People across the world seek out beautiful sounds in nature, such as a babbling brook or a nightingale song, for positive human experiences. However, it is unclear whether this positive aesthetic response is driven by a preference for the perceptual features typical of nature sounds versus a higher‐order association of nature with beauty. To test these hypotheses, participants provided aesthetic judgments for nature and urban soundscapes that varied on ease of recognition. Results demonstrated that the aesthetic preference for nature soundscapes was eliminated for the sounds hardest to recognize, and moreover the relationship between aesthetic ratings and several measured acoustic features significantly changed as a function of recognition. In a follow‐up experiment, requiring participants to classify these difficult‐to‐identify sounds into nature or urban categories resulted in a robust preference for nature sounds and a relationship between aesthetic ratings and our measured acoustic features that was more typical of easy‐to‐identify sounds. This pattern of results was replicated with computer‐generated artificial noises, which acoustically shared properties with the nature and urban soundscapes but by definition did not come from these environments. Taken together, these results support the conclusion that the recognition of a sound as either natural or urban dynamically organizes the relationship between aesthetic preference and perceptual features and that these preferences are not inherent to the acoustic features. Implications for nature's role in cognitive and affective restoration are discussed.
This content will become publicly available on July 31, 2025
Color composition (or color theme) is a key factor to determine how well a piece of art work or graphical design is perceived by humans. Despite a few color harmony models have been proposed, their results are often less satisfactory since they mostly neglect the variations of aesthetic cognition among individuals and treat the influence of all ratings equally as if they were all rated by the same anonymous user. To overcome this issue, in this article we propose a new color theme evaluation model by combining a back propagation neural network and a kernel probabilistic model to infer both the color theme rating and the user aesthetic preference. Our experiment results show that our model can predict more accurate and personalized color theme ratings than state of the art methods. Our work is also the first-of-its-kind effort to quantitatively evaluate the correlation between user aesthetic preferences and color harmonies of five-color themes, and study such a relation for users with different aesthetic cognition.
more » « less- Award ID(s):
- 2005430
- PAR ID:
- 10532346
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Applied Perception
- Volume:
- 21
- Issue:
- 3
- ISSN:
- 1544-3558
- Page Range / eLocation ID:
- 1 to 35
- Subject(s) / Keyword(s):
- Color harmony color theme machine learning crowdsourcing aesthetic cognition
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
In the past decade, both cognitive science and the learning sciences have been significantly altered by an increased attention to the theme of embodiment. Broadly speaking, this theme complements (or pushes back against) the notion of purely abstract, “disembodied” cognition and emphasizes the role of physical interaction with the environment in the course of learning and development. A common, if usually implicit, assumption in this work is that learners’ bodies are more or less constant from one era to another: after all, human senses, limbs, physiology, and the basic parameters of cognition are part of an ongoing evolutionary human endowment. This assumption, while historically reasonable, is likely to need reconsideration in the near future, as a variety of “transhumanist” technologies (enhanced senses, bodies, and internalized interfaces with the outside physical environment) become more prevalent in children’s lives. This paper discusses several foundational issues and questions that are poised to emerge, and to challenge our enduring ideas about children and education, in the foreseeable future.more » « less
-
null (Ed.)It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users’ distributions. In this work, we demonstrate that a lack of flatness in rating distributions is negatively correlated with recommendation performance. We propose a rating transformation model that compensates for skew in the rating distribution as well as its central tendency by converting ratings into percentile values as a pre-processing step before recommendation generation. This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance. We also show that a smoothed version of this transformation can yield more intuitive results for users with very narrow rating distributions. A comprehensive set of experiments, with state-of-the-art recommendation algorithms in four real-world datasets, show improved ranking performance for these percentile transformations.more » « less
-
Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current cross-domain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD) model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD.more » « less
-
Fleming, Roland W (Ed.)more » « less
Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty (‘feature-based aesthetics’), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver’s brain (‘processing-based aesthetics’), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty.
