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

Stimulus Complexity Can Enhance Art Appreciation: Phenomenological and Psychophysiological Evidence for the Pleasure-Interest Model of Aesthetic Liking

We tested predictions deriving from the "Pleasure-Interest Model of Aesthetic Liking" (PIA Model), whereby aesthetic preferences arise from two fluency-based processes: an initial automatic, percept-driven default process and a subsequent perceiver-driven reflective process. One key trigger for reflective processing is stimulus complexity. Moreover, if meaning can be derived from such complexity, then this can engender increased interest and elevated liking. Experiment 1 involved graffiti street-art images, pre-normed to elicit low, moderate and high levels of interest. Subjective reports indicated a predicted enhancement in liking across increasing interest levels. Electroencephalography (EEG) recordings during image viewing revealed different patterns of alpha power in temporal brain regions across interest levels. Experiment 2 enforced a brief initial image-viewing stage and a subsequent reflective image-viewing stage. Differences in alpha power arose in most EEG channels between the initial and deliberative viewing stages. A linear increase in aesthetic liking was again seen across interest levels, with different patterns of alpha activity in temporal and occipital regions across these levels. Overall, the phenomenological data support the PIA Model, while the physiological data suggest that enhanced aesthetic liking might be associated with "flow-feelings" indexed by alpha activity in brain regions linked to visual attention and reducing distraction.

Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings

Creativity is a compelling yet elusive phenomenon, especially when manifested in visual art, where its evaluation is often a subjective and complex process. Understanding how individuals judge creativity in visual art is a particularly intriguing question. Conventional linear approaches often fail to capture the intricate nature of human behavior underlying such judgments. Therefore, in this study, we employed interpretable machine learning to probe complex associations between 17 subjective art-attributes and creativity judgments across a diverse range of artworks. A cohort of 78 non-art expert participants assessed 54 artworks varying in styles and motifs. The applied Random Forests regressor models accounted for 30% of the variability in creativity judgments given our set of art-attributes. Our analyses revealed symbolism, emotionality, and imaginativeness as the primary attributes influencing creativity judgments. Abstractness, valence, and complexity also had an impact, albeit to a lesser degree. Notably, we observed non-linearity in the relationship between art-attribute scores and creativity judgments, indicating that changes in art-attributes did not consistently correspond to changes in creativity judgments. Employing statistical learning, this investigation presents the first attribute-integrating quantitative model of factors that contribute to creativity judgments in visual art among novice raters. Our research represents a significant stride forward building the groundwork for first causal models for future investigations in art and creativity research and offering implications for diverse practical applications. Beyond enhancing comprehension of the intricate interplay and specificity of attributes used in evaluating creativity, this work introduces machine learning as an innovative approach in the field of subjective judgment.

Gaze patterns reveal aesthetic distance while viewing art

For centuries, Western philosophers have argued that aesthetic experiences differ from common, everyday pleasing sensations, and further, that mental states, such as disinterested contemplation and aesthetic distance, underlie these complex experiences. We empirically tested whether basic perceptual processes of information intake reveal evidence for aesthetic distance, specifically toward visual art. We conducted two eye tracking experiments using appropriately matched visual stimuli (environmental scenes and representational paintings) with 59 participants using two different presentation durations (25 and 6 s). Linear mixed-effects models considering individual differences showed that affective content (pleasantness and arousal), but not stimulus composition (complexity), leads to differential effects when viewing representational paintings in comparison to environmental scenes. We demonstrate that an increase in aesthetic pleasantness induced by representational paintings during a free-viewing task leads to a slower and deeper processing mode than when viewing environmental scenes of motivational relevance, for which we observed the opposite effect. In addition, long presentation durations led to an increase in scanning behavior during visual art perception. These empirical findings inform the debate about how aesthetic experiences differ from everyday perceptual processes by showing that the notion of aesthetic distance may be better understood by examining different modes of viewing.

Spontaneous perception: a framework for task-free, self-paced perception

Flipping through social media feeds, viewing exhibitions in a museum, or walking through the botanical gardens, people consistently choose to engage with and disengage from visual content. Yet, in most laboratory settings, the visual stimuli, their presentation duration, and the task at hand are all controlled by the researcher. Such settings largely overlook the spontaneous nature of human visual experience, in which perception takes place independently from specific task constraints and its time course is determined by the observer as a self-governing agent. Currently, much remains unknown about how spontaneous perceptual experiences unfold in the brain. Are all perceptual categories extracted during spontaneous perception? Does spontaneous perception inherently involve volition? Is spontaneous perception segmented into discrete episodes? How do different neural networks interact over time during spontaneous perception? These questions are imperative to understand our conscious visual experience in daily life. In this article we propose a framework for spontaneous perception. We first define spontaneous perception as a task-free and self-paced experience. We propose that spontaneous perception is guided by four organizing principles that grant it temporal and spatial structures. These principles include coarse-to-fine processing, continuity and segmentation, agency and volition, and associative processing. We provide key suggestions illustrating how these principles may interact with one another in guiding the multifaceted experience of spontaneous perception. We point to testable predictions derived from this framework, including (but not limited to) the roles of the default-mode network and slow cortical potentials in underlying spontaneous perception. We conclude by suggesting several outstanding questions for future research, extending the relevance of this framework to consciousness and spontaneous brain activity. In conclusion, the spontaneous perception framework proposed herein integrates components in human perception and cognition, which have been traditionally studied in isolation, and opens the door to understand how visual perception unfolds in its most natural context.

The Relationship of Symmetry, Complexity, and Shape in Mobile Interface Aesthetics, from an Emotional Perspective—A Case Study of the Smartwatch

Products with interactive interfaces can be seen everywhere, and product interface design aesthetics is a topic that has begun to receive wide attention. Consumers’ perceptions of product interfaces come from their own emotions, and emotion plays a significant role in product interface design aesthetics. In other words, it must meet the users’ emotional and aesthetic requirements. Therefore, we need to better understand the aesthetic design criteria and how they stimulate specific emotional responses. This study takes the dial interface of smartwatches as its experimental sample and explores how the interaction effects of the screen shape (square and round) and the symmetry type and the complexity type of the interface design influence the users’ emotional arousal and valence. In addition, it analyzes the effects of the symmetry type, the complexity type, and the screen shape on the users’ arousal and valence. The results show that the attributes of interface design aesthetics (symmetry-asymmetry, complexity-simplicity, and square-round) affect the users’ emotional responses. Moreover, the interface shape is one of the important factors in the emotional response to an interface design. This paper, based on previous research, provides vital theoretical support for the relevant literature on interface design aesthetics and the users’ emotional state. In addition, it may provide a reference for designers and developers who wish to develop and implement emotional user interfaces that are designed to more effectively appeal to their emotions.

Tracking two pleasures

Can people track several pleasures? In everyday life, pleasing stimuli rarely appear in isolation. Yet, experiments on aesthetic pleasure usually present only one image at a time. Here, we ask whether people can reliably report the pleasure of either of two images seen in a single glimpse. Participants (N = 13 in the original; +25 in the preregistered replication) viewed 36 Open Affective Standardized Image Set (OASIS) images that span the entire range of pleasure and beauty. On each trial, the observer saw two images, side by side, for 200 ms. An arrow cue pointed, randomly, left, right, or bidirectionally. Left or right indicated which image (the target) to rate while ignoring the other (the distractor); bidirectional requested rating the combined pleasure of both images. In half the blocks, the cue came before the images (precuing). Otherwise, it came after (postcuing). Precuing allowed the observer to ignore the distractor, while postcuing demanded tracking both images. Finally, we obtained single-pleasure ratings for each image shown alone. Our replication confirms the original study. People have unbiased access to their felt pleasure from each image and the average of both. Furthermore, the variance of the observer's report is similar whether reporting the pleasure of one image or the average pleasure of two. The undiminished variance for reports of the average pleasure of two images indicates either that the underlying pleasure variances are highly correlated, or, more likely, that the variance arises in the common reporting process. In brief, observers can faithfully track at least two visual pleasures.

Better memory for intrinsic versus extrinsic details underlies the enhanced recollective experience of negative events

Why we remember emotional events with an increased subjective sense of remembering (SSR) is unclear. SSR for neutral events is linked to memory for various kinds of details. Using the Remember/Know paradigm, participants provided written justifications of their Remember responses indicating what they specifically recollected about a negative or neutral photo seen during encoding. Crucially, Remember responses for negative photos were more often linked to memory for details of the stimuli (intrinsic details) versus details related to external associations (extrinsic details) or emotional reaction at encoding, suggesting that memory for intrinsic details underlies the enhanced SSR of negative stimuli.

Visual complexity modelling based on image features fusion of multiple kernels

Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.

Visual Complexity and Affect: Ratings Reflect More Than Meets the Eye

Pictorial stimuli can vary on many dimensions, several aspects of which are captured by the term 'visual complexity.' Visual complexity can be described as, "a picture of a few objects, colors, or structures would be less complex than a very colorful picture of many objects that is composed of several components." Prior studies have reported a relationship between affect and visual complexity, where complex pictures are rated as more pleasant and arousing. However, a relationship in the opposite direction, an effect of affect on visual complexity, is also possible; emotional arousal and valence are known to influence selective attention and visual processing. In a series of experiments, we found that ratings of visual complexity correlated with affective ratings, and independently also with computational measures of visual complexity. These computational measures did not correlate with affect, suggesting that complexity ratings are separately related to distinct factors. We investigated the relationship between affect and ratings of visual complexity, finding an 'arousal-complexity bias' to be a robust phenomenon. Moreover, we found this bias could be attenuated when explicitly indicated but did not correlate with inter-individual difference measures of affective processing, and was largely unrelated to cognitive and eyetracking measures. Taken together, the arousal-complexity bias seems to be caused by a relationship between arousal and visual processing as it has been described for the greater vividness of arousing pictures. The described arousal-complexity bias is also of relevance from an experimental perspective because visual complexity is often considered a variable to control for when using pictorial stimuli.

Beauty at a glance: The feeling of beauty and the amplitude of pleasure are independent of stimulus duration

Over time, how does beauty develop and decay? Common sense suggests that beauty is intensely felt only after prolonged experience of the object. Here, we present one of various stimuli for a variable duration (1–30 s), measure the observers' pleasure over time, and, finally, ask whether they felt beauty. On each trial, participants (N = 21) either see an image that they had chosen as “movingly beautiful,” see an image with prerated valence, or suck a candy. During the stimulus and a further 60 s, participants rate pleasure continuously using a custom touchscreen web app, EmotionTracker.com. After each trial, participants judge whether they felt beauty. Across all stimulus kinds, durations, and beauty responses, the dynamic pleasure rating has a stereotypical time course that is well fit by a one-parameter model with a brief exponential onset (roughly 2.5 s), a sustained plateau during stimulus presentation, and a long exponential decay (roughly 70 s). Across conditions, only the plateau amplitude varies. Beauty and pleasure amplitude are nearly independent of stimulus duration. The final beauty rating is positively correlated with pleasure amplitude (r = 0.60), and nearly independent of duration (r = 0.10). Beauty's independence from duration is unlike Bentham's 18th-century notion of value (utility), which he supposed to depend on the product of pleasure amplitude and duration. Participants report having felt pleasure as strongly after a mere 1 s stimulus as after longer durations, up to 30 s. Thus, we find that amplitude of pleasure is independent of stimulus duration.

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.

The Ambiguity of Artworks -A Guideline for Empirical Aesthetics Research with Artworks as Stimuli

The aim of this work is to provide researchers from the field of aesthetics with a guideline on working with artworks as stimuli. Empirical aesthetics research is complicated by the uncertainty of the object of research. There is no way to unquestionably tell whether an object is an artwork or not. However, although the extension of the term artwork (i.e., the range of objects to which this concept applies) remains vague, the different intensions of the term artwork (i.e., the internal concept that constitutes a formal definition) are well defined. Here, I review the various concepts of artworks (i.e., intensions) that scientists from different fields use in current research in empirical aesthetics. The selection of stimuli is often not explained and/or does not match the focus of the study. An application of two or more intensions within one study leads to an indeterminacy of the stimuli and, thus, to systematic problems concerning the interpretation and comparability of the experimental results. Based on these intensions and the Pleasure-Interest Model of Aesthetic Liking (Graf and Landwehr, 2015), I compiled a decision tree in order to provide researchers with an instrument that allows a better control over their stimuli.

Berlyne Revisited: Evidence for the Multifaceted Nature of Hedonic Tone in the Appreciation of Paintings and Music

In his seminal book on esthetics, Berlyne (1971) posited an inverted-U relationship between complexity and hedonic tone in arts appreciation, however, converging evidence for his theory is still missing. The disregard of the multidimensionality of complexity may explain some of the divergent results. Here, we argue that definitions of hedonic tone are manifold and systematically examined whether the nature of the relationship between complexity and hedonic tone is determined by the specific measure of hedonic tone. In Experiment 1, we studied three picture categories with similar affective and semantic contents: 96 affective environmental scenes, which were also converted into 96 cartoons, and 96 representational paintings. Complexity varied along the dimension of elements. In a between-subjects design, each stimulus was presented for 5 s to 206 female participants. Subjective ratings of hedonic tone (either beauty, pleasantness or liking), arousal, complexity and familiarity were collected in three conditions per stimulus set. Complexity and arousal were positively associated in all conditions, with the strongest association observed for paintings. For environmental scenes and cartoons, there was no significant association between complexity and hedonic tone, and the three measures of hedonic tone were highly correlated (all rs > 0.85). As predicted, in paintings the measures of hedonic tone were less strongly correlated (all rs > 0.73), and when controlling for familiarity, the association with complexity was significantly positive for beauty (rs = 0.26), weakly negative for pleasantness (rs = -0.16) and not present for liking. Experiment 2 followed a similar approach and 77 female participants, all non-musicians, rated 92 musical excerpts (15 s) in three conditions of hedonic tone (either beauty, pleasantness or liking). Results indicated a strong relationship between complexity and arousal (all rs > 0.85). When controlling for familiarity effects, the relationship between complexity and beauty followed an inverted-U curve, whereas the relationship between complexity and pleasantness was negative (rs = -0.26) and the one between complexity and liking positive (rs = 0.29). We relate our results to Berlyne's theory and the latest findings in neuroaesthetics, proposing that future studies need to acknowledge the multifaceted nature of hedonic tone in esthetic experiences of artforms.

Liking versus Complexity: Decomposing the Inverted U-curve

The relationship between liking and stimulus complexity is commonly reported to follow an inverted U-curve. However, large individual differences among complexity preferences of participants have frequently been observed since the earliest studies on the topic. The common use of across-participant analysis methods that ignore these large individual differences in aesthetic preferences gives an impression of high agreement between individuals. In this study, we collected ratings of liking and perceived complexity from 30 participants for a set of digitally generated grayscale images. In addition, we calculated an objective measure of complexity for each image. Our results reveal that the inverted U-curve relationship between liking and stimulus complexity comes about as the combination of different individual liking functions. Specifically, after automatically clustering the participants based on their liking ratings, we determined that one group of participants in our sample had increasingly lower liking ratings for increasingly more complex stimuli, while a second group of participants had increasingly higher liking ratings for increasingly more complex stimuli. Based on our findings, we call for a focus on the individual differences in aesthetic preferences, adoption of alternative analysis methods that would account for these differences and a re-evaluation of established rules of human aesthetic preferences.