Towards an objective theory of subjective liking: A first step in understanding the sense of beauty

Abstract

The study of the electroencephalogram signals recorded from subjects during an experience is a way to understand the brain processes that underlie their physical and emotional involvement. Such signals have the form of time series, and their analysis could benefit from applying techniques that are specific to this kind of data. Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic judgments, such as liking or disliking a painting. Starting from a proprietary dataset of 248 trials from 16 subjects exposed to art paintings, using a real ecological context, this paper analyses the application of a novel symbolic machine learning technique, specifically designed to extract information from unstructured data and to express it in form of logical rules. Our purpose is to extract qualitative and quantitative logical rules, to relate the voltage at specific frequencies and in specific electrodes, and that, within the limits of the experiment, may help to understand the brain process that drives liking or disliking experiences in human subjects.

Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features—A mini-review

“…Nonetheless, brain entropy measures could be useful for visual aesthetic preference decoding. Mazzacane et al (2023) used a novel classification algorithm based on temporal decision trees (Sciavicco and Stan, 2020) to extract symbolic rules ralating EEG amplitudes of specific frequency bands and electrode locations to aesthetic liking or disliking in an ecological museum context (#subjects = 16). The authors report high classification performance using features from beta and gamma bands that could be related to affective and reward processing.…”