Although a number of studies have verified Daniel Berlyne's (1971) predicted maximum preference for intermediately complex stimuli, others have found that preference increased or decreased in relation to complexity. The objective of the present work was to assess whether differences in the kinds of stimuli used in prior studies or in the way complexity was defined could explain this divergence. In the first phase a set of 120 stimuli varying in complexity, abstraction, and artistry was assembled. In the second phase 94 participants were asked to rate the beauty of the stimuli. In the final phase the same participants rated 60 of the stimuli on seven complexity dimensions. We failed to detect any meaningful influence of complexity on beauty ratings for any of the kinds of stimuli. However, our results suggest that there are three different forms of complexity that contribute to people's perception of visual complexity: one related with the amount and variety of elements, another related with the way those elements are organized, and asymmetry. We suggest that each of these types of complexity influences beauty ratings in different ways, and that the unresolved relation between complexity and beauty appreciation is mainly due to differences in the conception, manipulation, and measurement of visual complexity. The knowledge that order and complexity influence people's appreciation of beauty goes back at least to the ancient Greeks. However, it was not until Fechner's (1876) work that this issue was systematically studied, paving the way for Birkhoff's (1932) mathematical approach, which predicted that appreciation would increase with order and decrease with complexity. Although Birkhoff accompanied his formulation of the aesthetic measure with detailed definitions of order and complexity, together with examples for a large number of polygons, Eysenck (1941) reported considerably low correlations between predicted aesthetic measure and the beauty ratings actually awarded by people. In order to find a more satisfactory alternative, Eysenck (1941) studied people's responses to a broad number of geometrical figures. His results suggested that different features of those objects, related with complexity and order, could be used to predict the beauty ratings awarded by human participants. However, the relation between both factors was not the one predicted by Birkhoff. In a simplification of his original formula, Eysenck (1942) showed both order and complexity contribute positively to the appreciation of beauty. It was not until Berlyne (1971) presented his influential framework that the study of complexity's influence on the appreciation of beauty was based on firm psychological and neurobiological grounds. In brief, Berlyne (1971) posited that the interaction of reward and aversion brain systems would lead people to prefer intermediate levels of complexity, which was defined according to such aspects as pattern regularity, amount of elements, their heterogeneity, or the irregularity of the forms (Berlyne, 1963 (Berlyne, , 1970 (Berlyne, , 1971 Berlyne, Ogilvie, & Parham, 1968) . A considerable amount of research has since been conducted to test this hypothesis, employing diverse visual stimuli. Some studies have been carried out with simple materials, such as geometric shapes (Aitken, 1974; Katz, 2002; Vitz, 1966) , or artificially generated images (Heath, Smith, & Lim, 2000; Ichikawa, 1985; Markovic & Gvozdenovic, 2001; Stamps, 2002) , varying along a specific, clearly defined, and objectively measurable complexity dimension. Other studies have used artworks, though often including only a single class, such as abstract paintings (Krupinski & Locher, 1988; Nicki & Moss, 1975; Osborne & Farley, 1970) , cubist artworks (Nicki, Lee, & Moss, 1981) , figurative images (Messinger, 1998 ), or portraits (Saklofske, 1975. These studies were not able to carry out such a straightforward measurement of complexity. A serious limitation of both sets of studies is that in most instances the number of participants was quite low (in some experiments as few as eight per condition) or the number of stimuli was reduced (only five in some instances). Additionally, the large majority of them used correlational methodology, which precluded the proposal of causal explanations of complexity's influence on aesthetic appreciation. The review of these prior studies reveals a considerable divergence in results. Some of them did actually find the expected inverted U-shaped distribution of preference and beauty scores as a function of complexity. Conversely, others found that preference and appreciation increased with complexity and, yet others, 174 / NADAL ET AL. found that ratings decreased as the stimuli's complexity grew. We believe that two reasons could explain this disparity: differences in the kinds of materials used in prior studies, and the disparate ways in which they conceived, measured, and manipulated visual complexity. The objective of the present study was, therefore, to clarify the influence of visual complexity on beauty appreciation, and examine the role of stimuli features such as degree of abstraction and artistry. Additionally, we wished to explore the notion of visual complexity. Specifically, we addressed the following questions: (i) Do people consistently rely on the same features to perform complexity judgments of diverse visual stimuli? (ii) Is visual complexity reducible to a single measure or is it multidimensional in nature?, and (iii) Do all forms of complexity influence beauty ratings in the same way? All participants were students enrolled in their fourth or fifth year of psychology, philosophy, or history studies, who voluntarily took part in the experiments. We included no one who had received formal art training or had studied art history. Participants were divided into two groups. The first one took part in the first phase of the experiment, aimed at reaching an adequate set of stimuli to be used in subsequent phases. The second group participated in phases two and three, which were designed to explore the relation among different kinds of visual stimuli, different levels and forms of complexity and aesthetic appreciation. Group 1 consisted of 240 participants: 112 men (46.7%) and 128 women (53.3%). Their ages ranged from 18 to 44 years, with a mean of 22.03 and a standard deviation of 3.75. Group 2 included 94 participants: 38 men (40.4%) and 56 women (59.9%). Ages ranged from 18 to 46 years, with a mean of 22.41 and a standard deviation of 4.1. Materials We collected over 1,500 digitalized images, which were either abstract or representational, and either artistic or not artistic. The distinction between abstract and representational stimuli referred to the absence and presence of explicit content, respectively. Artistic stimuli were reproductions of catalogued pieces created by renowned artists and exhibited in museums. Following Heinrichs and Cupchik's (1985) recommendation, we included images belonging to diverse styles and schools, such as realism, cubism, impressionism, and so on. As a guide for our initial selection we used the compendium Movements in Modern Art from the Tate Gallery, London (Cottington, 1998; Gooding, 2001; Malpas, 1997; VISUAL COMPLEXITY AND BEAUTY / 175 Thomson, 1998) and added reproductions of XVIII and XIX century American and European artworks. Non-artistic stimuli included postcards, photographs of landscapes, artifacts, urban scenes, and so on, taken from the series of books Boring Postcards (Parr, 1999 (Parr, , 2000 , photographs taken by ourselves, as well as digital images from the series of CDs Master Clips Premium Image Collection (ISMI, San Rafael, CA), used in industrial design, book illustrating, etc. The initial set of images was subjected to a series of modifications in order to eliminate the influence of potentially confounding variables. Only relatively unknown pieces were selected to avoid the impact of familiarity, as recommended by Eysenck (1940) . In order to avoid the influence of ecological variables we eliminated those stimuli that contained clear views of human figures and human faces, as well as those stimuli portraying scenes that could elicit strong emotional responses. The undesired influence of psychophysical variables was controlled by adjusting all stimuli to the same resolution of 150 ppi, setting a common size of 9 by 12 cm, standardizing the color spectrum, and adjusting luminance to between 370 and 390 lx. Stimuli that could not be reasonably modified to comply with this homogenization were discarded. Finally, the signature was removed from all signed pictures. This process of stimuli selection and modification was carried out to leave us with 800 images, 200 of which were abstract artistic, 200 were abstract non-artistic, 200 were representational artistic, and 200 were representational non-artistic. All stimuli were presented to participants and their responses registered by means of a specifically designed software running on Compaq EVO300 Pentium IV / 1,7 GHz computers with Windows 2000 SP4. After participants had introduced demographic information and understood the instructions, they went on to the actual experimental protocol. All stimuli were presented within a grey frame. In the upper segment of the frame there was a brief reminder of the task they were asked to perform. In the lower segment of the frame there was a reminder of the response scale they were required to use. Based on previous studies that showed no effect of viewing time on aesthetic appreciation (McWhinnie, 1993; Smith, Bousquet, Chang, & Smith, 2006), we decided not to impose a time limit on participants' responses. Hence, each stimulus was presented until participants responded. They did so by pressing a key between 1 and 5 in the phases 1 and 2, and a key between 1 and 9 in phase 3. If participants pressed any other key there was no response from the program. If the response was within the appropriate values it was fed-back on to the screen for 1.5 s, after which the 2-s masking screen appeared again. This same pattern was implemented for the 100, 120, and 60 stimuli in the first, second, and third phases, respectively (see in 176 / NADAL ET AL. the Procedure section). The computer program registered all the demographic information given by the participants and each of their responses to the stimuli in each phase. In order to achieve the objectives outlined above, the present work was structured into three phases. The first phase was designed to create a set of stimuli suitable to carry out both subsequent phases. The second phase consisted in the exploration of the relation between complexity and beauty appreciation by means of a beauty rating task using the set of stimuli created in the first phase. Finally, the aim of the third phase was to address the three issues we specified with regards to the nature of visual complexity. Our aim was to obtain a set of 120 stimuli equally divided into three complexity levels: low, intermediate, and high. Each of these complexity levels would include 10 abstract artistic (AA), 10 abstract non-artistic (AN), 10 representational artistic (RA), and 10 representational non-artistic (RN) stimuli. The 240 participants recruited for phase 1 were divided into eight different groups of 30 individuals, attempting to balance each of them in relation to sex. The set of 800 stimuli was divided into eight groups of 100, using a stratified randomized method, such that there were 25 stimuli of each kind (AA, AN, RA, RN) in each group. They were presented to all participants in the same random sequence. Participants were asked to rate the complexity of each of the 100 images on a 1 to 5 Likert scale (very simple-very complex). At this stage complexity was not defined to participants. Participants were only instructed to focus on their general impression of the visual complexity of each stimulus, not on the complexity involved in producing it. Two statistics were calculated for each stimulus: the average rating awarded by the 30 participants, considered as the complexity score, and the standard deviation, considered as the measure of participants' agreement on that score. The selection of stimuli for each complexity level was based on both statistics, and was carried out separately for each of the stimuli types (AA, AN, RA, RN) according to the following procedure. The 200 stimuli in each of the four kinds of images were ordered according to their complexity score. To select stimuli for the low complexity level, the experimenter began at the bottom of the list of stimuli (those with the lowest complexity score). If the standard deviation for the first stimulus was below .80, it was selected. If it was .81 or above, the stimulus was discarded and the operation was repeated with the stimulus immediately above in complexity score. This process was carried out for each of the four stimuli types until 10 stimuli of each particular type had been selected. In order to select stimuli for the high complexity level the same procedure was followed, VISUAL COMPLEXITY AND BEAUTY / 177 except that it began at the top of the list and moved down the complexity scores. Again, this finished when 10 stimuli of each kind had been selected. In order to select stimuli for the intermediate level of complexity, the median of the complexity scores was calculated for each stimulus kind. The experimenter started the selection at that point, using the same agreement criterion as mentioned above, only that he alternatively moved up and down the list to select or discard the stimuli. When the 10 images of each kind had been selected, the process was ended. This procedure was followed with the objective of maximizing the difference between the three complexity levels and to minimize the difference in complexity within levels. Choosing images whose complexity score had a small standard deviation was aimed at including stimuli for which people tended to agree on their degree of complexity. The objective of this phase was to explore the influence of complexity, degree of abstraction, and artistry on beauty appreciation. The 94 participants described above were asked to rate the beauty of the 120 stimuli selected in the previous phase on a 1 to 5 (very ugly-very beautiful) Likert scale. Stimuli were randomized and presented in the same order to all participants, who were sat at different computers in the same room. The average rating awarded by participants was calculated for each stimulus. This was the dependent variable used in our analysis of the influence of the three independent variables: Complexity (low, intermediate, high), Abstraction (abstract and representational), and Artistry (artistic and non-artistic). This phase was conceived to explore the influence of different features of visual stimuli on judgments of complexity, the relations between these features, and their relation with the appreciation of beauty. In order to do so we selected 60 of the 120 stimuli used in Phase 2. This subset was constituted by including five stimuli from each of the four kinds in each level of complexity from the 120 stimuli set. The median value of complexity was calculated for each of the 12 subgroups. We included the stimuli corresponding to the median value and the two adjacent stimuli on both sides. Based on our review of the literature on visual complexity and its influence on the appreciation of beauty, we selected seven features we believed could relate to different aspects of visual complexity. These dimensions were: Unintelligibility of the elements (difficulty to identify the elements in the image), Disorganization (difficulty to organize the elements into a coherent scene, Amount of elements, Variety of elements, Asymmetry, Variety of colors, and Threedimensional appearance. The same group of 94 participants who served as subjects in phase 2 took part in this third and last phase. In this case they were asked to rate each stimulus on a 1 to 9 Likert scale for each of the seven aforementioned scales. All the stimuli in the subset were rated on each dimension separately. Stimuli were presented in a different random order for each dimension. Before rating the stimuli on each particular dimension, participants received written and verbal instructions and a brief definition. After all participants had finished the task, their ratings were collected. The average rating awarded on each dimension were calculated for each stimulus.
The images used in their experiment contained 120 stimuli equally divided into three complexity levels: low, intermediate, and high REF .
Visual Complexity and Beauty Appreciation: Explaining the Divergence of Results:
Empirical Studies of The Arts
Psychology