Project description
Providing the missing link between objective and subjective aesthetics
The perception of beauty is something very personal, and for this reason the concept has not been researched enough. There are two main approaches to understanding aesthetics. One concerns statistical properties that are supposed to be universal and biologically determined. The other involves concepts such as style, meaning and personal associations that are dependent on cultural influences, art expertise and individual experiences. The EU-funded GRAPPA project will apply Gestalt psychology to develop and test a hypothesis that bridges the two. Machine learning will analyse new data from online studies with large samples of images and participants. This will result in a model that predicts aesthetic preference.
Objective
"""De gustibus et coloribus non disputandum est."" With this slogan philosophers and lay people alike have dismissed all attempts to understand taste, color perception, or aesthetic preferences. Sense of beauty may just be too individual and too complex to qualify as target of scientific inquiry. Yet, since Fechner (1876), empirical aesthetics has studied the factors determining people’s aesthetic responses to art works and objects, scenes or events encountered in everyday life. Most accounts focus either on high-level concepts such as style, meaning and personal associations, or on low-level statistical properties. While the latter are supposed to be universal and biologically determined, the former are subject to cultural influences, art expertise and individual experiences. Progress in this tradition has reached its limits, which this project overcomes by investigating how Gestalts Relate Aesthetic Preferences to Perceptual Analysis (GRAPPA). Its pioneering working hypothesis is that the way perceivers organize their sensory inputs into meaningful entities (Gestalts) provides the missing link between the two traditional sets of explanations. This hypothesis is fleshed out and tested in a coherent research program linking aesthetic preferences for images of paintings and everyday photographs to general principles of perceptual organization as well as to specific aesthetic concepts like composition, balance and visual rightness. New data from online studies with large samples of images and participants will be analyzed with state-of-the-art computational methods (machine learning) to reveal the critical mid-level factors. This will yield a model to predict aesthetic preference, which will be tested in well-controlled psychophysical and behavioral experiments (e.g. eye-movement recording) and validated also in ecologically richer settings (e.g. in galleries and art museums) and in unconventional cross-over collaborations with contemporary artists."
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
3000 Leuven
Belgium