figure 3. The relationship between PHOG complexity and average beauty rating (normalized). (A) The across-participant pattern (with an added quadratic function fit). (B, C) Results of the cluster analyses, showing two participant clusters with different relationships between PHOG complexity and beauty ratings (with added linear or quadratic function fits, respectively).



of representative objects within the observations — called medoids. Follow-ing this, each observation is assigned to the nearest medoid. Thus, the pack-age optimizes both the number of clusters and the specific medoids, based on the patterns of the data. For our analyses, we used the default method of the ‘pamk’ function, which examines solutions for two to 10 clusters, and decides on the optimal number of clusters based on the optimal average silhouette width. The silhouette width is a measure of the quality of clustering (by com-paring the dissimilarity of an item to the rest of its cluster, and comparing this to the dissimilarity to the closest neighbouring cluster). It indicates whether an observation is well-clustered (silhouette width approaching 1), in between clusters (around 0) or misclassified (negative numbers). When comparing the average silhouette width of all observations over different numbers of clusters tested, the optimal number of clusters can be determined (the largest overall average silhouette width) (Rousseeuw, 1987).

The cluster approach yielded two clusters of participants. We first examined whether our original across-participant U-shaped pattern could be explained by two different relationships for these clusters. Figure 3B plots the results of the two clusters together with a linear fit to their data (following the approach by Güçlütürk et al., 2016), while Fig. 3C shows the same information but with an added quadratic fit. In both cases, it is clear that the average pattern does not fit all participants. In fact, one cluster (n = 295, 65.12%) does seem to follow a similar U-curve pattern to the general pattern we found above — or a nega-tive relationship when using a linear fit. However, another cluster (n = 158, 34.88%) seems to follow rather the opposite pattern that lies closer to Ber-lyne’s inverted U-curve — or a positive relationship when using a linear fit. It is clear that depending on the specific fit we add to our data, our interpretations of these relationship will be different. Therefore, in the next section we com-pare the different models statistically to explore which model fits our data best.

3.5.1. Statistical Comparisons of Different ModelsThe following linear mixed effects models were compared, using the ‘lmer’ function of the lme4 package in R (Bates et al., 2014). Each model corre-sponds to one of the visualizations in Fig. 3 (the letters below correspond to the respective panels).

(A) Beauty = β0 + β1 PHOG complexity + β2 PHOG complexity2 + b0 Participant + ε

(B) Beauty = β0 + β1 PHOG complexity + β2Cluster + β3 PHOG complex-ity × Cluster + b0Participant + ε

(C) Beauty = β0 + β1 PHOG complexity + β2 PHOG complexity2 + β3 Cluster + β4 PHOG complexity × Cluster + β4 PHOG complexity2 × Cluster + b0Participant + ε



Table 1 shows the AIC and BIC criteria for the comparison of the three mod-els. Both converge towards Model C as the best fit for our data (lowest AIC and BIC criteria), indicating that our data are best explained by two different curvilinear relationships between PHOG complexity and beauty, one for each cluster — with the largest cluster (Cluster 1) showing a U-curve pattern, and the other cluster (Cluster 2) showing a trend towards an opposite U-curve pattern.

3.6. Can Individual Differences be Explained by Participant or Photograph Characteristics?

As the cluster approach is a hypothesis-free approach that just examines the patterns that show up in the data, we aimed to find out whether we could explain some of these differences through the other available information, namely participant characteristics or photograph characteristics.

3.6.1. Participant CharacteristicsOur participant group consisted of a broad audience that varied in age, gender and art background. Here, we explore whether the two clusters of participants show differences on these characteristics. Only one significant difference was found, that is the second cluster had a higher average for the ‘art books’ scale (M = 2.84, vs M = 2.50 in cluster 1; Welch t-test: t (331.85) = −3.01, p < 0.01), indicating that participants in the second cluster were closer to answer option 3 (at least 5, but less than 10 books), than to option 2 (at least 1, but less than 5 books), whereas the average for the first cluster was in the middle of these two options. Since the other artist-related questions did not show any significant differences, it is difficult to interpret this specific difference for the number of art books.

3.6.2. Photograph CharacteristicsWe were interested to see whether the specific content of the photograph could play a role in the differences between participant clusters. When examining the full set of photographs, some subgroups can be identified. For instance,

Table 1.AIC and BIC criteria for the three linear mixed effect models.

Model DF AIC BIC

Model A 5 33689 33726Model B 6 33307 33350Model C 8 32988 33046



we find four photographs that show an abstraction of book pages (top row in Fig. 1), four photographs that show variations of a certain rectangular theme (the four photographs with portrait orientation in Fig. 1) and the remaining photographs that are inspired more by natural scenes (varied with layers, add-ed manipulations, etc.). The photographer himself refers to these three sub-groups within his larger ‘On the Edge of Chaos’ project as ‘object’, ‘still-life’ and ‘nature’ respectively on his website (Genin, 2019).

It turns out that these specific photograph subgroups are somewhat con-founded with the calculated PHOG complexity levels. That is, the four simplest photographs in our subset are the four ‘object’ photographs of book pages (top row in Fig. 1). Luckily the nature and still-life categories were more spread out over the range of PHOG complexity values, both ranging from around 3–4 to around 10–11 in PHOG values (see Fig. 1).

To understand the role these subgroups of photographs play in determin-ing participants’ ratings, we explored visually (Fig. 4) and statistically how participant cluster and photograph subgroup (and their interaction) related to the average beauty ratings through a mixed ANOVA, taking into account that participants gave multiple ratings per subgroup of photographs. Significant

2.5

–0.5

0.0

0.5

Aver

age

beau

ty ra

ting

(nor

mal

ized

)

1.0

Cluster 1 (n = 295) Cluster 2 (n = 158)

Photograph subgroupobjectnaturestill-life

5.0 7.5 10.0 2.5

PHOG complexity

5.0 7.5 10.0

Figure 4. Average beauty ratings (normalized) per PHOG complexity value for three subgroups of photographs in the two participant clusters, with an added quadratic function fit per cluster (as in Fig. 1).



main effects were found for both cluster (F1,438 = 11.89, p < 0.001, η2G =

0.02) and photograph subgroup (F1.69,740.64 = 8.99, p < 0.001, η2G = 0.007),

along with a significant interaction between the two (F1.69,740.64 = 184.37, p < 0.001, η2

G = 0.12). Therefore, it is clear that participants in the two clusters responded differently to the different subgroups of photographs. Follow-up pairwise contrasts of the effects between clusters for each photograph sub-group revealed that participants in cluster 1 showed a higher preference for the ‘object’ subgroup [t(869) = 13.11, p < 0.0001], a lower preference for the ‘nature’ subgroup [t(869) = −5.75, p < 0.0001], but no significant difference for the ‘still-life’ subgroup [t(869) = 1.11, p = 0.27] compared to participants in cluster 2.

In fact, when visually inspecting Fig. 4, it becomes clear that the different subgroups (as well as individual variances for specific photographs) seem to drive the different patterns, and beauty ratings between clusters can be more consistently interpreted based on different responses to photograph subgroups than to the variance in PHOG complexity. This is noticeable when examining how beauty ratings vary according to the PHOG complexity levels within one subgroup. For example, when exploring the ‘nature’ photographs, the largest subgroup of photographs, it is difficult to see a consistent relationship between PHOG complexity and beauty ratings.

3.7. Extension to Other Aesthetic Responses (Pleasantness, Interest) and Predictor (Perceived Complexity)

In this section, we explore how different aesthetic responses and/or a different predictor (perceived complexity) might change our results.

3.7.1. Other Aesthetic Responses (Pleasantness, Interest)In Fig. 5, we repeat the same type of visualisations and cluster analyses as before — but now also for the responses of pleasantness and interest. Note that, for pleasantness and interest, there are some participants for which we could not do the calculations, either because of missing values in their rat-ings or because they gave all photographs the same score on pleasantness or interest. When exploring the resulting clusters of individuals and the visual-ization of the relationship between PHOG complexity and aesthetic response, pleasantness (Fig. 5B) shows a very similar pattern as the beauty results we described before (Fig. 5A), with two different curvilinear patterns (Cluster 1: n = 295; Cluster 2: n = 157; missing: n = 1). This is not surprising, given that beauty and pleasantness were correlated highly (see Fig. 2). For interest also (Fig. 5C), we again found two clusters of participants, where one cluster fol-lows the U-curve pattern (Cluster 1: n = 274), but the second cluster follows a linearly increasing pattern (as opposed to a somewhat more inverted U-curve pattern) (Cluster 2: n = 154; missing: n = 25).



Taking into account our previous finding that photograph subgroup seemed to be the most consistent driving factor of the different patterns for the beauty ratings, we explored how the different subgroups of photographs fit into this general pattern. Again, it becomes clear that photograph subgroup plays a more consistent role in differentiating the participant clusters, and that the role of PHOG complexity on aesthetic responses is less straightforward. Do note, however, that a more consistent pattern for PHOG complexity can be found for the response of interest (Fig. 5C), specifically in cluster 2 (n = 154), where a linear increasing trend seems to emerge that cannot be explained only based on different ratings for different photograph subgroups.

3.7.2. Perceived Complexity vs PHOG ComplexityTo understand whether the two clusters perceived the complexity of the pho-tographs differently, we explored the relationship between perceived and ob-jective complexity for the two clusters. Both clusters show only a moderately positive correlation between the PHOG complexity of the photographs and participants’ perceived complexity ratings (Cluster 1: r = 0.29; Cluster 2: r = 0.41), indicating that the calculated image statistic PHOG complexity and participants’ perceived complexity ratings are not fully interchangeable, and might therefore give us different insights into how complexity and aesthetic preference relate (or not relate). Moreover, perceived complexity ratings do not necessarily have a one-on-one relationship with specific photographs (as was the case with the PHOG values) and therefore the results should be less

–0.8

2.5 5.0 7.5 10.0PHOG complexity

2.5 5.0 7.5 10.0

–0.6

–0.4

–0.4

Aver

age

beau

ty ra

ting

(nor

mal

ized

)

0.0

0.2

0.4

0.6

0.8

1.0

Cluster 1 (n = 295) Cluster 2 (n = 158)A

–0.8


2.5 5.0 7.5 10.0

–0.6

–0.4

–0.4Av

erag

e be

auty

ratin

g (n

orm

aliz

ed)

0.0

0.2

0.4

0.6

0.8

1.0

Cluster 1 (n = 295) Cluster 2 (n = 157)B

–0.8


2.5 5.0 7.5 10.0

–0.6

–0.4

–0.4

Aver

age

beau

ty ra

ting

(nor

mal

ized

)

0.0

0.2

0.4

0.6

0.8

1.0

Cluster 1 (n = 274) Cluster 2 (n = 154)C

Photograph subgroup object nature still-life

Figure 5. (A) Relationship between PHOG complexity and average beauty (normalized), (B) average pleasantness (normalized) and (C) average interest (normalized) for participant clusters and photograph subgroups.



restricted by the confound between photograph subgroup and complexity than in our previous results.

Therefore, we explored whether the clusters of participants that were pre-viously found showed different relationships between perceived complexity ratings (normalized and binned into 23 bins, to get approximately the same amount of x-values as for the PHOG values) and aesthetic responses. Figure 6 shows the resulting visualisation for the previously found clusters for all three aesthetic responses. Again, the visualisation shows how the two clusters differ with respect to the type of photograph subgroup they prefer, but the results for perceived complexity levels are less consistent and more difficult to interpret. Moreover, the differences between the previously found clusters are less pro-nounced than before. This might indicate that the different patterns that were picked up in the cluster analyses that we described earlier were mainly driven by different aesthetic preferences for different photograph subgroups, rather than consistent differences in responding to the (calculated PHOG) complex-ity levels of the photographs. Again, however, we find that complexity might play a more consistent role for interest ratings, as there are some positive lin-ear trends between perceived complexity and interest (especially in the second cluster of participants for interest in Fig. 6C).

4. Discussion

Is there a relationship between the complexity in an art photograph and its aesthetic appeal? That is what we set out to answer in this project. Grounded in an art–science collaboration, 24 photographs — by art photographer Domi-nique Genin — were specifically created and selected for this research proj-ect. The resulting photographic series consists of semi-abstract photographs and plays with multiple layers, recognizability vs unrecognizability of the subject and different levels of complexity. It therefore proved a good starting point for a case study on complexity and aesthetics in artistic photographs. As far as we know, this was the first study to directly focus specifically on this relationship — a central and still inconclusive topic in psycho-aesthetic research — within a set of art photographs.

The large-scale online study resulted in a large sample (n = 453) of par-ticipants, varying in gender, age and art expertise. On average, viewers found the photographs aesthetically pleasing and complex. When it comes to the main relationship of interest, between complexity in the artistic photographs and aesthetic ratings, the result of our explorations could be summarized as a cautionary tale for empirical aesthetics. Our data-driven exploratory approach allowed us to approach the data from different perspectives with different anal-yses. Depending on the extent of our exploration, we could have concluded that the relationship between complexity and beauty is:



A

2.3


1.81.3

0.80.3

–0.2–0.7

–1.2–1.7

–2.2–2.7–3.2

0 5 10 15Perceived complexity rating (normalized and binned into 23 bins)

Aver

age

beau

ty ra

ting

(nor

mal

ized

)

20 0 5 10 15 20

B

2.3


1.81.3

0.80.3

–0.2–0.7

–1.2–1.7

–2.2–2.7–3.2


Aver

age

plea

sant

ness

ratin

g (n

orm

aliz

ed)

20 0 5 10 15 20

C

2.3


1.81.3

0.80.3

–0.2–0.7

–1.2–1.7

–2.2–2.7–3.2


Aver

age

inte

rest

ratin

g (n

orm

aliz

ed)

20 0 5 10 15 20

Photograph subgroup object nature still-life

Figure 6. Relationship between aesthetic responses (normalized) and perceived complexity ratings (normalized and binned into 23 bins) for the participant clusters and photograph subgroups.



– Slightly negative (PHOG complexity and beauty correlation:−0.11)

– Slightly positive (perceived complexity and beauty correlation: 0.15)

– Slightly U-curved (Fig. 3A), dependent on the viewer: one cluster of par-ticipants shows a positive linear pattern, the other a negative linear pattern (Fig. 3B) or even two curvilinear patterns (Fig. 3C)

– Misleading, and mainly explained by differences in how viewers reacted to specific subgroups of photographs (Figs. 4–6)

All of these conclusions would have been justified by statistical analyses that have been previously used in many empirical aesthetics, yet all of them would have resulted in very different conclusions regarding the aesthetics of photog-raphy. If one dataset, with a relatively straightforward design and ratings can already lead to such different conclusions, it should not come as a surprise that different studies have found inconsistent results for the role of complexity on aesthetics.

Still, we believe our study yields three insights that could be further devel-oped and studied in future research. First, and in line with the recommendation by Güçlütürk and colleagues (2016), a data-driven cluster approach highlight-ed that there were consistent clusters of individuals that responded differently to the different artistic photographs. This finding emphasizes the importance of examining substantial individual differences that might be underlying the across-participant patterns when it comes to aesthetic preferences. No sub-stantial and consistent differences were found between the two participant clusters in characteristics such as gender, age, or even artistic background and expertise. We did find a small, but significant difference for the number of artistic books (more books for those who showed the inverted U-shape func-tion). However, given the absence of other differences for artistic background or interest, it is difficult to interpret whether this difference is genuine or the result of mere sampling variation. Yet, the role of ‘expertise’ is one of the most studied individual differences in empirical aesthetics, and individuals are often a priori divided into these two groups and compared in terms of their aesthetic responses, risking to exaggerate random sample variation or small difference between groups. That we did not find any consistent differ-ences in terms of expertise between the two participant clusters in our study, even though they clearly reacted differently to the photographs, emphasizes that large-scale data-driven explorations of individual differences can have complementary value to explicit group-based approaches in aesthetics. Spe-cifically, data-driven approaches can yield nuances to previous findings and can point to new and unexplored individual difference characteristics.



A limitation of our study is that we asked participants about their art ex-perience, interest and practice before evaluating the photographs. Besides activating social desirability (making participants aware that we are interested in artistic expertise), it might have also biased participants’ answers through motives of social distinction, with art experts wanting to separate themselves from novices by accepting complexity and challenging art (Bourdieu, 1984), and art novices might feel more incompetent or insecure to do the task. Future research should take these possible biases into account when designing studies that focus on the role of artistic expertise.

Second, whereas the individual differences in preferences could be inter-preted based on different responses to (PHOG) complexity levels, our explo-rations revealed that they were more consistently explained by differences in ratings for different photograph subgroups. That is, even within our stylisti-cally coherent set of semi-abstract photographs, three subgroups of photo-graphs could be identified that differed in the type of ‘content’ that provided the starting point of the photographer’s photographic experiments (named ‘nature’, ‘object’ or ‘still-life’ by the photographer). The majority of partici-pants (around two thirds) showed the highest aesthetic appreciation for the ‘object’ photographs (abstract photographs of the leafs of a book), followed by the ‘still-life’ (abstract photographs with different rectangular shapes) and ‘nature’ photographs (abstract photographs starting from nature scenes, which looked more like textures really); while the other cluster showed the opposite pattern. This is an important finding that should be taken into account for future studies in empirical aesthetics with ecologically valid stimuli. Whereas lab studies with computer-generated stimuli could control confounding influ-ences of specific content, once the question of complexity and aesthetics is taken outside of this controlled environment, some confounds between ‘con-tent’ and complexity cannot be avoided. Researchers should be careful not to wrongly attribute differences between individuals to differences in image statistics, where it might in fact have been mostly driven by subtle differences in content or image subject.

Third, and as mentioned above, our main relationship of interest was be-tween complexity and aesthetic ratings. This did not follow Berlyne’s in-verted U-curve and was, even after cluster analyses, not straightforward to interpret (because of a mixture of participant and photograph factors). How-ever, complexity did seem to play a more consistent role in predicting interest ratings compared to the other aesthetic responses. This seems especially the case in the second cluster (around one third of participants), where a linear trend was found between both PHOG and perceived complexity and interest ratings. Interest was also less closely related to the other two aesthetic re-sponses (beauty and pleasantness) than they were to each other. These results highlight the multidimensional nature of aesthetic response, and are in line



with previous theoretical approaches (e.g., dual-process approach by Graf and Landwehr, 2015) and empirical findings (e.g., Marin et al., 2016; Van Geert and Wagemans, in press b), where pleasure and interest are disentangled and found to relate differently to complexity. Our explorations tentatively suggest that complexity’s influence on aesthetics may be best researched within the context of interest as aesthetic response.

It is important to note that some of our findings might be inherently tied to our specific focus on an art–science collaboration with only one photogra-pher. First, the photographs presented here (using multiple exposures, playing with recognizability, abstractions) only represent a small fraction of the broad world of artistic photography, and it is therefore difficult to generalize our results to other artistic photographs, especially those that might use a more documentary approach. The three general impression adjectives that were se-lected the most by participants were ‘abstract’, ‘interesting’ and ‘expressive’ emphasizing the specific type of photographs in this set. Second, and precisely because of the experimental and rather abstract photographic approach, it is somewhat difficult to fully understand the role of what we called ‘content’ in our findings. In fact, some viewers might not even have recognized the specif-ic ‘content’ of the subgroups (‘object’, ‘nature’, ‘still-life’) and their answers might have been driven by other factors which the photographs within these subgroups had in common (e.g., specific patterns or colour pallet). Third, and related to the former two, it might have been precisely this uncertainty in the photographs (not knowing what one is looking at; looking at something that does not look like a standard photograph) that challenged the viewers percep-tually and that might have been a source of their interest and enjoyment in the photographs (e.g., positive feelings after identifying the ‘content’ of the photograph, e.g., the book leaf photographs where we saw the biggest differ-ence between clusters). This perceptual challenge factor might have been a driver for aesthetic responses (and might have interacted with ‘content’ and complexity levels in our project), but might be less evident in other types of photography. Future studies using a similar type of photographic material could therefore incorporate some questions to ask the viewer whether they have identified the content of the photograph to further elucidate these plau-sible alternative explanations.

Still, reflecting back on the collaboration with artist Dominique Genin — this has been a rewarding experiment from both sides. It allowed us to explore the role of complexity within a homogeneous set of semi-abstract artistic pho-tographs, without too many additional confounds (e.g., large differences in style, colours, content), while still working with ecologically valid stimuli. The artist himself now has a better understanding of how people aesthetically react to his work. Beyond this, the study drew attention to his work. As a result, photographs from this project were part of a curated photography exhibition



for emerging photography talent in Belgium (Unknown Masterpieces 2018, Knokke) and they formed the basis of a solo exhibition in an art photography gallery in Leuven. This shows that the project was not only meaningful for us as scientists, but also to the artist himself. Therefore, we are looking forward to keeping the collaboration with artist Dominique Genin going and already have active plans for further research with his (current and new) work.

In conclusion, our aesthetic reactions to photographs are still largely un-charted territory and much of the mystery remains as to why people prefer certain photographs over others, but there do seem to be different clusters of people reacting differently to certain photograph characteristics. We therefore invite any interested researchers to join us in further exploring this highly in-triguing and — in a world filled with photographic material — highly relevant research quest.

Acknowledgements

We would like to thank professor Christoph Redies and his colleagues for as-sisting us with the calculations of image statistics on the photographs, Rudy Dekeerschieter for programming the online study, and Valeria Guiot and Sarah Delcourt for some practical aspects of setting up the study. Finally, we would also like to thank the editor and two anonymous reviewers for comments that have helped us to further improve the quality of the paper. This research was supported by a Methusalem grant (METH/14/02) awarded to Johan Wage-mans and a postdoctoral fellowship by the Research Foundation — Flanders awarded to Pieter Moors.

Notes

1. Within the field of empirical aesthetics, stimulus complexity is a broad concept that has been operationalized differently by different researchers, but could be broadly defined as being “related to the quantity and variety of information (an individual perceived) in a stimulus” (Van Geert and Wagemans, in press a, p. 3).

2. This photograph was intentionally made very complex by the artist by adding extra layers and manipulations.

ReferencesAdkins, O. C. and Norman, J. F. (2016). The visual aesthetics of snowflakes, Perception 45,

1304–1319. doi: 10.1177/2041669516661122.



Augustin, D., Wagemans, J. and Carbon, C.-C. (2012). All is beautiful? Generality vs. specific-ity of word usage in visual aesthetics, Acta Psychol. 139, 187–201. doi: 10.1016/j.actpsy .2011.10.004.

Armstrong, T. and Detweiler-Bedell, B. (2008). Beauty as an emotion: The exhilarating prospect of mastering a challenging world, Rev. Gen. Psychol. 12, 305–329. doi: 10.1037/a0012558.

Axelsson, Ö. (2007a). Individual differences in preferences to photographs, Psychol. Aesthet. Creat. Arts 1, 61–72. doi: 10.1037/1931-3896.1.2.61.

Axelsson, Ö. (2007b). Towards a psychology of photography: Dimensions underlying aesthetic appeal of photographs, Percept. Motor. Skill. 105, 411–434. doi: 10.2466/PMS.105.6.411 -434.

Bates, D., Mächler, M., Bolker, B. and Walker, S. (2014). Fitting linear mixed-effects models using lme4, J. Stat. Softw. 67, 1–48. doi: 10.18637/jss.v067.i01.

Berlyne, D. E. (1971). Aesthetics and Psychobiology, Appleton-Century-Crofts, New York, NY, USA.

Berlyne, D. E., Ogilvie, J. C. and Parham, L. C. (1968). The dimensionality of visual complex-ity, interestingness, and pleasingness, Can. J. Psych. 22, 376–387. doi: 10.1037/h0082777.

Birkhoff, G. D. (1932). Aesthetic Measure, Harvard University Press, Cambridge, MA, USA.Bosch, A., Zisserman, A. and Munoz, X. (2007). Representing shape with a spatial pyramid

kernel, CIVR ‘07 — Proc. 6th ACM Int. Conf. Image Video Retr., Amsterdam, Netherlands, pp. 401–408.

Bourdieu, P. (1984). Distinction: A Social Critique of the Judgement of Taste, Harvard Univer-sity Press, Cambridge, MA, USA.

Braun, D. M. [David Max Braun]. (2008, October 21). Sam Abell Cowboy Photo [Video file]. Retrieved from https://www.youtube.com/watch?v=ph7sdr44QRc.

Braun, J., Amirshahi, S. A., Denzler, J. and Redies, C. (2013). Statistical image properties of print advertisements, visual artworks and images of architecture, Front. Psychol. 4, 808. doi: 10.3389/fpsyg.2013.00808.

Carbon, C.-C. (2019). Empirical approaches to studying art experience, J. Percept. Imaging, 2, 10501-1–10501-7–7. doi: 10.2352/J.Percept.Imaging.2019.2.1.010501.

Chamberlain, R., Mullin, C., Scheerlinck, B. and Wagemans, J. (2017). Putting the art in ar-tificial : Aesthetic responses to computer-generated art, Psychol. Aesthet. Creat. Arts 12, 177–192. doi: 10.1037/aca0000136.

Chatterjee, A., Widick, P., Sternschein, R., Smith, W. B. and Bromberger, B. (2010). The as-sessment of art attributes, Empir. Stud. Arts 28, 207–222. doi: 10.2190/EM.28.2.f.

Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection, CVPR ‘05 — Int. Conf. Comp. Vis. Pattern Recogn., San Diego, CA, USA, pp. 886–893.

Eckblad, G. (1981). Assimilation resistance and affective response in problem solving, Scand. J. Psychol. 22, 1–16. doi: 10.1111/j.1467-9450.1981.tb00372.x.

Estes, W. K. (1956). The problem of inference from curves based on group data, Psychol. Bull. 53, 134–140. doi: 10.1037/h0045156.

Eysenck, H. J. (1941). The empirical determination of an aesthetic formula, Psychol. Rev 48, 83–92. doi: 10.1037/h0062483.

Fechner, G. T. (1876). Vorschule der Aesthetik, Breitkopf and Härtel, Leipzig, Germany.Foytik, L. R., Mullennix, J. W., Chan, C. H., Dragun, B. R., Maloney, M. and Polaski, L. (2013).

Automaticity and the processing of artistic photographs, Empir. Stud. Arts 31, 145–171.


https://www.youtube.com/watch?v=ph7sdr44QRc


Genin (2019). Art| Belgium| Dominique Genin Photography. Retrieved from https://www .dominiquegenin.com/.

Gordon, J. and Gridley, M. C. (2013). Musical preferences as a function of stimulus complexity of piano jazz, Creat. Res. J. 25, 143–146. doi: 10.1080/10400419.2013.752303.

Graf, L. K. and Landwehr, J. R. (2015). A dual-process perspective on fluency-based aesthetics: the pleasure–interest model of aesthetic liking, Pers. Soc. Psychol. Rev. 19, 395–410. doi: 10.1177/1088868315574978.

Graf, L. K. M. and Landwehr, J. R. (2017). Aesthetic pleasure versus aesthetic interest: The two routes to aesthetic liking, Front. Psychol. 8, 15. doi: 10.3389/ fpsyg.2017.00015.

Güçlütürk, Y., Jacobs, R. H. A. H. and van Lier, R. (2016). Liking versus complexity: Decompos-ing the inverted U-curve, Front. Hum. Neurosci. 10, 112. doi: 10.3389/fnhum.2016.00112.

Hennig, C. (2019). fpc: Flexible Procedures for Clustering. R package version 2.2–3. Retrieved from: https://CRAN.R-project.org/package=fpc.

Imamoglu, Ç. (2000). Complexity, liking and familiarity: Architecture and non-architecture Turkish students’ assessments of traditional and modern house facades. J. Environ. Psychol. 20, 5–16. doi: 10.1006/jevp.1999.0155.

Jacobsen, T. and Beudt, S. (2017). Stability and variability in aesthetic experience: A review. Front. Psychol. 8, 143. doi: 10.3389/fpsyg.2017.00143.

Jacobsen, T. and Höfel, L. (2002). Aesthetic judgments of novel graphic patterns: Analyses of individual judgments Percept. Mot. Skills 95, 755–766. doi: 10.2466/pms.2002.95.3.755.

Kaufman, L. and Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis, John Wiley and Sons, New York, NY, USA.

Krupinski, A. and Locher, P. (1988). Skin conductance and aesthetic evaluative responses to nonrepresentational works of art varying in symmetry, Bull. Psychon. Soc. 26, 355–358. doi: 10.3758/BF03337681.

Makin, A. D. J. (2017). The gap between aesthetic science and aesthetic experience, J. Con-scious. Stud. 24, 184–213.

Marin, M. M., Lampatz, A., Wandl, M. and Leder, H. (2016). Berlyne revisited: evidence for the multifaceted nature of hedonic tone in the appreciation of paintings and music, Front. Hum. Neurosci. 10, 536. doi: 10.3389/fnhum.2016.00536.

McManus, I. C. and Stöver, K. (2014). “Mute, motionless, variegated rectangles”: aesthetics and photography, in: P. P. L. Tinio and J. K. Smith (Eds.), The Cambridge Handbook of the Psychology of Aesthetics and the Arts, pp. 243–276. Cambridge University Press, Cam-bridge, UK. doi: 10.1017/CBO9781139207058.014.

McManus, I. C., Stöver, K. and Kim, D. (2011). Arnheim’s gestalt theory of visual balance: Examining the compositional structure of art photographs and abstract images, i-Perception 2, 615–647. doi: 10.1068/i0445aap.

Muth, C., Hesslinger, V. M. and Carbon, C.-C. (2015). The appeal of challenge in the percep-tion of art: How ambiguity, solvability of ambiguity and the opportunity for insight affect appreciation, Psychol. Aesthet. Creat. Arts 9, 206–216. doi: 10.1037/a0038814.

Nadal, M., Munar, E., Marty, G. and Cela-Conde, C. J. (2010). Visual complexity and beau-ty appreciation: Explaining the divergence of results, Empir. Stud. Arts 28, 173–191. doi: 10.2190/EM.28.2.d.




https://CRAN.R-project.org/package=fpc


R Core Team (2019). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. Retrieved from: https://www.R-project.org/.

Reber, R., Schwarz, N. and Winkielman, P. (2004). Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience? Pers. Soc. Psychol. Rev. 8, 364–382. doi: 10.1207/s15327957pspr0804_3.

Redies, C., Amirshahi, S. A., Koch, M. and Denzler, J. (2012) PHOG-derived aesthetic mea-sures applied to color photographs of artworks, natural scenes and objects, in: Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Com-puter Science, Vol. 7583, A. Fusiello, V. Murino and R. Cucchiara (Eds), pp. 522–531, Springer, Berlin, Germany.

Romero, J., Machado, P., Carballal, A. and Santos, A. (2012). Using complexity estimates in aes-thetic image classification, J. Math. Arts 6, 125–136. doi: 10.1080/17513472.2012.679514.

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math. 20, 53–65. doi: 10.1016/0377-0427(87)90125-7.

Silvia, P. J. (2005). Cognitive appraisals and interest in visual art: Exploring an appraisal theory of aesthetic emotions, Empir. Stud. Arts 23, 119–133. doi: 10.2190/12AV-AH2P-MCEH-289E.

Spehar, B., Walker, N. and Taylor, R. P. (2016). Taxonomy of individual variations in aesthetic re-sponses to fractal patterns, Front. Hum. Neurosci. 10, 350. doi: 10.3389/fnhum.2016.00350.

Sun, L., Yamasaki, T. and Aizawa, K. (2015). Relationship between visual complexity and aes-thetics: Application to beauty prediction of photos, in: Computer Vision — ECCV 2014 Work-shops. ECCV 2014. Lecture Notes in Computer Science, Vol. 8925, L. Agapito, M. Bronstein and C. Rother (Eds), pp. 20–34, Springer, Cham, Switzerland.10.1007/978-3-319-16199-0.

Szarkowski, J. (n.d.). Retrieved from https://www.photoquotes.com/showquotes.aspx?id=75&name=Szarkowski,John.

Tinio, P. P. L., Leder, H. and Strasser, M. (2011). Image quality and the aesthetic judgment of photographs: Contrast, sharpness, and grain teased apart and put together, Psychol. Aesthet. Creat. Arts 5, 165–176. doi: 10.1037/a0019542.

Van Geert, E. and Wagemans, J. (in press a). Order, complexity, and aesthetic appreciation. Psychol. Aesthet. Creat. Arts. doi: 10.1037/aca0000224.

Van Geert, E. and Wagemans, J. (in press b). Order, complexity, and aesthetic preferences for neatly organized compositions, Psychol. Aesthet. Creat. Arts. doi: 10.1037/aca0000276.

Wagemans, J. (2011). Towards a new kind of experimental psycho-aesthetics? Reflections on the Parallellepipeda project, i-Perception 2, 648–678. doi: 10.1068/i0464aap.


https://www.R-project.org/

https://www.photoquotes.com/showquotes.aspx?id=75&name=Szarkowski,John

https://www.photoquotes.com/showquotes.aspx?id=75&name=Szarkowski,John

exploring the role of complexity, content and individual ... et al artp 2020.pdf · photographs...

Documents