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Instead of relying on a predefined set of stimuli, it involved systematically sampling continuous two-dimensional spaces for shape, curvature, colour, musical harmony, and melody. The resulting preference spaces across these modalities and culture enabled the comparison of both shared and culturally variable patterns."},{"key":"10.1016\/j.copsyc.2026.102429_bib52","unstructured":"Introduces a computational method that combines human judgements with adaptive search to efficiently reconstruct high-dimensional preference spaces across modalities, including colour, speech, music, and human faces. Across 25 experiments and 5178 participants, the method is shown to reconstruct cast perceptual and preference spaces\u2014including the latent spaces of generative models\u2014that would be impractical to explore exhaustively. It provides a powerful framework for modelling complex aesthetic preferences beyond isolated ratings or predefined categories."},{"key":"10.1016\/j.copsyc.2026.102429_bib53","unstructured":"Uses learned embeddings of millions of songs and approximately 70 billion Spotify listening events to reconstruct patterns of musical taste from real-world behaviour. It demonstrates how large-scale digital traces can quantify individual taste and how it is shaped by engagement with recommendation systems."},{"key":"10.1016\/j.copsyc.2026.102429_bib54","unstructured":"This study develops a computational model showing that aesthetic preferences for visual art can be predicted from the integration of interpretable low- and high-level visual features. A common feature set predicted preferences both within and across individuals, while differences in how these features were weighted captured distinct individual preference profiles. Importantly, these features also emerged hierarchically within a deep neural network trained only for object recognition. It demonstrates how aesthetic value can be modelled as a structured computation over multiple properties of cultural objects."},{"key":"10.1016\/j.copsyc.2026.102429_bib55","unstructured":"Uses large-scale similarity judgements across 1854 natural objects to reconstruct 49 interpretable dimensions of human object representations, spanning perceptual, functional, and categorical properties. It demonstrates how simple similarity judgements combined with computational models can reveal rich multidimensional psychological representations that support flexible behaviour."},{"key":"10.1016\/j.copsyc.2026.102429_bib56","unstructured":"Reconstructs the structure of emotional experience from responses to 2185 videos, revealing at least 27 dimensions of emotion connected by continuous gradients. It demonstrates how large-scale behavioural experiments can uncover rich, multidimensional affective spaces underlying aesthetic experience."},{"key":"10.1016\/j.copsyc.2026.102429_bib57","unstructured":"Uses natural language processing to analyse free-response narratives imagined by 622 participants listening to instrumental music across two cultures. It shows that music evokes shared meanings within cultures but that these meanings diverge across cultures, demonstrating how language can reveal the rich and culturally shaped semantic representations underlying aesthetic experience."},{"key":"10.1016\/j.copsyc.2026.102429_bib58","unstructured":"This study tested whether deep neural networks trained only for visual recognition, without aesthetic or affective information, can predict human responses to natural images. Features extracted from these models explained a majority of the variance in ratings of beauty, valence, and arousal, suggesting that visually evoked feelings are strongly related to the perceptual structure learned by vision models. 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