What does it mean for a painting to fill us with wonder, for a sunset to be beautiful or for a film to move us? Why do some visual experiences have the power to reach inside and grab us, while others leave little impression? How can we measure these experiences scientifically?
In the VisNA Lab, we study the psychological and neural basis of aesthetic experiences, such as when a person is aesthetically “moved” by visual art, poetry, architecture, music, or natural landscapes. Much of our research relies on brain imaging (fMRI, EEG) and behavioral techniques. We also frequently use computational tools (e.g. machine learning), measurements of physiology and eyetracking.
Our core research areas are:
- Identifying the central cognitive and emotional processes that underly aesthetic experiences and the interacting neural systems that support them.
- Characterizing the conditions that lead to “shared taste” across a population of individuals in different aesthetic domains.
- Development of methods for studying aesthetic experiences in more naturalistic settings such as museums and performance halls.
Go here to sign up for the Institute's Research Participant Registry.
- Singing Voice Preferences
As suggested by the many singing contests and music programs in the media, the singing voice attracts ample attention. Recent studies showed that lay and expert listeners share similar definitions of what is “correct” when listening to ...
- Exploring Network Interactions During Aesthetic Experiences
- Aesthetic Appreciation Across Multiple Visual Domains
Individuals can be aesthetically engaged by objects from widely different visual aesthetic domains, such as paintings, mountain vistas, or buildings. The goal of this project is to understand whether aesthetic appreciation of different visual ...
- A Developmental Timeline of Hedonic Preferences
Adult liking judgments for visual scenes are strongly influenced by the semantic content of images, more so than by lower-level visual features (e.g. the presence of specific colors or line types). This results in a strong degree of shared taste ...
- Shivers down the spine in poetry and music
The wise reader reads the book of genius not with his heart, not so much with his brain, but with his spine. ...
- Aesthetic Responsiveness and Engagement Assessment (AREA): Psychometric analysis and test of measurement invariance across samples from the USA and Germany
It is generally accepted that most people are capable of having moving aesthetic experiences, with the caveat that the particular types of stimuli that are effective elicitors of positive aesthetic responses, and the intensity of aesthetic ...
- Aesthetically Moving Experiences and Creative Inspiration
Moments of creative inspiration are critical pivot points that mark the transition from creative ideation to actualization of an idea. We hypothesize that the state of being aesthetically moved, a critical moment during ...
- Electrophysiological Correlates of Aesthetically Moving Experiences
How does the brain support aesthetically “moving” experiences with visual art in situ? Our previous work using functional magnetic resonance imaging (fMRI) has identified several brain systems involved in observers’ subjective ratings ...
- Brain on Screen
When we go to the cinema, we partake in a complex experience. How does a series of two-dimensional images and sounds blend into an immersive, sometimes lifelike narrative experience? And how do different individuals in the movie theater become ...
- Dataset from Vessel et al. (2018) "Stronger shared taste for natural aesthetic domains than for artifacts of human culture."
Schlotz, W., Wallot, S., Omigie, D., Masucci, M. D., Hoelzmann, S. C., & Vessel, E. A. (2020).
The Aesthetic Responsiveness Assessment (AReA): A screening tool to assess individual differences in responsiveness to art
in English and German (Online First Posting). Psychology of Aesthetics, Creativity, and the Arts.
Appelhoff, S., Sanderson, M., Brooks, T. L., van Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K.,
Höchenberger, R., Welke, D., Brunner, C., Rockhill, A. P., Larson, E., Gramfort, A., & Jas, M. (2019).
MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis.
The Journal of Open Source Software,4(44): 1896. doi:10.21105/joss.01896.
Vessel, E. A., Isik, A. I., Belfi, A. M., Stahl, J. L., & Starr, a. G. G. (2019).
The default-mode network represents aesthetic appeal that generalizes across visual domains. Proceedings
of the National Academy of Sciences of the United States of America. doi:10.1073/pnas.1902650116.
Belfi, A. M., Vessel, E. A., Brielmann, A., Isik, A. I., Chatterjee, A., Leder, H., Pelli, D. G., & Starr, G. (2019).
Dynamics of aesthetic experience are reflected in the default-mode network. NeuroImage,188, 584-597. doi:10.1016/j.neuroimage.2018.12.017.
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre,
E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019).
fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature methods,16(1),
Belfi, A. M., Kasdan, A., Rowland, J., Vessel, E. A., Starr, G. G., & Poeppel, D. (2018).
Rapid timing of musical aesthetic judgments. Journal of Experimental Psychology: General,147(10), 1531-1543. doi:10.1037/xge0000474.
Vessel, E. A., Maurer, N., Denker, A. H., & Starr, G. G. (2018). Stronger shared
taste for natural aesthetic domains than for artifacts of human culture. Cognition,179, 121-131. doi:10.1016/j.cognition.2018.06.009.
Belfi, A. M., Vessel, E. A., & Starr, G. G. (2018). Individual ratings of vividness
predict aesthetic appeal in poetry. Psychology of Aesthetics, Creativity, and the Arts,12(3), 341-350. doi:10.1037/aca0000153.
Isik, A. I., Naumer, M. J., Kaiser, J., Buschenlange, C., Wiesmann, S., Czoschke, S., & Yalachkov, Y. (2017).
Automatized smoking-related action schemata are reflected by reduced fMRI activity in sensorimotor brain regions of smokers. NeuroImage: Clinical,15, 753-760. doi:10.1016/j.nicl.2017.06.021.
Vessel, E. A., Biederman, I., Subramaniam, S., & Greene, M. R. (2016). Effective
signaling of surface boundaries by L-vertices reflect the consistency of their contrast in natural images.
Journal of Vision,16(9): 15. doi:doi:10.1167/16.9.15.
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