Visual Neuroaesthetics (VisNA) Lab
FALL 2023: The VisNA Lab is moving! Dr. Vessel and his research team are in the process of setting up the lab in its new home at the City College of New York (CCNY). In the meantime, please visit edvessel.com.
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.
Want to volunteer for our studies?
Go here to sign up for the Institute's Research Participant Registry.
Events
Max Planck Institute for Empirical Aesthetics
[Online]
Our Studies
People
Edward A. Vessel
Team Leader
Ayse Ilkay Isik
Postdoctoral Researcher
Dominik Welke
PhD Candidate
Projects
- Exploring Network Interactions During Aesthetic Experiences
How does the brain support aesthetic experiences with visual stimuli such as artwork, landscapes, architecture, dance or movies? ...
- 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 ...
- Self Relevance and Aesthetic Appeal
While a degree of aesthetic appeal can be predicted from image features, a large proportion of variance in aesthetic ratings differs from person to person, particularly for visual art. We hypothesize that this is due to the ability of visual art ...
- Measuring neural and physiological responses to dynamically changing real-world aesthetic experiences
Aesthetic experiences with visual artwork often occur in real-world settings such as museums. Yet given the limitations of brain imaging technology, most of what we know about the neural and physiological basis of aesthetic experiences is derived ...
Latest Publications
Christensen, J. F., Muralikrishnan, R., Münzberg, M., Manias, B. C., Khorsandi, S., & Vessel, E. A. (2024).
Can 5 minutes of finger actions boost creative incubation? Journal of Cognitive Enhancement.
doi:10.1007/s41465-024-00306-0.
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Bignardi, G., Smit, D. J. A., Vessel, E. A., Trupp, M. D., Ticini, L. F., Fisher, S. E., & Polderman, T. J. C. (2024).
Genetic effects on variability in visual aesthetic evaluations are partially shared across visual domains.
Communications Biology,7: 55. doi:10.1038/s42003-023-05710-4.
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Vessel, E. A., Pasqualette, L., Uran, C., Koldehoff, S., Bignardi, G., & Vinck, M. (2023).
Self-relevance predicts the aesthetic appeal of real and synthetic artworks generated via neural style transfer.
Psychological Science,34(9), 1007-1023. doi:10.1177/09567976231188107.
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Notter, M. P., Herholz, P., Costa, S. D., Gulban, O. F., Isik, A. I., Gaglianese, A., & Murray, M. M. (2022).
fMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines.
Brain Topography. doi:10.1007/s10548-022-00935-8.
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Conwell, C., Graham, D., Konkle, T., & Vessel, E. A. (2022). Purely perceptual
machines robustly predict human visual arousal, valence, and aesthetics. Journal of Vision,22: 4266.
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Golbabaei, S., Christensen, J. F., Vessel, E. A., Kazemian, N., & Borhani, K. (2022).
The Aesthetic Responsiveness Assessment (AReA) in Farsi language: A scale validation and cultural adaptation study.
Psychology of Aesthetics, Creativity, and the Arts. Advance online publication. doi:10.1037/aca0000532.
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Vessel, E. A., Ishizu, T., & Bignardi, G. (2022). Neural correlates of visual aesthetic
appeal. In M. Skov, & M. Nadal (
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Welke, D., & Vessel, E. A. (2022). Naturalistic viewing conditions can increase
task engagement and aesthetic preference but have only minimal impact on EEG quality. NeuroImage,256: 119218. doi:10.1016/j.neuroimage.2022.119218.
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Vessel, E. A., & Starr, G. G. (2022). Imaging the subjective. In A. Chatterjee,
& E. Cardilo (
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Vessel, E. A., Yue, X., & Biederman, I. (2022). Scene preferences, aesthetic appeal,
and curiosity: Revisiting the neurobiology of the infovore. In A. Chatterjee, & E. Cardilo (
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Strijbosch, W., Vessel, E. A., Welke, D., Mitas, O., Gelissen, J., & Bastiaansen, M. (2022).
On the neuronal dynamics of aesthetic experience: Evidence from electroencephalographic oscillatory dynamics.
Journal of Cognitive Neuroscience,34(3), 461-479. doi:10.1162/jocn_a_01812.
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Vessel, E. A., Yue, X., & Biederman, I. (2022). Scene preferences, aesthetic appeal
and curiosity: Revisiting the neurobiology of the infovore. In A. Chatterjee, & E. Cardilo (
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Vessel, E. A., & Starr, G. G. (2022). Imaging the subjective. In A. Chatterjee,
& E. Cardilo (
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Pavlov, Y. G., Adamian, N., Appelhoff, S., Arvaneh, M., Benwell, C. S. Y., Beste, C., Bland, A. R., Bradford, D. E., Bublatzky,
F., Busch, N. A., Clayson, P. E., Cruse, D., Czeszumski, A., Dreber, A., Dumas, G., Ehinger, B., Ganis, G., He, X., Hinojosa,
J. A., Huber-Huber, C., Inzlicht, M., Jack, B. N., Johannesson, M., Jones, R., Kalenkovich, E., Kaltwasser, L., Karimi-Rouzbahani,
H., Keil, A., König, P., Kouara, L., Kulke, L., Ladouceur, C. D., Langer, N., Liesefeld, H. R., Luque, D., MacNamara, A.,
Mudrik, L., Muthuraman, M., Neal, L. B., Nilsonne, G., Niso, G., Ocklenburg, S., Oostenveld, R., Pernet, C. R., Pourtois,
G., Ruzzoli, M., Sass, S. M., Schaefer, A., Senderecka, M., Snyder, J. S., Tamnes, C. K., Tognoli, E., van Vugt, M. K., Verona,
E., Vloeberghs, R., Welke, D., Wessel, J. R., Zakharov, I., & Mushtaq, F. (2021).
#EEGManyLabs: Investigating the replicability of influential EEG experiments. Cortex,144, 213-229. doi:10.1016/j.cortex.2021.03.013.
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Schlotz, W., Wallot, S., Omigie, D., Masucci, M. D., Hoelzmann, S. C., & Vessel, E. A. (2021).
The Aesthetic Responsiveness Assessment (AReA): A screening tool to assess individual differences in responsiveness to art
in English and German. Psychology of Aesthetics, Creativity, and the Arts,15(4),
682-696. doi:10.1037/aca0000348.
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Vessel, E. A. (2021). Neuroaesthetics. In S. Della Sala (
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Isik, A. I., & Vessel, E. A. (2021). From visual perception to aesthetic appeal:
Brain responses to aesthetically appealing natural landscape movies. Frontiers in Human Neuroscience,15: 676032. doi:10.3389/fnhum.2021.676032.
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Welke, D., Purton, I., & Vessel, E. A. (2021). Inspired by art: Higher aesthetic
appeal elicits increased felt inspiration in a creative writing task (Advance online publication).
Psychology of Aesthetics, Creativity, and the Arts. doi:10.1037/aca0000393.
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Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford,
J. A., Adcock, R. A., Avesani, P., Baczkowski, B. M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton,
D., Beitner, J., Benoit, R. G., Berkers, R. M. W. J., Bhanji, J. P., Biswal, B. B., Bobadilla-Suarez, S., Bortolini, T., Bottenhorn,
K. L., Bowring, A., Braem, S., Brooks, H. R., Brudner, E. G., Calderon, C. B., Camilleri, J. A., Castrellon, J. J., Cecchetti,
L., Cieslik, E. C., Cole, Z. J., Collignon, O., Cox, R. W., Cunningham, W. A., Czoschke, S., Dadi, K., Davis, C. P., Luca,
A. D., Delgado, M. R., Demetriou, L., Dennison, J. B., Di, X., Dickie, E. W., Dobryakova, E., Donnat, C. L., Dukart, J., Duncan,
N. W., Durnez, J., Eed, A., Eickhoff, S. B., Erhart, A., Fontanesi, L., Fricke, G. M., Fu, S., Galván, A., Gau, R., Genon,
S., Glatard, T., Glerean, E., Goeman, J. J., Golowin, S. A. E., González-García, C., Gorgolewski, K. J., Grady, C. L., Green,
M. A., Moreira, J. F. G., Guest, O., Hakimi, S., Hamilton, J. P., Hancock, R., Handjaras, G., Harry, B. B., Hawco, C., Herholz,
P., Herman, G., Heunis, S., Hoffstaedter, F., Hogeveen, J., Holmes, S., Hu, C.-P., Huettel, S. A., Hughes, M. E., Iacovella,
V., Iordan, A. D., Isager, P. M., Isik, A. I., Jahn, A., Johnson, M. R., Johnstone, T., Joseph, M. J. E., Juliano, A. C.,
Kable, J. W., Kassinopoulos, M., Koba, C., Kong, X.-Z., Koscik, T. R., Kucukboyaci, N. E., Kuhl, B. A., Kupek, S., Laird,
A. R., Lamm, C., Langner, R., Lauharatanahirun, N., Lee, H., Lee, S., Leemans, A., Leo, A., Lesage, E., Li, F., Li, M. Y.
C., Lim, P. C., Lintz, E. N., Liphardt, S. W., Vermeer, A. B. L., Love, B. C., Mack, M. L., Malpica, N., Marins, T., Maumet,
C., McDonald, K., McGuire, J. T., Melero, H., Leal, A. S. M., Meyer, B., Meyer, K. N., Mihai, G., Mitsis, G. D., Moll, J.,
Nielson, D. M., Nilsonne, G., Notter, M. P., Olivetti, E., Onicas, A. I., Papale, P., Patil, K. R., Peelle, J. E., Pérez,
A., Pischedda, D., Poline, J.-B., Prystauka, Y., Ray, S., Reuter-Lorenz, P. A., Reynolds, R. C., Ricciardi, E., Rieck, J.
R., Rodriguez-Thompson, A. M., Romyn, A., Salo, T., Samanez-Larkin, G. R., Sanz-Morales, E., Schlichting, M. L., Schultz,
D. H., Shen, Q., Sheridan, M. A., Silvers, J. A., Skagerlund, K., Smith, A., Smith, D. V., Sokol-Hessner, P., Steinkamp, S.
R., Tashjian, S. M., Thirion, B., Thorp, J. N., Tinghög, G., Tisdall, L., Tompson, S. H., Toro-Serey, C., Tresols, J. J. T.,
Tozzi, L., Truong, V., Turella, L., van Veer, A. E. ‘., Verguts, T., Vettel, J. M., Vijayarajah, S., Vo, K., Wall, M. B.,
Weeda, W. D., Weis, S., White, D. J., Wisniewski, D., Xifra-Porxas, A., Yearling, E. A., Yoon, S., Yuan, R., Yuen, K. S. L.,
Zhang, L., Zhang, X., Zosky, J. E., Nichols, T. E., & Schonberg, R. A. P. &. T. (2020).
Variability in the analysis of a single neuroimaging dataset by many teams. Nature,582, 84-88. doi:10.1038/s41586-020-2314-9.
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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.
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Isik, A. I., & Vessel, E. A. (2019). Continuous ratings of movie watching reveal
idiosyncratic dynamics of aesthetic enjoyment. PLoS One,14(10):
e0223896. doi:10.1371/journal.pone.0223896.
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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.
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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.
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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),
111-116. doi:10.1038/s41592-018-0235-4.
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Vessel, E. A. (2019). How Do Aesthetic Experiences Function in the Brain? doi:10.21036/LTPUB10774.
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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.
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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.
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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.
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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.
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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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Contact
For more information on the VisNA lab, please contact Edward Vessel at
ed.vessel@ae.mpg.de
Team
Edward A. Vessel, PhD
For more information on the VisNA lab, please contact Edward Vessel at ed.vessel@ae.mpg.de