Social reputation influences on liking and willingness-to-pay for artworks: A multimethod design investigating choice behavior along with physiological measures and motivational factors

Using machine learning to predict judgments on Western visual art along content-representational and formal-perceptual attributes

Art research has long aimed to unravel the complex associations between specific attributes, such as color, complexity, and emotional expressiveness, and art judgments, including beauty, creativity, and liking. However, the fundamental distinction between attributes as inherent characteristics or features of the artwork and judgments as subjective evaluations remains an exciting topic. This paper reviews the literature of the last half century, to identify key attributes, and employs machine learning, specifically Gradient Boosted Decision Trees (GBDT), to predict 13 art judgments along 17 attributes. Ratings from 78 art novice participants were collected for 54 Western artworks. Our GBDT models successfully predicted 13 judgments significantly. Notably, judged creativity and disturbing/irritating judgments showed the highest predictability, with the models explaining 31% and 32% of the variance, respectively. The attributes emotional expressiveness, valence, symbolism, as well as complexity emerged as consistent and significant contributors to the models' performance. Content-representational attributes played a more prominent role than formal-perceptual attributes. Moreover, we found in some cases non-linear relationships between attributes and judgments with sudden inclines or declines around medium levels of the rating scales. By uncovering these underlying patterns and dynamics in art judgment behavior, our research provides valuable insights to advance the understanding of aesthetic experiences considering visual art, inform cultural practices, and inspire future research in the field of art appreciation.

Metaphors or mechanism? Predictive coding and a (brief) history of empirical study of the arts

Predictive processing (PP) offers an intriguing approach to perception, cognition, but also to appreciation of the arts. It does this by positing both a theoretical basis-one might say a 'metaphor'-for how we engage and respond, placing emphasis on mismatches rather than fluent overlap between schema and environment. Even more, it holds the promise for translating metaphor into neurobiological bases, suggesting a means for considering mechanisms-from basic perceptions to possibly even our complex, aesthetic experiences. However, while we share the excitement of this promise, the history of empirical or psychological aesthetics is also permeated by metaphors that have progressed our understanding but which also tend to elude translation into concrete, mechanistic operationalization-a challenge that can also be made to PP. We briefly consider this difficulty of convincing implementation of PP via a brief historical outline of some developments in the psychological study of aesthetics and art in order to show how these ideas have often anticipated PP but also how they have remained at the level of rather metaphorical and difficult-to-measure concepts. Although theoretical in scope, we hope that this commentary will spur researchers to reflect on PP with the aim of translating metaphorical explanations into well-defined mechanisms in future empirical study. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Bias against AI art can enhance perceptions of human creativity

The contemporary art world is conservatively estimated to be a $65 billion USD market that employs millions of human artists, sellers, and collectors globally. Recent attention paid to AI-made art in prestigious galleries, museums, and popular media has provoked debate around how these statistics will change. Unanswered questions fuel growing anxieties. Are AI-made and human-made art evaluated in the same ways? How will growing exposure to AI-made art impact evaluations of human creativity? Our research uses a psychological lens to explore these questions in the realm of visual art. We find that people devalue art labeled as AI-made across a variety of dimensions, even when they report it is indistinguishable from human-made art, and even when they believe it was produced collaboratively with a human. We also find that comparing images labeled as human-made to images labeled as AI-made increases perceptions of human creativity, an effect that can be leveraged to increase the value of human effort. Results are robust across six experiments (N = 2965) using a range of human-made and AI-made stimuli and incorporating representative samples of the US population. Finally, we highlight conditions that strengthen effects as well as dimensions where AI-devaluation effects are more pronounced.

Art and Perception: Using Empirical Aesthetics in Research on Consciousness

Understanding consciousness is a major frontier in the natural sciences. However, given the nuanced and ambiguous sets of conditions regarding how and when consciousness appears to manifest, it is also one of the most elusive topics for investigation. In this context, we argue that research in empirical aesthetics—specifically on the experience of art—holds strong potential for this research area. We suggest that empirical aesthetics of art provides a more exhaustive description of conscious perception than standard laboratory studies or investigations of the less artificial, more ecological perceptual conditions that dominate this research, leading to novel and better suited designs for natural science research on consciousness. Specifically, we discuss whether empirical aesthetics of art could be used for a more adequate picture of an observer’s attributions in the context of conscious perception. We point out that attributions in the course of conscious perception to (distal) objects versus to media (proximal objects) as origins of the contents of consciousness are typically swift and automatic. However, unconventional or novel object-media relations used in art can bring these attributions to the foreground of the observer’s conscious reflection. This is the reason that art may be ideally suited to study human attributions in conscious perception compared to protocols dedicated only to the most common and conventional perceptual abilities observed under standard laboratory or “natural”/ecological conditions alone. We also conclude that art provides an enormous stock of such unconventional and novel object-media relations, allowing more systematic falsification of tentative conclusions about conscious perception versus research protocols covering more conventional (ecological) perception only. We end with an outline of how this research could be carried out in general.