Neuroaesthetics and art's diversity and universality

Fluency, prediction and motivation: how processing dynamics, expectations and epistemic goals shape aesthetic judgements

What psychological mechanisms underlie aesthetic judgements? An influential account known as the Hedonic Marking of Fluency, later developed into a Processing Fluency Theory of Aesthetic Pleasure, posits that ease of processing elicits positive feelings and thus enhances stimulus evaluations. However, the theory faces empirical and conceptual challenges. In this paper, we extend it by integrating insights from predictive processing frameworks (PPF) and the epistemic motivation model (EMM). We propose four extensions. First, fluency of a stimulus depends on perceivers' expectations-their internal model of the world. Second, perceivers also form expectations about fluency itself and thus can experience surprising fluency. These expectations can come from the individual's history, their current task and their environment. Third, perceivers can value fluency but also disfluency, reflecting their non-directional epistemic goals. Fourth, perceivers also have directional epistemic goals, preferring specific conclusions or belief content. Consequently, affective reactions depend on whether the stimulus satisfies those goals. These directional epistemic goals may override concerns about fluency or change the value of fluency associated with specific content. We review supporting evidence and introduce novel predictions. By integrating insights from PPF and EMM, our framework can better capture established fluency effects and highlights their limitations and extensions. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Aesthetic experience models human learning

Aesthetic experiences have the potential to promote learning and creativity by enhancing the ability to understand complexity and to integrate novel or disparate information. Offering a theoretical framework for understanding the cognitive benefits of aesthetic experiences, this paper argues they are the necessary outcome of human learning, in which natural objects or artworks are evaluated in a multi-dimensional preference space shaped by Bayesian prediction. In addition, it contends that the brain-states underlying aesthetic experiences harness configurations of the apex three transmodal neural systems-the default mode network, the central executive network, and the salience network-that may offer information-processing advantages by recruiting the brain's high-power communication hubs, thus enhancing potential for learning gain.

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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.

EXPRESS: Investigating the Role of Working Memory Resources across Aesthetic and Non-Aesthetic Judgments

Aesthetic judgments dominate much of daily life by guiding how we evaluate objects, people, and experiences in our environment. One key question that remains unanswered is the extent to which more specialised or largely general cognitive resources support aesthetic judgments. To investigate this question in the context of working memory, we examined the extent to which a working memory load produces similar or different response time interference on aesthetic compared to non-aesthetic judgments. Across three pre-registered experiments that used Bayesian multi-level modelling approaches (N>100 per experiment), we found clear evidence that a working memory load produces similar response time interference on aesthetic judgments relative to non-aesthetic (motion) judgments. We also showed that this similarity in processing across aesthetic versus non-aesthetic judgments holds across variations in the form of art (people vs landscape; Exps. 1-3), medium type (artwork vs photographs; Exp. 2) and load content (art images vs letters; Exps. 1-3). These findings suggest that across a range of experimental contexts, as well as different processing streams in working memory (e.g., visual vs verbal), aesthetic and motion judgments commonly rely on a domain-general cognitive system, rather than a system that is more specifically tied to aesthetic judgments. In doing so, these findings shine new light on the working memory resources that supports aesthetic judgments, as well as how domain-general cognitive systems operate more generally in cognition.

Swipes and Saves: A Taxonomy of Factors Influencing Aesthetic Assessments and Perceived Beauty of Mobile Phone Photographs

Digital images taken by mobile phones are the most frequent class of images created today. Due to their omnipresence and the many ways they are encountered, they require a specific focus in research. However, to date, there is no systematic compilation of the various factors that may determine our evaluations of such images, and thus no explanation of how users select and identify relatively “better” or “worse” photos. Here, we propose a theoretical taxonomy of factors influencing the aesthetic appeal of mobile phone photographs. Beyond addressing relatively basic/universal image characteristics, perhaps more related to fast (bottom-up) perceptual processing of an image, we also consider factors involved in the slower (top-down) re-appraisal or deepened aesthetic appreciation of an image. We span this taxonomy across specific types of picture genres commonly taken—portraits of other people, selfies, scenes and food. We also discuss the variety of goals, uses, and contextual aspects of users of mobile phone photography. As a working hypothesis, we propose that two main decisions are often made with mobile phone photographs: (1) Users assess images at a first glance—by swiping through a stack of images—focusing on visual aspects that might be decisive to classify them from “low quality” (too dark, out of focus) to “acceptable” to, in rare cases, “an exceptionally beautiful picture.” (2) Users make more deliberate decisions regarding one’s “favorite” picture or the desire to preserve or share a picture with others, which are presumably tied to aspects such as content, framing, but also culture or personality, which have largely been overlooked in empirical research on perception of photographs. In sum, the present review provides an overview of current focal areas and gaps in research and offers a working foundation for upcoming research on the perception of mobile phone photographs as well as future developments in the fields of image recording and sharing technology.

More Than Meets the Eye: Art Engages the Social Brain

Here we present the viewpoint that art essentially engages the social brain, by demonstrating how art processing maps onto the social brain connectome-the most comprehensive diagram of the neural dynamics that regulate human social cognition to date. We start with a brief history of the rise of neuroaesthetics as the scientific study of art perception and appreciation, in relation to developments in contemporary art practice and theory during the same period. Building further on a growing awareness of the importance of social context in art production and appreciation, we then set out how art engages the social brain and outline candidate components of the "artistic brain connectome." We explain how our functional model for art as a social brain phenomenon may operate when engaging with artworks. We call for closer collaborations between the burgeoning field of neuroaesthetics and arts professionals, cultural institutions and diverse audiences in order to fully delineate and contextualize this model. Complementary to the unquestionable value of art for art's sake, we argue that its neural grounding in the social brain raises important practical implications for mental health, and the care of people living with dementia and other neurological conditions.

Similarity of gaze patterns across physical and virtual versions of an installation artwork

An experiment was conducted to compare museum visitors' gaze patterns using mobile eye-trackers, whilst they were engaging with a physical and a virtual reality (VR) installation of Piet Mondrian's Neo-plasticist room design. Visitors' eye movements produced approximately 25,000 fixations and were analysed using linear mixed-effects models. Absolute and area-normalized dwell time analyses yielded mostly non-significant main effects of the environment, indicating similarity of visual exploration patterns between physical and VR settings. One major difference observed was the decrease of average fixation duration in VR, where visitors tended to more rapidly switch focus in this environment with shorter bursts of attentional focus. The experiment demonstrated the ability to compare gaze data between physical and virtual environments as a proxy to measure the similarity of aesthetic experience. Similarity of viewing patterns along with questionnaire results suggested that virtual galleries can be treated as ecologically valid environments that are parallel to physical art galleries.

Stochasticity, Nonlinear Value Functions, and Update Rules in Learning Aesthetic Biases

A theoretical framework for the reinforcement learning of aesthetic biases was recently proposed based on brain circuitries revealed by neuroimaging. A model grounded on that framework accounted for interesting features of human aesthetic biases. These features included individuality, cultural predispositions, stochastic dynamics of learning and aesthetic biases, and the peak-shift effect. However, despite the success in explaining these features, a potential weakness was the linearity of the value function used to predict reward. This linearity meant that the learning process employed a value function that assumed a linear relationship between reward and sensory stimuli. Linearity is common in reinforcement learning in neuroscience. However, linearity can be problematic because neural mechanisms and the dependence of reward on sensory stimuli were typically nonlinear. Here, we analyze the learning performance with models including optimal nonlinear value functions. We also compare updating the free parameters of the value functions with the delta rule, which neuroscience models use frequently, vs. updating with a new Phi rule that considers the structure of the nonlinearities. Our computer simulations showed that optimal nonlinear value functions resulted in improvements of learning errors when the reward models were nonlinear. Similarly, the new Phi rule led to improvements in these errors. These improvements were accompanied by the straightening of the trajectories of the vector of free parameters in its phase space. This straightening meant that the process became more efficient in learning the prediction of reward. Surprisingly, however, this improved efficiency had a complex relationship with the rate of learning. Finally, the stochasticity arising from the probabilistic sampling of sensory stimuli, rewards, and motivations helped the learning process narrow the range of free parameters to nearly optimal outcomes. Therefore, we suggest that value functions and update rules optimized for social and ecological constraints are ideal for learning aesthetic biases.

The Embodied-Enactive-Interactive Brain: Bridging Neuroscience and Creative Arts Therapies

The recognition and incorporation of evidence-based neuroscientific concepts into creative arts therapeutic knowledge and practice seem valuable and advantageous for the purpose of integration and professional development. Moreover, exhilarating insights from the field of neuroscience coincide with the nature, conceptualization, goals, and methods of Creative Arts Therapies (CATs), enabling comprehensive understandings of the clinical landscape, from a translational perspective. This paper contextualizes and discusses dynamic brain functions that have been suggested to lie at the heart of intra- and inter-personal processes. Touching upon fundamental aspects of the self and self-other interaction, the state-of-the-art neuroscientific-informed views will shed light on mechanisms of the embodied, predictive and relational brain. The conceptual analysis introduces and interweaves the following contemporary perspectives of brain function: firstly, the grounding of mental activity in the lived, bodily experience will be delineated; secondly, the enactive account of internal models, or generative predictive representations, shaped by experience, will be defined and extensively deliberated; and thirdly, the interpersonal simulation and synchronization mechanisms that support empathy and mentalization will be thoroughly considered. Throughout the paper, the cross-talks between the brain and the body, within the brain through functionally connected neural networks and in the context of agent-environment dynamics, will be addressed. These communicative patterns will be elaborated on to unfold psychophysiological linkage, as well as psychopathological shifts, concluding with the neuroplastic change associated with the formulation of CATs. The manuscript suggests an integrative view of the brain-body-mind in contexts relevant to the therapeutic potential of the expressive creative arts and the main avenues by which neuroscience may ground, enlighten and enrich the clinical psychotherapeutic practice.

Why and How Should Cognitive Science Care about Aesthetics?

Empirical aesthetics has found its way into mainstream cognitive science. Until now, most research has focused either on identifying the internal processes that underlie a perceiver's aesthetic experience or on identifying the stimulus features that lead to a specific type of aesthetic experience. To progress, empirical aesthetics must integrate these approaches into a unified paradigm that encourages researchers to think in terms of temporal dynamics and interactions between: (i) the stimulus and the perceiver; (ii) different systems within the perceiver; and (iii) different layers of the stimulus. At this critical moment, empirical aesthetics must also clearly identify and define its key concepts, sketch out its agenda, and specify its approach to grow into a coherent and distinct discipline.

A Theoretical Framework for How We Learn Aesthetic Values

How do we come to like the things that we do? Each one of us starts from a relatively similar state at birth, yet we end up with vastly different sets of aesthetic preferences. These preferences go on to define us both as individuals and as members of our cultures. Therefore, it is important to understand how aesthetic preferences form over our lifetimes. This poses a challenging problem: to understand this process, one must account for the many factors at play in the formation of aesthetic values and how these factors influence each other over time. A general framework based on basic neuroscientific principles that can also account for this process is needed. Here, we present such a framework and illustrate it through a model that accounts for the trajectories of aesthetic values over time. Our framework is inspired by meta-analytic data of neuroimaging studies of aesthetic appraisal. This framework incorporates effects of sensory inputs, rewards, and motivational states. Crucially, each one of these effects is probabilistic. We model their interactions under a reinforcement-learning circuitry. Simulations of this model and mathematical analysis of the framework lead to three main findings. First, different people may develop distinct weighing of aesthetic variables because of individual variability in motivation. Second, individuals from different cultures and environments may develop different aesthetic values because of unique sensory inputs and social rewards. Third, because learning is stochastic, stemming from probabilistic sensory inputs, motivations, and rewards, aesthetic values vary in time. These three theoretical findings account for different lines of empirical research. Through our study, we hope to provide a general and unifying framework for understanding the various aspects involved in the formation of aesthetic values over time.

Comparison of Outlier-Tolerant Models for Measuring Visual Complexity

Providing the visual complexity of an image in terms of impact or aesthetic preference can be of great applicability in areas such as psychology or marketing. To this end, certain areas such as Computer Vision have focused on identifying features and computational models that allow for satisfactory results. This paper studies the application of recent ML models using input images evaluated by humans and characterized by features related to visual complexity. According to the experiments carried out, it was confirmed that one of these methods, Correlation by Genetic Search (CGS), based on the search for minimum sets of features that maximize the correlation of the model with respect to the input data, predicted human ratings of image visual complexity better than any other model referenced to date in terms of correlation, RMSE or minimum number of features required by the model. In addition, the variability of these terms were studied eliminating images considered as outliers in previous studies, observing the robustness of the method when selecting the most important variables to make the prediction.