Empirical Aesthetics and Artistic Perception
Summary
The field of empirical aesthetics investigates how individuals perceive, evaluate and emotionally respond to artistic stimuli. It integrates behavioural experiments, neuroimaging and computational modelling to analyse aesthetic appreciation as a dynamic interplay of sensory processing, cognitive appraisal and affective engagement. Research has elucidated neural networks underlying aesthetic judgment, revealing graded activity in sensory cortices and a distinct role for medial orbitofrontal regions in signalling beauty. Eye-tracking and psychophysiological measures have shown how expertise, context and stimulus properties modulate attention and emotional reactions. Computational frameworks, drawing on representation learning and information-theoretic measures, have begun to predict human ratings of beauty and interest from low-level image statistics. This interdisciplinary approach has practical implications for museum curation, design, digital media and human–machine collaboration, and underscores the universal and culture-specific dimensions of aesthetic experience.
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Empirical Aesthetics and Artistic Perception publication trend
The graph below shows the total number of articles in empirical aesthetics and artistic perception across all publications each year (not limited to Nature Index journals).
Technical terms
Valence: The positive or negative quality of an emotional response to a stimulus.
Arousal: The degree of physiological and psychological activation elicited by a stimulus.
Representation learning: The process by which computational models automatically discover features from raw data that capture salient patterns.
Minimum description length principle: An information-theoretic criterion that seeks the simplest model encoding data by balancing model complexity against goodness of fit.
References
- Does an emotional connection to art really require a human artist? Emotion and intentionality responses to AI- versus human-created art and impact on aesthetic experience. Computers in Human Behavior (2023).
- The perceptual primacy of feeling: Affectless visual machines explain a majority of variance in human visually evoked affect. Proceedings of the National Academy of Sciences of the United States of America (2025).
- Minimum description length clustering to measure meaningful image complexity. Pattern Recognition (2024).
- When Art Moves the Eyes: A Behavioral and Eye-Tracking Study. PLOS ONE (2012).
- Art in Time and Space: Context Modulates the Relation between Art Experience and Viewing Time. PLOS ONE (2014).
- Experiencing Art: The Influence of Expertise and Painting Abstraction Level. Frontiers in Human Neuroscience (2011).
- Toward A Brain-Based Theory of Beauty. PLOS ONE (2011).
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