Measuring icon complexity: an automated analysis

Responses to Raven matrices: Governed by visual complexity and centrality

Raven matrices are widely considered a pure test of cognitive abilities. Previous research has examined the extent to which cognitive strategies are predictive of the number of correct responses to Raven items. This study examined whether response times can be explained directly from the centrality and visual complexity of the matrix cells (edge density and perceived complexity). A total of 159 participants completed a 12-item version of the Raven Advanced Progressive Matrices. In addition to item number (an index of item difficulty), the findings demonstrated a positive correlation between the visual complexity of Raven items and both the mean response time and the number of fixations on the matrix (a strong correlate of response time). Moreover, more centrally placed cells as well as more complex cells received more fixations. It is concluded that response times on Raven matrices are impacted by low-level stimulus attributes, namely, visual complexity and eccentricity.

Diet-Related Mobile Apps to Promote Healthy Eating and Proper Nutrition: A Content Analysis and Quality Assessment

Dietary mobile applications (apps) continue to hold promise for facilitating a healthy diet and managing nutrition. However, few studies have objectively evaluated the content and quality of such apps in Korea. The present study assessed the content and quality of dietary mobile apps using the Mobile App Rating Scale (MARS). We selected 29 dietary apps based on keywords and eligibility criteria for inclusion in the analyses. We conducted regression analyses to examine the association between app content and MARS scores. Most of the apps featured a tracking tool, while few featured rewards or follow-up management. Our quality assessment revealed that the top-rated apps have distinct levels of quality in terms of MARS scores. The regression analyses showed that the ways in which the apps provide information and motivate the users are statistically significant predictors of app quality. Our findings may facilitate the selection of dietary apps in Korea and provide guidelines for app developers regarding potential improvements in terms of content and quality.

Aesthetic Evaluation of Computer Icons: Visual Pattern Differences Between Art-Trained and Lay Raters of Icons

Adopting eye-tracking measures, we explored the influence of art experience on the aesthetic evaluation of computer icons. Participants were 27 college students with art training and 27 laypersons. Both groups rated icons of varying complexity and symmetry for "beauty" while we recorded participants' eye movements. Results showed that art-trained participants viewed the icons with more eye fixations and had shorter scanning paths than participants in the non-art group, suggesting that art-trained participants processed the icons more deliberately. In addition, we observed an interaction effect between art experience and symmetry. For asymmetrical icons, art-trained participants' ratings tended to be higher than those of lay persons; for symmetric icons, there was no such rater difference. The different visual patterns associated with aesthetic evaluations by these two participant groups suggest that art experience plays a pivotal role in the aesthetic appreciation of icons and has important implications for icon design strategy.

Familiarity acts as a reduction in objective complexity

High-complexity stimuli are thought to place extra demands on working memory when processing and manipulating such stimuli; however, operational definitions of complexity are not well established, nor are the measures that would demonstrate such effects. Here, we argue that complexity is a relative quantity that is affected by preexisting experience. Experiment 1 compared cued-recall performance for Chinese and English speakers when the stimuli involved Chinese features that varied in the number of strokes or involved Ethiopic features unfamiliar to both groups. Chinese pseudocharacters (two radicals) had half the strokes of Chinese pseudowords (two characters). The response terms were English words familiar to both groups. English speakers performed equivalently with the Ethiopic and pseudocharacters, but much worse on the pseudowords. In contrast, Chinese speakers performed equivalently with pseudowords or pseudocharacters, but worse with Ethiopic cues. Experiment 2 showed that the lack of a complexity effect for Chinese speakers was not due to greater ease of rehearsal of pseudowords compared with pseudocharacters. Experiment 3 ruled out that Chinese speakers are just better at learning paired associates involving Mandarin by demonstrating that while complexity did not affect them, other features of the stimuli did. Taken together, it appears that complexity is not an absolute property based on the number of visual elements, but rather a relative property affected by one's prior knowledge.

Spatial complexity facilitates ordinal mapping with a novel symbol set

The representation of number symbols is assumed to be unique, and not shared with other ordinal sequences. However, little research has examined if this is the case, or whether properties of symbols (such as spatial complexity) affect ordinal learning. Two studies were conducted to investigate if the property of spatial complexity affects learning ordinal sequences. In Study 1, 46 adults made a series of judgements about two novel symbol sets (Gibson and Sunúz). The goal was to find a novel symbol set that could be ordered by spatial complexity. In Study 2, 84 adults learned to order nine novel symbols (Sunúz) with a paired comparison task, judging which symbol was 'larger' (whereby the larger symbol became physically larger as feedback), and were then asked to rank the symbols. Participants were assigned to either a condition where there was a relationship between spatial complexity and symbol order, or a condition where there was a random relationship. Of interest was whether learning an ordered list of symbols would be facilitated by the spatial complexity of the novel symbols. Findings suggest spatial complexity affected learning ability, and that pairing spatial complexity with relational information can facilitate learning ordinal sequences. This suggests that the implicit cognitive representation of number may be a more general feature of ordinal lists, and not exclusive to number per se.

Cartographic Symbology for Crisis Mapping: A Comparative Study

Cartographic symbols on crisis maps serve as means of depicting information about the position, properties, and/or numerical values of objects, phenomena or actions specific to crisis mapping. Many crisis cartographic visualisations require simple, clear, categorised and visually organised symbols that can be easily read and understood by a wide range of crisis map users. Cartographic symbol sets for crisis mapping depend on effective graphic design, good availability (sharing and promotion, dissemination and promulgation) and standardisation (ensuring the general and repeatable use of map symbols). In this research, our aim was to examine the extent of these challenges in current cartographic symbology for crisis mapping. Through a comparative study of prominent symbol sets, we analysed efforts invested so far and proposed future directions. The results of this study may be of assistance in understanding less unified or coherent symbologies currently in use, or in revising or amplifying existing sets for future publication.

Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A wide range of computational measures of complexity was calculated, further combined using linear models as well as machine learning (random forests), and compared with data from human evaluations. Our results confirm the adequacy of existing two-factor models of perceived visual complexity consisting of a quantitative and a structural factor (in our case mirror symmetry) for both of our stimulus sets. In addition, a non-linear transformation of mirror symmetry giving more influence to small deviations from symmetry greatly increased explained variance. Thus, we again demonstrate the multidimensional nature of human complexity perception and present comprehensive quantitative models of the visual complexity of abstract patterns, which might be useful for future experiments and applications.

Social dimension and complexity differentially influence brain responses during feedback processing

Recent research emphasizes the importance of social factors during performance monitoring. Thus, the current study investigated the impact of social stimuli -such as communicative gestures- on feedback processing. Moreover, it addressed a shortcoming of previous studies, which failed to consider stimulus complexity as potential confounding factor. Twenty-four volunteers performed a time estimation task while their electroencephalogram was recorded. Either social complex, social non-complex, non-social complex, or non-social non-complex stimuli were used to provide performance feedback. No effects of social dimension or complexity were found for task performance. In contrast, Feedback-Related Negativity (FRN) and P300 amplitudes were sensitive to both factors, with larger FRN and P300 amplitudes after social compared to non-social stimuli, and larger FRN amplitudes after complex positive than non-complex positive stimuli. P2 amplitudes were solely sensitive to feedback valence and social dimension. Subjectively, social complex stimuli were rated as more motivating than non-social complex ones. Independently of each other, social dimension and visual complexity influenced amplitude variation during performance monitoring. Social stimuli seem to be perceived as more salient, which is corroborated by P2, FRN and P300 results, as well as by subjective ratings. This could be explained due to their given relevance during every day social interactions.

A preference for some types of complexity comment on "perceived beauty of random texture patterns: A preference for complexity"

In two experiments, Friedenberg and Liby (2016) studied how a diversity of complexity estimates such as density, number of blocks, GIF compression rate and edge length impact the perception of beauty of semi-random two-dimensional patterns. They concluded that aesthetics ratings are positively linked with GIF compression metrics and edge length, but not with the number of blocks. They also found an inverse U-shaped link between aesthetic judgments and density. These mixed results originate in the variety of metrics used to estimate what is loosely called "complexity" in psychology and indeed refers to conflicting notions. Here, we reanalyze their data adding two more conventional and normative mathematical measures of complexity: entropy and algorithmic complexity. We show that their results can be interpreted as an aesthetic preference for low redundancy, balanced patterns and "crooked" figures, but not for high algorithmic complexity. We conclude that participants tend to have a preference for some types of complexity, but not for all. These findings may help understand divergent results in the study of perceived beauty and complexity, and illustrate the need to specify the notion of complexity used in psychology. The field would certainly benefit from a precise taxonomy of complexity measures.

Revisiting Rossion and Pourtois with new ratings for automated complexity, familiarity, beauty, and encounter

Differences between norm ratings collected when participants are asked to consider more than one picture characteristic are contrasted with the traditional methodological approaches of collecting ratings separately for image constructs. We present data that suggest that reporting normative data, based on methodological procedures that ask participants to consider multiple image constructs simultaneously, could potentially confounded norm data. We provide data for two new image constructs, beauty and the extent to which participants encountered the stimuli in their everyday lives. Analysis of this data suggests that familiarity and encounter are tapping different image constructs. The extent to which an observer encounters an object predicts human judgments of visual complexity. Encountering an image was also found to be an important predictor of beauty, but familiarity with that image was not. Taken together, these results suggest that continuing to collect complexity measures from human judgments is a pointless exercise. Automated measures are more reliable and valid measures, which are demonstrated here as predicting human preferences.

Predicting Complexity Perception of Real World Images

The aim of this work is to predict the complexity perception of real world images. We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images.

A Complex Story: Universal Preference vs. Individual Differences Shaping Aesthetic Response to Fractals Patterns

Fractal patterns offer one way to represent the rough complexity of the natural world. Whilst they dominate many of our visual experiences in nature, little large-scale perceptual research has been done to explore how we respond aesthetically to these patterns. Previous research (Taylor et al., 2011) suggests that the fractal patterns with mid-range fractal dimensions (FDs) have universal aesthetic appeal. Perceptual and aesthetic responses to visual complexity have been more varied with findings suggesting both linear (Forsythe et al., 2011) and curvilinear (Berlyne, 1970) relationships. Individual differences have been found to account for many of the differences we see in aesthetic responses but some, such as culture, have received little attention within the fractal and complexity research fields. This two-study article aims to test preference responses to FD and visual complexity, using a large cohort (N = 443) of participants from around the world to allow universality claims to be tested. It explores the extent to which age, culture and gender can predict our preferences for fractally complex patterns. Following exploratory analysis that found strong correlations between FD and visual complexity, a series of linear mixed-effect models were implemented to explore if each of the individual variables could predict preference. The first tested a linear complexity model (likelihood of selecting the more complex image from the pair of images) and the second a mid-range FD model (likelihood of selecting an image within mid-range). Results show that individual differences can reliably predict preferences for complexity across culture, gender and age. However, in fitting with current findings the mid-range models show greater consistency in preference not mediated by gender, age or culture. This article supports the established theory that the mid-range fractal patterns appear to be a universal construct underlying preference but also highlights the fragility of universal claims by demonstrating individual differences in preference for the interrelated concept of visual complexity. This highlights a current stalemate in the field of empirical aesthetics.

Effects of presentation duration on measures of complexity in affective environmental scenes and representational paintings

Complexity constitutes an integral part of humans' environment and is inherent to information processing. However, little is known about the dynamics of visual complexity perception of affective environmental scenes (IAPS pictures) and artworks, such as affective representational paintings. In three experiments, we studied the time course of visual complexity perception by varying presentation duration and comparing subjective ratings with objective measures of complexity. In Experiment 1, 60 females rated 96 IAPS pictures, presented either for 1, 5, or 25s, for familiarity, complexity, pleasantness and arousal. In Experiment 2, another 60 females rated 96 representational paintings. Mean ratings of complexity and pleasantness changed according to presentation duration in a similar vein in both experiments, suggesting an inverted U-shape. No common pattern of results was observed for arousal and familiarity ratings across the two picture sets. The correlations between subjective and objective measures of complexity increased with longer exposure durations for IAPS pictures, but results were more ambiguous for paintings. Experiment 3 explored the time course of the multidimensionality of visual complexity perception. Another 109 females rated the number of objects, their disorganization and the differentiation between a figure-ground vs. complex scene composition of pictures presented for 1 and 5s. The multidimensionality of visual complexity only clearly emerged in the 5-s condition. In both picture sets, the strength of the correlations with objective measures depended on the type of subdimension of complexity and was less affected by presentation duration than correlations with general complexity in Experiments 1 and 2. These results have clear implications for perceptual and cognitive theories, especially for those of esthetic experiences, in which the dynamical changes of complexity perception need to be integrated.

Visual Complexity Analysis Using Taxonomic Diagrams of Figures and Backgrounds in Japanese Residential Streetscapes

This study was conducted with the objective of finding the perceivable effect of figures and backgrounds on residential streetscapes and their connections in visual complexity. The visual complexity depends on the extent of information a viewer can observe from a visible area. The information includes a number of visual elements along the streetscapes and their diversity and interconnections. The aim of this research was to analyze the structural hierarchical visual complexity of the streetscapes, caused by varied spatial arrangement and numerous spatial connections of the perceivable visual elements. The visual elements of 60 residential streetscapes were classified into figures and backgrounds using human perception of 20 subjects. The identified figures and backgrounds were arranged in a taxonomic diagram representing their connections. These taxonomic diagrams reflect the structural hierarchical visual complexity. Finally, taxonomic entropy was applied to statistically analyze the structural hierarchical visual complexity. When the taxonomic diagram is vertically and horizontally lengthy and the arrangement of the elements of taxonomic diagram is irregular, the complexity increases depicting a large number of figures whose spatial connections impart a high visual complexity to the streetscapes.

Computerized measures of visual complexity

Visual complexity influences people's perception of, preference for, and behaviour toward many classes of objects, from artworks to web pages. The ability to predict people's impression of the complexity of different kinds of visual stimuli holds, therefore, great potential for many domains, basic and applied. Here we use edge detection operations and several image metrics based on image compression error and Zipf's law to estimate the visual complexity of images. The experiments involved 800 images, each previously rated by thirty participants on perceived complexity. In a first set of experiments we analysed the correlation of individual features with the average human response, obtaining correlations up to rs = .771. In a second set of experiments we employed Machine Learning techniques to predict the average visual complexity score attributed by humans to each stimuli. The best configurations obtained a correlation of rs = .832. The average prediction error of the Machine Learning system over the set of all stimuli was .096 in a normalized 0 to 1 interval, showing that it is possible to predict, with high accuracy human responses. Overall, edge density and compression error were the strongest predictors of human complexity ratings.

Measuring streetscape complexity based on the statistics of local contrast and spatial frequency

Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios.

Examining complexity across domains: relating subjective and objective measures of affective environmental scenes, paintings and music

Subjective complexity has been found to be related to hedonic measures of preference, pleasantness and beauty, but there is no consensus about the nature of this relationship in the visual and musical domains. Moreover, the affective content of stimuli has been largely neglected so far in the study of complexity but is crucial in many everyday contexts and in aesthetic experiences. We thus propose a cross-domain approach that acknowledges the multidimensional nature of complexity and that uses a wide range of objective complexity measures combined with subjective ratings. In four experiments, we employed pictures of affective environmental scenes, representational paintings, and Romantic solo and chamber music excerpts. Stimuli were pre-selected to vary in emotional content (pleasantness and arousal) and complexity (low versus high number of elements). For each set of stimuli, in a between-subjects design, ratings of familiarity, complexity, pleasantness and arousal were obtained for a presentation time of 25 s from 152 participants. In line with Berlyne's collative-motivation model, statistical analyses controlling for familiarity revealed a positive relationship between subjective complexity and arousal, and the highest correlations were observed for musical stimuli. Evidence for a mediating role of arousal in the complexity-pleasantness relationship was demonstrated in all experiments, but was only significant for females with regard to music. The direction and strength of the linear relationship between complexity and pleasantness depended on the stimulus type and gender. For environmental scenes, the root mean square contrast measures and measures of compressed file size correlated best with subjective complexity, whereas only edge detection based on phase congruency yielded equivalent results for representational paintings. Measures of compressed file size and event density also showed positive correlations with complexity and arousal in music, which is relevant for the discussion on which aspects of complexity are domain-specific and which are domain-general.

Searching for signs, symbols, and icons: effects of time of day, visual complexity, and grouping

Searching for icons, symbols, or signs is an integral part of tasks involving computer or radar displays, head-up displays in aircraft, or attending to road traffic signs. Icons therefore need to be designed to optimize search times, taking into account the factors likely to slow down visual search. Three factors likely to adversely affect visual search were examined: the time of day at which search was carried out, the visual complexity of the icons, and the extent to which information features in the icon were grouped together. The speed with which participants searched icon arrays for a target was slower early in the afternoon, when icons were visually complex and when information features in icons were not grouped together to form a single object. Theories of attention that account for both feature-based and object-based search best explain these findings and are used to form the basis for ways of improving icon design.

Sublexical and lexical representations in speech production: effects of phonotactic probability and onset density

Phonotactic probability, neighborhood density, and onset density were manipulated in 4 picture-naming tasks. Experiment 1 showed that pictures of words with high phonotactic probability were named more quickly than pictures of words with low phonotactic probability. This effect was consistent over multiple presentations of the pictures (Experiment 2). Manipulations of phonotactic probability and neighborhood density showed only an influence of phonotactic probability (Experiment 3). In Experiment 4, pictures of words with sparse onsets were named more quickly than pictures of words with dense onsets. The results of these experiments provide additional constraints on the architecture and processes involved in models of speech production, as well as constraints on the connections between the recognition and production systems.