Art and Perception: Using Empirical Aesthetics in Research on Consciousness

Environmental Lighting Conditions, Phenomenal Contrast, and the Conscious Perception of Near and Far

BACKGROUND

Recent evidence in systems neuroscience suggests that lighting conditions affect the whole chain of brain processing, from retina to high-level cortical networks, for perceptual and cognitive function. Here, visual adaptation levels to three different environmental lighting conditions, (1) darkness, (2) daylight, and (3) prolonged exposure to very bright light akin to sunlight, were simulated in lab to investigate the effects of light adaptation levels on classic cases of subjective contrast, assimilation, and contrast-induced relative depth in achromatic, i.e., ON-OFF pathway mediated visual configurations.

METHODS

After adaptation/exposure to a given lighting condition, configurations were shown in grouped and ungrouped conditions in random order to healthy young humans in computer-controlled two-alternative forced-choice procedures that consisted of deciding, as quickly as possible, which of two background patterns in a given configuration of achromatic contrast appeared lighter, or which of two foreground patterns appeared to stand out in front, as if it were nearer to the observer.

RESULTS

We found a statistically significant effect of the adaptation levels on the consciously perceived subjective contrast (F(2,23) = 20.73; p < 0.001) and the relative depth (F(2,23) = 12.67; p < 0.001), a statistically significant interaction between the adaptation levels and the grouping factor (F(2,23) = 4.73; p < 0.05) on subjective contrast, and a statistically significant effect of the grouping factor on the relative depth (F(2,23) = 13.71; p < 0.01).

CONCLUSIONS

Visual adaption to different lighting conditions significantly alters the conscious perception of contrast and assimilation, classically linked to non-linear functional synergies between ON and OFF processing channels in the visual brain, and modulates the repeatedly demonstrated effectiveness of luminance contrast as a depth cue; the physically brighter pattern regions in the configurations are no longer consistently perceived as nearer to a conscious observer under daylight and extreme bright light adapted (rod-saturated) conditions.

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

In today's digital information age, human exposure to visual artifacts has reached an unprecedented quasi-omnipresence. Some of these cultural artifacts are elevated to the status of artworks which indicates a special appreciation of these objects. For many persons, the perception of such artworks coincides with aesthetic experiences (AE) that can positively affect health and wellbeing. AEs are composed of complex cognitive and affective mental and physiological states. More profound scientific understanding of the neural dynamics behind AEs would allow the development of passive Brain-Computer-Interfaces (BCI) that offer personalized art presentation to improve AE without the necessity of explicit user feedback. However, previous empirical research in visual neuroaesthetics predominantly investigated functional Magnetic Resonance Imaging and Event-Related-Potentials correlates of AE in unnaturalistic laboratory conditions which might not be the best features for practical neuroaesthetic BCIs. Furthermore, AE has, until recently, largely been framed as the experience of beauty or pleasantness. Yet, these concepts do not encompass all types of AE. Thus, the scope of these concepts is too narrow to allow personalized and optimal art experience across individuals and cultures. This narrative mini-review summarizes the state-of-the-art in oscillatory Electroencephalography (EEG) based visual neuroaesthetics and paints a road map toward the development of ecologically valid neuroaesthetic passive BCI systems that could optimize AEs, as well as their beneficial consequences. We detail reported oscillatory EEG correlates of AEs, as well as machine learning approaches to classify AE. We also highlight current limitations in neuroaesthetics and suggest future directions to improve EEG decoding of AE.