Abstract
When we feel connected or engaged during social behavior, are our brains in fact “in sync” in a formal, quantifiable sense? Most studies addressing this question use highly controlled tasks with homogenous subject pools. In an effort to take a more naturalistic approach, we collaborated with art institutions to crowdsource neuroscience data: Over the course of 5 years, we collected electroencephalogram (EEG) data from thousands of museum and festival visitors who volunteered to engage in a 10-min face-to-face interaction. Pairs of participants with various levels of familiarity sat inside the Mutual Wave Machine—an artistic neurofeedback installation that translates real-time correlations of each pair's EEG activity into light patterns. Because such inter-participant EEG correlations are prone to noise contamination, in subsequent offline analyses we computed inter-brain coupling using Imaginary Coherence and Projected Power Correlations, two synchrony metrics that are largely immune to instantaneous, noise-driven correlations. When applying these methods to two subsets of recorded data with the most consistent protocols, we found that pairs’ trait empathy, social closeness, engagement, and social behavior (joint action and eye contact) consistently predicted the extent to which their brain activity became synchronized, most prominently in low alpha (~7–10 Hz) and beta (~20–22 Hz) oscillations. These findings support an account where shared engagement and joint action drive coupled neural activity and behavior during dynamic, naturalistic social interactions. To our knowledge, this work constitutes a first demonstration that an interdisciplinary, real-world, crowdsourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions. Additionally, it is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory. Institutions such as museums, galleries, or any other organization where the public actively engages out of self-motivation, can help facilitate this type of citizen science research, and support the collection of large datasets under scientifically controlled experimental conditions. To further enhance the public interest for the out-of-the-lab experimental approach, the data and results of this study are disseminated through a website tailored to the general public (wp.nyu.edu/mutualwavemachine).
Original language | English |
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Article number | 117436 |
Pages (from-to) | 1-14 |
Number of pages | 14 |
Journal | NeuroImage |
Volume | 227 |
Early online date | 8 Oct 2020 |
DOIs | |
Publication status | Published - 15 Feb 2021 |
Funding
This research was supported by the Netherlands Organization for Scientific Research Awards 275-89-018 and 406.18.GO.024. The Mutual Wave Machine was made possible with support by Creative Industries Fund NL, TodaysArt, Marina Abramovic Institute, de Hersenstichting, Lowlands Science, Utrecht University, and NEON. Design, tech & production: Peter Burr, Danielle Boelling, Diederik Schoorl, Jean Jacques Warmerdam, Matthew Patterson Curry, Pandelis Diamantides; Data collection and management: Annita Apostolaki, Dana Bevilacqua, Shaista Dhanesar, Imke Kruitwagen, Eletta Daemen, Orsa Rebouskou, Stella Papazisi, Aspa Papazisi, Karlijn Blommers, Sascha Couvee, Ella Bosch, Jorik Geutjes, Chris van Run.
Funders | Funder number |
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Ella Bosch | |
Jorik Geutjes | |
Netherlands Organization for Scientific Research | 275-89-018, 406.18 |
Sascha Couvee | |
Stimuleringsfonds Creatieve Industrie |
Keywords
- Brain-Computer-Interface Technology
- Brain-to-brain synchrony
- Hyperscanning
- Inter-brain coupling
- Neurofeedback
- Oscillations
- Real-world neuroscience