Parametric Cortical Representations of Complexity and Preference for Artistic and Computer-Generated Fractal Patterns Revealed by Single-Trial EEG Power Spectral Analysis

Abstract

Fractals are self-similar patterns that repeat at different scales, the complexity of which are expressed as a fractional Euclidean dimension D between 0 (a point) and 2 (a filled plane). The drip paintings of American painter Jackson Pollock (JP) are fractal in nature, and Pollock’s most illustrious works are of the high-D (~1.7) category. This would imply that people prefer more complex fractal patterns, but some research has instead suggested people prefer lower-D fractals. Furthermore, research has suggested that parietal and frontal brain activity tracks the complexity of fractal patterns, but previous research has artificially binned fractals depending on fractal dimension, rather than treating fractal dimension as a parametrically varying value. We used white layers extracted from JP artwork as stimuli, and constructed statistically matched 2-dimensional random Cantor sets as control stimuli. We recorded the electroencephalogram (EEG) while participants viewed the JP and matched random Cantor fractal patterns. Participants then rated their subjective preference for each pattern. We used a single-trial analysis to construct within-subject models relating subjective preference to fractal dimension D, as well as relating D and subjective preference to single-trial EEG power spectra. Results indicated that participants preferred higher-D images for both JP and Cantor stimuli. Power spectral analysis showed that, for artistic fractal images, parietal alpha and beta power parametrically tracked complexity of fractal patterns, while for matched mathematical fractals, parietal power tracked complexity of patterns over a range of frequencies, but most prominently in alpha band. Furthermore, parietal alpha power parametrically tracked aesthetic preference for both artistic and matched Cantor patterns. Overall, our results suggest that perception of complexity for artistic and computer-generated fractal images is reflected in parietal-occipital alpha and beta activity, and neural substrates of preference for complex stimuli are reflected in parietal alpha band activity.

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

“…Although correlates of visual attention are widely studied with EEG and harnessed in BCI (Nam et al, 2018), to our knowledge only Rawls et al (2021) report rhythm modulations commonly associated with visual attention that were informative of art preference. Instructing 44 subjects to give preference ratings to binarized Jackson Pollock paintings and computer generated cantor fractals after 4s viewing time, they found a suppression of the parietal alpha rhythm correlated with art preference.…”

“…Different artworks might evoke very different AEs and consequently different EEG signals. Additionally, various visual stimulus features are known to affect the EEG signal such as luminance (Eroglu et al, 2020) or complexity (Rawls et al, 2021). Therefore, it remains unclear, whether empirical results gathered with one type of art stimuli will generalize to other types of Last, but not least, natural visual AE is an embodied experience that involves motor processes such as eye movements.…”

Self Images: An Empirical Enquiry into Rembrandt’s Self-portraits and Portraits

“…Mid-range fractal values are aesthetically appealing. Lower and significantly higher values are less appealing in both synthetic images and in art (Spehar et al 2003;Taylor et al 2005Taylor et al , 2006Viengkham & Spehar, 2018) although there appears to be a preference for some natural images and abstract patterns with D values somewhat above 1.5 (Forsythe et al, 2011;Rawls et al, 2021).…”